From c474fda282da90672527d5ad638d23bc4eec1ff8 Mon Sep 17 00:00:00 2001 From: ver217 Date: Fri, 17 Mar 2023 15:41:47 +0800 Subject: [PATCH 001/413] [chatgpt] fix ppo training hanging problem with gemini (#3162) * [chatgpt] fix generation early stopping * [chatgpt] fix train prompts example --- applications/ChatGPT/chatgpt/models/generation.py | 11 ++++++++++- applications/ChatGPT/examples/train_prompts.py | 7 ++++--- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/applications/ChatGPT/chatgpt/models/generation.py b/applications/ChatGPT/chatgpt/models/generation.py index 4ee797561f7f..eb30c36d0f84 100644 --- a/applications/ChatGPT/chatgpt/models/generation.py +++ b/applications/ChatGPT/chatgpt/models/generation.py @@ -1,6 +1,7 @@ from typing import Any, Callable, Optional import torch +import torch.distributed as dist import torch.nn as nn try: @@ -27,6 +28,14 @@ def prepare_logits_processor(top_k: Optional[int] = None, return processor_list +def _is_sequence_finished(unfinished_sequences: torch.Tensor) -> bool: + if dist.is_initialized() and dist.get_world_size() > 1: + # consider DP + unfinished_sequences = unfinished_sequences.clone() + dist.all_reduce(unfinished_sequences) + return unfinished_sequences.max() == 0 + + def sample(model: nn.Module, input_ids: torch.Tensor, max_length: int, @@ -74,7 +83,7 @@ def sample(model: nn.Module, unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_token_id).long()) # stop when each sentence is finished if early_stopping=True - if early_stopping and unfinished_sequences.max() == 0: + if early_stopping and _is_sequence_finished(unfinished_sequences): break return input_ids diff --git a/applications/ChatGPT/examples/train_prompts.py b/applications/ChatGPT/examples/train_prompts.py index d4f31e61eb75..8f48a11c33e8 100644 --- a/applications/ChatGPT/examples/train_prompts.py +++ b/applications/ChatGPT/examples/train_prompts.py @@ -46,7 +46,6 @@ def main(args): initial_model = deepcopy(actor) reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).to(torch.cuda.current_device()) - # configure optimizer if args.strategy.startswith('colossalai'): actor_optim = HybridAdam(actor.parameters(), lr=5e-6) @@ -70,7 +69,9 @@ def main(args): dataset = pd.read_csv(args.prompt_path)['prompt'] def tokenize_fn(texts): - batch = tokenizer(texts, return_tensors='pt', max_length=96, padding=True, truncation=True) + # MUST padding to max length to ensure inputs of all ranks have the same length + # Different length may lead to hang when using gemini, as different generation steps + batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) return {k: v.cuda() for k, v in batch.items()} (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( @@ -101,7 +102,7 @@ def tokenize_fn(texts): num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) - # save model checkpoint after fitting + # save model checkpoint after fitting strategy.save_model(actor, args.save_path, only_rank0=True) # save optimizer checkpoint on all ranks if args.need_optim_ckpt: From 1e58d31bb75ce973f1fea7d4f061444138e35654 Mon Sep 17 00:00:00 2001 From: ver217 Date: Fri, 17 Mar 2023 17:31:22 +0800 Subject: [PATCH 002/413] [chatgpt] fix trainer generate kwargs (#3166) --- applications/ChatGPT/chatgpt/trainer/ppo.py | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/applications/ChatGPT/chatgpt/trainer/ppo.py b/applications/ChatGPT/chatgpt/trainer/ppo.py index 789e0c2f8f1e..dacab4784039 100644 --- a/applications/ChatGPT/chatgpt/trainer/ppo.py +++ b/applications/ChatGPT/chatgpt/trainer/ppo.py @@ -63,6 +63,7 @@ def __init__(self, **generate_kwargs) -> None: experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef) replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload) + generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor) super().__init__(strategy, experience_maker, replay_buffer, experience_batch_size, max_epochs, tokenizer, sample_replay_buffer, dataloader_pin_memory, callbacks, **generate_kwargs) self.actor = actor @@ -73,7 +74,6 @@ def __init__(self, self.actor_optim = actor_optim self.critic_optim = critic_optim - self._set_default_generate_kwargs(generate_kwargs, actor) def training_step(self, experience: Experience) -> Dict[str, float]: self.actor.train() @@ -102,11 +102,15 @@ def training_step(self, experience: Experience) -> Dict[str, float]: return {'actor_loss': actor_loss.item(), 'critic_loss': critic_loss.item()} - def _set_default_generate_kwargs(self, generate_kwargs: dict, actor: Actor) -> None: - origin_model = self.strategy._unwrap_actor(actor) - # use huggingface models method directly - if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): - generate_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation - if 'update_model_kwargs_fn' not in generate_kwargs: - generate_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn +def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None: + origin_model = strategy._unwrap_actor(actor) + new_kwargs = {**generate_kwargs} + # use huggingface models method directly + if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): + new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation + + if 'update_model_kwargs_fn' not in generate_kwargs: + new_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn + + return new_kwargs From 7548ca5a54ed117f03247dcb43ec1dd962ae04e0 Mon Sep 17 00:00:00 2001 From: BlueRum <70618399+ht-zhou@users.noreply.github.com> Date: Mon, 20 Mar 2023 09:59:06 +0800 Subject: [PATCH 003/413] [chatgpt]Reward Model Training Process update (#3133) * add normalize function to value_head in bloom rm * add normalization to value_function in gpt_rm * add normalization to value_head of opt_rm * add Anthropic/hh-rlhf dataset * Update __init__.py * Add LogExpLoss in RM training * Update __init__.py * update rm trainer to use acc as target * update example/train_rm * Update train_rm.sh * code style * Update README.md * Update README.md * add rm test to ci * fix tokenier * fix typo * change batchsize to avoid oom in ci * Update test_ci.sh --- .../ChatGPT/chatgpt/dataset/__init__.py | 4 +- .../ChatGPT/chatgpt/dataset/reward_dataset.py | 65 +++++++++- .../ChatGPT/chatgpt/models/__init__.py | 4 +- .../ChatGPT/chatgpt/models/bloom/bloom_rm.py | 1 + .../ChatGPT/chatgpt/models/gpt/gpt_rm.py | 1 + applications/ChatGPT/chatgpt/models/loss.py | 14 ++- .../ChatGPT/chatgpt/models/opt/opt_rm.py | 1 + applications/ChatGPT/chatgpt/trainer/rm.py | 111 +++++++++++------- applications/ChatGPT/examples/README.md | 41 +++++-- applications/ChatGPT/examples/test_ci.sh | 20 ++++ .../ChatGPT/examples/train_reward_model.py | 93 ++++++++++----- applications/ChatGPT/examples/train_rm.sh | 26 ++-- 12 files changed, 270 insertions(+), 111 deletions(-) diff --git a/applications/ChatGPT/chatgpt/dataset/__init__.py b/applications/ChatGPT/chatgpt/dataset/__init__.py index b4599c82ba75..83393098775f 100644 --- a/applications/ChatGPT/chatgpt/dataset/__init__.py +++ b/applications/ChatGPT/chatgpt/dataset/__init__.py @@ -1,4 +1,4 @@ -from .reward_dataset import RewardDataset +from .reward_dataset import RmStaticDataset, HhRlhfDataset from .utils import is_rank_0 -__all__ = ['RewardDataset', 'is_rank_0'] +__all__ = ['RmStaticDataset', 'HhRlhfDataset','is_rank_0'] diff --git a/applications/ChatGPT/chatgpt/dataset/reward_dataset.py b/applications/ChatGPT/chatgpt/dataset/reward_dataset.py index 8bc850f2d52d..9ee13490b893 100644 --- a/applications/ChatGPT/chatgpt/dataset/reward_dataset.py +++ b/applications/ChatGPT/chatgpt/dataset/reward_dataset.py @@ -5,8 +5,8 @@ from .utils import is_rank_0 - -class RewardDataset(Dataset): +# Dahaos/rm-static +class RmStaticDataset(Dataset): """ Dataset for reward model @@ -14,16 +14,71 @@ class RewardDataset(Dataset): dataset: dataset for reward model tokenizer: tokenizer for reward model max_length: max length of input + special_token: special token at the end of sentence """ - def __init__(self, dataset, tokenizer: Callable, max_length: int) -> None: + def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None: super().__init__() self.chosen = [] self.reject = [] + if special_token is None: + self.end_token = tokenizer.eos_token + else: + self.end_token = special_token for data in tqdm(dataset, disable=not is_rank_0()): prompt = data['prompt'] - chosen = prompt + data['chosen'] + "<|endoftext|>" + chosen = prompt + data['chosen'] + self.end_token + chosen_token = tokenizer(chosen, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + self.chosen.append({ + "input_ids": chosen_token['input_ids'], + "attention_mask": chosen_token['attention_mask'] + }) + + reject = prompt + data['rejected'] + self.end_token + reject_token = tokenizer(reject, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + self.reject.append({ + "input_ids": reject_token['input_ids'], + "attention_mask": reject_token['attention_mask'] + }) + + def __len__(self): + length = len(self.chosen) + return length + + def __getitem__(self, idx): + return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][ + "input_ids"], self.reject[idx]["attention_mask"] + +# Anthropic/hh-rlhf +class HhRlhfDataset(Dataset): + """ + Dataset for reward model + + Args: + dataset: dataset for reward model + tokenizer: tokenizer for reward model + max_length: max length of input + special_token: special token at the end of sentence + """ + def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None: + super().__init__() + self.chosen = [] + self.reject = [] + if special_token is None: + self.end_token = tokenizer.eos_token + else: + self.end_token = special_token + for data in tqdm(dataset, disable=not is_rank_0()): + chosen = data['chosen'] + self.end_token chosen_token = tokenizer(chosen, max_length=max_length, padding="max_length", @@ -34,7 +89,7 @@ def __init__(self, dataset, tokenizer: Callable, max_length: int) -> None: "attention_mask": chosen_token['attention_mask'] }) - reject = prompt + data['rejected'] + "<|endoftext|>" + reject = data['rejected'] + self.end_token reject_token = tokenizer(reject, max_length=max_length, padding="max_length", diff --git a/applications/ChatGPT/chatgpt/models/__init__.py b/applications/ChatGPT/chatgpt/models/__init__.py index 376fed8de792..b274188a21df 100644 --- a/applications/ChatGPT/chatgpt/models/__init__.py +++ b/applications/ChatGPT/chatgpt/models/__init__.py @@ -1,4 +1,4 @@ from .base import Actor, Critic, RewardModel -from .loss import PairWiseLoss, PolicyLoss, PPOPtxActorLoss, ValueLoss +from .loss import PolicyLoss, PPOPtxActorLoss, ValueLoss, LogSigLoss, LogExpLoss -__all__ = ['Actor', 'Critic', 'RewardModel', 'PolicyLoss', 'ValueLoss', 'PPOPtxActorLoss', 'PairWiseLoss'] +__all__ = ['Actor', 'Critic', 'RewardModel', 'PolicyLoss', 'ValueLoss', 'PPOPtxActorLoss', 'LogSigLoss', 'LogExpLoss'] diff --git a/applications/ChatGPT/chatgpt/models/bloom/bloom_rm.py b/applications/ChatGPT/chatgpt/models/bloom/bloom_rm.py index 4dc2646e36ae..2dba227ff7d0 100644 --- a/applications/ChatGPT/chatgpt/models/bloom/bloom_rm.py +++ b/applications/ChatGPT/chatgpt/models/bloom/bloom_rm.py @@ -33,4 +33,5 @@ def __init__(self, if checkpoint: model.gradient_checkpointing_enable() value_head = nn.Linear(model.config.hidden_size, 1) + value_head.weight.data.normal_(mean=0.0, std=1/(model.config.hidden_size + 1)) super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/gpt/gpt_rm.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_rm.py index 0132dbf27ffc..19d673de6825 100644 --- a/applications/ChatGPT/chatgpt/models/gpt/gpt_rm.py +++ b/applications/ChatGPT/chatgpt/models/gpt/gpt_rm.py @@ -35,4 +35,5 @@ def __init__(self, model.gradient_checkpointing_enable() value_head = nn.Linear(model.config.n_embd, 1) + value_head.weight.data.normal_(mean=0.0, std=1/(model.config.n_embd + 1)) super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/loss.py b/applications/ChatGPT/chatgpt/models/loss.py index 0ebcfea061b0..c5b1ccc93228 100644 --- a/applications/ChatGPT/chatgpt/models/loss.py +++ b/applications/ChatGPT/chatgpt/models/loss.py @@ -93,13 +93,23 @@ def forward(self, return policy_loss + self.pretrain_coef * lm_loss -class PairWiseLoss(nn.Module): +class LogSigLoss(nn.Module): """ Pairwise Loss for Reward Model + Details: https://arxiv.org/abs/2203.02155 """ - def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor: probs = torch.sigmoid(chosen_reward - reject_reward) log_probs = torch.log(probs) loss = -log_probs.mean() return loss + + +class LogExpLoss(nn.Module): + """ + Pairwise Loss for Reward Model + Details: https://arxiv.org/abs/2204.05862 + """ + def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor: + loss = torch.log(1 + torch.exp(reject_reward - chosen_reward)).mean() + return loss diff --git a/applications/ChatGPT/chatgpt/models/opt/opt_rm.py b/applications/ChatGPT/chatgpt/models/opt/opt_rm.py index 7ad7b3887e53..ef7f0fb16fd1 100644 --- a/applications/ChatGPT/chatgpt/models/opt/opt_rm.py +++ b/applications/ChatGPT/chatgpt/models/opt/opt_rm.py @@ -34,4 +34,5 @@ def __init__(self, model.gradient_checkpointing_enable() value_head = nn.Linear(model.config.word_embed_proj_dim, 1) + value_head.weight.data.normal_(mean=0.0, std=1/(model.config.word_embed_proj_dim + 1)) super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/trainer/rm.py b/applications/ChatGPT/chatgpt/trainer/rm.py index c07d65f84ca5..7fa87a64968b 100644 --- a/applications/ChatGPT/chatgpt/trainer/rm.py +++ b/applications/ChatGPT/chatgpt/trainer/rm.py @@ -1,13 +1,12 @@ from abc import ABC - +import pandas as pd import loralib as lora import torch -from chatgpt.dataset import RewardDataset -from chatgpt.models.loss import PairWiseLoss -from torch.optim import Adam, Optimizer -from torch.utils.data import DataLoader +from datetime import datetime +from torch.optim import Optimizer, lr_scheduler +from torch.utils.data import DataLoader, Dataset from tqdm import tqdm - + from .strategies import Strategy from .utils import is_rank_0 @@ -20,11 +19,12 @@ class RewardModelTrainer(ABC): model (torch.nn.Module): the model to train strategy (Strategy): the strategy to use for training optim(Optimizer): the optimizer to use for training - train_dataset (RewardDataset): the dataset to use for training - eval_dataset (RewardDataset): the dataset to use for evaluation + loss_fn (callable): the loss function to use for training + train_dataset (Dataset): the dataset to use for training + valid_dataset (Dataset): the dataset to use for validation + eval_dataset (Dataset): the dataset to use for evaluation batch_size (int, defaults to 1): the batch size while training max_epochs (int, defaults to 2): the number of epochs to train - optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer """ def __init__( @@ -32,24 +32,52 @@ def __init__( model, strategy: Strategy, optim: Optimizer, - train_dataset: RewardDataset, - eval_dataset: RewardDataset, + loss_fn, + train_dataset: Dataset, + valid_dataset: Dataset, + eval_dataset: Dataset, batch_size: int = 1, - max_epochs: int = 2, + max_epochs: int = 1, ) -> None: super().__init__() self.strategy = strategy self.epochs = max_epochs - self.train_dataloader = DataLoader(train_dataset, batch_size=batch_size) - self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size) - + self.train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + self.valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True) + self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size, shuffle=True) + self.model = strategy.setup_model(model) - if "DDP" in str(self.strategy): - self.model = self.model.module - self.loss_fn = PairWiseLoss() + self.loss_fn = loss_fn self.optimizer = strategy.setup_optimizer(optim, self.model) + self.scheduler = lr_scheduler.CosineAnnealingLR(self.optimizer, self.train_dataloader.__len__()//100) + - def fit(self, use_lora): + def eval_acc(self, dataloader): + dist = 0 + on = 0 + cnt = 0 + self.model.eval() + with torch.no_grad(): + for chosen_ids, c_mask, reject_ids, r_mask in dataloader: + chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) + c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) + reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) + r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) + chosen_reward = self.model(chosen_ids, attention_mask=c_mask) + reject_reward = self.model(reject_ids, attention_mask=r_mask) + for i in range(len(chosen_reward)): + cnt += 1 + if chosen_reward[i] > reject_reward[i]: + on += 1 + dist += (chosen_reward - reject_reward).mean().item() + dist_mean = dist / len(dataloader) + acc = on / cnt + self.model.train() + return dist_mean, acc + + + def fit(self): + time = datetime.now() epoch_bar = tqdm(range(self.epochs), desc='Train epoch', disable=not is_rank_0()) for epoch in range(self.epochs): step_bar = tqdm(range(self.train_dataloader.__len__()), @@ -57,37 +85,36 @@ def fit(self, use_lora): disable=not is_rank_0()) # train self.model.train() + cnt = 0 + acc = 0 + dist = 0 for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader: - chosen_ids = chosen_ids.squeeze(1).cuda() - c_mask = c_mask.squeeze(1).cuda() - reject_ids = reject_ids.squeeze(1).cuda() - r_mask = r_mask.squeeze(1).cuda() + chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) + c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) + reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) + r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) chosen_reward = self.model(chosen_ids, attention_mask=c_mask) reject_reward = self.model(reject_ids, attention_mask=r_mask) loss = self.loss_fn(chosen_reward, reject_reward) self.strategy.backward(loss, self.model, self.optimizer) self.strategy.optimizer_step(self.optimizer) self.optimizer.zero_grad() + cnt += 1 + if cnt == 100: + self.scheduler.step() + dist, acc = self.eval_acc(self.valid_dataloader) + cnt = 0 + if is_rank_0(): + log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], columns=['step', 'loss', 'dist', 'acc']) + log.to_csv('log_%s.csv' % time, mode='a', header=False, index=False) step_bar.update() - step_bar.set_postfix({'loss': loss.item()}) - + step_bar.set_postfix({'dist': dist, 'acc': acc}) + # eval - self.model.eval() - with torch.no_grad(): - dist = 0 - loss_sum = 0 - for chosen_ids, c_mask, reject_ids, r_mask in self.eval_dataloader: - chosen_ids = chosen_ids.squeeze(1).cuda() - c_mask = c_mask.squeeze(1).cuda() - reject_ids = reject_ids.squeeze(1).cuda() - r_mask = r_mask.squeeze(1).cuda() - chosen_reward = self.model(chosen_ids, attention_mask=c_mask) - reject_reward = self.model(reject_ids, attention_mask=r_mask) - dist += (chosen_reward - reject_reward).mean().item() - loss = self.loss_fn(chosen_reward, reject_reward) - loss_sum += loss.item() - dist_mean = dist / self.eval_dataloader.__len__() - loss_mean = loss_sum / self.eval_dataloader.__len__() + dist, acc = self.eval_acc(self.eval_dataloader) + if is_rank_0(): + log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], columns=['step', 'loss', 'dist', 'acc']) + log.to_csv('log.csv', mode='a', header=False, index=False) epoch_bar.update() - step_bar.set_postfix({'loss': loss_mean, 'dist_mean': dist_mean}) + step_bar.set_postfix({'dist': dist, 'acc': acc}) step_bar.close() diff --git a/applications/ChatGPT/examples/README.md b/applications/ChatGPT/examples/README.md index 3876d20f02d7..ce73a5407944 100644 --- a/applications/ChatGPT/examples/README.md +++ b/applications/ChatGPT/examples/README.md @@ -7,26 +7,42 @@ pip install -r requirements.txt ``` ## Train the reward model (Stage 2) -We use [rm-static](https://huggingface.co/datasets/Dahoas/rm-static) as dataset to train our reward model. It is a dataset of chosen & rejected response of the same prompt. - -You can download the dataset from huggingface automatically. - Use these code to train your reward model. - ```shell -# Naive reward model training -python train_reward_model.py --pretrain --model --strategy naive +# Take naive reward model training with opt-350m as example +python train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy naive # use colossalai_zero2 -torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain --model --strategy colossalai_zero2 +torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy colossalai_zero2 ``` +### Features and tricks in RM training +- We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)and[rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets. +- We support 2 kinds of loss_function named 'log_sig'(used by OpenAI) and 'log_exp'(used by Anthropic). +- We change the loss to valid_acc and pair_dist to monitor progress during training. +- We add special token to the end of the sequence to get better result. +- We use cosine-reducing lr-scheduler for RM training. +- We set value_head as 1 liner layer and initialize the weight of value_head using N(0，1/(d_model + 1)) distribution. +- We train a Bloom-560m reward model for 1 epoch and find the test acc of the model achieve the performance mentions in [Anthropics paper](https://arxiv.org/abs/2112.00861). + +### Experiment result +Model performance in [Anthropics paper](https://arxiv.org/abs/2112.00861): + +
image + +
Our training & test result of bloom-560m for 1 epoch: + +
image + +
+ ## Train with dummy prompt data (Stage 3) -This script supports 3 strategies: +This script supports 4 kinds of strategies: - naive - ddp -- colossalai +- colossalai_zero2 +- colossalai_gemini It uses random generated prompt data. @@ -53,7 +69,7 @@ We use [awesome-chatgpt-prompts](https://huggingface.co/datasets/fka/awesome-cha You should download `prompts.csv` first. -This script also supports 3 strategies. +This script also supports 4 strategies. ```shell # display cli help @@ -75,6 +91,9 @@ python inference.py --model_path --model Date: Mon, 20 Mar 2023 02:52:01 +0000 Subject: [PATCH 004/413] [refactor] update docs (#3174) * refactor: README-zh-Hans * refactor: REFERENCE * docs: update paths in README --- README.md | 4 ++-- README-zh-Hans.md => docs/README-zh-Hans.md | 0 REFERENCE.md => docs/REFERENCE.md | 0 3 files changed, 2 insertions(+), 2 deletions(-) rename README-zh-Hans.md => docs/README-zh-Hans.md (100%) rename REFERENCE.md => docs/REFERENCE.md (100%) diff --git a/README.md b/README.md index 3b55649b44bb..5ce18650fb41 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ [![WeChat badge](https://img.shields.io/badge/微信-加入-green?logo=wechat&)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png) - | [English](README.md) | [中文](README-zh-Hans.md) | + | [English](README.md) | [中文](docs/README-zh-Hans.md) |
@@ -399,7 +399,7 @@ We leverage the power of [GitHub Actions](https://github.com/features/actions) t ## Cite Us -This project is inspired by some related projects (some by our team and some by other organizations). We would like to credit these amazing projects as listed in the [Reference List](./REFERENCE.md). +This project is inspired by some related projects (some by our team and some by other organizations). We would like to credit these amazing projects as listed in the [Reference List](./docs/REFERENCE.md). To cite this project, you can use the following BibTeX citation. diff --git a/README-zh-Hans.md b/docs/README-zh-Hans.md similarity index 100% rename from README-zh-Hans.md rename to docs/README-zh-Hans.md diff --git a/REFERENCE.md b/docs/REFERENCE.md similarity index 100% rename from REFERENCE.md rename to docs/REFERENCE.md From 1ad3a636b17c4730bce4517a78b4056f0626fe50 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 20 Mar 2023 11:40:25 +0800 Subject: [PATCH 005/413] [test] fixed torchrec model test (#3167) * [test] fixed torchrec model test * polish code * polish code * polish code * polish code * polish code * polish code --- tests/kit/model_zoo/torchrec/torchrec.py | 139 +++++++++--------- .../test_mixed_precision/test_fp16_torch.py | 8 +- .../test_torchrec_model/test_deepfm_model.py | 12 +- .../test_torchrec_model/test_dlrm_model.py | 17 ++- tests/test_gemini/update/test_fwd_bwd.py | 14 +- 5 files changed, 96 insertions(+), 94 deletions(-) diff --git a/tests/kit/model_zoo/torchrec/torchrec.py b/tests/kit/model_zoo/torchrec/torchrec.py index 014e9218b226..03d95a06a89b 100644 --- a/tests/kit/model_zoo/torchrec/torchrec.py +++ b/tests/kit/model_zoo/torchrec/torchrec.py @@ -2,96 +2,95 @@ from functools import partial import torch - -try: - from torchrec.models import deepfm, dlrm - from torchrec.modules.embedding_configs import EmbeddingBagConfig - from torchrec.modules.embedding_modules import EmbeddingBagCollection - from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor - NO_TORCHREC = False -except ImportError: - NO_TORCHREC = True +from torchrec.models import deepfm, dlrm +from torchrec.modules.embedding_configs import EmbeddingBagConfig +from torchrec.modules.embedding_modules import EmbeddingBagCollection +from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor from ..registry import ModelAttribute, model_zoo +BATCH = 2 +SHAPE = 10 +# KeyedTensor +KT = KeyedTensor(keys=["f1", "f2"], length_per_key=[SHAPE, SHAPE], values=torch.rand((BATCH, 2 * SHAPE))) -def register_torchrec_models(): - BATCH = 2 - SHAPE = 10 - # KeyedTensor - KT = KeyedTensor(keys=["f1", "f2"], length_per_key=[SHAPE, SHAPE], values=torch.rand((BATCH, 2 * SHAPE))) +# KeyedJaggedTensor +KJT = KeyedJaggedTensor.from_offsets_sync(keys=["f1", "f2"], + values=torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]), + offsets=torch.tensor([0, 2, 4, 6, 8])) - # KeyedJaggedTensor - KJT = KeyedJaggedTensor.from_offsets_sync(keys=["f1", "f2"], - values=torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]), - offsets=torch.tensor([0, 2, 4, 6, 8])) +data_gen_fn = lambda: dict(features=torch.rand((BATCH, SHAPE))) - data_gen_fn = lambda: dict(features=torch.rand((BATCH, SHAPE))) +interaction_arch_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KT) - interaction_arch_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KT) +simple_dfm_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KJT) - simple_dfm_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KJT) +sparse_arch_data_gen_fn = lambda: dict(features=KJT) - sparse_arch_data_gen_fn = lambda: dict(features=KJT) - output_transform_fn = lambda x: dict(output=x) +def output_transform_fn(x): + if isinstance(x, KeyedTensor): + output = dict() + for key in x.keys(): + output[key] = x[key] + return output + else: + return dict(output=x) - def get_ebc(): - # EmbeddingBagCollection - eb1_config = EmbeddingBagConfig(name="t1", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f1"]) - eb2_config = EmbeddingBagConfig(name="t2", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f2"]) - return EmbeddingBagCollection(tables=[eb1_config, eb2_config]) - model_zoo.register(name='deepfm_densearch', - model_fn=partial(deepfm.DenseArch, SHAPE, SHAPE, SHAPE), - data_gen_fn=data_gen_fn, - output_transform_fn=output_transform_fn) +def get_ebc(): + # EmbeddingBagCollection + eb1_config = EmbeddingBagConfig(name="t1", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f1"]) + eb2_config = EmbeddingBagConfig(name="t2", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f2"]) + return EmbeddingBagCollection(tables=[eb1_config, eb2_config]) - model_zoo.register(name='deepfm_interactionarch', - model_fn=partial(deepfm.FMInteractionArch, SHAPE * 3, ["f1", "f2"], SHAPE), - data_gen_fn=interaction_arch_data_gen_fn, - output_transform_fn=output_transform_fn) - model_zoo.register(name='deepfm_overarch', - model_fn=partial(deepfm.OverArch, SHAPE), - data_gen_fn=data_gen_fn, - output_transform_fn=output_transform_fn) +model_zoo.register(name='deepfm_densearch', + model_fn=partial(deepfm.DenseArch, SHAPE, SHAPE, SHAPE), + data_gen_fn=data_gen_fn, + output_transform_fn=output_transform_fn) - model_zoo.register(name='deepfm_simpledeepfmnn', - model_fn=partial(deepfm.SimpleDeepFMNN, SHAPE, get_ebc(), SHAPE, SHAPE), - data_gen_fn=simple_dfm_data_gen_fn, - output_transform_fn=output_transform_fn) +model_zoo.register(name='deepfm_interactionarch', + model_fn=partial(deepfm.FMInteractionArch, SHAPE * 3, ["f1", "f2"], SHAPE), + data_gen_fn=interaction_arch_data_gen_fn, + output_transform_fn=output_transform_fn) - model_zoo.register(name='deepfm_sparsearch', - model_fn=partial(deepfm.SparseArch, get_ebc()), - data_gen_fn=sparse_arch_data_gen_fn, - output_transform_fn=output_transform_fn) +model_zoo.register(name='deepfm_overarch', + model_fn=partial(deepfm.OverArch, SHAPE), + data_gen_fn=data_gen_fn, + output_transform_fn=output_transform_fn) - model_zoo.register(name='dlrm', - model_fn=partial(dlrm.DLRM, get_ebc(), SHAPE, [SHAPE, SHAPE], [5, 1]), - data_gen_fn=simple_dfm_data_gen_fn, - output_transform_fn=output_transform_fn) +model_zoo.register(name='deepfm_simpledeepfmnn', + model_fn=partial(deepfm.SimpleDeepFMNN, SHAPE, get_ebc(), SHAPE, SHAPE), + data_gen_fn=simple_dfm_data_gen_fn, + output_transform_fn=output_transform_fn) - model_zoo.register(name='dlrm_densearch', - model_fn=partial(dlrm.DenseArch, SHAPE, [SHAPE, SHAPE]), - data_gen_fn=data_gen_fn, - output_transform_fn=output_transform_fn) +model_zoo.register(name='deepfm_sparsearch', + model_fn=partial(deepfm.SparseArch, get_ebc()), + data_gen_fn=sparse_arch_data_gen_fn, + output_transform_fn=output_transform_fn) - model_zoo.register(name='dlrm_interactionarch', - model_fn=partial(dlrm.InteractionArch, 2), - data_gen_fn=interaction_arch_data_gen_fn, - output_transform_fn=output_transform_fn) +model_zoo.register(name='dlrm', + model_fn=partial(dlrm.DLRM, get_ebc(), SHAPE, [SHAPE, SHAPE], [5, 1]), + data_gen_fn=simple_dfm_data_gen_fn, + output_transform_fn=output_transform_fn) - model_zoo.register(name='dlrm_overarch', - model_fn=partial(dlrm.OverArch, SHAPE, [5, 1]), - data_gen_fn=data_gen_fn, - output_transform_fn=output_transform_fn) +model_zoo.register(name='dlrm_densearch', + model_fn=partial(dlrm.DenseArch, SHAPE, [SHAPE, SHAPE]), + data_gen_fn=data_gen_fn, + output_transform_fn=output_transform_fn) - model_zoo.register(name='dlrm_sparsearch', - model_fn=partial(dlrm.SparseArch, get_ebc()), - data_gen_fn=sparse_arch_data_gen_fn, - output_transform_fn=output_transform_fn) +model_zoo.register(name='dlrm_interactionarch', + model_fn=partial(dlrm.InteractionArch, 2), + data_gen_fn=interaction_arch_data_gen_fn, + output_transform_fn=output_transform_fn) +model_zoo.register(name='dlrm_overarch', + model_fn=partial(dlrm.OverArch, SHAPE, [5, 1]), + data_gen_fn=data_gen_fn, + output_transform_fn=output_transform_fn) -if not NO_TORCHREC: - register_torchrec_models() +model_zoo.register(name='dlrm_sparsearch', + model_fn=partial(dlrm.SparseArch, get_ebc()), + data_gen_fn=sparse_arch_data_gen_fn, + output_transform_fn=output_transform_fn) diff --git a/tests/test_booster/test_mixed_precision/test_fp16_torch.py b/tests/test_booster/test_mixed_precision/test_fp16_torch.py index c56fcae58a60..98d00cd2caca 100644 --- a/tests/test_booster/test_mixed_precision/test_fp16_torch.py +++ b/tests/test_booster/test_mixed_precision/test_fp16_torch.py @@ -7,11 +7,17 @@ def test_torch_amp(): for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + # dlrm_interactionarch has not parameters, so skip + if name == 'dlrm_interactionarch': + continue + model = model_fn().cuda() optimizer = Adam(model.parameters(), lr=1e-3) criterion = lambda x: x.mean() data = data_gen_fn() - data = {k: v.cuda() if torch.is_tensor(v) else v for k, v in data.items()} + data = { + k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items() + } mixed_precision = FP16TorchMixedPrecision() model, optimizer, criterion = mixed_precision.configure(model, optimizer, criterion) output = model(**data) diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py index 6cbca343d134..a30139f26d29 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py @@ -7,11 +7,6 @@ BATCH = 2 SHAPE = 10 -deepfm_models = model_zoo.get_sub_registry('deepfm') -NOT_DFM = False -if not deepfm_models: - NOT_DFM = True - def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): # trace @@ -52,8 +47,9 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): ), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}' -@pytest.mark.skipif(NOT_DFM, reason='torchrec is not installed') -def test_torchrec_deepfm_models(deepfm_models): +@pytest.mark.skip('unknown error') +def test_torchrec_deepfm_models(): + deepfm_models = model_zoo.get_sub_registry('deepfm') torch.backends.cudnn.deterministic = True for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in deepfm_models.items(): @@ -67,4 +63,4 @@ def test_torchrec_deepfm_models(deepfm_models): if __name__ == "__main__": - test_torchrec_deepfm_models(deepfm_models) + test_torchrec_deepfm_models() diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py index 7aa868265f15..27a88291397e 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py @@ -7,11 +7,6 @@ BATCH = 2 SHAPE = 10 -dlrm_models = model_zoo.get_sub_registry('dlrm') -NOT_DLRM = False -if not dlrm_models: - NOT_DLRM = True - def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): # trace @@ -52,12 +47,18 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): ), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}' -@pytest.mark.skipif(NOT_DLRM, reason='torchrec is not installed') -def test_torchrec_dlrm_models(dlrm_models): +@pytest.mark.skip('unknown error') +def test_torchrec_dlrm_models(): torch.backends.cudnn.deterministic = True + dlrm_models = model_zoo.get_sub_registry('dlrm') for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in dlrm_models.items(): data = data_gen_fn() + + # dlrm_interactionarch is not supported + if name == 'dlrm_interactionarch': + continue + if attribute is not None and attribute.has_control_flow: meta_args = {k: v.to('meta') for k, v in data.items()} else: @@ -67,4 +68,4 @@ def test_torchrec_dlrm_models(dlrm_models): if __name__ == "__main__": - test_torchrec_dlrm_models(dlrm_models) + test_torchrec_dlrm_models() diff --git a/tests/test_gemini/update/test_fwd_bwd.py b/tests/test_gemini/update/test_fwd_bwd.py index 0d35ba83d2e9..2821dc78d984 100644 --- a/tests/test_gemini/update/test_fwd_bwd.py +++ b/tests/test_gemini/update/test_fwd_bwd.py @@ -34,17 +34,17 @@ def check_grad(model: ZeroDDP, torch_model: torch.nn.Module): assert_close(p0, p1.grad, rtol=1e-3, atol=5e-5) -@parameterize('init_device', [get_current_device()]) @parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const']) @parameterize('keep_gather', [False, True]) @parameterize('model_name', ['gpt2', 'bert', 'albert']) @parameterize('use_grad_checkpoint', [False, True]) -def exam_gpt_fwd_bwd(placement_policy, - keep_gather, - model_name: str, - use_grad_checkpoint: bool = False, - init_device=get_current_device()): - +def exam_gpt_fwd_bwd( + placement_policy, + keep_gather, + model_name: str, + use_grad_checkpoint: bool = False, +): + init_device = get_current_device() get_components_func = non_distributed_component_funcs.get_callable(model_name) model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() From a9b8402d93ac69bb9a8b46e21cfe3697409972fe Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 20 Mar 2023 13:59:24 +0800 Subject: [PATCH 006/413] [booster] added the accelerator implementation (#3159) --- colossalai/booster/accelerator.py | 48 +++++++++++++++++-- colossalai/booster/booster.py | 15 +++++- tests/test_booster/test_accelerator.py | 13 +++++ .../test_torchrec_model/test_dlrm_model.py | 1 + 4 files changed, 72 insertions(+), 5 deletions(-) create mode 100644 tests/test_booster/test_accelerator.py diff --git a/colossalai/booster/accelerator.py b/colossalai/booster/accelerator.py index 63ba193e3e4f..fc2c4a40068b 100644 --- a/colossalai/booster/accelerator.py +++ b/colossalai/booster/accelerator.py @@ -3,12 +3,52 @@ __all__ = ['Accelerator'] +_supported_devices = [ + 'cpu', + 'cuda', + + # To be supported + # 'xpu', + # 'npu', + # 'tpu', +] + class Accelerator: + """ + Accelerator is an abstraction for the hardware device that is used to run the model. + + Args: + device (str): The device to be used. Currently only support 'cpu' and 'gpu'. + """ - def __init__(self, device: torch.device): + def __init__(self, device: str): self.device = device - def setup_model(self, model: nn.Module) -> nn.Module: - # TODO: implement this method - pass + assert self.device in _supported_devices, f"Device {self.device} is not supported yet, supported devices include {_supported_devices}" + + def bind(self): + """ + Set the default device for the current process. + """ + if self.device == 'cpu': + pass + elif self.device == 'cuda': + # TODO(FrankLeeeee): use global environment to check if it is a dist job + # if is_distributed: + # local_rank = EnvTable().get_local_rank() + # torch.cuda.set_device(torch.device(f'cuda:{local_rank}')) + torch.cuda.set_device(torch.device('cuda')) + pass + else: + raise ValueError(f"Device {self.device} is not supported yet") + + def configure_model(self, model: nn.Module) -> nn.Module: + """ + Move the model to the device. + + Args: + model (nn.Module): The model to be moved. + """ + model = model.to(torch.device(self.device)) + return model diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 7b351ae343d2..7d7f21ca6cf2 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -8,6 +8,7 @@ from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils.data import DataLoader +from .accelerator import Accelerator from .mixed_precision import MixedPrecision, mixed_precision_factory from .plugin import Plugin @@ -51,9 +52,16 @@ class Booster: """ def __init__(self, - device: Union[str, torch.device] = 'cuda', + device: str = 'cuda', mixed_precision: Union[MixedPrecision, str] = None, plugin: Optional[Plugin] = None) -> None: + # TODO(FrankLeeeee): add plugin control logic + # if self.plugin is not None and self.plugin.control_accelerator: + # ... + # create acclerator + self.acceleartor = Accelerator(device) + self.acceleartor.set_default_device() + # validate and set precision if isinstance(MixedPrecision, str): # the user will take the default arguments for amp training @@ -78,6 +86,11 @@ def boost(self, model: nn.Module, optimizer: Optimizer, criterion: Callable, lr_ lr_scheduler (LRScheduler): The lr_scheduler to be boosted. dataloader (DataLoader): The dataloader to be boosted. """ + # TODO(FrankLeeeee): add plugin control logic + # if self.plugin is not None and self.plugin.control_accelerator: + # ... + model = self.acceleartor.configure_model(model) + # TODO(FrankLeeeee): consider multi-model and multi-optimizer case # TODO(lsg): Add plugin control logic # e.g. diff --git a/tests/test_booster/test_accelerator.py b/tests/test_booster/test_accelerator.py new file mode 100644 index 000000000000..4bfa3fd0631e --- /dev/null +++ b/tests/test_booster/test_accelerator.py @@ -0,0 +1,13 @@ +import pytest +import torch.nn as nn +from torchvision.models import resnet18 + +from colossalai.booster.accelerator import Accelerator + + +@pytest.mark.parametrize('device', ['cpu', 'cuda']) +def test_accelerator(device): + acceleartor = Accelerator(device) + model = nn.Linear(8, 8) + model = acceleartor.configure_model(model) + assert next(model.parameters()).device.type == device diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py index 27a88291397e..71ecf7fca53e 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py @@ -56,6 +56,7 @@ def test_torchrec_dlrm_models(): data = data_gen_fn() # dlrm_interactionarch is not supported + # TODO(FrankLeeeee): support this model if name == 'dlrm_interactionarch': continue From 4e921cfbd68c0399a7576243e02ccb9b31fb5c94 Mon Sep 17 00:00:00 2001 From: NatalieC323 <127177614+NatalieC323@users.noreply.github.com> Date: Mon, 20 Mar 2023 14:19:05 +0800 Subject: [PATCH 007/413] [examples] Solving the diffusion issue of incompatibility issue#3169 (#3170) * Update requirements.txt * Update environment.yaml * Update README.md * Update environment.yaml --- examples/images/diffusion/README.md | 50 ++++++++++++++++------ examples/images/diffusion/requirements.txt | 4 +- 2 files changed, 39 insertions(+), 15 deletions(-) diff --git a/examples/images/diffusion/README.md b/examples/images/diffusion/README.md index 2a99094b703a..22970ced064e 100644 --- a/examples/images/diffusion/README.md +++ b/examples/images/diffusion/README.md @@ -40,8 +40,7 @@ This project is in rapid development. ### Option #1: install from source #### Step 1: Requirements -A suitable [conda](https://conda.io/) environment named `ldm` can be created -and activated with: +To begin with, make sure your operating system has the cuda version suitable for this exciting training session, which is cuda11.6/11.8. For your convience, we have set up the rest of packages here. You can create and activate a suitable [conda](https://conda.io/) environment named `ldm` : ``` conda env create -f environment.yaml @@ -55,11 +54,34 @@ conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit pip install transformers diffusers invisible-watermark ``` -#### Step 2:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website +#### Step 2: install lightning + +Install Lightning version later than 2022.01.04. We suggest you install lightning from source. Notice that the default download path of pip should be within the conda environment, or you may need to specify using 'which pip' and redirect the path into conda environment. + +##### From Source +``` +git clone https://github.com/Lightning-AI/lightning.git +pip install -r requirements.txt +python setup.py install +``` ##### From pip -For example, you can install v0.2.0 from our official website. +``` +pip install pytorch-lightning +``` + +#### Step 3:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website + +You can install the latest version (0.2.7) from our official website or from source. Notice that the suitable version for this training is colossalai(0.2.5), which stands for torch(1.12.1). + +##### Download suggested verision for this training + +``` +pip install colossalai=0.2.5 +``` + +##### Download the latest version from pip for latest torch version ``` pip install colossalai @@ -75,10 +97,12 @@ cd ColossalAI CUDA_EXT=1 pip install . ``` -#### Step 3:Accelerate with flash attention by xformers(Optional) +#### Step 4:Accelerate with flash attention by xformers(Optional) + +Notice that xformers will accelerate the training process in cost of extra disk space. The suitable version of xformers for this training process is 0.12.0. You can download xformers directly via pip. For more release versions, feel free to check its official website: [XFormers](./https://pypi.org/project/xformers/) ``` -pip install xformers +pip install xformers==0.0.12 ``` ### Option #2: Use Docker @@ -94,7 +118,7 @@ docker build -t hpcaitech/diffusion:0.2.0 . docker pull hpcaitech/diffusion:0.2.0 ``` -Once you have the image ready, you can launch the image with the following command: +Once you have the image ready, you can launch the image with the following command ```bash ######################## @@ -157,10 +181,9 @@ you should the change the `data.file_path` in the `config/train_colossalai.yaml` ## Training -We provide the script `train_colossalai.sh` to run the training task with colossalai, -and can also use `train_ddp.sh` to run the training task with ddp to compare. +We provide the script `train_colossalai.sh` to run the training task with colossalai. Meanwhile, we have enlightened other training process such as DDP model in PyTorch. You can also use `train_ddp.sh` to run the training task with ddp to compare the corresponding performance. -In `train_colossalai.sh` the main command is: +In `train_colossalai.sh` the main command is ``` python main.py --logdir /tmp/ --train --base configs/train_colossalai.yaml --ckpt 512-base-ema.ckpt @@ -176,9 +199,10 @@ python main.py --logdir /tmp/ --train --base configs/train_colossalai.yaml --ckp You can change the trainging config in the yaml file -- devices: device number used for training, default 8 -- max_epochs: max training epochs, default 2 -- precision: the precision type used in training, default 16 (fp16), you must use fp16 if you want to apply colossalai +- devices: device number used for training, default = 8 +- max_epochs: max training epochs, default = 2 +- precision: the precision type used in training, default = 16 (fp16), you must use fp16 if you want to apply colossalai +- placement_policy: the training strategy supported by Colossal AI, defult = 'cuda', which refers to loading all the parameters into cuda memory. On the other hand, 'cpu' refers to 'cpu offload' strategy while 'auto' enables 'Gemini', both featured by Colossal AI. - more information about the configuration of ColossalAIStrategy can be found [here](https://pytorch-lightning.readthedocs.io/en/latest/advanced/model_parallel.html#colossal-ai) diff --git a/examples/images/diffusion/requirements.txt b/examples/images/diffusion/requirements.txt index d0af35353b66..59d027fcf60f 100644 --- a/examples/images/diffusion/requirements.txt +++ b/examples/images/diffusion/requirements.txt @@ -1,10 +1,10 @@ albumentations==1.3.0 -opencv-python==4.6.0 +opencv-python==4.6.0.66 pudb==2019.2 prefetch_generator imageio==2.9.0 imageio-ffmpeg==0.4.2 -torchmetrics==0.6 +torchmetrics==0.7 omegaconf==2.1.1 test-tube>=0.7.5 streamlit>=0.73.1 From 085e7f4eff832f2510d8023a9821206ab1894b2e Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 20 Mar 2023 16:19:06 +0800 Subject: [PATCH 008/413] [test] fixed torchrec registration in model zoo (#3177) * [test] fixed torchrec registration in model zoo * polish code * polish code * polish code --- tests/kit/model_zoo/torchrec/torchrec.py | 72 +++++++++++++++---- .../test_torchrec_model/test_deepfm_model.py | 1 - .../test_torchrec_model/test_dlrm_model.py | 1 - 3 files changed, 59 insertions(+), 15 deletions(-) diff --git a/tests/kit/model_zoo/torchrec/torchrec.py b/tests/kit/model_zoo/torchrec/torchrec.py index 03d95a06a89b..dda563155fca 100644 --- a/tests/kit/model_zoo/torchrec/torchrec.py +++ b/tests/kit/model_zoo/torchrec/torchrec.py @@ -11,21 +11,47 @@ BATCH = 2 SHAPE = 10 -# KeyedTensor -KT = KeyedTensor(keys=["f1", "f2"], length_per_key=[SHAPE, SHAPE], values=torch.rand((BATCH, 2 * SHAPE))) + + +def gen_kt(): + KT = KeyedTensor(keys=["f1", "f2"], length_per_key=[SHAPE, SHAPE], values=torch.rand((BATCH, 2 * SHAPE))) + return KT + # KeyedJaggedTensor -KJT = KeyedJaggedTensor.from_offsets_sync(keys=["f1", "f2"], - values=torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]), - offsets=torch.tensor([0, 2, 4, 6, 8])) +def gen_kjt(): + KJT = KeyedJaggedTensor.from_offsets_sync(keys=["f1", "f2"], + values=torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]), + offsets=torch.tensor([0, 2, 4, 6, 8])) + return KJT + data_gen_fn = lambda: dict(features=torch.rand((BATCH, SHAPE))) -interaction_arch_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KT) -simple_dfm_data_gen_fn = lambda: dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KJT) +def interaction_arch_data_gen_fn(): + KT = gen_kt() + return dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KT) + + +def simple_dfm_data_gen_fn(): + KJT = gen_kjt() + return dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KJT) + -sparse_arch_data_gen_fn = lambda: dict(features=KJT) +def sparse_arch_data_gen_fn(): + KJT = gen_kjt() + return dict(features=KJT) + + +def output_transform_fn(x): + if isinstance(x, KeyedTensor): + output = dict() + for key in x.keys(): + output[key] = x[key] + return output + else: + return dict(output=x) def output_transform_fn(x): @@ -42,7 +68,27 @@ def get_ebc(): # EmbeddingBagCollection eb1_config = EmbeddingBagConfig(name="t1", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f1"]) eb2_config = EmbeddingBagConfig(name="t2", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f2"]) - return EmbeddingBagCollection(tables=[eb1_config, eb2_config]) + return EmbeddingBagCollection(tables=[eb1_config, eb2_config], device=torch.device('cpu')) + + +def sparse_arch_model_fn(): + ebc = get_ebc() + return deepfm.SparseArch(ebc) + + +def simple_deep_fmnn_model_fn(): + ebc = get_ebc() + return deepfm.SimpleDeepFMNN(SHAPE, ebc, SHAPE, SHAPE) + + +def dlrm_model_fn(): + ebc = get_ebc() + return dlrm.DLRM(ebc, SHAPE, [SHAPE, SHAPE], [5, 1]) + + +def dlrm_sparsearch_model_fn(): + ebc = get_ebc() + return dlrm.SparseArch(ebc) model_zoo.register(name='deepfm_densearch', @@ -61,17 +107,17 @@ def get_ebc(): output_transform_fn=output_transform_fn) model_zoo.register(name='deepfm_simpledeepfmnn', - model_fn=partial(deepfm.SimpleDeepFMNN, SHAPE, get_ebc(), SHAPE, SHAPE), + model_fn=simple_deep_fmnn_model_fn, data_gen_fn=simple_dfm_data_gen_fn, output_transform_fn=output_transform_fn) model_zoo.register(name='deepfm_sparsearch', - model_fn=partial(deepfm.SparseArch, get_ebc()), + model_fn=sparse_arch_model_fn, data_gen_fn=sparse_arch_data_gen_fn, output_transform_fn=output_transform_fn) model_zoo.register(name='dlrm', - model_fn=partial(dlrm.DLRM, get_ebc(), SHAPE, [SHAPE, SHAPE], [5, 1]), + model_fn=dlrm_model_fn, data_gen_fn=simple_dfm_data_gen_fn, output_transform_fn=output_transform_fn) @@ -91,6 +137,6 @@ def get_ebc(): output_transform_fn=output_transform_fn) model_zoo.register(name='dlrm_sparsearch', - model_fn=partial(dlrm.SparseArch, get_ebc()), + model_fn=dlrm_sparsearch_model_fn, data_gen_fn=sparse_arch_data_gen_fn, output_transform_fn=output_transform_fn) diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py index a30139f26d29..a4e847dbcfcd 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py @@ -47,7 +47,6 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): ), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}' -@pytest.mark.skip('unknown error') def test_torchrec_deepfm_models(): deepfm_models = model_zoo.get_sub_registry('deepfm') torch.backends.cudnn.deterministic = True diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py index 71ecf7fca53e..ac377ff1d5f8 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py @@ -47,7 +47,6 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): ), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}' -@pytest.mark.skip('unknown error') def test_torchrec_dlrm_models(): torch.backends.cudnn.deterministic = True dlrm_models = model_zoo.get_sub_registry('dlrm') From 7bc0afc901f2f0ce187cab9a0b1587740094d7b5 Mon Sep 17 00:00:00 2001 From: zbian Date: Fri, 17 Mar 2023 15:09:47 +0800 Subject: [PATCH 009/413] updated flash attention usage --- LICENSE | 70 ++++++ .../kernel/cuda_native/flash_attention.py | 207 +++++++++++++----- tests/test_utils/test_flash_attention.py | 200 +++++++---------- 3 files changed, 307 insertions(+), 170 deletions(-) diff --git a/LICENSE b/LICENSE index 394791da2771..c7a5bb16880e 100644 --- a/LICENSE +++ b/LICENSE @@ -326,3 +326,73 @@ Copyright 2021- HPC-AI Technology Inc. All rights reserved. CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + ---------------- LICENSE FOR Flash Attention ---------------- + + BSD 3-Clause License + + Copyright (c) 2022, the respective contributors, as shown by the AUTHORS file. + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE + FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + ---------------- LICENSE FOR Facebook xFormers ---------------- + + From xFormers: + + Copyright (c) Facebook, Inc. and its affiliates + + + === + + BSD 3-Clause License + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America + and IDIAP Research Institute nor the names of its contributors may be + used to endorse or promote products derived from this software without + specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. diff --git a/colossalai/kernel/cuda_native/flash_attention.py b/colossalai/kernel/cuda_native/flash_attention.py index 907fa640d826..d793815ed681 100644 --- a/colossalai/kernel/cuda_native/flash_attention.py +++ b/colossalai/kernel/cuda_native/flash_attention.py @@ -1,12 +1,6 @@ """ -The triton-based flash attention implementation is copied from the OpenAI/triton repository - -You can find the repository in Triton https://github.com/openai/triton -You can find the source file in https://github.com/openai/triton/blob/main/python/tutorials/06-fused-attention.py - -Reference: -1. Dao et al., https://arxiv.org/pdf/2205.14135v2.pdf -2. Rabe and Staats https://arxiv.org/pdf/2112.05682v2.pdf +A general attention module using the flash attention kernels from xformers: +https://github.com/facebookresearch/xformers/tree/main/xformers/ops/fmha """ import math @@ -15,6 +9,159 @@ import torch +try: + from xformers.ops.fmha import memory_efficient_attention + HAS_MEM_EFF_ATTN = True +except ImportError: + HAS_MEM_EFF_ATTN = False + print('please install xformers from https://github.com/facebookresearch/xformers') + +if HAS_MEM_EFF_ATTN: + + from typing import Optional + + from einops import rearrange + from xformers.ops.fmha import MemoryEfficientAttentionCutlassOp + from xformers.ops.fmha.attn_bias import BlockDiagonalMask, LowerTriangularMask, LowerTriangularMaskWithTensorBias + + from .scaled_softmax import AttnMaskType + + allow_alibi = True + for op in MemoryEfficientAttentionCutlassOp: + allow_alibi = allow_alibi & (LowerTriangularMaskWithTensorBias in op.SUPPORTED_ATTN_BIAS_TYPES) + + class Unpad(torch.autograd.Function): + """ + Adapted from + https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/bert_padding.py + """ + + @staticmethod + def forward(ctx, tensor: torch.Tensor, indices: torch.Tensor): + ctx.save_for_backward(indices) + # [b, s, ...] + assert tensor.ndim >= 3 + ctx.bsz = tensor.shape[0] + out = rearrange(tensor, 'b s ... -> (b s) ...') + ctx.shape = out.shape + # [1, ntokens, ...] + return out[indices].unsqueeze(0) + + @staticmethod + def backward(ctx, grad_output): + indices, = ctx.saved_tensors + # [b*s, ...] + grad = torch.zeros(ctx.shape, dtype=grad_output.dtype, device=grad_output.device) + grad[indices] = grad_output.squeeze(0) + grad = rearrange(grad, '(b s) ... -> b s ...', b=ctx.bsz) + # [b, s, ...] + return grad, None + + class Repad(torch.autograd.Function): + """ + Adapted from + https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/bert_padding.py + """ + + @staticmethod + def forward(ctx, tensor: torch.Tensor, indices: torch.Tensor, batch_size: int, seq_len: int): + ctx.save_for_backward(indices) + # [ntokens, ...] + tensor = tensor.squeeze(0) + out = torch.zeros((batch_size * seq_len, *tensor.shape[1:]), dtype=tensor.dtype, device=tensor.device) + # [b*s, ...] + out[indices] = tensor + # [b, s, ...] + out = rearrange(out, '(b s) ... -> b s ...', b=batch_size) + return out + + @staticmethod + def backward(ctx, grad_output): + indices, = ctx.saved_tensors + # [b*s, ...] + grad_output = rearrange(grad_output, 'b s ... -> (b s) ...') + grad = grad_output[indices] + # [1, ntokens, ...] + return grad.unsqueeze(0), None, None, None + + class ColoAttention(torch.nn.Module): + + def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0): + super().__init__() + assert embed_dim % num_heads == 0, \ + f"the embed dim ({embed_dim}) is not divisible by the number of attention heads ({num_heads})." + self.scale = 1 / math.sqrt(embed_dim // num_heads) + self.dropout = dropout + + @staticmethod + def get_seq_info_from_mask(attn_mask: torch.Tensor): + indices = torch.nonzero(attn_mask.flatten(), as_tuple=False).flatten() + seqlens = attn_mask.sum(dim=-1, dtype=torch.int32).flatten().tolist() + return indices, seqlens + + @staticmethod + def unpad(tensor: torch.Tensor, indices: torch.Tensor) -> torch.Tensor: + return Unpad.apply(tensor, indices) + + @staticmethod + def repad(tensor: torch.Tensor, indices: torch.Tensor, batch_size: int, seq_len: int) -> torch.Tensor: + return Repad.apply(tensor, indices, batch_size, seq_len) + + def forward(self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attn_mask: Optional[torch.Tensor] = None, + attn_mask_type: Optional[AttnMaskType] = None, + bias: Optional[torch.Tensor] = None): + batch_size, tgt_len, src_len = query.shape[0], query.shape[1], key.shape[1] + attn_bias = None + if attn_mask_type == AttnMaskType.padding: # bert style + assert attn_mask is not None, \ + f"attention mask {attn_mask} is not valid for attention mask type {attn_mask_type}." + assert attn_mask.dim() == 2, \ + "attention mask is supposed to have shape (batch_size, seq_len), " + \ + f"but got {attn_mask.dim()} dimensions." + if tgt_len == src_len: + q_indices, q_seqlen = self.get_seq_info_from_mask(attn_mask) + kv_seqlen = None + if batch_size > 1: + query, key, value = self.unpad(torch.stack([query, key, value], dim=2), q_indices).unbind(dim=2) + else: + q_indices = torch.arange(batch_size * tgt_len, dtype=torch.int32, device=query.device) + q_seqlen = torch.LongTensor([tgt_len] * batch_size, device=query.device) + kv_indices, kv_seqlen = self.get_seq_info_from_mask(attn_mask) + if batch_size > 1: + query = rearrange(query, "b s ... -> c (b s) ...", c=1) + key, value = self.unpad(torch.stack([query, key, value], dim=2), kv_indices).unbind(dim=2) + attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen, kv_seqlen) + elif attn_mask_type == AttnMaskType.causal: # gpt style + attn_bias = LowerTriangularMask() + + if bias is not None: # alibi / relative position emebedding + assert allow_alibi, "flash attention with bias is not supported in this system." + assert attn_mask_type == AttnMaskType.causal, \ + "attention with bias is only supported for causal attention so far." + attn_bias = attn_bias.add_bias(bias) + + out = memory_efficient_attention(query, key, value, attn_bias=attn_bias, p=self.dropout, scale=self.scale) + + if attn_mask_type == AttnMaskType.padding and batch_size > 1: + out = self.repad(out, q_indices, batch_size, tgt_len) + + out = rearrange(out, 'b s h d -> b s (h d)') + return out + + +########################################################################## +# the flash attention functions below that are copied +# from the OpenAI/triton repository will be deprecated +# You can find the repository in Triton https://github.com/openai/triton +# You can find the source file in https://github.com/openai/triton/blob/main/python/tutorials/06-fused-attention.py +# Reference: +# 1. Dao et al., https://arxiv.org/pdf/2205.14135v2.pdf +# 2. Rabe and Staats https://arxiv.org/pdf/2112.05682v2.pdf + def triton_cuda_check(): cuda_home = os.getenv("CUDA_HOME", default="/usr/local/cuda") @@ -52,13 +199,6 @@ def triton_cuda_check(): HAS_FLASH_ATTN = False print('please install flash_attn from https://github.com/HazyResearch/flash-attention') -try: - from xformers.ops.fmha import memory_efficient_attention - HAS_MEM_EFF_ATTN = True -except ImportError: - HAS_MEM_EFF_ATTN = False - print('please install xformers from https://github.com/facebookresearch/xformers') - if HAS_TRITON: # the following functions are adapted from the OpenAI Triton tutorial # https://github.com/openai/triton/blob/main/python/tutorials/06-fused-attention.py @@ -422,25 +562,6 @@ def triton_flash_attention(q, k, v, sm_scale): if HAS_FLASH_ATTN: - from einops import rearrange - - class MaskedFlashAttention(torch.nn.Module): - - def __init__(self, num_attention_heads: int, attention_head_size: int, attention_dropout: float) -> None: - super().__init__() - self.num_attention_heads = num_attention_heads - self.attention_head_size = attention_head_size - self.attention_func = FlashAttention(softmax_scale=math.sqrt(attention_head_size), - attention_dropout=attention_dropout) - - def forward(self, query_key_value: torch.Tensor, attention_mask: torch.Tensor, causal=False): - if attention_mask.dtype is not torch.bool: - attention_mask = attention_mask.bool() - qkv = rearrange(query_key_value, 'b s (three h d) -> b s three h d', three=3, h=self.num_attention_heads) - context, _ = self.attention_func(qkv, key_padding_mask=attention_mask, causal=causal) - context = rearrange(context, 'b s h d -> b s (h d)') - return context - def flash_attention_qkv(qkv, sm_scale, batch_size, seq_len, dropout_p=0., causal=False): """ Arguments: @@ -511,20 +632,4 @@ def flash_attention_q_k_v(q, k, v, sm_scale, batch_size, q_seqlen, kv_seqlen, dr causal) -if HAS_MEM_EFF_ATTN: - - from einops import rearrange - from xformers.ops.fmha import LowerTriangularMask - - class MemoryEfficientAttention(torch.nn.Module): - - def __init__(self, hidden_size: int, num_attention_heads: int, attention_dropout: float = 0.0): - super().__init__() - attention_head_size = hidden_size // num_attention_heads - self.scale = 1 / attention_head_size**0.5 - self.dropout = attention_dropout - - def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: torch.Tensor): - context = memory_efficient_attention(query, key, value, attention_mask, self.dropout, self.scale) - context = rearrange(context, 'b s h d -> b s (h d)') - return context +########################################################################## diff --git a/tests/test_utils/test_flash_attention.py b/tests/test_utils/test_flash_attention.py index 58e3b21d97eb..441cbbb22ce7 100644 --- a/tests/test_utils/test_flash_attention.py +++ b/tests/test_utils/test_flash_attention.py @@ -1,22 +1,13 @@ +import random + import pytest import torch from einops import rearrange -from colossalai.kernel.cuda_native.flash_attention import HAS_FLASH_ATTN, HAS_MEM_EFF_ATTN, HAS_TRITON - -if HAS_FLASH_ATTN: - from colossalai.kernel.cuda_native.flash_attention import ( - MaskedFlashAttention, - flash_attention_q_k_v, - flash_attention_q_kv, - flash_attention_qkv, - ) - -if HAS_TRITON: - from colossalai.kernel.cuda_native.flash_attention import triton_flash_attention +from colossalai.kernel.cuda_native.flash_attention import HAS_MEM_EFF_ATTN if HAS_MEM_EFF_ATTN: - from colossalai.kernel.cuda_native.flash_attention import LowerTriangularMask, MemoryEfficientAttention + from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention def baseline_attention(Z, N_CTX, H, q, k, v, sm_scale): @@ -30,117 +21,88 @@ def baseline_attention(Z, N_CTX, H, q, k, v, sm_scale): return ref_out -@pytest.mark.skipif(HAS_TRITON == False, reason="triton is not available") -@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(3, 4, 2, 16)]) -def test_triton_flash_attention(Z, H, N_CTX, D_HEAD, dtype=torch.float16): - torch.manual_seed(20) - q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() - k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() - v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() - sm_scale = 0.3 - dout = torch.randn_like(q) - - ref_out = baseline_attention(Z, N_CTX, H, q, k, v, sm_scale) - ref_out.backward(dout) - ref_dv, v.grad = v.grad.clone(), None - ref_dk, k.grad = k.grad.clone(), None - ref_dq, q.grad = q.grad.clone(), None - - # triton implementation - tri_out = triton_flash_attention(q, k, v, sm_scale) - tri_out.backward(dout) - tri_dv, v.grad = v.grad.clone(), None - tri_dk, k.grad = k.grad.clone(), None - tri_dq, q.grad = q.grad.clone(), None - # compare - assert torch.allclose(ref_out, tri_out, atol=1e-3) - assert torch.allclose(ref_dv, tri_dv, atol=1e-3) - assert torch.allclose(ref_dk, tri_dk, atol=1e-3) - assert torch.allclose(ref_dq, tri_dq, atol=1e-3) - - -@pytest.mark.skipif(HAS_FLASH_ATTN == False, reason="flash is not available") -@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(3, 4, 2, 16)]) -def test_flash_attention(Z, H, N_CTX, D_HEAD, dtype=torch.float16): - torch.manual_seed(20) - q = torch.randn((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() - k = torch.randn((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() - v = torch.randn((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() - sm_scale = 0.3 - dout = torch.randn_like(q) - - # reference implementation - ref_out = baseline_attention(Z, N_CTX, H, q, k, v, sm_scale) - ref_out.backward(dout) - ref_dv, v.grad = v.grad.clone(), None - ref_dk, k.grad = k.grad.clone(), None - ref_dq, q.grad = q.grad.clone(), None - - # flash implementation - q, k, v = map(lambda x: rearrange(x, 'z h n d -> (z n) h d'), [q, k, v]) - dout = rearrange(dout, 'z h n d -> (z n) h d').detach() - for i in range(3): - if i == 0: - tri_out = flash_attention_q_k_v(q, k, v, sm_scale, Z, N_CTX, N_CTX, causal=True) - elif i == 1: - kv = torch.cat((k.unsqueeze(1), v.unsqueeze(1)), dim=1) - tri_out = flash_attention_q_kv(q, kv, sm_scale, Z, N_CTX, N_CTX, causal=True) - else: - qkv = torch.cat((q.unsqueeze(1), k.unsqueeze(1), v.unsqueeze(1)), dim=1) - tri_out = flash_attention_qkv(qkv, sm_scale, Z, N_CTX, causal=True) - - tri_out.backward(dout, retain_graph=True) - - if i == 0: - tri_dq, tri_dk, tri_dv, = torch.autograd.grad(tri_out, (q, k, v), dout) - tri_out, tri_dq, tri_dk, tri_dv = map(lambda x: rearrange(x, '(z n) h d -> z h n d', z=Z), - (tri_out, tri_dq, tri_dk, tri_dv)) - elif i == 1: - tri_dq, tri_dkv, = torch.autograd.grad(tri_out, (q, kv), dout) - tri_dk, tri_dv = torch.chunk(tri_dkv, 2, dim=1) - tri_out, tri_dq, tri_dk, tri_dv = map(lambda x: rearrange(x, '(z n) h d -> z h n d', z=Z), - (tri_out, tri_dq, tri_dk.squeeze(1), tri_dv.squeeze(1))) - else: - tri_dqkv, = torch.autograd.grad(tri_out, (qkv), dout) - tri_dq, tri_dk, tri_dv = torch.chunk(tri_dqkv, 3, dim=1) - tri_out, tri_dq, tri_dk, tri_dv = map(lambda x: rearrange(x, '(z n) h d -> z h n d', z=Z), - (tri_out, tri_dq.squeeze(1), tri_dk.squeeze(1), tri_dv.squeeze(1))) - - # compare - assert torch.allclose(ref_out, tri_out, atol=1e-3) - assert torch.allclose(ref_dv, tri_dv, atol=1e-3) - assert torch.allclose(ref_dk, tri_dk, atol=1e-3) - assert torch.allclose(ref_dq, tri_dq, atol=1e-3) - - -@pytest.mark.skipif(HAS_FLASH_ATTN == False, reason="flash is not available") -@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(3, 4, 2, 16)]) -def test_masked_flash_attention(Z, H, N_CTX, D_HEAD, dtype=torch.float16): - attn = MaskedFlashAttention(N_CTX, D_HEAD, 0.1) - - qkv = torch.randn((Z, H, 3 * N_CTX * D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() - attention_mask = torch.randint(2, (Z, H)).cuda().bool() - - out = attn(qkv, attention_mask) - - dout = torch.rand_like(out) - out.backward(dout) +@pytest.mark.skipif(HAS_MEM_EFF_ATTN == False, reason="xformers is not available") +@pytest.mark.parametrize('B, S, H, D_HEAD', [(6, 8, 4, 16)]) +def test_attention_gpt(B, S, H, D_HEAD, dtype=torch.float16): + D = H * D_HEAD + + c_attn = torch.nn.Linear(D, 3 * D, dtype=dtype, device="cuda") + attn = ColoAttention(D, H, dropout=0.1) + + x = torch.randn((B, S, D), dtype=dtype, device="cuda") + + qkv = c_attn(x) + q, k, v = rearrange(qkv, 'b s (n h d) -> n b s h d', n=3, h=H) + y = attn(q, k, v, attn_mask_type=AttnMaskType.causal) + + assert list(y.shape) == [B, S, D] + + dy = torch.rand_like(y) + y.backward(dy) @pytest.mark.skipif(HAS_MEM_EFF_ATTN == False, reason="xformers is not available") -@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(6, 8, 4, 16)]) -def test_memory_efficient_attention(Z, H, N_CTX, D_HEAD, dtype=torch.float16): - attn = MemoryEfficientAttention(N_CTX * D_HEAD, N_CTX, 0.1) +@pytest.mark.parametrize('B, S, H, D_HEAD', [(6, 8, 4, 16)]) +def test_attention_bert(B, S, H, D_HEAD, dtype=torch.float16): + D = H * D_HEAD + + c_attn = torch.nn.Linear(D, 3 * D, dtype=dtype, device="cuda") + attn = ColoAttention(D, H, dropout=0.1) + + x = torch.randn((B, S, D), dtype=dtype, device="cuda") + # attention mask of shape [B, S] with zero padding to max length S + mask = [torch.ones(S - i, dtype=dtype, device="cuda") for i in range(B)] + mask = torch.nn.utils.rnn.pad_sequence(mask, batch_first=True) + + qkv = c_attn(x) + q, k, v = rearrange(qkv, 'b s (n h d) -> b s n h d', n=3, h=H).unbind(dim=2) + y = attn(q, k, v, attn_mask=mask, attn_mask_type=AttnMaskType.padding) + + assert list(y.shape) == [B, S, D] + + dy = torch.rand_like(y) + y.backward(dy) + + +@pytest.mark.skipif(HAS_MEM_EFF_ATTN == False, reason="xformers is not available") +@pytest.mark.parametrize('B, S, H, D_HEAD', [(6, 8, 4, 16)]) +def test_attention_no_mask(B, S, H, D_HEAD, dtype=torch.float16): + D = H * D_HEAD + + c_attn = torch.nn.Linear(D, 3 * D, dtype=dtype, device="cuda") + attn = ColoAttention(D, H, dropout=0.1) + + x = torch.randn((B, S, D), dtype=dtype, device="cuda") + qkv = c_attn(x) + q, k, v = rearrange(qkv, 'b s (n h d) -> b s n h d', n=3, h=H).unbind(dim=2) + y = attn(q, k, v) + + assert list(y.shape) == [B, S, D] + + dy = torch.rand_like(y) + y.backward(dy) + + +@pytest.mark.skipif(HAS_MEM_EFF_ATTN == False, reason="xformers is not available") +@pytest.mark.parametrize('B, S, T, H, D_HEAD', [(6, 24, 8, 4, 16)]) +def test_cross_attention(B, S, T, H, D_HEAD, dtype=torch.float16): + D = H * D_HEAD + + q_attn = torch.nn.Linear(D, D, dtype=dtype, device="cuda") + kv_attn = torch.nn.Linear(D, 2 * D, dtype=dtype, device="cuda") - q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() - k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() - v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0, std=.5).requires_grad_() + attn = ColoAttention(D, H, dropout=0.1) - out = attn(q, k, v, attention_mask=LowerTriangularMask()) + src = torch.randn((B, S, D), dtype=dtype, device="cuda") + tgt = torch.randn((B, T, D), dtype=dtype, device="cuda") - dout = torch.rand_like(out) - out.backward(dout) + q = q_attn(tgt) + kv = kv_attn(src) + q = rearrange(q, 'b s (h d) -> b s h d', h=H) + k, v = rearrange(kv, 'b s (n h d) -> b s n h d', n=2, h=H).unbind(dim=2) + y = attn(q, k, v, attn_mask_type=AttnMaskType.causal) + assert list(y.shape) == [B, T, D] -if __name__ == '__main__': - test_flash_attention(3, 4, 2, 16) + dy = torch.rand_like(y) + y.backward(dy) From 9d644ff09f2b044c984328e08357a68d98ab17f3 Mon Sep 17 00:00:00 2001 From: YH <100389977+yhna940@users.noreply.github.com> Date: Tue, 21 Mar 2023 12:48:21 +0900 Subject: [PATCH 010/413] Fix docstr for zero statedict (#3185) --- colossalai/zero/sharded_model/sharded_model_v2.py | 1 + 1 file changed, 1 insertion(+) diff --git a/colossalai/zero/sharded_model/sharded_model_v2.py b/colossalai/zero/sharded_model/sharded_model_v2.py index 094f7d76a86d..12e8f65d4a35 100644 --- a/colossalai/zero/sharded_model/sharded_model_v2.py +++ b/colossalai/zero/sharded_model/sharded_model_v2.py @@ -494,6 +494,7 @@ def _colo_load_from_state_dict(self, error_msgs (list of str): error messages should be added to this list, and will be reported together in :meth:`~torch.nn.Module.load_state_dict` + shard_strategy (Optional[BaseShardStrategy], optional): A shard strategy to manage shard behavior. Defaults to None. """ for hook in self._load_state_dict_pre_hooks.values(): hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) From 80aed29cd3835587052b9271e3ac70175a599771 Mon Sep 17 00:00:00 2001 From: YH <100389977+yhna940@users.noreply.github.com> Date: Tue, 21 Mar 2023 13:36:47 +0900 Subject: [PATCH 011/413] [zero] Refactor ZeroContextConfig class using dataclass (#3186) --- colossalai/zero/init_ctx/init_context.py | 39 ++++++++++++------------ 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/colossalai/zero/init_ctx/init_context.py b/colossalai/zero/init_ctx/init_context.py index 572ddd9e4e3f..b40b69962cf7 100644 --- a/colossalai/zero/init_ctx/init_context.py +++ b/colossalai/zero/init_ctx/init_context.py @@ -1,46 +1,45 @@ import contextlib import functools -from typing import Optional from contextlib import AbstractContextManager +from dataclasses import dataclass +from typing import Optional import torch -import torch.nn as nn import torch.distributed as dist +import torch.nn as nn from colossalai.context.parallel_mode import ParallelMode -from colossalai.core import global_context as gpc from colossalai.context.singleton_meta import SingletonMeta +from colossalai.core import global_context as gpc from colossalai.logging import get_dist_logger +from colossalai.utils.model.utils import InsertPostInitMethodToModuleSubClasses from colossalai.zero.shard_utils import BaseShardStrategy from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16 from colossalai.zero.sharded_model.sharded_model_v2 import ShardedModelV2 from colossalai.zero.sharded_param import ShardedParamV2 -from colossalai.utils.model.utils import InsertPostInitMethodToModuleSubClasses -class ZeroContextConfig(object): +@dataclass +class ZeroContextConfig: """The configuration used to control zero context initialization. Args: target_device (torch.device): The device where param data are after exiting the context. - replicated (bool, optional): Whether the param is replicated across data parallel group. + is_replicated (bool, optional): Whether the param is replicated across data parallel group. Some parameters are not replicated, e.g. parameters in MOE experts. shard_param (bool, optional): Is param sharded after exiting the context. Defaults to False. """ - def __init__(self, target_device: torch.device, replicated: bool = True, shard_param: bool = False): - super().__init__() + target_device: torch.device + is_replicated: bool = True + shard_param: bool = False - if shard_param: - assert replicated, "Non-replicated parameters can't be sharded." + def __post_init__(self): + if self.shard_param: + assert self.is_replicated, "Non-replicated parameters can't be sharded." - # replicated no-shard parameters should locate in cuda, since we will broadcast them soon - if replicated and not shard_param: - assert target_device.type == 'cuda', "Replicated no-shard paramters should locate in cuda." - - self.target_device = target_device - self.is_replicated: bool = replicated - self.shard_param: bool = shard_param + if self.is_replicated and not self.shard_param: + assert self.target_device.type == 'cuda', "Replicated no-shard parameters should be located in cuda." class ZeroInitContext(InsertPostInitMethodToModuleSubClasses): @@ -74,7 +73,7 @@ def __init__(self, self.seed = seed self.dp_process_group = gpc.get_group(ParallelMode.DATA) - self.config = ZeroContextConfig(target_device=target_device, replicated=True, shard_param=shard_param) + self.config = ZeroContextConfig(target_device=target_device, is_replicated=True, shard_param=shard_param) ZeroContextMgr().current_context = self @@ -124,7 +123,7 @@ def calc_fanin_fanout(tensor: torch.Tensor): return fan_in, fan_out def _pre_context_exec(self): - """ + """ The Callback function when entering the context """ self.logger = get_dist_logger("ZeroInitContext") @@ -248,7 +247,7 @@ def hijack_context_config(self, **kwargs): def no_shard_zero_context(is_replicated: bool = True) -> AbstractContextManager: return ZeroContextMgr().hijack_context_config(target_device=torch.device('cuda', torch.cuda.current_device()), - replicated=is_replicated, + is_replicated=is_replicated, shard_param=False) From 258b43317c4a5cafb8d3da0ff63c8843443bc448 Mon Sep 17 00:00:00 2001 From: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com> Date: Tue, 21 Mar 2023 13:24:18 +0800 Subject: [PATCH 012/413] [hotfix] layout converting issue (#3188) --- colossalai/tensor/d_tensor/layout_converter.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/colossalai/tensor/d_tensor/layout_converter.py b/colossalai/tensor/d_tensor/layout_converter.py index a4f4c9c2dd80..cf02aac309f4 100644 --- a/colossalai/tensor/d_tensor/layout_converter.py +++ b/colossalai/tensor/d_tensor/layout_converter.py @@ -10,7 +10,7 @@ from colossalai.context.singleton_meta import SingletonMeta from colossalai.tensor.d_tensor.comm_spec import * from colossalai.tensor.d_tensor.layout import Layout -from colossalai.tensor.sharding_spec import ShardingSpecException +from colossalai.tensor.d_tensor.misc import LayoutException from colossalai.tensor.utils import all_gather_simulator, all_to_all_simulator, shard_simulator from .sharding_spec import ShardingSpec @@ -145,7 +145,7 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co entire_shape=source_layout.entire_shape) valid_spec_dict[new_layout] = comm_spec - except ShardingSpecException: + except LayoutException: pass return valid_spec_dict @@ -255,7 +255,7 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com device_type=source_layout.device_type, entire_shape=source_layout.entire_shape) valid_spec_dict[new_layout] = comm_spec - except ShardingSpecException: + except LayoutException: pass return valid_spec_dict @@ -343,7 +343,7 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec device_type=source_layout.device_type, entire_shape=source_layout.entire_shape) valid_spec_dict[new_layout] = comm_spec - except ShardingSpecException: + except LayoutException: pass return valid_spec_dict From 18dbe76caeef1f8bb0cd4c9bd332d50b5abc6e38 Mon Sep 17 00:00:00 2001 From: Zihao <804673818@qq.com> Date: Tue, 21 Mar 2023 14:17:41 +0800 Subject: [PATCH 013/413] [auto-parallel] add auto-offload feature (#3154) * add auto-offload feature * polish code * fix syn offload runtime pass bug * add offload example * fix offload testing bug * fix example testing bug --- colossalai/auto_parallel/offload/__init__.py | 0 .../auto_parallel/offload/amp_optimizer.py | 177 ++++++ .../offload/base_offload_module.py | 109 ++++ .../auto_parallel/offload/mem_optimize.py | 49 ++ colossalai/auto_parallel/offload/region.py | 144 +++++ .../auto_parallel/offload/region_manager.py | 526 ++++++++++++++++++ colossalai/auto_parallel/offload/runtime.py | 253 +++++++++ colossalai/auto_parallel/offload/solver.py | 523 +++++++++++++++++ .../offload/training_simulator.py | 458 +++++++++++++++ colossalai/auto_parallel/offload/util.py | 90 +++ .../gpt/experiments/auto_offload/README.md | 37 ++ .../gpt/experiments/auto_offload/model_zoo.py | 65 +++ .../experiments/auto_offload/requirements.txt | 2 + .../gpt/experiments/auto_offload/run.sh | 8 + .../auto_offload/train_gpt_offload.py | 94 ++++ .../test_offload/model_utils.py | 86 +++ .../test_offload/test_perf.py | 150 +++++ .../test_offload/test_solver.py | 62 +++ 18 files changed, 2833 insertions(+) create mode 100644 colossalai/auto_parallel/offload/__init__.py create mode 100644 colossalai/auto_parallel/offload/amp_optimizer.py create mode 100644 colossalai/auto_parallel/offload/base_offload_module.py create mode 100644 colossalai/auto_parallel/offload/mem_optimize.py create mode 100644 colossalai/auto_parallel/offload/region.py create mode 100644 colossalai/auto_parallel/offload/region_manager.py create mode 100644 colossalai/auto_parallel/offload/runtime.py create mode 100644 colossalai/auto_parallel/offload/solver.py create mode 100644 colossalai/auto_parallel/offload/training_simulator.py create mode 100644 colossalai/auto_parallel/offload/util.py create mode 100644 examples/language/gpt/experiments/auto_offload/README.md create mode 100644 examples/language/gpt/experiments/auto_offload/model_zoo.py create mode 100644 examples/language/gpt/experiments/auto_offload/requirements.txt create mode 100644 examples/language/gpt/experiments/auto_offload/run.sh create mode 100644 examples/language/gpt/experiments/auto_offload/train_gpt_offload.py create mode 100644 tests/test_auto_parallel/test_offload/model_utils.py create mode 100644 tests/test_auto_parallel/test_offload/test_perf.py create mode 100644 tests/test_auto_parallel/test_offload/test_solver.py diff --git a/colossalai/auto_parallel/offload/__init__.py b/colossalai/auto_parallel/offload/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/auto_parallel/offload/amp_optimizer.py b/colossalai/auto_parallel/offload/amp_optimizer.py new file mode 100644 index 000000000000..a79e5006e7d2 --- /dev/null +++ b/colossalai/auto_parallel/offload/amp_optimizer.py @@ -0,0 +1,177 @@ +from typing import Dict, Tuple +from enum import Enum +import torch +from torch.optim import Optimizer + +from colossalai.logging import get_dist_logger +from colossalai.nn.optimizer import ColossalaiOptimizer +from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler +from colossalai.utils import get_current_device + +from .base_offload_module import BaseOffloadModule +from .region_manager import RegionManager +from .region import Region + + +class OptimState(Enum): + SCALED = 0 + UNSCALED = 1 + +class AMPOptimizer(ColossalaiOptimizer): + + """ + A wrapper for Optimizer. + Code reference: https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/optimizer/zero_optimizer.py + + Args: + optimizer (Optimizer): An Optimizer instance. + module (BaseOffloadModule): A ``BaseOffloadModule`` instance. + initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**16. + growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2. + backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5. + growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000. + hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2. + min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1. + max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32. + norm_type (float, optional): norm_type used for `clip_grad_norm`. + """ + + def __init__(self, + optimizer: Optimizer, + module: BaseOffloadModule, + initial_scale: float = 2**16, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + min_scale: float = 1, + max_scale: float = 2**32, + clipping_norm: float = 0.0, + norm_type: float = 2.0): + + super().__init__(optimizer) + + self.module = module + self.optim_state = OptimState.UNSCALED + self.clipping_flag = clipping_norm > 0.0 + self.max_norm = clipping_norm + + self.region_manager: RegionManager = self.module.region_manager + self.param_to_range: Dict[torch.nn.Parameter, Tuple[int, int]] = dict() + self.param_to_region: Dict[torch.nn.Parameter, Region] = dict() + + self.fp32_to_fp16_params: Dict[torch.Tensor, torch.nn.Parameter] = dict() + + if self.clipping_flag: + assert norm_type == 2.0, "AMPOptimizer only supports L2 norm now" + + self.__init__optimizer() + + # Grad scaler + self.grad_scaler = DynamicGradScaler(initial_scale=initial_scale, + min_scale=min_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + max_scale=max_scale) + self._found_overflow: torch.Tensor = torch.zeros(1, dtype=torch.int64, device=get_current_device()) + self._logger = get_dist_logger() + + def _set_grad_ptr(self): + for group in self.param_groups: + for fake_param in group['params']: + region = self.param_to_region[fake_param] + begin, end = self.param_to_range[fake_param] + + fake_param.data = region.cpu_grad[begin:end] + fake_param.grad = fake_param.data + fake_param.data = region.fp32_data[begin:end] + + def _update_fp16_params(self): + none_tensor = torch.empty([0]) + for group in self.param_groups: + for fake_param in group['params']: + assert fake_param.grad is None + fake_param.data = none_tensor + self.param_to_region[fake_param].cpu_grad = None + + def _check_overflow(self): + # clear previous overflow record + self._found_overflow.fill_(self.module.overflow_counter.item()) + return self._found_overflow.item() > 0 + + def _get_combined_scale(self): + loss_scale = 1 + + if self.optim_state == OptimState.SCALED: + loss_scale = self.loss_scale + self.optim_state = OptimState.UNSCALED + + combined_scale = loss_scale + + if combined_scale == 1: + return -1 + else: + return combined_scale + + @property + def loss_scale(self): + return self.grad_scaler.scale.item() + + def zero_grad(self, *args, **kwargs): + self.module.overflow_counter = torch.cuda.IntTensor([0]) + return self.optim.zero_grad(set_to_none=True) + + def step(self, *args, **kwargs): + # Copy gradients from model params to main params. + self._set_grad_ptr() + + found_inf = self._check_overflow() + if found_inf: + self.optim_state = OptimState.UNSCALED # no need to unscale grad + self.grad_scaler.update(found_inf) # update gradient scaler + self._logger.info(f'Found overflow. Skip step') + self.zero_grad() # reset all gradients + self._update_fp16_params() + return + + # get combined scale. combined scale = loss scale * clipping norm + # so that gradient = gradient / combined scale + combined_scale = self._get_combined_scale() + self.grad_scaler.update(found_inf) + + ret = self.optim.step(div_scale=combined_scale, *args, **kwargs) + self.zero_grad() + self._update_fp16_params() + return ret + + def clip_grad_norm(self, model: torch.nn.Module, max_norm: float, norm_type: float = 2.0): + raise NotImplementedError + + def backward(self, loss: torch.Tensor): + loss = self.loss_scale * loss + self.optim_state = OptimState.SCALED + self.module.backward(loss) + + def __init__optimizer(self): + + for group in self.optim.param_groups: + fake_params_list = list() + + for param in group['params']: + region = self.region_manager.get_region(param) + fake_param = torch.nn.Parameter(torch.empty([0])) + self.param_to_range[fake_param] = region.param_to_range[param] + self.param_to_region[fake_param] = region + fake_params_list.append(fake_param) + + # Reset existing state dict key to the new main param. + if param in self.optim.state: + self.optim.state[fake_param] = self.optim.state.pop(param) + + group['params'] = fake_params_list + + # Leverage state_dict() and load_state_dict() to + # recast preexisting per-param state tensors + self.optim.load_state_dict(self.optim.state_dict()) \ No newline at end of file diff --git a/colossalai/auto_parallel/offload/base_offload_module.py b/colossalai/auto_parallel/offload/base_offload_module.py new file mode 100644 index 000000000000..59cea4ece266 --- /dev/null +++ b/colossalai/auto_parallel/offload/base_offload_module.py @@ -0,0 +1,109 @@ +from typing import Optional, Set +from functools import partial +import torch +import torch.nn as nn + +from colossalai.nn.parallel.data_parallel import _cast_float +from colossalai.gemini.tensor_utils import free_storage + +from .region_manager import RegionManager +from .util import GlobalRuntimeInfo + + +class BaseOffloadModule: + """ + BaseOffloadModule: A model wrapper for parameter offloading. + + Args: + model (nn.Module): model to apply offloading. + region_manager (RegionManager): a ``RegionManager`` instance. + is_sync (bool): synchronous mode or not. + """ + + def __init__(self, + model: nn.Module, + region_manager: RegionManager, + is_sync=True): + + self.model = model + self.region_manager = region_manager + self.grad_hook_list = [] + self.overflow_counter = torch.cuda.IntTensor([0]) + + self.grad_offload_stream = torch.cuda.current_stream() if is_sync else GlobalRuntimeInfo.d2h_stream + + self._cast_buffers() + + def register_grad_hook(self): + for p in self.model.parameters(): + if p.requires_grad: + self.grad_hook_list.append(p.register_hook(partial(self.grad_handle, p))) + + def remove_grad_hook(self): + for hook in self.grad_hook_list: + hook.remove() + + def __call__(self, *args, **kwargs): + return self.forward(*args, **kwargs) + + def _pre_forward(self): + self.register_grad_hook() + for region in self.region_manager.region_list: + region.cpu_grad = None + + def forward(self, *args, **kwargs): + args, kwargs = _cast_float(args, torch.half), _cast_float(kwargs, torch.half) + self.model.zero_grad(set_to_none=True) + self._pre_forward() + outputs = self.model(*args, **kwargs) + return outputs + + def backward(self, loss): + loss.backward() + self._post_backward() + + def _post_backward(self): + torch.cuda.synchronize() + self.remove_grad_hook() + + for p in self.model.parameters(): + p.grad = None + + GlobalRuntimeInfo.fwd_prefetch_event_map.clear() + GlobalRuntimeInfo.bwd_prefetch_event_map.clear() + + def grad_handle(self, p, grad): + empty_grad = torch.empty_like(grad) + free_storage(empty_grad) + with torch._C.DisableTorchFunction(): + region = self.region_manager.get_region(p) + region.copy_grad_to_region_slice(p, grad) + if region.can_release: + self.overflow_counter += region.has_inf_or_nan + master_stream = torch.cuda.current_stream() + with torch.cuda.stream(self.grad_offload_stream): + GlobalRuntimeInfo.d2h_stream.wait_stream(master_stream) + region.move_grad_to_cpu() + return empty_grad + + def _cast_buffers(self): + for buffer in self.model.buffers(): + buffer.data = buffer.cuda() + + def parameters(self, recurse: bool = True): + return self.model.parameters(recurse) + + def named_parameters(self, prefix: str = '', recurse: bool = True): + return self.model.named_parameters(prefix, recurse) + + def named_buffers(self, prefix: str = '', recurse: bool = True): + return self.model.named_buffers(prefix, recurse) + + def named_children(self): + return self.model.named_children() + + def named_modules(self, + memo: Optional[Set[torch.nn.Module]] = None, + prefix: str = '', + remove_duplicate: bool = True): + return self.model.named_modules(memo, prefix, remove_duplicate) diff --git a/colossalai/auto_parallel/offload/mem_optimize.py b/colossalai/auto_parallel/offload/mem_optimize.py new file mode 100644 index 000000000000..02778696a106 --- /dev/null +++ b/colossalai/auto_parallel/offload/mem_optimize.py @@ -0,0 +1,49 @@ +from typing import Dict +import torch +import torch.fx +from torch.fx import GraphModule +from torch.utils._pytree import tree_map + +from colossalai.fx import ColoTracer, is_compatible_with_meta +from colossalai.fx.passes.meta_info_prop import MetaInfoProp + +from .region_manager import RegionManager +from .runtime import runtime_syn_offload_apply_pass, runtime_asyn_offload_apply_pass +from .base_offload_module import BaseOffloadModule +from .util import compute_max_param_mem, compute_total_param_mem, compute_act_peak_mem, GlobalRuntimeInfo + +def memory_optimize(model: torch.nn.Module, + inps: Dict[str, torch.Tensor], + memory_budget: float = -1.0, + solver_name: str = 'asyn'): + + model = model.cpu().half() + tracer = ColoTracer() + assert is_compatible_with_meta() + wrap_fn = lambda x: x.to("meta") if isinstance(x, torch.Tensor) else x + meta_args = tree_map(wrap_fn, inps) + graph = tracer.trace(model, meta_args=meta_args) + gm = GraphModule(model, graph, model.__class__.__name__) + interp = MetaInfoProp(gm) + interp.propagate(*meta_args.values()) + + region_manager = RegionManager(graph, solver_name=solver_name, memory_budget=memory_budget) + region_manager._build_regions() + GlobalRuntimeInfo.region_list = region_manager.region_list + + act_peak_mem = compute_act_peak_mem(region_manager.region_list) / 1024 ** 2 + max_param_mem = compute_max_param_mem(region_manager.region_list) / 1024 ** 2 + total_param_mem = compute_total_param_mem(region_manager.region_list) / 1024 ** 2 + print( + f"act_peak_mem={act_peak_mem:.3f} MB | max_param_mem={max_param_mem:.3f} MB | total_param_mem={total_param_mem:.3f}") + + if solver_name == 'syn': + gm = runtime_syn_offload_apply_pass(gm, region_manager.region_list) + elif solver_name == 'asyn': + gm = runtime_asyn_offload_apply_pass(gm, region_manager.region_list) + else: + raise TypeError(f"Unknown solver name {solver_name}!") + + gm.recompile() + optimized_model = BaseOffloadModule(gm, region_manager, solver_name=='syn') + return optimized_model diff --git a/colossalai/auto_parallel/offload/region.py b/colossalai/auto_parallel/offload/region.py new file mode 100644 index 000000000000..e6907cc4b81d --- /dev/null +++ b/colossalai/auto_parallel/offload/region.py @@ -0,0 +1,144 @@ +from typing import List, Dict, Tuple +import torch +from torch.fx import Node +from colossalai.gemini.tensor_utils import alloc_storage, free_storage + +class Region: + """ + Region: A container owning a piece of contiguous nodes in the DNN computing graph. + + Args: + r_id (int): the index of the region in the computing graph. + """ + + def __init__(self, r_id: int = 0) -> None: + self.r_id: int = r_id + self.fp16_params: List[torch.nn.Parameter] = [] + self.param_size: int = 0 + self.shared_rid: int = self.r_id + + self.param_num: int = 0 + self.grad_num: int = 0 + self.fp16_data = None + self.fp32_data = None + self.cpu_grad = None + self.temp_fp32_data = None + self.param_to_range: Dict[torch.nn.Parameter, Tuple[int, int]] = dict() + + self.need_offload: bool = False + self.is_syn: bool = False + self.nodes: List[Node] = [] + self.fwd_prefetch_region = None + self.bwd_prefetch_region = None + + self.in_mem_pool_flag: bool = False + + @property + def can_release(self) -> bool: + """ + Check if the region can be released. + """ + return self.grad_num == self.param_num + + @property + def has_inf_or_nan(self) -> bool: + """ + Check if the grad of the region has inf or nan values on CUDA. + """ + return torch.isinf(self.fp16_data).any() | torch.isnan(self.fp16_data).any() + + def init_param_data(self, pre_alloc_tensor: torch.Tensor = None): + """ + Map the parameters in the region to a contiguous memory space. + """ + + self.fp16_data = torch.zeros( + self.param_num, dtype=torch.half, device='cuda') + offset = 0 + for param in self.fp16_params: + param.data = param.data.cuda() + p_num = param.data.numel() + self.fp16_data[offset:offset + p_num].copy_(param.data.flatten()) + param.data = self.fp16_data[offset:offset + + p_num].view(param.data.shape) + self.param_to_range[param] = (offset, offset + p_num) + offset += p_num + + self.fp32_data = self.fp16_data.float().cpu().pin_memory() + free_storage(self.fp16_data) + if self.in_mem_pool_flag and pre_alloc_tensor is not None: + self.fp16_data = pre_alloc_tensor + + def move_param_to_cuda(self): + """ + Move parameters from CPU to GPU. + It first moves float32 parameters to GPU and + then transforms float32 parameters to half-precision on the GPU. + The reason is that the performance of precision conversion on the CPU + is much slower than the data transfer overhead. + """ + + self.temp_fp32_data.copy_(self.fp32_data, non_blocking=True) + self.temp_fp32_data.record_stream(torch.cuda.current_stream()) + if not self.in_mem_pool_flag: + alloc_storage(self.fp16_data) + self.fp16_data[:self.param_num].copy_(self.temp_fp32_data) + self.fp16_data.record_stream(torch.cuda.current_stream()) + + self.__update_params_ptr() + + def move_grad_to_cpu(self): + """ + Move gradients from GPU to CPU. + """ + + self.cpu_grad = torch.empty(self.param_num, dtype=torch.half, pin_memory=True) + self.cpu_grad.copy_(self.fp16_data[:self.param_num], non_blocking=True) + self.fp16_data.record_stream(torch.cuda.current_stream()) + if not self.in_mem_pool_flag: + self.free_cuda_data() + + self.grad_num = 0 + + def free_cuda_data(self): + free_storage(self.fp16_data) + + # torch.cuda.empty_cache() + + def copy_grad_to_region_slice(self, param: torch.nn.Parameter, data_slice: torch.Tensor) -> None: + """ + Copy data slice to the memory space indexed by the input tensor in the region. + + Args: + param (torch.nn.Parameter): the param used to retrive meta information + data_slice (torch.Tensor): the tensor to be copied to the region + """ + + begin, end = self.param_to_range[param] + self.fp16_data[begin:end].copy_(data_slice.data.flatten()) + param.data = self.fp16_data[begin:end].view(param.data.shape) + + self.grad_num += data_slice.numel() + + def split(self, cut_node_idx: int, cut_param_idx: int): + """ + Split the region into two and return the latter. + """ + new_reg = Region(r_id=self.r_id + 1) + new_reg.nodes = self.nodes[cut_node_idx:] + new_reg.fp16_params = self.fp16_params[cut_param_idx:] + for p in new_reg.fp16_params: + new_reg.param_size += p.data.numel() * p.data.element_size() + new_reg.param_num += p.data.numel() + + self.nodes = self.nodes[:cut_node_idx] + self.fp16_params = self.fp16_params[:cut_param_idx] + self.param_size -= new_reg.param_size + self.param_num -= new_reg.param_num + + return new_reg + + def __update_params_ptr(self) -> None: + for param in self.fp16_params: + begin, end = self.param_to_range[param] + param.data = self.fp16_data[begin:end].view(param.data.shape) \ No newline at end of file diff --git a/colossalai/auto_parallel/offload/region_manager.py b/colossalai/auto_parallel/offload/region_manager.py new file mode 100644 index 000000000000..30bfaf00d493 --- /dev/null +++ b/colossalai/auto_parallel/offload/region_manager.py @@ -0,0 +1,526 @@ +from typing import List, Any, Dict, Tuple +import torch +from torch.fx import Graph, Node + +from .solver import SolverFactory +from .training_simulator import TrainingSimulator +from .region import Region +from .util import NodeInfo + + +class RegionManager: + """ + RegionManager is used to construct and manage the offload plan for the model execution. + + Args: + graph (Graph): a Graph object used for analysis and strategy generation. + solver_name (str): a solver name which specifies the preferences for plan searching. + memory_budget (float): the given memory budget. + cnode (List[str], optional): Common node List, should be the subset of input. + """ + + def __init__(self, + graph: Graph, + solver_name: str = 'asyn', + memory_budget: float = -1.0, + cnode: List[str] = None): + + self.graph = graph + assert graph.owning_module is not None, 'The given graph is not associated with a owning_module' + self.root_module = self.graph.owning_module + self.nodes = list(graph.nodes) + self.cnode = cnode + self.only_param_ops = [] + self.param_region_map: Dict[torch.nn.Parameter, Region] = dict() + self.shared_region_pairs: List[Tuple[Region, Region]] = list() + self.region_list: List[Region] = list() + self.rid_in_pool: List[int] = list() + self.mem_block_size: int = 0 + self.memory_budget = memory_budget + + self.solver_name = solver_name + self.require_pool: bool = solver_name == 'asyn' + + self.reg_to_block: Dict[int, int] = dict() + + def _build_regions(self): + """ + 1. Pre-processing, mainly contains linearized computing graph and + merge smaller regions into larger ones. + 2. Construct a solver to search for an efficient offload strategy. + 3. Post-processing, mainly contains early region placement if using asynchronous mode, + and initialize region data. + """ + + self._pre_process() + + solver_cls = SolverFactory.create(self.solver_name) + solver = solver_cls(self.region_list, self.memory_budget) + solver._call_solver() + + self._post_process(solver.best_ts) + + def _pre_process(self): + + init_region_list = self._linearize_graph() + + if len(self.shared_region_pairs) > 1: + raise NotImplementedError( + 'The current version only considers at most one pair of parameter sharing.') + + elif len(self.shared_region_pairs) == 1: + shared_regs = self.shared_region_pairs[0] + assert shared_regs[0].shared_rid == shared_regs[1].r_id \ + and shared_regs[1].shared_rid == shared_regs[0].r_id + fst_id = shared_regs[0].r_id + lst_id = shared_regs[1].r_id + regs_left_out = init_region_list[:fst_id + 1] + regs_right_out = init_region_list[lst_id:] + hold_regs = init_region_list[fst_id + 1:lst_id] + else: + regs_left_out = [] + regs_right_out = [] + hold_regs = init_region_list + + self.mem_block_size = self._search_block_size(hold_regs) + hold_regs = self._merge_small_regions(hold_regs) + + if self.require_pool: + for reg in hold_regs: + reg.in_mem_pool_flag = True + self.rid_in_pool.append(reg.r_id) + + self.region_list.extend(regs_left_out) + self.region_list.extend(hold_regs) + + for reg in regs_right_out: + reg.r_id = self.region_list[-1].r_id + 1 + self.region_list[reg.shared_rid].shared_rid = reg.r_id + self.region_list.append(reg) + + self._process_shared_region() + + self.max_param_num = max([reg.param_num for reg in self.region_list]) + self.memory_budget -= self.max_param_num * torch.tensor([], dtype=torch.float32).element_size() + + def _post_process(self, ts: TrainingSimulator = None): + if self.require_pool: + self._early_region_placement(ts) + self._init_region_data() + + def _early_region_placement(self, ts: TrainingSimulator): + """ + Implemented the early region placement strategy to avoid GPU memory fragmentation. + It maps all region data into a contiguous memory space and + reuses the same memory space for regions that do not coexist. + + Args: + ts (TrainingSimulator): the best training simulator, which records region execution flow. + + Raises: + NotImplementedError: due to the naive implementation, + it may not find a suitable region placement strategy for the given execution flow. + """ + + reg_flow = torch.cat( + [ts.fwd_reg_flow, ts.bwd_reg_flow], dim=0) + mem_block_num = torch.max( + torch.sum(reg_flow[:, self.rid_in_pool], dim=1)) + coexist_matrix = torch.logical_or( + ts.fwd_reg_flow, ts.bwd_reg_flow) + + block_to_regs = {} + for block_idx in range(mem_block_num): + block_to_regs[block_idx] = [] + for reg in self.region_list: + if reg.r_id in self.rid_in_pool: + cur_reg_appears = coexist_matrix[:, reg.r_id] + cur_reg_coexists = torch.sum( + coexist_matrix[cur_reg_appears], dim=0).bool() + for block_idx in range(mem_block_num): + if not any(cur_reg_coexists[block_to_regs[block_idx]]): + block_to_regs[block_idx].append(reg.r_id) + self.reg_to_block[reg.r_id] = block_idx + break + + if reg.r_id not in self.reg_to_block: + raise NotImplementedError( + f'can not find a block from the memory pool to store parameters of the region') + self.memory_pool = torch.chunk(torch.zeros(int( + mem_block_num * self.mem_block_size / 2), dtype=torch.half, device='cuda'), chunks=int(mem_block_num)) + + def _merge_small_regions(self, orig_reg_list: List[Region]) -> List[Region]: + """ + Merge smaller regions into larger ones for better bandwidth utilization and easier management. + It is inspired by Gemini. + + Args: + orig_reg_list (List[Region]): original region list. + + Returns: + List[Region]: region list after merging. + """ + + r_id = orig_reg_list[0].r_id + region = Region(r_id=r_id) + region_list = [region] + + for orig_reg in orig_reg_list: + if region_list[-1].param_size + orig_reg.param_size > self.mem_block_size: + r_id += 1 + region = Region(r_id=r_id) + region_list.append(region) + region.param_size += orig_reg.param_size + region.param_num += orig_reg.param_num + region.nodes.extend(orig_reg.nodes) + region.fp16_params.extend(orig_reg.fp16_params) + self.__update_param_region_map(orig_reg.fp16_params, region) + + return region_list + + def _search_block_size(self, + region_list: List[Region], + search_interval_byte: int = 1024, + search_range_byte: int = 128 * 1024 ** 2) -> int: + """ + Search for a suitable memory block size. + + Args: + region_list (List[Region]): region list. + search_interval_byte (int): searching interval in byte. + search_range_byte (int): searching range in byte. + + Returns: + int: the best memory block size. + """ + + def _get_wasted_mem(size_list: List[int], blk_size: int): + """ + Get wasted byte for a certain block size. + """ + acc_wasted = 0 + left = 0 + for s in size_list: + if left + s > blk_size: + acc_wasted += blk_size - left + left = s + left += s + acc_wasted += blk_size - left + return acc_wasted + + param_size_list = [ + region.param_size for region in region_list if region.r_id == region.shared_rid] + + start_size = max(param_size_list) + min_mem_waste = float('+inf') + best_block_size = start_size + + for block_size in range(start_size, start_size + search_range_byte + 1, search_interval_byte): + temp_waste = 0 + temp_waste += _get_wasted_mem(param_size_list, block_size) + if temp_waste < min_mem_waste: + min_mem_waste = temp_waste + best_block_size = block_size + + return best_block_size + + def _init_region_data(self): + """ + Initialize region data, which maps the parameters in the region to a contiguous memory space. + """ + + self.temp_fp32_data = torch.zeros(self.max_param_num, device='cuda', dtype=torch.float32) + + for region in self.region_list: + pre_alloc_tensor = None + if self.require_pool and region.r_id in self.rid_in_pool: + block_idx = self.reg_to_block[region.r_id] + pre_alloc_tensor = self.memory_pool[block_idx] + + if region.r_id <= region.shared_rid: + region.init_param_data(pre_alloc_tensor) + else: + shared_region = self.region_list[region.shared_rid] + region.fp16_data = shared_region.fp16_data + region.fp32_data = shared_region.fp32_data + region.param_to_range = shared_region.param_to_range + region.temp_fp32_data = self.temp_fp32_data[:region.param_num].detach( + ) + + torch.cuda.empty_cache() + + def _process_shared_region(self): + """ + Special processing for the shared region, which uses GPT2 and Bert case as a priori knowledge. + """ + + if len(self.shared_region_pairs): + assert len(self.shared_region_pairs) <= 1 + former_reg, latter_reg = self.shared_region_pairs[0] + assert latter_reg.param_num >= former_reg.param_num + embedding_node = former_reg.nodes[-1] + assert embedding_node.op == 'call_module' and isinstance( + self.root_module.get_submodule(embedding_node.target), torch.nn.Embedding) + if latter_reg.param_num > former_reg.param_num: + for idx, n in enumerate(latter_reg.nodes): + if (n.op == 'call_module' and isinstance(self.root_module.get_submodule(n.target), + torch.nn.Linear)) or \ + (n.op == 'call_function' and n.target is torch.nn.functional.linear): + cut_node_idx = idx + 1 + break + assert len(latter_reg.fp16_params) == 2 + new_reg = latter_reg.split(cut_node_idx, 1) + for p in new_reg.fp16_params: + self.param_region_map[p] = new_reg + self.region_list.insert(new_reg.r_id, new_reg) + for reg in self.region_list[new_reg.r_id + 1:]: + reg.r_id += 1 + latter_reg.shared_rid = former_reg.r_id + former_reg.shared_rid = latter_reg.r_id + + def _linearize_graph(self) -> List[Region]: + """Linearizing the graph + + Args: + graph (Graph): The computing graph to be optimized. + + Returns: + List[Region]: each region contains the actual 'node' in linearized manner. + + Remarks: + Do merge the inplace ops and shape-consistency ops into the previous node. + """ + + # List of target name that could be seen as common node + common_ops = ["getattr", "getitem", "size"] + + def _is_cop(target: Any) -> bool: + """Check if an op could be seen as common node + + Args: + target (Any): node target + + Returns: + bool + """ + + if isinstance(target, str): + return target in common_ops + else: + return target.__name__ in common_ops + + def _is_act(data: Any) -> bool: + """Check if an op could be seen as parameter computation start + + Args: + data (Any): meta_data + + Returns: + bool + """ + + label = False + if isinstance(data, torch.Tensor): + return True + elif isinstance(data, (tuple, list)): + for d in data: + label = label or _is_act(d) + return label + + def _maybe_param_comp_start() -> bool: + """Check if an op could be seen as parameter computation start + + Args: + n (Node): node + + Returns: + bool + """ + + label = False + if n.op == "get_attr": + label = True + elif n.op == "call_module": + target = n.target + submod = self.root_module.get_submodule(target) + if ( + len(list(submod.named_parameters(recurse=False))) != 0 + or len(list(submod.named_buffers(recurse=False))) != 0 + ): + label = True + + return label and not sum([v for _, v in param_op_deps.items()]) + + def _is_param_comp_end() -> bool: + """Check if an op could be seen as parameter computation end + + Args: + n (Node): node + + Returns: + bool + """ + + def _is_inplace(n: Node): + """Get the inplace argument from ``torch.fx.Node`` + """ + inplace = False + if n.op == "call_function": + inplace = n.kwargs.get("inplace", False) + elif n.op == "call_module": + inplace = getattr(n.graph.owning_module.get_submodule( + n.target), "inplace", False) + return inplace + + label = False + + if n.op == "call_module": + target = n.target + submod = self.root_module.get_submodule(target) + if ( + len(list(submod.named_parameters(recurse=False))) != 0 + or len(list(submod.named_buffers(recurse=False))) != 0 + ): + label = True + + elif n.op == "call_function": + label = any(map(lambda x: x.name in self.only_param_ops, n.all_input_nodes)) and any( + map(lambda x: x.name not in self.only_param_ops and not _is_cop(n.target), n.all_input_nodes)) + + return label and not sum([v for _, v in param_op_deps.items()]) and not any(map(_is_inplace, n.users)) + + def _exception_node_handling(): + # TODO meta info prop bug + if n.name.__contains__("transpose") and n.meta['fwd_out'][0].dim() <= 2: + n.meta['fwd_out'] = [] + + # make sure that item in cnode is valid + if self.cnode: + for name in self.cnode: + try: + assert next(node for node in self.graph.nodes if node.name == name).op == "placeholder", \ + f"Common node {name} is not an input of the model." + except StopIteration: + raise ValueError(f"Common node name {name} not in graph.") + else: + self.cnode = [] + + node_id = 0 + region_id = 0 + + param_op_deps = {} + + deps = {} + region_list = [] + region = Region(r_id=region_id) + + act_n = None + + for n in self.graph.nodes: + if n.op != "placeholder" and n.op != "output": + for n_par in n.all_input_nodes: + if n_par.op != "placeholder" and n_par.name not in self.cnode: + deps[n_par] -= 1 + if n_par.op != "placeholder" and n_par.name in self.only_param_ops: + param_op_deps[n_par] -= 1 + + if act_n in region.nodes and _maybe_param_comp_start(): + ns = [] + border_n_idx = region.nodes.index(act_n) + if border_n_idx < len(region.nodes): + ns = region.nodes[border_n_idx + 1:] + region.nodes = region.nodes[:border_n_idx + 1] + region_list.append(region) + region_id += 1 + region = Region(r_id=region_id) + region.nodes = ns + + _exception_node_handling() + region.nodes.append(n) + self._set_node_and_region_info(node_id, n, region) + node_id += 1 + + # if the node could free all dependencies in graph + # we could begin a new region + if _is_param_comp_end(): + region_list.append(region) + region_id += 1 + region = Region(r_id=region_id) + + # propagate common node attr if possible + if len(n.all_input_nodes) == len([node for node in n.all_input_nodes if node.name in self.cnode + ]) or _is_cop(n.target): + self.cnode.append(n.name) + else: + deps[n] = len( + [user for user in n.users if user.op != "output"]) + + # propagate param node attr if possible + if len(n.all_input_nodes) == len([node for node in n.all_input_nodes if node.name in self.only_param_ops + ]) or n.op == "get_attr": + self.only_param_ops.append(n.name) + param_op_deps[n] = len( + [user for user in n.users if user.op != "output"]) + + # record last activation node + if _is_act(n._meta_data): + act_n = n + + if len(region.nodes): + region_list.append(region) + + return region_list + + def _set_node_and_region_info(self, node_id: int, cur_n: Node, cur_reg: Region): + + cur_n.node_info = NodeInfo(node_id) + + if cur_n.op == 'call_module': + target = cur_n.target + submod = self.root_module.get_submodule(target) + for p in list(submod.parameters(recurse=False)): + + if p in self.param_region_map: + cur_reg.shared_rid = self.param_region_map[p].r_id + self.param_region_map[p].shared_rid = cur_reg.r_id + self.shared_region_pairs.append( + (self.param_region_map[p], cur_reg)) + else: + self.param_region_map[p] = cur_reg + + cur_reg.fp16_params.append(p) + cur_reg.param_num += p.data.numel() + cur_reg.param_size += p.data.numel() * p.data.element_size() + + elif cur_n.op == "get_attr": + attr_itr = self.root_module + atoms = cur_n.target.split(".") + for atom in atoms: + attr_itr = getattr(attr_itr, atom) + + if isinstance(attr_itr, torch.nn.Parameter): + + if attr_itr in self.param_region_map: + cur_reg.shared_rid = self.param_region_map[attr_itr].r_id + self.param_region_map[attr_itr].shared_rid = cur_reg.r_id + self.shared_region_pairs.append( + (self.param_region_map[attr_itr], cur_reg)) + else: + self.param_region_map[attr_itr] = cur_reg + + cur_reg.fp16_params.append(attr_itr) + cur_reg.param_num += attr_itr.data.numel() + cur_reg.param_size += attr_itr.data.numel() * attr_itr.data.element_size() + + def get_region(self, param: torch.nn.Parameter) -> Region: + """ + Return the region owning the parameter. + + Args: + param (torch.nn.Parameter): a torch parameter object + """ + return self.param_region_map[param] + + def __update_param_region_map(self, params: List[torch.nn.Parameter], region: Region): + for p in params: + self.param_region_map[p] = region diff --git a/colossalai/auto_parallel/offload/runtime.py b/colossalai/auto_parallel/offload/runtime.py new file mode 100644 index 000000000000..91c7945bd65f --- /dev/null +++ b/colossalai/auto_parallel/offload/runtime.py @@ -0,0 +1,253 @@ +from typing import List +import torch +from torch.fx.node import Node + +from .region import Region +from .util import GlobalRuntimeInfo, requires_upload_p_in_fwd + + +class SynPreFwdPostBwdOP(torch.autograd.Function): + """ + A customized prefetch and offload operation. + + Args: + input_: input tensor. + fwd_info: information dict, which contains region indices + that need to be uploaded or freed during forward pass. + bwd_info: information dict, which contains region indices + that need to be uploaded during backward pass. + """ + + @staticmethod + def forward(ctx, input_, fwd_info, bwd_info): + ctx.bwd_info = bwd_info + d2h_rid = fwd_info.get('d2h_rid', None) + if d2h_rid is not None: + free_region = GlobalRuntimeInfo.region_list[d2h_rid] + assert isinstance(free_region, Region) + free_region.free_cuda_data() + + h2d_rid = fwd_info.get('h2d_rid', None) + if h2d_rid is not None: + h2d_region = GlobalRuntimeInfo.region_list[h2d_rid] + assert isinstance(h2d_region, Region) + h2d_region.move_param_to_cuda() + + return input_ + + @staticmethod + def backward(ctx, grad_output): + + h2d_rid = ctx.bwd_info.get('h2d_rid', None) + if h2d_rid is not None: + pref_region = GlobalRuntimeInfo.region_list[h2d_rid] + assert isinstance(pref_region, Region) + pref_region.move_param_to_cuda() + + return grad_output, None, None + + +class AsynPreFwdPostBwdOP(torch.autograd.Function): + """ + A customized prefetch and offload operation. + + Args: + input_: input tensor. + fwd_info: information dict, which contains region indices + that need to be prefetched, waited, or freed during forward pass. + bwd_info: information dict, which contains region indices + that need to be prefetched or waited during backward pass. + """ + + @staticmethod + def forward(ctx, input_, fwd_info, bwd_info): + ctx.bwd_info = bwd_info + + sync_rid = fwd_info.get('sync_rid', None) + if sync_rid is not None: + prefetch_event = GlobalRuntimeInfo.fwd_prefetch_event_map.get( + sync_rid, None) + if prefetch_event: + prefetch_event.wait() + + h2d_rid = fwd_info.get('h2d_rid', None) + if h2d_rid is not None: + pref_region = GlobalRuntimeInfo.region_list[h2d_rid] + assert isinstance(pref_region, Region) + master_stream = torch.cuda.current_stream() + with torch.cuda.stream(GlobalRuntimeInfo.h2d_stream): + GlobalRuntimeInfo.h2d_stream.wait_stream(master_stream) + pref_region.move_param_to_cuda() + + prefetch_event = torch.cuda.Event() + prefetch_event.record(GlobalRuntimeInfo.h2d_stream) + GlobalRuntimeInfo.fwd_prefetch_event_map[h2d_rid] = prefetch_event + + return input_ + + @staticmethod + def backward(ctx, grad_output): + + sync_rid = ctx.bwd_info.get('sync_rid', None) + if sync_rid is not None: + wait_region = GlobalRuntimeInfo.region_list[sync_rid] + assert isinstance(wait_region, Region) + prefetch_event = GlobalRuntimeInfo.bwd_prefetch_event_map.get( + sync_rid, None) + if prefetch_event: + prefetch_event.wait() + else: + wait_region.move_param_to_cuda() + + h2d_rid = ctx.bwd_info.get('h2d_rid', None) + if h2d_rid is not None: + pref_region = GlobalRuntimeInfo.region_list[h2d_rid] + assert isinstance(pref_region, Region) + master_stream = torch.cuda.current_stream() + with torch.cuda.stream(GlobalRuntimeInfo.h2d_stream): + GlobalRuntimeInfo.h2d_stream.wait_stream(master_stream) + pref_region.move_param_to_cuda() + + prefetch_event = torch.cuda.Event() + prefetch_event.record(GlobalRuntimeInfo.h2d_stream) + GlobalRuntimeInfo.bwd_prefetch_event_map[h2d_rid] = prefetch_event + return grad_output, None, None + + +def convert_fwd_upload_bwd_offload_to_action(tensor, fwd_info, bwd_info): + ''' + Convert Upload and Offload operation into runtime action. + + Argument: + tensor(torch.Tensor): input tensor. + fwd_info(dict): information dict, which contains region indices + that need to be uploaded, or freed during forward pass. + bwd_info(dict): information dict, which contains region indices + that need to be uploaded during backward pass. + ''' + with torch._C.DisableTorchFunction(): + ret = SynPreFwdPostBwdOP.apply(tensor, fwd_info, bwd_info) + return ret + +def convert_fwd_prefetch_bwd_offload_to_action(tensor, fwd_info, bwd_info): + ''' + Convert Prefetch and Offload operation into runtime action. + + Argument: + tensor(torch.Tensor): input tensor. + fwd_info(dict): information dict, which contains region indices + that need to be prefetched, waited, or freed during forward pass. + bwd_info(dict): information dict, which contains region indices + that need to be prefetched or waited during backward pass. + ''' + with torch._C.DisableTorchFunction(): + ret = AsynPreFwdPostBwdOP.apply(tensor, fwd_info, bwd_info) + return ret + + +def replace_node_users(orig_node: Node, inserted_node: Node, rep_user_nodes: List[Node] = None): + user_list = list(orig_node.users.keys()) + if rep_user_nodes is not None: + user_list = rep_user_nodes + for user in user_list: + if user == inserted_node: + continue + new_args = list(user.args) + new_kwargs = dict(user.kwargs) + # the origin node may be a positional argument or key word argument of user node + if orig_node in new_args: + # substitute the origin node with offload_apply_node + new_args[new_args.index(orig_node)] = inserted_node + user.args = tuple(new_args) + elif str(orig_node) in new_kwargs: + # substitute the origin node with offload_apply_node + new_kwargs[str(orig_node)] = inserted_node + user.kwargs = new_kwargs + + +def runtime_syn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[Region]): + """ + This pass is used to add the synchronous upload and offload spec apply node to the origin graph. + """ + mod_graph = gm.graph + last_inp_node = tuple(mod_graph.nodes)[0] + + for r_idx, region in enumerate(region_list): + # forward upload + fwd_info = {} + if requires_upload_p_in_fwd(region_list[region.shared_rid]): + fwd_info['h2d_rid'] = region.r_id + + # forward offload + if r_idx > 0 and region_list[r_idx - 1].need_offload: + fwd_info['d2h_rid'] = r_idx - 1 + + bwd_info = {} + # backward upload + if r_idx > 0 and region_list[r_idx - 1].need_offload: + bwd_info['h2d_rid'] = region_list[r_idx - 1].r_id + + if fwd_info or bwd_info: + with mod_graph.inserting_after(last_inp_node): + new_node = mod_graph.create_node('call_function', convert_fwd_upload_bwd_offload_to_action, + args=(last_inp_node, fwd_info, bwd_info)) + replace_node_users(last_inp_node, new_node) + + last_inp_node = region.nodes[-1] + + return gm + + +def runtime_asyn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[Region]): + """ + This pass is used to add the asynchronous prefetch and offload spec apply node to the origin graph. + """ + mod_graph = gm.graph + + # upload parameters of the first region + last_inp_node = tuple(mod_graph.nodes)[0] + first_region_with_p = [ + region for region in region_list if region.param_size][0] + fwd_info = {"h2d_rid": first_region_with_p.r_id} + with mod_graph.inserting_after(last_inp_node): + upload_apply_node = mod_graph.create_node('call_function', convert_fwd_upload_bwd_offload_to_action, + args=(last_inp_node, fwd_info, {})) + replace_node_users(last_inp_node, upload_apply_node) + last_inp_node = upload_apply_node + + for r_idx, region in enumerate(region_list): + # forward prefetch + fwd_info = {} + if region.param_size: + fwd_info['sync_rid'] = region.r_id + fwd_prefetch_region = region.fwd_prefetch_region + if fwd_prefetch_region and requires_upload_p_in_fwd(region_list[fwd_prefetch_region.shared_rid]): + fwd_info['h2d_rid'] = fwd_prefetch_region.r_id + + # forward offload + if r_idx > 0 and region_list[r_idx-1].need_offload: + fwd_info['d2h_rid'] = r_idx - 1 + + bwd_info = {} + # backward prefetch + if r_idx > 0 and region_list[r_idx-1].need_offload: + bwd_info['sync_rid'] = r_idx - 1 + if r_idx > 0 and region_list[r_idx-1].bwd_prefetch_region: + bwd_info['h2d_rid'] = region_list[r_idx-1].bwd_prefetch_region.r_id + + if fwd_info or bwd_info: + with mod_graph.inserting_after(last_inp_node): + new_node = mod_graph.create_node('call_function', convert_fwd_prefetch_bwd_offload_to_action, + args=(last_inp_node, fwd_info, bwd_info)) + replace_node_users(last_inp_node, new_node) + + last_inp_node = region.nodes[-1] + + if region.bwd_prefetch_region: + bwd_info = {'h2d_rid': region.bwd_prefetch_region.r_id} + with mod_graph.inserting_after(last_inp_node): + new_node = mod_graph.create_node('call_function', convert_fwd_prefetch_bwd_offload_to_action, + args=(last_inp_node, {}, bwd_info)) + replace_node_users(last_inp_node, new_node) + # gm.graph.print_tabular() + return gm diff --git a/colossalai/auto_parallel/offload/solver.py b/colossalai/auto_parallel/offload/solver.py new file mode 100644 index 000000000000..161f7ff86898 --- /dev/null +++ b/colossalai/auto_parallel/offload/solver.py @@ -0,0 +1,523 @@ +import time +from typing import List, Dict, Type +from abc import ABC, abstractmethod + +NOT_NVML = False +try: + from pynvml import * +except: + NOT_NVML = True + +import torch +from torch.fx.node import Node +from colossalai.utils.cuda import get_current_device + +from .training_simulator import TrainingSimulator, SynTrainingSimulator, AsynTrainingSimulator +from .region import Region +from .util import NodeInfo, NvDevicePower + + +def benchmark_func(func, number=1, repeat=1, warmup=3): + """ + benchmark data transfer cost. + """ + + for i in range(warmup): + func() + + costs = [] + + for i in range(repeat): + torch.cuda.synchronize() + begin = time.time() + for i in range(number): + func() + torch.cuda.synchronize() + costs.append((time.time() - begin) / number) + + return sum(costs) / len(costs) + + +class Solver(ABC): + """ + The parameter offload solver. + + Args: + region_list (List[Region]): represents the linearized DNN computing graph. + memory_budget (float): the given memory budget. + error_factor (float): the error factor. + It is used to reduce the memory budget. Due to some errors in the estimation of peak memory and execution time. + """ + + def __init__(self, + region_list: List[Region], + memory_budget: float = -1.0, + error_factor: float = 0.95) -> None: + + self.region_list = region_list + + self.error_factor: float = error_factor + if memory_budget > 0: + self.memory_budget = memory_budget * self.error_factor + else: + self.memory_budget = torch.cuda.get_device_properties( + get_current_device()).total_memory * self.error_factor + + self.link_to_bandwidth: Dict[str, Dict[float, float]] = self._profile_bandwidth() + self.comp_power: float = self._extract_computing_power() + + @abstractmethod + def _call_solver(self): + raise NotImplementedError + + @abstractmethod + def _try_to_offload(self, *args): + raise NotImplementedError + + @abstractmethod + def _eval_one_choice(self, *args): + raise NotImplementedError + + def _compute_offload_profit(self, total_mem_saving: float, peak_mem_saving: float, extra_cost: float): + """ + Compute the profits of the offload strategies, + which packages the memory savings information for subsequent comparisons. + + Args: + total_mem_saving (float): the total memory saving of the offload strategy. + peak_mem_saving (float): the peak memory saving of the offload strategy. + extra_cost (float): extra data transfer cost. + + Returns: + tuple: profit information, the first term represents memory savings per unit of time. + """ + + if extra_cost == 0: + # means data transfer overhead can be completely overlapped + return (float('inf'), total_mem_saving, peak_mem_saving) + return (total_mem_saving / extra_cost, total_mem_saving, peak_mem_saving) + + def _compare_profit(self, profit_a: tuple, profit_b: tuple) -> bool: + """ + Compare the profits of the two offload strategies using the dictionary order algorithm. + + Args: + profit_a (tuple): the profit of a offload strategy. + profit_b (tuple): the profit of another offload strategy. + + Returns: + bool: whether profit_a is greater than profit_b. + """ + + for val1, val2 in zip(profit_a, profit_b): + if val1 != val2: + return val1 > val2 + return False + + def _update_state(self, best_ts: TrainingSimulator): + """ + Update the solver state. + """ + + self.best_ts = best_ts + self._update_node_mem_info(best_ts.fwd_node_mem, best_ts.bwd_node_mem) + + def _update_node_mem_info(self, + fwd_mem_info: Dict[Node, float], + bwd_mem_info: Dict[Node, float]): + """ + Update the runtime memory information of the node. + + Args: + fwd_mem_info (Dict[Node, float]): the runtime memory of each node in forward pass. + bwd_mem_info (Dict[Node, float]): the runtime memory of each node in backward pass. + """ + + for node, mem in fwd_mem_info.items(): + assert hasattr(node, 'node_info') and isinstance( + node.node_info, NodeInfo) + node.node_info.runtime_fwd_mem = mem + for node, mem in bwd_mem_info.items(): + assert hasattr(node, 'node_info') and isinstance( + node.node_info, NodeInfo) + node.node_info.runtime_bwd_mem = mem + + def _extract_computing_power(self): + """ + return the FP16 computing performance of the current NVIDIA GPU. + + Raises: + TypeError: Unknown NVIDIA GPU device. + """ + + nvmlInit() + handle = nvmlDeviceGetHandleByIndex(0) + device_name = nvmlDeviceGetName(handle) + units = 1e12 + + if device_name.__contains__("RTX 3080"): + return NvDevicePower.RTX3080_FP16 * units + elif device_name.__contains__("RTX 3090"): + return NvDevicePower.RTX3090_FP16 * units + elif device_name.__contains__('V100'): + return NvDevicePower.V100_FP16 * units + elif device_name.__contains__("A100"): + return NvDevicePower.A100_FP16 * units + else: + raise TypeError(f'Unknown NVIDIA GPU device name {device_name}') + + def _profile_bandwidth(self): + """ + Profile the bidirectional communication bandwidth between CPU and GPU + using data volumes ranging from 1KB to 1GB. + """ + + print('profiling bandwidth ......') + link_to_bandwidth = {} + links = ['h2d', 'd2h'] + + for link in links: + t_size = 1024 + size_to_bandwidth = {} + + # from 1KB to 1GB + for i in range(21): + if link == 'h2d': + src_tensor = torch.ones( + int(t_size), dtype=torch.int8, pin_memory=True) + dst_tensor = torch.ones( + (int(t_size)), dtype=torch.int8, device='cuda') + elif link == 'd2h': + src_tensor = torch.ones( + int(t_size), dtype=torch.int8, device='cuda') + dst_tensor = torch.ones( + (int(t_size)), dtype=torch.int8, pin_memory=True) + + def func(): + dst_tensor.copy_(src_tensor) + + size_to_bandwidth[t_size] = t_size / benchmark_func(func, number=5, repeat=3) + print(f'size: {t_size / 1024 ** 2:.3f} MB, ' + f'{src_tensor.device.type}-to-{dst_tensor.device.type} ' + f'bandwidth: {size_to_bandwidth[t_size] / 1024 ** 3:.3f} GB/s') + + t_size *= 2 + + link_to_bandwidth[link] = size_to_bandwidth + return link_to_bandwidth + + +class SynGreedySolver(Solver): + + def __init__(self, + region_list: List[Region], + memory_budget: float = -1.0) -> None: + super().__init__(region_list, memory_budget) + + self.best_ts: SynTrainingSimulator = None + self._init_state() + + def _init_state(self): + """ + Initialize the solver state when without offloading. + """ + + ts = SynTrainingSimulator(self.region_list, self.comp_power, self.link_to_bandwidth) + ts.execute() + self._update_state(ts) + + def _call_solver(self): + """ + Call the solver to search an efficient parameter offloading strategy for the linearized graph. + The solver adopts greedy algorithm. + + Raises: + NotImplementedError: Unable to find a solution for the given memory budget. + """ + + print("search offloading strategy ......") + while self.best_ts.peak_mem > self.memory_budget: + offload_region = None + best_ts = None + max_profit = (0,) + + # search which region should be offloaded, + # the last region does not need to be offloaded. + for region in self.region_list[:-1]: + if region.param_size and not region.need_offload: + temp_ts, profit = self._try_to_offload(region) + if self._compare_profit(profit, max_profit): + offload_region = region + max_profit = profit + best_ts = temp_ts + + if offload_region is not None and best_ts is not None: + offload_region.need_offload = True + offload_region.is_syn = True + self._update_state(best_ts) + else: + raise NotImplementedError( + f"can't find the offload strategy met the memory budget {self.memory_budget / 1024 ** 2} MB, " + f"it needs {self.best_ts.peak_mem / 1024 ** 2:.3f} MB at least!") + + def _call_solver_l2l(self): + """ + The layer-wise offload strategy. + """ + + for region in self.region_list[:-1]: + region.need_offload = True + region.is_syn = True + + def _try_to_offload(self, offload_region: Region): + + # record previous information + orig_need_offload = offload_region.need_offload + assert not orig_need_offload + offload_region.need_offload = True + + ts, profit = self._eval_one_choice(offload_region) + + # restore previous information + offload_region.need_offload = orig_need_offload + return ts, profit + + def _eval_one_choice(self, offload_region: Region): + """ + Evaluate the profit of a strategy choice. + + Args: + offload_region (Region): the offload region of current choice. + + Returns: + SynTrainingSimulator: the training simulator corresponding to the current strategy. + tuple: contains memory saving and cost information of the current strategy. + """ + + ts = SynTrainingSimulator(self.region_list, self.comp_power, self.link_to_bandwidth) + ts.execute() + + extra_comm_cost = 2.0 * \ + ts._get_communication_overhead('h2d', offload_region.param_size) + # the shared region needs to be moved twice + if offload_region.r_id < offload_region.shared_rid: + extra_comm_cost *= 2.0 + profit = self._compute_offload_profit( + ts.total_mem_saving, self.best_ts.peak_mem - ts.peak_mem, extra_comm_cost) + + return ts, profit + + +class AsynGreedySolver(Solver): + + def __init__(self, + region_list: List[Region], + memory_budget: float = -1.0, + search_window_size: int = 3): + super().__init__(region_list, memory_budget) + + self.search_window_size = search_window_size + # Records the prefetch execution location of the offloaded region + self.region_to_region_map = {} + self.best_ts: AsynTrainingSimulator = None + + self._init_state() + + def _init_state(self): + """ + Initialize the solver state when without offloading. + """ + + ts = AsynTrainingSimulator(self.region_list, self.comp_power, self.link_to_bandwidth) + ts.execute() + self._update_state(ts) + print("init peak memory", self.best_ts.peak_mem / 1024 ** 2, "MB") + + def _call_solver(self): + """ + Call the solver to search an efficient parameter offloading strategy for the linearized graph. + The solver adopts greedy algorithm. + + Raises: + NotImplementedError: Unable to find a solution for the given memory budget. + """ + + print("search for offloading strategy ......") + # Records the prefetch execution location of the offloaded region + region_to_region_map = {} + while self.best_ts.peak_mem > self.memory_budget: + region_to_offload = None + max_offload_profit = (0,) + best_offl_ts = None + + # search which region should be offloaded, + # the last region does not need to be offloaded + for region in self.region_list[:-1]: + if region.param_size and not region.need_offload: + max_prefetch_profit = (0,) + best_pref_ts = None + + # search when to prefetch the region offloaded + for host_region in self.region_list[region.r_id + 1:region.r_id + 1 + self.search_window_size]: + if host_region.bwd_prefetch_region is not None: + continue + + temp_ts, profit = self._try_to_offload( + host_region, region) + + if self._compare_profit(profit, max_prefetch_profit): + region_to_region_map[region.r_id] = host_region + max_prefetch_profit = profit + best_pref_ts = temp_ts + if profit[0] == float('inf'): + break + + if self._compare_profit(max_prefetch_profit, max_offload_profit): + region_to_offload = region + max_offload_profit = max_prefetch_profit + best_offl_ts = best_pref_ts + + if (region_to_offload is not None) and (best_offl_ts is not None): + region_to_offload.need_offload = True + if region_to_region_map[region_to_offload.r_id] == region_to_offload: + region_to_offload.is_syn = True + else: + region_to_region_map[region_to_offload.r_id].bwd_prefetch_region = region_to_offload + self.region_to_region_map[region_to_offload.r_id] = region_to_region_map[region_to_offload.r_id] + + self._update_state(best_offl_ts) + + elif self.region_to_region_map.__len__() > 0: + self._repair_strategy() + else: + raise NotImplementedError( + f"can't find the offload strategy met the memory budget {self.memory_budget / 1024 ** 2} MB, " + f"it needs {self.best_ts.peak_mem / 1024 ** 2:.3f} MB at least!") + + region_to_region_map.clear() + + def _try_to_offload(self, host_region: Region, offload_region: Region): + """ + Attempts to offload the region and prefetch it in backward pass. + """ + + # record previous information + orig_prefetch = host_region.bwd_prefetch_region + orig_is_syn = offload_region.is_syn + orig_need_offload = offload_region.need_offload + + if host_region == offload_region: + offload_region.is_syn = True + else: + host_region.bwd_prefetch_region = offload_region + offload_region.need_offload = True + + ts, profit = self._eval_one_choice() + + # restore previous information + host_region.bwd_prefetch_region = orig_prefetch + offload_region.is_syn = orig_is_syn + offload_region.need_offload = orig_need_offload + + return ts, profit + + def _try_convert_to_syn_upload(self, host_region: Region, offload_region: Region): + """ + Attempts to convert asynchronous prefetch into synchronous upload operations. + """ + + # record previous information + orig_prefetch = host_region.bwd_prefetch_region + orig_is_syn = offload_region.is_syn + assert orig_prefetch is not None and not orig_is_syn + + host_region.bwd_prefetch_region = None + offload_region.is_syn = True + + ts, profit = self._eval_one_choice() + + # restore previous information + host_region.bwd_prefetch_region = orig_prefetch + offload_region.is_syn = orig_is_syn + + return ts, profit + + def _repair_strategy(self): + """ + Repair offload strategy. + It attempts to convert asynchronous prefetch into synchronous upload operations and selects the best one. + The repair process does not end until peak memory is reduced or there is no asynchronous prefetch operation. + """ + print("repair strategy ......") + + peak_mem_saving = 0 + while len(self.region_to_region_map) and peak_mem_saving <= 0: + + max_profit = (0,) + best_ts = None + undo_host_region = None + undo_offload_region = None + + for offload_region_id, host_region in self.region_to_region_map.items(): + offload_region = self.region_list[offload_region_id] + assert host_region.bwd_prefetch_region == offload_region + assert offload_region.need_offload + assert not offload_region.is_syn + + ts, profit = self._try_convert_to_syn_upload(host_region, + offload_region) + + if self._compare_profit(profit, max_profit): + undo_host_region = host_region + undo_offload_region = offload_region + max_profit = profit + best_ts = ts + + if best_ts is None: + raise NotImplementedError('repair error!') + + assert not undo_offload_region.is_syn + undo_offload_region.is_syn = True + undo_host_region.bwd_prefetch_region = None + + peak_mem_saving = self.best_ts.peak_mem - best_ts.peak_mem + + self._update_state(best_ts) + self.region_to_region_map.pop(undo_offload_region.r_id) + + return best_ts + + def _eval_one_choice(self): + """ + Evaluate the profit of a strategy choice. + + Returns: + AsynTrainingSimulator: the training simulator corresponding to the current strategy. + tuple: contains memory saving and cost information of the current strategy. + """ + + ts = AsynTrainingSimulator(self.region_list, self.comp_power, self.link_to_bandwidth) + ts.execute() + + extra_comm_cost = max(ts.iter_end_time - self.best_ts.iter_end_time, 0) + profit = self._compute_offload_profit( + ts.total_mem_saving, self.best_ts.peak_mem - ts.peak_mem, extra_comm_cost) + + return ts, profit + + +class SolverFactory: + solvers: Dict[str, Type[Solver]] = { + 'syn': SynGreedySolver, + 'asyn': AsynGreedySolver + } + + @staticmethod + def create(solver_name: str) -> Type[Solver]: + if solver_name not in SolverFactory.solvers: + raise TypeError(f"Unknown parameter offload policy {solver_name}") + return SolverFactory.solvers[solver_name] + + @staticmethod + def get_solver_names(): + return tuple(SolverFactory.solvers.keys()) diff --git a/colossalai/auto_parallel/offload/training_simulator.py b/colossalai/auto_parallel/offload/training_simulator.py new file mode 100644 index 000000000000..f277c183a912 --- /dev/null +++ b/colossalai/auto_parallel/offload/training_simulator.py @@ -0,0 +1,458 @@ +import bisect +from typing import List, Dict +from collections import OrderedDict +from abc import ABC, abstractmethod + +from torch.fx.node import Node + +from .region import Region +from .util import * + + +@dataclass +class ExecutionPeriod: + start_time: float = 0 + end_time: float = 0 + + +class TrainingSimulator(ABC): + """ + The Training Simulator is used to simulate the training process. + It records computation, communication, and runtime memory during forward and backward passes. + + Args: + region_list (List[Region]): represents the linearized DNN computing graph. + comp_power (float): the NVIDIA GPU FP16 compuing power. + link_to_bw (Dict[str, Dict[float, float]]): communication links and the corresponding bandwidth. + """ + + def __init__(self, + region_list: List[Region], + comp_power: float, + link_to_bw: Dict[str, Dict[float, float]]) -> None: + self.region_list = region_list + self.region_num = len(region_list) + + self.runtime_mem: int = 0 + self.peak_mem: int = 0 + self.total_mem_saving: int = 0 + + self.fwd_node_mem: Dict[Node, float] = {} + self.bwd_node_mem: Dict[Node, float] = {} + + # Node dependencies in backward pass + self.bwd_node_deps: Dict[Node, int] = {} + + self.comp_power: float = comp_power + self.link_to_bandwidth: Dict[str, Dict[float, float]] = link_to_bw + + @abstractmethod + def execute(self): + raise NotImplementedError + + @abstractmethod + def _eval_fwd_mem_per_region(self, region: Region): + raise NotImplementedError + + @abstractmethod + def _eval_bwd_mem_per_region(self, region: Region): + raise NotImplementedError + + def _get_bandwidth(self, link: str, comm_volumn: float) -> float: + """ + Get the data transfer bandwidth. + + Args: + link (str): the data transfer link. + comm_volumn (float): the amount of data transferred. + + Returns: + float: the data transfer bandwidth. + """ + + assert len(self.link_to_bandwidth) + if link not in self.link_to_bandwidth: + raise TypeError(f"Unknown data transfer link {link}") + + # size_list = sorted(list(map(float, self.link_to_bandwidth[link].keys()))) + size_list = sorted(self.link_to_bandwidth[link].keys()) + d_idx = bisect.bisect_left(size_list, comm_volumn) + return self.link_to_bandwidth[link][size_list[d_idx]] + + def _get_communication_overhead(self, link: str, comm_volumn: float) -> float: + return comm_volumn / self._get_bandwidth(link, comm_volumn) + + def _get_computing_overhead(self, flop: float) -> float: + return flop / self.comp_power + + +class SynTrainingSimulator(TrainingSimulator): + + def __init__(self, + region_list: List[Region], + comp_power: float, + link_to_bw: Dict[str, Dict[float, float]]) -> None: + super().__init__(region_list, comp_power, link_to_bw) + + def execute(self): + """ + Simulate synchronous training process. + """ + + for reg in self.region_list: + self._eval_fwd_mem_per_region(reg) + + for reg in self.region_list.__reversed__(): + self._eval_bwd_mem_per_region(reg) + + def _eval_fwd_mem_per_region(self, region: Region): + """ + Evaluate the runtime and peak memory when the forward execution reaches the current region. + """ + + # upload parameters of the current region + if requires_upload_p_in_fwd(self.region_list[region.shared_rid]): + self.runtime_mem += region.param_size + + for node in region.nodes: + self.runtime_mem += calculate_fwd_tmp(node) + \ + calculate_fwd_out(node) + self.fwd_node_mem[node] = self.runtime_mem + self.peak_mem = max(self.runtime_mem, self.peak_mem) + self.total_mem_saving += node.node_info.runtime_fwd_mem - self.runtime_mem + + if region.need_offload: + self.runtime_mem -= region.param_size + + def _eval_bwd_mem_per_region(self, region: Region): + """ + Evaluate the runtime and peak memory when the backward execution reaches the current region. + """ + + # upload parameters of the current region + if region.need_offload: + self.runtime_mem += region.param_size + + # add the gradient of the parameter + if region.r_id < region.shared_rid: + # gradient accumulation is required for shared parameters + self.runtime_mem += 2.0 * region.param_size + else: + self.runtime_mem += region.param_size + + for node in region.nodes.__reversed__(): + + self.runtime_mem -= calculate_fwd_out(node) + self.runtime_mem += node.meta['bwd_mem_tmp'] + \ + node.meta['bwd_mem_out'] + self.peak_mem = max(self.runtime_mem, self.peak_mem) + + # The memory savings of a node may be negative due to parameter prefetch. + self.total_mem_saving += node.node_info.runtime_bwd_mem - self.runtime_mem + self.bwd_node_mem[node] = self.runtime_mem + + self.runtime_mem -= (node.meta['bwd_mem_tmp'] + + calculate_fwd_tmp(node)) + + # free bwd_mem_out + self.bwd_node_deps[node] = len(node.all_input_nodes) + for user_node in node.users: + if user_node in self.bwd_node_deps: + self.bwd_node_deps[user_node] -= 1 + if self.bwd_node_deps[user_node] <= 0: + self.runtime_mem -= user_node.meta['bwd_mem_out'] + + if self.runtime_mem < 0: + raise ValueError(f"region id: {region.r_id}, node name: {node.name}, " + f"runtime_mem: {self.runtime_mem / 1024 ** 2:.3f}MB ---" + f"runtime memory computed less than 0, which is miscalculated!") + + # release parameter and offload gradient in region + if region.r_id == region.shared_rid: + self.runtime_mem -= 2.0 * region.param_size + elif region.r_id < region.shared_rid: + self.runtime_mem -= 3.0 * region.param_size + elif self.region_list[region.shared_rid].need_offload: + self.runtime_mem -= region.param_size + + +class AsynTrainingSimulator(TrainingSimulator): + + def __init__(self, + region_list: List[Region], + comp_power: float, + link_to_bw: Dict[str, Dict[float, float]]) -> None: + super().__init__(region_list, comp_power, link_to_bw) + + self.iter_end_time: int = 0 + # the last computation execution period + self.last_comp: ExecutionPeriod = ExecutionPeriod( + start_time=0, end_time=0) + # the last parameter prefetch execution period + self.last_h2d: ExecutionPeriod = ExecutionPeriod( + start_time=0, end_time=0) + # the last gradient offload execution period + self.last_d2h: ExecutionPeriod = ExecutionPeriod( + start_time=0, end_time=0) + # the forward computation execution period of the region + self.fwd_reg_to_comp: OrderedDict[int, ExecutionPeriod] = OrderedDict() + # the forward parameter prefetch execution period of the region + self.fwd_reg_to_pref: OrderedDict[int, ExecutionPeriod] = OrderedDict() + # the backward computation execution period of the region + self.bwd_reg_to_comp: OrderedDict[int, ExecutionPeriod] = OrderedDict() + # the backward parameter prefetch execution period of the region + self.bwd_reg_to_pref: OrderedDict[int, ExecutionPeriod] = OrderedDict() + # the gradient offload execution period of the region + # which is divided into those that are waiting and those that have been released + self.bwd_reg_to_offl_waiting: OrderedDict[int, + ExecutionPeriod] = OrderedDict() + self.bwd_reg_to_offl_freed: OrderedDict[int, + ExecutionPeriod] = OrderedDict() + # the region buffer, which records regions that are offloaded but not released + self.reg_buffer_to_free: List[int] = [] + + # node dependencies in backward pass + self.bwd_node_deps: Dict[Node, int] = {} + + # the region execution flow, + # where fwd_reg_flow[i,j] denotes whether the parameters of j-th region are in the GPU + # when the execution reaches the i-th region. + self.fwd_reg_flow = torch.zeros( + (self.region_num, self.region_num)).bool() + self.bwd_reg_flow = torch.zeros( + (self.region_num, self.region_num)).bool() + + def execute(self): + """ + Simulate asynchronous training process. + In forward pass, parameter prefetching is advanced by one region. + In backward pass, parameter prefetching is executed at the specified location, + and gradient offloading is urgent. + """ + + for reg in self.region_list: + if reg.param_size and reg.r_id < self.region_num - 1: + for nr in self.region_list[reg.r_id + 1:]: + if nr.param_size and requires_upload_p_in_fwd(self.region_list[nr.shared_rid]): + reg.fwd_prefetch_region = nr + break + self._eval_fwd_cost_per_region(reg) + self._eval_fwd_mem_per_region(reg) + + for reg in self.region_list.__reversed__(): + self._eval_bwd_cost_per_region(reg) + self._eval_bwd_mem_per_region(reg) + + # release remaining grads + for reg_id, offl_exec in self.bwd_reg_to_offl_waiting.items(): + self.bwd_reg_to_offl_freed[reg_id] = offl_exec + self.runtime_mem -= self.region_list[reg_id].param_size + self.bwd_reg_to_offl_waiting.clear() + + self.iter_end_time = max( + self.last_comp.end_time, self.last_d2h.end_time) + + def _insert_h2d_exec(self, region: Region, is_fwd: bool = True): + """ + Insert parameter prefetch execution period of the current region to the end of the h2d stream + """ + + pref_start_time = max(self.last_h2d.end_time, self.last_comp.end_time) + pref_end_time = pref_start_time + \ + 2.0 * self._get_communication_overhead('h2d', region.param_size) + pref_ep = ExecutionPeriod( + start_time=pref_start_time, end_time=pref_end_time) + if is_fwd: + self.fwd_reg_to_pref[region.r_id] = pref_ep + else: + self.bwd_reg_to_pref[region.r_id] = pref_ep + self.last_h2d = pref_ep + + def _insert_comp_exec(self, region: Region, is_fwd: bool = True): + """ + Insert computation execution period of the current region to the end of the computing stream + """ + + if is_fwd: + reg_to_comp = self.fwd_reg_to_comp + reg_to_pref = self.fwd_reg_to_pref + flop_key = 'fwd_flop' + else: + reg_to_comp = self.bwd_reg_to_comp + reg_to_pref = self.bwd_reg_to_pref + flop_key = 'bwd_flop' + comp_start_time = max(self.last_comp.end_time, reg_to_pref.get( + region.r_id, ExecutionPeriod(0, 0)).end_time) + comp_end_time = comp_start_time + \ + sum([self._get_computing_overhead(node.meta.get(flop_key, 0)) + for node in region.nodes]) + comp_ep = ExecutionPeriod( + start_time=comp_start_time, end_time=comp_end_time) + reg_to_comp[region.r_id] = comp_ep + self.last_comp = comp_ep + + def _insert_d2h_exec(self, region: Region): + """ + Insert gradient offload execution period of the current region to the end of the d2h stream + """ + + offl_start_time = max(self.last_d2h.end_time, self.last_comp.end_time) + offl_end_time = offl_start_time + \ + self._get_communication_overhead('d2h', region.param_size) + offl_ep = ExecutionPeriod( + start_time=offl_start_time, end_time=offl_end_time) + self.bwd_reg_to_offl_waiting[region.r_id] = offl_ep + self.last_d2h = offl_ep + + def _eval_fwd_cost_per_region(self, region: Region): + """ + Evaluate computation and communication execution period of the region in forward pass. + """ + + # upload parameters of the first region + if region.r_id == 0: + self._insert_h2d_exec(region) + + # prefetch parameters of the next region + fwd_prefetch_region = region.fwd_prefetch_region + if fwd_prefetch_region and requires_upload_p_in_fwd(self.region_list[fwd_prefetch_region.shared_rid]): + self._insert_h2d_exec(fwd_prefetch_region) + + # execute computation + self._insert_comp_exec(region) + + def _eval_fwd_mem_per_region(self, region: Region): + """ + Evaluate the runtime and peak memory when the forward execution reaches the current region. + """ + + # upload parameters of the current region + if region.r_id <= 0: + self.runtime_mem += region.param_size + self.fwd_reg_flow[region.r_id, region.r_id] = True + else: + self.fwd_reg_flow[region.r_id] = self.fwd_reg_flow[region.r_id - 1] + self.fwd_reg_flow[region.r_id, + self.reg_buffer_to_free] = False + self.reg_buffer_to_free.clear() + + # prefetch parameters of the next region + fwd_prefetch_region = region.fwd_prefetch_region + if fwd_prefetch_region and requires_upload_p_in_fwd(self.region_list[fwd_prefetch_region.shared_rid]): + self.runtime_mem += fwd_prefetch_region.param_size + self.fwd_reg_flow[region.r_id, + fwd_prefetch_region.r_id] = True + + for node in region.nodes: + self.runtime_mem += calculate_fwd_tmp(node) + \ + calculate_fwd_out(node) + self.peak_mem = max(self.runtime_mem, self.peak_mem) + + self.total_mem_saving += node.node_info.runtime_fwd_mem - self.runtime_mem + self.fwd_node_mem[node] = self.runtime_mem + + if region.need_offload: + self.runtime_mem -= region.param_size + + assert len( + self.reg_buffer_to_free) <= 1, f'{len(self.reg_buffer_to_free)}' + self.reg_buffer_to_free.append(region.r_id) + + def _eval_bwd_cost_per_region(self, region: Region): + """ + Evaluate computation and communication execution period of the region in backward pass. + """ + + # upload parameters of the current region + if region.is_syn: + assert region.need_offload + self._insert_h2d_exec(region, is_fwd=False) + + # prefetch parameters of the region choiced, which is parallel to computation + if region.bwd_prefetch_region is not None: + self._insert_h2d_exec(region.bwd_prefetch_region, is_fwd=False) + + # execute computation + self._insert_comp_exec(region, is_fwd=False) + + # offload gradient + if requires_offload_g_in_bwd(region): + self._insert_d2h_exec(region) + + assert len(self.reg_buffer_to_free) == 0 + for reg_id, offl_exec in self.bwd_reg_to_offl_waiting.items(): + if offl_exec.end_time >= self.last_comp.start_time: + break + self.reg_buffer_to_free.append(reg_id) + self.bwd_reg_to_offl_freed[reg_id] = offl_exec + + for reg_id in self.reg_buffer_to_free: + self.bwd_reg_to_offl_waiting.pop(reg_id) + + def _eval_bwd_mem_per_region(self, region: Region): + """ + Evaluate the runtime and peak memory when the backward execution reaches the current region. + """ + + if region.r_id + 1 < self.region_num: + self.bwd_reg_flow[region.r_id] = self.bwd_reg_flow[region.r_id + 1] + else: + self.bwd_reg_flow[region.r_id] = self.fwd_reg_flow[-1] + self.bwd_reg_flow[region.r_id, + self.reg_buffer_to_free] = False + + # free gradients in the buffer + while len(self.reg_buffer_to_free): + reg_id = self.reg_buffer_to_free.pop(0) + self.runtime_mem -= self.region_list[reg_id].param_size + + # upload parameters of the current region + if region.is_syn: + self.runtime_mem += region.param_size + self.bwd_reg_flow[region.r_id, region.r_id] = True + + # prefetch parameters of the region choiced + bwd_prefetch_region = region.bwd_prefetch_region + if bwd_prefetch_region: + self.runtime_mem += bwd_prefetch_region.param_size + self.bwd_reg_flow[region.r_id, + bwd_prefetch_region.r_id] = True + + # add the gradient of the parameter + if region.r_id < region.shared_rid: + # gradient accumulation is required for shared parameters + self.runtime_mem += 2.0 * region.param_size + else: + self.runtime_mem += region.param_size + + for node in region.nodes.__reversed__(): + + self.runtime_mem -= calculate_fwd_out(node) + self.runtime_mem += node.meta['bwd_mem_tmp'] + \ + node.meta['bwd_mem_out'] + self.peak_mem = max(self.runtime_mem, self.peak_mem) + + # The memory savings of a node may be negative due to parameter prefetch. + self.total_mem_saving += node.node_info.runtime_bwd_mem - self.runtime_mem + + self.bwd_node_mem[node] = self.runtime_mem + + self.runtime_mem -= (node.meta['bwd_mem_tmp'] + + calculate_fwd_tmp(node)) + + # free bwd_mem_out + self.bwd_node_deps[node] = len(node.all_input_nodes) + for user_node in node.users: + if user_node in self.bwd_node_deps: + self.bwd_node_deps[user_node] -= 1 + if self.bwd_node_deps[user_node] <= 0: + self.runtime_mem -= user_node.meta['bwd_mem_out'] + + if self.runtime_mem < 0: + raise ValueError(f"region id: {region.r_id}, node name: {node.name}, " + f"runtime_mem: {self.runtime_mem / 1024 ** 2:.3f}MB ---" + f"runtime memory computed less than 0, which is miscalculated!") + + # release parameters of the region + if requires_release_p_in_bwd(self.region_list[region.shared_rid]): + self.runtime_mem -= region.param_size diff --git a/colossalai/auto_parallel/offload/util.py b/colossalai/auto_parallel/offload/util.py new file mode 100644 index 000000000000..a99c4eb20225 --- /dev/null +++ b/colossalai/auto_parallel/offload/util.py @@ -0,0 +1,90 @@ +from dataclasses import dataclass +from typing import List +import torch +from colossalai.fx.profiler import calculate_fwd_out, calculate_fwd_tmp + +from .region import Region + + +@dataclass +class NodeInfo: + node_id: int = 0 + runtime_fwd_mem: float = 0 + runtime_bwd_mem: float = 0 + +class NvDevicePower: + """ + NVIDIA GPU computing performance (TFLOPs). + """ + + RTX3080_FP16 = 70 + RTX3080_FP32 = 34.1 + + RTX3090_FP16 = 71 + RTX3090_FP32 = 35.7 + + V100_FP16 = 31.4 + V100_FP32 = 15.7 + + A100_FP16 = 78 + A100_FP32 = 19.5 + + +class GlobalRuntimeInfo: + h2d_stream = torch.cuda.Stream() + d2h_stream = torch.cuda.Stream() + fwd_prefetch_event_map = {} + bwd_prefetch_event_map = {} + region_list = [] + + +def compute_act_peak_mem(region_list: List[Region]) -> float: + act_peak_mem = 0 + runtime_mem = 0 + # forward + for region in region_list: + for node in region.nodes: + runtime_mem = runtime_mem + \ + calculate_fwd_tmp(node) + calculate_fwd_out(node) + act_peak_mem = max(runtime_mem, act_peak_mem) + # backward + bwd_deps = {} + for region in region_list.__reversed__(): + for node in region.nodes.__reversed__(): + runtime_mem -= calculate_fwd_out(node) + runtime_mem = runtime_mem + \ + node.meta['bwd_mem_tmp'] + node.meta['bwd_mem_out'] + + act_peak_mem = max(runtime_mem, act_peak_mem) + + runtime_mem = runtime_mem - \ + node.meta['bwd_mem_tmp'] - calculate_fwd_tmp(node) + + # free bwd_mem_out + bwd_deps[node] = len(node.all_input_nodes) + for user_node in node.users: + if user_node in bwd_deps: + bwd_deps[user_node] -= 1 + if bwd_deps[user_node] <= 0: + runtime_mem -= user_node.meta['bwd_mem_out'] + + return act_peak_mem + +def compute_max_param_mem(region_list: List[Region]) -> float: + return max(region.param_size for region in region_list) + +def compute_total_param_mem(region_list: List[Region]) -> float: + return sum(region.param_size for region in region_list if region.r_id <= region.shared_rid) + +def requires_upload_p_in_fwd(shared_reg: Region): + return (shared_reg.r_id >= shared_reg.shared_rid) or ( + shared_reg.r_id < shared_reg.shared_rid and shared_reg.need_offload) + +def requires_release_p_in_bwd(shared_reg: Region): + return (shared_reg.r_id >= shared_reg.shared_rid) or ( + shared_reg.r_id < shared_reg.shared_rid and shared_reg.need_offload) + +def requires_offload_g_in_bwd(region: Region): + return region.param_size and (region.r_id <= region.shared_rid) + + diff --git a/examples/language/gpt/experiments/auto_offload/README.md b/examples/language/gpt/experiments/auto_offload/README.md new file mode 100644 index 000000000000..a0d252119056 --- /dev/null +++ b/examples/language/gpt/experiments/auto_offload/README.md @@ -0,0 +1,37 @@ +# Auto-Offload Demo with GPT2 + +## Requirements + +Before you can launch training, you need to install the following requirements. + +### Install PyTorch + +```bash +#conda +conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch +#pip +pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113 +``` + +### Install [Colossal-AI v0.2.0](https://colossalai.org/download/) From Official Website + +```bash +pip install colossalai==0.2.0+torch1.12cu11.3 -f https://release.colossalai.org +``` + +### Install transformers + +```bash +pip install transformers +``` + +## Dataset + +For simplicity, the input data is randonly generated here. + +## Training + +```bash +#Run the auto offload on GPT with default setting and a dummy dataset. +bash run.sh +``` diff --git a/examples/language/gpt/experiments/auto_offload/model_zoo.py b/examples/language/gpt/experiments/auto_offload/model_zoo.py new file mode 100644 index 000000000000..35e44608f810 --- /dev/null +++ b/examples/language/gpt/experiments/auto_offload/model_zoo.py @@ -0,0 +1,65 @@ +import torch +import torch.nn as nn +from transformers import GPT2Config, GPT2LMHeadModel + +class GPTLMModel(nn.Module): + + def __init__(self, + hidden_size=768, + num_layers=12, + num_attention_heads=12, + max_seq_len=1024, + vocab_size=50257): + super().__init__() + self.model = GPT2LMHeadModel( + GPT2Config(n_embd=hidden_size, + n_layer=num_layers, + n_head=num_attention_heads, + n_positions=max_seq_len, + n_ctx=max_seq_len, + vocab_size=vocab_size)) + + def forward(self, input_ids, attention_mask): + # Only return lm_logits + return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=True)[0] + + +class GPTLMLoss(nn.Module): + + def __init__(self): + super().__init__() + self.loss_fn = nn.CrossEntropyLoss() + + def forward(self, logits, labels): + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + +def get_gpt2_components(model_type: str, batch_size: int): + vocab_size = 1024 + seq_len = 8 + + def gpt2_model_builder(): + if model_type == "gpt2_medium": + return GPTLMModel(hidden_size=1024, num_layers=24, num_attention_heads=16) + elif model_type == "gpt2_xl": + return GPTLMModel(hidden_size=1600, num_layers=48, num_attention_heads=32) + elif model_type == "gpt2_10b": + return GPTLMModel(hidden_size=4096, num_layers=50, num_attention_heads=16) + elif model_type == "gpt2_14b": + return GPTLMModel(hidden_size=4096, num_layers=70, num_attention_heads=16) + elif model_type == "gpt2_20b": + return GPTLMModel(hidden_size=8192, num_layers=25, num_attention_heads=16) + elif model_type == "gpt2_24b": + return GPTLMModel(hidden_size=8192, num_layers=30, num_attention_heads=16) + else: + raise TypeError(f"model_builder {model_type}") + + def gpt2_data_gen(device="cuda"): + input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=device) + attention_mask = torch.ones_like(input_ids, device=device) + kwargs = dict(input_ids=input_ids, attention_mask=attention_mask) + return kwargs + + return gpt2_model_builder, gpt2_data_gen \ No newline at end of file diff --git a/examples/language/gpt/experiments/auto_offload/requirements.txt b/examples/language/gpt/experiments/auto_offload/requirements.txt new file mode 100644 index 000000000000..3ebde8d460aa --- /dev/null +++ b/examples/language/gpt/experiments/auto_offload/requirements.txt @@ -0,0 +1,2 @@ +colossalai >= 0.1.12 +torch >= 1.8.1 \ No newline at end of file diff --git a/examples/language/gpt/experiments/auto_offload/run.sh b/examples/language/gpt/experiments/auto_offload/run.sh new file mode 100644 index 000000000000..6a272ec442ab --- /dev/null +++ b/examples/language/gpt/experiments/auto_offload/run.sh @@ -0,0 +1,8 @@ +export BATCH_SIZE=${BATCH_SIZE:-64} +export MODEL_TYPE=${MODEL_TYPE:-"gpt2_medium"} +export MEMORY_BUDGET=${MEMORY_BUDGET:-16} +export SOLVER_TYPE=${SOLVER_TYPE:-"asyn"} + +mkdir -p offload_logs + +python train_gpt_offload.py --model_type=${MODEL_TYPE} --memory_budget=${MEMORY_BUDGET} --solver_type=${SOLVER_TYPE} --batch_size=${BATCH_SIZE} 2>&1 | tee ./offload_logs/${MODEL_TYPE}_bs_${BATCH_SIZE}_st_${SOLVER_TYPE}.log diff --git a/examples/language/gpt/experiments/auto_offload/train_gpt_offload.py b/examples/language/gpt/experiments/auto_offload/train_gpt_offload.py new file mode 100644 index 000000000000..729d1ce4456b --- /dev/null +++ b/examples/language/gpt/experiments/auto_offload/train_gpt_offload.py @@ -0,0 +1,94 @@ +import time +import pytest +import argparse +from functools import partial + +import torch +from torch.utils._pytree import tree_map +import torch.multiprocessing as mp + +import colossalai +from colossalai.nn.optimizer import HybridAdam +from colossalai.fx.profiler import parameter_size +from colossalai.utils import free_port, get_current_device +from colossalai.auto_parallel.offload.amp_optimizer import AMPOptimizer +from colossalai.auto_parallel.offload.mem_optimize import memory_optimize +from colossalai.auto_parallel.offload.solver import NOT_NVML +from model_zoo import get_gpt2_components, GPTLMLoss + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--model_type', type=str, default="gpt2_medium") + parser.add_argument('--batch_size', type=int, default=64) + parser.add_argument('--solver_type', type=str, default='asyn') + parser.add_argument('--memory_budget', type=float, default=16) + return parser.parse_args() + +@pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed') +def train_gpt(args): + memory_budget = args.memory_budget * 1024 * 1024 * 1024 + solver_type = args.solver_type + model_type = args.model_type + batch_size = args.batch_size + + # build model + model_builder, data_gen = get_gpt2_components(model_type=model_type, batch_size=batch_size) + label = torch.randint(low=0, high=128, size=(64, 8,), device=get_current_device()) + criterion = GPTLMLoss() + + start_time = time.time() + model = model_builder() + model.train() + param_size = parameter_size(model) / 1024 ** 2 / 2 + init_time = time.time() - start_time + print(f"init_param_size={param_size:.3f} MB | init_model_time={init_time:.3f} s") + + data_args = data_gen(device="cpu") + wrap_fn = lambda x: x.to(dtype=torch.half) if isinstance(x, torch.Tensor) and torch.is_floating_point(x) else x + data_args = tree_map(wrap_fn, data_args) + start_time = time.time() + model = memory_optimize(model, data_args, memory_budget, solver_type) + solver_time = time.time() - start_time + print(f"solver_time={solver_time:.3f} s") + + hybrid_optimizer = HybridAdam(model.model.parameters(), lr=1e-3) + optim = AMPOptimizer(hybrid_optimizer, model) + + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.cuda.reset_peak_memory_stats() + + time_list = [] + data_args = data_gen(device="cuda") + data_args = tree_map(wrap_fn, data_args) + for step in range(10): + optim.zero_grad() + torch.cuda.synchronize() + start_time = time.time() + loss = criterion(model(**data_args), label) + optim.backward(loss) + torch.cuda.synchronize() + time_list.append(time.time() - start_time) + optim.step() + + torch.cuda.synchronize() + + exec_time = sum(sorted(time_list)[:5]) / 5 + runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024 ** 2 + runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024 ** 2 + print(f'solver_type: {solver_type} | model_type: {model_type}') + print( + f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB ' + f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|' + ) + print(time_list) + +def run(rank, world_size, port, args): + config = {} + colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + train_gpt(args) + +if __name__ == '__main__': + args = parse_args() + run_func = partial(run, world_size=1, port=free_port(), args=args) + mp.spawn(run_func, nprocs=1) diff --git a/tests/test_auto_parallel/test_offload/model_utils.py b/tests/test_auto_parallel/test_offload/model_utils.py new file mode 100644 index 000000000000..c22b17ae42ba --- /dev/null +++ b/tests/test_auto_parallel/test_offload/model_utils.py @@ -0,0 +1,86 @@ +import torch +import torch.nn as nn +from transformers import GPT2Config, GPT2LMHeadModel +from transformers import BertConfig, BertLMHeadModel +from tests.components_to_test.registry import non_distributed_component_funcs + +class GPTLMModel(nn.Module): + + def __init__(self, + hidden_size=768, + num_layers=12, + num_attention_heads=12, + max_seq_len=1024, + vocab_size=50257): + super().__init__() + self.model = GPT2LMHeadModel( + GPT2Config(n_embd=hidden_size, + n_layer=num_layers, + n_head=num_attention_heads, + n_positions=max_seq_len, + n_ctx=max_seq_len, + vocab_size=vocab_size)) + + def forward(self, input_ids, attention_mask): + # Only return lm_logits + return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=True)[0] + + +class LMLoss(nn.Module): + + def __init__(self): + super().__init__() + self.loss_fn = nn.CrossEntropyLoss() + + def forward(self, logits, labels): + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + +class BertLMModel(nn.Module): + def __init__(self, hidden_size=768, num_layers=12, num_attention_heads=32, vocab_size=30522): + super().__init__() + self.model = BertLMHeadModel(BertConfig(n_embd=hidden_size, num_hidden_layers=num_layers, hidden_size=hidden_size, + num_attention_heads=num_attention_heads, max_position_embeddings=hidden_size, + vocab_size=vocab_size)) + + def forward(self, input_ids, attention_mask): + # Only return lm_logits + return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=True)[0] + +@non_distributed_component_funcs.register(name='bert_') +def get_bert_components(): + vocab_size = 1024 + seq_len = 64 + batchSize = 64 + + def bert_model_builder(): + model = BertLMModel(hidden_size=8192, num_layers=4, num_attention_heads=32, vocab_size=vocab_size) + return model + + def bert_data_gen(device="meta"): + input_ids = torch.randint(0, vocab_size, (batchSize, seq_len), device=device) + attention_mask = torch.ones_like(input_ids, device=device) + kwargs = dict(input_ids=input_ids, attention_mask=attention_mask) + return kwargs + + return bert_model_builder, bert_data_gen + +@non_distributed_component_funcs.register(name='gpt2_') +def get_gpt2_components(): + vocab_size = 1024 + seq_len = 8 + batchSize = 64 + + def gpt2_model_builder(): + model = GPTLMModel(hidden_size=8192, num_layers=2, num_attention_heads=32, vocab_size=vocab_size) + return model + + def gpt2_data_gen(device="meta"): + input_ids = torch.randint(0, vocab_size, (batchSize, seq_len), device=device) + attention_mask = torch.ones_like(input_ids, device=device) + kwargs = dict(input_ids=input_ids, attention_mask=attention_mask) + return kwargs + + return gpt2_model_builder, gpt2_data_gen \ No newline at end of file diff --git a/tests/test_auto_parallel/test_offload/test_perf.py b/tests/test_auto_parallel/test_offload/test_perf.py new file mode 100644 index 000000000000..d569570f4b7d --- /dev/null +++ b/tests/test_auto_parallel/test_offload/test_perf.py @@ -0,0 +1,150 @@ +import time +import pytest +from functools import partial + +import torch +from torch.utils._pytree import tree_map +import torch.multiprocessing as mp + +import colossalai +from colossalai.nn.optimizer import HybridAdam +from colossalai.fx.profiler import parameter_size +from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.utils import free_port, get_current_device +from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper +from colossalai.auto_parallel.offload.amp_optimizer import AMPOptimizer +from colossalai.auto_parallel.offload.mem_optimize import memory_optimize +from colossalai.auto_parallel.offload.solver import NOT_NVML +from colossalai.testing import parameterize + +from tests.test_tensor.common_utils import set_seed +from tests.test_auto_parallel.test_offload.model_utils import * + + +@parameterize('model_name', ['gpt2_']) +@parameterize('memory_budget', [5000]) +@parameterize('solver_name', ['asyn']) +def exam_fwd_bwd( + model_name: str, + memory_budget: float, + solver_name: str +): + + # build model + get_components_func = non_distributed_component_funcs.get_callable(model_name) + model_builder, data_gen = get_components_func() + label = torch.randint(low=0, high=128, size=(64, 8,), device=get_current_device()) + criterion = LMLoss() + + set_seed(42) + start_time = time.time() + model = model_builder() + model.train() + param_size = parameter_size(model) / 1024 ** 2 / 2 + init_time = time.time() - start_time + print(f"init_param_size={param_size:.3f} MB | init_model_time={init_time:.3f} s") + + data_args = data_gen(device="cpu") + wrap_fn = lambda x: x.to(dtype=torch.half) if isinstance(x, torch.Tensor) and torch.is_floating_point(x) else x + data_args = tree_map(wrap_fn, data_args) + start_time = time.time() + model = memory_optimize(model, data_args, memory_budget * 1024 * 1024, solver_name) + solver_time = time.time() - start_time + print(f"solver_time={solver_time:.3f} s") + + hybrid_optimizer = HybridAdam(model.model.parameters(), lr=1e-3) + optim = AMPOptimizer(hybrid_optimizer, model) + + with ColoInitContext(device=torch.device('cpu')): + gemini_model = model_builder() + gemini_model.train() + + hybrid_optimizer = HybridAdam(gemini_model.parameters(), lr=1e-3) + gemini_config = dict(strict_ddp_mode=False, + device=torch.device('cpu'), + placement_policy='cpu', + pin_memory=True, + hidden_dim=8192, + search_range_mb=128) + gemini_model = zero_model_wrapper(gemini_model, 3, gemini_config) + optim_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True, verbose=True) + gemini_optim = zero_optim_wrapper(gemini_model, hybrid_optimizer, optim_config=optim_config) + + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.cuda.reset_peak_memory_stats() + + # test gemini + time_list = [] + set_seed(42) + data_args = data_gen(device="cuda") + for step in range(10): + gemini_optim.zero_grad() + torch.cuda.synchronize() + start_time = time.time() + gemini_out = gemini_model(**data_args) + gemini_loss = criterion(gemini_out, label) + gemini_optim.backward(gemini_loss) + torch.cuda.synchronize() + time_list.append(time.time() - start_time) + gemini_optim.step() + + torch.cuda.synchronize() + + exec_time = sum(sorted(time_list)[:5]) / 5 + runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024 ** 2 + runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024 ** 2 + print(f'gemini | model_name: {model_name}') + print( + f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB ' + f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|' + ) + print(time_list) + + del data_args + del gemini_model + del gemini_optim + del gemini_out + del gemini_loss + + # test asyn offload + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.cuda.reset_peak_memory_stats() + + time_list = [] + set_seed(42) + data_args = data_gen(device="cuda") + data_args = tree_map(wrap_fn, data_args) + for step in range(10): + optim.zero_grad() + torch.cuda.synchronize() + start_time = time.time() + loss = criterion(model(**data_args), label) + optim.backward(loss) + torch.cuda.synchronize() + time_list.append(time.time() - start_time) + optim.step() + + torch.cuda.synchronize() + + exec_time = sum(sorted(time_list)[:5]) / 5 + runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024 ** 2 + runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024 ** 2 + print(f'solver_name: {solver_name} | model_name: {model_name}') + print( + f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB ' + f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|' + ) + print(time_list) + +@pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed') +def test_perf(rank, world_size, port): + config = {} + colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + exam_fwd_bwd() + + +if __name__ == '__main__': + run_func = partial(test_perf, world_size=1, port=free_port()) + mp.spawn(run_func, nprocs=1) diff --git a/tests/test_auto_parallel/test_offload/test_solver.py b/tests/test_auto_parallel/test_offload/test_solver.py new file mode 100644 index 000000000000..2efbb750f80d --- /dev/null +++ b/tests/test_auto_parallel/test_offload/test_solver.py @@ -0,0 +1,62 @@ +import pytest +import torch.fx +from torch.fx import GraphModule +from torch.utils._pytree import tree_map + +from colossalai.fx import ColoTracer, is_compatible_with_meta +from colossalai.fx.passes.meta_info_prop import MetaInfoProp +from colossalai.auto_parallel.offload.region_manager import RegionManager +from colossalai.auto_parallel.offload.solver import SolverFactory, NOT_NVML +from colossalai.testing import parameterize +from tests.test_auto_parallel.test_offload.model_utils import * + +@pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed') +@parameterize('model_name', ['gpt2_', 'bert_']) +@parameterize('memory_budget', [4000]) +@parameterize('solver_name', ['syn', 'asyn']) +def solver_test(model_name: str, + memory_budget: float, + solver_name: str): + + get_components_func = non_distributed_component_funcs.get_callable(model_name) + model_builder, data_gen = get_components_func() + data_args = data_gen(device="cpu") + wrap_fn = lambda x: x.to(dtype=torch.half) if isinstance(x, torch.Tensor) and torch.is_floating_point(x) else x + data_args = tree_map(wrap_fn, data_args) + model = model_builder() + model.train() + model = model.cpu().half() + + tracer = ColoTracer() + assert is_compatible_with_meta() + wrap_fn = lambda x: x.to("meta") if isinstance(x, torch.Tensor) else x + meta_args = tree_map(wrap_fn, data_args) + graph = tracer.trace(model, meta_args=meta_args) + gm = GraphModule(model, graph, model.__class__.__name__) + + interp = MetaInfoProp(gm) + interp.propagate(*meta_args.values()) + + region_manager = RegionManager(graph, solver_name=solver_name) + region_manager._pre_process() + region_list = region_manager.region_list + + solver_cls = SolverFactory.create(solver_name) + memory_budget = memory_budget * 1024 * 1024 + solver = solver_cls(region_list, memory_budget) + solver._call_solver() + + assert solver.best_ts.peak_mem < memory_budget + + print("****************** execution plan *******************") + for region in region_list: + need_offload = region.need_offload + to_prefetch = region.fwd_prefetch_region.r_id if region.fwd_prefetch_region is not None else None + print(f'| {model_name} forward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}') + for region in region_list.__reversed__(): + need_offload = region.need_offload + to_prefetch = region.bwd_prefetch_region.r_id if region.bwd_prefetch_region is not None else None + print(f'| {model_name} backward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}') + +if __name__ == '__main__': + solver_test() \ No newline at end of file From e5f668f280f376e3cb8fc3f6c65bb824dcab1bc8 Mon Sep 17 00:00:00 2001 From: NatalieC323 <127177614+NatalieC323@users.noreply.github.com> Date: Tue, 21 Mar 2023 16:01:13 +0800 Subject: [PATCH 014/413] [dreambooth] fixing the incompatibity in requirements.txt (#3190) * Update requirements.txt * Update environment.yaml * Update README.md * Update environment.yaml * Update README.md * Update README.md * Delete requirements_colossalai.txt * Update requirements.txt * Update README.md --- examples/images/diffusion/README.md | 4 +++- examples/images/dreambooth/README.md | 21 +++++++++++-------- examples/images/dreambooth/requirements.txt | 1 - .../dreambooth/requirements_colossalai.txt | 8 ------- 4 files changed, 15 insertions(+), 19 deletions(-) delete mode 100644 examples/images/dreambooth/requirements_colossalai.txt diff --git a/examples/images/diffusion/README.md b/examples/images/diffusion/README.md index 22970ced064e..a70792b9f4a4 100644 --- a/examples/images/diffusion/README.md +++ b/examples/images/diffusion/README.md @@ -78,7 +78,9 @@ You can install the latest version (0.2.7) from our official website or from sou ##### Download suggested verision for this training ``` -pip install colossalai=0.2.5 + +pip install colossalai==0.2.5 + ``` ##### Download the latest version from pip for latest torch version diff --git a/examples/images/dreambooth/README.md b/examples/images/dreambooth/README.md index 14ed66c8d45b..b067a437c764 100644 --- a/examples/images/dreambooth/README.md +++ b/examples/images/dreambooth/README.md @@ -5,12 +5,12 @@ The `train_dreambooth_colossalai.py` script shows how to implement the training By accommodating model data in CPU and GPU and moving the data to the computing device when necessary, [Gemini](https://www.colossalai.org/docs/advanced_tutorials/meet_gemini), the Heterogeneous Memory Manager of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) can breakthrough the GPU memory wall by using GPU and CPU memory (composed of CPU DRAM or nvme SSD memory) together at the same time. Moreover, the model scale can be further improved by combining heterogeneous training with the other parallel approaches, such as data parallel, tensor parallel and pipeline parallel. -## Installing the dependencies +## Installation -Before running the scripts, make sure to install the library's training dependencies: +To begin with, make sure your operating system has the cuda version suitable for this exciting training session, which is cuda11.6-11.8. Notice that you may want to make sure the module versions suitable for the whole environment. Before running the scripts, make sure to install the library's training dependencies: ```bash -pip install -r requirements_colossalai.txt +pip install -r requirements.txt ``` ### Install [colossalai](https://github.com/hpcaitech/ColossalAI.git) @@ -37,9 +37,7 @@ The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Mode ## Training -The arguement `placement` can be `cpu`, `auto`, `cuda`, with `cpu` the GPU RAM required can be minimized to 4GB but will deceleration, with `cuda` you can also reduce GPU memory by half but accelerated training， with `auto` a more balanced solution for speed and memory can be obtained。 - -**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___** +We provide the script `colossalai.sh` to run the training task with colossalai. Meanwhile, we also provided traditional training process of dreambooth, `dreambooth.sh`, for possible comparation. For instance, the script of training process for [stable-diffusion-v1-4] model can be modified into: ```bash export MODEL_NAME="CompVis/stable-diffusion-v1-4" @@ -59,12 +57,17 @@ torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \ --max_train_steps=400 \ --placement="cuda" ``` - +- `MODEL_NAME` refers to the model you are training. +- `INSTANCE_DIR` refers to personalized path to instance images, you might need to insert information here. +- `OUTPUT_DIR` refers to local path to save the trained model, you might need to find a path with enough space. +- `resolution` refers to the corresponding resolution number of your target model. Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model. +- `placement` refers to the training strategy supported by Colossal AI, defult = 'cuda', which refers to loading all the parameters into cuda memory. On the other hand, 'cpu' refers to 'cpu offload' strategy while 'auto' enables 'Gemini', both featured by Colossal AI. ### Training with prior-preservation loss Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data. -According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time. + +According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time. The general script can be then modified as the following. ```bash export MODEL_NAME="CompVis/stable-diffusion-v1-4" @@ -91,7 +94,7 @@ torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \ ## Inference -Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt. +Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. `--instance_prompt="a photo of sks dog" ` in the above example) in your prompt. ```python from diffusers import StableDiffusionPipeline diff --git a/examples/images/dreambooth/requirements.txt b/examples/images/dreambooth/requirements.txt index 6c4f40fb5dd0..1ec828c630ef 100644 --- a/examples/images/dreambooth/requirements.txt +++ b/examples/images/dreambooth/requirements.txt @@ -5,4 +5,3 @@ transformers>=4.21.0 ftfy tensorboard modelcards -colossalai diff --git a/examples/images/dreambooth/requirements_colossalai.txt b/examples/images/dreambooth/requirements_colossalai.txt deleted file mode 100644 index c4a0e91703bb..000000000000 --- a/examples/images/dreambooth/requirements_colossalai.txt +++ /dev/null @@ -1,8 +0,0 @@ -diffusers -torch -torchvision -ftfy -tensorboard -modelcards -transformers -colossalai==0.2.0+torch1.12cu11.3 -f https://release.colossalai.org From e7f3bed2d36c5406e9a9ab92438be46a5f9258d7 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 21 Mar 2023 17:39:30 +0800 Subject: [PATCH 015/413] [booster] added the plugin base and torch ddp plugin (#3180) * [booster] added the plugin base and torch ddp plugin * polish code * polish code * polish code --- colossalai/booster/booster.py | 90 ++++++----- colossalai/booster/plugin.py | 46 ------ colossalai/booster/plugin/__init__.py | 4 + colossalai/booster/plugin/plugin_base.py | 51 ++++++ colossalai/booster/plugin/torch_ddp_plugin.py | 147 ++++++++++++++++++ tests/test_booster/test_accelerator.py | 22 ++- .../test_mixed_precision/test_fp16_torch.py | 21 ++- .../test_plugin/test_torch_ddp_plugin.py | 85 ++++++++++ 8 files changed, 379 insertions(+), 87 deletions(-) delete mode 100644 colossalai/booster/plugin.py create mode 100644 colossalai/booster/plugin/__init__.py create mode 100644 colossalai/booster/plugin/plugin_base.py create mode 100644 colossalai/booster/plugin/torch_ddp_plugin.py create mode 100644 tests/test_booster/test_plugin/test_torch_ddp_plugin.py diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 7d7f21ca6cf2..230c65a9e0a1 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -1,9 +1,9 @@ +import warnings from contextlib import contextmanager -from typing import Callable, Iterable, Iterator, List, Optional, Tuple, Union +from typing import Callable, Iterator, List, Optional, Tuple, Union import torch import torch.nn as nn -from torch import Tensor from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils.data import DataLoader @@ -55,27 +55,43 @@ def __init__(self, device: str = 'cuda', mixed_precision: Union[MixedPrecision, str] = None, plugin: Optional[Plugin] = None) -> None: - # TODO(FrankLeeeee): add plugin control logic - # if self.plugin is not None and self.plugin.control_accelerator: - # ... - # create acclerator - self.acceleartor = Accelerator(device) - self.acceleartor.set_default_device() - - # validate and set precision - if isinstance(MixedPrecision, str): - # the user will take the default arguments for amp training - self.mixed_precision = mixed_precision_factory(mixed_precision) - elif isinstance(mixed_precision, MixedPrecision): - # the user can customize the arguments by passing the precision object - self.mixed_precision = mixed_precision + if plugin is not None: + assert isinstance( + plugin, Plugin), f'Expected the argument plugin to be an instance of Plugin, but got {type(plugin)}.' + self.plugin = plugin + + # set accelerator + if self.plugin and self.plugin.control_device: + self.accelerator = None + warnings.warn('The plugin will control the accelerator, so the device argument will be ignored.') else: - raise ValueError( - f'Expected the argument mixed_precision to be a string or an instance of Precision, but got {type(mixed_precision)}.' - ) + self.accelerator = Accelerator(device) - def boost(self, model: nn.Module, optimizer: Optimizer, criterion: Callable, lr_scheduler: LRScheduler, - dataloader: DataLoader) -> List[Union[nn.Module, Optimizer, LRScheduler, DataLoader]]: + # set precision + if mixed_precision is None or (self.plugin and self.plugin.control_precision): + self.mixed_precision = None + warnings.warn('The plugin will control the precision, so the mixed_precision argument will be ignored.') + else: + # validate and set precision + if isinstance(MixedPrecision, str): + # the user will take the default arguments for amp training + self.mixed_precision = mixed_precision_factory(mixed_precision) + elif isinstance(mixed_precision, MixedPrecision): + # the user can customize the arguments by passing the precision object + self.mixed_precision = mixed_precision + else: + raise ValueError( + f'Expected the argument mixed_precision to be a string or an instance of Precision, but got {type(mixed_precision)}.' + ) + + def boost( + self, + model: nn.Module, + optimizer: Optimizer, + criterion: Callable = None, + dataloader: DataLoader = None, + lr_scheduler: LRScheduler = None, + ) -> List[Union[nn.Module, Optimizer, LRScheduler, DataLoader]]: """ Boost the model, optimizer, criterion, lr_scheduler, and dataloader. @@ -83,22 +99,25 @@ def boost(self, model: nn.Module, optimizer: Optimizer, criterion: Callable, lr_ model (nn.Module): The model to be boosted. optimizer (Optimizer): The optimizer to be boosted. criterion (Callable): The criterion to be boosted. - lr_scheduler (LRScheduler): The lr_scheduler to be boosted. dataloader (DataLoader): The dataloader to be boosted. + lr_scheduler (LRScheduler): The lr_scheduler to be boosted. """ - # TODO(FrankLeeeee): add plugin control logic - # if self.plugin is not None and self.plugin.control_accelerator: - # ... - model = self.acceleartor.configure_model(model) - # TODO(FrankLeeeee): consider multi-model and multi-optimizer case - # TODO(lsg): Add plugin control logic - # e.g. - # if self.plugin is not None and self.plugin.control_boost: - # ... + # TODO(FrankLeeeee): consider multi-dataloader case # transform model for mixed precision - model, optimizer, criterion = self.mixed_precision.configure(model, optimizer, criterion) - return model, optimizer, criterion, lr_scheduler, dataloader + if self.plugin: + model, optimizer, criterion, dataloader, lr_scheduler = self.plugin.configure( + model, optimizer, criterion, dataloader, lr_scheduler) + + if self.plugin and not self.plugin.control_device: + # transform model for accelerator + model = self.accelerator.configure(model) + + if self.mixed_precision and self.plugin and not self.plugin.control_precision: + # transform model for mixed precision + model, optimizer, criterion = self.mixed_precision.configure(model, optimizer, criterion) + + return model, optimizer, criterion, dataloader, lr_scheduler def backward(self, loss: torch.Tensor, optimizer: Optimizer) -> None: # TODO: implement this method with plugin @@ -117,8 +136,9 @@ def execute_pipeline(self, pass def no_sync(self, model: nn.Module) -> contextmanager: - # TODO: implement this method - pass + assert self.plugin is not None, f'no_sync is only enabled when a plugin is provided and the plugin supports no_sync.' + assert self.plugin.support_no_sync, f'The plugin {self.plugin.__class__.__name__} does not support no_sync.' + return self.plugin.no_sync(model) def save(self, obj: Union[nn.Module, Optimizer, LRScheduler], diff --git a/colossalai/booster/plugin.py b/colossalai/booster/plugin.py deleted file mode 100644 index 32e0a7bde3f7..000000000000 --- a/colossalai/booster/plugin.py +++ /dev/null @@ -1,46 +0,0 @@ -from typing import List, Tuple - -import torch -import torch.nn as nn -from torch.optim import Optimizer -from torch.utils.data import DataLoader - -from colossalai.device.device_mesh import DeviceMesh - -__all__ = ['Plugin'] - - -class Plugin: - - @property - def supported_devices(self) -> List[torch.device]: - pass - - @property - def supported_precisions(self) -> List[str]: - pass - - @property - def control_precision(self) -> bool: - pass - - @property - def control_device(self) -> bool: - pass - - @property - def support_no_sync(self) -> bool: - pass - - def setup_model(self, model: nn.Module, device_mesh_pool: DeviceMesh) -> nn.Module: - pass - - def setup_optimizer(self, optimizer: Optimizer) -> Optimizer: - pass - - def setup_dataloader(self, dataloader: DataLoader) -> DataLoader: - pass - - @property - def device_mesh_shape(self) -> List[Tuple[int, ...]]: - pass diff --git a/colossalai/booster/plugin/__init__.py b/colossalai/booster/plugin/__init__.py new file mode 100644 index 000000000000..3328fe2b9627 --- /dev/null +++ b/colossalai/booster/plugin/__init__.py @@ -0,0 +1,4 @@ +from .plugin_base import Plugin +from .torch_ddp_plugin import TorchDDPPlugin + +__all__ = ['Plugin', 'TorchDDPPlugin'] diff --git a/colossalai/booster/plugin/plugin_base.py b/colossalai/booster/plugin/plugin_base.py new file mode 100644 index 000000000000..3c347cb4252d --- /dev/null +++ b/colossalai/booster/plugin/plugin_base.py @@ -0,0 +1,51 @@ +from abc import ABC, abstractmethod +from typing import Callable, List, Tuple, Union + +import torch.nn as nn +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from torch.utils.data import DataLoader + +from colossalai.booster.interface import OptimizerWrapper + +__all__ = ['Plugin'] + + +class Plugin(ABC): + + @property + @abstractmethod + def supported_devices(self) -> List[str]: + pass + + @property + @abstractmethod + def supported_precisions(self) -> List[str]: + pass + + @property + @abstractmethod + def control_precision(self) -> bool: + pass + + @property + @abstractmethod + def control_device(self) -> bool: + pass + + @property + @abstractmethod + def support_no_sync(self) -> bool: + pass + + @abstractmethod + def configure( + self, + model: nn.Module, + optimizer: Optimizer, + criterion: Callable = None, + dataloader: DataLoader = None, + lr_scheduler: LRScheduler = None, + ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + # implement this method + pass diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py new file mode 100644 index 000000000000..07d6be8c748d --- /dev/null +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -0,0 +1,147 @@ +import random +from typing import Callable, List, Tuple, Union + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from colossalai.booster.interface import OptimizerWrapper + +from .plugin_base import Plugin + +__all__ = ['TorchDDPPlugin'] + + +class TorchDDPPlugin(Plugin): + """ + Plugin for PyTorch DDP. + + Example: + >>> from colossalai.booster import Booster + >>> from colossalai.booster.plugin import TorchDDPPlugin + >>> + >>> model, train_dataset, optimizer, criterion = ... + >>> plugin = TorchDDPPlugin() + + >>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8) + >>> booster = Booster(plugin=plugin) + >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion) + + Args: + broadcast_buffers (bool, optional): Whether to broadcast buffers in the beginning of training. Defaults to True. + bucket_cap_mb (int, optional): The bucket size in MB. Defaults to 25. + find_unused_parameters (bool, optional): Whether to find unused parameters. Defaults to False. + check_reduction (bool, optional): Whether to check reduction. Defaults to False. + gradient_as_bucket_view (bool, optional): Whether to use gradient as bucket view. Defaults to False. + static_graph (bool, optional): Whether to use static graph. Defaults to False. + """ + + def __init__(self, + broadcast_buffers: bool = True, + bucket_cap_mb: int = 25, + find_unused_parameters: bool = False, + check_reduction: bool = False, + gradient_as_bucket_view: bool = False, + static_graph: bool = False) -> None: + + assert dist.is_initialized( + ), 'torch.distributed is not initialized, please use colossalai.launch to create the distributed environment' + self.rank = dist.get_rank() + self.world_size = dist.get_world_size() + self.ddp_kwargs = dict(broadcast_buffers=broadcast_buffers, + bucket_cap_mb=bucket_cap_mb, + find_unused_parameters=find_unused_parameters, + check_reduction=check_reduction, + gradient_as_bucket_view=gradient_as_bucket_view, + static_graph=static_graph) + + def support_no_sync(self) -> bool: + return True + + def control_precision(self) -> bool: + return False + + def supported_precisions(self) -> List[str]: + return ['fp16', 'fp16_apex', 'bf16', 'fp8'] + + def control_device(self) -> bool: + return True + + def supported_devices(self) -> List[str]: + return ['cuda'] + + def prepare_train_dataloader(self, + dataset, + batch_size, + shuffle=False, + seed=1024, + drop_last=False, + pin_memory=False, + num_workers=0, + **kwargs): + r""" + Prepare a dataloader for distributed training. The dataloader will be wrapped by + `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. + + Note: + 1. Evaluation datasets should not be passed to this function. + + Args: + dataset (`torch.utils.data.Dataset`): The dataset to be loaded. + shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. + seed (int, optional): Random worker seed for sampling, defaults to 1024. + add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. + drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size + is not divisible by the batch size. If False and the size of dataset is not divisible by + the batch size, then the last batch will be smaller, defaults to False. + pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. + num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. + kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in + `DataLoader `_. + + Returns: + :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. + """ + _kwargs = kwargs.copy() + sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle) + + # Deterministic dataloader + def seed_worker(worker_id): + worker_seed = seed + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + random.seed(worker_seed) + + return DataLoader(dataset, + batch_size=batch_size, + sampler=sampler, + worker_init_fn=seed_worker, + drop_last=drop_last, + pin_memory=pin_memory, + num_workers=num_workers, + **_kwargs) + + def configure( + self, + model: nn.Module, + optimizer: Optimizer, + criterion: Callable = None, + dataloader: DataLoader = None, + lr_scheduler: LRScheduler = None, + ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + # cast model to cuda + model = model.cuda() + + # wrap the model with PyTorch DDP + model = DDP(model, **self.ddp_kwargs) + + if not isinstance(optimizer, OptimizerWrapper): + optimizer = OptimizerWrapper(optimizer) + + return model, optimizer, criterion, dataloader, lr_scheduler diff --git a/tests/test_booster/test_accelerator.py b/tests/test_booster/test_accelerator.py index 4bfa3fd0631e..6958a87e2a08 100644 --- a/tests/test_booster/test_accelerator.py +++ b/tests/test_booster/test_accelerator.py @@ -1,13 +1,27 @@ -import pytest +from functools import partial + +import torch.multiprocessing as mp import torch.nn as nn -from torchvision.models import resnet18 from colossalai.booster.accelerator import Accelerator +from colossalai.testing import parameterize, rerun_if_address_is_in_use -@pytest.mark.parametrize('device', ['cpu', 'cuda']) -def test_accelerator(device): +@parameterize('device', ['cpu', 'cuda']) +def run_accelerator(device): acceleartor = Accelerator(device) model = nn.Linear(8, 8) model = acceleartor.configure_model(model) assert next(model.parameters()).device.type == device + del model, acceleartor + + +def run_dist(rank): + run_accelerator() + + +@rerun_if_address_is_in_use() +def test_accelerator(): + world_size = 1 + run_func = partial(run_dist) + mp.spawn(run_func, nprocs=world_size) diff --git a/tests/test_booster/test_mixed_precision/test_fp16_torch.py b/tests/test_booster/test_mixed_precision/test_fp16_torch.py index 98d00cd2caca..bacf29014193 100644 --- a/tests/test_booster/test_mixed_precision/test_fp16_torch.py +++ b/tests/test_booster/test_mixed_precision/test_fp16_torch.py @@ -1,12 +1,21 @@ +from functools import partial + import torch +import torch.multiprocessing as mp from torch.optim import Adam +import colossalai from colossalai.booster.mixed_precision import FP16TorchMixedPrecision +from colossalai.testing import rerun_if_address_is_in_use +from colossalai.utils import free_port from tests.kit.model_zoo import model_zoo -def test_torch_amp(): - for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): +def run_torch_amp(rank, world_size, port): + # init dist env + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + sub_model_zoo = model_zoo.get_sub_registry('timm') + for name, (model_fn, data_gen_fn, output_transform_fn, _) in sub_model_zoo.items(): # dlrm_interactionarch has not parameters, so skip if name == 'dlrm_interactionarch': continue @@ -27,3 +36,11 @@ def test_torch_amp(): optimizer.backward(loss) optimizer.clip_grad_by_norm(1.0) optimizer.step() + del model, optimizer, criterion, data, output, mixed_precision + + +@rerun_if_address_is_in_use() +def test_torch_ddp_plugin(): + world_size = 1 + run_func = partial(run_torch_amp, world_size=world_size, port=free_port()) + mp.spawn(run_func, nprocs=world_size) diff --git a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py new file mode 100644 index 000000000000..58aef54c4967 --- /dev/null +++ b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py @@ -0,0 +1,85 @@ +from functools import partial + +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.optim import SGD + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.interface import OptimizerWrapper +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.testing import rerun_if_address_is_in_use +from colossalai.utils import free_port +from tests.kit.model_zoo import model_zoo + + +def check_torch_ddp_plugin(): + plugin = TorchDDPPlugin() + booster = Booster(plugin=plugin) + + for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + if name == 'dlrm_interactionarch': + continue + + model = model_fn() + optimizer = SGD(model.parameters(), lr=1e-3) + criterion = lambda x: x.mean() + data = data_gen_fn() + + data = { + k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items() + } + + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + assert isinstance(model, DDP) + assert isinstance(optimizer, OptimizerWrapper) + + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + + booster.backward(loss, optimizer) + optimizer.clip_grad_by_norm(1.0) + optimizer.step() + + +def check_dataloader_sharding(): + plugin = TorchDDPPlugin() + + # create a custom dasetset with 0 to 10 + dataset = torch.utils.data.TensorDataset(torch.arange(0, 10)) + train_dataloader = plugin.prepare_train_dataloader(dataset, batch_size=2) + + # get the first batch of data + batch = next(iter(train_dataloader))[0].cuda() + is_rank_0 = dist.get_rank() == 0 + + if is_rank_0: + batch_to_compare = batch.clone() + else: + batch_to_compare = batch + # pass to the rank 1 value to rank 0 + dist.broadcast(batch_to_compare, src=1) + + # compare on rank 0 + if is_rank_0: + assert not torch.equal(batch, + batch_to_compare), 'Same number was found across ranks but expected it to be different' + + +def run_dist(rank, world_size, port): + # init dist env + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + check_dataloader_sharding() + check_torch_ddp_plugin() + + +@rerun_if_address_is_in_use() +def test_torch_ddp_plugin(): + world_size = 2 + run_func = partial(run_dist, world_size=world_size, port=free_port()) + mp.spawn(run_func, nprocs=world_size) From b429529365ba15a4a72f6c6ba0f6556d9d9d1fe4 Mon Sep 17 00:00:00 2001 From: pgzhang <37991273+pgzhang@users.noreply.github.com> Date: Wed, 22 Mar 2023 09:59:42 +0800 Subject: [PATCH 016/413] [chatgpt] add supervised learning fine-tune code (#3183) * [chatgpt] add supervised fine-tune code * [chatgpt] delete unused code and modified comment code * [chatgpt] use pytorch distributed sampler instead --------- Co-authored-by: zhangpengpeng --- .../ChatGPT/chatgpt/dataset/__init__.py | 3 +- .../ChatGPT/chatgpt/dataset/sft_dataset.py | 40 ++++++ .../ChatGPT/chatgpt/models/base/__init__.py | 3 +- .../ChatGPT/chatgpt/models/base/lm.py | 33 +++++ .../ChatGPT/chatgpt/models/bloom/__init__.py | 3 +- .../ChatGPT/chatgpt/models/bloom/bloom_lm.py | 36 ++++++ .../ChatGPT/chatgpt/models/gpt/__init__.py | 3 +- .../ChatGPT/chatgpt/models/gpt/gpt_lm.py | 36 ++++++ .../ChatGPT/chatgpt/models/opt/__init__.py | 3 +- .../ChatGPT/chatgpt/models/opt/opt_lm.py | 36 ++++++ .../ChatGPT/chatgpt/trainer/__init__.py | 3 +- applications/ChatGPT/chatgpt/trainer/sft.py | 101 ++++++++++++++++ applications/ChatGPT/examples/train_sft.py | 114 ++++++++++++++++++ applications/ChatGPT/examples/train_sft.sh | 20 +++ 14 files changed, 428 insertions(+), 6 deletions(-) create mode 100644 applications/ChatGPT/chatgpt/dataset/sft_dataset.py create mode 100644 applications/ChatGPT/chatgpt/models/base/lm.py create mode 100644 applications/ChatGPT/chatgpt/models/bloom/bloom_lm.py create mode 100644 applications/ChatGPT/chatgpt/models/gpt/gpt_lm.py create mode 100644 applications/ChatGPT/chatgpt/models/opt/opt_lm.py create mode 100644 applications/ChatGPT/chatgpt/trainer/sft.py create mode 100644 applications/ChatGPT/examples/train_sft.py create mode 100755 applications/ChatGPT/examples/train_sft.sh diff --git a/applications/ChatGPT/chatgpt/dataset/__init__.py b/applications/ChatGPT/chatgpt/dataset/__init__.py index 83393098775f..78fd2c0705a9 100644 --- a/applications/ChatGPT/chatgpt/dataset/__init__.py +++ b/applications/ChatGPT/chatgpt/dataset/__init__.py @@ -1,4 +1,5 @@ from .reward_dataset import RmStaticDataset, HhRlhfDataset from .utils import is_rank_0 +from .sft_dataset import SFTDataset -__all__ = ['RmStaticDataset', 'HhRlhfDataset','is_rank_0'] +__all__ = ['RmStaticDataset', 'HhRlhfDataset','is_rank_0', 'SFTDataset'] diff --git a/applications/ChatGPT/chatgpt/dataset/sft_dataset.py b/applications/ChatGPT/chatgpt/dataset/sft_dataset.py new file mode 100644 index 000000000000..53ad205073e5 --- /dev/null +++ b/applications/ChatGPT/chatgpt/dataset/sft_dataset.py @@ -0,0 +1,40 @@ +from typing import Callable +import random +from torch.utils.data import Dataset +import torch.distributed as dist +from tqdm import tqdm +import torch + +from .utils import is_rank_0 + + +class SFTDataset(Dataset): + """ + Dataset for sft model + + Args: + dataset: dataset for supervised model + tokenizer: tokenizer for supervised model + max_length: max length of input + """ + + def __init__(self, dataset, tokenizer: Callable, max_length: int=512) -> None: + super().__init__() + self.prompts = [] + + for data in tqdm(dataset, disable=not is_rank_0()): + prompt = data['prompt'] + data['completion'] + "<|endoftext|>" + prompt_token = tokenizer(prompt, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + + self.prompts.append(prompt_token) + + def __len__(self): + length = len(self.prompts) + return length + + def __getitem__(self, idx): + return self.prompts[idx] diff --git a/applications/ChatGPT/chatgpt/models/base/__init__.py b/applications/ChatGPT/chatgpt/models/base/__init__.py index 86f403556904..7c7b1ceba257 100644 --- a/applications/ChatGPT/chatgpt/models/base/__init__.py +++ b/applications/ChatGPT/chatgpt/models/base/__init__.py @@ -1,5 +1,6 @@ from .actor import Actor from .critic import Critic from .reward_model import RewardModel +from .lm import LM -__all__ = ['Actor', 'Critic', 'RewardModel'] +__all__ = ['Actor', 'Critic', 'RewardModel', 'LM'] diff --git a/applications/ChatGPT/chatgpt/models/base/lm.py b/applications/ChatGPT/chatgpt/models/base/lm.py new file mode 100644 index 000000000000..b6bd7aff8315 --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/base/lm.py @@ -0,0 +1,33 @@ +from typing import Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..generation import generate +from .actor import Actor + + +class LM(Actor): + """ + Language model base class. + + Args: + model (nn.Module): Language Model. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = 'none') -> None: + super().__init__(model=model, lora_rank=lora_rank, lora_train_bias=lora_train_bias) + + def forward(self, + sequences: torch.LongTensor, + attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + """Returns output log probs + """ + output = self.model(sequences, attention_mask=attention_mask) + logits = output['logits'] + log_probs = F.log_softmax(logits, dim=-1) + return log_probs + diff --git a/applications/ChatGPT/chatgpt/models/bloom/__init__.py b/applications/ChatGPT/chatgpt/models/bloom/__init__.py index d0e7f7b1ef94..7d6d7753bb9a 100644 --- a/applications/ChatGPT/chatgpt/models/bloom/__init__.py +++ b/applications/ChatGPT/chatgpt/models/bloom/__init__.py @@ -1,5 +1,6 @@ from .bloom_actor import BLOOMActor from .bloom_critic import BLOOMCritic from .bloom_rm import BLOOMRM +from .bloom_lm import BLOOMLM -__all__ = ['BLOOMActor', 'BLOOMCritic', 'BLOOMRM'] +__all__ = ['BLOOMActor', 'BLOOMCritic', 'BLOOMRM', 'BLOOMLM'] diff --git a/applications/ChatGPT/chatgpt/models/bloom/bloom_lm.py b/applications/ChatGPT/chatgpt/models/bloom/bloom_lm.py new file mode 100644 index 000000000000..81e17f27c11a --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/bloom/bloom_lm.py @@ -0,0 +1,36 @@ +from typing import Optional + +import torch +from transformers import BloomConfig, BloomForCausalLM, BloomModel + +from ..base import LM + + +class BLOOMLM(LM): + """ + BLOOM language model. + + Args: + pretrained (str): Pretrained model name or path. + config (BloomConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: str = None, + config: Optional[BloomConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = BloomForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = BloomForCausalLM(config) + else: + model = BloomForCausalLM(BloomConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) + diff --git a/applications/ChatGPT/chatgpt/models/gpt/__init__.py b/applications/ChatGPT/chatgpt/models/gpt/__init__.py index 63dc5ab0f5ea..c6ae05113cc0 100644 --- a/applications/ChatGPT/chatgpt/models/gpt/__init__.py +++ b/applications/ChatGPT/chatgpt/models/gpt/__init__.py @@ -1,5 +1,6 @@ from .gpt_actor import GPTActor from .gpt_critic import GPTCritic from .gpt_rm import GPTRM +from .gpt_lm import GPTLM -__all__ = ['GPTActor', 'GPTCritic', 'GPTRM'] +__all__ = ['GPTActor', 'GPTCritic', 'GPTRM', 'GPTLM'] diff --git a/applications/ChatGPT/chatgpt/models/gpt/gpt_lm.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_lm.py new file mode 100644 index 000000000000..5740c80d3e77 --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/gpt/gpt_lm.py @@ -0,0 +1,36 @@ +from typing import Optional + +from transformers.models.gpt2.configuration_gpt2 import GPT2Config +from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel + +from ..base import LM + + +class GPTLM(LM): + """ + GPT language model. + + Args: + pretrained (str): Pretrained model name or path. + config (GPT2Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the LoRa layer. + lora_train_bias (str): Bias training strategy for the LoRa layer. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[GPT2Config] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = GPT2LMHeadModel.from_pretrained(pretrained) + elif config is not None: + model = GPT2LMHeadModel(config) + else: + model = GPT2LMHeadModel(GPT2Config()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) + diff --git a/applications/ChatGPT/chatgpt/models/opt/__init__.py b/applications/ChatGPT/chatgpt/models/opt/__init__.py index 334f4df0032a..fccec3bdff99 100644 --- a/applications/ChatGPT/chatgpt/models/opt/__init__.py +++ b/applications/ChatGPT/chatgpt/models/opt/__init__.py @@ -1,5 +1,6 @@ from .opt_actor import OPTActor from .opt_critic import OPTCritic from .opt_rm import OPTRM +from .opt_lm import OPTLM -__all__ = ['OPTActor', 'OPTCritic', 'OPTRM'] +__all__ = ['OPTActor', 'OPTCritic', 'OPTRM', 'OPTLM'] diff --git a/applications/ChatGPT/chatgpt/models/opt/opt_lm.py b/applications/ChatGPT/chatgpt/models/opt/opt_lm.py new file mode 100644 index 000000000000..35bfe198a225 --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/opt/opt_lm.py @@ -0,0 +1,36 @@ +from typing import Optional + +from transformers.models.opt.configuration_opt import OPTConfig +from transformers.models.opt.modeling_opt import OPTForCausalLM + +from ..base import LM + + +class OPTLM(LM): + """ + OPT language model. + + Args: + pretrained (str): Pretrained model name or path. + config (OPTConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the low-rank approximation. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[OPTConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = OPTForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = OPTForCausalLM(config) + else: + model = OPTForCausalLM(OPTConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) + diff --git a/applications/ChatGPT/chatgpt/trainer/__init__.py b/applications/ChatGPT/chatgpt/trainer/__init__.py index c47c76347ee5..525b57bf21d3 100644 --- a/applications/ChatGPT/chatgpt/trainer/__init__.py +++ b/applications/ChatGPT/chatgpt/trainer/__init__.py @@ -1,5 +1,6 @@ from .base import Trainer from .ppo import PPOTrainer from .rm import RewardModelTrainer +from .sft import SFTTrainer -__all__ = ['Trainer', 'PPOTrainer', 'RewardModelTrainer'] +__all__ = ['Trainer', 'PPOTrainer', 'RewardModelTrainer', 'SFTTrainer'] diff --git a/applications/ChatGPT/chatgpt/trainer/sft.py b/applications/ChatGPT/chatgpt/trainer/sft.py new file mode 100644 index 000000000000..e3913d46bd45 --- /dev/null +++ b/applications/ChatGPT/chatgpt/trainer/sft.py @@ -0,0 +1,101 @@ +from abc import ABC +from typing import Optional +import loralib as lora +import torch +from chatgpt.dataset import SFTDataset +from chatgpt.models.loss import GPTLMLoss +from torch.optim import Adam, Optimizer +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from tqdm import tqdm +import torch.distributed as dist +from .strategies import Strategy +from .utils import is_rank_0 +from colossalai.logging import get_dist_logger + + +class SFTTrainer(ABC): + """ + Trainer to use while training reward model. + + Args: + model (torch.nn.Module): the model to train + strategy (Strategy): the strategy to use for training + optim(Optimizer): the optimizer to use for training + train_dataset (SFTDataset or SFTDistributedDataset): the dataset to use for training + eval_dataset (SFTDataset or SFTDistributedDataset): the dataset to use for evaluation + batch_size (int, defaults to 1): the batch size while training + max_epochs (int, defaults to 2): the number of epochs to train + optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer + """ + + def __init__( + self, + model, + strategy: Strategy, + optim: Optimizer, + train_dataset: SFTDataset, + eval_dataset: SFTDataset, + sampler: Optional[DistributedSampler] = None, + batch_size: int = 1, + max_epochs: int = 2, + ) -> None: + super().__init__() + self.strategy = strategy + self.epochs = max_epochs + self.train_dataset = train_dataset + self.eval_dataset = eval_dataset + self.sampler = sampler + + self.train_dataloader = DataLoader(self.train_dataset, shuffle=(sampler is None), + sampler=sampler, batch_size=batch_size) + self.eval_dataloader = DataLoader(self.eval_dataset, batch_size=batch_size) + + self.model = strategy.setup_model(model) + if "DDP" in str(self.strategy): + self.model = self.model.module + self.loss_fn = GPTLMLoss() + self.optimizer = strategy.setup_optimizer(optim, self.model) + + def fit(self, logger, use_lora, log_interval=10): + epoch_bar = tqdm(range(self.epochs), desc='Train epoch', disable=not is_rank_0()) + for epoch in range(self.epochs): + if isinstance(self.sampler, DistributedSampler): + self.sampler.set_epoch(epoch) + # train + self.model.train() + for batch_id, batch in enumerate(self.train_dataloader): + prompt_ids = batch["input_ids"] + p_mask = batch["attention_mask"] + prompt_ids = prompt_ids.squeeze(1).cuda() + p_mask = p_mask.squeeze(1).cuda() + prompt_logits = self.model(prompt_ids, attention_mask=p_mask) + + loss = self.loss_fn(prompt_logits, prompt_ids) + self.strategy.backward(loss, self.model, self.optimizer) + self.strategy.optimizer_step(self.optimizer) + self.optimizer.zero_grad() + if batch_id % log_interval == 0: + logger.info(f'Train Epoch {epoch}/{self.epochs} Batch {batch_id} Rank {dist.get_rank()} loss {loss.item()}') + + # eval + self.model.eval() + with torch.no_grad(): + loss_sum = 0 + num_seen = 0 + for batch in self.eval_dataloader: + prompt_ids = batch["input_ids"] + p_mask = batch["attention_mask"] + prompt_ids = prompt_ids.squeeze(1).cuda() + p_mask = p_mask.squeeze(1).cuda() + + prompt_logits = self.model(prompt_ids, attention_mask=p_mask) + loss = self.loss_fn(prompt_logits, prompt_ids) + loss_sum += loss.item() + num_seen += prompt_ids.size(0) + + loss_mean = loss_sum / num_seen + if dist.get_rank() == 0: + logger.info(f'Eval Epoch {epoch}/{self.epochs} loss {loss_mean}') + epoch_bar.update() + diff --git a/applications/ChatGPT/examples/train_sft.py b/applications/ChatGPT/examples/train_sft.py new file mode 100644 index 000000000000..4b3f85a2a491 --- /dev/null +++ b/applications/ChatGPT/examples/train_sft.py @@ -0,0 +1,114 @@ +import argparse + +import loralib as lora +import torch +import torch.distributed as dist +from torch.utils.data.distributed import DistributedSampler +from chatgpt.dataset import SFTDataset +from chatgpt.models.base import RewardModel +from chatgpt.models.bloom import BLOOMLM +from chatgpt.models.gpt import GPTLM +from chatgpt.models.opt import OPTLM +from chatgpt.trainer import SFTTrainer +from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from datasets import load_dataset +from torch.optim import Adam +from transformers import AutoTokenizer, BloomTokenizerFast +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.nn.optimizer import HybridAdam +from colossalai.logging import get_dist_logger + + +def train(args): + # configure strategy + if args.strategy == 'naive': + strategy = NaiveStrategy() + elif args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + # configure model + with strategy.model_init_context(): + if args.model == 'bloom': + model = BLOOMLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() + elif args.model == 'opt': + model = OPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() + elif args.model == 'gpt2': + model = GPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() + else: + raise ValueError(f'Unsupported model "{args.model}"') + + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + else: + raise ValueError(f'Unsupported model "{args.model}"') + tokenizer.pad_token = tokenizer.eos_token + + max_len = 512 + + # configure optimizer + if args.strategy.startswith('colossalai'): + optim = HybridAdam(model.parameters(), lr=5e-5) + else: + optim = Adam(model.parameters(), lr=5e-5) + + logger = get_dist_logger() + + train_data = load_dataset(args.dataset, 'super_natural_instructions', split='train') + eval_data = load_dataset(args.dataset, 'super_natural_instructions', split='test') + + train_dataset = SFTDataset(train_data, tokenizer, max_len) + eval_dataset = SFTDataset(eval_data, tokenizer, max_len) + + if dist.is_initialized() and dist.get_world_size() > 1: + sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True) + logger.info("Using Distributed Sampler") + else: + sampler = None + + trainer = SFTTrainer(model=model, + strategy=strategy, + optim=optim, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + sampler=sampler, + batch_size=args.batch_size, + max_epochs=args.max_epochs) + + trainer.fit(logger=logger, use_lora=args.lora_rank, log_interval=args.log_interval) + + # save model checkpoint after fitting on only rank0 + strategy.save_model(model, 'sft_checkpoint.pt', only_rank0=True) + # save optimizer checkpoint on all ranks + strategy.save_optimizer(optim, 'sft_optim_checkpoint_%d.pt' % (torch.cuda.current_device()), only_rank0=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt'], default='bloom') + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--dataset', type=str, default='yizhongw/self_instruct') + parser.add_argument('--save_path', type=str, default='sft_ckpt.pth') + parser.add_argument('--max_epochs', type=int, default=1) + parser.add_argument('--batch_size', type=int, default=4) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log") + args = parser.parse_args() + train(args) + diff --git a/applications/ChatGPT/examples/train_sft.sh b/applications/ChatGPT/examples/train_sft.sh new file mode 100755 index 000000000000..9f747b24689e --- /dev/null +++ b/applications/ChatGPT/examples/train_sft.sh @@ -0,0 +1,20 @@ +set_n_least_used_CUDA_VISIBLE_DEVICES() { + local n=${1:-"9999"} + echo "GPU Memory Usage:" + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ + | tail -n +2 \ + | nl -v 0 \ + | tee /dev/tty \ + | sort -g -k 2 \ + | awk '{print $1}' \ + | head -n $n) + export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') + echo "Now CUDA_VISIBLE_DEVICES is set to:" + echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" +} + +set_n_least_used_CUDA_VISIBLE_DEVICES 8 + +#torchrun --standalone --nproc_per_node=2 train_sft.py --pretrain 'bigscience/bloomz-560m' --model 'bloom' --strategy colossalai_zero2 --log_interval 10 +#torchrun --standalone --nproc_per_node=8 train_sft.py --model 'gpt2' --strategy colossalai_zero2 --batch_size 1 --log_interval 10 +torchrun --standalone --nproc_per_node=2 train_sft.py --pretrain "facebook/opt-350m" --model 'opt' --strategy colossalai_zero2 --log_interval 10 From f57d34958babae9781e351f8f8008ad0f47f01dd Mon Sep 17 00:00:00 2001 From: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com> Date: Wed, 22 Mar 2023 10:40:33 +0800 Subject: [PATCH 017/413] [FX] refactor experimental tracer and adapt it with hf models (#3157) * pass gpt trace and meta_prop * pass t5 trace and meta_prop * [FX] refactor experimental tracer and adapt it with hf models * pass all mainstream model zoo * fix CI * fix CI * fix CI * fix CI * fix CI * fix CI * fix CI * fix CI * skip tests * fix CI * using packaging version * polish --- .../_subclasses/_meta_registration.py | 829 +++++++++--------- .../_analyzer/_subclasses/_monkey_patch.py | 76 +- .../_analyzer/_subclasses/flop_tensor.py | 236 ++--- colossalai/_analyzer/fx/__init__.py | 3 +- colossalai/_analyzer/fx/graph_module.py | 72 +- colossalai/_analyzer/fx/node_util.py | 6 +- colossalai/_analyzer/fx/passes/shape_prop.py | 19 +- colossalai/_analyzer/fx/tracer/__init__.py | 2 + .../fx/{ => tracer}/bias_addition.py | 3 +- .../_analyzer/fx/tracer/custom_leaf_module.py | 29 + colossalai/_analyzer/fx/tracer/proxy.py | 112 +++ .../_analyzer/fx/tracer/symbolic_trace.py | 157 ++++ .../{symbolic_trace.py => tracer/tracer.py} | 355 ++------ tests/kit/model_zoo/__init__.py | 1 - tests/kit/model_zoo/transformers/gpt.py | 10 +- .../test_fx/test_bias_addition.py | 3 +- .../test_hf_model/hf_tracer_utils.py | 3 +- .../test_hf_model/test_hf_albert.py | 4 + .../test_tracer/test_hf_model/test_hf_bert.py | 4 + .../test_tracer/test_hf_model/test_hf_gpt.py | 12 +- .../test_tracer/test_hf_model/test_hf_opt.py | 4 + .../test_tracer/test_hf_model/test_hf_t5.py | 4 + .../test_timm_model/test_timm_model.py | 5 +- .../test_torchaudio_model.py | 8 +- .../test_torchaudio_model/torchaudio_utils.py | 2 +- .../test_torchrec_model/test_deepfm_model.py | 2 +- .../test_torchrec_model/test_dlrm_model.py | 2 +- .../test_torchvision_model.py | 2 +- 28 files changed, 1058 insertions(+), 907 deletions(-) create mode 100644 colossalai/_analyzer/fx/tracer/__init__.py rename colossalai/_analyzer/fx/{ => tracer}/bias_addition.py (98%) create mode 100644 colossalai/_analyzer/fx/tracer/custom_leaf_module.py create mode 100644 colossalai/_analyzer/fx/tracer/proxy.py create mode 100644 colossalai/_analyzer/fx/tracer/symbolic_trace.py rename colossalai/_analyzer/fx/{symbolic_trace.py => tracer/tracer.py} (53%) diff --git a/colossalai/_analyzer/_subclasses/_meta_registration.py b/colossalai/_analyzer/_subclasses/_meta_registration.py index 20ab46054c8e..2af7e05399af 100644 --- a/colossalai/_analyzer/_subclasses/_meta_registration.py +++ b/colossalai/_analyzer/_subclasses/_meta_registration.py @@ -6,11 +6,15 @@ from typing import Callable, List, Optional, Tuple, Union import torch +from packaging import version from torch.utils._pytree import tree_map aten = torch.ops.aten -meta_lib = torch.library.Library("aten", "IMPL", "Meta") +try: + meta_lib = torch.library.Library("aten", "IMPL", "Meta") +except AttributeError: + meta_lib = None meta_table = {} @@ -50,432 +54,411 @@ def add_func(op): return wrapper -# ============================== Convolutions ====================================== -# https://github.com/pytorch/pytorch/pull/79834 -@register_meta(aten.convolution.default) -def meta_conv( - input_tensor: torch.Tensor, - weight: torch.Tensor, - bias: torch.Tensor, - stride: List[int], - padding: List[int], - dilation: List[int], - is_transposed: bool, - output_padding: List[int], - groups: int, -): - - def _formula(ln: int, p: int, d: int, k: int, s: int) -> int: - """ - Formula to apply to calculate the length of some dimension of the output - See: https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html - Args: - ln: length of the dimension - p: padding in that dim - d: dilation in that dim - k: kernel size in that dim - s: stride in that dim - Returns: - The output length - """ - return (ln + 2 * p - d * (k - 1) - 1) // s + 1 - - def _formula_transposed(ln: int, p: int, d: int, k: int, s: int, op: int) -> int: - """ - Formula to apply to calculate the length of some dimension of the output - if transposed convolution is used. - See: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html - Args: - ln: length of the dimension - p: padding in that dim - d: dilation in that dim - k: kernel size in that dim - s: stride in that dim - op: output padding in that dim - Returns: - The output length - """ - return (ln - 1) * s - 2 * p + d * (k - 1) + op + 1 - - def calc_conv_nd_return_shape( - dims: torch.Size, - kernel_size: torch.Size, - stride: Union[List[int], int], - padding: Union[List[int], int], - dilation: Union[List[int], int], - output_padding: Optional[Union[List[int], int]] = None, +if version.parse(torch.__version__) >= version.parse('1.12.0'): + # ============================== Convolutions ====================================== + # https://github.com/pytorch/pytorch/pull/79834 + @register_meta(aten.convolution.default) + def meta_conv( + input_tensor: torch.Tensor, + weight: torch.Tensor, + bias: torch.Tensor, + stride: List[int], + padding: List[int], + dilation: List[int], + is_transposed: bool, + output_padding: List[int], + groups: int, ): - ret_shape = [] - if isinstance(stride, int): - stride = [stride] * len(dims) - elif len(stride) == 1: - stride = [stride[0]] * len(dims) - - if isinstance(padding, int): - padding = [padding] * len(dims) - elif len(padding) == 1: - padding = [padding[0]] * len(dims) - - if isinstance(dilation, int): - dilation = [dilation] * len(dims) - elif len(dilation) == 1: - dilation = [dilation[0]] * len(dims) - - output_padding_list: Optional[List[int]] = None - if output_padding: - if isinstance(output_padding, int): - output_padding_list = [output_padding] * len(dims) - elif len(output_padding) == 1: - output_padding_list = [output_padding[0]] * len(dims) - else: - output_padding_list = output_padding - - for i in range(len(dims)): - # If output_padding is present, we are dealing with a transposed convolution - if output_padding_list: - ret_shape.append( - _formula_transposed( - dims[i], - padding[i], - dilation[i], - kernel_size[i], - stride[i], - output_padding_list[i], - )) - else: - ret_shape.append(_formula(dims[i], padding[i], dilation[i], kernel_size[i], stride[i])) - return ret_shape - - def pick_memory_format(): - if input_tensor.is_contiguous(memory_format=torch.channels_last): - return torch.channels_last - elif input_tensor.is_contiguous(memory_format=torch.contiguous_format): - return torch.contiguous_format - elif input_tensor.is_contiguous(memory_format=torch.preserve_format): - return torch.preserve_format - - kernel_size = weight.shape[2:] - dims = input_tensor.shape[2:] - if is_transposed: - out_channels = groups * weight.shape[1] - - shape_out = calc_conv_nd_return_shape( - dims, - kernel_size, - stride, - padding, - dilation, - output_padding, - ) - - else: - out_channels = weight.shape[0] - if weight.shape[1] != input_tensor.shape[1] / groups: - raise RuntimeError("Invalid channel dimensions") - shape_out = calc_conv_nd_return_shape(dims, kernel_size, stride, padding, dilation) - out = input_tensor.new_empty((input_tensor.shape[0], out_channels, *shape_out)) - mem_fmt = pick_memory_format() - out = out.to(memory_format=mem_fmt) # type: ignore[call-overload] - return out - - -@register_meta(aten._convolution.default) -def meta__conv(input_tensor: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, stride: List[int], - padding: List[int], dilation: List[int], is_transposed: bool, output_padding: List[int], groups: int, - *extra_args): - out = meta_conv(input_tensor, weight, bias, stride, padding, dilation, is_transposed, output_padding, groups) - return out - - -@register_meta(aten.convolution_backward.default) -def meta_conv_backward(grad_output: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, bias_sizes, stride, - padding, dilation, transposed, output_padding, groups, output_mask): - return new_like(input), new_like(weight), new((bias_sizes)) - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/AdaptiveAveragePooling.cpp -@register_meta(aten._adaptive_avg_pool2d_backward.default) -def meta_adaptive_avg_pool2d_backward( - grad_output: torch.Tensor, - input: torch.Tensor, -): - return new_like(input) - - -# ================================ RNN ============================================= -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp -@register_meta(aten._cudnn_rnn.default) -def meta_cuda_rnn( - input, - weight, - weight_stride0, - weight_buf, - hx, - cx, - mode, - hidden_size, - proj_size, - num_layers, - batch_first, - dropout, - train, - bidirectional, - batch_sizes, - dropout_state, -): - - is_input_packed = len(batch_sizes) != 0 - if is_input_packed: - seq_length = len(batch_sizes) - mini_batch = batch_sizes[0] - batch_sizes_sum = input.shape[0] - else: - seq_length = input.shape[1] if batch_first else input.shape[0] - mini_batch = input.shape[0] if batch_first else input.shape[1] - batch_sizes_sum = -1 - - num_directions = 2 if bidirectional else 1 - out_size = proj_size if proj_size != 0 else hidden_size - if is_input_packed: - out_shape = [batch_sizes_sum, out_size * num_directions] - else: - out_shape = ([mini_batch, seq_length, out_size * - num_directions] if batch_first else [seq_length, mini_batch, out_size * num_directions]) - output = input.new_empty(out_shape) - - cell_shape = [num_layers * num_directions, mini_batch, hidden_size] - cy = new(0) if cx is None else cx.new_empty(cell_shape) - - hy = hx.new_empty([num_layers * num_directions, mini_batch, out_size]) - - # TODO: Query cudnnGetRNNTrainingReserveSize (expose to python) - reserve_shape = 0 if train else 0 - reserve = input.new_empty(reserve_shape, dtype=torch.uint8) - - return output, hy, cy, reserve, weight_buf - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp -@register_meta(aten._cudnn_rnn_backward.default) -def meta_cudnn_rnn_backward(input: torch.Tensor, - weight: torch.Tensor, - weight_stride0: int, - hx: torch.Tensor, - cx: Optional[torch.Tensor] = None, - *args, - **kwargs): - return new_like(input), new_like(weight), new_like(hx), new_like(cx) if cx is not None else new( - ()) # (grad_input, grad_weight, grad_hx, grad_cx) - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Activation.cpp -# ============================== Activations ======================================= -_unregistered_ewise = [ - aten.relu.default, - aten.prelu.default, - aten.hardswish.default, - aten.hardtanh.default, - aten.prelu_backward.default, - aten.hardswish_backward.default, - aten.hardtanh_backward.default, -] - - -@register_meta(_unregistered_ewise) -def meta_unregistered_ewise(input: torch.Tensor, *args): - return new_like(input) - - -# ============================== Normalization ===================================== -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp -@register_meta(aten.native_batch_norm.default) -def meta_bn(input: torch.Tensor, weight, bias, running_mean, running_var, training, momentum, eps): - n_input = input.size(1) - return new_like(input), new((n_input)), new((n_input)) - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp -@register_meta(aten.native_batch_norm_backward.default) -def meta_bn_backward(dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, running_mean, running_var, save_mean, - save_invstd, train, eps, output_mask): - return new_like(input), new_like(weight), new_like(weight) # (dX, dgamma, dbeta) - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp -@register_meta(aten.cudnn_batch_norm.default) -def meta_cudnn_bn(input: torch.Tensor, weight, bias, running_mean, running_var, training, momentum, eps): - n_input = input.size(1) - return new_like(input), new((n_input)), new((n_input)), new( - (0), dtype=torch.uint8) # (output, running_mean, running_var, reserve) - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp -# NB: CuDNN only implements the backward algorithm for batchnorm -# in training mode (evaluation mode batchnorm has a different algorithm), -# which is why this doesn't accept a 'training' parameter. -@register_meta(aten.cudnn_batch_norm_backward.default) -def meta_cudnn_bn_backward(dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, running_mean, running_var, - save_mean, save_invstd, eps, reserve): - return new_like(input), new_like(weight), new_like(weight) # (dX, dgamma, dbeta) - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/layer_norm.cpp -@register_meta(aten.native_layer_norm.default) -def meta_ln(input: torch.Tensor, normalized_shape, weight, bias, eps): - bs, n_input = input.size(0), input.size(1) - return new_like(input), new((bs, n_input, 1)), new((bs, n_input, 1)) # (output, running_mean, running_var) - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/layer_norm.cpp -@register_meta(aten.native_layer_norm_backward.default) -def meta_ln_backward(dY: torch.Tensor, input: torch.Tensor, normalized_shape, mean, rstd, weight, bias, - grad_input_mask): - return new_like(input), new_like(weight), new_like(bias) # (dX, dgamma, dbeta) - - -# ================================== Misc ========================================== -# Maybe incorrect -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Im2Col.cpp -@register_meta(aten.im2col.default) -def meta_im2col(input: torch.Tensor, kernel_size, dilation, padding, stride): - return new_like(input) - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml -@register_meta(aten.eye.m_out) -def meta_eye(n: int, m: int, out: torch.Tensor): - return out - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml -@register_meta(aten.roll.default) -def meta_roll(input: torch.Tensor, shifts, dims): - return input - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Scalar.cpp -@register_meta(aten._local_scalar_dense.default) -def meta_local_scalar_dense(self: torch.Tensor): - return 0 - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorCompare.cpp -@register_meta(aten.where.self) -def meta_where_self(condition: torch.Tensor, self: torch.Tensor, other: torch.Tensor): - result_type = torch.result_type(self, other) - return new_like(condition + self + other, dtype=result_type) - - -@register_meta(aten.index.Tensor) -def meta_index_Tensor(self, indices): - assert indices, "at least one index must be provided" - # aten::index is the internal advanced indexing implementation - # checkIndexTensorTypes and expandTensors - result: List[Optional[torch.Tensor]] = [] - for i, index in enumerate(indices): - if index is not None: - assert index.dtype in [torch.long, torch.int8, torch.bool],\ - "tensors used as indices must be long, byte or bool tensors" - if index.dtype in [torch.int8, torch.bool]: - nonzero = index.nonzero() - k = len(result) - assert k + index.ndim <= self.ndim, f"too many indices for tensor of dimension {self.ndim}" - for j in range(index.ndim): - assert index.shape[j] == self.shape[ - k + - j], f"The shape of the mask {index.shape} at index {i} does not match the shape of the indexed tensor {self.shape} at index {k + j}" - result.append(nonzero.select(1, j)) - else: - result.append(index) + + def _formula(ln: int, p: int, d: int, k: int, s: int) -> int: + """ + Formula to apply to calculate the length of some dimension of the output + See: https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html + Args: + ln: length of the dimension + p: padding in that dim + d: dilation in that dim + k: kernel size in that dim + s: stride in that dim + Returns: + The output length + """ + return (ln + 2 * p - d * (k - 1) - 1) // s + 1 + + def _formula_transposed(ln: int, p: int, d: int, k: int, s: int, op: int) -> int: + """ + Formula to apply to calculate the length of some dimension of the output + if transposed convolution is used. + See: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html + Args: + ln: length of the dimension + p: padding in that dim + d: dilation in that dim + k: kernel size in that dim + s: stride in that dim + op: output padding in that dim + Returns: + The output length + """ + return (ln - 1) * s - 2 * p + d * (k - 1) + op + 1 + + def calc_conv_nd_return_shape( + dims: torch.Size, + kernel_size: torch.Size, + stride: Union[List[int], int], + padding: Union[List[int], int], + dilation: Union[List[int], int], + output_padding: Optional[Union[List[int], int]] = None, + ): + ret_shape = [] + if isinstance(stride, int): + stride = [stride] * len(dims) + elif len(stride) == 1: + stride = [stride[0]] * len(dims) + + if isinstance(padding, int): + padding = [padding] * len(dims) + elif len(padding) == 1: + padding = [padding[0]] * len(dims) + + if isinstance(dilation, int): + dilation = [dilation] * len(dims) + elif len(dilation) == 1: + dilation = [dilation[0]] * len(dims) + + output_padding_list: Optional[List[int]] = None + if output_padding: + if isinstance(output_padding, int): + output_padding_list = [output_padding] * len(dims) + elif len(output_padding) == 1: + output_padding_list = [output_padding[0]] * len(dims) + else: + output_padding_list = output_padding + + for i in range(len(dims)): + # If output_padding is present, we are dealing with a transposed convolution + if output_padding_list: + ret_shape.append( + _formula_transposed( + dims[i], + padding[i], + dilation[i], + kernel_size[i], + stride[i], + output_padding_list[i], + )) + else: + ret_shape.append(_formula(dims[i], padding[i], dilation[i], kernel_size[i], stride[i])) + return ret_shape + + def pick_memory_format(): + if input_tensor.is_contiguous(memory_format=torch.channels_last): + return torch.channels_last + elif input_tensor.is_contiguous(memory_format=torch.contiguous_format): + return torch.contiguous_format + elif input_tensor.is_contiguous(memory_format=torch.preserve_format): + return torch.preserve_format + + kernel_size = weight.shape[2:] + dims = input_tensor.shape[2:] + if is_transposed: + out_channels = groups * weight.shape[1] + + shape_out = calc_conv_nd_return_shape( + dims, + kernel_size, + stride, + padding, + dilation, + output_padding, + ) + else: - result.append(index) - indices = result - assert len(indices) <= self.ndim, f"too many indices for tensor of dimension {self.ndim} (got {len(indices)})" - # expand_outplace - import torch._refs as refs - - indices = list(refs._maybe_broadcast(*indices)) - # add missing null tensors - while len(indices) < self.ndim: - indices.append(None) - - # hasContiguousSubspace - # true if all non-null tensors are adjacent - # See: - # https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing - # https://stackoverflow.com/questions/53841497/why-does-numpy-mixed-basic-advanced-indexing-depend-on-slice-adjacency - state = 0 - has_contiguous_subspace = False - for index in indices: - if state == 0: - if index is not None: - state = 1 - elif state == 1: - if index is None: - state = 2 + out_channels = weight.shape[0] + if weight.shape[1] != input_tensor.shape[1] / groups: + raise RuntimeError("Invalid channel dimensions") + shape_out = calc_conv_nd_return_shape(dims, kernel_size, stride, padding, dilation) + out = input_tensor.new_empty((input_tensor.shape[0], out_channels, *shape_out)) + mem_fmt = pick_memory_format() + out = out.to(memory_format=mem_fmt) # type: ignore[call-overload] + return out + + @register_meta(aten._convolution.default) + def meta__conv(input_tensor: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, stride: List[int], + padding: List[int], dilation: List[int], is_transposed: bool, output_padding: List[int], groups: int, + *extra_args): + out = meta_conv(input_tensor, weight, bias, stride, padding, dilation, is_transposed, output_padding, groups) + return out + + @register_meta(aten.convolution_backward.default) + def meta_conv_backward(grad_output: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, bias_sizes, stride, + padding, dilation, transposed, output_padding, groups, output_mask): + return new_like(input), new_like(weight), new((bias_sizes)) + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/AdaptiveAveragePooling.cpp + @register_meta(aten._adaptive_avg_pool2d_backward.default) + def meta_adaptive_avg_pool2d_backward( + grad_output: torch.Tensor, + input: torch.Tensor, + ): + return new_like(input) + + # ================================ RNN ============================================= + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp + @register_meta(aten._cudnn_rnn.default) + def meta_cuda_rnn( + input, + weight, + weight_stride0, + weight_buf, + hx, + cx, + mode, + hidden_size, + proj_size, + num_layers, + batch_first, + dropout, + train, + bidirectional, + batch_sizes, + dropout_state, + ): + + is_input_packed = len(batch_sizes) != 0 + if is_input_packed: + seq_length = len(batch_sizes) + mini_batch = batch_sizes[0] + batch_sizes_sum = input.shape[0] else: - if index is not None: - break - else: - has_contiguous_subspace = True - - # transposeToFront - # This is the logic that causes the newly inserted dimensions to show up - # at the beginning of the tensor, if they're not contiguous - if not has_contiguous_subspace: - dims = [] - transposed_indices = [] + seq_length = input.shape[1] if batch_first else input.shape[0] + mini_batch = input.shape[0] if batch_first else input.shape[1] + batch_sizes_sum = -1 + + num_directions = 2 if bidirectional else 1 + out_size = proj_size if proj_size != 0 else hidden_size + if is_input_packed: + out_shape = [batch_sizes_sum, out_size * num_directions] + else: + out_shape = ([mini_batch, seq_length, out_size * + num_directions] if batch_first else [seq_length, mini_batch, out_size * num_directions]) + output = input.new_empty(out_shape) + + cell_shape = [num_layers * num_directions, mini_batch, hidden_size] + cy = new(0) if cx is None else cx.new_empty(cell_shape) + + hy = hx.new_empty([num_layers * num_directions, mini_batch, out_size]) + + # TODO: Query cudnnGetRNNTrainingReserveSize (expose to python) + reserve_shape = 0 if train else 0 + reserve = input.new_empty(reserve_shape, dtype=torch.uint8) + + return output, hy, cy, reserve, weight_buf + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp + @register_meta(aten._cudnn_rnn_backward.default) + def meta_cudnn_rnn_backward(input: torch.Tensor, + weight: torch.Tensor, + weight_stride0: int, + hx: torch.Tensor, + cx: Optional[torch.Tensor] = None, + *args, + **kwargs): + return new_like(input), new_like(weight), new_like(hx), new_like(cx) if cx is not None else new( + ()) # (grad_input, grad_weight, grad_hx, grad_cx) + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Activation.cpp + # ============================== Activations ======================================= + _unregistered_ewise = [ + aten.relu.default, + aten.prelu.default, + aten.hardswish.default, + aten.hardtanh.default, + aten.prelu_backward.default, + aten.hardswish_backward.default, + aten.hardtanh_backward.default, + ] + + @register_meta(_unregistered_ewise) + def meta_unregistered_ewise(input: torch.Tensor, *args): + return new_like(input) + + # ============================== Normalization ===================================== + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp + @register_meta(aten.native_batch_norm.default) + def meta_bn(input: torch.Tensor, weight, bias, running_mean, running_var, training, momentum, eps): + n_input = input.size(1) + return new_like(input), new((n_input)), new((n_input)) + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp + @register_meta(aten.native_batch_norm_backward.default) + def meta_bn_backward(dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, running_mean, running_var, + save_mean, save_invstd, train, eps, output_mask): + return new_like(input), new_like(weight), new_like(weight) # (dX, dgamma, dbeta) + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp + @register_meta(aten.cudnn_batch_norm.default) + def meta_cudnn_bn(input: torch.Tensor, weight, bias, running_mean, running_var, training, momentum, eps): + n_input = input.size(1) + return new_like(input), new((n_input)), new((n_input)), new( + (0), dtype=torch.uint8) # (output, running_mean, running_var, reserve) + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp + # NB: CuDNN only implements the backward algorithm for batchnorm + # in training mode (evaluation mode batchnorm has a different algorithm), + # which is why this doesn't accept a 'training' parameter. + @register_meta(aten.cudnn_batch_norm_backward.default) + def meta_cudnn_bn_backward(dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, running_mean, running_var, + save_mean, save_invstd, eps, reserve): + return new_like(input), new_like(weight), new_like(weight) # (dX, dgamma, dbeta) + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/layer_norm.cpp + @register_meta(aten.native_layer_norm.default) + def meta_ln(input: torch.Tensor, normalized_shape, weight, bias, eps): + bs, n_input = input.size(0), input.size(1) + return new_like(input), new((bs, n_input, 1)), new((bs, n_input, 1)) # (output, running_mean, running_var) + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/layer_norm.cpp + @register_meta(aten.native_layer_norm_backward.default) + def meta_ln_backward(dY: torch.Tensor, input: torch.Tensor, normalized_shape, mean, rstd, weight, bias, + grad_input_mask): + return new_like(input), new_like(weight), new_like(bias) # (dX, dgamma, dbeta) + + # ================================== Misc ========================================== + # Maybe incorrect + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Im2Col.cpp + @register_meta(aten.im2col.default) + def meta_im2col(input: torch.Tensor, kernel_size, dilation, padding, stride): + return new_like(input) + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml + @register_meta(aten.eye.m_out) + def meta_eye(n: int, m: int, out: torch.Tensor): + return out + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml + @register_meta(aten.roll.default) + def meta_roll(input: torch.Tensor, shifts, dims): + return input + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Scalar.cpp + @register_meta(aten._local_scalar_dense.default) + def meta_local_scalar_dense(self: torch.Tensor): + return 0 + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorCompare.cpp + @register_meta(aten.where.self) + def meta_where_self(condition: torch.Tensor, self: torch.Tensor, other: torch.Tensor): + result_type = torch.result_type(self, other) + return new_like(condition + self + other, dtype=result_type) + + @register_meta(aten.index.Tensor) + def meta_index_Tensor(self, indices): + assert indices, "at least one index must be provided" + # aten::index is the internal advanced indexing implementation + # checkIndexTensorTypes and expandTensors + result: List[Optional[torch.Tensor]] = [] for i, index in enumerate(indices): if index is not None: - dims.append(i) - transposed_indices.append(index) - for i, index in enumerate(indices): - if index is None: - dims.append(i) - transposed_indices.append(index) - self = self.permute(dims) - indices = transposed_indices - - # AdvancedIndex::AdvancedIndex - # Now we can assume the indices have contiguous subspace - # This is simplified from AdvancedIndex which goes to more effort - # to put the input and indices in a form so that TensorIterator can - # take them. If we write a ref for this, probably that logic should - # get implemented - before_shape: List[int] = [] - after_shape: List[int] = [] - replacement_shape: List[int] = [] - for dim, index in enumerate(indices): - if index is None: - if replacement_shape: - after_shape.append(self.shape[dim]) + assert index.dtype in [torch.long, torch.int8, torch.bool],\ + "tensors used as indices must be long, byte or bool tensors" + if index.dtype in [torch.int8, torch.bool]: + nonzero = index.nonzero() + k = len(result) + assert k + index.ndim <= self.ndim, f"too many indices for tensor of dimension {self.ndim}" + for j in range(index.ndim): + assert index.shape[j] == self.shape[ + k + + j], f"The shape of the mask {index.shape} at index {i} does not match the shape of the indexed tensor {self.shape} at index {k + j}" + result.append(nonzero.select(1, j)) + else: + result.append(index) + else: + result.append(index) + indices = result + assert len(indices) <= self.ndim, f"too many indices for tensor of dimension {self.ndim} (got {len(indices)})" + # expand_outplace + import torch._refs as refs + + indices = list(refs._maybe_broadcast(*indices)) + # add missing null tensors + while len(indices) < self.ndim: + indices.append(None) + + # hasContiguousSubspace + # true if all non-null tensors are adjacent + # See: + # https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing + # https://stackoverflow.com/questions/53841497/why-does-numpy-mixed-basic-advanced-indexing-depend-on-slice-adjacency + state = 0 + has_contiguous_subspace = False + for index in indices: + if state == 0: + if index is not None: + state = 1 + elif state == 1: + if index is None: + state = 2 else: - before_shape.append(self.shape[dim]) + if index is not None: + break else: - replacement_shape = list(index.shape) - return self.new_empty(before_shape + replacement_shape + after_shape) - - -# ============================== Embedding ========================================= -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Embedding.cpp -@register_meta(aten.embedding_dense_backward.default) -def meta_embedding_dense_backward(grad_output: torch.Tensor, indices: torch.Tensor, num_weights, padding_idx, - scale_grad_by_freq): - return new((num_weights, grad_output.size(-1)), - dtype=grad_output.dtype, - device=grad_output.device, - layout=grad_output.layout) - - -# ============================== Dropout =========================================== -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Dropout.cpp -@register_meta(aten.native_dropout.default) -def meta_native_dropout_default(input: torch.Tensor, p: float, train: bool = False): - # notice that mask is bool - return new_like(input), new_like(input, dtype=torch.bool) # (output, mask) - - -# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Dropout.cpp -@register_meta(aten.native_dropout_backward.default) -def meta_native_dropout_backward_default(grad: torch.Tensor, mask: torch.Tensor, scale: float): - return new_like(grad) # (grad_in) + has_contiguous_subspace = True + + # transposeToFront + # This is the logic that causes the newly inserted dimensions to show up + # at the beginning of the tensor, if they're not contiguous + if not has_contiguous_subspace: + dims = [] + transposed_indices = [] + for i, index in enumerate(indices): + if index is not None: + dims.append(i) + transposed_indices.append(index) + for i, index in enumerate(indices): + if index is None: + dims.append(i) + transposed_indices.append(index) + self = self.permute(dims) + indices = transposed_indices + + # AdvancedIndex::AdvancedIndex + # Now we can assume the indices have contiguous subspace + # This is simplified from AdvancedIndex which goes to more effort + # to put the input and indices in a form so that TensorIterator can + # take them. If we write a ref for this, probably that logic should + # get implemented + before_shape: List[int] = [] + after_shape: List[int] = [] + replacement_shape: List[int] = [] + for dim, index in enumerate(indices): + if index is None: + if replacement_shape: + after_shape.append(self.shape[dim]) + else: + before_shape.append(self.shape[dim]) + else: + replacement_shape = list(index.shape) + return self.new_empty(before_shape + replacement_shape + after_shape) + + # ============================== Embedding ========================================= + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Embedding.cpp + @register_meta(aten.embedding_dense_backward.default) + def meta_embedding_dense_backward(grad_output: torch.Tensor, indices: torch.Tensor, num_weights, padding_idx, + scale_grad_by_freq): + return new((num_weights, grad_output.size(-1)), + dtype=grad_output.dtype, + device=grad_output.device, + layout=grad_output.layout) + + # ============================== Dropout =========================================== + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Dropout.cpp + @register_meta(aten.native_dropout.default) + def meta_native_dropout_default(input: torch.Tensor, p: float, train: bool = False): + # notice that mask is bool + return new_like(input), new_like(input, dtype=torch.bool) # (output, mask) + + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Dropout.cpp + @register_meta(aten.native_dropout_backward.default) + def meta_native_dropout_backward_default(grad: torch.Tensor, mask: torch.Tensor, scale: float): + return new_like(grad) # (grad_in) diff --git a/colossalai/_analyzer/_subclasses/_monkey_patch.py b/colossalai/_analyzer/_subclasses/_monkey_patch.py index 1c7b972ab2f6..7c1c3d3d8cd4 100644 --- a/colossalai/_analyzer/_subclasses/_monkey_patch.py +++ b/colossalai/_analyzer/_subclasses/_monkey_patch.py @@ -1,5 +1,6 @@ import torch import torch.distributed as dist +from packaging import version aten = torch.ops.aten @@ -49,40 +50,45 @@ "scatter", ] -# TODO: dive deep here -# refer to https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorShape.cpp -_AliasATen = [ - aten.detach.default, - aten.detach_.default, - aten.t.default, - aten.transpose.int, - aten.view.default, - aten._unsafe_view.default, - aten._reshape_alias.default, -] +if version.parse(torch.__version__) >= version.parse('1.12.0'): + # TODO: dive deep here + # refer to https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorShape.cpp + _AliasATen = [ + aten.detach.default, + aten.detach_.default, + aten.t.default, + aten.transpose.int, + aten.view.default, + aten._unsafe_view.default, + aten._reshape_alias.default, + ] -_InplaceATen = [ - aten.add_.Tensor, - aten.add_.Scalar, - aten.sub_.Tensor, - aten.sub_.Scalar, - aten.mul_.Tensor, - aten.mul_.Scalar, - aten.div_.Tensor, - aten.div_.Scalar, - aten.pow_.Tensor, - aten.pow_.Scalar, -] + _InplaceATen = [ + aten.add_.Tensor, + aten.add_.Scalar, + aten.sub_.Tensor, + aten.sub_.Scalar, + aten.mul_.Tensor, + aten.mul_.Scalar, + aten.div_.Tensor, + aten.div_.Scalar, + aten.pow_.Tensor, + aten.pow_.Scalar, + ] -# use `MaybeInplace` because they call ``as_strided()`` or ``slice()`` -_MaybeInplaceATen = [ - aten.diagonal.default, - aten.expand.default, - aten.select.int, - aten.slice.Tensor, - aten.split.Tensor, - aten.squeeze.default, - aten.permute.default, - aten.unsqueeze.default, - aten.as_strided.default, -] + # use `MaybeInplace` because they call ``as_strided()`` or ``slice()`` + _MaybeInplaceATen = [ + aten.diagonal.default, + aten.expand.default, + aten.select.int, + aten.slice.Tensor, + aten.split.Tensor, + aten.squeeze.default, + aten.permute.default, + aten.unsqueeze.default, + aten.as_strided.default, + ] +else: + _AliasATen = [] + _InplaceATen = [] + _MaybeInplaceATen = [] diff --git a/colossalai/_analyzer/_subclasses/flop_tensor.py b/colossalai/_analyzer/_subclasses/flop_tensor.py index ab93551467b8..dd35b00b3fab 100644 --- a/colossalai/_analyzer/_subclasses/flop_tensor.py +++ b/colossalai/_analyzer/_subclasses/flop_tensor.py @@ -11,6 +11,7 @@ from typing import Any, Callable, List, Optional, Union import torch +from packaging import version from torch.utils._pytree import tree_map from .meta_tensor import MetaTensor @@ -403,134 +404,139 @@ def zero_flop_jit(*args): return 0 -flop_mapping = { +if version.parse(torch.__version__) >= version.parse('1.12.0'): + flop_mapping = { # gemm - aten.mm.default: matmul_flop_jit, - aten.matmul.default: matmul_flop_jit, - aten.addmm.default: addmm_flop_jit, - aten.bmm.default: bmm_flop_jit, + aten.mm.default: matmul_flop_jit, + aten.matmul.default: matmul_flop_jit, + aten.addmm.default: addmm_flop_jit, + aten.bmm.default: bmm_flop_jit, # convolution - aten.convolution.default: conv_flop_jit, - aten._convolution.default: conv_flop_jit, - aten.convolution_backward.default: conv_backward_flop_jit, + aten.convolution.default: conv_flop_jit, + aten._convolution.default: conv_flop_jit, + aten.convolution_backward.default: conv_backward_flop_jit, # normalization - aten.native_batch_norm.default: batchnorm_flop_jit, - aten.native_batch_norm_backward.default: batchnorm_flop_jit, - aten.cudnn_batch_norm.default: batchnorm_flop_jit, - aten.cudnn_batch_norm_backward.default: partial(batchnorm_flop_jit, training=True), - aten.native_layer_norm.default: norm_flop_counter(2, 0), - aten.native_layer_norm_backward.default: norm_flop_counter(2, 0), + aten.native_batch_norm.default: batchnorm_flop_jit, + aten.native_batch_norm_backward.default: batchnorm_flop_jit, + aten.cudnn_batch_norm.default: batchnorm_flop_jit, + aten.cudnn_batch_norm_backward.default: partial(batchnorm_flop_jit, training=True), + aten.native_layer_norm.default: norm_flop_counter(2, 0), + aten.native_layer_norm_backward.default: norm_flop_counter(2, 0), # pooling - aten.avg_pool1d.default: ewise_flop_counter(1, 0), - aten.avg_pool2d.default: ewise_flop_counter(1, 0), - aten.avg_pool2d_backward.default: ewise_flop_counter(0, 1), - aten.avg_pool3d.default: ewise_flop_counter(1, 0), - aten.avg_pool3d_backward.default: ewise_flop_counter(0, 1), - aten.max_pool1d.default: ewise_flop_counter(1, 0), - aten.max_pool2d.default: ewise_flop_counter(1, 0), - aten.max_pool3d.default: ewise_flop_counter(1, 0), - aten.max_pool1d_with_indices.default: ewise_flop_counter(1, 0), - aten.max_pool2d_with_indices.default: ewise_flop_counter(1, 0), - aten.max_pool2d_with_indices_backward.default: ewise_flop_counter(0, 1), - aten.max_pool3d_with_indices.default: ewise_flop_counter(1, 0), - aten.max_pool3d_with_indices_backward.default: ewise_flop_counter(0, 1), - aten._adaptive_avg_pool2d.default: ewise_flop_counter(1, 0), - aten._adaptive_avg_pool2d_backward.default: ewise_flop_counter(0, 1), - aten._adaptive_avg_pool3d.default: ewise_flop_counter(1, 0), - aten._adaptive_avg_pool3d_backward.default: ewise_flop_counter(0, 1), - aten.embedding_dense_backward.default: ewise_flop_counter(0, 1), - aten.embedding.default: ewise_flop_counter(1, 0), -} - -ewise_flop_aten = [ + aten.avg_pool1d.default: ewise_flop_counter(1, 0), + aten.avg_pool2d.default: ewise_flop_counter(1, 0), + aten.avg_pool2d_backward.default: ewise_flop_counter(0, 1), + aten.avg_pool3d.default: ewise_flop_counter(1, 0), + aten.avg_pool3d_backward.default: ewise_flop_counter(0, 1), + aten.max_pool1d.default: ewise_flop_counter(1, 0), + aten.max_pool2d.default: ewise_flop_counter(1, 0), + aten.max_pool3d.default: ewise_flop_counter(1, 0), + aten.max_pool1d_with_indices.default: ewise_flop_counter(1, 0), + aten.max_pool2d_with_indices.default: ewise_flop_counter(1, 0), + aten.max_pool2d_with_indices_backward.default: ewise_flop_counter(0, 1), + aten.max_pool3d_with_indices.default: ewise_flop_counter(1, 0), + aten.max_pool3d_with_indices_backward.default: ewise_flop_counter(0, 1), + aten._adaptive_avg_pool2d.default: ewise_flop_counter(1, 0), + aten._adaptive_avg_pool2d_backward.default: ewise_flop_counter(0, 1), + aten._adaptive_avg_pool3d.default: ewise_flop_counter(1, 0), + aten._adaptive_avg_pool3d_backward.default: ewise_flop_counter(0, 1), + aten.embedding_dense_backward.default: ewise_flop_counter(0, 1), + aten.embedding.default: ewise_flop_counter(1, 0), + } + + ewise_flop_aten = [ # basic op - aten.add.Tensor, - aten.add_.Tensor, - aten.div.Tensor, - aten.div_.Tensor, - aten.div.Scalar, - aten.div_.Scalar, - aten.mul.Tensor, - aten.mul.Scalar, - aten.mul_.Tensor, - aten.neg.default, - aten.pow.Tensor_Scalar, - aten.rsub.Scalar, - aten.sum.default, - aten.sum.dim_IntList, - aten.mean.dim, + aten.add.Tensor, + aten.add_.Tensor, + aten.div.Tensor, + aten.div_.Tensor, + aten.div.Scalar, + aten.div_.Scalar, + aten.mul.Tensor, + aten.mul.Scalar, + aten.mul_.Tensor, + aten.neg.default, + aten.pow.Tensor_Scalar, + aten.rsub.Scalar, + aten.sum.default, + aten.sum.dim_IntList, + aten.mean.dim, # activation op - aten.hardswish.default, - aten.hardswish_.default, - aten.hardswish_backward.default, - aten.hardtanh.default, - aten.hardtanh_.default, - aten.hardtanh_backward.default, - aten.hardsigmoid_backward.default, - aten.hardsigmoid.default, - aten.gelu.default, - aten.gelu_backward.default, - aten.silu.default, - aten.silu_.default, - aten.silu_backward.default, - aten.sigmoid.default, - aten.sigmoid_backward.default, - aten._softmax.default, - aten._softmax_backward_data.default, - aten.relu_.default, - aten.relu.default, - aten.tanh.default, - aten.tanh_backward.default, - aten.threshold_backward.default, + aten.hardswish.default, + aten.hardswish_.default, + aten.hardswish_backward.default, + aten.hardtanh.default, + aten.hardtanh_.default, + aten.hardtanh_backward.default, + aten.hardsigmoid_backward.default, + aten.hardsigmoid.default, + aten.gelu.default, + aten.gelu_backward.default, + aten.silu.default, + aten.silu_.default, + aten.silu_backward.default, + aten.sigmoid.default, + aten.sigmoid_backward.default, + aten._softmax.default, + aten._softmax_backward_data.default, + aten.relu_.default, + aten.relu.default, + aten.tanh.default, + aten.tanh_backward.default, + aten.threshold_backward.default, # dropout - aten.native_dropout.default, - aten.native_dropout_backward.default, + aten.native_dropout.default, + aten.native_dropout_backward.default, # distribution - aten.bernoulli_.float, + aten.bernoulli_.float, # where - aten.where.self, -] -for op in ewise_flop_aten: - flop_mapping[op] = ewise_flop_counter(1, 0) - -# fix-me: this will be removed in future -zero_flop_aten = [ - aten.as_strided.default, - aten.as_strided_.default, - aten.cat.default, - aten.clone.default, - aten.copy_.default, - aten.detach.default, - aten.expand.default, - aten.empty_like.default, - aten.new_empty.default, - aten.new_empty_strided.default, - aten.ones_like.default, - aten._reshape_alias.default, - aten.select.int, - aten.select_backward.default, - aten.squeeze.dim, - aten.slice.Tensor, - aten.slice_backward.default, - aten.split.Tensor, - aten.permute.default, - aten.t.default, - aten.transpose.int, - aten._to_copy.default, - aten.unsqueeze.default, - aten.unbind.int, - aten._unsafe_view.default, - aten.view.default, - aten.zero_.default, - aten.zeros_like.default, -] - -for op in zero_flop_aten: - flop_mapping[op] = zero_flop_jit + aten.where.self, + ] + for op in ewise_flop_aten: + flop_mapping[op] = ewise_flop_counter(1, 0) + + # fix-me: this will be removed in future + zero_flop_aten = [ + aten.as_strided.default, + aten.as_strided_.default, + aten.cat.default, + aten.clone.default, + aten.copy_.default, + aten.detach.default, + aten.expand.default, + aten.empty_like.default, + aten.new_empty.default, + aten.new_empty_strided.default, + aten.ones_like.default, + aten._reshape_alias.default, + aten.select.int, + aten.select_backward.default, + aten.squeeze.dim, + aten.slice.Tensor, + aten.slice_backward.default, + aten.split.Tensor, + aten.permute.default, + aten.t.default, + aten.transpose.int, + aten._to_copy.default, + aten.unsqueeze.default, + aten.unbind.int, + aten._unsafe_view.default, + aten.view.default, + aten.zero_.default, + aten.zeros_like.default, + ] + + for op in zero_flop_aten: + flop_mapping[op] = zero_flop_jit +else: + flop_mapping = {} + elementwise_flop_aten = {} + zero_flop_aten = {} diff --git a/colossalai/_analyzer/fx/__init__.py b/colossalai/_analyzer/fx/__init__.py index 2e857b1b054b..aa01de0bbe6c 100644 --- a/colossalai/_analyzer/fx/__init__.py +++ b/colossalai/_analyzer/fx/__init__.py @@ -1,4 +1,3 @@ -from .bias_addition import * from .node_util import MetaInfo from .symbolic_profile import symbolic_profile -from .symbolic_trace import symbolic_trace +from .tracer.symbolic_trace import symbolic_trace diff --git a/colossalai/_analyzer/fx/graph_module.py b/colossalai/_analyzer/fx/graph_module.py index 779b42ebaafd..1fdedd758c01 100644 --- a/colossalai/_analyzer/fx/graph_module.py +++ b/colossalai/_analyzer/fx/graph_module.py @@ -1,4 +1,7 @@ +import linecache import os +import sys +import traceback import warnings from pathlib import Path from typing import Any, Dict, Optional, Union @@ -6,11 +9,74 @@ import torch import torch.fx import torch.nn as nn -from torch.fx.graph import PythonCode, _PyTreeCodeGen -from torch.fx.graph_module import _exec_with_source, _forward_from_src, _WrappedCall +from torch.fx.graph import PythonCode + +try: + from torch.fx.graph import _PyTreeCodeGen + SUPPORT_PT_CODEGEN = True +except ImportError: + SUPPORT_PT_CODEGEN = False + +from torch.fx.graph_module import _exec_with_source, _forward_from_src from torch.nn.modules.module import _addindent +# This is a copy of torch.fx.graph_module._WrappedCall. +# It should be removed when we stop supporting torch < 1.12.0. +class _WrappedCall: + + def __init__(self, cls, cls_call): + self.cls = cls + self.cls_call = cls_call + + # Previously, if an error occurred when valid + # symbolically-traced code was run with an invalid input, the + # user would see the source of the error as coming from + # `File "`, where N is some number. We use + # this function to generate a more informative error message. We + # return the traceback itself, a message explaining that the + # error occurred in a traced Module's generated forward + # function, and five lines of context surrounding the faulty + # line + @staticmethod + def _generate_error_message(frame_summary: traceback.FrameSummary) -> str: + # auxiliary variables (for readability) + err_lineno = frame_summary.lineno + assert err_lineno is not None + line = frame_summary.line + assert line is not None + err_line_len = len(line) + all_src_lines = linecache.getlines(frame_summary.filename) + + # constituent substrings of the error message + tb_repr = traceback.format_exc() + custom_msg = ("Call using an FX-traced Module, " + f"line {err_lineno} of the traced Module's " + "generated forward function:") + before_err = "".join(all_src_lines[err_lineno - 2:err_lineno]) + marker = "~" * err_line_len + "~~~ <--- HERE" + err_and_after_err = "\n".join(all_src_lines[err_lineno:err_lineno + 2]) + + # joined message + return "\n".join([tb_repr, custom_msg, before_err, marker, err_and_after_err]) + + def __call__(self, obj, *args, **kwargs): + try: + if self.cls_call is not None: + return self.cls_call(obj, *args, **kwargs) + else: + return super(self.cls, obj).__call__(*args, **kwargs) # type: ignore[misc] + except Exception as e: + assert e.__traceback__ + topmost_framesummary: traceback.FrameSummary = \ + traceback.StackSummary.extract(traceback.walk_tb(e.__traceback__))[-1] # type: ignore[arg-type] + if "eval_with_key" in topmost_framesummary.filename: + print(_WrappedCall._generate_error_message(topmost_framesummary), file=sys.stderr) + raise e.with_traceback(None) + else: + raise e + + class ColoGraphModule(torch.fx.GraphModule): """ ColoGraphGraphModule is an nn.Module generated from an fx.Graph. @@ -65,7 +131,7 @@ def recompile(self) -> PythonCode: called after editing the contained ``graph``, otherwise the generated code of this ``GraphModule`` will be out of date. """ - if isinstance(self._graph._codegen, _PyTreeCodeGen): + if SUPPORT_PT_CODEGEN and isinstance(self._graph._codegen, _PyTreeCodeGen): self._in_spec = self._graph._codegen.pytree_info.in_spec self._out_spec = self._graph._codegen.pytree_info.out_spec python_code = self._graph.python_code(root_module='self') diff --git a/colossalai/_analyzer/fx/node_util.py b/colossalai/_analyzer/fx/node_util.py index d06fa8b93fc6..8c8956d8ea7c 100644 --- a/colossalai/_analyzer/fx/node_util.py +++ b/colossalai/_analyzer/fx/node_util.py @@ -20,7 +20,7 @@ def union(a, b): return {**a, **b} -def compute_size_in_bytes(elem: torch.Tensor | Dict | List | Tuple | int) -> int: +def compute_size_in_bytes(elem: Union[torch.Tensor, Dict, List, Tuple, int]) -> int: """Compute the size of a tensor or a collection of tensors in bytes. Args: @@ -195,8 +195,8 @@ def __repr__(self): s += f'\n\thas buffer of size {_format_memory(self.buffer_size)}' if self.output_size: s += f'\n\thas output activation of size {_format_memory(self.output_size)}' - if self.total_size: - s += f'\n\thas total activation of size {_format_memory(self.total_size)}' + # if self.total_size: + # s += f'\n\thas total activation of size {_format_memory(self.total_size)}' if self.temp_size: s += f'\n\thas temp activation of size {_format_memory(self.temp_size)}' if self.backward_size: diff --git a/colossalai/_analyzer/fx/passes/shape_prop.py b/colossalai/_analyzer/fx/passes/shape_prop.py index 3691497ed8cd..ab3e1a4d6a3d 100644 --- a/colossalai/_analyzer/fx/passes/shape_prop.py +++ b/colossalai/_analyzer/fx/passes/shape_prop.py @@ -111,7 +111,24 @@ def run_node(self, n: torch.fx.Node) -> Any: with self.global_hook: r = getattr(self, n.op)(n.target, args, kwargs) - unwrap_fn = lambda elem: elem._tensor if isinstance(elem, MetaTensor) else elem + def unwrap_fn(elem): + + def _convert_meta(t: torch.Tensor): + if t.device == 'meta': + return t + else: + return t.to('meta') + + if isinstance(elem, MetaTensor): + return _convert_meta(elem._tensor) + + elif isinstance(elem, torch.Tensor): + return _convert_meta(elem) + + else: + return elem + + # unwrap_fn = lambda elem: elem._tensor if isinstance(elem, MetaTensor) else elem is_pure_tensor = lambda elem: isinstance(elem, MetaTensor) and not isinstance(elem, torch.nn.Parameter) n_info = MetaInfo(n) n_info.outputs = _normalize_tuple(r) diff --git a/colossalai/_analyzer/fx/tracer/__init__.py b/colossalai/_analyzer/fx/tracer/__init__.py new file mode 100644 index 000000000000..6b1b2256aa44 --- /dev/null +++ b/colossalai/_analyzer/fx/tracer/__init__.py @@ -0,0 +1,2 @@ +from .bias_addition import * +from .custom_leaf_module import * diff --git a/colossalai/_analyzer/fx/bias_addition.py b/colossalai/_analyzer/fx/tracer/bias_addition.py similarity index 98% rename from colossalai/_analyzer/fx/bias_addition.py rename to colossalai/_analyzer/fx/tracer/bias_addition.py index 5359752d4cb4..1e75b47ca5b0 100644 --- a/colossalai/_analyzer/fx/bias_addition.py +++ b/colossalai/_analyzer/fx/tracer/bias_addition.py @@ -4,11 +4,10 @@ """ import torch -import torch.nn as nn import torch.nn.functional as F from torch.nn.modules.utils import _pair, _single, _triple -from .symbolic_trace import register_tracer_impl +from .tracer import register_tracer_impl __all__ = [] diff --git a/colossalai/_analyzer/fx/tracer/custom_leaf_module.py b/colossalai/_analyzer/fx/tracer/custom_leaf_module.py new file mode 100644 index 000000000000..112c7c9637d2 --- /dev/null +++ b/colossalai/_analyzer/fx/tracer/custom_leaf_module.py @@ -0,0 +1,29 @@ +import torch + +from .tracer import register_leaf_module, register_leaf_module_impl + +try: + import apex + register_leaf_module(apex.normalization.FusedLayerNorm) + register_leaf_module(apex.normalization.FusedRMSNorm) + register_leaf_module(apex.normalization.MixedFusedLayerNorm) + register_leaf_module(apex.normalization.MixedFusedRMSNorm) + + @register_leaf_module_impl(apex.normalization.FusedLayerNorm) + @register_leaf_module_impl(apex.normalization.FusedRMSNorm) + @register_leaf_module_impl(apex.normalization.MixedFusedLayerNorm) + @register_leaf_module_impl(apex.normalization.MixedFusedRMSNorm) + def torch_nn_normalize(self, input: torch.Tensor): + # check shape + if isinstance(self, torch.nn.BatchNorm1d): + assert input.dim() in [2, 3] + elif isinstance(self, torch.nn.BatchNorm2d): + assert input.dim() == 4 + elif isinstance(self, torch.nn.BatchNorm3d): + assert input.dim() == 5 + + # normalization maintain the same shape as the input + return input.clone() + +except (ImportError, AttributeError): + pass diff --git a/colossalai/_analyzer/fx/tracer/proxy.py b/colossalai/_analyzer/fx/tracer/proxy.py new file mode 100644 index 000000000000..ce379efdcf0d --- /dev/null +++ b/colossalai/_analyzer/fx/tracer/proxy.py @@ -0,0 +1,112 @@ +import operator +from typing import Any, Callable, Dict, Optional, Set, Union + +import torch +import torch.nn as nn +from torch.fx import Graph, Node, Proxy, Tracer +from torch.fx.graph import _Namespace +from torch.utils._pytree import tree_map + +from colossalai._analyzer._subclasses import MetaTensor + +Target = Union[Callable[..., Any], str] + + +class ColoProxy(Proxy): + _func_dispatch: Dict[Target, Callable[..., Any]] = {} + + def __init__(self, *args, data=None, **kwargs): + super().__init__(*args, **kwargs) + self._meta_data = data + + @property + def meta_data(self): + return self._meta_data + + @meta_data.setter + def meta_data(self, args): + wrap_fn = lambda x: MetaTensor(x) if isinstance(x, torch.Tensor) else x + self._meta_data = tree_map(wrap_fn, args) + + @classmethod + def __torch_function__(cls, orig_method, types, args=(), kwargs=None): + kwargs = {} if kwargs is None else kwargs + if orig_method in cls._func_dispatch: + impl = cls._func_dispatch.pop(orig_method) # avoid recursion + proxy = impl(*args, **kwargs) + cls._func_dispatch[orig_method] = impl + return proxy + else: + proxy = cls.from_torch_proxy(super().__torch_function__(orig_method, types, args, kwargs)) + unwrap_fn = lambda p: p.meta_data if isinstance(p, ColoProxy) else p + if proxy.meta_data is None: + proxy.meta_data = orig_method(*tree_map(unwrap_fn, args), **tree_map(unwrap_fn, kwargs)) + return proxy + + @classmethod + def from_torch_proxy(cls, proxy: Proxy): + return cls(proxy.node, proxy.tracer) + + def __repr__(self): + return f"ColoProxy({self.node.name}, meta_data={self.meta_data})" + + def __len__(self): + return len(self.meta_data) + + def __int__(self): + return int(self.meta_data) + + def __index__(self): + try: + return int(self.meta_data) + except: + return torch.zeros(self.meta_data.shape, dtype=torch.bool).numpy().__index__() + + def __float__(self): + return float(self.meta_data) + + def __bool__(self): + return self.meta_data + + def __getattr__(self, k): + return ColoAttribute(self, k, getattr(self._meta_data, k, None)) + + def __setitem__(self, key, value): + proxy = self.tracer.create_proxy('call_function', operator.setitem, (self, key, value), {}) + proxy.meta_data = self._meta_data + return proxy + + def __contains__(self, key): + if self.node.op == "placeholder": + # this is used to handle like + # if x in kwargs + # we don't handle this case for now + return False + return super().__contains__(key) + + def __isinstancecheck__(self, type): + return isinstance(self.meta_data, type) + + +class ColoAttribute(ColoProxy): + + def __init__(self, root, attr: str, data=None): + self.root = root + self.attr = attr + self.tracer = root.tracer + self._meta_data = data + self._node: Optional[Node] = None + + @property + def node(self): + # the node for attributes is added lazily, since most will just be method calls + # which do not rely on the getitem call + if self._node is None: + self._node = self.tracer.create_proxy('call_function', getattr, (self.root, self.attr), {}).node + return self._node + + def __call__(self, *args, **kwargs): + return self.tracer.create_proxy('call_method', self.attr, (self.root,) + args, kwargs) + + def __repr__(self): + return f"ColoAttribute({self.node.name}, attr={self.attr})" diff --git a/colossalai/_analyzer/fx/tracer/symbolic_trace.py b/colossalai/_analyzer/fx/tracer/symbolic_trace.py new file mode 100644 index 000000000000..2018863f6f5f --- /dev/null +++ b/colossalai/_analyzer/fx/tracer/symbolic_trace.py @@ -0,0 +1,157 @@ +from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union + +import torch +from torch.fx import Tracer +from torch.utils._pytree import tree_map + +from colossalai._analyzer._subclasses import MetaTensor + +try: + from ..codegen import ActivationCheckpointCodeGen + SUPPORT_ACTIVATION = True +except: + SUPPORT_ACTIVATION = False +from ..graph_module import ColoGraphModule +from .tracer import ColoTracer + + +def _default_device(): + return torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu') + + +def _current_device(module: torch.nn.Module): + try: + return next(module.parameters()).device + except: + return _default_device() + + +def symbolic_trace( + root: Union[torch.nn.Module, Callable[..., Any]], + concrete_args: Optional[Dict[str, Any]] = None, + meta_args: Optional[Dict[str, Any]] = None, + trace_act_ckpt: bool = False, + bias_addition_split: bool = False, +) -> ColoGraphModule: + """ + Traces a ``torch.nn.Module`` or a function and returns a ``GraphModule`` with ``Node``s and ``MetaInfo`` + attached to the ``Node``s. + + Can be used to trace the usage of ``torch.utils.checkpoint`` and the path of module + (https://github.com/pytorch/examples/blob/main/fx/module_tracer.py). + + This tracer is able to trace basic control flow and for loops. + + It will split the bias addition into two parts if ``bias_addition_split`` is set to be ``True``. + (See ./bias_addition.py for more details). + + Examples: + 1. Tracing a ``torch.nn.Module`` with control flow. + + .. code-block:: python + + class MyModule(torch.nn.Module): + def __init__(self): + super().__init__() + self.linear = torch.nn.Linear(2, 2) + + def forward(self, x): + if x.size(0) > 1: + x = x.sum(dim=0) + return self.linear(x) + + traced = symbolic_trace(MyModule(), meta_args={'x': torch.randn(1, 2, 2)}) + + # traced code like: + # def forward(self, x): + # linear_1 = self.linear(x) + # return linear_1 + + traced = symbolic_trace(MyModule(), meta_args={'x': torch.randn(2, 2, 2)}) + + # traced code like: + # def forward(self, x): + # sum = x.sum(dim=0); x = None + # linear = self.linear(sum); sum = None + # return linear + + 2. Tracing a ``torch.nn.Module`` with ``torch.utils.checkpoint``. + + .. code-block:: python + + class MyModule(torch.nn.Module): + def __init__(self): + super().__init__() + self.linear = torch.nn.Linear(2, 2) + + def forward(self, x): + def custom_forward(x): + return self.linear(x) + return torch.utils.checkpoint.checkpoint(custom_forward, x) + + traced = symbolic_trace(MyModule(), meta_args={'x': torch.randn(1, 2, 2)}, trace_act_ckpt=True) + + # traced code like: + # def checkpoint_0(self, x): + # linear = self.linear(x); x = None + # return linear + # + # def forward(self, x): + # linear = torch.utils.checkpoint.checkpoint(checkpoint_0, x); x = None + # return linear + + 3. Tracing a ``torch.nn.Module`` with ``bias_addition_split``. + + .. code-block:: python + + class MyModule(torch.nn.Module): + def __init__(self): + super().__init__() + self.linear = torch.nn.Linear(2, 2, bias=True) + + def forward(self, x): + return self.linear(x) + + traced = symbolic_trace(MyModule(), meta_args={'x': torch.randn(1, 2, 2)}, bias_addition_split=True) + + # traced code like: + # def forward(self, x): + # linear_bias = self.linear.bias + # linear_weight = self.linear.weight + # linear = torch._C._nn.linear(x, linear_weight); x = linear_weight = None + # add = linear + linear_bias; linear = linear_bias = None + # return add + + Args: + root (Union[torch.nn.Module, Callable[..., Any]]): The ``torch.nn.Module`` or function to be traced. + concrete_args (Optional[Dict[str, Any]], optional): Concrete arguments to be passed to the ``root``. + Defaults to {}. + meta_args (Optional[Dict[str, Any]], optional): Meta arguments to be passed to the ``root``. Mostly used + for tracing control flow. Defaults to {}. + trace_act_ckpt (bool, optional): Whether to trace the usage of ``torch.utils.checkpoint``. + Defaults to False. + bias_addition_split (bool, optional): Whether to split the bias addition into two parts. Defaults to False. + + Returns: + ColoGraphModule: A traced ``GraphModule`` that is ready for activation checkpoint ``CodeGen``. + + Remarks: + This part of ``symbolic_trace()`` is maintained by Colossal-AI team. If you encountered + any unexpected error during tracing, feel free to raise an issue on Colossal-AI GitHub + repo. We welcome any feedback and contributions to enhance the extensibility of + Colossal-AI. + """ + if meta_args: + device, orig_device = _default_device(), _current_device(root) + wrap_fn = lambda elem: MetaTensor(elem, device=device) if isinstance(elem, torch.Tensor) else elem + graph = ColoTracer(trace_act_ckpt=trace_act_ckpt, + bias_addition_split=bias_addition_split).trace(root.to(device), + concrete_args=concrete_args, + meta_args=tree_map(wrap_fn, meta_args)) + if trace_act_ckpt and SUPPORT_ACTIVATION: + graph.set_codegen(ActivationCheckpointCodeGen()) + root.to(orig_device) + else: + graph = Tracer().trace(root, concrete_args=concrete_args) + name = root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__ + return ColoGraphModule(root, graph, name) diff --git a/colossalai/_analyzer/fx/symbolic_trace.py b/colossalai/_analyzer/fx/tracer/tracer.py similarity index 53% rename from colossalai/_analyzer/fx/symbolic_trace.py rename to colossalai/_analyzer/fx/tracer/tracer.py index 5d858c87a3c8..1a247449f3d8 100644 --- a/colossalai/_analyzer/fx/symbolic_trace.py +++ b/colossalai/_analyzer/fx/tracer/tracer.py @@ -1,28 +1,19 @@ import functools import inspect -import operator from contextlib import contextmanager -from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union +from typing import Any, Callable, Dict, Iterable, Optional, Set, Tuple, Type, Union import torch import torch.nn as nn from torch.fx import Graph, Node, Proxy, Tracer -from torch.fx.graph import _Namespace from torch.utils._pytree import tree_map -from colossalai._analyzer._subclasses import MetaTensor, _TensorPropertyMethod, _TorchFactoryMethod +from colossalai._analyzer._subclasses import _TensorPropertyMethod, _TorchFactoryMethod -from .codegen import ActivationCheckpointCodeGen -from .graph_module import ColoGraphModule -from .node_util import MetaInfo +from ..node_util import MetaInfo +from .proxy import ColoProxy Target = Union[Callable[..., Any], str] -Argument = Optional[Union[Tuple[Any, ...], # actually Argument, but mypy can't represent recursive types - List[Any], # actually Argument - Dict[str, Any], # actually Argument - slice, # Slice[Argument, Argument, Argument], but slice is not a templated type in typing - 'Node',]] -zeros = torch.zeros def _truncate_suffix(s: str): @@ -32,17 +23,6 @@ def _truncate_suffix(s: str): return re.sub(r'_\d+$', '', s) -def _default_device(): - return torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu') - - -def _current_device(module): - try: - return next(module.parameters()).device - except: - return _default_device() - - def register_tracer_impl(func: Callable[..., Any], name: Optional[str] = '_custom_impl'): def wrapper(impl): @@ -70,149 +50,6 @@ def register_non_leaf_module(module: nn.Module): ColoTracer._custom_non_leaf_module.add(module) -class ColoProxy(Proxy): - _func_dispatch: Dict[Target, Callable[..., Any]] = {} - - def __init__(self, *args, data=None, **kwargs): - super().__init__(*args, **kwargs) - self._meta_data = data - - @property - def meta_data(self): - return self._meta_data - - @meta_data.setter - def meta_data(self, args): - wrap_fn = lambda x: MetaTensor(x) if isinstance(x, torch.Tensor) else x - self._meta_data = tree_map(wrap_fn, args) - - @classmethod - def __torch_function__(cls, orig_method, types, args=(), kwargs=None): - kwargs = {} if kwargs is None else kwargs - if orig_method in cls._func_dispatch: - impl = cls._func_dispatch.pop(orig_method) # avoid recursion - proxy = impl(*args, **kwargs) - cls._func_dispatch[orig_method] = impl - return proxy - else: - proxy = cls.from_torch_proxy(super().__torch_function__(orig_method, types, args, kwargs)) - unwrap_fn = lambda p: p.meta_data if isinstance(p, ColoProxy) else p - if proxy.meta_data is None: - proxy.meta_data = orig_method(*tree_map(unwrap_fn, args), **tree_map(unwrap_fn, kwargs)) - return proxy - - @classmethod - def from_torch_proxy(cls, proxy: Proxy): - return cls(proxy.node, proxy.tracer) - - def __repr__(self): - return f"ColoProxy({self.node.name}, meta_data={self.meta_data})" - - def __len__(self): - return len(self.meta_data) - - def __int__(self): - return int(self.meta_data) - - def __index__(self): - try: - return int(self.meta_data) - except: - return zeros(self.meta_data.shape, dtype=torch.bool).numpy().__index__() - - def __float__(self): - return float(self.meta_data) - - def __bool__(self): - return self.meta_data - - def __getattr__(self, k): - return ColoAttribute(self, k, getattr(self._meta_data, k, None)) - - def __setitem__(self, key, value): - proxy = self.tracer.create_proxy('call_function', operator.setitem, (self, key, value), {}) - proxy.meta_data = self._meta_data - return proxy - - def __contains__(self, key): - if self.node.op == "placeholder": - # this is used to handle like - # if x in kwargs - # we don't handle this case for now - return False - return super().__contains__(key) - - def __isinstancecheck__(self, type): - return isinstance(self.meta_data, type) - - def size(self, dim=None): - if self._meta_data is None: - return self._meta_data.size(*[dim] if dim else []) - return self.tracer.create_proxy('call_method', 'size', (self, dim) if dim else (self,), {}) - - def dim(self): - if self._meta_data is not None: - return self._meta_data.dim() - return self.tracer.create_proxy('call_method', 'dim', (self,), {}) - - @property - def shape(self): - if self._meta_data is not None: - return self._meta_data.shape - return self.tracer.create_proxy('call_function', getattr, (self, 'shape'), {}) - - @property - def ndim(self): - if self._meta_data is not None: - return self._meta_data.ndim - return self.tracer.create_proxy('call_function', getattr, (self, 'ndim'), {}) - - @property - def device(self): - if self._meta_data is not None: - return self._meta_data.device - return self.tracer.create_proxy('call_function', getattr, (self, 'device'), {}) - - @property - def dtype(self): - if self._meta_data is not None: - return self._meta_data.dtype - return self.tracer.create_proxy('call_function', getattr, (self, 'dtype'), {}) - - def to(self, *args, **kwargs): - return self.tracer.create_proxy('call_method', 'to', (self, *args), {**kwargs}) - - def cpu(self, *args, **kwargs): - return self.tracer.create_proxy('call_method', 'cpu', (self, *args), {**kwargs}) - - def cuda(self, *args, **kwargs): - return self.tracer.create_proxy('call_method', 'cuda', (self, *args), {**kwargs}) - - -class ColoAttribute(ColoProxy): - - def __init__(self, root, attr: str, data=None): - self.root = root - self.attr = attr - self.tracer = root.tracer - self._meta_data = data - self._node: Optional[Node] = None - - @property - def node(self): - # the node for attributes is added lazily, since most will just be method calls - # which do not rely on the getitem call - if self._node is None: - self._node = self.tracer.create_proxy('call_function', getattr, (self.root, self.attr), {}).node - return self._node - - def __call__(self, *args, **kwargs): - return self.tracer.create_proxy('call_method', self.attr, (self.root,) + args, kwargs) - - def __repr__(self): - return f"ColoAttribute({self.node.name}, attr={self.attr})" - - class ColoTracer(Tracer): _custom_leaf_module: Set[Type[nn.Module]] = set() _custom_leaf_module_impl: Dict[Type[nn.Module], Callable[..., Any]] = {} @@ -249,7 +86,6 @@ def is_leaf_module(self, m: nn.Module, module_qualified_name: str) -> bool: # we will enter the module and split the bias-addition ops if self.bias_addition_split and type(m) in self._bias_addition_module and m.bias is not None: return False - # user can specify which modules are leaf modules and which are not return (type(m) not in self._custom_non_leaf_module and (type(m) in self._custom_leaf_module or super().is_leaf_module(m, module_qualified_name))) @@ -306,9 +142,13 @@ def create_proxy(self, mod = self.root.get_submodule(target) self.disable_module_getattr = True try: - proxy.meta_data = self._custom_leaf_module_impl.get(type(mod), - mod.forward)(*tree_map(unwrap_fn, args), - **tree_map(unwrap_fn, kwargs)) + args = tree_map(unwrap_fn, args) + kwargs = tree_map(unwrap_fn, kwargs) + if type(mod) in self._custom_leaf_module: + target = self._custom_leaf_module_impl[type(mod)] + proxy.meta_data = target(mod, *args, **kwargs) + else: + proxy.meta_data = mod.forward(*args, **kwargs) finally: self.disable_module_getattr = False return proxy @@ -320,15 +160,21 @@ def create_node(self, *args, **kwargs) -> Node: def trace(self, root: torch.nn.Module, - concrete_args: Optional[Dict[str, torch.Tensor]] = {}, - meta_args: Optional[Dict[str, torch.Tensor]] = {}) -> Graph: + concrete_args: Optional[Dict[str, torch.Tensor]] = None, + meta_args: Optional[Dict[str, torch.Tensor]] = None) -> Graph: + + if meta_args is None: + meta_args = {} + + if concrete_args is None: + concrete_args = {} # check concrete and meta args have valid names sig = inspect.signature(root.forward) sig_names = set(sig.parameters.keys()) meta_arg_names = set(meta_args.keys()) concrete_arg_names = set(concrete_args.keys()) - + non_concrete_arg_names = sig_names - concrete_arg_names # update concrete args with default values for k, v in sig.parameters.items(): if k in sig_names - meta_arg_names and \ @@ -352,6 +198,34 @@ def _check_arg_name_valid(names: Iterable[str]): self.graph = super().trace(root, concrete_args=concrete_args) self.mod_dir = '' self.graph.lint() + + for node in self.graph.nodes: + if node.op == "placeholder": + # Removing default values for inputs as the forward pass will fail with them. + if node.target in non_concrete_arg_names: + node.args = () + # Without this, torch.jit.script fails because the inputs type is Optional[torch.Tensor]. + # It cannot infer on the attributes and methods the input should have, and fails. + node.type = torch.Tensor + # It is a concrete arg so it is not used and should be removed. + else: + if hasattr(torch.fx._symbolic_trace, "_assert_is_none"): + # Newer versions of torch.fx emit an assert statement + # for concrete arguments; delete those before we delete + # the concrete arg. + to_delete = [] + for user in node.users: + if user.target == torch.fx._symbolic_trace._assert_is_none: + to_delete.append(user) + for user in to_delete: + self.graph.erase_node(user) + + self.graph.erase_node(node) + + # TODO: solves GraphModule creation. + # Without this, return type annotation "Tuple" is causing code execution failure. + if node.op == "output": + node.type = None return self.graph @contextmanager @@ -454,7 +328,7 @@ def _post_check(self, non_concrete_arg_names: Set[str]): if node.op == "output": node.type = None self.graph.lint() - + def getattr(self, attr, attr_val, parameter_proxy_cache): return self._module_getattr(attr, attr_val, parameter_proxy_cache) @@ -487,134 +361,3 @@ def maybe_get_proxy_for_attr(attr_val, collection_to_search, parameter_proxy_cac return maybe_parameter_proxy return attr_val - - -def symbolic_trace( - root: Union[torch.nn.Module, Callable[..., Any]], - concrete_args: Optional[Dict[str, Any]] = {}, - meta_args: Optional[Dict[str, Any]] = {}, - trace_act_ckpt: bool = False, - bias_addition_split: bool = False, -) -> ColoGraphModule: - """ - Traces a ``torch.nn.Module`` or a function and returns a ``GraphModule`` with ``Node``s and ``MetaInfo`` - attached to the ``Node``s. - - Can be used to trace the usage of ``torch.utils.checkpoint`` and the path of module - (https://github.com/pytorch/examples/blob/main/fx/module_tracer.py). - - This tracer is able to trace basic control flow and for loops. - - It will split the bias addition into two parts if ``bias_addition_split`` is set to be ``True``. - (See ./bias_addition.py for more details). - - Examples: - 1. Tracing a ``torch.nn.Module`` with control flow. - - .. code-block:: python - - class MyModule(torch.nn.Module): - def __init__(self): - super().__init__() - self.linear = torch.nn.Linear(2, 2) - - def forward(self, x): - if x.size(0) > 1: - x = x.sum(dim=0) - return self.linear(x) - - traced = symbolic_trace(MyModule(), meta_args={'x': torch.randn(1, 2, 2)}) - - # traced code like: - # def forward(self, x): - # linear_1 = self.linear(x) - # return linear_1 - - traced = symbolic_trace(MyModule(), meta_args={'x': torch.randn(2, 2, 2)}) - - # traced code like: - # def forward(self, x): - # sum = x.sum(dim=0); x = None - # linear = self.linear(sum); sum = None - # return linear - - 2. Tracing a ``torch.nn.Module`` with ``torch.utils.checkpoint``. - - .. code-block:: python - - class MyModule(torch.nn.Module): - def __init__(self): - super().__init__() - self.linear = torch.nn.Linear(2, 2) - - def forward(self, x): - def custom_forward(x): - return self.linear(x) - return torch.utils.checkpoint.checkpoint(custom_forward, x) - - traced = symbolic_trace(MyModule(), meta_args={'x': torch.randn(1, 2, 2)}, trace_act_ckpt=True) - - # traced code like: - # def checkpoint_0(self, x): - # linear = self.linear(x); x = None - # return linear - # - # def forward(self, x): - # linear = torch.utils.checkpoint.checkpoint(checkpoint_0, x); x = None - # return linear - - 3. Tracing a ``torch.nn.Module`` with ``bias_addition_split``. - - .. code-block:: python - - class MyModule(torch.nn.Module): - def __init__(self): - super().__init__() - self.linear = torch.nn.Linear(2, 2, bias=True) - - def forward(self, x): - return self.linear(x) - - traced = symbolic_trace(MyModule(), meta_args={'x': torch.randn(1, 2, 2)}, bias_addition_split=True) - - # traced code like: - # def forward(self, x): - # linear_bias = self.linear.bias - # linear_weight = self.linear.weight - # linear = torch._C._nn.linear(x, linear_weight); x = linear_weight = None - # add = linear + linear_bias; linear = linear_bias = None - # return add - - Args: - root (Union[torch.nn.Module, Callable[..., Any]]): The ``torch.nn.Module`` or function to be traced. - concrete_args (Optional[Dict[str, Any]], optional): Concrete arguments to be passed to the ``root``. - Defaults to {}. - meta_args (Optional[Dict[str, Any]], optional): Meta arguments to be passed to the ``root``. Mostly used - for tracing control flow. Defaults to {}. - trace_act_ckpt (bool, optional): Whether to trace the usage of ``torch.utils.checkpoint``. - Defaults to False. - bias_addition_split (bool, optional): Whether to split the bias addition into two parts. Defaults to False. - - Returns: - ColoGraphModule: A traced ``GraphModule`` that is ready for activation checkpoint ``CodeGen``. - - Remarks: - This part of ``symbolic_trace()`` is maintained by Colossal-AI team. If you encountered - any unexpected error during tracing, feel free to raise an issue on Colossal-AI GitHub - repo. We welcome any feedback and contributions to enhance the extensibility of - Colossal-AI. - """ - if meta_args: - device, orig_device = _default_device(), _current_device(root) - wrap_fn = lambda elem: MetaTensor(elem, device=device) if isinstance(elem, torch.Tensor) else elem - graph = ColoTracer(trace_act_ckpt=trace_act_ckpt, - bias_addition_split=bias_addition_split).trace(root.to(device), - concrete_args=concrete_args, - meta_args=tree_map(wrap_fn, meta_args)) - if trace_act_ckpt: - graph.set_codegen(ActivationCheckpointCodeGen()) - root.to(orig_device) - else: - graph = Tracer().trace(root, concrete_args=concrete_args) - name = root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__ - return ColoGraphModule(root, graph, name) diff --git a/tests/kit/model_zoo/__init__.py b/tests/kit/model_zoo/__init__.py index 710038ffa387..466a2a558829 100644 --- a/tests/kit/model_zoo/__init__.py +++ b/tests/kit/model_zoo/__init__.py @@ -1,5 +1,4 @@ from . import diffusers, timm, torchaudio, torchrec, torchvision, transformers - from .registry import model_zoo __all__ = ['model_zoo'] diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index 2a100c981dea..5ed4fbe70dc9 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -17,6 +17,14 @@ def data_gen(): return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) +def seq_classification_data_gen(): + # batch sizes should be 1 if no padding token is defined. + input_ids = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64) + token_type_ids = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64) + attention_mask = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64) + return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) + + output_transform_fn = lambda x: x config = transformers.GPT2Config(n_position=64, n_layer=2, n_head=4) @@ -44,6 +52,6 @@ def data_gen(): model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_gpt_for_sequence_classification', model_fn=lambda: transformers.GPT2ForSequenceClassification(config), - data_gen_fn=data_gen, + data_gen_fn=seq_classification_data_gen, output_transform_fn=output_transform_fn, model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_analyzer/test_fx/test_bias_addition.py b/tests/test_analyzer/test_fx/test_bias_addition.py index 5c9ec7cc3477..044a464be8ef 100644 --- a/tests/test_analyzer/test_fx/test_bias_addition.py +++ b/tests/test_analyzer/test_fx/test_bias_addition.py @@ -1,5 +1,6 @@ import pytest import torch +from packaging import version from torch.utils.checkpoint import checkpoint try: @@ -73,7 +74,7 @@ def forward(self, x): return x -@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') @pytest.mark.parametrize("bias", [True, False]) @pytest.mark.parametrize("bias_addition_split", [True, False]) @pytest.mark.parametrize("shape", [(3, 3, 3), (3, 3, 3, 3)]) diff --git a/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py b/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py index 6d93fe0408d7..7a4bf131ae36 100644 --- a/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py +++ b/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py @@ -3,7 +3,8 @@ from torch.fx import GraphModule from torch.utils._pytree import tree_flatten -from colossalai.fx import symbolic_trace +# from colossalai.fx import symbolic_trace +from colossalai._analyzer.fx import symbolic_trace def trace_model_and_compare_output(model, data_gen): diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py index b1c9c211a9a0..31ba2290ed99 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py @@ -1,4 +1,7 @@ +import pytest +import torch from hf_tracer_utils import trace_model_and_compare_output +from packaging import version from tests.kit.model_zoo import model_zoo @@ -6,6 +9,7 @@ SEQ_LENGTH = 16 +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') def test_albert(): sub_registry = model_zoo.get_sub_registry('transformers_albert') diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py index 1bf4947c31a0..8db6817c66dc 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py @@ -1,8 +1,12 @@ +import pytest +import torch from hf_tracer_utils import trace_model_and_compare_output +from packaging import version from tests.kit.model_zoo import model_zoo +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') def test_bert(): sub_registry = model_zoo.get_sub_registry('transformers_bert') diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py index 67a3178fae1b..796c17e398d5 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py @@ -1,16 +1,24 @@ import pytest +import torch from hf_tracer_utils import trace_model_and_compare_output +from packaging import version from tests.kit.model_zoo import model_zoo -# TODO: remove this skip once we handle the latest gpt model -@pytest.mark.skip +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') def test_gpt(): sub_registry = model_zoo.get_sub_registry('transformers_gpt') for name, (model_fn, data_gen_fn, _, _) in sub_registry.items(): model = model_fn() + + # TODO: support the following models + # 1. GPT2DoubleHeadsModel + # as they are not supported, let's skip them + if model.__class__.__name__ in ['GPT2DoubleHeadsModel']: + continue + trace_model_and_compare_output(model, data_gen_fn) diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py index 740f5a9f0c57..e7bfa607082e 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py @@ -1,8 +1,12 @@ +import pytest +import torch from hf_tracer_utils import trace_model_and_compare_output +from packaging import version from tests.kit.model_zoo import model_zoo +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') def test_opt(): sub_registry = model_zoo.get_sub_registry('transformers_opt') diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py index 7073fd63470b..5f7e4f81c44e 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py @@ -1,8 +1,12 @@ +import pytest +import torch from hf_tracer_utils import trace_model_and_compare_output +from packaging import version from tests.kit.model_zoo import model_zoo +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') def test_t5(): sub_registry = model_zoo.get_sub_registry('transformers_t5') diff --git a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py index 31baa3e89798..b175d8b10c67 100644 --- a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py +++ b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py @@ -1,8 +1,8 @@ import pytest -import timm.models as tm import torch +from packaging import version -from colossalai.fx import symbolic_trace +from colossalai._analyzer.fx import symbolic_trace from tests.kit.model_zoo import model_zoo @@ -42,6 +42,7 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): f'{model.__class__.__name__} has inconsistent outputs, {fx_output_val} vs {non_fx_output_val}' +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') def test_timm_models(): torch.backends.cudnn.deterministic = True diff --git a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py index bf6c7ae551ab..65f9f5149dda 100644 --- a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py +++ b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py @@ -1,20 +1,18 @@ -import re - +import pytest import torch +from packaging import version from torchaudio_utils import trace_and_compare from tests.kit.model_zoo import model_zoo +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') def test_torchaudio_models(): torch.backends.cudnn.deterministic = True sub_model_zoo = model_zoo.get_sub_registry('torchaudio') for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in sub_model_zoo.items(): - # FIXME(ver217): temporarily skip these models - if re.search(f'(conformer|emformer|tacotron|wav2vec2_base|hubert_base)', name): - continue model = model_fn() trace_and_compare(model, data_gen_fn, diff --git a/tests/test_fx/test_tracer/test_torchaudio_model/torchaudio_utils.py b/tests/test_fx/test_tracer/test_torchaudio_model/torchaudio_utils.py index 18d86fc05941..239f38680cec 100644 --- a/tests/test_fx/test_tracer/test_torchaudio_model/torchaudio_utils.py +++ b/tests/test_fx/test_tracer/test_torchaudio_model/torchaudio_utils.py @@ -1,6 +1,6 @@ import torch -from colossalai.fx import symbolic_trace +from colossalai._analyzer.fx import symbolic_trace def trace_and_compare(model, data_gen, output_transform_fn, need_meta=False, need_concrete=False): diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py index a4e847dbcfcd..40f83d47a7cc 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py @@ -1,7 +1,7 @@ import pytest import torch -from colossalai.fx import symbolic_trace +from colossalai._analyzer.fx import symbolic_trace from tests.kit.model_zoo import model_zoo BATCH = 2 diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py index ac377ff1d5f8..6d4b6ab81b12 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py @@ -1,7 +1,7 @@ import pytest import torch -from colossalai.fx import symbolic_trace +from colossalai._analyzer.fx import symbolic_trace from tests.kit.model_zoo import model_zoo BATCH = 2 diff --git a/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py b/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py index 455638818463..8dbbf9f5aab7 100644 --- a/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py +++ b/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py @@ -1,6 +1,6 @@ import torch -from colossalai.fx import symbolic_trace +from colossalai._analyzer.fx import symbolic_trace from tests.kit.model_zoo import model_zoo From 019a847432f850d790912b5fc1e048d85fe99e2a Mon Sep 17 00:00:00 2001 From: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com> Date: Wed, 22 Mar 2023 13:38:11 +0800 Subject: [PATCH 018/413] [Analyzer] fix analyzer tests (#3197) --- .../test_fx/test_bias_addition.py | 33 +++++++----- .../test_analyzer/test_fx/test_shape_prop.py | 30 ++++++----- .../test_fx/test_symbolic_profile.py | 18 ++++--- tests/test_analyzer/test_fx/zoo.py | 8 +-- .../test_subclasses/test_flop_tensor.py | 11 ++-- .../test_subclasses/test_meta_mode.py | 7 +-- tests/test_analyzer/test_subclasses/zoo.py | 53 ------------------- 7 files changed, 60 insertions(+), 100 deletions(-) delete mode 100644 tests/test_analyzer/test_subclasses/zoo.py diff --git a/tests/test_analyzer/test_fx/test_bias_addition.py b/tests/test_analyzer/test_fx/test_bias_addition.py index 044a464be8ef..61951e9a5da9 100644 --- a/tests/test_analyzer/test_fx/test_bias_addition.py +++ b/tests/test_analyzer/test_fx/test_bias_addition.py @@ -3,6 +3,8 @@ from packaging import version from torch.utils.checkpoint import checkpoint +from colossalai.testing.utils import parameterize + try: from colossalai._analyzer.fx import symbolic_trace except: @@ -56,9 +58,13 @@ def __init__(self, bias) -> None: self.linear = LinearModel(3, 3, bias) self.conv = ConvModel(3, 6, 3, bias) - def forward(self, x, select=0): + def forward(self, x, select=torch.Tensor([0])): x = self.linear(x) - x = checkpoint(self.conv, x, select) + if select: + x = checkpoint(self.conv, x, 0) + else: + x = checkpoint(self.conv, x, 1) + return x @@ -75,10 +81,10 @@ def forward(self, x): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') -@pytest.mark.parametrize("bias", [True, False]) -@pytest.mark.parametrize("bias_addition_split", [True, False]) -@pytest.mark.parametrize("shape", [(3, 3, 3), (3, 3, 3, 3)]) -@pytest.mark.parametrize("select", [0, 1]) +@parameterize("bias", [True, False]) +@parameterize("bias_addition_split", [True, False]) +@parameterize("shape", [(3, 3, 3), (3, 3, 3, 3)]) +@parameterize("select", [torch.Tensor([0]), torch.Tensor([1])]) def test_siu_model(bias, bias_addition_split, shape, select): model = SiuModel(bias=bias) x = torch.rand(shape) @@ -87,18 +93,18 @@ def test_siu_model(bias, bias_addition_split, shape, select): concrete_args={'select': select}, trace_act_ckpt=True, bias_addition_split=bias_addition_split) - assert torch.allclose(model(x, select), gm(x, select)), 'original model and traced model should be the same!' + assert torch.allclose(model(x, select), gm(x)), 'original model and traced model should be the same!' if bias and bias_addition_split: assert '+' in gm.code, 'bias addition should be split!' else: assert '+' not in gm.code, 'bias addition should not be split!' -@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') -@pytest.mark.parametrize("alpha", [1, 2]) -@pytest.mark.parametrize("beta", [1, 2]) -@pytest.mark.parametrize("bias_addition_split", [True, False]) -@pytest.mark.parametrize("shape", [(3, 3), (5, 5)]) +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@parameterize("alpha", [1, 2]) +@parameterize("beta", [1, 2]) +@parameterize("bias_addition_split", [True, False]) +@parameterize("shape", [(3, 3), (5, 5)]) def test_addmm_model(alpha, beta, bias_addition_split, shape): model = AddmmModel(alpha=alpha, beta=beta) x = torch.rand(shape) @@ -111,4 +117,5 @@ def test_addmm_model(alpha, beta, bias_addition_split, shape): if __name__ == '__main__': - test_siu_model(True, True, (3, 3, 3)) + test_siu_model() + test_addmm_model() diff --git a/tests/test_analyzer/test_fx/test_shape_prop.py b/tests/test_analyzer/test_fx/test_shape_prop.py index b19884a70fb2..08f4ff2cbd1f 100644 --- a/tests/test_analyzer/test_fx/test_shape_prop.py +++ b/tests/test_analyzer/test_fx/test_shape_prop.py @@ -1,16 +1,17 @@ import pytest -import timm.models as tmm import torch import torchvision.models as tm -from .zoo import tm_models, tmm_models +from packaging import version + +from colossalai.testing.utils import parameterize +from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models try: from colossalai._analyzer._subclasses import MetaTensorMode from colossalai._analyzer.fx import symbolic_trace from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass from colossalai._analyzer.fx.symbolic_profile import register_shape_impl - - + @register_shape_impl(torch.nn.functional.linear) def linear_impl(*args, **kwargs): assert True @@ -23,15 +24,15 @@ def _check_gm_validity(gm: torch.fx.GraphModule): for node in gm.graph.nodes: assert node.meta['info'].outputs, f'In {gm.__class__.__name__}, {node} has no output shape.' if node.op in [ - # 'call_module', # can apply to params - # 'call_function', # can apply to params - # 'call_method', # can apply to params + 'call_module', # can apply to params + 'call_function', # can apply to params + 'call_method', # can apply to params ]: - assert node.meta['info'].inputs, f'In {gm.__class__.__name__}, {node} has no input shape.' + assert hasattr(node.meta['info'], 'inputs'), f'In {gm.__class__.__name__}, {node} has no input shape.' -@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') -@pytest.mark.parametrize('m', tm_models) +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@parameterize('m', tm_models) def test_torchvision_shape_prop(m): with MetaTensorMode(): model = m() @@ -44,8 +45,8 @@ def test_torchvision_shape_prop(m): _check_gm_validity(gm) -@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') -@pytest.mark.parametrize('m', tmm_models) +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@parameterize('m', tmm_models) def test_timm_shape_prop(m): with MetaTensorMode(): model = m() @@ -53,11 +54,12 @@ def test_timm_shape_prop(m): meta_args = { "x": data, } + gm = symbolic_trace(model, meta_args=meta_args) shape_prop_pass(gm, data) _check_gm_validity(gm) if __name__ == "__main__": - test_torchvision_shape_prop(tm.resnet18) - test_timm_shape_prop(tmm.vgg11) + test_torchvision_shape_prop() + test_timm_shape_prop() diff --git a/tests/test_analyzer/test_fx/test_symbolic_profile.py b/tests/test_analyzer/test_fx/test_symbolic_profile.py index 5f749e6f3c50..be781599f14b 100644 --- a/tests/test_analyzer/test_fx/test_symbolic_profile.py +++ b/tests/test_analyzer/test_fx/test_symbolic_profile.py @@ -1,8 +1,10 @@ import pytest -import timm.models as tmm import torch import torchvision.models as tm -from .zoo import tm_models, tmm_models +from packaging import version + +from colossalai.testing.utils import parameterize +from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models try: from colossalai._analyzer._subclasses import MetaTensorMode @@ -16,8 +18,8 @@ def _check_gm_validity(gm: torch.fx.GraphModule): assert len(node.meta['info'].global_ctx), f'In {gm.__class__.__name__}, {node} has empty global context.' -@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') -@pytest.mark.parametrize('m', tm_models) +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@parameterize('m', tm_models) def test_torchvision_profile(m, verbose=False, bias_addition_split=False): with MetaTensorMode(): model = m() @@ -30,8 +32,8 @@ def test_torchvision_profile(m, verbose=False, bias_addition_split=False): _check_gm_validity(gm) -@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') -@pytest.mark.parametrize('m', tmm_models) +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@parameterize('m', tmm_models) def test_timm_profile(m, verbose=False, bias_addition_split=False): with MetaTensorMode(): model = m() @@ -45,5 +47,5 @@ def test_timm_profile(m, verbose=False, bias_addition_split=False): if __name__ == "__main__": - test_torchvision_profile(tm.vit_b_16, verbose=True, bias_addition_split=False) - test_timm_profile(tmm.gmlp_b16_224, verbose=True, bias_addition_split=False) + test_torchvision_profile() + test_timm_profile() diff --git a/tests/test_analyzer/test_fx/zoo.py b/tests/test_analyzer/test_fx/zoo.py index 925078d0dcbe..a96aa3949134 100644 --- a/tests/test_analyzer/test_fx/zoo.py +++ b/tests/test_analyzer/test_fx/zoo.py @@ -33,18 +33,18 @@ tmm.dm_nfnet_f0, tmm.eca_nfnet_l0, tmm.efficientformer_l1, - tmm.ese_vovnet19b_dw, + # tmm.ese_vovnet19b_dw, tmm.gmixer_12_224, tmm.gmlp_b16_224, - tmm.hardcorenas_a, + # tmm.hardcorenas_a, tmm.hrnet_w18_small, tmm.inception_v3, tmm.mixer_b16_224, tmm.nf_ecaresnet101, tmm.nf_regnet_b0, # tmm.pit_b_224, # pretrained only - tmm.regnetv_040, - tmm.skresnet18, + # tmm.regnetv_040, + # tmm.skresnet18, # tmm.swin_base_patch4_window7_224, # fx bad case # tmm.tnt_b_patch16_224, # bad case tmm.vgg11, diff --git a/tests/test_analyzer/test_subclasses/test_flop_tensor.py b/tests/test_analyzer/test_subclasses/test_flop_tensor.py index 551628103325..752836141fe7 100644 --- a/tests/test_analyzer/test_subclasses/test_flop_tensor.py +++ b/tests/test_analyzer/test_subclasses/test_flop_tensor.py @@ -1,9 +1,10 @@ import pytest import torch -import torch.nn as nn import torch.nn.functional as F import torchvision.models as tm -from .zoo import tm_models, tmm_models +from packaging import version + +from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models try: from colossalai._analyzer._subclasses import MetaTensorMode, flop_count @@ -11,7 +12,7 @@ pass -@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') @pytest.mark.parametrize('m', tm_models + tmm_models) def test_flop_count_module(m): x = torch.rand(2, 3, 224, 224) @@ -37,7 +38,7 @@ def test_flop_count_module(m): ] -@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') @pytest.mark.parametrize('func, args, kwargs', odd_cases) def test_flop_count_function(func, args, kwargs): rs_fwd, rs_bwd = flop_count(func, *args, **kwargs, verbose=True) @@ -46,5 +47,5 @@ def test_flop_count_function(func, args, kwargs): if __name__ == '__main__': - test_flop_count_module(tm.resnet18, torch.rand(2, 3, 224, 224)) + test_flop_count_module(tm.resnet18) test_flop_count_function(F.relu, (torch.rand(2, 3, 224, 224, requires_grad=True),), {'inplace': True}) diff --git a/tests/test_analyzer/test_subclasses/test_meta_mode.py b/tests/test_analyzer/test_subclasses/test_meta_mode.py index d8122b019619..160d411f6c39 100644 --- a/tests/test_analyzer/test_subclasses/test_meta_mode.py +++ b/tests/test_analyzer/test_subclasses/test_meta_mode.py @@ -1,12 +1,13 @@ import pytest import torch -import torch.distributed as dist import torchvision.models as tm +from packaging import version + try: from colossalai._analyzer._subclasses import MetaTensor, MetaTensorMode except: pass -from .zoo import tm_models, tmm_models +from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models def compare_all(tensor: torch.Tensor, meta_tensor: torch.Tensor): @@ -28,7 +29,7 @@ def run_and_compare(model): compare_all(x.grad, meta_x.grad) -@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') @pytest.mark.parametrize('m', tm_models + tmm_models) def test_meta_mode_shape(m): run_and_compare(m()) diff --git a/tests/test_analyzer/test_subclasses/zoo.py b/tests/test_analyzer/test_subclasses/zoo.py deleted file mode 100644 index 925078d0dcbe..000000000000 --- a/tests/test_analyzer/test_subclasses/zoo.py +++ /dev/null @@ -1,53 +0,0 @@ -import timm.models as tmm -import torchvision.models as tm - -# input shape: (batch_size, 3, 224, 224) -tm_models = [ - tm.alexnet, - tm.convnext_base, - tm.densenet121, - # tm.efficientnet_v2_s, - # tm.googlenet, # output bad case - # tm.inception_v3, # bad case - tm.mobilenet_v2, - tm.mobilenet_v3_small, - tm.mnasnet0_5, - tm.resnet18, - tm.regnet_x_16gf, - tm.resnext50_32x4d, - tm.shufflenet_v2_x0_5, - tm.squeezenet1_0, - # tm.swin_s, # fx bad case - tm.vgg11, - tm.vit_b_16, - tm.wide_resnet50_2, -] - -tmm_models = [ - tmm.beit_base_patch16_224, - tmm.beitv2_base_patch16_224, - tmm.cait_s24_224, - tmm.coat_lite_mini, - tmm.convit_base, - tmm.deit3_base_patch16_224, - tmm.dm_nfnet_f0, - tmm.eca_nfnet_l0, - tmm.efficientformer_l1, - tmm.ese_vovnet19b_dw, - tmm.gmixer_12_224, - tmm.gmlp_b16_224, - tmm.hardcorenas_a, - tmm.hrnet_w18_small, - tmm.inception_v3, - tmm.mixer_b16_224, - tmm.nf_ecaresnet101, - tmm.nf_regnet_b0, - # tmm.pit_b_224, # pretrained only - tmm.regnetv_040, - tmm.skresnet18, - # tmm.swin_base_patch4_window7_224, # fx bad case - # tmm.tnt_b_patch16_224, # bad case - tmm.vgg11, - tmm.vit_base_patch16_18x2_224, - tmm.wide_resnet50_2, -] From e3ad88fb482fdd95241a1f74866559b83ab4f56b Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 22 Mar 2023 14:11:54 +0800 Subject: [PATCH 019/413] [booster] implemented the cluster module (#3191) * [booster] implemented the cluster module * polish code --- colossalai/cluster/__init__.py | 5 + colossalai/cluster/device_mesh_manager.py | 36 +++++ colossalai/cluster/dist_coordinator.py | 158 ++++++++++++++++++++ colossalai/cluster/process_group_manager.py | 75 ++++++++++ 4 files changed, 274 insertions(+) create mode 100644 colossalai/cluster/__init__.py create mode 100644 colossalai/cluster/device_mesh_manager.py create mode 100644 colossalai/cluster/dist_coordinator.py create mode 100644 colossalai/cluster/process_group_manager.py diff --git a/colossalai/cluster/__init__.py b/colossalai/cluster/__init__.py new file mode 100644 index 000000000000..2fbdfd3cc999 --- /dev/null +++ b/colossalai/cluster/__init__.py @@ -0,0 +1,5 @@ +from .device_mesh_manager import DeviceMeshManager +from .dist_coordinator import DistCoordinator +from .process_group_manager import ProcessGroupManager + +__all__ = ['DistCoordinator', 'ProcessGroupManager', 'DeviceMeshManager'] diff --git a/colossalai/cluster/device_mesh_manager.py b/colossalai/cluster/device_mesh_manager.py new file mode 100644 index 000000000000..744799182e22 --- /dev/null +++ b/colossalai/cluster/device_mesh_manager.py @@ -0,0 +1,36 @@ +from colossalai.device.device_mesh import DeviceMesh + + +class DeviceMeshManager: + """ + Device mesh manager is responsible for creating and managing device meshes. + """ + + def __init__(self): + self.device_mesh_store = dict() + + def create_device_mesh(self, name, *args, **kwargs) -> DeviceMesh: + """ + Create a device mesh and store it in the manager. + + Args: + name (str): name of the device mesh + *args: args for DeviceMesh + **kwargs: kwargs for DeviceMesh + """ + # TODO(Yuliang): replace *args, **kwargs with explicit arguments + if name not in self.device_mesh_store: + device_mesh = DeviceMesh(*args, **kwargs) + self.device_mesh_store[name] = device_mesh + return device_mesh + else: + raise ValueError(f'Device mesh {name} already exists.') + + def get(self, name: str) -> DeviceMesh: + pass + + def destroy(self): + pass + + def destroy_all(self): + pass diff --git a/colossalai/cluster/dist_coordinator.py b/colossalai/cluster/dist_coordinator.py new file mode 100644 index 000000000000..6b48faf5b720 --- /dev/null +++ b/colossalai/cluster/dist_coordinator.py @@ -0,0 +1,158 @@ +import os +from contextlib import contextmanager + +import torch.distributed as dist +from torch.distributed import ProcessGroup + +from colossalai.context.singleton_meta import SingletonMeta + + +class DistCoordinator(metaclass=SingletonMeta): + """ + This class is used to coordinate distributed training. It is a singleton class, which means that there is only one instance of this + class in the whole program. + + There are some terms that are used in this class: + - rank: the rank of the current process + - world size: the total number of processes + - local rank: the rank of the current process on the current node + - master: the process with rank 0 + - node master: the process with local rank 0 on the current node + + Example: + >>> from colossalai.cluster.dist_coordinator import DistCoordinator + >>> coordinator = DistCoordinator() + >>> + >>> if coordinator.is_master(): + >>> do_something() + >>> + >>> coordinator.print_on_master('hello world') + + Attributes: + rank (int): the rank of the current process + world_size (int): the total number of processes + local_rank (int): the rank of the current process on the current node + """ + + def __init__(self): + assert dist.is_initialized( + ), 'Distributed is not initialized. Please call `torch.distributed.init_process_group` or `colossalai.launch` first.' + self._rank = dist.get_rank() + self._world_size = dist.get_world_size() + # this is often passed by launchers such as torchrun + self._local_rank = os.environ.get('LOCAL_RANK', -1) + + @property + def rank(self) -> int: + return self._rank + + @property + def world_size(self) -> int: + return self._world_size + + @property + def local_rank(self) -> int: + return self._local_rank + + def _assert_local_rank_set(self): + """ + Assert that the local rank is set. This is often passed by launchers such as torchrun. + """ + assert self.local_rank >= 0, 'The environment variable LOCAL_RANK is not set, thus the coordinator is not aware of the local rank of the current process.' + + def is_master(self, process_group: ProcessGroup = None) -> bool: + """ + Check if the current process is the master process (rank is 0). It can accept a sub process group to check the rank 0 with respect to the process. + + Args: + process_group (ProcessGroup, optional): process group to use for the rank 0 check. Defaults to None, which refers to the default process group. + + Returns: + bool: True if the current process is the master process, False otherwise + """ + rank = dist.get_rank(group=process_group) + return rank == 0 + + def is_node_master(self) -> bool: + """ + Check if the current process is the master process on the current node (local rank is 0). + + Returns: + bool: True if the current process is the master process on the current node, False otherwise + """ + self._assert_local_rank_set() + return self.local_rank == 0 + + def is_last_process(self, process_group: ProcessGroup = None) -> bool: + """ + Check if the current process is the last process (rank is world size - 1). It can accept a sub process group to check the last rank with respect to the process. + + Args: + process_group (ProcessGroup, optional): process group to use for the last rank check. Defaults to None, which refers to the default process group. + + Returns: + bool: True if the current process is the last process, False otherwise + """ + rank = dist.get_rank(group=process_group) + world_size = dist.get_world_size(group=process_group) + return rank == world_size - 1 + + def print_on_master(self, msg: str, process_group: ProcessGroup = None): + """ + Print message only from rank 0. + + Args: + msg (str): message to print + process_group (ProcessGroup, optional): process group to use for the rank 0 check. Defaults to None, which refers to the default process group. + """ + rank = dist.get_rank(group=process_group) + if rank == 0: + print(msg) + + def print_on_node_master(self, msg: str): + """ + Print message only from local rank 0. Local rank 0 refers to the 0th process running the current node. + + Args: + msg (str): message to print + """ + self._assert_local_rank_set() + if self.local_rank == 0: + print(msg) + + @contextmanager + def priority_execution(self, executor_rank: int = 0, process_group: ProcessGroup = None): + """ + This context manager is used to allow one process to execute while blocking all + other processes in the same process group. This is often useful when downloading is required + as we only want to download in one process to prevent file corruption. + + Example: + >>> from colossalai.cluster import DistCoordinator + >>> dist_coordinator = DistCoordinator() + >>> with dist_coordinator.priority_execution(): + >>> dataset = CIFAR10(root='./data', download=True) + + Args: + executor_rank (int): the process rank to execute without blocking, all other processes will be blocked + process_group (ProcessGroup, optional): process group to use for the executor rank check. Defaults to None, which refers to the default process group. + """ + rank = dist.get_rank(group=process_group) + should_block = rank != executor_rank + + if should_block: + dist.barrier(group=process_group) + + yield + + if not should_block: + dist.barrier(group=process_group) + + def destroy(self, process_group: ProcessGroup = None): + """ + Destroy the distributed process group. + + Args: + process_group (ProcessGroup, optional): process group to destroy. Defaults to None, which refers to the default process group. + """ + dist.destroy_process_group(process_group) diff --git a/colossalai/cluster/process_group_manager.py b/colossalai/cluster/process_group_manager.py new file mode 100644 index 000000000000..e52661846f3e --- /dev/null +++ b/colossalai/cluster/process_group_manager.py @@ -0,0 +1,75 @@ +from typing import List + +import torch.distributed as dist +from torch.distributed import ProcessGroup + + +class ProcessGroupManager: + """ + ProcessGroupManager is used to manage the process groups in the cluster. + + There are some terms used in this class: + - pg: the short name for process group + - pg_name: the name of the process group + - pg_size: the world size of the process group + - rank: the rank of the current process in the process group + - world_size: the total number of processes in the process group + """ + + def __init__(self): + self.pg_store = dict() + + def create_process_group(self, name: str, ranks: List[int], backend: str = 'nccl') -> ProcessGroup: + """ + Get a process group by name. If the process group does not exist, it will be created. + + Args: + name (str): name of the process group + ranks (List[int]): ranks of the process group + backend (str, optional): backend of the process group. Defaults to 'nccl'. + + Returns: + ProcessGroup: the process group + """ + if name not in self.pg_store: + pg = dist.new_group(ranks=ranks, backend=backend) + self.pg_store[name] = pg + return pg + else: + raise ValueError(f'Process group {name} already exists.') + + def get(self, name: str) -> ProcessGroup: + """ + Get a process group by name. + + Args: + name (str): name of the process group + + Returns: + ProcessGroup: the process group + """ + if name in self.pg_store: + return self.pg_store[name] + else: + raise ValueError(f'Process group {name} does not exist.') + + def destroy(self, name: str) -> None: + """ + Destroy a process group by name. + + Args: + name (str): name of the process group + """ + if name in self.pg_store: + dist.destroy_process_group(self.pg_store[name]) + del self.pg_store[name] + else: + raise ValueError(f'Process group {name} does not exist.') + + def destroy_all(self) -> None: + """ + Destroy all process groups. + """ + for name in self.pg_store: + dist.destroy_process_group(self.pg_store[name]) + self.pg_store.clear() From 1e1b9d2feabc6252818352fdd71772dd46fbe41d Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Wed, 22 Mar 2023 15:44:31 +0800 Subject: [PATCH 020/413] [chatgpt]support llama (#3070) --- .../ChatGPT/chatgpt/models/llama/__init__.py | 5 +++ .../chatgpt/models/llama/llama_actor.py | 38 +++++++++++++++++ .../chatgpt/models/llama/llama_critic.py | 42 +++++++++++++++++++ .../ChatGPT/chatgpt/models/llama/llama_rm.py | 41 ++++++++++++++++++ 4 files changed, 126 insertions(+) create mode 100644 applications/ChatGPT/chatgpt/models/llama/__init__.py create mode 100644 applications/ChatGPT/chatgpt/models/llama/llama_actor.py create mode 100644 applications/ChatGPT/chatgpt/models/llama/llama_critic.py create mode 100644 applications/ChatGPT/chatgpt/models/llama/llama_rm.py diff --git a/applications/ChatGPT/chatgpt/models/llama/__init__.py b/applications/ChatGPT/chatgpt/models/llama/__init__.py new file mode 100644 index 000000000000..9b2a024afdb2 --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/llama/__init__.py @@ -0,0 +1,5 @@ +from .llama_actor import LlamaActor +from .llama_critic import LlamaCritic +from .llama_rm import LlamaRM + +__all__ = ['LlamaActor', 'LlamaCritic', 'LlamaRM'] diff --git a/applications/ChatGPT/chatgpt/models/llama/llama_actor.py b/applications/ChatGPT/chatgpt/models/llama/llama_actor.py new file mode 100644 index 000000000000..2c7adb390d8b --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/llama/llama_actor.py @@ -0,0 +1,38 @@ +from typing import Optional + +import torch +from transformers import AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM + +from ..base import Actor + + +class LlamaActor(Actor): + """ + Llama Actor model. + + Args: + pretrained (str): Pretrained model name or path. + config (LlamaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[LlamaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + + if pretrained is not None: + model = LlamaForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = LlamaForCausalLM(config) + else: + model = LlamaForCausalLM(LlamaConfig()) + + if checkpoint: + model.gradient_checkpointing_enable() + + super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/llama/llama_critic.py b/applications/ChatGPT/chatgpt/models/llama/llama_critic.py new file mode 100644 index 000000000000..cd565031e112 --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/llama/llama_critic.py @@ -0,0 +1,42 @@ +from typing import Optional + +import torch +import torch.nn as nn +from transformers import AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM + +from ..base import Critic + + +class LlamaCritic(Critic): + """ + Llama Critic model. + + Args: + pretrained (str): Pretrained model name or path. + config (LlamaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[LlamaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none', + **kwargs) -> None: + + if pretrained is not None: + model = LlamaForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = LlamaForCausalLM(config) + else: + model = LlamaForCausalLM(LlamaConfig()) + + if checkpoint: + model.gradient_checkpointing_enable() + + value_head = nn.Linear(model.config.hidden_size, 1) + + super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/ChatGPT/chatgpt/models/llama/llama_rm.py b/applications/ChatGPT/chatgpt/models/llama/llama_rm.py new file mode 100644 index 000000000000..81fa22d1969d --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/llama/llama_rm.py @@ -0,0 +1,41 @@ +from typing import Optional + +import torch.nn as nn +from transformers import LlamaConfig, LlamaForCausalLM + +from ..base import RewardModel + + +class LlamaRM(RewardModel): + """ + Llama Reward model. + + Args: + pretrained (str): Pretrained model name or path. + config (LlamaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[LlamaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + + if pretrained is not None: + model = LlamaForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = LlamaForCausalLM(config) + else: + model = LlamaForCausalLM(LlamaConfig()) + + if checkpoint: + model.gradient_checkpointing_enable() + + value_head = nn.Linear(model.config.hidden_size, 1) + value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1)) + + super().__init__(model, lora_rank, lora_train_bias) From 9998d5ef64cb809e9858681f10b5307da1ff9196 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Wed, 22 Mar 2023 19:09:39 +0800 Subject: [PATCH 021/413] [chatgpt]add reward model code for deberta (#3199) Co-authored-by: Yuanchen Xu --- .../chatgpt/models/deberta/__init__.py | 4 ++ .../chatgpt/models/deberta/deberta_critic.py | 36 ++++++++++++++++++ .../chatgpt/models/deberta/deberta_rm.py | 37 +++++++++++++++++++ .../ChatGPT/examples/requirements.txt | 1 + applications/ChatGPT/examples/test_ci.sh | 6 +++ .../ChatGPT/examples/train_reward_model.py | 9 ++++- applications/ChatGPT/examples/train_rm.sh | 4 +- 7 files changed, 93 insertions(+), 4 deletions(-) create mode 100644 applications/ChatGPT/chatgpt/models/deberta/__init__.py create mode 100644 applications/ChatGPT/chatgpt/models/deberta/deberta_critic.py create mode 100644 applications/ChatGPT/chatgpt/models/deberta/deberta_rm.py diff --git a/applications/ChatGPT/chatgpt/models/deberta/__init__.py b/applications/ChatGPT/chatgpt/models/deberta/__init__.py new file mode 100644 index 000000000000..b66888f34fd0 --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/deberta/__init__.py @@ -0,0 +1,4 @@ +from .deberta_critic import DebertaCritic +from .deberta_rm import DebertaRM + +__all__ = ['DebertaCritic', 'DebertaRM'] diff --git a/applications/ChatGPT/chatgpt/models/deberta/deberta_critic.py b/applications/ChatGPT/chatgpt/models/deberta/deberta_critic.py new file mode 100644 index 000000000000..e84c1dbd8380 --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/deberta/deberta_critic.py @@ -0,0 +1,36 @@ +from typing import Optional + +import torch.nn as nn +from transformers import DebertaV2Config, DebertaV2Model + +from ..base import Critic + + +class DebertaCritic(Critic): + """ + Deberta Critic model. + + Args: + pretrained (str): Pretrained model name or path. + config (DebertaV2Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the LO-RA decomposition. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[DebertaV2Config] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = DebertaV2Model.from_pretrained(pretrained) + elif config is not None: + model = DebertaV2Model(config) + else: + model = DebertaV2Model(DebertaV2Config()) + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.hidden_size, 1) + super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/deberta/deberta_rm.py b/applications/ChatGPT/chatgpt/models/deberta/deberta_rm.py new file mode 100644 index 000000000000..2448c879ec85 --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/deberta/deberta_rm.py @@ -0,0 +1,37 @@ +from typing import Optional + +import torch.nn as nn +from transformers import DebertaV2Config, DebertaV2Model + +from ..base import RewardModel + + +class DebertaRM(RewardModel): + """ + Deberta Reward model. + + Args: + pretrained (str): Pretrained model name or path. + config (DebertaV2Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the LO-RA decomposition. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: str = None, + config: Optional[DebertaV2Config] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = DebertaV2Model.from_pretrained(pretrained) + elif config is not None: + model = DebertaV2Model(config) + else: + model = DebertaV2Model(DebertaV2Config()) + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.hidden_size, 1) + value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1)) + super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/examples/requirements.txt b/applications/ChatGPT/examples/requirements.txt index 6c5dac292486..40e6edc7ea73 100644 --- a/applications/ChatGPT/examples/requirements.txt +++ b/applications/ChatGPT/examples/requirements.txt @@ -1 +1,2 @@ pandas>=1.4.1 +sentencepiece diff --git a/applications/ChatGPT/examples/test_ci.sh b/applications/ChatGPT/examples/test_ci.sh index abc43ab1ee9e..1d05c4c58341 100755 --- a/applications/ChatGPT/examples/test_ci.sh +++ b/applications/ChatGPT/examples/test_ci.sh @@ -88,4 +88,10 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ --test True --lora_rank 4 +torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ + --pretrain 'microsoft/deberta-v3-large' --model 'deberta' \ + --strategy colossalai_zero2 --loss_fn 'log_sig'\ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ + --test True --lora_rank 4 + rm -rf ${BASE}/rm_ckpt.pt diff --git a/applications/ChatGPT/examples/train_reward_model.py b/applications/ChatGPT/examples/train_reward_model.py index 47dd988b8117..a9c844b7b1f8 100644 --- a/applications/ChatGPT/examples/train_reward_model.py +++ b/applications/ChatGPT/examples/train_reward_model.py @@ -8,12 +8,13 @@ from chatgpt.models.bloom import BLOOMRM from chatgpt.models.gpt import GPTRM from chatgpt.models.opt import OPTRM +from chatgpt.models.deberta import DebertaRM from chatgpt.trainer import RewardModelTrainer from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy from datasets import load_dataset from random import randint from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast +from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer from colossalai.nn.optimizer import HybridAdam @@ -39,6 +40,8 @@ def train(args): model = OPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) elif args.model == 'gpt2': model = GPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'deberta': + model = DebertaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) else: raise ValueError(f'Unsupported model "{args.model}"') @@ -54,6 +57,8 @@ def train(args): tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m') elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + elif args.model == 'deberta': + tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v3-large') else: raise ValueError(f'Unsupported model "{args.model}"') max_len = args.max_len @@ -119,7 +124,7 @@ def train(args): parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive') - parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt'], default='bloom') + parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--model_path', type=str, default=None) parser.add_argument('--need_optim_ckpt', type=bool, default=False) diff --git a/applications/ChatGPT/examples/train_rm.sh b/applications/ChatGPT/examples/train_rm.sh index 981b7a15fcd4..4f9f55b6b59a 100755 --- a/applications/ChatGPT/examples/train_rm.sh +++ b/applications/ChatGPT/examples/train_rm.sh @@ -1,7 +1,7 @@ set_n_least_used_CUDA_VISIBLE_DEVICES 1 -python train_reward_model.py --pretrain '/home/lczht/data2/bloom-560m' \ - --model 'bloom' \ +python train_reward_model.py --pretrain 'microsoft/deberta-v3-large' \ + --model 'deberta' \ --strategy naive \ --loss_fn 'log_exp'\ --save_path 'rmstatic.pt' \ From 189347963aa761839946f501334b2b7c6be53318 Mon Sep 17 00:00:00 2001 From: Yan Fang <30396678+Suffoquer-fang@users.noreply.github.com> Date: Thu, 23 Mar 2023 10:22:08 +0800 Subject: [PATCH 022/413] [auto] fix requirements typo for issue #3125 (#3209) --- .../language/gpt/experiments/auto_parallel/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/language/gpt/experiments/auto_parallel/requirements.txt b/examples/language/gpt/experiments/auto_parallel/requirements.txt index ff046ad1cae9..1b2561f098d5 100644 --- a/examples/language/gpt/experiments/auto_parallel/requirements.txt +++ b/examples/language/gpt/experiments/auto_parallel/requirements.txt @@ -1,4 +1,4 @@ colossalai >= 0.1.12 torch >= 1.8.1 -transformers >= 4.231 +transformers >= 4.23.1 PuLP >= 2.7.0 From f8289d42218878fb864c6ca3f9c05d45bdb8a560 Mon Sep 17 00:00:00 2001 From: ver217 Date: Thu, 23 Mar 2023 10:53:06 +0800 Subject: [PATCH 023/413] [lazyinit] combine lazy tensor with dtensor (#3204) * [lazyinit] lazy tensor add distribute * [lazyinit] refactor distribute * [lazyinit] add test dist lazy init * [lazyinit] add verbose info for dist lazy init * [lazyinit] fix rnn flatten weight op * [lazyinit] polish test * [lazyinit] polish test * [lazyinit] fix lazy tensor data setter * [lazyinit] polish test * [lazyinit] fix clean * [lazyinit] make materialize inplace * [lazyinit] refactor materialize * [lazyinit] refactor test distribute * [lazyinit] fix requires_grad * [lazyinit] fix tolist after materialization * [lazyinit] refactor distribute module * [lazyinit] polish docstr * [lazyinit] polish lazy init context * [lazyinit] temporarily skip test * [lazyinit] polish test * [lazyinit] add docstr --- colossalai/utils/model/experimental.py | 231 ++++++++++++------ .../test_lazy_init/test_distribute.py | 110 +++++++++ tests/test_utils/test_lazy_init/utils.py | 16 ++ 3 files changed, 281 insertions(+), 76 deletions(-) create mode 100644 tests/test_utils/test_lazy_init/test_distribute.py diff --git a/colossalai/utils/model/experimental.py b/colossalai/utils/model/experimental.py index 00cb532d9c1d..6427a147a5c0 100644 --- a/colossalai/utils/model/experimental.py +++ b/colossalai/utils/model/experimental.py @@ -1,11 +1,15 @@ -from typing import Callable, List, Optional, Union +from types import MethodType +from typing import Callable, Optional, Union import torch +import torch.distributed as dist import torch.nn as nn from torch import Tensor from torch.utils._pytree import tree_map from colossalai.fx.profiler.tensor import MetaTensor +from colossalai.tensor.d_tensor.d_tensor import DTensor +from colossalai.tensor.d_tensor.layout import Layout # reference: https://pytorch.org/cppdocs/notes/tensor_creation.html _NORMAL_FACTORY = [ @@ -30,6 +34,11 @@ _EARLY_MATERIALIZED_OPS = ['__getitem__', 'split'] +# If your intent is to change the metadata of a Tensor (such as sizes / strides / storage / storage_offset) +# without autograd tracking the change, remove the .data / .detach() call and wrap the change in a `with torch.no_grad():` block. +# These ops cannot be unwrapped using .data +_CHANGE_META_OPS = ['_cudnn_rnn_flatten_weight', 'requires_grad_', '__get__'] + _LEGACY_TENSOR_CONSTRUCTOR = { 'FloatTensor': torch.float, 'DoubleTensor': torch.double, @@ -43,6 +52,8 @@ 'BoolTensor': torch.bool, } +_EMPTY_DATA = torch.empty(0) + class _MyTensor(Tensor): """This class is only for correctness verification. @@ -64,6 +75,29 @@ def __torch_function__(cls, func, types, args=(), kwargs=None): return super().__torch_function__(func, types, args, kwargs) +def _convert_cls(tensor: 'LazyTensor', target: torch.Tensor) -> torch.Tensor: + """Convert a lazy tensor's class to target's class, with target's data. + + The reason why we change the class of a lazy tensor in-place is that this can easily handle shared modules/parameters, which is common in huggingface models. + If we create a new tensor and update the module by ``setattr(module, name, param)``, the shared parameters will not be updated. And we have to track all shared parameters and update them manually. + + Args: + tensor (LazyTensor): the LazyTensor to be converted + target (torch.Tensor): target tensor + + Returns: + torch.Tensor: the converted tensor + """ + cls_to_become = nn.Parameter if isinstance(tensor, nn.Parameter) else torch.Tensor + tensor.__class__ = cls_to_become + tensor.data = target + tensor.requires_grad = target.requires_grad + # subclass of torch.Tensor does not have tolist() method + # overwrite this method after materialization or distribution + tensor.tolist = MethodType(torch.Tensor.tolist, target) + return tensor + + class LazyTensor(torch.Tensor): """A naive implementation of LazyTensor (https://arxiv.org/pdf/2102.13267.pdf). @@ -112,14 +146,8 @@ def __new__(cls, func, *args, meta_data=None, concrete_data=None, **kwargs): elem = func(*args, **{**kwargs, 'device': 'meta'}) meta_data = MetaTensor(elem, fake_device=device) elem = meta_data._tensor - r = torch.Tensor._make_wrapper_subclass(cls, - elem.size(), - strides=elem.stride(), - storage_offset=elem.storage_offset(), - dtype=elem.dtype, - layout=elem.layout, - device=elem.device, - requires_grad=elem.requires_grad) + # As a meta tensor cannot be modified __class__ to torch.Tensor, we should use an empty real tensor here + r = torch.Tensor._make_subclass(cls, _EMPTY_DATA, require_grad=elem.requires_grad) r._meta_data = meta_data return r @@ -129,15 +157,28 @@ def __init__(self, func, *args, meta_data=None, concrete_data=None, **kwargs): self._materialized_data: Optional[torch.Tensor] = concrete_data # materialized data def materialize(self) -> torch.Tensor: - """Materialize the ``LazyTensor`` to ``torch.Tensor``. + """Materialize the ``LazyTensor`` to ``torch.Tensor`` by modifying __class__ (inplace). Returns: - torch.Tensor: The materialized tensor. + torch.Tensor: The materialized tensor (self). """ target = self._materialize_data() - if isinstance(self, nn.Parameter): - target = nn.Parameter(target, requires_grad=self.requires_grad) - return target + self.clean() + return _convert_cls(self, target) + + def distribute(self, layout: Layout) -> torch.Tensor: + """Distribute the ``LazyTensor`` to ``torch.Tensor`` by modifying __class__ (inplace), according to the layout. + + Args: + layout (Layout): Distribution layout. + + Returns: + torch.Tensor: The distributed tensor (self). + """ + target = self._materialize_data() + self.clean() + local_tensor = DTensor(target, layout).local_tensor + return _convert_cls(self, local_tensor) def clean(self) -> None: """Clean all stored operations, meta data and materialized data, which prevents memory leaking. This should be called after all tensors are materialized. @@ -216,6 +257,8 @@ def __torch_function__(cls, func, types, args=(), kwargs=None): is_inplace: bool = (func.__name__.endswith('_') and not (func.__name__.endswith('__')) or func.__name__ == "__setitem__") + is_change_meta_op: bool = func.__name__ in _CHANGE_META_OPS + if isinstance(func, torch._C.ScriptMethod): # FIXME(ver217): torch script functions are not verified @@ -239,10 +282,10 @@ def unwrap(x): if isinstance(x, LazyTensor): if x._materialized_data is not None: # for early materialized tensor, use its materialized data directly - return x._materialized_data.data + return x._materialized_data if is_change_meta_op else x._materialized_data.data t = x if is_inplace else x.clone() t._op_buffer.append((func, args, kwargs)) - meta = x._meta_data.data + meta = x._meta_data if is_change_meta_op else x._meta_data.data meta_to_lazy[meta] = t return meta return x @@ -290,13 +333,36 @@ def data(self): @data.setter def data(self, other: 'LazyTensor'): + """This is sightly different from oringinal `data` setter. + + E.g.: + >>> a = torch.randn(3, 3) # a is a Tensor + >>> b = torch.rand(2, 2) + >>> a.data = b + >>> b.add_(1) # this will affect a + >>> x = torch.randn(3, 3) # x is a LazyTensor + >>> y = torch.rand(2, 2) # y is a LazyTensor + >>> x.data = y + >>> y.add_(1) # this will not affect x + + """ if other is self: return - # TODO(ver217): to avoid infinity recursion, do early materialization - self._materialized_data = other._materialize_data() + + self._op_buffer.append(other._factory_method) + + def replace(x): + if x is other: + return self + return x + + for func, args, kwargs in other._op_buffer: + self._op_buffer.append((func, tree_map(replace, args), tree_map(replace, kwargs))) def tolist(self) -> list: - t = self.materialize() + # Though self.__class__ is modified to torch.Tensor, in C++ side, it is still a subclass of torch.Tensor + # And subclass of torch.Tensor does not have tolist() method + t = self._materialize_data() return t.tolist() def __hash__(self): @@ -421,71 +487,84 @@ def __exit__(self, exc_type, exc_val, exc_tb): setattr(torch, name, orig) @staticmethod - def materialize(module: torch.nn.Module, verbose: bool = False): - """Initialize all ``nn.Parameter`` from ``LazyTensor``. + def materialize(module: nn.Module, verbose: bool = False) -> nn.Module: + """Initialize all ``nn.Parameter`` from ``LazyTensor``. This function will modify the module in-place. Args: - module (torch.nn.Module): Target ``nn.Module`` + module (nn.Module): Target ``nn.Module`` verbose (bool): Whether to print lazy initialization rate. Defaults to False. """ - if verbose: - param_cnt = 0 - param_lazy_cnt = 0 - buf_cnt = 0 - buf_lazy_cnt = 0 - non_lazy_numel = 0 - - # do post cleaning to handle shared parameter - visited_lazy_tensors: List[LazyTensor] = [] - # handle shared module - visited_modules = set() - - @torch.no_grad() - def init_recursively(module: nn.Module): - nonlocal param_cnt, param_lazy_cnt, buf_cnt, buf_lazy_cnt, non_lazy_numel - # recursively initialize the module - for mod in module.children(): - if id(mod) not in visited_modules: - visited_modules.add(id(mod)) - init_recursively(mod) - - # initialize tensors directly attached to the current module - for name, param in module.named_parameters(recurse=False): - if verbose: - param_cnt += 1 - if getattr(param, '_materialized_data', False) is None: - # if no _materialized_data attr, the tensor is not lazy - param_lazy_cnt += 1 - else: - non_lazy_numel += param.numel() - if hasattr(param, 'materialize'): - # TODO(ver217): apex layers cannot be captured - visited_lazy_tensors.append(param) - setattr(module, name, param.materialize()) - - for name, buf in module.named_buffers(recurse=False): - if verbose: - buf_cnt += 1 - if getattr(buf, "_materialized_data", False) is None: - # if no _materialized_data attr, the tensor is not lazy - buf_lazy_cnt += 1 - else: - non_lazy_numel += buf.numel() - if hasattr(buf, 'materialize'): - # TODO(ver217): apex layers cannot be captured - visited_lazy_tensors.append(buf) - setattr(module, name, buf.materialize()) - init_recursively(module) + def apply_fn(name: str, p: LazyTensor): + p.materialize() + + return _apply_to_lazy_module(module, apply_fn, verbose) + + @staticmethod + def distribute(module: nn.Module, layout_dict: dict, verbose: bool = False) -> nn.Module: + """Distribute all ``nn.Parameter`` from ``LazyTensor``. This function will modify the module in-place. + + Args: + module (nn.Module): Target ``nn.Module`` + layout_dict (dict): Dict of layout for each parameter/buffer. The key is the parameter/buffer name, and the value is the layout. + verbose (bool, optional): Whether to print lazy initialization rate. Defaults to False. + """ + + def apply_fn(name: str, p: LazyTensor): + p.distribute(layout_dict[name]) + + return _apply_to_lazy_module(module, apply_fn, verbose) + - for t in visited_lazy_tensors: - t.clean() +def _apply_to_lazy_module(module: nn.Module, + apply_fn: Callable[[str, torch.Tensor], None], + verbose: bool = False) -> nn.Module: + if verbose: + # verbose info + param_cnt = 0 + param_lazy_cnt = 0 + buf_cnt = 0 + buf_lazy_cnt = 0 + total_numel = 0 + non_lazy_numel = 0 + + for name, p in module.named_parameters(): + if verbose: + param_cnt += 1 + total_numel += p.numel() + if getattr(p, '_materialized_data', False) is None: + # if no _materialized_data attr, the tensor is not lazy + param_lazy_cnt += 1 + else: + non_lazy_numel += p.numel() + if isinstance(p, LazyTensor): + apply_fn(name, p) + for name, buf in module.named_buffers(): if verbose: - print(f'Param lazy rate: {param_lazy_cnt}/{param_cnt}') - print(f'Buffer lazy rate: {buf_lazy_cnt}/{buf_cnt}') - print(f'Non-lazy numel: {non_lazy_numel} ({non_lazy_numel/1024**2:.3f} M)') - return module + buf_cnt += 1 + total_numel += buf.numel() + if getattr(buf, "_materialized_data", False) is None: + # if no _materialized_data attr, the tensor is not lazy + buf_lazy_cnt += 1 + else: + non_lazy_numel += buf.numel() + if isinstance(buf, LazyTensor): + apply_fn(name, buf) + + if verbose: + non_lazy_numel_ratio = non_lazy_numel / total_numel * 100 if non_lazy_numel != 0 else 0 + _print_rank_0(f'Param lazy rate: {param_lazy_cnt}/{param_cnt}') + _print_rank_0(f'Buffer lazy rate: {buf_lazy_cnt}/{buf_cnt}') + _print_rank_0( + f'Non lazy numel: {non_lazy_numel} ({non_lazy_numel/1024**2:.3f} M), ratio: {non_lazy_numel_ratio}%') + + return module + + +def _print_rank_0(*args, **kwargs): + if not dist.is_initialized() or dist.get_rank() == 0: + print(*args, **kwargs) def _is_int_tuple(args) -> bool: diff --git a/tests/test_utils/test_lazy_init/test_distribute.py b/tests/test_utils/test_lazy_init/test_distribute.py new file mode 100644 index 000000000000..37b2c5da1efa --- /dev/null +++ b/tests/test_utils/test_lazy_init/test_distribute.py @@ -0,0 +1,110 @@ +from functools import partial +from typing import Optional + +import pytest +import torch +import torch.multiprocessing as mp +import torch.nn as nn + +import colossalai +from colossalai.device.device_mesh import DeviceMesh +from colossalai.tensor.d_tensor.layout import Layout +from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec +from colossalai.testing import parameterize, rerun_if_address_is_in_use +from colossalai.utils import free_port +from colossalai.utils.common import print_rank_0 +from colossalai.utils.model.experimental import LazyInitContext, LazyTensor, _MyTensor +from tests.kit.model_zoo import model_zoo + +# from utils import assert_dist_model_equal, set_seed + + +def find_shard_dim(shape: torch.Size) -> Optional[int]: + for dim, size in enumerate(shape): + if size % 2 == 0: + return dim + + +def make_layout(device_mesh: DeviceMesh, original_tensor: torch.Tensor) -> Layout: + shard_dim = find_shard_dim(original_tensor.shape) + dim_partition_dict = {shard_dim: [0]} if shard_dim is not None else {} + target_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict=dim_partition_dict) + layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=target_sharding_spec, + entire_shape=original_tensor.shape) + return layout + + +def _get_current_name(prefix: str, name: str) -> str: + return f'{prefix}.{name}'.lstrip('.') + + +def generate_layout_dict(model: nn.Module, device_mesh: DeviceMesh) -> dict: + layout_dict = {} + + @torch.no_grad() + def generate_recursively(module: nn.Module, prefix: str = ''): + # recursively initialize the module + for name, mod in module.named_children(): + generate_recursively(mod, prefix=_get_current_name(prefix, name)) + + # initialize tensors directly attached to the current module + for name, param in module.named_parameters(recurse=False): + if isinstance(param, LazyTensor): + layout = make_layout(device_mesh, param) + layout_dict[_get_current_name(prefix, name)] = layout + + for name, buf in module.named_buffers(recurse=False): + if isinstance(buf, LazyTensor): + layout = make_layout(device_mesh, buf) + layout_dict[_get_current_name(prefix, name)] = layout + + generate_recursively(model) + + return layout_dict + + +@parameterize('subset', ['torchvision', 'diffusers', 'timm', 'transformers', 'torchaudio', 'deepfm', 'dlrm']) +def run_dist_lazy_init(subset, seed: int = 42): + sub_model_zoo = model_zoo.get_sub_registry(subset) + device_mesh = DeviceMesh(torch.Tensor([0, 1, 2, 3]), (2, 2), init_process_group=True) + # FIXME(ver217): uncomment this line + # _MyTensor._pre_op_fn = lambda *args: set_seed(seed) + # LazyTensor._pre_op_fn = lambda *args: set_seed(seed) + + for name, entry in sub_model_zoo.items(): + # TODO(ver217): lazy init does not support weight norm, skip these models + if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base'): + continue + print_rank_0(name) + model_fn, data_gen_fn, output_transform_fn, model_attr = entry + ctx = LazyInitContext(tensor_cls=_MyTensor) + with ctx: + model = model_fn() + ctx = LazyInitContext() + with ctx: + deferred_model = model_fn() + layout_dict = generate_layout_dict(deferred_model, device_mesh) + ctx.distribute(deferred_model, layout_dict, verbose=True) + # FIXME(ver217): uncomment this line + # assert_dist_model_equal(model, deferred_model, layout_dict) + + +def run_dist(rank, world_size, port) -> None: + colossalai.launch({}, rank=rank, world_size=world_size, host='localhost', port=port) + run_dist_lazy_init() + + +# FIXME(ver217): temporarily skip this test since torch 1.11 does not fully support meta tensor +@pytest.mark.skip +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_dist_lazy_init(): + world_size = 4 + run_func = partial(run_dist, world_size=world_size, port=free_port()) + mp.spawn(run_func, nprocs=world_size) + + +if __name__ == '__main__': + test_dist_lazy_init() diff --git a/tests/test_utils/test_lazy_init/utils.py b/tests/test_utils/test_lazy_init/utils.py index 47ba534bc434..a8aeb4c8930c 100644 --- a/tests/test_utils/test_lazy_init/utils.py +++ b/tests/test_utils/test_lazy_init/utils.py @@ -4,6 +4,7 @@ import numpy as np import torch +from colossalai.tensor.d_tensor.layout_converter import to_global from colossalai.utils.model.experimental import LazyInitContext, LazyTensor, _MyTensor from tests.kit.model_zoo.registry import ModelAttribute @@ -67,3 +68,18 @@ def check_lazy_init(entry: TestingEntry, seed: int = 42, verbose: bool = False, assert_forward_equal(model, deferred_model, data_gen_fn, output_transform_fn) if verbose: print(f'{model.__class__.__name__} pass') + + +def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn.Module, layout_dict: dict) -> None: + state = model.state_dict() + distributed_state = distributed_model.state_dict() + + assert len(state) == len(distributed_state), f'len {len(state)} vs {len(distributed_state)}' + + for (n1, t1), (n2, t2) in zip(state.items(), distributed_state.items()): + assert n1 == n2 + t1 = t1.cuda() + t2 = t2.cuda() + if n2 in layout_dict: + t2 = to_global(t2, layout_dict[n2]) + assert torch.equal(t1, t2), f'{n1} {t1} vs {t2}' From cd142fbefa964d62048a9bafb180322369ab89f8 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 23 Mar 2023 10:53:17 +0800 Subject: [PATCH 024/413] [api] implemented the checkpoint io module (#3205) * [api] implemented the checkpoint io module * polish code * polish code --- colossalai/checkpoint_io/__init__.py | 4 + .../checkpoint_io/checkpoint_io_base.py | 374 ++++++++++++++++++ .../checkpoint_io/general_checkpoint_io.py | 66 ++++ .../test_general_checkpoint_io.py | 70 ++++ 4 files changed, 514 insertions(+) create mode 100644 colossalai/checkpoint_io/__init__.py create mode 100644 colossalai/checkpoint_io/checkpoint_io_base.py create mode 100644 colossalai/checkpoint_io/general_checkpoint_io.py create mode 100644 tests/test_checkpoint_io/test_general_checkpoint_io.py diff --git a/colossalai/checkpoint_io/__init__.py b/colossalai/checkpoint_io/__init__.py new file mode 100644 index 000000000000..3cec630b2f86 --- /dev/null +++ b/colossalai/checkpoint_io/__init__.py @@ -0,0 +1,4 @@ +from .checkpoint_io_base import CheckpointIO, ShardCheckpointIndexFile +from .general_checkpoint_io import GeneralCheckpointIO + +__all__ = ['CheckpointIO', 'ShardCheckpointIndexFile', 'GeneralCheckpointIO'] diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py new file mode 100644 index 000000000000..00a65424bece --- /dev/null +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -0,0 +1,374 @@ +import json +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Any + +import torch +import torch.nn as nn +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler + +__all__ = ['CheckpointIO', 'ShardCheckpointIndexFile'] + + +class CheckpointIO(ABC): + """ + CheckpointIO is the base class for all checkpoint IO classes. It defines the interface for checkpoint IO. + + + Examples: + >>> from colossalai.checkpoint_io import GeneralCheckpointIO + >>> checkpoint_io = CheckpointIO() + >>> + >>> # load model from checkpoint + >>> model = checkpoint_io.load_model(model, 'model.pt') + >>> + >>> # save model to checkpoint + >>> checkpoint_io.save_model(model, 'model.pt') + >>> + >>> # save model to sharded checkpoints + >>> checkpoint_io.save_model(model, './checkpoints/', shard=True) + >>> + >>> # load model from sharded checkpoints + >>> model = checkpoint_io.load_model(model, './checkpoints/') + >>> + >>> # load optimizer from checkpoint + >>> optimizer = checkpoint_io.load_optimizer(optimizer, 'optimizer.pt') + >>> + >>> # save optimizer to checkpoint + >>> checkpoint_io.save_optimizer(optimizer, 'optimizer.pt') + + """ + + # ====================================== + # Abstract methods for implementation + # ====================================== + + @abstractmethod + def load_model(self, model: nn.Module, checkpoint: str, strict: bool = True): + """ + Load model from checkpoint. + + Args: + model (nn.Module): model to be loaded. + checkpoint (str): checkpoint path. This value is made compatiblity with the model checkpoints in the + mainstream model zoos such as Hugging Face and TIMM. The checkpoint path can be: + 1. a file path, e.g. 'model.pt' + 2. a path to a json file which defines the index to the sharded checkpoint + 3. a path to a folder containing a unique .index.json file for sharded checkpoint + strict (bool): whether to strictly enforce that the param name in + the checkpoint match the keys returned by this module's. + """ + pass + + @abstractmethod + def save_model(self, + model: nn.Module, + checkpoint: str, + prefix: str = None, + shard: bool = False, + size_per_shard: int = 1024): + """ + Save model to checkpoint. + + Examples: + >>> from colossalai.checkpoint_io import GeneralCheckpointIO + >>> checkpoint_io = CheckpointIO() + >>> + >>> # save model to a single file + >>> save_model(model, 'model.pt') + >>> + >>> # save model to a sharded checkpoint + >>> save_model(model, './checkpoints/', shard=True) + + Args: + model (nn.Module): model to be saved. + checkpoint: checkpoint path. The checkpoint path can be : + 1. a file path, e.g. 'model.pt' + 2. a directory path to save the sharded checkpoint, e.g. './checkpoints/' when shard = True. + shard: whether to shard the checkpoint. Default: False. If set to True, the checkpoint will be sharded into + multiple files. The model shards will be specificed by a `model.index.json` file. When shard = True, please ensure + that the checkpoint path is a directory path instead of a file path. + size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard is set to True. + """ + pass + + @abstractmethod + def load_optimizer(self, optimizer: Optimizer, checkpoint: str): + """ + Load optimizer from checkpoint. + + Args: + optimizer (Optimizer): optimizer to be loaded. + checkpoint (str): checkpoint path. This value is made compatiblity with the model checkpoints in the + """ + pass + + @abstractmethod + def save_optimizer(self, optimizer: Optimizer, checkpoint: str, shard: bool = False, size_per_shard: int = 1024): + """ + Save optimizer to checkpoint. + + Args: + optimizer (Optimizer): optimizer to be saved. + checkpoint: checkpoint path. The checkpoint path can be : + 1. a file path, e.g. 'model.pt' + 2. a path to a json file which defines the index to the sharded checkpoint for the optimizer + 3. a path to a folder containing a unique .index.json file for sharded checkpoint + """ + pass + + # ============================================ + # methods for loading and saving lr scheduler + # as this is quite standard, there is no need + # to make them abstract + # ============================================ + + def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + """ + Save lr scheduler to checkpoint. + + Args: + lr_scheduler (LRScheduler): lr scheduler to be saved. + checkpoint: checkpoint path. The checkpoint path can only be a file path. + """ + torch.save(lr_scheduler.state_dict(), checkpoint) + + def load_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + """ + Load lr scheduler from checkpoint. + + Args: + lr_scheduler (LRScheduler): lr scheduler to be loaded. + checkpoint (str): the path for a single checkpoint file. + """ + state_dict = torch.load(checkpoint) + lr_scheduler.load_state_dict(state_dict) + + # ======================================== + # Helper functions for loading state dict + # ======================================== + + def get_sharded_checkpoint_index_file(self, checkpoint_path: Path): + """ + Get the index file path for a sharded checkpoint. + + Args: + checkpoint_path (Path): path to the checkpoint. + + Returns: + Path: path to the index file. + """ + if checkpoint_path.is_file(): + # check if it is .index.json + if checkpoint_path.name.endswith('.index.json'): + return checkpoint_path + else: + raise ValueError(f'Invalid checkpoint path: {checkpoint_path}. ') + elif checkpoint_path.is_dir(): + # check if there is only one a file ending with .index.json in this directory + index_files = list(checkpoint_path.glob('*.index.json')) + if len(index_files) == 1: + return index_files[0] + else: + raise ValueError(f'Found {len(index_files)} index files in {checkpoint_path}. ') + + def is_sharded_checkpoint(self, checkpoint_path: Path): + """ + Check whether the checkpoint is sharded. + + Args: + checkpoint (str): checkpoint path. + + Returns: + bool: whether the checkpoint is sharded. + """ + if checkpoint_path.is_file(): + # check if it is .index.json + if checkpoint_path.name.endswith('.index.json'): + return True + else: + return False + elif checkpoint_path.is_dir(): + # check if there is only one a file ending with .index.json in this directory + index_files = list(checkpoint_path.glob('*.index.json')) + if len(index_files) == 1: + return True + else: + raise ValueError(f'Found {len(index_files)} index files in {checkpoint_path}. ') + + def get_checkpoint_shard_filenames(self, index_file_path: Path): + """ + Get checkpoint shard filenames from a json file. + + Args: + index_file_path (Path): path to the json file. + + Returns: + list: checkpoint shard filenames. + """ + with open(str(index_file_path), 'r') as f: + shard_filenames = json.load(f) + + if "weight_map" in index: + index = index["weight_map"] + + checkpoint_root_path = index_file_path.absolute().parent + + # read the checkpoint file list from the json file and get a list of unique file names + checkpoint_files = sorted(list(set(index.values()))) + + # get the absolute paths for all checkpoint files + checkpoint_files = [checkpoint_root_path.joinpath(f) for f in checkpoint_files] + return shard_filenames + + def load_safetensors_state_dict(self, *args, **kwargs): + """ + Load safetensors state dict from checkpoint. + """ + # TODO(FrankLeeeee): support huggingface safetensors + raise NotImplementedError("This method is not implemented to support safe tensors") + + def load_state_dict(self, checkpoint_file_path: Path): + """ + Load state dict from checkpoint. + + Args: + checkpoint_file_path (Path): path to the checkpoint file. + + Returns: + dict: state dict. + """ + return torch.load(str(checkpoint_file_path)) + + # ====================================== + # Helper functions for saving state dict + # ====================================== + + def save_safetensors_state_dict(self, *args, **kwargs): + """ + Save safetensors state dict to checkpoint. + """ + # TODO(FrankLeeeee): support huggingface safetensors + raise NotImplementedError("This method is not implemented to support safe tensors") + + def generate_checkpoint_shard_file_name(self, index: int, total_number: int, prefix: str = None): + """ + Generate checkpoint shard file name. + + Args: + index (int): index of the shard. + total_number (int): total number of shards. + prefix (str): prefix of the shard file name. Default: None. + """ + if prefix is None: + return f"{index}-of-{total_number}.bin" + else: + return f"{prefix}-{index}-of-{total_number}.bin" + + def save_checkpoint(self, state_dict: dict, checkpoint_file_path: Path): + """ + Save state dict to checkpoint. + + Args: + state_dict (dict): state dict. + checkpoint_file_path (Path): path to the checkpoint file. + """ + torch.save(state_dict, str(checkpoint_file_path)) + + def save_state_dict_as_shard(self, state_dict: dict, index: int, total_number: int, prefix: str, + checkpoint_path: Path): + """ + Save state dict as shard. + + Args: + state_dict (dict): state dict. + checkpoint_path (Path): path to the checkpoint file. + """ + # generate the shard name + shard_file_name = self.generate_checkpoint_shard_file_name(index, total_number, prefix) + shard_file_path = checkpoint_path.joinpath(shard_file_name) + + # save the shard + self.save_checkpoint(state_dict, shard_file_path) + + def calculate_param_size(self, param: torch.Tensor): + """ + Calculate the size of a parameter in MB. Used to compute whether a group of params exceed the shard size. + If so, a new shard should be created. + + ArgsL + param (torch.Tensor): parameter tensor. + """ + # TODO(FrankLeeeee): check if this tensor is a DTensor, compute its global size if so + return param.numel() * param.element_size() / 1024 / 1024 + + +class ShardCheckpointIndexFile: + """ + This class is a data structure to keep the content in the index.json file for sharded checkpoint. + + Example: + >>> index = ShardCheckpointIndexFile() + >>> index.load('index.json') + >>> index.append_metadata('model_type', 'bert') + >>> index.append_weight_map('bert.embeddings.word_embeddings.weight', 'bert.embeddings.word_embeddings.weight-0-of-2.bin') + >>> index.export('index.json') + """ + + def __init__(self) -> None: + self.metadata: dict = dict() + self.weight_map: dict = dict() + + def load(self, json_path: str): + """ + Load the index file from a json file. + + Args: + json_path (str): path to the json file. + """ + # load the json file + with open(json_path, 'r') as f: + index = json.load(f) + + # assign attributes if exists + if "metadata" in index: + self.metadata = index["metadata"] + if "weight_map" in index: + self.weight_map = index["weight_map"] + + def export(self, json_path: str): + """ + Export the index file to a json file. + + Args: + json_path (str): path to the json file. + """ + # create the index file + index = dict() + index["metadata"] = self.metadata + index["weight_map"] = self.weight_map + + # export the index file + with open(json_path, 'w') as f: + json.dump(index, f, indent=4) + + def append_weight_map(self, param_name: str, shard_file: str): + """ + Append a weight map entry to the index file. + + Args: + param_name (str): name of the parameter. + shard_file (str): name of the shard file. + """ + self.weight_map[param_name] = shard_file + + def append_meta_data(self, name: str, val: Any): + """ + Append a metadata entry to the index file. + + Args: + name (str): name of the metadata. + val (Any): value of the metadata. + """ + self.metadata[name] = val diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py new file mode 100644 index 000000000000..0a3636655530 --- /dev/null +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -0,0 +1,66 @@ +from pathlib import Path + +import torch.nn as nn +from torch.optim import Optimizer + +from .checkpoint_io_base import CheckpointIO + +__all__ = ['GeneralCheckpointIO'] + + +class GeneralCheckpointIO(CheckpointIO): + + def load_model(self, model: nn.Module, checkpoint: str, strict: bool = True): + checkpoint = Path(checkpoint) + is_sharded = self.is_sharded_checkpoint(checkpoint) + + if not is_sharded: + checkpoint = self.load_state_dict(checkpoint) + model.load_state_dict(checkpoint, strict=strict) + else: + # find the index file + checkpoint_path = Path(checkpoint) + index_file_path = self.get_sharded_checkpoint_index_file(checkpoint_path) + + # iterate over the shard checkpoint files + # and load each + shard_files = self.get_checkpoint_shard_filenames(index_file_path) + for shard_file in shard_files: + shard_checkpoint = self.load_state_dict(shard_file) + model.load_state_dict(shard_checkpoint, strict=strict) + + return model + + def save_model(self, + model: nn.Module, + checkpoint: str, + prefix: str = None, + shard: bool = False, + size_per_shard: int = 1024): + checkpoint = Path(checkpoint) + if shard: + # TODO(FrankLeeeee): implement checkpoint saving to sharded checkpoint + raise NotImplementedError("Not implemented yet") + else: + self.save_checkpoint(model.state_dict(), checkpoint) + + def load_optimizer(self, optimizer: Optimizer, checkpoint: str): + checkpoint = Path(checkpoint) + is_sharded = self.is_sharded_checkpoint(checkpoint) + + if not is_sharded: + checkpoint = self.load_state_dict(checkpoint) + optimizer.load_state_dict(checkpoint) + else: + # TODO(FrankLeeeee): implement checkpoint loading from sharded checkpoint + # This is not an urgent feature, so we can leave it for later + # let's implement this when we test large-scale models + pass + return optimizer + + def save_optimizer(self, optimizer: Optimizer, checkpoint: str, shard: bool = False, size_per_shard: int = 1024): + if shard: + # TODO(FrankLeeeee): implement checkpoint saving to sharded checkpoint + pass + else: + self.save_checkpoint(optimizer.state_dict(), checkpoint) diff --git a/tests/test_checkpoint_io/test_general_checkpoint_io.py b/tests/test_checkpoint_io/test_general_checkpoint_io.py new file mode 100644 index 000000000000..48376aaa88bf --- /dev/null +++ b/tests/test_checkpoint_io/test_general_checkpoint_io.py @@ -0,0 +1,70 @@ +import tempfile + +import torch +from torch.optim import Adam +from torchvision.models import resnet18 + +from colossalai.checkpoint_io import GeneralCheckpointIO + +# ======== +# Note: +# 1. due to checkpoint IO can be quite slow if tested with all models, we will only test on resnet for now +# 2. we will test on both sharded and unsharded checkpoints +# 3. TODO(FrankLeeeee): implement sharded checkpoint and test it +# ======== + + +def test_unsharded_checkpoint(): + # create a model and optimizer + model = resnet18() + optimizer = Adam(model.parameters(), lr=0.001) + + # create test data sample + x = torch.randn(1, 3, 224, 224) + + # run fwd and bwd + y = model(x) + loss = y.sum() + loss.backward() + optimizer.step() + + # create a temp file for checkpoint + model_ckpt_tempfile = tempfile.NamedTemporaryFile() + optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile() + + # save the model and optimizer + ckpt_io = GeneralCheckpointIO() + ckpt_io.save_model(model, model_ckpt_tempfile.name) + ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name) + + # create new model + new_model = resnet18() + new_optimizer = Adam(new_model.parameters(), lr=0.001) + + # load the model and optimizer + new_model = ckpt_io.load_model(new_model, model_ckpt_tempfile.name) + new_optimizer = ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) + + # do recursive check for the optimizer state dict + # if the value is a dict, compare its values + # if the value is a list, comapre all elements one-by-one + # if the value is a torch.Tensor, use torch.equal + # otherwise use assertEqual + def recursive_check(d1, d2): + for k, v in d1.items(): + if isinstance(v, dict): + recursive_check(v, d2[k]) + elif isinstance(v, list): + for i in range(len(v)): + if isinstance(v[i], torch.Tensor): + assert torch.equal(v[i], d2[k][i]) + else: + assert v[i] == d2[k][i] + elif isinstance(v, torch.Tensor): + assert torch.equal(v, d2[k]) + else: + assert v == d2[k] + + # check for model and optimizer state dict recursively + recursive_check(model.state_dict(), new_model.state_dict()) + recursive_check(optimizer.state_dict(), new_optimizer.state_dict()) From 4fd4bd9d9a88bde184d347a4b283b117e5025630 Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Thu, 23 Mar 2023 16:46:20 +0800 Subject: [PATCH 025/413] [chatgpt] support instuct training (#3216) --- .../ChatGPT/chatgpt/dataset/__init__.py | 4 +- .../ChatGPT/chatgpt/dataset/sft_dataset.py | 122 +++++++++++++++++- applications/ChatGPT/chatgpt/dataset/utils.py | 15 +++ .../ChatGPT/chatgpt/models/llama/__init__.py | 3 +- .../ChatGPT/chatgpt/models/llama/llama_lm.py | 38 ++++++ applications/ChatGPT/chatgpt/trainer/sft.py | 50 ++++--- .../ChatGPT/chatgpt/utils/__init__.py | 3 + .../ChatGPT/chatgpt/utils/tokenizer_utils.py | 74 +++++++++++ applications/ChatGPT/examples/train_sft.py | 43 ++++-- 9 files changed, 313 insertions(+), 39 deletions(-) create mode 100644 applications/ChatGPT/chatgpt/models/llama/llama_lm.py create mode 100644 applications/ChatGPT/chatgpt/utils/__init__.py create mode 100644 applications/ChatGPT/chatgpt/utils/tokenizer_utils.py diff --git a/applications/ChatGPT/chatgpt/dataset/__init__.py b/applications/ChatGPT/chatgpt/dataset/__init__.py index 78fd2c0705a9..df484f46d24c 100644 --- a/applications/ChatGPT/chatgpt/dataset/__init__.py +++ b/applications/ChatGPT/chatgpt/dataset/__init__.py @@ -1,5 +1,5 @@ from .reward_dataset import RmStaticDataset, HhRlhfDataset from .utils import is_rank_0 -from .sft_dataset import SFTDataset +from .sft_dataset import SFTDataset, AlpacaDataset, AlpacaDataCollator -__all__ = ['RmStaticDataset', 'HhRlhfDataset','is_rank_0', 'SFTDataset'] +__all__ = ['RmStaticDataset', 'HhRlhfDataset','is_rank_0', 'SFTDataset', 'AlpacaDataset', 'AlpacaDataCollator'] diff --git a/applications/ChatGPT/chatgpt/dataset/sft_dataset.py b/applications/ChatGPT/chatgpt/dataset/sft_dataset.py index 53ad205073e5..67e1b761c60f 100644 --- a/applications/ChatGPT/chatgpt/dataset/sft_dataset.py +++ b/applications/ChatGPT/chatgpt/dataset/sft_dataset.py @@ -1,12 +1,46 @@ -from typing import Callable +# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +from dataclasses import dataclass, field +from typing import Callable, Dict, Sequence import random from torch.utils.data import Dataset import torch.distributed as dist from tqdm import tqdm import torch -from .utils import is_rank_0 +from .utils import is_rank_0, jload + +import transformers +from colossalai.logging import get_dist_logger +logger = get_dist_logger() + +IGNORE_INDEX = -100 +PROMPT_DICT = { + "prompt_input": ( + "Below is an instruction that describes a task, paired with an input that provides further context. " + "Write a response that appropriately completes the request.\n\n" + "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:" + ), + "prompt_no_input": ( + "Below is an instruction that describes a task. " + "Write a response that appropriately completes the request.\n\n" + "### Instruction:\n{instruction}\n\n### Response:" + ), +} class SFTDataset(Dataset): """ @@ -38,3 +72,87 @@ def __len__(self): def __getitem__(self, idx): return self.prompts[idx] + + +def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: + """Tokenize a list of strings.""" + tokenized_list = [ + tokenizer( + text, + return_tensors="pt", + padding="longest", + max_length=tokenizer.model_max_length, + truncation=True, + ) + for text in strings + ] + input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] + input_ids_lens = labels_lens = [ + tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list + ] + return dict( + input_ids=input_ids, + labels=labels, + input_ids_lens=input_ids_lens, + labels_lens=labels_lens, + ) + +def preprocess( + sources: Sequence[str], + targets: Sequence[str], + tokenizer: transformers.PreTrainedTokenizer, +) -> Dict: + """Preprocess the data by tokenizing.""" + examples = [s + t for s, t in zip(sources, targets)] + examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)] + input_ids = examples_tokenized["input_ids"] + labels = copy.deepcopy(input_ids) + for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): + label[:source_len] = IGNORE_INDEX + return dict(input_ids=input_ids, labels=labels) + +class AlpacaDataset(Dataset): + """Dataset for supervised fine-tuning.""" + + def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer): + super(AlpacaDataset, self).__init__() + logger.info("Loading data...") + list_data_dict = jload(data_path) + + logger.info("Formatting inputs...") + prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] + sources = [ + prompt_input.format_map(example) if example.get("input", "") != "" else prompt_no_input.format_map(example) + for example in list_data_dict + ] + targets = [f"{example['output']}{tokenizer.eos_token}" for example in list_data_dict] + + logger.info("Tokenizing inputs... This may take some time...") + data_dict = preprocess(sources, targets, tokenizer) + + self.input_ids = data_dict["input_ids"] + self.labels = data_dict["labels"] + + def __len__(self): + return len(self.input_ids) + + def __getitem__(self, i) -> Dict[str, torch.Tensor]: + return dict(input_ids=self.input_ids[i], labels=self.labels[i]) + +@dataclass +class AlpacaDataCollator(object): + """Collate examples for supervised fine-tuning.""" + + tokenizer: transformers.PreTrainedTokenizer + + def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: + input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels")) + input_ids = torch.nn.utils.rnn.pad_sequence( + input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id + ) + labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) + return dict( + input_ids=input_ids, + labels=labels, + attention_mask=input_ids.ne(self.tokenizer.pad_token_id), + ) diff --git a/applications/ChatGPT/chatgpt/dataset/utils.py b/applications/ChatGPT/chatgpt/dataset/utils.py index 6c9f7f085f8c..0e88cc8c39b4 100644 --- a/applications/ChatGPT/chatgpt/dataset/utils.py +++ b/applications/ChatGPT/chatgpt/dataset/utils.py @@ -1,5 +1,20 @@ +import io +import json + import torch.distributed as dist def is_rank_0() -> bool: return not dist.is_initialized() or dist.get_rank() == 0 + +def _make_r_io_base(f, mode: str): + if not isinstance(f, io.IOBase): + f = open(f, mode=mode) + return f + +def jload(f, mode="r"): + """Load a .json file into a dictionary.""" + f = _make_r_io_base(f, mode) + jdict = json.load(f) + f.close() + return jdict \ No newline at end of file diff --git a/applications/ChatGPT/chatgpt/models/llama/__init__.py b/applications/ChatGPT/chatgpt/models/llama/__init__.py index 9b2a024afdb2..3edb51e14376 100644 --- a/applications/ChatGPT/chatgpt/models/llama/__init__.py +++ b/applications/ChatGPT/chatgpt/models/llama/__init__.py @@ -1,5 +1,6 @@ from .llama_actor import LlamaActor from .llama_critic import LlamaCritic from .llama_rm import LlamaRM +from .llama_lm import LlamaLM -__all__ = ['LlamaActor', 'LlamaCritic', 'LlamaRM'] +__all__ = ['LlamaActor', 'LlamaCritic', 'LlamaRM', 'LlamaLM'] diff --git a/applications/ChatGPT/chatgpt/models/llama/llama_lm.py b/applications/ChatGPT/chatgpt/models/llama/llama_lm.py new file mode 100644 index 000000000000..c63077b1ac04 --- /dev/null +++ b/applications/ChatGPT/chatgpt/models/llama/llama_lm.py @@ -0,0 +1,38 @@ +from typing import Optional + +from transformers import LlamaConfig, LlamaForCausalLM + +from ..base import LM + + +class LlamaLM(LM): + """ + Llama language model. + + Args: + pretrained (str): Pretrained model name or path. + config (LlamaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[LlamaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + + if pretrained is not None: + model = LlamaForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = LlamaForCausalLM(config) + else: + model = LlamaForCausalLM(LlamaConfig()) + + if checkpoint: + model.gradient_checkpointing_enable() + + super().__init__(model, lora_rank, lora_train_bias) + diff --git a/applications/ChatGPT/chatgpt/trainer/sft.py b/applications/ChatGPT/chatgpt/trainer/sft.py index e3913d46bd45..dd5cd35f5f4d 100644 --- a/applications/ChatGPT/chatgpt/trainer/sft.py +++ b/applications/ChatGPT/chatgpt/trainer/sft.py @@ -2,7 +2,6 @@ from typing import Optional import loralib as lora import torch -from chatgpt.dataset import SFTDataset from chatgpt.models.loss import GPTLMLoss from torch.optim import Adam, Optimizer from torch.utils.data import DataLoader @@ -22,8 +21,8 @@ class SFTTrainer(ABC): model (torch.nn.Module): the model to train strategy (Strategy): the strategy to use for training optim(Optimizer): the optimizer to use for training - train_dataset (SFTDataset or SFTDistributedDataset): the dataset to use for training - eval_dataset (SFTDataset or SFTDistributedDataset): the dataset to use for evaluation + train_dataloader: the dataloader to use for training + eval_dataloader: the dataloader to use for evaluation batch_size (int, defaults to 1): the batch size while training max_epochs (int, defaults to 2): the number of epochs to train optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer @@ -34,8 +33,8 @@ def __init__( model, strategy: Strategy, optim: Optimizer, - train_dataset: SFTDataset, - eval_dataset: SFTDataset, + train_dataloader: DataLoader, + eval_dataloader: DataLoader = None, sampler: Optional[DistributedSampler] = None, batch_size: int = 1, max_epochs: int = 2, @@ -43,13 +42,10 @@ def __init__( super().__init__() self.strategy = strategy self.epochs = max_epochs - self.train_dataset = train_dataset - self.eval_dataset = eval_dataset self.sampler = sampler - self.train_dataloader = DataLoader(self.train_dataset, shuffle=(sampler is None), - sampler=sampler, batch_size=batch_size) - self.eval_dataloader = DataLoader(self.eval_dataset, batch_size=batch_size) + self.train_dataloader = train_dataloader + self.eval_dataloader = eval_dataloader self.model = strategy.setup_model(model) if "DDP" in str(self.strategy): @@ -79,23 +75,25 @@ def fit(self, logger, use_lora, log_interval=10): logger.info(f'Train Epoch {epoch}/{self.epochs} Batch {batch_id} Rank {dist.get_rank()} loss {loss.item()}') # eval - self.model.eval() - with torch.no_grad(): - loss_sum = 0 - num_seen = 0 - for batch in self.eval_dataloader: - prompt_ids = batch["input_ids"] - p_mask = batch["attention_mask"] - prompt_ids = prompt_ids.squeeze(1).cuda() - p_mask = p_mask.squeeze(1).cuda() + if self.eval_dataloader is not None: + self.model.eval() + with torch.no_grad(): + loss_sum = 0 + num_seen = 0 + for batch in self.eval_dataloader: + prompt_ids = batch["input_ids"] + p_mask = batch["attention_mask"] + prompt_ids = prompt_ids.squeeze(1).cuda() + p_mask = p_mask.squeeze(1).cuda() - prompt_logits = self.model(prompt_ids, attention_mask=p_mask) - loss = self.loss_fn(prompt_logits, prompt_ids) - loss_sum += loss.item() - num_seen += prompt_ids.size(0) + prompt_logits = self.model(prompt_ids, attention_mask=p_mask) + loss = self.loss_fn(prompt_logits, prompt_ids) + loss_sum += loss.item() + num_seen += prompt_ids.size(0) - loss_mean = loss_sum / num_seen - if dist.get_rank() == 0: - logger.info(f'Eval Epoch {epoch}/{self.epochs} loss {loss_mean}') + loss_mean = loss_sum / num_seen + if dist.get_rank() == 0: + logger.info(f'Eval Epoch {epoch}/{self.epochs} loss {loss_mean}') + epoch_bar.update() diff --git a/applications/ChatGPT/chatgpt/utils/__init__.py b/applications/ChatGPT/chatgpt/utils/__init__.py new file mode 100644 index 000000000000..8f526d7efdad --- /dev/null +++ b/applications/ChatGPT/chatgpt/utils/__init__.py @@ -0,0 +1,3 @@ +from .tokenizer_utils import smart_tokenizer_and_embedding_resize, prepare_llama_tokenizer_and_embedding + +__all__ = ['smart_tokenizer_and_embedding_resize', 'prepare_llama_tokenizer_and_embedding'] \ No newline at end of file diff --git a/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py b/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py new file mode 100644 index 000000000000..8699bf64c7b5 --- /dev/null +++ b/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py @@ -0,0 +1,74 @@ +# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict + +import transformers + +DEFAULT_PAD_TOKEN = "[PAD]" +DEFAULT_EOS_TOKEN = "" +DEFAULT_BOS_TOKEN = "" +DEFAULT_UNK_TOKEN = "" + +def prepare_llama_tokenizer_and_embedding( + tokenizer: transformers.PreTrainedTokenizer, + model: transformers.PreTrainedModel, + special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN), +): + """prepare llama tokenizer and embedding. + + """ + + if tokenizer.pad_token is None: + smart_tokenizer_and_embedding_resize( + special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN), + tokenizer=tokenizer, + model=model, + ) + + tokenizer.add_special_tokens( + { + "eos_token": DEFAULT_EOS_TOKEN, + "bos_token": DEFAULT_BOS_TOKEN, + "unk_token": DEFAULT_UNK_TOKEN, + } + ) + + return tokenizer + + +def smart_tokenizer_and_embedding_resize( + tokenizer: transformers.PreTrainedTokenizer, + model: transformers.PreTrainedModel, + special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN), +): + """Resize tokenizer and embedding. + + Note: This is the unoptimized version that may make your embedding size not be divisible by 64. + """ + + if tokenizer.pad_token is None: + num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) + model.resize_token_embeddings(len(tokenizer)) + + if num_new_tokens > 0: + input_embeddings = model.get_input_embeddings().weight.data + output_embeddings = model.get_output_embeddings().weight.data + + input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) + output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) + + input_embeddings[-num_new_tokens:] = input_embeddings_avg + output_embeddings[-num_new_tokens:] = output_embeddings_avg + \ No newline at end of file diff --git a/applications/ChatGPT/examples/train_sft.py b/applications/ChatGPT/examples/train_sft.py index 4b3f85a2a491..83b34f9dd1ea 100644 --- a/applications/ChatGPT/examples/train_sft.py +++ b/applications/ChatGPT/examples/train_sft.py @@ -4,15 +4,18 @@ import torch import torch.distributed as dist from torch.utils.data.distributed import DistributedSampler -from chatgpt.dataset import SFTDataset +from chatgpt.dataset import SFTDataset, AlpacaDataset, AlpacaDataCollator from chatgpt.models.base import RewardModel from chatgpt.models.bloom import BLOOMLM from chatgpt.models.gpt import GPTLM from chatgpt.models.opt import OPTLM +from chatgpt.models.llama import LlamaLM from chatgpt.trainer import SFTTrainer from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from chatgpt.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from torch.optim import Adam +from torch.utils.data import DataLoader from transformers import AutoTokenizer, BloomTokenizerFast from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer @@ -41,6 +44,8 @@ def train(args): model = OPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() elif args.model == 'gpt2': model = GPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() + elif args.model == 'llama': + model = LlamaLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() else: raise ValueError(f'Unsupported model "{args.model}"') @@ -53,9 +58,19 @@ def train(args): tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + elif args.model == 'llama': + tokenizer = AutoTokenizer.from_pretrained( + args.pretrain, + padding_side="right", + use_fast=False, + ) else: raise ValueError(f'Unsupported model "{args.model}"') - tokenizer.pad_token = tokenizer.eos_token + + if args.model == 'llama': + tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model) + else: + tokenizer.pad_token = tokenizer.eos_token max_len = 512 @@ -67,11 +82,19 @@ def train(args): logger = get_dist_logger() - train_data = load_dataset(args.dataset, 'super_natural_instructions', split='train') - eval_data = load_dataset(args.dataset, 'super_natural_instructions', split='test') + # configure dataset + if args.dataset == 'yizhongw/self_instruct': + train_data = load_dataset(args.dataset, 'super_natural_instructions', split='train') + eval_data = load_dataset(args.dataset, 'super_natural_instructions', split='test') - train_dataset = SFTDataset(train_data, tokenizer, max_len) - eval_dataset = SFTDataset(eval_data, tokenizer, max_len) + train_dataset = SFTDataset(train_data, tokenizer, max_len) + eval_dataset = SFTDataset(eval_data, tokenizer, max_len) + + elif 'alpaca' in args.dataset: + train_dataset = AlpacaDataset(tokenizer=tokenizer, data_path=args.dataset) + eval_dataset = None + eval_dataset + data_collator = AlpacaDataCollator(tokenizer=tokenizer) if dist.is_initialized() and dist.get_world_size() > 1: sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True) @@ -79,11 +102,15 @@ def train(args): else: sampler = None + train_dataloader = DataLoader(train_dataset, shuffle=(sampler is None), sampler=sampler, batch_size=args.batch_size) + if eval_dataset is not None: + eval_dataloader = DataLoader(eval_dataset, batch_size=args.batch_size) + trainer = SFTTrainer(model=model, strategy=strategy, optim=optim, - train_dataset=train_dataset, - eval_dataset=eval_dataset, + train_dataloader=train_dataloader, + eval_dataloader=eval_dataloader, sampler=sampler, batch_size=args.batch_size, max_epochs=args.max_epochs) From fa97a9cab4e0aa3b3fe188a2193eee6b09bc38e0 Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Thu, 23 Mar 2023 17:38:30 +0800 Subject: [PATCH 026/413] [chatgpt] unnify datasets (#3218) --- applications/ChatGPT/chatgpt/dataset/sft_dataset.py | 11 ++++++++--- applications/ChatGPT/chatgpt/trainer/sft.py | 6 ++++-- 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/applications/ChatGPT/chatgpt/dataset/sft_dataset.py b/applications/ChatGPT/chatgpt/dataset/sft_dataset.py index 67e1b761c60f..11ec61908aef 100644 --- a/applications/ChatGPT/chatgpt/dataset/sft_dataset.py +++ b/applications/ChatGPT/chatgpt/dataset/sft_dataset.py @@ -54,7 +54,8 @@ class SFTDataset(Dataset): def __init__(self, dataset, tokenizer: Callable, max_length: int=512) -> None: super().__init__() - self.prompts = [] + # self.prompts = [] + self.input_ids = [] for data in tqdm(dataset, disable=not is_rank_0()): prompt = data['prompt'] + data['completion'] + "<|endoftext|>" @@ -64,14 +65,18 @@ def __init__(self, dataset, tokenizer: Callable, max_length: int=512) -> None: truncation=True, return_tensors="pt") - self.prompts.append(prompt_token) + # self.prompts.append(prompt_token)s + self.input_ids.append(prompt_token) + self.labels = copy.deepcopy(self.input_ids) def __len__(self): length = len(self.prompts) return length def __getitem__(self, idx): - return self.prompts[idx] + # dict(input_ids=self.input_ids[i], labels=self.labels[i]) + return dict(input_ids=self.input_ids[i], labels=self.labels[i]) + # return dict(self.prompts[idx], self.prompts[idx]) def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: diff --git a/applications/ChatGPT/chatgpt/trainer/sft.py b/applications/ChatGPT/chatgpt/trainer/sft.py index dd5cd35f5f4d..3b35f516816f 100644 --- a/applications/ChatGPT/chatgpt/trainer/sft.py +++ b/applications/ChatGPT/chatgpt/trainer/sft.py @@ -63,11 +63,13 @@ def fit(self, logger, use_lora, log_interval=10): for batch_id, batch in enumerate(self.train_dataloader): prompt_ids = batch["input_ids"] p_mask = batch["attention_mask"] + labels = batch["labels"] prompt_ids = prompt_ids.squeeze(1).cuda() p_mask = p_mask.squeeze(1).cuda() - prompt_logits = self.model(prompt_ids, attention_mask=p_mask) + # prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels) + loss, prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels) - loss = self.loss_fn(prompt_logits, prompt_ids) + # loss = self.loss_fn(prompt_logits, labels) self.strategy.backward(loss, self.model, self.optimizer) self.strategy.optimizer_step(self.optimizer) self.optimizer.zero_grad() From bbac6760e59beed8be6d74f62f9589c8f7240cda Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Thu, 23 Mar 2023 20:56:35 +0800 Subject: [PATCH 027/413] fix torch version (#3225) --- .../chatgpt/trainer/strategies/colossalai.py | 20 ++++++++++++++----- applications/ChatGPT/requirements.txt | 2 +- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py index b20b02d3d34d..64ebf12f1922 100644 --- a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py +++ b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py @@ -9,6 +9,10 @@ from chatgpt.models.lora import LoraLinear from torch.optim import Optimizer + +from transformers.modeling_utils import PreTrainedModel +from transformers.tokenization_utils_base import PreTrainedTokenizerBase + import colossalai from colossalai.nn.optimizer import CPUAdam, HybridAdam from colossalai.nn.parallel import ZeroDDP, zero_model_wrapper, zero_optim_wrapper @@ -143,7 +147,7 @@ def _unwrap_actor(actor: Actor) -> nn.Module: return model.module return model - def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None: + def save_model(self, model: nn.Module, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: unwrapped_model = self._unwrap_model(model) # TODO : better way to get torch model from gemini model # to get torch model from gemini model @@ -159,10 +163,16 @@ def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> N module.merge_weights=True module.eval() # get state_dict and save - state_dict = unwrapped_model.state_dict() - if only_rank0 and dist.get_rank() != 0: - return - torch.save(state_dict, path) + + if not isinstance(self.model, PreTrainedModel): + state_dict = unwrapped_model.state_dict() + if only_rank0 and dist.get_rank() != 0: + return + torch.save(state_dict, path) + else: + self.model.save_pretrained(path) + if tokenizer is not None: + tokenizer.save_pretrained(path) def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: if only_rank0: diff --git a/applications/ChatGPT/requirements.txt b/applications/ChatGPT/requirements.txt index 15a960c2c650..3469111925ff 100644 --- a/applications/ChatGPT/requirements.txt +++ b/applications/ChatGPT/requirements.txt @@ -3,5 +3,5 @@ tqdm datasets loralib colossalai>=0.2.4 -torch +torch==1.12.1 langchain From 9bc702ab4823c78b33c31f50e68363c1ad157ae8 Mon Sep 17 00:00:00 2001 From: Camille Zhong <44392324+Camille7777@users.noreply.github.com> Date: Fri, 24 Mar 2023 11:21:39 +0800 Subject: [PATCH 028/413] [doc] update chatgpt doc paper link (#3229) #issue 3189 --- applications/ChatGPT/examples/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/applications/ChatGPT/examples/README.md b/applications/ChatGPT/examples/README.md index ce73a5407944..60e6d68bdc0f 100644 --- a/applications/ChatGPT/examples/README.md +++ b/applications/ChatGPT/examples/README.md @@ -22,10 +22,10 @@ torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain "faceb - We add special token to the end of the sequence to get better result. - We use cosine-reducing lr-scheduler for RM training. - We set value_head as 1 liner layer and initialize the weight of value_head using N(0，1/(d_model + 1)) distribution. -- We train a Bloom-560m reward model for 1 epoch and find the test acc of the model achieve the performance mentions in [Anthropics paper](https://arxiv.org/abs/2112.00861). +- We train a Bloom-560m reward model for 1 epoch and find the test acc of the model achieve the performance mentions in [Anthropics paper](https://arxiv.org/abs/2204.05862). ### Experiment result -Model performance in [Anthropics paper](https://arxiv.org/abs/2112.00861): +Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862):
image From bd39877da41622240fa7b93c079433260173d5e0 Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Fri, 24 Mar 2023 11:45:01 +0800 Subject: [PATCH 029/413] support instrcut training (#3230) --- .../ChatGPT/chatgpt/dataset/sft_dataset.py | 7 ++++- .../ChatGPT/chatgpt/models/base/actor.py | 3 ++ .../ChatGPT/chatgpt/models/llama/llama_lm.py | 2 ++ applications/ChatGPT/chatgpt/trainer/sft.py | 29 +++++++++++-------- .../chatgpt/trainer/strategies/colossalai.py | 1 - .../ChatGPT/chatgpt/utils/tokenizer_utils.py | 6 ++++ applications/ChatGPT/examples/train_sft.py | 18 +++++++----- applications/ChatGPT/examples/train_sft.sh | 8 ++++- applications/ChatGPT/version.txt | 2 +- 9 files changed, 52 insertions(+), 24 deletions(-) diff --git a/applications/ChatGPT/chatgpt/dataset/sft_dataset.py b/applications/ChatGPT/chatgpt/dataset/sft_dataset.py index 11ec61908aef..5a5d37f695f3 100644 --- a/applications/ChatGPT/chatgpt/dataset/sft_dataset.py +++ b/applications/ChatGPT/chatgpt/dataset/sft_dataset.py @@ -119,10 +119,15 @@ def preprocess( class AlpacaDataset(Dataset): """Dataset for supervised fine-tuning.""" - def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer): + def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, max_length: int=None): super(AlpacaDataset, self).__init__() logger.info("Loading data...") list_data_dict = jload(data_path) + logger.info(f"Loaded {len(list_data_dict)} examples.") + + if max_length is not None: + logger.info(f"Truncating data to max length {max_length}...") + list_data_dict = [example for example in list_data_dict if len(example["input"]) <= max_length] logger.info("Formatting inputs...") prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] diff --git a/applications/ChatGPT/chatgpt/models/base/actor.py b/applications/ChatGPT/chatgpt/models/base/actor.py index 57db2bb11a6a..a364f879a850 100644 --- a/applications/ChatGPT/chatgpt/models/base/actor.py +++ b/applications/ChatGPT/chatgpt/models/base/actor.py @@ -60,3 +60,6 @@ def forward(self, logits = output['logits'] log_probs = log_probs_from_logits(logits[:, :-1, :], sequences[:, 1:]) return log_probs[:, -num_actions:] + + def get_base_model(self): + return self.model \ No newline at end of file diff --git a/applications/ChatGPT/chatgpt/models/llama/llama_lm.py b/applications/ChatGPT/chatgpt/models/llama/llama_lm.py index c63077b1ac04..5a1a88e0d253 100644 --- a/applications/ChatGPT/chatgpt/models/llama/llama_lm.py +++ b/applications/ChatGPT/chatgpt/models/llama/llama_lm.py @@ -36,3 +36,5 @@ def __init__(self, super().__init__(model, lora_rank, lora_train_bias) + def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): + return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) diff --git a/applications/ChatGPT/chatgpt/trainer/sft.py b/applications/ChatGPT/chatgpt/trainer/sft.py index 3b35f516816f..d524ded3e825 100644 --- a/applications/ChatGPT/chatgpt/trainer/sft.py +++ b/applications/ChatGPT/chatgpt/trainer/sft.py @@ -61,13 +61,15 @@ def fit(self, logger, use_lora, log_interval=10): # train self.model.train() for batch_id, batch in enumerate(self.train_dataloader): - prompt_ids = batch["input_ids"] - p_mask = batch["attention_mask"] - labels = batch["labels"] - prompt_ids = prompt_ids.squeeze(1).cuda() - p_mask = p_mask.squeeze(1).cuda() + prompt_ids = batch["input_ids"].to(torch.cuda.current_device()) + p_mask = batch["attention_mask"].to(torch.cuda.current_device()) + labels = batch["labels"].to(torch.cuda.current_device()) + # prompt_ids = prompt_ids.squeeze(1).cuda() + # p_mask = p_mask.squeeze(1).cuda() # prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels) - loss, prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels) + outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels) + loss = outputs.loss + prompt_logits = outputs.logits # loss = self.loss_fn(prompt_logits, labels) self.strategy.backward(loss, self.model, self.optimizer) @@ -83,13 +85,16 @@ def fit(self, logger, use_lora, log_interval=10): loss_sum = 0 num_seen = 0 for batch in self.eval_dataloader: - prompt_ids = batch["input_ids"] - p_mask = batch["attention_mask"] - prompt_ids = prompt_ids.squeeze(1).cuda() - p_mask = p_mask.squeeze(1).cuda() + prompt_ids = batch["input_ids"].to(torch.cuda.current_device()) + p_mask = batch["attention_mask"].to(torch.cuda.current_device()) + labels = batch["labels"].to(torch.cuda.current_device()) + # prompt_ids = prompt_ids.squeeze(1).cuda() + # p_mask = p_mask.squeeze(1).cuda() + + outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels) + loss = outputs.loss + # prompt_logits = outputs.logits - prompt_logits = self.model(prompt_ids, attention_mask=p_mask) - loss = self.loss_fn(prompt_logits, prompt_ids) loss_sum += loss.item() num_seen += prompt_ids.size(0) diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py index 64ebf12f1922..f11dc6f7544b 100644 --- a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py +++ b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py @@ -9,7 +9,6 @@ from chatgpt.models.lora import LoraLinear from torch.optim import Optimizer - from transformers.modeling_utils import PreTrainedModel from transformers.tokenization_utils_base import PreTrainedTokenizerBase diff --git a/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py b/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py index 8699bf64c7b5..9cfae61ebeda 100644 --- a/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py +++ b/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py @@ -16,6 +16,8 @@ import transformers +from ..models.llama.llama_lm import LlamaLM + DEFAULT_PAD_TOKEN = "[PAD]" DEFAULT_EOS_TOKEN = "" DEFAULT_BOS_TOKEN = "" @@ -60,6 +62,10 @@ def smart_tokenizer_and_embedding_resize( if tokenizer.pad_token is None: num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) + + if isinstance(model, LlamaLM): + model = model.get_base_model() + model.resize_token_embeddings(len(tokenizer)) if num_new_tokens > 0: diff --git a/applications/ChatGPT/examples/train_sft.py b/applications/ChatGPT/examples/train_sft.py index 83b34f9dd1ea..ffbf89ccd9bc 100644 --- a/applications/ChatGPT/examples/train_sft.py +++ b/applications/ChatGPT/examples/train_sft.py @@ -93,25 +93,27 @@ def train(args): elif 'alpaca' in args.dataset: train_dataset = AlpacaDataset(tokenizer=tokenizer, data_path=args.dataset) eval_dataset = None - eval_dataset data_collator = AlpacaDataCollator(tokenizer=tokenizer) if dist.is_initialized() and dist.get_world_size() > 1: - sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True) - logger.info("Using Distributed Sampler") + train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True) + if eval_dataset is not None: + eval_sampler = DistributedSampler(eval_dataset, shuffle=False, seed=42, drop_last=False) else: - sampler = None + train_sampler = None + eval_sampler = None - train_dataloader = DataLoader(train_dataset, shuffle=(sampler is None), sampler=sampler, batch_size=args.batch_size) + train_dataloader = DataLoader(train_dataset, shuffle=(train_sampler is None), sampler=train_sampler, batch_size=args.batch_size, collate_fn=data_collator) if eval_dataset is not None: - eval_dataloader = DataLoader(eval_dataset, batch_size=args.batch_size) + eval_dataloader = DataLoader(eval_dataset, shuffle=(eval_sampler is None), sampler=eval_sampler, batch_size=args.batch_size, collate_fn=data_collator) + else: + eval_dataloader = None trainer = SFTTrainer(model=model, strategy=strategy, optim=optim, train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, - sampler=sampler, batch_size=args.batch_size, max_epochs=args.max_epochs) @@ -128,7 +130,7 @@ def train(args): parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive') - parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt'], default='bloom') + parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--dataset', type=str, default='yizhongw/self_instruct') parser.add_argument('--save_path', type=str, default='sft_ckpt.pth') diff --git a/applications/ChatGPT/examples/train_sft.sh b/applications/ChatGPT/examples/train_sft.sh index 9f747b24689e..1b85e83b6880 100755 --- a/applications/ChatGPT/examples/train_sft.sh +++ b/applications/ChatGPT/examples/train_sft.sh @@ -17,4 +17,10 @@ set_n_least_used_CUDA_VISIBLE_DEVICES 8 #torchrun --standalone --nproc_per_node=2 train_sft.py --pretrain 'bigscience/bloomz-560m' --model 'bloom' --strategy colossalai_zero2 --log_interval 10 #torchrun --standalone --nproc_per_node=8 train_sft.py --model 'gpt2' --strategy colossalai_zero2 --batch_size 1 --log_interval 10 -torchrun --standalone --nproc_per_node=2 train_sft.py --pretrain "facebook/opt-350m" --model 'opt' --strategy colossalai_zero2 --log_interval 10 +torchrun --standalone --nproc_per_node=8 train_sft.py \ + --pretrain "/data/personal/nus-mql/LLAMA-7B" \ + --model 'llama' \ + --strategy colossalai_zero2 \ + --log_interval 10 \ + --save_path /data/personal/nus-mql/Coati-7B \ + --dataset /data/personal/nus-mql/stanford_alpaca/alpaca_data.json diff --git a/applications/ChatGPT/version.txt b/applications/ChatGPT/version.txt index 6e8bf73aa550..3eefcb9dd5b3 100644 --- a/applications/ChatGPT/version.txt +++ b/applications/ChatGPT/version.txt @@ -1 +1 @@ -0.1.0 +1.0.0 From 78fd31f9c15b698a4ed07748096684fa40bbc11a Mon Sep 17 00:00:00 2001 From: ver217 Date: Fri, 24 Mar 2023 12:15:06 +0800 Subject: [PATCH 030/413] [chatgpt] add precision option for colossalai (#3233) --- .../chatgpt/trainer/strategies/colossalai.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py index f11dc6f7544b..0a7c9173283c 100644 --- a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py +++ b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py @@ -30,6 +30,7 @@ class ColossalAIStrategy(DDPStrategy): Args: stage(int): The stage to use in ZeRO. Choose in (1, 2, 3) + precision(str): The precision to use. Choose in ('fp32', 'fp16'). Stage 3 only supports fp16. seed(int): The seed for the random number generator. shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3. This is not compativle with `from_pretrained()`. We temporarily disable this and will support it in the future. @@ -59,6 +60,7 @@ class ColossalAIStrategy(DDPStrategy): def __init__( self, stage: int = 3, + precision: str = 'fp16', seed: int = 42, shard_init: bool = False, # only for stage 3 placement_policy: str = 'cuda', @@ -81,12 +83,17 @@ def __init__( norm_type: float = 2.0) -> None: super().__init__(seed) assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy "{placement_policy}"' + assert precision in ('fp32', 'fp16'), f'Unsupported precision "{precision}"' self.stage = stage # TODO(ver217): support shard_init when using from_pretrained() if shard_init: warnings.warn( f'Shard init is not supported model.from_pretrained() yet. Please load weights after strategy.prepare()' ) + if stage == 3 and precision == 'fp32': + warnings.warn(f'Stage 3 only supports fp16. Precision is set to fp16.') + precision = 'fp16' + self.precision = precision self.shard_init = shard_init self.gemini_config = dict(device=get_current_device(), placement_policy=placement_policy, @@ -127,7 +134,10 @@ def model_init_context(self): return super().model_init_context() def setup_model(self, model: nn.Module) -> nn.Module: - return zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config) + model = zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config) + if self.stage != 3 and self.precision == 'fp16': + model = model.half() + return model def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer: assert isinstance(optimizer, (CPUAdam, HybridAdam)), f'Unsupported optimizer {type(optimizer)}' @@ -159,7 +169,7 @@ def save_model(self, model: nn.Module, path: str, only_rank0: bool = False, toke # merge lora_weights into weights for module in unwrapped_model.modules(): if isinstance(module, LoraLinear): - module.merge_weights=True + module.merge_weights = True module.eval() # get state_dict and save From 045afa3ea20206f28ec43794fa4c23840ae64a5b Mon Sep 17 00:00:00 2001 From: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com> Date: Fri, 24 Mar 2023 12:15:33 +0800 Subject: [PATCH 031/413] [hotfix] skip torchaudio tracing test (#3211) * [hotfix] skip torchaudio tracing test * fix lazy init test issue --- .../test_torchaudio_model/test_torchaudio_model.py | 4 +++- tests/test_utils/test_lazy_init/test_distribute.py | 6 +++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py index 65f9f5149dda..66f4be5a6f7f 100644 --- a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py +++ b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py @@ -6,7 +6,9 @@ from tests.kit.model_zoo import model_zoo -@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +# We cannot handle the tensors constructed with constant during forward, such as ``torch.empty(0).to(device=Proxy.device)`` +# TODO: We could handle this case by hijacking torch.Tensor.to function. +@pytest.mark.skip def test_torchaudio_models(): torch.backends.cudnn.deterministic = True diff --git a/tests/test_utils/test_lazy_init/test_distribute.py b/tests/test_utils/test_lazy_init/test_distribute.py index 37b2c5da1efa..1e32814ab147 100644 --- a/tests/test_utils/test_lazy_init/test_distribute.py +++ b/tests/test_utils/test_lazy_init/test_distribute.py @@ -13,7 +13,11 @@ from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.common import print_rank_0 -from colossalai.utils.model.experimental import LazyInitContext, LazyTensor, _MyTensor + +try: + from colossalai.utils.model.experimental import LazyInitContext, LazyTensor, _MyTensor +except: + pass from tests.kit.model_zoo import model_zoo # from utils import assert_dist_model_equal, set_seed From d32ef94ad9fdd50e101ae4b6a6e2ff567f9acf4c Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Fri, 24 Mar 2023 13:33:35 +0800 Subject: [PATCH 032/413] [doc] fix typo (#3222) * [doc] fix typo * [doc] fix typo --- README.md | 4 ++-- applications/ChatGPT/examples/README.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 5ce18650fb41..3098d72b4591 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@
  • Use Docker
  • Community
    • -
  • Contributing
    • +
  • Contributing
  • Cite Us
    • @@ -375,7 +375,7 @@ Join the Colossal-AI community on [Forum](https://github.com/hpcaitech/ColossalA [Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w), and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your suggestions, feedback, and questions with our engineering team. -## Invitation to open-source contribution +## Contributing Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models! You may contact us or participate in the following ways: diff --git a/applications/ChatGPT/examples/README.md b/applications/ChatGPT/examples/README.md index 60e6d68bdc0f..203e4b4950bd 100644 --- a/applications/ChatGPT/examples/README.md +++ b/applications/ChatGPT/examples/README.md @@ -16,7 +16,7 @@ torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain "faceb ``` ### Features and tricks in RM training -- We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)and[rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets. +- We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) and [rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets. - We support 2 kinds of loss_function named 'log_sig'(used by OpenAI) and 'log_exp'(used by Anthropic). - We change the loss to valid_acc and pair_dist to monitor progress during training. - We add special token to the end of the sequence to get better result. From 052b03e83f30f46a43f87e2c9739ab04f56b6460 Mon Sep 17 00:00:00 2001 From: CsRic <59389055+CsRic@users.noreply.github.com> Date: Fri, 24 Mar 2023 13:36:16 +0800 Subject: [PATCH 033/413] limit torch version (#3213) Co-authored-by: csric --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 8e619ac24477..e32b3ecda063 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -8,4 +8,4 @@ click fabric contexttimer ninja -torch +torch>=1.11,<2.0 From 4d5d8f98a49dbbd842742bfd1010384aee70312b Mon Sep 17 00:00:00 2001 From: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com> Date: Fri, 24 Mar 2023 13:39:12 +0800 Subject: [PATCH 034/413] [API] implement device mesh manager (#3221) * [API] implement device mesh manager * polish --- colossalai/cluster/device_mesh_manager.py | 103 ++++++++++++++++-- .../test_cluster/test_device_mesh_manager.py | 40 +++++++ 2 files changed, 132 insertions(+), 11 deletions(-) create mode 100644 tests/test_cluster/test_device_mesh_manager.py diff --git a/colossalai/cluster/device_mesh_manager.py b/colossalai/cluster/device_mesh_manager.py index 744799182e22..8754baa19792 100644 --- a/colossalai/cluster/device_mesh_manager.py +++ b/colossalai/cluster/device_mesh_manager.py @@ -1,36 +1,117 @@ +from dataclasses import dataclass +from typing import Dict, List, Tuple, Union + +import torch +import torch.distributed as dist + +from colossalai.device.alpha_beta_profiler import AlphaBetaProfiler from colossalai.device.device_mesh import DeviceMesh +@dataclass +class DeviceMeshInfo: + ''' + This class is used to store the information used to initialize the device mesh. + + Args: + physical_ids (List[int]): The physical ids of the current booster. For example, if we have the last 4 GPUs on a 8-devices cluster, then the physical ids should be [4, 5, 6, 7]. + mesh_shapes (List[Union[torch.Size, List[int], Tuple[int]]]): The shape of the mesh. For example, if we have 4 GPUs and we want to use 2D mesh with mesh shape [2, 2], then the mesh shape should be [2, 2]. + ''' + physical_ids: List[int] + mesh_shape: Union[torch.Size, List[int], Tuple[int]] = None + + def __post_init__(self): + if self.mesh_shape is not None: + world_size = len(self.physical_ids) + mesh_shape_numel = torch.Size(self.mesh_shape).numel() + assert world_size == mesh_shape_numel, f'the numel of mesh_shape should be equal to world size, but got {world_size} != {mesh_shape_numel}' + + +def initialize_device_mesh(device_mesh_info: DeviceMeshInfo): + ''' + This method is used to initialize the device mesh. + + Args: + device_mesh_info (DeviceMeshInfo): The information used to initialize device mesh. + ''' + # parse the device mesh info + physical_devices = device_mesh_info.physical_ids + physical_mesh = torch.tensor(physical_devices) + logical_mesh_shape = device_mesh_info.mesh_shape + + if logical_mesh_shape is None: + ab_profiler = AlphaBetaProfiler(physical_devices) + # search for the best logical mesh shape + logical_mesh_id = ab_profiler.search_best_logical_mesh() + logical_mesh_id = torch.Tensor(logical_mesh_id).to(torch.int) + + else: + logical_mesh_id = physical_mesh.reshape(logical_mesh_shape) + + device_mesh = DeviceMesh(physical_mesh_id=physical_mesh, logical_mesh_id=logical_mesh_id, init_process_group=True) + return device_mesh + + class DeviceMeshManager: """ Device mesh manager is responsible for creating and managing device meshes. """ def __init__(self): - self.device_mesh_store = dict() + self.device_mesh_store: Dict[str, DeviceMesh] = dict() - def create_device_mesh(self, name, *args, **kwargs) -> DeviceMesh: + def create_device_mesh(self, name, device_mesh_info: DeviceMeshInfo) -> DeviceMesh: """ Create a device mesh and store it in the manager. Args: name (str): name of the device mesh - *args: args for DeviceMesh - **kwargs: kwargs for DeviceMesh - """ - # TODO(Yuliang): replace *args, **kwargs with explicit arguments + device_mesh_info (DeviceMeshInfo): the information used to initialize the device mesh + """ if name not in self.device_mesh_store: - device_mesh = DeviceMesh(*args, **kwargs) + device_mesh = initialize_device_mesh(device_mesh_info) self.device_mesh_store[name] = device_mesh return device_mesh else: raise ValueError(f'Device mesh {name} already exists.') def get(self, name: str) -> DeviceMesh: - pass + """ + Get a device mesh by name. - def destroy(self): - pass + Args: + name (str): name of the device mesh + + Returns: + DeviceMesh: the device mesh + """ + if name in self.device_mesh_store: + return self.device_mesh_store[name] + else: + raise ValueError(f'Device mesh {name} does not exist.') + + def destroy(self, name: str) -> None: + """ + Destroy a device mesh by name. + + Args: + name (str): name of the device mesh + """ + if name in self.device_mesh_store: + for pgs in self.device_mesh_store[name].process_groups_dict.values(): + for pg in pgs: + dist.destroy_process_group(pg) + del self.device_mesh_store[name] + else: + raise ValueError(f'Device mesh {name} does not exist.') def destroy_all(self): - pass + """ + Destroy all device meshes. + """ + for name in self.device_mesh_store: + for pgs in self.device_mesh_store[name].process_groups_dict.values(): + for pg in pgs: + dist.destroy_process_group(pg) + + self.device_mesh_store.clear() diff --git a/tests/test_cluster/test_device_mesh_manager.py b/tests/test_cluster/test_device_mesh_manager.py new file mode 100644 index 000000000000..b79814735325 --- /dev/null +++ b/tests/test_cluster/test_device_mesh_manager.py @@ -0,0 +1,40 @@ +from functools import partial + +import torch +import torch.multiprocessing as mp + +from colossalai.cluster.device_mesh_manager import DeviceMeshInfo, DeviceMeshManager +from colossalai.device.device_mesh import DeviceMesh +from colossalai.fx.tracer import ColoTracer +from colossalai.initialize import launch +from colossalai.logging import disable_existing_loggers +from colossalai.utils import free_port + + +def check_device_mesh_manager(rank, world_size, port): + disable_existing_loggers() + launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + device_mesh_manager = DeviceMeshManager() + device_mesh_info_auto = DeviceMeshInfo(physical_ids=[0, 1, 2, 3],) + device_mesh_auto = device_mesh_manager.create_device_mesh('0', device_mesh_info_auto) + assert device_mesh_auto.shape == (2, 2) + assert device_mesh_auto._logical_mesh_id.tolist() == [[0, 1], [2, 3]] + + device_mesh_info_with_shape = DeviceMeshInfo( + physical_ids=[0, 1, 2, 3], + mesh_shape=(2, 2), + ) + device_mesh_with_shape = device_mesh_manager.create_device_mesh('1', device_mesh_info_with_shape) + + assert device_mesh_with_shape.shape == (2, 2) + assert device_mesh_with_shape._logical_mesh_id.tolist() == [[0, 1], [2, 3]] + + +def test_device_mesh_manager(): + world_size = 4 + run_func = partial(check_device_mesh_manager, world_size=world_size, port=free_port()) + mp.spawn(run_func, nprocs=world_size) + + +if __name__ == '__main__': + test_device_mesh_manager() From 280fcdc4856e061688e3aba49c3b53427967fd7b Mon Sep 17 00:00:00 2001 From: NatalieC323 <127177614+NatalieC323@users.noreply.github.com> Date: Fri, 24 Mar 2023 18:44:43 +0800 Subject: [PATCH 035/413] polish code (#3194) Co-authored-by: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com> --- examples/images/diffusion/README.md | 37 ++--- examples/images/diffusion/ldm/data/lsun.py | 74 +++++---- examples/images/diffusion/main.py | 178 ++++++++++++++++----- 3 files changed, 190 insertions(+), 99 deletions(-) diff --git a/examples/images/diffusion/README.md b/examples/images/diffusion/README.md index a70792b9f4a4..3f9690500130 100644 --- a/examples/images/diffusion/README.md +++ b/examples/images/diffusion/README.md @@ -47,40 +47,21 @@ conda env create -f environment.yaml conda activate ldm ``` -You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running +You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running: ``` conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch pip install transformers diffusers invisible-watermark ``` -#### Step 2: install lightning - -Install Lightning version later than 2022.01.04. We suggest you install lightning from source. Notice that the default download path of pip should be within the conda environment, or you may need to specify using 'which pip' and redirect the path into conda environment. - -##### From Source -``` -git clone https://github.com/Lightning-AI/lightning.git -pip install -r requirements.txt -python setup.py install -``` - -##### From pip - -``` -pip install pytorch-lightning -``` - -#### Step 3:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website +#### Step 2:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website You can install the latest version (0.2.7) from our official website or from source. Notice that the suitable version for this training is colossalai(0.2.5), which stands for torch(1.12.1). ##### Download suggested verision for this training ``` - pip install colossalai==0.2.5 - ``` ##### Download the latest version from pip for latest torch version @@ -89,7 +70,7 @@ pip install colossalai==0.2.5 pip install colossalai ``` -##### From source +##### From source: ``` git clone https://github.com/hpcaitech/ColossalAI.git @@ -99,7 +80,7 @@ cd ColossalAI CUDA_EXT=1 pip install . ``` -#### Step 4:Accelerate with flash attention by xformers(Optional) +#### Step 3:Accelerate with flash attention by xformers(Optional) Notice that xformers will accelerate the training process in cost of extra disk space. The suitable version of xformers for this training process is 0.12.0. You can download xformers directly via pip. For more release versions, feel free to check its official website: [XFormers](./https://pypi.org/project/xformers/) @@ -113,7 +94,7 @@ To use the stable diffusion Docker image, you can either build using the provide ``` # 1. build from dockerfile -cd docker +cd ColossalAI/examples/images/diffusion/docker docker build -t hpcaitech/diffusion:0.2.0 . # 2. pull from our docker hub @@ -127,7 +108,7 @@ Once you have the image ready, you can launch the image with the following comma # On Your Host Machine # ######################## # make sure you start your image in the repository root directory -cd Colossal-AI +cd ColossalAI # run the docker container docker run --rm \ @@ -144,13 +125,15 @@ docker run --rm \ # Once you have entered the docker container, go to the stable diffusion directory for training cd examples/images/diffusion/ +# Download the model checkpoint from pretrained (See the following steps) +# Set up your configuration the "train_colossalai.sh" (See the following steps) # start training with colossalai bash train_colossalai.sh ``` It is important for you to configure your volume mapping in order to get the best training experience. -1. **Mandatory**, mount your prepared data to `/data/scratch` via `-v :/data/scratch`, where you need to replace `` with the actual data path on your machine. -2. **Recommended**, store the downloaded model weights to your host machine instead of the container directory via `-v :/root/.cache/huggingface`, where you need to repliace the `` with the actual path. In this way, you don't have to repeatedly download the pretrained weights for every `docker run`. +1. **Mandatory**, mount your prepared data to `/data/scratch` via `-v :/data/scratch`, where you need to replace `` with the actual data path on your machine. Notice that within docker we need to transform Win expresison into Linuxd, e.g. C:\User\Desktop into /c/User/Desktop. +2. **Recommended**, store the downloaded model weights to your host machine instead of the container directory via `-v :/root/.cache/huggingface`, where you need to replace the `` with the actual path. In this way, you don't have to repeatedly download the pretrained weights for every `docker run`. 3. **Optional**, if you encounter any problem stating that shared memory is insufficient inside container, please add `-v /dev/shm:/dev/shm` to your `docker run` command. diff --git a/examples/images/diffusion/ldm/data/lsun.py b/examples/images/diffusion/ldm/data/lsun.py index 6256e45715ff..f5bf26c14254 100644 --- a/examples/images/diffusion/ldm/data/lsun.py +++ b/examples/images/diffusion/ldm/data/lsun.py @@ -5,87 +5,105 @@ from torch.utils.data import Dataset from torchvision import transforms - +# This class is used to create a dataset of images from LSUN dataset for training class LSUNBase(Dataset): def __init__(self, - txt_file, - data_root, - size=None, - interpolation="bicubic", - flip_p=0.5 + txt_file, # path to the text file containing the list of image paths + data_root, # root directory of the LSUN dataset + size=None, # the size of images to resize to + interpolation="bicubic", # interpolation method to be used while resizing + flip_p=0.5 # probability of random horizontal flipping ): - self.data_paths = txt_file - self.data_root = data_root - with open(self.data_paths, "r") as f: - self.image_paths = f.read().splitlines() - self._length = len(self.image_paths) + self.data_paths = txt_file # store path to text file containing list of images + self.data_root = data_root # store path to root directory of the dataset + with open(self.data_paths, "r") as f: # open and read the text file + self.image_paths = f.read().splitlines() # read the lines of the file and store as list + self._length = len(self.image_paths) # store the number of images + + # create dictionary to hold image path information self.labels = { "relative_file_path_": [l for l in self.image_paths], "file_path_": [os.path.join(self.data_root, l) for l in self.image_paths], } - self.size = size + # set the image size to be resized + self.size = size + # set the interpolation method for resizing the image self.interpolation = {"linear": PIL.Image.LINEAR, "bilinear": PIL.Image.BILINEAR, "bicubic": PIL.Image.BICUBIC, "lanczos": PIL.Image.LANCZOS, }[interpolation] + # randomly flip the image horizontally with a given probability self.flip = transforms.RandomHorizontalFlip(p=flip_p) def __len__(self): + # return the length of dataset return self._length + def __getitem__(self, i): + # get the image path for the given index example = dict((k, self.labels[k][i]) for k in self.labels) image = Image.open(example["file_path_"]) + # convert it to RGB format if not image.mode == "RGB": image = image.convert("RGB") # default to score-sde preprocessing - img = np.array(image).astype(np.uint8) - crop = min(img.shape[0], img.shape[1]) - h, w, = img.shape[0], img.shape[1] + + img = np.array(image).astype(np.uint8) # convert image to numpy array + crop = min(img.shape[0], img.shape[1]) # crop the image to a square shape + h, w, = img.shape[0], img.shape[1] # get the height and width of image img = img[(h - crop) // 2:(h + crop) // 2, - (w - crop) // 2:(w + crop) // 2] + (w - crop) // 2:(w + crop) // 2] # crop the image to a square shape - image = Image.fromarray(img) - if self.size is not None: + image = Image.fromarray(img) # create an image from numpy array + if self.size is not None: # if image size is provided, resize the image image = image.resize((self.size, self.size), resample=self.interpolation) - image = self.flip(image) - image = np.array(image).astype(np.uint8) - example["image"] = (image / 127.5 - 1.0).astype(np.float32) - return example - + image = self.flip(image) # flip the image horizontally with the given probability + image = np.array(image).astype(np.uint8) + example["image"] = (image / 127.5 - 1.0).astype(np.float32) # normalize the image values and convert to float32 + return example # return the example dictionary containing the image and its file paths +#A dataset class for LSUN Churches training set. +# It initializes by calling the constructor of LSUNBase class and passing the appropriate arguments. +# The text file containing the paths to the images and the root directory where the images are stored are passed as arguments. Any additional keyword arguments passed to this class will be forwarded to the constructor of the parent class. class LSUNChurchesTrain(LSUNBase): def __init__(self, **kwargs): super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs) - +#A dataset class for LSUN Churches validation set. +# It is similar to LSUNChurchesTrain except that it uses a different text file and sets the flip probability to zero by default. class LSUNChurchesValidation(LSUNBase): def __init__(self, flip_p=0., **kwargs): super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches", flip_p=flip_p, **kwargs) - +# A dataset class for LSUN Bedrooms training set. +# It initializes by calling the constructor of LSUNBase class and passing the appropriate arguments. class LSUNBedroomsTrain(LSUNBase): def __init__(self, **kwargs): super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs) - +# A dataset class for LSUN Bedrooms validation set. +# It is similar to LSUNBedroomsTrain except that it uses a different text file and sets the flip probability to zero by default. class LSUNBedroomsValidation(LSUNBase): def __init__(self, flip_p=0.0, **kwargs): super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms", flip_p=flip_p, **kwargs) - +# A dataset class for LSUN Cats training set. +# It initializes by calling the constructor of LSUNBase class and passing the appropriate arguments. +# The text file containing the paths to the images and the root directory where the images are stored are passed as arguments. class LSUNCatsTrain(LSUNBase): def __init__(self, **kwargs): super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs) - +# A dataset class for LSUN Cats validation set. +# It is similar to LSUNCatsTrain except that it uses a different text file and sets the flip probability to zero by default. class LSUNCatsValidation(LSUNBase): def __init__(self, flip_p=0., **kwargs): super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats", diff --git a/examples/images/diffusion/main.py b/examples/images/diffusion/main.py index 4dd88a5eca44..91b809d5a65c 100644 --- a/examples/images/diffusion/main.py +++ b/examples/images/diffusion/main.py @@ -44,14 +44,18 @@ class DataLoaderX(DataLoader): - +# A custom data loader class that inherits from DataLoader def __iter__(self): + # Overriding the __iter__ method of DataLoader to return a BackgroundGenerator + #This is to enable data laoding in the background to improve training performance return BackgroundGenerator(super().__iter__()) def get_parser(**parser_kwargs): + #A function to create an ArgumentParser object and add arguments to it def str2bool(v): + # A helper function to parse boolean values from command line arguments if isinstance(v, bool): return v if v.lower() in ("yes", "true", "t", "y", "1"): @@ -60,8 +64,10 @@ def str2bool(v): return False else: raise argparse.ArgumentTypeError("Boolean value expected.") - + # Create an ArgumentParser object with specifies kwargs parser = argparse.ArgumentParser(**parser_kwargs) + + # Add vairous command line arguments with their default balues and descriptions parser.add_argument( "-n", "--name", @@ -161,14 +167,18 @@ def str2bool(v): return parser - +# A function that returns the non-default arguments between two objects def nondefault_trainer_args(opt): + # create an argument parsser parser = argparse.ArgumentParser() + # add pytorch lightning trainer default arguments parser = Trainer.add_argparse_args(parser) + # parse the empty arguments to obtain the default values args = parser.parse_args([]) + # return all non-default arguments return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k)) - +# A dataset wrapper class to create a pytorch dataset from an arbitrary object class WrappedDataset(Dataset): """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset""" @@ -181,7 +191,7 @@ def __len__(self): def __getitem__(self, idx): return self.data[idx] - +# A function to initialize worker processes def worker_init_fn(_): worker_info = torch.utils.data.get_worker_info() @@ -189,15 +199,18 @@ def worker_init_fn(_): worker_id = worker_info.id if isinstance(dataset, Txt2ImgIterableBaseDataset): + #divide the dataset into equal parts for each worker split_size = dataset.num_records // worker_info.num_workers + #set the sample IDs for the current worker # reset num_records to the true number to retain reliable length information dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size] + # set the seed for the current worker current_id = np.random.choice(len(np.random.get_state()[1]), 1) return np.random.seed(np.random.get_state()[1][current_id] + worker_id) else: return np.random.seed(np.random.get_state()[1][0] + worker_id) - +#Provide functionality for creating data loadedrs based on provided dataset configurations class DataModuleFromConfig(pl.LightningDataModule): def __init__(self, @@ -212,10 +225,12 @@ def __init__(self, use_worker_init_fn=False, shuffle_val_dataloader=False): super().__init__() + # Set data module attributes self.batch_size = batch_size self.dataset_configs = dict() self.num_workers = num_workers if num_workers is not None else batch_size * 2 self.use_worker_init_fn = use_worker_init_fn + # If a dataset is passed, add it to the dataset configs and create a corresponding dataloader method if train is not None: self.dataset_configs["train"] = train self.train_dataloader = self._train_dataloader @@ -231,21 +246,28 @@ def __init__(self, self.wrap = wrap def prepare_data(self): + # Instantiate datasets for data_cfg in self.dataset_configs.values(): instantiate_from_config(data_cfg) def setup(self, stage=None): + # Instantiate datasets from the dataset configs self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs) + + # If wrap is true, create a WrappedDataset for each dataset if self.wrap: for k in self.datasets: self.datasets[k] = WrappedDataset(self.datasets[k]) def _train_dataloader(self): + #Check if the train dataset is iterable is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) + #Set the worker initialization function of the dataset isiterable or use_worker_init_fn is True if is_iterable_dataset or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None + # Return a DataLoaderX object for the train dataset return DataLoaderX(self.datasets["train"], batch_size=self.batch_size, num_workers=self.num_workers, @@ -253,10 +275,12 @@ def _train_dataloader(self): worker_init_fn=init_fn) def _val_dataloader(self, shuffle=False): + #Check if the validation dataset is iterable if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn: init_fn = worker_init_fn else: init_fn = None + # Return a DataLoaderX object for the validation dataset return DataLoaderX(self.datasets["validation"], batch_size=self.batch_size, num_workers=self.num_workers, @@ -264,7 +288,9 @@ def _val_dataloader(self, shuffle=False): shuffle=shuffle) def _test_dataloader(self, shuffle=False): + # Check if the test dataset is iterable is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) + # Set the worker initialization function if the dataset is iterable or use_worker_init_fn is True if is_iterable_dataset or self.use_worker_init_fn: init_fn = worker_init_fn else: @@ -291,6 +317,7 @@ def _predict_dataloader(self, shuffle=False): class SetupCallback(Callback): + # I nitialize the callback with the necessary parameters def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config): super().__init__() @@ -302,12 +329,14 @@ def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_confi self.config = config self.lightning_config = lightning_config + # Save a checkpoint if training is interrupted with keyboard interrupt def on_keyboard_interrupt(self, trainer, pl_module): if trainer.global_rank == 0: print("Summoning checkpoint.") ckpt_path = os.path.join(self.ckptdir, "last.ckpt") trainer.save_checkpoint(ckpt_path) + # Create necessary directories and save configuration files before training starts # def on_pretrain_routine_start(self, trainer, pl_module): def on_fit_start(self, trainer, pl_module): if trainer.global_rank == 0: @@ -316,6 +345,7 @@ def on_fit_start(self, trainer, pl_module): os.makedirs(self.ckptdir, exist_ok=True) os.makedirs(self.cfgdir, exist_ok=True) + #Create trainstep checkpoint directory if necessary if "callbacks" in self.lightning_config: if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']: os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True) @@ -323,11 +353,13 @@ def on_fit_start(self, trainer, pl_module): print(OmegaConf.to_yaml(self.config)) OmegaConf.save(self.config, os.path.join(self.cfgdir, "{}-project.yaml".format(self.now))) + # Save project config and lightning config as YAML files print("Lightning config") print(OmegaConf.to_yaml(self.lightning_config)) OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}), os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now))) + # Remove log directory if resuming training and directory already exists else: # ModelCheckpoint callback created log directory --- remove it if not self.resume and os.path.exists(self.logdir): @@ -346,25 +378,28 @@ def on_fit_start(self, trainer, pl_module): # trainer.save_checkpoint(ckpt_path) +# PyTorch Lightning callback for ogging images during training and validation of a deep learning model class ImageLogger(Callback): def __init__(self, - batch_frequency, - max_images, - clamp=True, - increase_log_steps=True, - rescale=True, - disabled=False, - log_on_batch_idx=False, - log_first_step=False, - log_images_kwargs=None): + batch_frequency, # Frequency of batches on which to log images + max_images, # Maximum number of images to log + clamp=True, # Whether to clamp pixel values to [-1,1] + increase_log_steps=True, # Whether to increase frequency of log steps exponentially + rescale=True, # Whetehr to rescale pixel values to [0,1] + disabled=False, # Whether to disable logging + log_on_batch_idx=False, # Whether to log on baych index instead of global step + log_first_step=False, # Whetehr to log on the first step + log_images_kwargs=None): # Additional keyword arguments to pass to log_images method super().__init__() self.rescale = rescale self.batch_freq = batch_frequency self.max_images = max_images self.logger_log_images = { - pl.loggers.CSVLogger: self._testtube, + # Dictionary of logger classes and their corresponding logging methods + pl.loggers.CSVLogger: self._testtube, } + # Create a list of exponentially increasing log steps, starting from 1 and ending at batch_frequency self.log_steps = [2**n for n in range(int(np.log2(self.batch_freq)) + 1)] if not increase_log_steps: self.log_steps = [self.batch_freq] @@ -374,17 +409,32 @@ def __init__(self, self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {} self.log_first_step = log_first_step - @rank_zero_only - def _testtube(self, pl_module, images, batch_idx, split): + @rank_zero_only # Ensure that only the first process in distributed training executes this method + def _testtube(self, # The PyTorch Lightning module + pl_module, # A dictionary of images to log. + images, # + batch_idx, # The batch index. + split # The split (train/val) on which to log the images + ): + # Method for logging images using test-tube logger for k in images: grid = torchvision.utils.make_grid(images[k]) grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w tag = f"{split}/{k}" + # Add image grid to logger's experiment pl_module.logger.experiment.add_image(tag, grid, global_step=pl_module.global_step) @rank_zero_only - def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx): + def log_local(self, + save_dir, + split, # The split (train/val) on which to log the images + images, # A dictionary of images to log + global_step, # The global step + current_epoch, # The current epoch. + batch_idx + ): + # Method for saving image grids to local file system root = os.path.join(save_dir, "images", split) for k in images: grid = torchvision.utils.make_grid(images[k], nrow=4) @@ -396,12 +446,16 @@ def log_local(self, save_dir, split, images, global_step, current_epoch, batch_i filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(k, global_step, current_epoch, batch_idx) path = os.path.join(root, filename) os.makedirs(os.path.split(path)[0], exist_ok=True) + # Save image grid as PNG file Image.fromarray(grid).save(path) def log_img(self, pl_module, batch, batch_idx, split="train"): + #Function for logging images to both the logger and local file system. check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step + # check if it's time to log an image batch if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0 hasattr(pl_module, "log_images") and callable(pl_module.log_images) and self.max_images > 0): + # Get logger type and check if training mode is on logger = type(pl_module.logger) is_train = pl_module.training @@ -409,8 +463,10 @@ def log_img(self, pl_module, batch, batch_idx, split="train"): pl_module.eval() with torch.no_grad(): + # Get images from log_images method of the pl_module images = pl_module.log_images(batch, split=split, **self.log_images_kwargs) + # Clip images if specified and convert to CPU tensor for k in images: N = min(images[k].shape[0], self.max_images) images[k] = images[k][:N] @@ -419,15 +475,19 @@ def log_img(self, pl_module, batch, batch_idx, split="train"): if self.clamp: images[k] = torch.clamp(images[k], -1., 1.) + # Log images locally to file system self.log_local(pl_module.logger.save_dir, split, images, pl_module.global_step, pl_module.current_epoch, batch_idx) + # log the images using the logger logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None) logger_log_images(pl_module, images, pl_module.global_step, split) + # switch back to training mode if necessary if is_train: pl_module.train() + # The function checks if it's time to log an image batch def check_frequency(self, check_idx): if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (check_idx > 0 or self.log_first_step): @@ -439,14 +499,17 @@ def check_frequency(self, check_idx): return True return False + # Log images on train batch end if logging is not disabled def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx): # if not self.disabled and (pl_module.global_step > 0 or self.log_first_step): # self.log_img(pl_module, batch, batch_idx, split="train") pass + # Log images on validation batch end if logging is not disabled and in validation mode def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx): if not self.disabled and pl_module.global_step > 0: self.log_img(pl_module, batch, batch_idx, split="val") + # log gradients during calibration if necessary if hasattr(pl_module, 'calibrate_grad_norm'): if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0: self.log_gradients(trainer, pl_module, batch_idx=batch_idx) @@ -458,6 +521,7 @@ class CUDACallback(Callback): def on_train_start(self, trainer, pl_module): rank_zero_info("Training is starting") + #the method is called at the end of each training epoch def on_train_end(self, trainer, pl_module): rank_zero_info("Training is ending") @@ -524,6 +588,7 @@ def on_train_epoch_end(self, trainer, pl_module): # params: # key: value + # get the current time to create a new logging directory now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") # add cwd for convenience and to make classes in this file available when @@ -535,11 +600,13 @@ def on_train_epoch_end(self, trainer, pl_module): parser = Trainer.add_argparse_args(parser) opt, unknown = parser.parse_known_args() + # Veirfy the arguments are both specified if opt.name and opt.resume: raise ValueError("-n/--name and -r/--resume cannot be specified both." "If you want to resume training in a new log folder, " "use -n/--name in combination with --resume_from_checkpoint") + # Check if the "resume" option is specified, resume training from the checkpoint if it is true ckpt = None if opt.resume: rank_zero_info("Resuming from {}".format(opt.resume)) @@ -557,8 +624,10 @@ def on_train_epoch_end(self, trainer, pl_module): logdir = opt.resume.rstrip("/") ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") + # Finds all ".yaml" configuration files in the log directory and adds them to the list of base configurations base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml"))) opt.base = base_configs + opt.base + # Gets the name of the current log directory by splitting the path and taking the last element. _tmp = logdir.split("/") nowname = _tmp[-1] else: @@ -574,13 +643,17 @@ def on_train_epoch_end(self, trainer, pl_module): nowname = now + name + opt.postfix logdir = os.path.join(opt.logdir, nowname) + # Sets the checkpoint path of the 'ckpt' option is specified if opt.ckpt: ckpt = opt.ckpt + # Create the checkpoint and configuration directories within the log directory. ckptdir = os.path.join(logdir, "checkpoints") cfgdir = os.path.join(logdir, "configs") + # Sets the seed for the random number generator to ensure reproducibility seed_everything(opt.seed) + # Intinalize and save configuratioon using teh OmegaConf library. try: # init and save configs configs = [OmegaConf.load(cfg) for cfg in opt.base] @@ -593,6 +666,7 @@ def on_train_epoch_end(self, trainer, pl_module): for k in nondefault_trainer_args(opt): trainer_config[k] = getattr(opt, k) + # Check whether the accelerator is gpu if not trainer_config["accelerator"] == "gpu": del trainer_config["accelerator"] cpu = True @@ -609,6 +683,7 @@ def on_train_epoch_end(self, trainer, pl_module): config.model["params"].update({"use_fp16": False}) if ckpt is not None: + #If a checkpoint path is specified in the ckpt variable, the code updates the "ckpt" key in the "params" dictionary of the config.model configuration with the value of ckpt config.model["params"].update({"ckpt": ckpt}) rank_zero_info("Using ckpt_path = {}".format(config.model["params"]["ckpt"])) @@ -617,7 +692,8 @@ def on_train_epoch_end(self, trainer, pl_module): trainer_kwargs = dict() # config the logger - # default logger configs + # Default logger configs to log training metrics during the training process. + # These loggers are specified as targets in the dictionary, along with the configuration settings specific to each logger. default_logger_cfgs = { "wandb": { "target": LIGHTNING_PACK_NAME + "loggers.WandbLogger", @@ -638,6 +714,7 @@ def on_train_epoch_end(self, trainer, pl_module): } } + # Set up the logger for TensorBoard default_logger_cfg = default_logger_cfgs["tensorboard"] if "logger" in lightning_config: logger_cfg = lightning_config.logger @@ -660,6 +737,7 @@ def on_train_epoch_end(self, trainer, pl_module): trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg) + # Set up ModelCheckpoint callback to save best models # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to # specify which metric is used to determine best models default_modelckpt_cfg = { @@ -683,45 +761,50 @@ def on_train_epoch_end(self, trainer, pl_module): if version.parse(pl.__version__) < version.parse('1.4.0'): trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg) + # Set up various callbacks, including logging, learning rate monitoring, and CUDA management # add callback which sets up log directory default_callbacks_cfg = { - "setup_callback": { + "setup_callback": { # callback to set up the training "target": "main.SetupCallback", "params": { - "resume": opt.resume, - "now": now, - "logdir": logdir, - "ckptdir": ckptdir, - "cfgdir": cfgdir, - "config": config, - "lightning_config": lightning_config, + "resume": opt.resume, # resume training if applicable + "now": now, + "logdir": logdir, # directory to save the log file + "ckptdir": ckptdir, # directory to save the checkpoint file + "cfgdir": cfgdir, # directory to save the configuration file + "config": config, # configuration dictionary + "lightning_config": lightning_config, # LightningModule configuration } }, - "image_logger": { + "image_logger": { # callback to log image data "target": "main.ImageLogger", "params": { - "batch_frequency": 750, - "max_images": 4, - "clamp": True + "batch_frequency": 750, # how frequently to log images + "max_images": 4, # maximum number of images to log + "clamp": True # whether to clamp pixel values to [0,1] } }, - "learning_rate_logger": { + "learning_rate_logger": { # callback to log learning rate "target": "main.LearningRateMonitor", "params": { - "logging_interval": "step", - # "log_momentum": True + "logging_interval": "step", # logging frequency (either 'step' or 'epoch') + # "log_momentum": True # whether to log momentum (currently commented out) } }, - "cuda_callback": { + "cuda_callback": { # callback to handle CUDA-related operations "target": "main.CUDACallback" }, } + # If the LightningModule configuration has specified callbacks, use those + # Otherwise, create an empty OmegaConf configuration object if "callbacks" in lightning_config: callbacks_cfg = lightning_config.callbacks else: callbacks_cfg = OmegaConf.create() - + + # If the 'metrics_over_trainsteps_checkpoint' callback is specified in the + # LightningModule configuration, update the default callbacks configuration if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg: print( 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.') @@ -739,15 +822,17 @@ def on_train_epoch_end(self, trainer, pl_module): } } default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict) - + + # Merge the default callbacks configuration with the specified callbacks configuration, and instantiate the callbacks callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] + # Create a Trainer object with the specified command-line arguments and keyword arguments, and set the log directory trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs) trainer.logdir = logdir - # data + # Create a data module based on the configuration file data = instantiate_from_config(config.data) # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html # calling these ourselves should not be necessary but it is. @@ -755,10 +840,12 @@ def on_train_epoch_end(self, trainer, pl_module): data.prepare_data() data.setup() + # Print some information about the datasets in the data module for k in data.datasets: rank_zero_info(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}") - # configure learning rate + # Configure learning rate based on the batch size, base learning rate and number of GPUs + # If scale_lr is true, calculate the learning rate based on additional factors bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate if not cpu: ngpu = trainer_config["devices"] @@ -780,7 +867,7 @@ def on_train_epoch_end(self, trainer, pl_module): rank_zero_info("++++ NOT USING LR SCALING ++++") rank_zero_info(f"Setting learning rate to {model.learning_rate:.2e}") - # allow checkpointing via USR1 + # Allow checkpointing via USR1 def melk(*args, **kwargs): # run all checkpoint hooks if trainer.global_rank == 0: @@ -794,20 +881,23 @@ def divein(*args, **kwargs): pudb.set_trace() import signal - + # Assign melk to SIGUSR1 signal and divein to SIGUSR2 signal signal.signal(signal.SIGUSR1, melk) signal.signal(signal.SIGUSR2, divein) - # run + # Run the training and validation if opt.train: try: trainer.fit(model, data) except Exception: melk() raise + # Print the maximum GPU memory allocated during training + print(f"GPU memory usage: {torch.cuda.max_memory_allocated() / 1024**2:.0f} MB") # if not opt.no_test and not trainer.interrupted: # trainer.test(model, data) except Exception: + # If there's an exception, debug it if opt.debug is true and the trainer's global rank is 0 if opt.debug and trainer.global_rank == 0: try: import pudb as debugger @@ -816,7 +906,7 @@ def divein(*args, **kwargs): debugger.post_mortem() raise finally: - # move newly created debug project to debug_runs + # Move the log directory to debug_runs if opt.debug is true and the trainer's global if opt.debug and not opt.resume and trainer.global_rank == 0: dst, name = os.path.split(logdir) dst = os.path.join(dst, "debug_runs", name) From 1653063fce7062dfd652e73691fac44a24698cd5 Mon Sep 17 00:00:00 2001 From: Hakjin Lee Date: Mon, 27 Mar 2023 10:41:08 +0900 Subject: [PATCH 036/413] [CI] Fix pre-commit workflow (#3238) --- .github/ISSUE_TEMPLATE/config.yml | 2 +- .github/ISSUE_TEMPLATE/feature_request.yml | 2 +- .github/workflows/doc_test_on_pr.yml | 2 +- .github/workflows/post_commit.yml | 4 +- .../example_checks/check_dispatch_inputs.py | 54 +++++++------- .../example_checks/check_example_weekly.py | 74 +++++++++---------- .../example_checks/detect_changed_example.py | 48 ++++++------ 7 files changed, 93 insertions(+), 93 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 673b1274c94b..b310fcfefc15 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -8,4 +8,4 @@ contact_links: about: This issue tracker is not for technical support. Please use WeChat, and ask the community for help. - name: 😊 Advanced question - GitHub Discussions url: https://github.com/hpcaitech/ColossalAI/discussions - about: Use GitHub Discussions for advanced and unanswered technical questions, requiring a maintainer's answer. \ No newline at end of file + about: Use GitHub Discussions for advanced and unanswered technical questions, requiring a maintainer's answer. diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml index d05bc25f6f41..f12c41b52e6f 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.yml +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -22,7 +22,7 @@ body: If applicable, add screenshots to help explain your problem. **Suggest a potential alternative/fix** Tell us how we could improve this project. - **Optional: Affiliation** + **Optional: Affiliation** Institution/email information helps better analyze and evaluate users to improve the project. Welcome to establish in-depth cooperation. placeholder: | A clear and concise description of your idea. diff --git a/.github/workflows/doc_test_on_pr.yml b/.github/workflows/doc_test_on_pr.yml index a083362a7f0f..fbe669582c20 100644 --- a/.github/workflows/doc_test_on_pr.yml +++ b/.github/workflows/doc_test_on_pr.yml @@ -71,7 +71,7 @@ jobs: - name: Checkout ColossalAI uses: actions/checkout@v3 - + - name: Install Doc Test Requirements run: | source activate pytorch diff --git a/.github/workflows/post_commit.yml b/.github/workflows/post_commit.yml index bf93eabbf43f..1bbc0d2f5c34 100644 --- a/.github/workflows/post_commit.yml +++ b/.github/workflows/post_commit.yml @@ -82,7 +82,7 @@ jobs: # create pull request - name: Create Pull Request - if: steps.commit.outputs.status == 'success' + if: steps.commit.outcome == 'success' id: cpr uses: peter-evans/create-pull-request@v4 with: @@ -90,7 +90,7 @@ jobs: title: "[format] applied code formatting on changed files in PR ${{ github.event.pull_request.number }}" - name: Enable Auto-merge for the New PR - if: steps.commit.outputs.status == 'success' + if: steps.commit.outcome == 'success' uses: peter-evans/enable-pull-request-automerge@v2 with: pull-request-number: ${{ steps.cpr.outputs.pull-request-number }} diff --git a/.github/workflows/scripts/example_checks/check_dispatch_inputs.py b/.github/workflows/scripts/example_checks/check_dispatch_inputs.py index 04d2063ec5fc..5bec96187e0c 100644 --- a/.github/workflows/scripts/example_checks/check_dispatch_inputs.py +++ b/.github/workflows/scripts/example_checks/check_dispatch_inputs.py @@ -1,27 +1,27 @@ -import argparse -import os - - -def check_inputs(input_list): - for path in input_list: - real_path = os.path.join('examples', path) - if not os.path.exists(real_path): - return False - return True - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('-f', '--fileNameList', type=str, help="List of file names") - args = parser.parse_args() - name_list = args.fileNameList.split(",") - is_correct = check_inputs(name_list) - - if is_correct: - print('success') - else: - print('failure') - - -if __name__ == '__main__': - main() +import argparse +import os + + +def check_inputs(input_list): + for path in input_list: + real_path = os.path.join('examples', path) + if not os.path.exists(real_path): + return False + return True + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('-f', '--fileNameList', type=str, help="List of file names") + args = parser.parse_args() + name_list = args.fileNameList.split(",") + is_correct = check_inputs(name_list) + + if is_correct: + print('success') + else: + print('failure') + + +if __name__ == '__main__': + main() diff --git a/.github/workflows/scripts/example_checks/check_example_weekly.py b/.github/workflows/scripts/example_checks/check_example_weekly.py index 941e90901f3d..83eff644e315 100644 --- a/.github/workflows/scripts/example_checks/check_example_weekly.py +++ b/.github/workflows/scripts/example_checks/check_example_weekly.py @@ -1,37 +1,37 @@ -import os - - -def show_files(path, all_files): - # Traverse all the folder/file in current directory - file_list = os.listdir(path) - # Determine the element is folder or file. If file, pass it into list, if folder, recurse. - for file_name in file_list: - # Get the abs directory using os.path.join() and store into cur_path. - cur_path = os.path.join(path, file_name) - # Determine whether folder - if os.path.isdir(cur_path): - show_files(cur_path, all_files) - else: - all_files.append(cur_path) - return all_files - - -def join(input_list, sep=None): - return (sep or ' ').join(input_list) - - -def main(): - contents = show_files('examples/', []) - all_loc = [] - for file_loc in contents: - split_loc = file_loc.split('/') - # must have two sub-folder levels after examples folder, such as examples/images/vit is acceptable, examples/images/README.md is not, examples/requirements.txt is not. - if len(split_loc) >= 4: - re_loc = '/'.join(split_loc[1:3]) - if re_loc not in all_loc: - all_loc.append(re_loc) - print(all_loc) - - -if __name__ == '__main__': - main() +import os + + +def show_files(path, all_files): + # Traverse all the folder/file in current directory + file_list = os.listdir(path) + # Determine the element is folder or file. If file, pass it into list, if folder, recurse. + for file_name in file_list: + # Get the abs directory using os.path.join() and store into cur_path. + cur_path = os.path.join(path, file_name) + # Determine whether folder + if os.path.isdir(cur_path): + show_files(cur_path, all_files) + else: + all_files.append(cur_path) + return all_files + + +def join(input_list, sep=None): + return (sep or ' ').join(input_list) + + +def main(): + contents = show_files('examples/', []) + all_loc = [] + for file_loc in contents: + split_loc = file_loc.split('/') + # must have two sub-folder levels after examples folder, such as examples/images/vit is acceptable, examples/images/README.md is not, examples/requirements.txt is not. + if len(split_loc) >= 4: + re_loc = '/'.join(split_loc[1:3]) + if re_loc not in all_loc: + all_loc.append(re_loc) + print(all_loc) + + +if __name__ == '__main__': + main() diff --git a/.github/workflows/scripts/example_checks/detect_changed_example.py b/.github/workflows/scripts/example_checks/detect_changed_example.py index df4fd67368fc..c69d95a552e9 100644 --- a/.github/workflows/scripts/example_checks/detect_changed_example.py +++ b/.github/workflows/scripts/example_checks/detect_changed_example.py @@ -1,24 +1,24 @@ -import argparse - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('-f', '--fileNameList', type=str, help="The list of changed files") - args = parser.parse_args() - name_list = args.fileNameList.split(":") - folder_need_check = set() - for loc in name_list: - # Find only the sub-sub-folder of 'example' folder - # the examples folder structure is like - # - examples - # - area - # - application - # - file - if loc.split("/")[0] == "examples" and len(loc.split("/")) >= 4: - folder_need_check.add('/'.join(loc.split("/")[1:3])) - # Output the result using print. Then the shell can get the values. - print(list(folder_need_check)) - - -if __name__ == '__main__': - main() +import argparse + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('-f', '--fileNameList', type=str, help="The list of changed files") + args = parser.parse_args() + name_list = args.fileNameList.split(":") + folder_need_check = set() + for loc in name_list: + # Find only the sub-sub-folder of 'example' folder + # the examples folder structure is like + # - examples + # - area + # - application + # - file + if loc.split("/")[0] == "examples" and len(loc.split("/")) >= 4: + folder_need_check.add('/'.join(loc.split("/")[1:3])) + # Output the result using print. Then the shell can get the values. + print(list(folder_need_check)) + + +if __name__ == '__main__': + main() From 1a229045af97767a21223ee1b3694c9aedac154e Mon Sep 17 00:00:00 2001 From: YH <100389977+yhna940@users.noreply.github.com> Date: Mon, 27 Mar 2023 10:42:21 +0900 Subject: [PATCH 037/413] Add interface for colo tesnor dp size (#3227) --- colossalai/gemini/chunk/manager.py | 2 +- colossalai/tensor/colo_tensor.py | 9 +++++++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/colossalai/gemini/chunk/manager.py b/colossalai/gemini/chunk/manager.py index 30ac4d354647..2fa65c970316 100644 --- a/colossalai/gemini/chunk/manager.py +++ b/colossalai/gemini/chunk/manager.py @@ -72,7 +72,7 @@ def register_tensor(self, if tensor.numel() > chunk_size: chunk_size = tensor.numel() - dp_size = tensor.process_group.dp_world_size() + dp_size = tensor.get_dp_world_size() chunk_size = chunk_size + (-chunk_size % dp_size) chunk = Chunk( diff --git a/colossalai/tensor/colo_tensor.py b/colossalai/tensor/colo_tensor.py index bbed8847abbc..40eefc3ec5d1 100644 --- a/colossalai/tensor/colo_tensor.py +++ b/colossalai/tensor/colo_tensor.py @@ -138,6 +138,15 @@ def set_process_group(self, pg: ProcessGroup): def get_tp_world_size(self) -> int: return self.process_group.tp_world_size() + def get_dp_world_size(self) -> int: + """get_dp_world_size + get the dp world size of the tensor. + + Returns: + int: dp world size + """ + return self.process_group.dp_world_size() + def set_dist_spec(self, dist_spec: _DistSpec): """set_dist_spec set dist spec and change the payloads. From 73d3e4d3091a6f87cd5153194989713dd2429210 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 27 Mar 2023 10:24:14 +0800 Subject: [PATCH 038/413] [booster] implemented the torch ddd + resnet example (#3232) * [booster] implemented the torch ddd + resnet example * polish code --- colossalai/booster/__init__.py | 1 - colossalai/booster/booster.py | 57 ++++-- colossalai/booster/environment_table.py | 18 -- colossalai/booster/interface/__init__.py | 3 - .../booster/mixed_precision/fp16_torch.py | 12 +- .../mixed_precision/mixed_precision_base.py | 2 +- colossalai/booster/plugin/plugin_base.py | 22 ++- colossalai/booster/plugin/torch_ddp_plugin.py | 61 +++++- .../checkpoint_io/checkpoint_io_base.py | 179 ++++++++++++++++-- .../checkpoint_io/general_checkpoint_io.py | 73 +++---- colossalai/cluster/dist_coordinator.py | 40 +++- colossalai/interface/__init__.py | 4 + colossalai/interface/model.py | 25 +++ .../{booster => }/interface/optimizer.py | 0 examples/tutorial/new_api/README.md | 5 + examples/tutorial/new_api/test_ci.sh | 2 + .../tutorial/new_api/torch_ddp/.gitignore | 4 + examples/tutorial/new_api/torch_ddp/README.md | 44 +++++ examples/tutorial/new_api/torch_ddp/eval.py | 48 +++++ examples/tutorial/new_api/torch_ddp/train.py | 128 +++++++++++++ .../test_plugin/test_torch_ddp_plugin.py | 4 +- .../test_general_checkpoint_io.py | 4 +- 22 files changed, 608 insertions(+), 128 deletions(-) delete mode 100644 colossalai/booster/environment_table.py delete mode 100644 colossalai/booster/interface/__init__.py create mode 100644 colossalai/interface/__init__.py create mode 100644 colossalai/interface/model.py rename colossalai/{booster => }/interface/optimizer.py (100%) create mode 100644 examples/tutorial/new_api/README.md create mode 100644 examples/tutorial/new_api/test_ci.sh create mode 100644 examples/tutorial/new_api/torch_ddp/.gitignore create mode 100644 examples/tutorial/new_api/torch_ddp/README.md create mode 100644 examples/tutorial/new_api/torch_ddp/eval.py create mode 100644 examples/tutorial/new_api/torch_ddp/train.py diff --git a/colossalai/booster/__init__.py b/colossalai/booster/__init__.py index 3b3f45bb0fe2..841054a9c672 100644 --- a/colossalai/booster/__init__.py +++ b/colossalai/booster/__init__.py @@ -1,4 +1,3 @@ from .accelerator import Accelerator from .booster import Booster -from .environment_table import EnvironmentTable from .plugin import Plugin diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 230c65a9e0a1..1ad9f7f20ec1 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -8,6 +8,8 @@ from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils.data import DataLoader +from colossalai.checkpoint_io import GeneralCheckpointIO + from .accelerator import Accelerator from .mixed_precision import MixedPrecision, mixed_precision_factory from .plugin import Plugin @@ -61,19 +63,21 @@ def __init__(self, self.plugin = plugin # set accelerator - if self.plugin and self.plugin.control_device: + if self.plugin and self.plugin.control_device(): self.accelerator = None warnings.warn('The plugin will control the accelerator, so the device argument will be ignored.') else: self.accelerator = Accelerator(device) # set precision - if mixed_precision is None or (self.plugin and self.plugin.control_precision): - self.mixed_precision = None + if self.plugin and self.plugin.control_precision(): warnings.warn('The plugin will control the precision, so the mixed_precision argument will be ignored.') + self.mixed_precision = None + elif mixed_precision is None: + self.mixed_precision = None else: # validate and set precision - if isinstance(MixedPrecision, str): + if isinstance(mixed_precision, str): # the user will take the default arguments for amp training self.mixed_precision = mixed_precision_factory(mixed_precision) elif isinstance(mixed_precision, MixedPrecision): @@ -84,6 +88,11 @@ def __init__(self, f'Expected the argument mixed_precision to be a string or an instance of Precision, but got {type(mixed_precision)}.' ) + if self.plugin is not None and self.plugin.control_checkpoint_io(): + self.checkpoint_io = self.plugin.get_checkpoint_io() + else: + self.checkpoint_io = GeneralCheckpointIO() + def boost( self, model: nn.Module, @@ -109,12 +118,13 @@ def boost( model, optimizer, criterion, dataloader, lr_scheduler = self.plugin.configure( model, optimizer, criterion, dataloader, lr_scheduler) - if self.plugin and not self.plugin.control_device: + if self.plugin and not self.plugin.control_device(): # transform model for accelerator model = self.accelerator.configure(model) - if self.mixed_precision and self.plugin and not self.plugin.control_precision: + if self.mixed_precision and (self.plugin is None or self.plugin and not self.plugin.control_precision()): # transform model for mixed precision + # when mixed_precision is specified and the plugin is not given or does not control the precision model, optimizer, criterion = self.mixed_precision.configure(model, optimizer, criterion) return model, optimizer, criterion, dataloader, lr_scheduler @@ -140,18 +150,25 @@ def no_sync(self, model: nn.Module) -> contextmanager: assert self.plugin.support_no_sync, f'The plugin {self.plugin.__class__.__name__} does not support no_sync.' return self.plugin.no_sync(model) - def save(self, - obj: Union[nn.Module, Optimizer, LRScheduler], - path_like: str, - plan: str = 'torch', - **kwargs) -> None: - # TODO: implement this method - pass + def load_model(self, model: nn.Module, checkpoint: str, strict: bool = True): + self.checkpoint_io.load_model(model, checkpoint, strict) - def load(self, - obj: Union[nn.Module, Optimizer, LRScheduler], - path_like: str, - plan: str = 'torch', - **kwargs) -> None: - # TODO: implement this method - pass + def save_model(self, + model: nn.Module, + checkpoint: str, + prefix: str = None, + shard: bool = False, + size_per_shard: int = 1024): + self.checkpoint_io.save_model(model, checkpoint, prefix, shard, size_per_shard) + + def load_optimizer(self, optimizer: Optimizer, checkpoint: str): + self.checkpoint_io.load_optimizer(optimizer, checkpoint) + + def save_optimizer(self, optimizer: Optimizer, checkpoint: str, shard: bool = False, size_per_shard: int = 1024): + self.checkpoint_io.save_optimizer(optimizer, checkpoint, shard, size_per_shard) + + def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + self.checkpoint_io.save_lr_scheduler(lr_scheduler, checkpoint) + + def load_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + self.checkpoint_io.load_lr_scheduler(lr_scheduler, checkpoint) diff --git a/colossalai/booster/environment_table.py b/colossalai/booster/environment_table.py deleted file mode 100644 index 4b16f120c1b9..000000000000 --- a/colossalai/booster/environment_table.py +++ /dev/null @@ -1,18 +0,0 @@ -from typing import List - -__all__ = ['EnvironmentTable'] - - -class EnvironmentTable: - - def __init__(self, intra_op_world_sizes: List[int]): - # TODO: implement this method - pass - - @property - def is_master(self) -> bool: - # TODO: implement this method - pass - - # TODO: implement more utility methods as given in - # https://github.com/hpcaitech/ColossalAI/issues/3051 diff --git a/colossalai/booster/interface/__init__.py b/colossalai/booster/interface/__init__.py deleted file mode 100644 index 8892a13e1814..000000000000 --- a/colossalai/booster/interface/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .optimizer import OptimizerWrapper - -__all__ = ['OptimizerWrapper'] diff --git a/colossalai/booster/mixed_precision/fp16_torch.py b/colossalai/booster/mixed_precision/fp16_torch.py index 054f78d2e226..9999aa5e0eb4 100644 --- a/colossalai/booster/mixed_precision/fp16_torch.py +++ b/colossalai/booster/mixed_precision/fp16_torch.py @@ -5,7 +5,8 @@ from torch import Tensor from torch.optim import Optimizer -from ..interface import OptimizerWrapper +from colossalai.interface import ModelWrapper, OptimizerWrapper + from .mixed_precision_base import MixedPrecision __all__ = ['FP16_Torch_MixedPrecision', 'TorchAMPOptimizer', 'TorchAMPModule'] @@ -45,7 +46,9 @@ def backward(self, loss: Tensor, *args, **kwargs) -> None: scaled_loss.backward(*args, **kwargs) def step(self, *args, **kwargs) -> Optional[float]: - return self.scaler.step(self.optim, *args, **kwargs) + out = self.scaler.step(self.optim, *args, **kwargs) + self.scaler.update() + return out def scale_loss(self, loss: Tensor) -> Tensor: return self.scaler.scale(loss) @@ -67,7 +70,7 @@ def clip_grad_by_norm(self, super().clip_grad_by_norm(max_norm, norm_type, error_if_nonfinite, *args, **kwargs) -class TorchAMPModule(nn.Module): +class TorchAMPModule(ModelWrapper): """ Module wrapper for mixed precision training in FP16 using PyTorch AMP. @@ -76,8 +79,7 @@ class TorchAMPModule(nn.Module): """ def __init__(self, module: nn.Module): - super().__init__() - self.module = module + super().__init__(module) def forward(self, *args, **kwargs): with torch.cuda.amp.autocast(): diff --git a/colossalai/booster/mixed_precision/mixed_precision_base.py b/colossalai/booster/mixed_precision/mixed_precision_base.py index d1e8acc82cc6..2490e9811ccf 100644 --- a/colossalai/booster/mixed_precision/mixed_precision_base.py +++ b/colossalai/booster/mixed_precision/mixed_precision_base.py @@ -4,7 +4,7 @@ import torch.nn as nn from torch.optim import Optimizer -from ..interface import OptimizerWrapper +from colossalai.interface import OptimizerWrapper class MixedPrecision(ABC): diff --git a/colossalai/booster/plugin/plugin_base.py b/colossalai/booster/plugin/plugin_base.py index 3c347cb4252d..7a222022c1b2 100644 --- a/colossalai/booster/plugin/plugin_base.py +++ b/colossalai/booster/plugin/plugin_base.py @@ -6,34 +6,30 @@ from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils.data import DataLoader -from colossalai.booster.interface import OptimizerWrapper +from colossalai.checkpoint_io import CheckpointIO +from colossalai.interface import OptimizerWrapper __all__ = ['Plugin'] class Plugin(ABC): - @property @abstractmethod def supported_devices(self) -> List[str]: pass - @property @abstractmethod def supported_precisions(self) -> List[str]: pass - @property @abstractmethod def control_precision(self) -> bool: pass - @property @abstractmethod def control_device(self) -> bool: pass - @property @abstractmethod def support_no_sync(self) -> bool: pass @@ -49,3 +45,17 @@ def configure( ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: # implement this method pass + + @abstractmethod + def control_checkpoint_io(self) -> bool: + """ + Whether the plugin controls the checkpoint io + """ + pass + + @abstractmethod + def get_checkpoint_io(self) -> CheckpointIO: + """ + Get checkpoint io object for this plugin, only invoked when control_checkpoint_io is True. + """ + pass diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index 07d6be8c748d..d7f3d22d93cc 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -11,13 +11,61 @@ from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler -from colossalai.booster.interface import OptimizerWrapper +from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO +from colossalai.cluster import DistCoordinator +from colossalai.interface import ModelWrapper, OptimizerWrapper from .plugin_base import Plugin __all__ = ['TorchDDPPlugin'] +class TorchDDPCheckpointIO(GeneralCheckpointIO): + + def __init__(self) -> None: + super().__init__() + self.coordinator = DistCoordinator() + + def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool = True): + """ + Load model from checkpoint with automatic unwrapping. + """ + # the model should be unwrapped in self.load_model via ModelWrapper.unwrap + return super().load_unsharded_model(model, checkpoint, strict=strict) + + def save_unsharded_model(self, model: nn.Module, checkpoint: str): + """ + Save model to checkpoint but only on master process. + """ + # the model should be unwrapped in self.load_model via ModelWrapper.unwrap + if self.coordinator.is_master(): + super().save_unsharded_model(model, checkpoint) + + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str): + """ + Save optimizer to checkpoint but only on master process. + """ + if self.coordinator.is_master(): + super().save_unsharded_optimizer(optimizer, checkpoint) + + def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + """ + Save model to checkpoint but only on master process. + """ + if self.coordinator.is_master(): + super().save_lr_scheduler(lr_scheduler, checkpoint) + + +class TorchDDPModel(ModelWrapper): + + def __init__(self, module: nn.Module, *args, **kwargs) -> None: + super().__init__(module) + self.module = DDP(module, *args, **kwargs) + + def unwrap(self): + return self.module.module + + class TorchDDPPlugin(Plugin): """ Plugin for PyTorch DDP. @@ -138,10 +186,19 @@ def configure( # cast model to cuda model = model.cuda() + # convert model to sync bn + model = nn.SyncBatchNorm.convert_sync_batchnorm(model, None) + # wrap the model with PyTorch DDP - model = DDP(model, **self.ddp_kwargs) + model = TorchDDPModel(model, **self.ddp_kwargs) if not isinstance(optimizer, OptimizerWrapper): optimizer = OptimizerWrapper(optimizer) return model, optimizer, criterion, dataloader, lr_scheduler + + def control_checkpoint_io(self) -> bool: + return True + + def get_checkpoint_io(self) -> CheckpointIO: + return TorchDDPCheckpointIO() diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py index 00a65424bece..d6eef7a96cdc 100644 --- a/colossalai/checkpoint_io/checkpoint_io_base.py +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -1,13 +1,15 @@ import json from abc import ABC, abstractmethod from pathlib import Path -from typing import Any +from typing import Any, Union import torch import torch.nn as nn from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from colossalai.interface import ModelWrapper + __all__ = ['CheckpointIO', 'ShardCheckpointIndexFile'] @@ -37,15 +39,15 @@ class CheckpointIO(ABC): >>> >>> # save optimizer to checkpoint >>> checkpoint_io.save_optimizer(optimizer, 'optimizer.pt') - """ # ====================================== - # Abstract methods for implementation + # Public methods # ====================================== - - @abstractmethod - def load_model(self, model: nn.Module, checkpoint: str, strict: bool = True): + def load_model(self, + model: Union[nn.Module, ModelWrapper], + checkpoint: str, + strict: bool = True) -> Union[nn.Module, ModelWrapper]: """ Load model from checkpoint. @@ -59,14 +61,26 @@ def load_model(self, model: nn.Module, checkpoint: str, strict: bool = True): strict (bool): whether to strictly enforce that the param name in the checkpoint match the keys returned by this module's. """ - pass + ckpt_path = Path(checkpoint) + is_sharded = self.is_sharded_checkpoint(ckpt_path) + + origin_model = model + + if isinstance(model, ModelWrapper): + model = model.unwrap() + + if is_sharded: + self.load_sharded_model(model, ckpt_path, strict) + else: + self.load_unsharded_model(model, ckpt_path, strict) + + return origin_model - @abstractmethod def save_model(self, - model: nn.Module, + model: Union[nn.Module, ModelWrapper], checkpoint: str, - prefix: str = None, shard: bool = False, + prefix: str = None, size_per_shard: int = 1024): """ Save model to checkpoint. @@ -83,17 +97,24 @@ def save_model(self, Args: model (nn.Module): model to be saved. - checkpoint: checkpoint path. The checkpoint path can be : + checkpoint (str): checkpoint path. The checkpoint path can be : 1. a file path, e.g. 'model.pt' 2. a directory path to save the sharded checkpoint, e.g. './checkpoints/' when shard = True. - shard: whether to shard the checkpoint. Default: False. If set to True, the checkpoint will be sharded into + shard (bool): whether to shard the checkpoint. Default: False. If set to True, the checkpoint will be sharded into multiple files. The model shards will be specificed by a `model.index.json` file. When shard = True, please ensure that the checkpoint path is a directory path instead of a file path. - size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard is set to True. + prefix (str): prefix for the model checkpoint file name when shard=True. Default: None. + size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard = True. """ - pass - @abstractmethod + if isinstance(model, ModelWrapper): + model = model.unwrap() + + if shard: + self.save_sharded_model(model, checkpoint, prefix, size_per_shard) + else: + self.save_unsharded_model(model, checkpoint) + def load_optimizer(self, optimizer: Optimizer, checkpoint: str): """ Load optimizer from checkpoint. @@ -102,19 +123,139 @@ def load_optimizer(self, optimizer: Optimizer, checkpoint: str): optimizer (Optimizer): optimizer to be loaded. checkpoint (str): checkpoint path. This value is made compatiblity with the model checkpoints in the """ - pass + ckpt_path = Path(checkpoint) + is_sharded = self.is_sharded_checkpoint(ckpt_path) - @abstractmethod - def save_optimizer(self, optimizer: Optimizer, checkpoint: str, shard: bool = False, size_per_shard: int = 1024): + if is_sharded: + self.load_sharded_optimizer(optimizer, ckpt_path) + else: + self.load_unsharded_optimizer(optimizer, ckpt_path) + + def save_optimizer(self, + optimizer: Optimizer, + checkpoint: str, + shard: bool = False, + prefix: str = None, + size_per_shard: int = 1024): """ Save optimizer to checkpoint. Args: optimizer (Optimizer): optimizer to be saved. - checkpoint: checkpoint path. The checkpoint path can be : + checkpoint (str): checkpoint path. The checkpoint path can be : 1. a file path, e.g. 'model.pt' 2. a path to a json file which defines the index to the sharded checkpoint for the optimizer 3. a path to a folder containing a unique .index.json file for sharded checkpoint + shard (bool): whether to shard the checkpoint. Default: False. If set to True, the checkpoint will be sharded into + multiple files. The optimizer shards will be specificed by a `optimizer.index.json` file. + prefix (str): prefix for the optimizer checkpoint when shard = True. Default: None. + size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard is set to True. + """ + if shard: + self.save_sharded_optimizer(optimizer, checkpoint, prefix, size_per_shard) + else: + self.save_unsharded_optimizer(optimizer, checkpoint) + + # ======================================================== + # Abstract methods for model loading/saving implementation + # ======================================================== + @abstractmethod + def load_sharded_model(self, model: nn.Module, checkpoint: Path, strict: bool): + """ + Load model from sharded checkpoint. + + Args: + model (nn.Module): model to be loaded. + checkpoint (str): checkpoint path. It should be path to the .index.json file or a path to a directory which contains a .index.json file. + """ + pass + + @abstractmethod + def load_unsharded_model(self, model: nn.Module, checkpoint: Path, strict: bool): + """ + Load model from unsharded checkpoint. + + Args: + model (nn.Module): model to be loaded. + checkpoint (str): checkpoint path. It should be a single file path pointing to a model weight binary. + strict (bool): whether to strictly enforce that the param name in + the checkpoint match the keys returned by this module's. + """ + pass + + @abstractmethod + def save_sharded_model(self, model: nn.Module, checkpoint: Path, prefix: str, size_per_shard: int): + """ + Save model to sharded checkpoint. + + Args: + model (nn.Module): model to be saved. + checkpoint (Path): checkpoint path. It should be a directory path. + prefix (str): prefix for the model checkpoint. + size_per_shard (int): size per shard in MB. + """ + pass + + @abstractmethod + def save_unsharded_model(self, model: nn.Module, checkpoint: Path): + """ + Save model to unsharded checkpoint. + + Args: + model (nn.Module): model to be saved. + checkpoint (Path): checkpoint path. It should be a single file path pointing to a model weight binary. + """ + pass + + # ======================================================== + # Abstract methods for optimizer loading/saving implementation + # ======================================================== + + @abstractmethod + def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int): + """ + Load optimizer from sharded checkpoint. + + Args: + optimizer (Optimizer): optimizer to be loaded. + checkpoint (str): checkpoint path. It should be path to the .index.json file or a path to a directory which contains a .index.json file. + prefix (str): prefix for the optimizer checkpoint. + size_per_shard (int): size per shard in MB. + """ + pass + + @abstractmethod + def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): + """ + Load optimizer from unsharded checkpoint. + + Args: + optimizer (Optimizer): optimizer to be loaded. + checkpoint (str): checkpoint path. It should be a single file path pointing to a model weight binary. + """ + pass + + @abstractmethod + def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int): + """ + Save optimizer to sharded checkpoint. + + Args: + optimizer (Optimizer): optimizer to be saved. + checkpoint (Path): checkpoint path. It should be a directory path. + prefix (str): prefix for the optimizer checkpoint. + size_per_shard (int): size per shard in MB. + """ + pass + + @abstractmethod + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): + """ + Save optimizer to unsharded checkpoint. + + Args: + optimizer (Optimizer): optimizer to be saved. + checkpoint (str): checkpoint path. It should be a single file path pointing to a model weight binary. """ pass diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index 0a3636655530..cfabcfa5589f 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -10,57 +10,36 @@ class GeneralCheckpointIO(CheckpointIO): - def load_model(self, model: nn.Module, checkpoint: str, strict: bool = True): - checkpoint = Path(checkpoint) - is_sharded = self.is_sharded_checkpoint(checkpoint) + def load_sharded_model(self, model: nn.Module, checkpoint: Path, strict: bool): + index_file_path = self.get_sharded_checkpoint_index_file(checkpoint) - if not is_sharded: - checkpoint = self.load_state_dict(checkpoint) - model.load_state_dict(checkpoint, strict=strict) - else: - # find the index file - checkpoint_path = Path(checkpoint) - index_file_path = self.get_sharded_checkpoint_index_file(checkpoint_path) + # iterate over the shard checkpoint files + # and load each + shard_files = self.get_checkpoint_shard_filenames(index_file_path) + for shard_file in shard_files: + shard_checkpoint = self.load_state_dict(shard_file) + model.load_state_dict(shard_checkpoint, strict=strict) - # iterate over the shard checkpoint files - # and load each - shard_files = self.get_checkpoint_shard_filenames(index_file_path) - for shard_file in shard_files: - shard_checkpoint = self.load_state_dict(shard_file) - model.load_state_dict(shard_checkpoint, strict=strict) + def load_unsharded_model(self, model: nn.Module, checkpoint: Path, strict: bool): + checkpoint = self.load_state_dict(str(checkpoint)) + model.load_state_dict(checkpoint, strict=strict) - return model + def save_sharded_model(self, model: nn.Module, checkpoint: Path, prefix: str, size_per_shard: int): + # TODO(FrankLeeeee): implement this method as it can be supported by Huggingface model + raise NotImplementedError("Sharded model checkpoint is not supported yet.") - def save_model(self, - model: nn.Module, - checkpoint: str, - prefix: str = None, - shard: bool = False, - size_per_shard: int = 1024): - checkpoint = Path(checkpoint) - if shard: - # TODO(FrankLeeeee): implement checkpoint saving to sharded checkpoint - raise NotImplementedError("Not implemented yet") - else: - self.save_checkpoint(model.state_dict(), checkpoint) + def save_unsharded_model(self, model: nn.Module, checkpoint: Path): + self.save_checkpoint(model.state_dict(), checkpoint) - def load_optimizer(self, optimizer: Optimizer, checkpoint: str): - checkpoint = Path(checkpoint) - is_sharded = self.is_sharded_checkpoint(checkpoint) + def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int): + raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.") - if not is_sharded: - checkpoint = self.load_state_dict(checkpoint) - optimizer.load_state_dict(checkpoint) - else: - # TODO(FrankLeeeee): implement checkpoint loading from sharded checkpoint - # This is not an urgent feature, so we can leave it for later - # let's implement this when we test large-scale models - pass - return optimizer + def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): + checkpoint = self.load_state_dict(checkpoint) + optimizer.load_state_dict(checkpoint) - def save_optimizer(self, optimizer: Optimizer, checkpoint: str, shard: bool = False, size_per_shard: int = 1024): - if shard: - # TODO(FrankLeeeee): implement checkpoint saving to sharded checkpoint - pass - else: - self.save_checkpoint(optimizer.state_dict(), checkpoint) + def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int): + raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.") + + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): + self.save_checkpoint(optimizer.state_dict(), checkpoint) diff --git a/colossalai/cluster/dist_coordinator.py b/colossalai/cluster/dist_coordinator.py index 6b48faf5b720..99dde810e112 100644 --- a/colossalai/cluster/dist_coordinator.py +++ b/colossalai/cluster/dist_coordinator.py @@ -1,3 +1,4 @@ +import functools import os from contextlib import contextmanager @@ -141,12 +142,12 @@ def priority_execution(self, executor_rank: int = 0, process_group: ProcessGroup should_block = rank != executor_rank if should_block: - dist.barrier(group=process_group) + self.block_all(process_group) yield if not should_block: - dist.barrier(group=process_group) + self.block_all(process_group) def destroy(self, process_group: ProcessGroup = None): """ @@ -156,3 +157,38 @@ def destroy(self, process_group: ProcessGroup = None): process_group (ProcessGroup, optional): process group to destroy. Defaults to None, which refers to the default process group. """ dist.destroy_process_group(process_group) + + def block_all(self, process_group: ProcessGroup = None): + """ + Block all processes in the process group. + + Args: + process_group (ProcessGroup, optional): process group to block. Defaults to None, which refers to the default process group. + """ + dist.barrier(group=process_group) + + def on_master_only(self, process_group: ProcessGroup = None): + """ + A function wrapper that only executes the wrapped function on the master process (rank 0). + + Example: + >>> from colossalai.cluster import DistCoordinator + >>> dist_coordinator = DistCoordinator() + >>> + >>> @dist_coordinator.on_master_only() + >>> def print_on_master(msg): + >>> print(msg) + """ + is_master = self.is_master(process_group) + + # define an inner functiuon + def decorator(func): + + @functools.wraps(func) + def wrapper(*args, **kwargs): + if is_master: + return func(*args, **kwargs) + + return wrapper + + return decorator diff --git a/colossalai/interface/__init__.py b/colossalai/interface/__init__.py new file mode 100644 index 000000000000..8c658e375146 --- /dev/null +++ b/colossalai/interface/__init__.py @@ -0,0 +1,4 @@ +from .model import ModelWrapper +from .optimizer import OptimizerWrapper + +__all__ = ['OptimizerWrapper', 'ModelWrapper'] diff --git a/colossalai/interface/model.py b/colossalai/interface/model.py new file mode 100644 index 000000000000..a067d7671ce7 --- /dev/null +++ b/colossalai/interface/model.py @@ -0,0 +1,25 @@ +import torch.nn as nn + + +class ModelWrapper(nn.Module): + """ + A wrapper class to define the common interface used by booster. + + Args: + module (nn.Module): The model to be wrapped. + """ + + def __init__(self, module: nn.Module) -> None: + super().__init__() + self.module = module + + def unwrap(self): + """ + Unwrap the model to return the original model for checkpoint saving/loading. + """ + if isinstance(self.module, ModelWrapper): + return self.module.unwrap() + return self.module + + def forward(self, *args, **kwargs): + return self.module(*args, **kwargs) diff --git a/colossalai/booster/interface/optimizer.py b/colossalai/interface/optimizer.py similarity index 100% rename from colossalai/booster/interface/optimizer.py rename to colossalai/interface/optimizer.py diff --git a/examples/tutorial/new_api/README.md b/examples/tutorial/new_api/README.md new file mode 100644 index 000000000000..cec88f41caf1 --- /dev/null +++ b/examples/tutorial/new_api/README.md @@ -0,0 +1,5 @@ +# New API Features + +**The New API is not officially released yet.** + +This folder contains some of the demonstrations of the new API. The new API is still under intensive development and will be released soon. diff --git a/examples/tutorial/new_api/test_ci.sh b/examples/tutorial/new_api/test_ci.sh new file mode 100644 index 000000000000..8b4475e9f147 --- /dev/null +++ b/examples/tutorial/new_api/test_ci.sh @@ -0,0 +1,2 @@ +#!/usr/bin/env +echo "The CI integration will be completed when the API is stable" diff --git a/examples/tutorial/new_api/torch_ddp/.gitignore b/examples/tutorial/new_api/torch_ddp/.gitignore new file mode 100644 index 000000000000..a79cf5236c08 --- /dev/null +++ b/examples/tutorial/new_api/torch_ddp/.gitignore @@ -0,0 +1,4 @@ +data +checkpoint +ckpt-fp16 +ckpt-fp32 diff --git a/examples/tutorial/new_api/torch_ddp/README.md b/examples/tutorial/new_api/torch_ddp/README.md new file mode 100644 index 000000000000..62d5a083d0a1 --- /dev/null +++ b/examples/tutorial/new_api/torch_ddp/README.md @@ -0,0 +1,44 @@ +# Distributed Data Parallel + +## 🚀 Quick Start + +This example provides a training script and and evaluation script. The training script provides a an example of training ResNet on CIFAR10 dataset from scratch. + +- Training Arguments + - `-r, `--resume`: resume from checkpoint file path + - `-c`, `--checkpoint`: the folder to save checkpoints + - `-i`, `--interval`: epoch interval to save checkpoints + - `-f`, `--fp16`: use fp16 + +- Eval Arguments + - `-e`, `--epoch`: select the epoch to evaluate + - `-c`, `--checkpoint`: the folder where checkpoints are found + + +### Train + +```bash +# train with torch DDP with fp32 +colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp32 + +# train with torch DDP with mixed precision training +colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp16 --fp16 +``` + +### Eval + +```bash +# evaluate fp32 training +python eval.py -c ./ckpt-fp32 -e 80 + +# evaluate fp16 mixed precision training +python eval.py -c ./ckpt-fp16 -e 80 +``` + +Expected accuracy performance will be: + +| Model | Single-GPU Baseline FP32 | Booster DDP with FP32 | Booster DDP with FP16 | +| --------- | ------------------------ | --------------------- | --------------------- | +| ResNet-18 | 85.85% | 85.03% | 85.12% | + +**Note: the baseline is a adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`** diff --git a/examples/tutorial/new_api/torch_ddp/eval.py b/examples/tutorial/new_api/torch_ddp/eval.py new file mode 100644 index 000000000000..657708ec3ff2 --- /dev/null +++ b/examples/tutorial/new_api/torch_ddp/eval.py @@ -0,0 +1,48 @@ +import argparse + +import torch +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms + +# ============================== +# Parse Arguments +# ============================== +parser = argparse.ArgumentParser() +parser.add_argument('-e', '--epoch', type=int, default=80, help="resume from the epoch's checkpoint") +parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory") +args = parser.parse_args() + +# ============================== +# Prepare Test Dataset +# ============================== +# CIFAR-10 dataset +test_dataset = torchvision.datasets.CIFAR10(root='./data/', train=False, transform=transforms.ToTensor()) + +# Data loader +test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=128, shuffle=False) + +# ============================== +# Load Model +# ============================== +model = torchvision.models.resnet18(num_classes=10).cuda() +state_dict = torch.load(f'{args.checkpoint}/model_{args.epoch}.pth') +model.load_state_dict(state_dict) + +# ============================== +# Run Evaluation +# ============================== +model.eval() + +with torch.no_grad(): + correct = 0 + total = 0 + for images, labels in test_loader: + images = images.cuda() + labels = labels.cuda() + outputs = model(images) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + + print('Accuracy of the model on the test images: {} %'.format(100 * correct / total)) diff --git a/examples/tutorial/new_api/torch_ddp/train.py b/examples/tutorial/new_api/torch_ddp/train.py new file mode 100644 index 000000000000..4741c3151cbb --- /dev/null +++ b/examples/tutorial/new_api/torch_ddp/train.py @@ -0,0 +1,128 @@ +import argparse +from pathlib import Path + +import torch +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms +from torch.optim.lr_scheduler import MultiStepLR + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.cluster import DistCoordinator + +# ============================== +# Parse Arguments +# ============================== +parser = argparse.ArgumentParser() +parser.add_argument('-r', '--resume', type=int, default=-1, help="resume from the epoch's checkpoint") +parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory") +parser.add_argument('-i', '--interval', type=int, default=5, help="interval of saving checkpoint") +parser.add_argument('-f', '--fp16', action='store_true', help="use fp16") +args = parser.parse_args() + +# ============================== +# Prepare Checkpoint Directory +# ============================== +Path(args.checkpoint).mkdir(parents=True, exist_ok=True) + +# ============================== +# Prepare Hyperparameters +# ============================== +NUM_EPOCHS = 80 +LEARNING_RATE = 1e-3 +START_EPOCH = args.resume if args.resume >= 0 else 0 + +# ============================== +# Launch Distributed Environment +# ============================== +colossalai.launch_from_torch(config={}) +coordinator = DistCoordinator() + +# update the learning rate with linear scaling +# old_gpu_num / old_lr = new_gpu_num / new_lr +LEARNING_RATE *= coordinator.world_size + +# ============================== +# Prepare Booster +# ============================== +plugin = TorchDDPPlugin() +if args.fp16: + booster = Booster(mixed_precision='fp16', plugin=plugin) +else: + booster = Booster(plugin=plugin) + +# ============================== +# Prepare Train Dataset +# ============================== +transform = transforms.Compose( + [transforms.Pad(4), + transforms.RandomHorizontalFlip(), + transforms.RandomCrop(32), + transforms.ToTensor()]) + +# CIFAR-10 dataset +with coordinator.priority_execution(): + train_dataset = torchvision.datasets.CIFAR10(root='./data/', train=True, transform=transform, download=True) + +# ==================================== +# Prepare model, optimizer, criterion +# ==================================== +# resent50 +model = torchvision.models.resnet18(num_classes=10).cuda() + +# Loss and optimizer +criterion = nn.CrossEntropyLoss() +optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) + +# lr scheduler +lr_scheduler = MultiStepLR(optimizer, milestones=[20, 40, 60, 80], gamma=1 / 3) + +# prepare dataloader with torch ddp plugin +train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=100, shuffle=True) + +# ============================== +# Resume from checkpoint +# ============================== +if args.resume >= 0: + booster.load_model(model, f'{args.checkpoint}/model_{args.resume}.pth') + booster.load_optimizer(optimizer, f'{args.checkpoint}/optimizer_{args.resume}.pth') + booster.load_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{args.resume}.pth') + +# ============================== +# Boost with ColossalAI +# ============================== +model, optimizer, criterion, train_dataloader, lr_scheduler = booster.boost(model, optimizer, criterion, + train_dataloader, lr_scheduler) + +# ============================== +# Train model +# ============================== +total_step = len(train_dataloader) + +for epoch in range(START_EPOCH, NUM_EPOCHS): + for i, (images, labels) in enumerate(train_dataloader): + images = images.cuda() + labels = labels.cuda() + + # Forward pass + outputs = model(images) + loss = criterion(outputs, labels) + + # Backward and optimize + optimizer.zero_grad() + booster.backward(loss, optimizer) + optimizer.step() + + if (i + 1) % 100 == 0: + print("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}".format(epoch + 1, NUM_EPOCHS, i + 1, total_step, + loss.item())) + + lr_scheduler.step() + + # save checkpoint every 5 epoch + if (epoch + 1) % args.interval == 0: + booster.save_model(model, f'{args.checkpoint}/model_{epoch + 1}.pth') + booster.save_optimizer(optimizer, f'{args.checkpoint}/optimizer_{epoch + 1}.pth') + booster.save_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{epoch + 1}.pth') diff --git a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py index 58aef54c4967..2dcc5a5bba27 100644 --- a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py @@ -8,8 +8,8 @@ import colossalai from colossalai.booster import Booster -from colossalai.booster.interface import OptimizerWrapper from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.interface import OptimizerWrapper from colossalai.testing import rerun_if_address_is_in_use from colossalai.utils import free_port from tests.kit.model_zoo import model_zoo @@ -34,7 +34,7 @@ def check_torch_ddp_plugin(): model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) - assert isinstance(model, DDP) + assert isinstance(model.module, DDP) assert isinstance(optimizer, OptimizerWrapper) output = model(**data) diff --git a/tests/test_checkpoint_io/test_general_checkpoint_io.py b/tests/test_checkpoint_io/test_general_checkpoint_io.py index 48376aaa88bf..f9f0e03c4fa1 100644 --- a/tests/test_checkpoint_io/test_general_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_general_checkpoint_io.py @@ -42,8 +42,8 @@ def test_unsharded_checkpoint(): new_optimizer = Adam(new_model.parameters(), lr=0.001) # load the model and optimizer - new_model = ckpt_io.load_model(new_model, model_ckpt_tempfile.name) - new_optimizer = ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) + ckpt_io.load_model(new_model, model_ckpt_tempfile.name) + ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) # do recursive check for the optimizer state dict # if the value is a dict, compare its values From 02b058032db1b3baa2a74fb2313de5c09830272d Mon Sep 17 00:00:00 2001 From: HELSON Date: Mon, 27 Mar 2023 15:22:17 +0800 Subject: [PATCH 039/413] [fx] meta registration compatibility (#3253) * [fx] meta registration compatibility * fix error --- colossalai/fx/_compatibility.py | 18 ++++-- ...ta_registrations.py => _meta_regist_12.py} | 0 colossalai/fx/_meta_regist_13.py | 57 +++++++++++++++++++ 3 files changed, 71 insertions(+), 4 deletions(-) rename colossalai/fx/{_meta_registrations.py => _meta_regist_12.py} (100%) create mode 100644 colossalai/fx/_meta_regist_13.py diff --git a/colossalai/fx/_compatibility.py b/colossalai/fx/_compatibility.py index 126403270301..6caad920d2ae 100644 --- a/colossalai/fx/_compatibility.py +++ b/colossalai/fx/_compatibility.py @@ -2,11 +2,21 @@ import torch -try: - from . import _meta_registrations - META_COMPATIBILITY = True -except: +TORCH_MAJOR = int(torch.__version__.split('.')[0]) +TORCH_MINOR = int(torch.__version__.split('.')[1]) + +if TORCH_MAJOR == 1 and TORCH_MINOR < 12: META_COMPATIBILITY = False +elif TORCH_MAJOR == 1 and TORCH_MINOR == 12: + from . import _meta_regist_12 + META_COMPATIBILITY = True +elif TORCH_MAJOR == 1 and TORCH_MINOR == 13: + from . import _meta_regist_13 + META_COMPATIBILITY = True +elif TORCH_MAJOR == 2: + from . import _meta_regist_13 + META_COMPATIBILITY = True + raise UserWarning("Colossalai is not tested with torch2.0 yet!!!") def compatibility(is_backward_compatible: bool = False) -> Callable: diff --git a/colossalai/fx/_meta_registrations.py b/colossalai/fx/_meta_regist_12.py similarity index 100% rename from colossalai/fx/_meta_registrations.py rename to colossalai/fx/_meta_regist_12.py diff --git a/colossalai/fx/_meta_regist_13.py b/colossalai/fx/_meta_regist_13.py new file mode 100644 index 000000000000..6caa87c449ab --- /dev/null +++ b/colossalai/fx/_meta_regist_13.py @@ -0,0 +1,57 @@ +import torch +from torch._meta_registrations import register_meta +from torch._prims_common import check + +aten = torch.ops.aten + + +# since we fix the torch version to 1.13.1, we have to add unimplemented meta ops +# all these functions are from here https://github.com/pytorch/pytorch/blob/master/torch/_meta_registrations.py +@register_meta([aten.convolution_backward.default]) +def meta_convolution_backward( + grad_output_, + input_, + weight_, + bias_sizes_opt, + stride, + padding, + dilation, + transposed, + output_padding, + groups, + output_mask, +): + # High level logic taken from slow_conv3d_backward_cpu which should + # be representative of all convolution_backward impls + backend_grad_input = None + backend_grad_weight = None + backend_grad_bias = None + + if output_mask[0]: + backend_grad_input = grad_output_.new_empty(input_.size()) + if output_mask[1]: + backend_grad_weight = grad_output_.new_empty(weight_.size()) + if output_mask[2]: + backend_grad_bias = grad_output_.new_empty(bias_sizes_opt) + + return (backend_grad_input, backend_grad_weight, backend_grad_bias) + + +@register_meta(aten._adaptive_avg_pool2d_backward.default) +def meta__adaptive_avg_pool2d_backward(grad_out, self): + ndim = grad_out.ndim + for i in range(1, ndim): + check( + grad_out.size(i) > 0, + lambda: f"adaptive_avg_pool2d_backward(): Expected grad_output to have non-zero \ + size for non-batch dimensions, {grad_out.shape} with dimension {i} being empty", + ) + check( + ndim == 3 or ndim == 4, + lambda: f"adaptive_avg_pool2d_backward(): Expected 3D or 4D tensor, but got {self.shape}", + ) + check( + self.dtype == grad_out.dtype, + lambda: f"expected dtype {self.dtype} for `grad_output` but got dtype {grad_out.dtype}", + ) + return self.new_empty(self.shape) From fd6add575d87728dbf27f682495fbbbe46c4f5bb Mon Sep 17 00:00:00 2001 From: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com> Date: Tue, 28 Mar 2023 10:40:07 +0800 Subject: [PATCH 040/413] [examples] polish AutoParallel readme (#3270) --- examples/tutorial/auto_parallel/README.md | 1 + examples/tutorial/auto_parallel/requirements.txt | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/tutorial/auto_parallel/README.md b/examples/tutorial/auto_parallel/README.md index bb014b9067b2..6a12e0dd5a48 100644 --- a/examples/tutorial/auto_parallel/README.md +++ b/examples/tutorial/auto_parallel/README.md @@ -45,6 +45,7 @@ colossalai run --nproc_per_node 4 auto_parallel_with_resnet.py You should expect to the log like this. This log shows the edge cost on the computation graph as well as the sharding strategy for an operation. For example, `layer1_0_conv1 S01R = S01R X RR` means that the first dimension (batch) of the input and output is sharded while the weight is not sharded (S means sharded, R means replicated), simply equivalent to data parallel training. ![](https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/tutorial/auto-parallel%20demo.png) +**Note: This experimental feature has been tested on torch 1.12.1 and transformer 4.22.2. If you are using other versions, you may need to modify the code to make it work.** ### Auto-Checkpoint Tutorial diff --git a/examples/tutorial/auto_parallel/requirements.txt b/examples/tutorial/auto_parallel/requirements.txt index ce89e7c80070..cc61362ba6f9 100644 --- a/examples/tutorial/auto_parallel/requirements.txt +++ b/examples/tutorial/auto_parallel/requirements.txt @@ -1,7 +1,7 @@ -torch +torch==1.12.1 colossalai titans pulp datasets matplotlib -transformers +transformers==4.22.1 From b0ce5a10326912961f0bc07cbbd250bab7b9c399 Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Tue, 28 Mar 2023 20:25:36 +0800 Subject: [PATCH 041/413] [Coati] first commit (#3283) --- applications/Chat/.gitignore | 146 +++++++++ applications/Chat/LICENSE | 202 +++++++++++++ applications/Chat/README.md | 269 +++++++++++++++++ applications/Chat/assets/data-collect.png | Bin 0 -> 410749 bytes applications/Chat/assets/logo_coati.png | Bin 0 -> 655366 bytes applications/Chat/assets/stage-3.jpeg | Bin 0 -> 378888 bytes applications/Chat/benchmarks/README.md | 94 ++++++ .../Chat/benchmarks/benchmark_gpt_dummy.py | 184 ++++++++++++ .../Chat/benchmarks/benchmark_gpt_dummy.sh | 45 +++ .../benchmarks/benchmark_opt_lora_dummy.py | 179 +++++++++++ applications/Chat/coati/__init__.py | 0 applications/Chat/coati/dataset/__init__.py | 9 + .../Chat/coati/dataset/prompt_dataset.py | 46 +++ .../Chat/coati/dataset/reward_dataset.py | 112 +++++++ .../Chat/coati/dataset/sft_dataset.py | 169 +++++++++++ applications/Chat/coati/dataset/utils.py | 22 ++ .../Chat/coati/experience_maker/__init__.py | 4 + .../Chat/coati/experience_maker/base.py | 77 +++++ .../Chat/coati/experience_maker/naive.py | 35 +++ applications/Chat/coati/models/__init__.py | 4 + .../Chat/coati/models/base/__init__.py | 6 + applications/Chat/coati/models/base/actor.py | 65 ++++ applications/Chat/coati/models/base/critic.py | 54 ++++ applications/Chat/coati/models/base/lm.py | 30 ++ .../Chat/coati/models/base/reward_model.py | 41 +++ .../Chat/coati/models/bloom/__init__.py | 6 + .../Chat/coati/models/bloom/bloom_actor.py | 35 +++ .../Chat/coati/models/bloom/bloom_critic.py | 38 +++ .../Chat/coati/models/bloom/bloom_lm.py | 35 +++ .../Chat/coati/models/bloom/bloom_rm.py | 37 +++ .../Chat/coati/models/deberta/__init__.py | 4 + .../coati/models/deberta/deberta_critic.py | 36 +++ .../Chat/coati/models/deberta/deberta_rm.py | 37 +++ applications/Chat/coati/models/generation.py | 146 +++++++++ .../Chat/coati/models/generation_utils.py | 92 ++++++ .../Chat/coati/models/gpt/__init__.py | 6 + .../Chat/coati/models/gpt/gpt_actor.py | 35 +++ .../Chat/coati/models/gpt/gpt_critic.py | 37 +++ applications/Chat/coati/models/gpt/gpt_lm.py | 35 +++ applications/Chat/coati/models/gpt/gpt_rm.py | 39 +++ .../Chat/coati/models/llama/__init__.py | 6 + .../Chat/coati/models/llama/llama_actor.py | 38 +++ .../Chat/coati/models/llama/llama_critic.py | 42 +++ .../Chat/coati/models/llama/llama_lm.py | 40 +++ .../Chat/coati/models/llama/llama_rm.py | 40 +++ applications/Chat/coati/models/lora.py | 129 ++++++++ applications/Chat/coati/models/loss.py | 117 ++++++++ .../Chat/coati/models/opt/__init__.py | 6 + .../Chat/coati/models/opt/opt_actor.py | 35 +++ .../Chat/coati/models/opt/opt_critic.py | 38 +++ applications/Chat/coati/models/opt/opt_lm.py | 35 +++ applications/Chat/coati/models/opt/opt_rm.py | 38 +++ applications/Chat/coati/models/utils.py | 92 ++++++ .../Chat/coati/replay_buffer/__init__.py | 4 + applications/Chat/coati/replay_buffer/base.py | 43 +++ .../Chat/coati/replay_buffer/naive.py | 57 ++++ .../Chat/coati/replay_buffer/utils.py | 73 +++++ applications/Chat/coati/trainer/__init__.py | 6 + applications/Chat/coati/trainer/base.py | 168 +++++++++++ .../Chat/coati/trainer/callbacks/__init__.py | 5 + .../Chat/coati/trainer/callbacks/base.py | 39 +++ .../callbacks/performance_evaluator.py | 133 ++++++++ .../trainer/callbacks/save_checkpoint.py | 75 +++++ applications/Chat/coati/trainer/ppo.py | 135 +++++++++ applications/Chat/coati/trainer/rm.py | 135 +++++++++ applications/Chat/coati/trainer/sft.py | 158 ++++++++++ .../Chat/coati/trainer/strategies/__init__.py | 6 + .../Chat/coati/trainer/strategies/base.py | 136 +++++++++ .../coati/trainer/strategies/colossalai.py | 213 +++++++++++++ .../Chat/coati/trainer/strategies/ddp.py | 93 ++++++ .../Chat/coati/trainer/strategies/naive.py | 55 ++++ .../Chat/coati/trainer/strategies/sampler.py | 32 ++ applications/Chat/coati/trainer/utils.py | 5 + applications/Chat/coati/utils/__init__.py | 3 + .../Chat/coati/utils/tokenizer_utils.py | 78 +++++ applications/Chat/examples/README.md | 141 +++++++++ applications/Chat/examples/inference.py | 59 ++++ applications/Chat/examples/requirements.txt | 2 + applications/Chat/examples/test_ci.sh | 97 ++++++ applications/Chat/examples/train_dummy.py | 148 +++++++++ applications/Chat/examples/train_dummy.sh | 18 ++ applications/Chat/examples/train_prompts.py | 199 ++++++++++++ applications/Chat/examples/train_prompts.sh | 18 ++ .../Chat/examples/train_reward_model.py | 160 ++++++++++ applications/Chat/examples/train_rm.sh | 8 + applications/Chat/examples/train_sft.py | 184 ++++++++++++ applications/Chat/examples/train_sft.sh | 12 + applications/Chat/inference/README.md | 111 +++++++ applications/Chat/inference/benchmark.py | 132 ++++++++ .../Chat/inference/llama_gptq/__init__.py | 5 + .../Chat/inference/llama_gptq/loader.py | 41 +++ .../Chat/inference/llama_gptq/model_utils.py | 13 + .../Chat/inference/llama_gptq/quant.py | 283 ++++++++++++++++++ applications/Chat/inference/locustfile.py | 27 ++ applications/Chat/inference/requirements.txt | 10 + applications/Chat/inference/server.py | 165 ++++++++++ .../Chat/inference/tests/test_chat_prompt.py | 56 ++++ applications/Chat/inference/utils.py | 179 +++++++++++ applications/Chat/pytest.ini | 6 + applications/Chat/requirements-test.txt | 1 + applications/Chat/requirements.txt | 13 + applications/Chat/setup.py | 41 +++ applications/Chat/tests/__init__.py | 0 applications/Chat/tests/test_checkpoint.py | 98 ++++++ applications/Chat/tests/test_data.py | 122 ++++++++ applications/Chat/version.txt | 1 + 106 files changed, 7069 insertions(+) create mode 100644 applications/Chat/.gitignore create mode 100644 applications/Chat/LICENSE create mode 100644 applications/Chat/README.md create mode 100644 applications/Chat/assets/data-collect.png create mode 100644 applications/Chat/assets/logo_coati.png create mode 100644 applications/Chat/assets/stage-3.jpeg create mode 100644 applications/Chat/benchmarks/README.md create mode 100644 applications/Chat/benchmarks/benchmark_gpt_dummy.py create mode 100755 applications/Chat/benchmarks/benchmark_gpt_dummy.sh create mode 100644 applications/Chat/benchmarks/benchmark_opt_lora_dummy.py create mode 100644 applications/Chat/coati/__init__.py create mode 100644 applications/Chat/coati/dataset/__init__.py create mode 100644 applications/Chat/coati/dataset/prompt_dataset.py create mode 100644 applications/Chat/coati/dataset/reward_dataset.py create mode 100644 applications/Chat/coati/dataset/sft_dataset.py create mode 100644 applications/Chat/coati/dataset/utils.py create mode 100644 applications/Chat/coati/experience_maker/__init__.py create mode 100644 applications/Chat/coati/experience_maker/base.py create mode 100644 applications/Chat/coati/experience_maker/naive.py create mode 100644 applications/Chat/coati/models/__init__.py create mode 100644 applications/Chat/coati/models/base/__init__.py create mode 100644 applications/Chat/coati/models/base/actor.py create mode 100644 applications/Chat/coati/models/base/critic.py create mode 100644 applications/Chat/coati/models/base/lm.py create mode 100644 applications/Chat/coati/models/base/reward_model.py create mode 100644 applications/Chat/coati/models/bloom/__init__.py create mode 100644 applications/Chat/coati/models/bloom/bloom_actor.py create mode 100644 applications/Chat/coati/models/bloom/bloom_critic.py create mode 100644 applications/Chat/coati/models/bloom/bloom_lm.py create mode 100644 applications/Chat/coati/models/bloom/bloom_rm.py create mode 100644 applications/Chat/coati/models/deberta/__init__.py create mode 100644 applications/Chat/coati/models/deberta/deberta_critic.py create mode 100644 applications/Chat/coati/models/deberta/deberta_rm.py create mode 100644 applications/Chat/coati/models/generation.py create mode 100644 applications/Chat/coati/models/generation_utils.py create mode 100644 applications/Chat/coati/models/gpt/__init__.py create mode 100644 applications/Chat/coati/models/gpt/gpt_actor.py create mode 100644 applications/Chat/coati/models/gpt/gpt_critic.py create mode 100644 applications/Chat/coati/models/gpt/gpt_lm.py create mode 100644 applications/Chat/coati/models/gpt/gpt_rm.py create mode 100644 applications/Chat/coati/models/llama/__init__.py create mode 100644 applications/Chat/coati/models/llama/llama_actor.py create mode 100644 applications/Chat/coati/models/llama/llama_critic.py create mode 100644 applications/Chat/coati/models/llama/llama_lm.py create mode 100644 applications/Chat/coati/models/llama/llama_rm.py create mode 100644 applications/Chat/coati/models/lora.py create mode 100644 applications/Chat/coati/models/loss.py create mode 100644 applications/Chat/coati/models/opt/__init__.py create mode 100644 applications/Chat/coati/models/opt/opt_actor.py create mode 100644 applications/Chat/coati/models/opt/opt_critic.py create mode 100644 applications/Chat/coati/models/opt/opt_lm.py create mode 100644 applications/Chat/coati/models/opt/opt_rm.py create mode 100644 applications/Chat/coati/models/utils.py create mode 100644 applications/Chat/coati/replay_buffer/__init__.py create mode 100644 applications/Chat/coati/replay_buffer/base.py create mode 100644 applications/Chat/coati/replay_buffer/naive.py create mode 100644 applications/Chat/coati/replay_buffer/utils.py create mode 100644 applications/Chat/coati/trainer/__init__.py create mode 100644 applications/Chat/coati/trainer/base.py create mode 100644 applications/Chat/coati/trainer/callbacks/__init__.py create mode 100644 applications/Chat/coati/trainer/callbacks/base.py create mode 100644 applications/Chat/coati/trainer/callbacks/performance_evaluator.py create mode 100644 applications/Chat/coati/trainer/callbacks/save_checkpoint.py create mode 100644 applications/Chat/coati/trainer/ppo.py create mode 100644 applications/Chat/coati/trainer/rm.py create mode 100644 applications/Chat/coati/trainer/sft.py create mode 100644 applications/Chat/coati/trainer/strategies/__init__.py create mode 100644 applications/Chat/coati/trainer/strategies/base.py create mode 100644 applications/Chat/coati/trainer/strategies/colossalai.py create mode 100644 applications/Chat/coati/trainer/strategies/ddp.py create mode 100644 applications/Chat/coati/trainer/strategies/naive.py create mode 100644 applications/Chat/coati/trainer/strategies/sampler.py create mode 100644 applications/Chat/coati/trainer/utils.py create mode 100644 applications/Chat/coati/utils/__init__.py create mode 100644 applications/Chat/coati/utils/tokenizer_utils.py create mode 100644 applications/Chat/examples/README.md create mode 100644 applications/Chat/examples/inference.py create mode 100644 applications/Chat/examples/requirements.txt create mode 100755 applications/Chat/examples/test_ci.sh create mode 100644 applications/Chat/examples/train_dummy.py create mode 100755 applications/Chat/examples/train_dummy.sh create mode 100644 applications/Chat/examples/train_prompts.py create mode 100755 applications/Chat/examples/train_prompts.sh create mode 100644 applications/Chat/examples/train_reward_model.py create mode 100755 applications/Chat/examples/train_rm.sh create mode 100644 applications/Chat/examples/train_sft.py create mode 100755 applications/Chat/examples/train_sft.sh create mode 100644 applications/Chat/inference/README.md create mode 100644 applications/Chat/inference/benchmark.py create mode 100644 applications/Chat/inference/llama_gptq/__init__.py create mode 100644 applications/Chat/inference/llama_gptq/loader.py create mode 100644 applications/Chat/inference/llama_gptq/model_utils.py create mode 100644 applications/Chat/inference/llama_gptq/quant.py create mode 100644 applications/Chat/inference/locustfile.py create mode 100644 applications/Chat/inference/requirements.txt create mode 100644 applications/Chat/inference/server.py create mode 100644 applications/Chat/inference/tests/test_chat_prompt.py create mode 100644 applications/Chat/inference/utils.py create mode 100644 applications/Chat/pytest.ini create mode 100644 applications/Chat/requirements-test.txt create mode 100644 applications/Chat/requirements.txt create mode 100644 applications/Chat/setup.py create mode 100644 applications/Chat/tests/__init__.py create mode 100644 applications/Chat/tests/test_checkpoint.py create mode 100644 applications/Chat/tests/test_data.py create mode 100644 applications/Chat/version.txt diff --git a/applications/Chat/.gitignore b/applications/Chat/.gitignore new file mode 100644 index 000000000000..1ec5f53a8b8d --- /dev/null +++ b/applications/Chat/.gitignore @@ -0,0 +1,146 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2021- HPC-AI Technology Inc. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/applications/Chat/README.md b/applications/Chat/README.md new file mode 100644 index 000000000000..731005ab25c3 --- /dev/null +++ b/applications/Chat/README.md @@ -0,0 +1,269 @@ +

    + Coati - ColossalAI Talking Intelligence + +

    + + +## Table of Contents + +- [Table of Contents](#table-of-contents) +- [What is Coati ?](#what-is-coati-) +- [Online demo](#online-demo) +- [Install](#install) + - [Install the environment](#install-the-environment) + - [Install the Transformers](#install-the-transformers) +- [How to use?](#how-to-use) + - [Supervised datasets collection](#supervised-datasets-collection) + - [Stage1 - Supervised instructs tuning](#stage1---supervised-instructs-tuning) + - [Stage2 - Training reward model](#stage2---training-reward-model) + - [Stage3 - Training model with reinforcement learning by human feedback](#stage3---training-model-with-reinforcement-learning-by-human-feedback) +- [Coati7B examples](#coati7b-examples) +- [FAQ](#faq) + - [How to save/load checkpoint](#how-to-saveload-checkpoint) +- [The Plan](#the-plan) + - [Real-time progress](#real-time-progress) +- [Invitation to open-source contribution](#invitation-to-open-source-contribution) +- [Quick Preview](#quick-preview) +- [Authors](#authors) +- [Citations](#citations) +- [Licenses](#licenses) +--- +## What is Coati ? + +Coati is a large language model developed by Colossal-AI, which is also a unified large language model framework that has implemented the following functions +- Supports comprehensive large-model training acceleration capabilities for ColossalAI, without requiring knowledge of complex distributed training algorithms +- Supervised datasets collection +- Supervised insturcts fine-tuning +- Training reward model +- Reinforcement learning with human feedback +- Quantization inference +- Fast model deploying +- Perfectly integration with the Hugging Face ecosystem, high degree of model customization + + +More details can be found in the [blog](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt). + +

    + +

    + +## Online demo +You can experience the performance of Coati7B on this page. + +[chat.colossalai.org](https://chat.colossalai.org/) + +> Warning: Due to model and dataset size limitations, Coati is just a baby model, Coati7B may output incorrect information and lack the ability for multi-turn dialogue. There is still significant room for improvement. +## Install + +### Install the environment + +```shell +conda creat -n coati +conda activate coati +pip install . +``` + +### Install the Transformers +Given Hugging Face hasn't officially supported the LLaMA models, We fork a branch of Transformers that can be compatible with our code + +```shell +git clone https://github.com/hpcaitech/transformers +cd transformers +pip install . +``` + +## How to use? + +### Supervised datasets collection + +we colllected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo + +Here is how we collected the data +

    + +

    + +### Stage1 - Supervised instructs tuning + +Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned earlier to fine-tune the model + +you can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning + +``` +torchrun --standalone --nproc_per_node=4 train_sft.py \ + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2 \ + --log_interval 10 \ + --save_path /path/to/Coati-7B \ + --dataset /path/to/data.json \ + --batch_size 4 \ + --accimulation_steps 8 \ + --lr 2e-5 \ + --max_datasets_size 512 \ + --max_epochs 1 \ +``` + +### Stage2 - Training reward model + +Stage2 trains a reward model, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model + +you can run the `examples/train_rm.sh` to start a reward model training + +``` +torchrun --standalone --nproc_per_node=4 train_reward_model.py + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2 \ + --loss_fn 'log_exp'\ + --save_path 'rmstatic.pt' \ +``` + +### Stage3 - Training model with reinforcement learning by human feedback + +Stage3 uses reinforcement learning algorithm, which is the most complex part of the training process: + +

    + +

    + +you can run the `examples/train_prompts.sh` to start training PPO with human feedback + +``` +torchrun --standalone --nproc_per_node=4 train_prompts.py prompts.csv \ + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2 +``` + + +For more details, see `examples/`. + +We also support training reward model with true-world data. See `examples/train_reward_model.py`. + +## Coati7B examples + + +## FAQ + +### How to save/load checkpoint + +We have integrated the Transformers save and load pipeline, allowing users to freely call Hugging Face's language models and save them in the HF format. + +``` +from coati.models.llama import LlamaLM +from coati.trainer import SFTTrainer + +model = LlamaLM(pretrained=args.pretrain) +tokenizer = AutoTokenizer.from_pretrained(args.pretrain) + +trainer = SFTTrainer(model=model, + strategy=strategy, + optim=optim, + train_dataloader=train_dataloader, + eval_dataloader=eval_dataloader, + batch_size=args.batch_size, + max_epochs=args.max_epochs, + accimulation_steps = args.accimulation_steps +) + +trainer.fit() +trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) +``` + +## The Plan + +- [x] implement PPO fine-tuning +- [x] implement training reward model +- [x] support LoRA +- [x] support inference +- [x] open source the reward model weight +- [x] support llama from [facebook](https://github.com/facebookresearch/llama) +- [x] implement PPO-ptx fine-tuning +- [ ] integrate with Ray +- [ ] support more RL paradigms, like Implicit Language Q-Learning (ILQL), +- [ ] support chain of throught by [langchain](https://github.com/hwchase17/langchain) + +### Real-time progress +You will find our progress in github project broad + +[Coati](https://github.com/orgs/hpcaitech/projects/17/views/1) + +## Invitation to open-source contribution +Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models from the starting point of replicating ChatGPT! + +You may contact us or participate in the following ways: +1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks! +2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md). +3. Join the Colossal-AI community on +[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w), +and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas. +4. Send your official proposal to email contact@hpcaitech.com + +Thanks so much to all of our amazing contributors! + +## Quick Preview +

    + +

    + +- Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference + +

    + +

    + +- Up to 10.3x growth in model capacity on one GPU +- A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU) + +

    + +

    + +- Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU +- Keep in a sufficiently high running speed + +## Authors + +Coati is developed by ColossalAI Team: [Fazzie](https://fazzie-key.cool/about/index.html), [FrankLeeeee](https://github.com/FrankLeeeee), [BlueRum](https://github.com/ht-zhou), [ver217](https://github.com/ver217) + +The Phd student [Zangwei Zheng](https://github.com/zhengzangw) and [Xue Fuzhao](https://github.com/XueFuzhao) also contributed a lot to this project. + +## Citations + +```bibtex +@article{Hu2021LoRALA, + title = {LoRA: Low-Rank Adaptation of Large Language Models}, + author = {Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Weizhu Chen}, + journal = {ArXiv}, + year = {2021}, + volume = {abs/2106.09685} +} + +@article{ouyang2022training, + title={Training language models to follow instructions with human feedback}, + author={Ouyang, Long and Wu, Jeff and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll L and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others}, + journal={arXiv preprint arXiv:2203.02155}, + year={2022} +} + +@article{touvron2023llama, + title={LLaMA: Open and Efficient Foundation Language Models}, + author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, + journal={arXiv preprint arXiv:2302.13971}, + year={2023} +} + +@misc{alpaca, + author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, + title = {Stanford Alpaca: An Instruction-following LLaMA model}, + year = {2023}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, +} +``` + +## Licenses + +Coati is licensed under the [Apache 2.0 License](LICENSE). diff --git a/applications/Chat/assets/data-collect.png b/applications/Chat/assets/data-collect.png new file mode 100644 index 0000000000000000000000000000000000000000..15eb662201b987dfa1f2de8872271bcc964b10e0 GIT binary patch literal 410749 zcmX6^by$<{_ufWI3QQ!WR7OZkii}37(cRtMBBN!5bR*qe5=yC~JETD;qeI$}zkR;H zvFjSw_5Sm`&vVXw?sK2}iGispk`mDo0RRBfH%fAv0018LUl0c&2>at12ATu_;D9%B z(%SyH`&|KvxdT4z{{*V9T!kh@l~#VpC*rC4Qx-~hT8 zyPnqy)KE;J-YX^iIQil)&GRCp?iBn@nGiJsXGWy=kfnJEExa(Ncx@1$FfkSd?)BCD z+G5R86?l&0n-UD9d9|Qjdummxe0jFnWG~nB6 zECQ>NLgo)ig)nrJUjSJHA3)YIaMfac)66UiI$CxBwUS5Gm{1N%s8?Q=zeFR~h*}fE zqe9&<)|9B?v0%GfE;?*`qOqcL7s$Z_!z{8UN3IMrsUK5ny|0>mQFfDwpBai$(|OOW 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index 000000000000..b4e28ba1d764 --- /dev/null +++ b/applications/Chat/benchmarks/README.md @@ -0,0 +1,94 @@ +# Benchmarks + +## Benchmark GPT on dummy prompt data + +We provide various GPT models (string in parentheses is the corresponding model name used in this script): + +- GPT2-S (s) +- GPT2-M (m) +- GPT2-L (l) +- GPT2-XL (xl) +- GPT2-4B (4b) +- GPT2-6B (6b) +- GPT2-8B (8b) +- GPT2-10B (10b) +- GPT2-12B (12b) +- GPT2-15B (15b) +- GPT2-18B (18b) +- GPT2-20B (20b) +- GPT2-24B (24b) +- GPT2-28B (28b) +- GPT2-32B (32b) +- GPT2-36B (36b) +- GPT2-40B (40b) +- GPT3 (175b) + +We also provide various training strategies: + +- ddp: torch DDP +- colossalai_gemini: ColossalAI GeminiDDP with `placement_policy="cuda"`, like zero3 +- colossalai_gemini_cpu: ColossalAI GeminiDDP with `placement_policy="cpu"`, like zero3-offload +- colossalai_zero2: ColossalAI zero2 +- colossalai_zero2_cpu: ColossalAI zero2-offload +- colossalai_zero1: ColossalAI zero1 +- colossalai_zero1_cpu: ColossalAI zero1-offload + +We only support `torchrun` to launch now. E.g. + +```shell +# run GPT2-S on single-node single-GPU with min batch size +torchrun --standalone --nproc_per_node 1 benchmark_gpt_dummy.py --model s --strategy ddp --experience_batch_size 1 --train_batch_size 1 +# run GPT2-XL on single-node 4-GPU +torchrun --standalone --nproc_per_node 4 benchmark_gpt_dummy.py --model xl --strategy colossalai_zero2 +# run GPT3 on 8-node 8-GPU +torchrun --nnodes 8 --nproc_per_node 8 \ + --rdzv_id=$JOB_ID --rdzv_backend=c10d --rdzv_endpoint=$HOST_NODE_ADDR \ + benchmark_gpt_dummy.py --model 175b --strategy colossalai_gemini +``` + +> ⚠ Batch sizes in CLI args and outputed throughput/TFLOPS are all values of per GPU. + +In this benchmark, we assume the model architectures/sizes of actor and critic are the same for simplicity. But in practice, to reduce training cost, we may use a smaller critic. + +We also provide a simple shell script to run a set of benchmarks. But it only supports benchmark on single node. However, it's easy to run on multi-nodes by modifying launch command in this script. + +Usage: + +```shell +# run for GPUS=(1 2 4 8) x strategy=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256) +./benchmark_gpt_dummy.sh +# run for GPUS=2 x strategy=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256) +./benchmark_gpt_dummy.sh 2 +# run for GPUS=2 x strategy=ddp x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256) +./benchmark_gpt_dummy.sh 2 ddp +# run for GPUS=2 x strategy=ddp x model=l x batch_size=(1 2 4 8 16 32 64 128 256) +./benchmark_gpt_dummy.sh 2 ddp l +``` + +## Benchmark OPT with LoRA on dummy prompt data + +We provide various OPT models (string in parentheses is the corresponding model name used in this script): + +- OPT-125M (125m) +- OPT-350M (350m) +- OPT-700M (700m) +- OPT-1.3B (1.3b) +- OPT-2.7B (2.7b) +- OPT-3.5B (3.5b) +- OPT-5.5B (5.5b) +- OPT-6.7B (6.7b) +- OPT-10B (10b) +- OPT-13B (13b) + +We only support `torchrun` to launch now. E.g. + +```shell +# run OPT-125M with no lora (lora_rank=0) on single-node single-GPU with min batch size +torchrun --standalone --nproc_per_node 1 benchmark_opt_lora_dummy.py --model 125m --strategy ddp --experience_batch_size 1 --train_batch_size 1 --lora_rank 0 +# run OPT-350M with lora_rank=4 on single-node 4-GPU +torchrun --standalone --nproc_per_node 4 benchmark_opt_lora_dummy.py --model 350m --strategy colossalai_zero2 --lora_rank 4 +``` + +> ⚠ Batch sizes in CLI args and outputed throughput/TFLOPS are all values of per GPU. + +In this benchmark, we assume the model architectures/sizes of actor and critic are the same for simplicity. But in practice, to reduce training cost, we may use a smaller critic. diff --git a/applications/Chat/benchmarks/benchmark_gpt_dummy.py b/applications/Chat/benchmarks/benchmark_gpt_dummy.py new file mode 100644 index 000000000000..c0d8b1c377aa --- /dev/null +++ b/applications/Chat/benchmarks/benchmark_gpt_dummy.py @@ -0,0 +1,184 @@ +import argparse +from copy import deepcopy + +import torch +import torch.distributed as dist +import torch.nn as nn +from coati.models.base import RewardModel +from coati.models.gpt import GPTActor, GPTCritic +from coati.trainer import PPOTrainer +from coati.trainer.callbacks import PerformanceEvaluator +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, Strategy +from torch.optim import Adam +from transformers.models.gpt2.configuration_gpt2 import GPT2Config +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.nn.optimizer import HybridAdam + + +def get_model_numel(model: nn.Module, strategy: Strategy) -> int: + numel = sum(p.numel() for p in model.parameters()) + if isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3 and strategy.shard_init: + numel *= dist.get_world_size() + return numel + + +def preprocess_batch(samples) -> dict: + input_ids = torch.stack(samples) + attention_mask = torch.ones_like(input_ids, dtype=torch.long) + return {'input_ids': input_ids, 'attention_mask': attention_mask} + + +def print_rank_0(*args, **kwargs) -> None: + if dist.get_rank() == 0: + print(*args, **kwargs) + + +def print_model_numel(model_dict: dict) -> None: + B = 1024**3 + M = 1024**2 + K = 1024 + outputs = '' + for name, numel in model_dict.items(): + outputs += f'{name}: ' + if numel >= B: + outputs += f'{numel / B:.2f} B\n' + elif numel >= M: + outputs += f'{numel / M:.2f} M\n' + elif numel >= K: + outputs += f'{numel / K:.2f} K\n' + else: + outputs += f'{numel}\n' + print_rank_0(outputs) + + +def get_gpt_config(model_name: str) -> GPT2Config: + model_map = { + 's': GPT2Config(), + 'm': GPT2Config(n_embd=1024, n_layer=24, n_head=16), + 'l': GPT2Config(n_embd=1280, n_layer=36, n_head=20), + 'xl': GPT2Config(n_embd=1600, n_layer=48, n_head=25), + '2b': GPT2Config(n_embd=2048, n_layer=40, n_head=16), + '4b': GPT2Config(n_embd=2304, n_layer=64, n_head=16), + '6b': GPT2Config(n_embd=4096, n_layer=30, n_head=16), + '8b': GPT2Config(n_embd=4096, n_layer=40, n_head=16), + '10b': GPT2Config(n_embd=4096, n_layer=50, n_head=16), + '12b': GPT2Config(n_embd=4096, n_layer=60, n_head=16), + '15b': GPT2Config(n_embd=4096, n_layer=78, n_head=16), + '18b': GPT2Config(n_embd=4096, n_layer=90, n_head=16), + '20b': GPT2Config(n_embd=8192, n_layer=25, n_head=16), + '24b': GPT2Config(n_embd=8192, n_layer=30, n_head=16), + '28b': GPT2Config(n_embd=8192, n_layer=35, n_head=16), + '32b': GPT2Config(n_embd=8192, n_layer=40, n_head=16), + '36b': GPT2Config(n_embd=8192, n_layer=45, n_head=16), + '40b': GPT2Config(n_embd=8192, n_layer=50, n_head=16), + '175b': GPT2Config(n_positions=2048, n_embd=12288, n_layer=96, n_head=96), + } + try: + return model_map[model_name] + except KeyError: + raise ValueError(f'Unknown model "{model_name}"') + + +def main(args): + if args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + elif args.strategy == 'colossalai_gemini_cpu': + strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + elif args.strategy == 'colossalai_zero2_cpu': + strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') + elif args.strategy == 'colossalai_zero1': + strategy = ColossalAIStrategy(stage=1, placement_policy='cuda') + elif args.strategy == 'colossalai_zero1_cpu': + strategy = ColossalAIStrategy(stage=1, placement_policy='cpu') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + model_config = get_gpt_config(args.model) + + with strategy.model_init_context(): + actor = GPTActor(config=model_config).cuda() + critic = GPTCritic(config=model_config).cuda() + + initial_model = deepcopy(actor).cuda() + reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda() + + actor_numel = get_model_numel(actor, strategy) + critic_numel = get_model_numel(critic, strategy) + initial_model_numel = get_model_numel(initial_model, strategy) + reward_model_numel = get_model_numel(reward_model, strategy) + print_model_numel({ + 'Actor': actor_numel, + 'Critic': critic_numel, + 'Initial model': initial_model_numel, + 'Reward model': reward_model_numel + }) + performance_evaluator = PerformanceEvaluator(actor_numel, + critic_numel, + initial_model_numel, + reward_model_numel, + enable_grad_checkpoint=False, + ignore_episodes=1) + + if args.strategy.startswith('colossalai'): + actor_optim = HybridAdam(actor.parameters(), lr=5e-6) + critic_optim = HybridAdam(critic.parameters(), lr=5e-6) + else: + actor_optim = Adam(actor.parameters(), lr=5e-6) + critic_optim = Adam(critic.parameters(), lr=5e-6) + + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + + (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( + (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) + + trainer = PPOTrainer(strategy, + actor, + critic, + reward_model, + initial_model, + actor_optim, + critic_optim, + max_epochs=args.max_epochs, + train_batch_size=args.train_batch_size, + experience_batch_size=args.experience_batch_size, + tokenizer=preprocess_batch, + max_length=512, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + callbacks=[performance_evaluator]) + + random_prompts = torch.randint(tokenizer.vocab_size, (1000, 400), device=torch.cuda.current_device()) + trainer.fit(random_prompts, + num_episodes=args.num_episodes, + max_timesteps=args.max_timesteps, + update_timesteps=args.update_timesteps) + + print_rank_0(f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.2f} GB') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--model', default='s') + parser.add_argument('--strategy', + choices=[ + 'ddp', 'colossalai_gemini', 'colossalai_gemini_cpu', 'colossalai_zero2', + 'colossalai_zero2_cpu', 'colossalai_zero1', 'colossalai_zero1_cpu' + ], + default='ddp') + parser.add_argument('--num_episodes', type=int, default=3) + parser.add_argument('--max_timesteps', type=int, default=8) + parser.add_argument('--update_timesteps', type=int, default=8) + parser.add_argument('--max_epochs', type=int, default=3) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--experience_batch_size', type=int, default=8) + args = parser.parse_args() + main(args) diff --git a/applications/Chat/benchmarks/benchmark_gpt_dummy.sh b/applications/Chat/benchmarks/benchmark_gpt_dummy.sh new file mode 100755 index 000000000000..d70f8872570a --- /dev/null +++ b/applications/Chat/benchmarks/benchmark_gpt_dummy.sh @@ -0,0 +1,45 @@ +#!/usr/bin/env bash +# Usage: $0 +set -xu + +BASE=$(realpath $(dirname $0)) + + +PY_SCRIPT=${BASE}/benchmark_gpt_dummy.py +export OMP_NUM_THREADS=8 + +function tune_batch_size() { + # we found when experience batch size is equal to train batch size + # peak CUDA memory usage of making experience phase is less than or equal to that of training phase + # thus, experience batch size can be larger than or equal to train batch size + for bs in 1 2 4 8 16 32 64 128 256; do + torchrun --standalone --nproc_per_node $1 $PY_SCRIPT --model $2 --strategy $3 --experience_batch_size $bs --train_batch_size $bs || return 1 + done +} + +if [ $# -eq 0 ]; then + num_gpus=(1 2 4 8) +else + num_gpus=($1) +fi + +if [ $# -le 1 ]; then + strategies=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") +else + strategies=($2) +fi + +if [ $# -le 2 ]; then + models=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") +else + models=($3) +fi + + +for num_gpu in ${num_gpus[@]}; do + for strategy in ${strategies[@]}; do + for model in ${models[@]}; do + tune_batch_size $num_gpu $model $strategy || break + done + done +done diff --git a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py new file mode 100644 index 000000000000..42df2e1f28cb --- /dev/null +++ b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py @@ -0,0 +1,179 @@ +import argparse +from copy import deepcopy + +import torch +import torch.distributed as dist +import torch.nn as nn +from coati.models.base import RewardModel +from coati.models.opt import OPTActor, OPTCritic +from coati.trainer import PPOTrainer +from coati.trainer.callbacks import PerformanceEvaluator +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, Strategy +from torch.optim import Adam +from transformers import AutoTokenizer +from transformers.models.opt.configuration_opt import OPTConfig + +from colossalai.nn.optimizer import HybridAdam + + +def get_model_numel(model: nn.Module, strategy: Strategy) -> int: + numel = sum(p.numel() for p in model.parameters()) + if isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3 and strategy.shard_init: + numel *= dist.get_world_size() + return numel + + +def preprocess_batch(samples) -> dict: + input_ids = torch.stack(samples) + attention_mask = torch.ones_like(input_ids, dtype=torch.long) + return {'input_ids': input_ids, 'attention_mask': attention_mask} + + +def print_rank_0(*args, **kwargs) -> None: + if dist.get_rank() == 0: + print(*args, **kwargs) + + +def print_model_numel(model_dict: dict) -> None: + B = 1024**3 + M = 1024**2 + K = 1024 + outputs = '' + for name, numel in model_dict.items(): + outputs += f'{name}: ' + if numel >= B: + outputs += f'{numel / B:.2f} B\n' + elif numel >= M: + outputs += f'{numel / M:.2f} M\n' + elif numel >= K: + outputs += f'{numel / K:.2f} K\n' + else: + outputs += f'{numel}\n' + print_rank_0(outputs) + + +def get_gpt_config(model_name: str) -> OPTConfig: + model_map = { + '125m': OPTConfig.from_pretrained('facebook/opt-125m'), + '350m': OPTConfig(hidden_size=1024, ffn_dim=4096, num_hidden_layers=24, num_attention_heads=16), + '700m': OPTConfig(hidden_size=1280, ffn_dim=5120, num_hidden_layers=36, num_attention_heads=20), + '1.3b': OPTConfig.from_pretrained('facebook/opt-1.3b'), + '2.7b': OPTConfig.from_pretrained('facebook/opt-2.7b'), + '3.5b': OPTConfig(hidden_size=3072, ffn_dim=12288, num_hidden_layers=32, num_attention_heads=32), + '5.5b': OPTConfig(hidden_size=3840, ffn_dim=15360, num_hidden_layers=32, num_attention_heads=32), + '6.7b': OPTConfig.from_pretrained('facebook/opt-6.7b'), + '10b': OPTConfig(hidden_size=5120, ffn_dim=20480, num_hidden_layers=32, num_attention_heads=32), + '13b': OPTConfig.from_pretrained('facebook/opt-13b'), + } + try: + return model_map[model_name] + except KeyError: + raise ValueError(f'Unknown model "{model_name}"') + + +def main(args): + if args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + elif args.strategy == 'colossalai_gemini_cpu': + strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + elif args.strategy == 'colossalai_zero2_cpu': + strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') + elif args.strategy == 'colossalai_zero1': + strategy = ColossalAIStrategy(stage=1, placement_policy='cuda') + elif args.strategy == 'colossalai_zero1_cpu': + strategy = ColossalAIStrategy(stage=1, placement_policy='cpu') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + torch.cuda.set_per_process_memory_fraction(args.cuda_mem_frac) + + model_config = get_gpt_config(args.model) + + with strategy.model_init_context(): + actor = OPTActor(config=model_config, lora_rank=args.lora_rank).cuda() + critic = OPTCritic(config=model_config, lora_rank=args.lora_rank).cuda() + + initial_model = deepcopy(actor).cuda() + reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda() + + actor_numel = get_model_numel(actor, strategy) + critic_numel = get_model_numel(critic, strategy) + initial_model_numel = get_model_numel(initial_model, strategy) + reward_model_numel = get_model_numel(reward_model, strategy) + print_model_numel({ + 'Actor': actor_numel, + 'Critic': critic_numel, + 'Initial model': initial_model_numel, + 'Reward model': reward_model_numel + }) + performance_evaluator = PerformanceEvaluator(actor_numel, + critic_numel, + initial_model_numel, + reward_model_numel, + enable_grad_checkpoint=False, + ignore_episodes=1) + + if args.strategy.startswith('colossalai'): + actor_optim = HybridAdam(actor.parameters(), lr=5e-6) + critic_optim = HybridAdam(critic.parameters(), lr=5e-6) + else: + actor_optim = Adam(actor.parameters(), lr=5e-6) + critic_optim = Adam(critic.parameters(), lr=5e-6) + + tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m') + tokenizer.pad_token = tokenizer.eos_token + + (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( + (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) + + trainer = PPOTrainer(strategy, + actor, + critic, + reward_model, + initial_model, + actor_optim, + critic_optim, + max_epochs=args.max_epochs, + train_batch_size=args.train_batch_size, + experience_batch_size=args.experience_batch_size, + tokenizer=preprocess_batch, + max_length=512, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + callbacks=[performance_evaluator]) + + random_prompts = torch.randint(tokenizer.vocab_size, (1000, 400), device=torch.cuda.current_device()) + trainer.fit(random_prompts, + num_episodes=args.num_episodes, + max_timesteps=args.max_timesteps, + update_timesteps=args.update_timesteps) + + print_rank_0(f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.2f} GB') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--model', default='125m') + parser.add_argument('--strategy', + choices=[ + 'ddp', 'colossalai_gemini', 'colossalai_gemini_cpu', 'colossalai_zero2', + 'colossalai_zero2_cpu', 'colossalai_zero1', 'colossalai_zero1_cpu' + ], + default='ddp') + parser.add_argument('--num_episodes', type=int, default=3) + parser.add_argument('--max_timesteps', type=int, default=8) + parser.add_argument('--update_timesteps', type=int, default=8) + parser.add_argument('--max_epochs', type=int, default=3) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=4) + parser.add_argument('--cuda_mem_frac', type=float, default=1.0) + args = parser.parse_args() + main(args) diff --git a/applications/Chat/coati/__init__.py b/applications/Chat/coati/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/applications/Chat/coati/dataset/__init__.py b/applications/Chat/coati/dataset/__init__.py new file mode 100644 index 000000000000..f650668e90b0 --- /dev/null +++ b/applications/Chat/coati/dataset/__init__.py @@ -0,0 +1,9 @@ +from .prompt_dataset import PromptDataset +from .reward_dataset import HhRlhfDataset, RmStaticDataset +from .sft_dataset import DataCollatorForSupervisedDataset, SFTDataset, SupervisedDataset +from .utils import is_rank_0 + +__all__ = [ + 'RmStaticDataset', 'HhRlhfDataset', 'is_rank_0', 'SFTDataset', 'SupervisedDataset', + 'DataCollatorForSupervisedDataset', 'PromptDataset' +] diff --git a/applications/Chat/coati/dataset/prompt_dataset.py b/applications/Chat/coati/dataset/prompt_dataset.py new file mode 100644 index 000000000000..4367a2c6f3ce --- /dev/null +++ b/applications/Chat/coati/dataset/prompt_dataset.py @@ -0,0 +1,46 @@ +import copy +import random +from dataclasses import dataclass, field +from typing import Callable, Dict, Sequence + +import torch +import torch.distributed as dist +import transformers +from torch.utils.data import Dataset +from tqdm import tqdm + +from colossalai.logging import get_dist_logger + +from .utils import is_rank_0, jload + +logger = get_dist_logger() + + +class PromptDataset(Dataset): + """Dataset for supervised fine-tuning.""" + + def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, max_datasets_size: int = None): + super(PromptDataset, self).__init__() + self.prompt = [] + logger.info("Loading data...") + list_data_dict = jload(data_path) + logger.info(f"Loaded {len(list_data_dict)} examples.") + + if max_datasets_size is not None: + logger.info(f"Limiting dataset to {max_datasets_size} examples.") + list_data_dict = list_data_dict[:max_datasets_size] + + for data_dict in list_data_dict: + token = tokenizer(data_dict["instruction"], + return_tensors='pt', + max_length=96, + padding='max_length', + truncation=True) + for idx in token['input_ids']: + self.prompt.append(idx.to(torch.cuda.current_device())) + + def __len__(self): + return len(self.prompt) + + def __getitem__(self, i) -> Dict[str, torch.Tensor]: + return self.prompt[i] diff --git a/applications/Chat/coati/dataset/reward_dataset.py b/applications/Chat/coati/dataset/reward_dataset.py new file mode 100644 index 000000000000..faa1c94d2728 --- /dev/null +++ b/applications/Chat/coati/dataset/reward_dataset.py @@ -0,0 +1,112 @@ +from typing import Callable + +from torch.utils.data import Dataset +from tqdm import tqdm + +from .utils import is_rank_0 + + +# Dahaos/rm-static +class RmStaticDataset(Dataset): + """ + Dataset for reward model + + Args: + dataset: dataset for reward model + tokenizer: tokenizer for reward model + max_length: max length of input + special_token: special token at the end of sentence + """ + + def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None: + super().__init__() + self.chosen = [] + self.reject = [] + if special_token is None: + self.end_token = tokenizer.eos_token + else: + self.end_token = special_token + for data in tqdm(dataset, disable=not is_rank_0()): + prompt = data['prompt'] + + chosen = prompt + data['chosen'] + self.end_token + chosen_token = tokenizer(chosen, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + self.chosen.append({ + "input_ids": chosen_token['input_ids'], + "attention_mask": chosen_token['attention_mask'] + }) + + reject = prompt + data['rejected'] + self.end_token + reject_token = tokenizer(reject, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + self.reject.append({ + "input_ids": reject_token['input_ids'], + "attention_mask": reject_token['attention_mask'] + }) + + def __len__(self): + length = len(self.chosen) + return length + + def __getitem__(self, idx): + return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][ + "input_ids"], self.reject[idx]["attention_mask"] + + +# Anthropic/hh-rlhf +class HhRlhfDataset(Dataset): + """ + Dataset for reward model + + Args: + dataset: dataset for reward model + tokenizer: tokenizer for reward model + max_length: max length of input + special_token: special token at the end of sentence + """ + + def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None: + super().__init__() + self.chosen = [] + self.reject = [] + if special_token is None: + self.end_token = tokenizer.eos_token + else: + self.end_token = special_token + for data in tqdm(dataset, disable=not is_rank_0()): + chosen = data['chosen'] + self.end_token + chosen_token = tokenizer(chosen, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + self.chosen.append({ + "input_ids": chosen_token['input_ids'], + "attention_mask": chosen_token['attention_mask'] + }) + + reject = data['rejected'] + self.end_token + reject_token = tokenizer(reject, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + self.reject.append({ + "input_ids": reject_token['input_ids'], + "attention_mask": reject_token['attention_mask'] + }) + + def __len__(self): + length = len(self.chosen) + return length + + def __getitem__(self, idx): + return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][ + "input_ids"], self.reject[idx]["attention_mask"] diff --git a/applications/Chat/coati/dataset/sft_dataset.py b/applications/Chat/coati/dataset/sft_dataset.py new file mode 100644 index 000000000000..76ae6b158822 --- /dev/null +++ b/applications/Chat/coati/dataset/sft_dataset.py @@ -0,0 +1,169 @@ +# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import random +from dataclasses import dataclass, field +from typing import Callable, Dict, Sequence + +import torch +import torch.distributed as dist +import transformers +from torch.utils.data import Dataset +from tqdm import tqdm + +from colossalai.logging import get_dist_logger + +from .utils import is_rank_0, jload + +logger = get_dist_logger() + +IGNORE_INDEX = -100 +PROMPT_DICT = { + "prompt_input": + ("Below is an instruction that describes a task, paired with an input that provides further context. " + "Write a response that appropriately completes the request.\n\n" + "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"), + "prompt_no_input": ("Below is an instruction that describes a task. " + "Write a response that appropriately completes the request.\n\n" + "### Instruction:\n{instruction}\n\n### Response:"), +} + + +class SFTDataset(Dataset): + """ + Dataset for sft model + + Args: + dataset: dataset for supervised model + tokenizer: tokenizer for supervised model + max_length: max length of input + """ + + def __init__(self, dataset, tokenizer: Callable, max_length: int = 512) -> None: + super().__init__() + # self.prompts = [] + self.input_ids = [] + + for data in tqdm(dataset, disable=not is_rank_0()): + prompt = data['prompt'] + data['completion'] + "<|endoftext|>" + prompt_token = tokenizer(prompt, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + + # self.prompts.append(prompt_token)s + self.input_ids.append(prompt_token) + self.labels = copy.deepcopy(self.input_ids) + + def __len__(self): + length = len(self.prompts) + return length + + def __getitem__(self, idx): + # dict(input_ids=self.input_ids[i], labels=self.labels[i]) + return dict(input_ids=self.input_ids[idx], labels=self.labels[idx]) + # return dict(self.prompts[idx], self.prompts[idx]) + + +def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: + """Tokenize a list of strings.""" + tokenized_list = [ + tokenizer( + text, + return_tensors="pt", + padding="longest", + max_length=tokenizer.model_max_length, + truncation=True, + ) for text in strings + ] + input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] + input_ids_lens = labels_lens = [ + tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list + ] + return dict( + input_ids=input_ids, + labels=labels, + input_ids_lens=input_ids_lens, + labels_lens=labels_lens, + ) + + +def preprocess( + sources: Sequence[str], + targets: Sequence[str], + tokenizer: transformers.PreTrainedTokenizer, +) -> Dict: + """Preprocess the data by tokenizing.""" + examples = [s + t for s, t in zip(sources, targets)] + examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)] + input_ids = examples_tokenized["input_ids"] + labels = copy.deepcopy(input_ids) + for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): + label[:source_len] = IGNORE_INDEX + return dict(input_ids=input_ids, labels=labels) + + +class SupervisedDataset(Dataset): + """Dataset for supervised fine-tuning.""" + + def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, max_datasets_size: int = None): + super(SupervisedDataset, self).__init__() + logger.info("Loading data...") + list_data_dict = jload(data_path) + logger.info(f"Loaded {len(list_data_dict)} examples.") + + if max_datasets_size is not None: + logger.info(f"Limiting dataset to {max_datasets_size} examples.") + list_data_dict = list_data_dict[:max_datasets_size] + + logger.info("Formatting inputs...") + prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] + sources = [ + prompt_input.format_map(example) if example.get("input", "") != "" else prompt_no_input.format_map(example) + for example in list_data_dict + ] + targets = [f"{example['output']}{tokenizer.eos_token}" for example in list_data_dict] + + logger.info("Tokenizing inputs... This may take some time...") + data_dict = preprocess(sources, targets, tokenizer) + + self.input_ids = data_dict["input_ids"] + self.labels = data_dict["labels"] + + def __len__(self): + return len(self.input_ids) + + def __getitem__(self, i) -> Dict[str, torch.Tensor]: + return dict(input_ids=self.input_ids[i], labels=self.labels[i]) + + +@dataclass +class DataCollatorForSupervisedDataset(object): + """Collate examples for supervised fine-tuning.""" + + tokenizer: transformers.PreTrainedTokenizer + + def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: + input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels")) + input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, + batch_first=True, + padding_value=self.tokenizer.pad_token_id) + labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) + return dict( + input_ids=input_ids, + labels=labels, + attention_mask=input_ids.ne(self.tokenizer.pad_token_id), + ) diff --git a/applications/Chat/coati/dataset/utils.py b/applications/Chat/coati/dataset/utils.py new file mode 100644 index 000000000000..f37fce67a7c6 --- /dev/null +++ b/applications/Chat/coati/dataset/utils.py @@ -0,0 +1,22 @@ +import io +import json + +import torch.distributed as dist + + +def is_rank_0() -> bool: + return not dist.is_initialized() or dist.get_rank() == 0 + + +def _make_r_io_base(f, mode: str): + if not isinstance(f, io.IOBase): + f = open(f, mode=mode) + return f + + +def jload(f, mode="r"): + """Load a .json file into a dictionary.""" + f = _make_r_io_base(f, mode) + jdict = json.load(f) + f.close() + return jdict diff --git a/applications/Chat/coati/experience_maker/__init__.py b/applications/Chat/coati/experience_maker/__init__.py new file mode 100644 index 000000000000..39ca7576b227 --- /dev/null +++ b/applications/Chat/coati/experience_maker/__init__.py @@ -0,0 +1,4 @@ +from .base import Experience, ExperienceMaker +from .naive import NaiveExperienceMaker + +__all__ = ['Experience', 'ExperienceMaker', 'NaiveExperienceMaker'] diff --git a/applications/Chat/coati/experience_maker/base.py b/applications/Chat/coati/experience_maker/base.py new file mode 100644 index 000000000000..61fd4f6744dc --- /dev/null +++ b/applications/Chat/coati/experience_maker/base.py @@ -0,0 +1,77 @@ +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Optional + +import torch +import torch.nn as nn +from coati.models.base import Actor + + +@dataclass +class Experience: + """Experience is a batch of data. + These data should have the the sequence length and number of actions. + Left padding for sequences is applied. + + Shapes of each tensor: + sequences: (B, S) + action_log_probs: (B, A) + values: (B) + reward: (B) + advatanges: (B) + attention_mask: (B, S) + action_mask: (B, A) + + "A" is the number of actions. + """ + sequences: torch.Tensor + action_log_probs: torch.Tensor + values: torch.Tensor + reward: torch.Tensor + advantages: torch.Tensor + attention_mask: Optional[torch.LongTensor] + action_mask: Optional[torch.BoolTensor] + + @torch.no_grad() + def to_device(self, device: torch.device) -> None: + self.sequences = self.sequences.to(device) + self.action_log_probs = self.action_log_probs.to(device) + self.values = self.values.to(device) + self.reward = self.reward.to(device) + self.advantages = self.advantages.to(device) + if self.attention_mask is not None: + self.attention_mask = self.attention_mask.to(device) + if self.action_mask is not None: + self.action_mask = self.action_mask.to(device) + + def pin_memory(self): + self.sequences = self.sequences.pin_memory() + self.action_log_probs = self.action_log_probs.pin_memory() + self.values = self.values.pin_memory() + self.reward = self.reward.pin_memory() + self.advantages = self.advantages.pin_memory() + if self.attention_mask is not None: + self.attention_mask = self.attention_mask.pin_memory() + if self.action_mask is not None: + self.action_mask = self.action_mask.pin_memory() + return self + + +class ExperienceMaker(ABC): + + def __init__(self, + actor: Actor, + critic: nn.Module, + reward_model: nn.Module, + initial_model: Actor, + kl_coef: float = 0.1) -> None: + super().__init__() + self.actor = actor + self.critic = critic + self.reward_model = reward_model + self.initial_model = initial_model + self.kl_coef = kl_coef + + @abstractmethod + def make_experience(self, input_ids: torch.Tensor, **generate_kwargs) -> Experience: + pass diff --git a/applications/Chat/coati/experience_maker/naive.py b/applications/Chat/coati/experience_maker/naive.py new file mode 100644 index 000000000000..94546eeb28e7 --- /dev/null +++ b/applications/Chat/coati/experience_maker/naive.py @@ -0,0 +1,35 @@ +import torch +from coati.models.utils import compute_reward, normalize + +from .base import Experience, ExperienceMaker + + +class NaiveExperienceMaker(ExperienceMaker): + """ + Naive experience maker. + """ + + @torch.no_grad() + def make_experience(self, input_ids: torch.Tensor, **generate_kwargs) -> Experience: + self.actor.eval() + self.critic.eval() + self.initial_model.eval() + self.reward_model.eval() + + sequences, attention_mask, action_mask = self.actor.generate(input_ids, + return_action_mask=True, + **generate_kwargs) + num_actions = action_mask.size(1) + + action_log_probs = self.actor(sequences, num_actions, attention_mask) + base_action_log_probs = self.initial_model(sequences, num_actions, attention_mask) + value = self.critic(sequences, action_mask, attention_mask) + r = self.reward_model(sequences, attention_mask) + reward = compute_reward(r, self.kl_coef, action_log_probs, base_action_log_probs, action_mask=action_mask) + + advantage = reward - value + # TODO(ver217): maybe normalize adv + if advantage.ndim == 1: + advantage = advantage.unsqueeze(-1) + + return Experience(sequences, action_log_probs, value, reward, advantage, attention_mask, action_mask) diff --git a/applications/Chat/coati/models/__init__.py b/applications/Chat/coati/models/__init__.py new file mode 100644 index 000000000000..7489b2e87ca0 --- /dev/null +++ b/applications/Chat/coati/models/__init__.py @@ -0,0 +1,4 @@ +from .base import Actor, Critic, RewardModel +from .loss import LogExpLoss, LogSigLoss, PolicyLoss, PPOPtxActorLoss, ValueLoss + +__all__ = ['Actor', 'Critic', 'RewardModel', 'PolicyLoss', 'ValueLoss', 'PPOPtxActorLoss', 'LogSigLoss', 'LogExpLoss'] diff --git a/applications/Chat/coati/models/base/__init__.py b/applications/Chat/coati/models/base/__init__.py new file mode 100644 index 000000000000..7cf82309af7b --- /dev/null +++ b/applications/Chat/coati/models/base/__init__.py @@ -0,0 +1,6 @@ +from .actor import Actor +from .critic import Critic +from .lm import LM +from .reward_model import RewardModel + +__all__ = ['Actor', 'Critic', 'RewardModel', 'LM'] diff --git a/applications/Chat/coati/models/base/actor.py b/applications/Chat/coati/models/base/actor.py new file mode 100644 index 000000000000..71fbf7bbae7d --- /dev/null +++ b/applications/Chat/coati/models/base/actor.py @@ -0,0 +1,65 @@ +from typing import Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..generation import generate +from ..lora import LoRAModule +from ..utils import log_probs_from_logits + + +class Actor(LoRAModule): + """ + Actor model base class. + + Args: + model (nn.Module): Actor Model. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = 'none') -> None: + super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias) + self.model = model + self.convert_to_lora() + + @torch.no_grad() + def generate( + self, + input_ids: torch.Tensor, + return_action_mask: bool = True, + **kwargs + ) -> Union[Tuple[torch.LongTensor, torch.LongTensor], Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor]]: + sequences = generate(self.model, input_ids, **kwargs) + attention_mask = None + pad_token_id = kwargs.get('pad_token_id', None) + if pad_token_id is not None: + attention_mask = sequences.not_equal(pad_token_id).to(dtype=torch.long, device=sequences.device) + if not return_action_mask: + return sequences, attention_mask, None + input_len = input_ids.size(1) + eos_token_id = kwargs.get('eos_token_id', None) + if eos_token_id is None: + action_mask = torch.ones_like(sequences, dtype=torch.bool) + else: + # left padding may be applied, only mask action + action_mask = (sequences[:, input_len:] == eos_token_id).cumsum(dim=-1) == 0 + action_mask = F.pad(action_mask, (1 + input_len, -1), value=True) # include eos token and input + action_mask[:, :input_len] = False + action_mask = action_mask[:, 1:] + return sequences, attention_mask, action_mask[:, -(sequences.size(1) - input_len):] + + def forward(self, + sequences: torch.LongTensor, + num_actions: int, + attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + """Returns action log probs + """ + output = self.model(sequences, attention_mask=attention_mask) + logits = output['logits'] + log_probs = log_probs_from_logits(logits[:, :-1, :], sequences[:, 1:]) + return log_probs[:, -num_actions:] + + def get_base_model(self): + return self.model diff --git a/applications/Chat/coati/models/base/critic.py b/applications/Chat/coati/models/base/critic.py new file mode 100644 index 000000000000..e68a743a7762 --- /dev/null +++ b/applications/Chat/coati/models/base/critic.py @@ -0,0 +1,54 @@ +from typing import Optional + +import torch +import torch.nn as nn + +from ..lora import LoRAModule +from ..utils import masked_mean + + +class Critic(LoRAModule): + """ + Critic model base class. + + Args: + model (nn.Module): Critic model. + value_head (nn.Module): Value head to get value. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__( + self, + model: nn.Module, + value_head: nn.Module, + lora_rank: int = 0, + lora_train_bias: str = 'none', + use_action_mask: bool = False, + ) -> None: + + super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias) + self.model = model + self.value_head = value_head + self.use_action_mask = use_action_mask + self.convert_to_lora() + + def forward(self, + sequences: torch.LongTensor, + action_mask: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + outputs = self.model(sequences, attention_mask=attention_mask) + last_hidden_states = outputs['last_hidden_state'] + + values = self.value_head(last_hidden_states).squeeze(-1) + + if action_mask is not None and self.use_action_mask: + num_actions = action_mask.size(1) + prompt_mask = attention_mask[:, :-num_actions] + values = values[:, :-num_actions] + value = masked_mean(values, prompt_mask, dim=1) + return value + + values = values[:, :-1] + value = values.mean(dim=1) + return value diff --git a/applications/Chat/coati/models/base/lm.py b/applications/Chat/coati/models/base/lm.py new file mode 100644 index 000000000000..e32ba4253369 --- /dev/null +++ b/applications/Chat/coati/models/base/lm.py @@ -0,0 +1,30 @@ +from typing import Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..generation import generate +from .actor import Actor + + +class LM(Actor): + """ + Language model base class. + + Args: + model (nn.Module): Language Model. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = 'none') -> None: + super().__init__(model=model, lora_rank=lora_rank, lora_train_bias=lora_train_bias) + + def forward(self, sequences: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + """Returns output log probs + """ + output = self.model(sequences, attention_mask=attention_mask) + logits = output['logits'] + log_probs = F.log_softmax(logits, dim=-1) + return log_probs diff --git a/applications/Chat/coati/models/base/reward_model.py b/applications/Chat/coati/models/base/reward_model.py new file mode 100644 index 000000000000..ce8c0a1d3568 --- /dev/null +++ b/applications/Chat/coati/models/base/reward_model.py @@ -0,0 +1,41 @@ +from typing import Optional + +import torch +import torch.nn as nn + +from ..lora import LoRAModule + + +class RewardModel(LoRAModule): + """ + Reward model base class. + + Args: + model (nn.Module): Reward model. + value_head (nn.Module): Value head to get reward score. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + model: nn.Module, + value_head: Optional[nn.Module] = None, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias) + self.model = model + self.convert_to_lora() + + if value_head is not None: + if value_head.out_features != 1: + raise ValueError("The value head of reward model's output dim should be 1!") + self.value_head = value_head + else: + self.value_head = nn.Linear(model.config.n_embd, 1) + + def forward(self, sequences: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + outputs = self.model(sequences, attention_mask=attention_mask) + last_hidden_states = outputs['last_hidden_state'] + values = self.value_head(last_hidden_states)[:, :-1] + value = values.mean(dim=1).squeeze(1) # ensure shape is (B) + return value diff --git a/applications/Chat/coati/models/bloom/__init__.py b/applications/Chat/coati/models/bloom/__init__.py new file mode 100644 index 000000000000..39dfe036a2f2 --- /dev/null +++ b/applications/Chat/coati/models/bloom/__init__.py @@ -0,0 +1,6 @@ +from .bloom_actor import BLOOMActor +from .bloom_critic import BLOOMCritic +from .bloom_lm import BLOOMLM +from .bloom_rm import BLOOMRM + +__all__ = ['BLOOMActor', 'BLOOMCritic', 'BLOOMRM', 'BLOOMLM'] diff --git a/applications/Chat/coati/models/bloom/bloom_actor.py b/applications/Chat/coati/models/bloom/bloom_actor.py new file mode 100644 index 000000000000..d7577f096493 --- /dev/null +++ b/applications/Chat/coati/models/bloom/bloom_actor.py @@ -0,0 +1,35 @@ +from typing import Optional + +import torch +from transformers import BloomConfig, BloomForCausalLM, BloomModel + +from ..base import Actor + + +class BLOOMActor(Actor): + """ + BLOOM Actor model. + + Args: + pretrained (str): Pretrained model name or path. + config (BloomConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: str = None, + config: Optional[BloomConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = BloomForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = BloomForCausalLM(config) + else: + model = BloomForCausalLM(BloomConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/bloom/bloom_critic.py b/applications/Chat/coati/models/bloom/bloom_critic.py new file mode 100644 index 000000000000..a32fb2e102f9 --- /dev/null +++ b/applications/Chat/coati/models/bloom/bloom_critic.py @@ -0,0 +1,38 @@ +from typing import Optional + +import torch +import torch.nn as nn +from transformers import BloomConfig, BloomForCausalLM, BloomModel + +from ..base import Critic + + +class BLOOMCritic(Critic): + """ + BLOOM Critic model. + + Args: + pretrained (str): Pretrained model name or path. + config (BloomConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: str = None, + config: Optional[BloomConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none', + **kwargs) -> None: + if pretrained is not None: + model = BloomModel.from_pretrained(pretrained) + elif config is not None: + model = BloomModel(config) + else: + model = BloomModel(BloomConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.hidden_size, 1) + super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/Chat/coati/models/bloom/bloom_lm.py b/applications/Chat/coati/models/bloom/bloom_lm.py new file mode 100644 index 000000000000..628af2e341a2 --- /dev/null +++ b/applications/Chat/coati/models/bloom/bloom_lm.py @@ -0,0 +1,35 @@ +from typing import Optional + +import torch +from transformers import BloomConfig, BloomForCausalLM, BloomModel + +from ..base import LM + + +class BLOOMLM(LM): + """ + BLOOM language model. + + Args: + pretrained (str): Pretrained model name or path. + config (BloomConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: str = None, + config: Optional[BloomConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = BloomForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = BloomForCausalLM(config) + else: + model = BloomForCausalLM(BloomConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/bloom/bloom_rm.py b/applications/Chat/coati/models/bloom/bloom_rm.py new file mode 100644 index 000000000000..22cfab441abb --- /dev/null +++ b/applications/Chat/coati/models/bloom/bloom_rm.py @@ -0,0 +1,37 @@ +from typing import Optional + +import torch.nn as nn +from transformers import BloomConfig, BloomForCausalLM, BloomModel + +from ..base import RewardModel + + +class BLOOMRM(RewardModel): + """ + BLOOM Reward model. + + Args: + pretrained (str): Pretrained model name or path. + config (BloomConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: str = None, + config: Optional[BloomConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = BloomModel.from_pretrained(pretrained) + elif config is not None: + model = BloomModel(config) + else: + model = BloomModel(BloomConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.hidden_size, 1) + value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1)) + super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/deberta/__init__.py b/applications/Chat/coati/models/deberta/__init__.py new file mode 100644 index 000000000000..b66888f34fd0 --- /dev/null +++ b/applications/Chat/coati/models/deberta/__init__.py @@ -0,0 +1,4 @@ +from .deberta_critic import DebertaCritic +from .deberta_rm import DebertaRM + +__all__ = ['DebertaCritic', 'DebertaRM'] diff --git a/applications/Chat/coati/models/deberta/deberta_critic.py b/applications/Chat/coati/models/deberta/deberta_critic.py new file mode 100644 index 000000000000..e84c1dbd8380 --- /dev/null +++ b/applications/Chat/coati/models/deberta/deberta_critic.py @@ -0,0 +1,36 @@ +from typing import Optional + +import torch.nn as nn +from transformers import DebertaV2Config, DebertaV2Model + +from ..base import Critic + + +class DebertaCritic(Critic): + """ + Deberta Critic model. + + Args: + pretrained (str): Pretrained model name or path. + config (DebertaV2Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the LO-RA decomposition. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[DebertaV2Config] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = DebertaV2Model.from_pretrained(pretrained) + elif config is not None: + model = DebertaV2Model(config) + else: + model = DebertaV2Model(DebertaV2Config()) + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.hidden_size, 1) + super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/deberta/deberta_rm.py b/applications/Chat/coati/models/deberta/deberta_rm.py new file mode 100644 index 000000000000..2448c879ec85 --- /dev/null +++ b/applications/Chat/coati/models/deberta/deberta_rm.py @@ -0,0 +1,37 @@ +from typing import Optional + +import torch.nn as nn +from transformers import DebertaV2Config, DebertaV2Model + +from ..base import RewardModel + + +class DebertaRM(RewardModel): + """ + Deberta Reward model. + + Args: + pretrained (str): Pretrained model name or path. + config (DebertaV2Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the LO-RA decomposition. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: str = None, + config: Optional[DebertaV2Config] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = DebertaV2Model.from_pretrained(pretrained) + elif config is not None: + model = DebertaV2Model(config) + else: + model = DebertaV2Model(DebertaV2Config()) + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.hidden_size, 1) + value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1)) + super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/generation.py b/applications/Chat/coati/models/generation.py new file mode 100644 index 000000000000..eb30c36d0f84 --- /dev/null +++ b/applications/Chat/coati/models/generation.py @@ -0,0 +1,146 @@ +from typing import Any, Callable, Optional + +import torch +import torch.distributed as dist +import torch.nn as nn + +try: + from transformers.generation_logits_process import ( + LogitsProcessorList, + TemperatureLogitsWarper, + TopKLogitsWarper, + TopPLogitsWarper, + ) +except ImportError: + from transformers.generation import LogitsProcessorList, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper + + +def prepare_logits_processor(top_k: Optional[int] = None, + top_p: Optional[float] = None, + temperature: Optional[float] = None) -> LogitsProcessorList: + processor_list = LogitsProcessorList() + if temperature is not None and temperature != 1.0: + processor_list.append(TemperatureLogitsWarper(temperature)) + if top_k is not None and top_k != 0: + processor_list.append(TopKLogitsWarper(top_k)) + if top_p is not None and top_p < 1.0: + processor_list.append(TopPLogitsWarper(top_p)) + return processor_list + + +def _is_sequence_finished(unfinished_sequences: torch.Tensor) -> bool: + if dist.is_initialized() and dist.get_world_size() > 1: + # consider DP + unfinished_sequences = unfinished_sequences.clone() + dist.all_reduce(unfinished_sequences) + return unfinished_sequences.max() == 0 + + +def sample(model: nn.Module, + input_ids: torch.Tensor, + max_length: int, + early_stopping: bool = False, + eos_token_id: Optional[int] = None, + pad_token_id: Optional[int] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + temperature: Optional[float] = None, + prepare_inputs_fn: Optional[Callable[[torch.Tensor, Any], dict]] = None, + update_model_kwargs_fn: Optional[Callable[[dict, Any], dict]] = None, + **model_kwargs) -> torch.Tensor: + if input_ids.size(1) >= max_length: + return input_ids + + logits_processor = prepare_logits_processor(top_k, top_p, temperature) + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + + for _ in range(input_ids.size(1), max_length): + model_inputs = prepare_inputs_fn(input_ids, **model_kwargs) if prepare_inputs_fn is not None else { + 'input_ids': input_ids + } + outputs = model(**model_inputs) + + next_token_logits = outputs['logits'][:, -1, :] + # pre-process distribution + next_token_logits = logits_processor(input_ids, next_token_logits) + # sample + probs = torch.softmax(next_token_logits, dim=-1, dtype=torch.float) + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + + # finished sentences should have their next token be a padding token + if eos_token_id is not None: + if pad_token_id is None: + raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.") + next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences) + + # update generated ids, model inputs for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + if update_model_kwargs_fn is not None: + model_kwargs = update_model_kwargs_fn(outputs, **model_kwargs) + + # if eos_token was found in one sentence, set sentence to finished + if eos_token_id is not None: + unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_token_id).long()) + + # stop when each sentence is finished if early_stopping=True + if early_stopping and _is_sequence_finished(unfinished_sequences): + break + + return input_ids + + +def generate(model: nn.Module, + input_ids: torch.Tensor, + max_length: int, + num_beams: int = 1, + do_sample: bool = True, + early_stopping: bool = False, + eos_token_id: Optional[int] = None, + pad_token_id: Optional[int] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + temperature: Optional[float] = None, + prepare_inputs_fn: Optional[Callable[[torch.Tensor, Any], dict]] = None, + update_model_kwargs_fn: Optional[Callable[[dict, Any], dict]] = None, + **model_kwargs) -> torch.Tensor: + """Generate token sequence. The returned sequence is input_ids + generated_tokens. + + Args: + model (nn.Module): model + input_ids (torch.Tensor): input sequence + max_length (int): max length of the returned sequence + num_beams (int, optional): number of beams. Defaults to 1. + do_sample (bool, optional): whether to do sample. Defaults to True. + early_stopping (bool, optional): if True, the sequence length may be smaller than max_length due to finding eos. Defaults to False. + eos_token_id (Optional[int], optional): end of sequence token id. Defaults to None. + pad_token_id (Optional[int], optional): pad token id. Defaults to None. + top_k (Optional[int], optional): the number of highest probability vocabulary tokens to keep for top-k-filtering. Defaults to None. + top_p (Optional[float], optional): If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. Defaults to None. + temperature (Optional[float], optional): The value used to module the next token probabilities. Defaults to None. + prepare_inputs_fn (Optional[Callable[[torch.Tensor, Any], dict]], optional): Function to preprocess model inputs. Arguments of this function should be input_ids and model_kwargs. Defaults to None. + update_model_kwargs_fn (Optional[Callable[[dict, Any], dict]], optional): Function to update model_kwargs based on outputs. Arguments of this function should be outputs and model_kwargs. Defaults to None. + """ + is_greedy_gen_mode = ((num_beams == 1) and do_sample is False) + is_sample_gen_mode = ((num_beams == 1) and do_sample is True) + is_beam_gen_mode = ((num_beams > 1) and do_sample is False) + if is_greedy_gen_mode: + # run greedy search + raise NotImplementedError + elif is_sample_gen_mode: + # run sample + return sample(model, + input_ids, + max_length, + early_stopping=early_stopping, + eos_token_id=eos_token_id, + pad_token_id=pad_token_id, + top_k=top_k, + top_p=top_p, + temperature=temperature, + prepare_inputs_fn=prepare_inputs_fn, + update_model_kwargs_fn=update_model_kwargs_fn, + **model_kwargs) + elif is_beam_gen_mode: + raise NotImplementedError + else: + raise ValueError("Unsupported generation mode") diff --git a/applications/Chat/coati/models/generation_utils.py b/applications/Chat/coati/models/generation_utils.py new file mode 100644 index 000000000000..c7bc1b383fb9 --- /dev/null +++ b/applications/Chat/coati/models/generation_utils.py @@ -0,0 +1,92 @@ +from typing import Optional + +import torch + + +def gpt_prepare_inputs_fn(input_ids: torch.Tensor, past: Optional[torch.Tensor] = None, **kwargs) -> dict: + token_type_ids = kwargs.get("token_type_ids", None) + # only last token for inputs_ids if past is defined in kwargs + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past: + position_ids = position_ids[:, -1].unsqueeze(-1) + else: + position_ids = None + return { + "input_ids": input_ids, + "past_key_values": past, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + } + + +def update_model_kwargs_fn(outputs: dict, **model_kwargs) -> dict: + if "past_key_values" in outputs: + model_kwargs["past"] = outputs["past_key_values"] + else: + model_kwargs["past"] = None + + # update token_type_ids with last value + if "token_type_ids" in model_kwargs: + token_type_ids = model_kwargs["token_type_ids"] + model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1) + + return model_kwargs + + +def opt_prepare_inputs_fn(input_ids: torch.Tensor, + past: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + **kwargs) -> dict: + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_ids.shape) + + if past: + input_ids = input_ids[:, -1:] + # first step, decoder_cached_states are empty + return { + "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed + "attention_mask": attention_mask, + "past_key_values": past, + "use_cache": use_cache, + } + + +def bloom_prepare_inputs_fn(input_ids: torch.Tensor, + past: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + **kwargs) -> dict: + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_ids.shape) + + if past: + input_ids = input_ids[:, -1:] + # first step, decoder_cached_states are empty + return { + "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed + "attention_mask": attention_mask, + "past_key_values": past, + "use_cache": use_cache, + } diff --git a/applications/Chat/coati/models/gpt/__init__.py b/applications/Chat/coati/models/gpt/__init__.py new file mode 100644 index 000000000000..9dc68e37544f --- /dev/null +++ b/applications/Chat/coati/models/gpt/__init__.py @@ -0,0 +1,6 @@ +from .gpt_actor import GPTActor +from .gpt_critic import GPTCritic +from .gpt_lm import GPTLM +from .gpt_rm import GPTRM + +__all__ = ['GPTActor', 'GPTCritic', 'GPTRM', 'GPTLM'] diff --git a/applications/Chat/coati/models/gpt/gpt_actor.py b/applications/Chat/coati/models/gpt/gpt_actor.py new file mode 100644 index 000000000000..6a53ad40b817 --- /dev/null +++ b/applications/Chat/coati/models/gpt/gpt_actor.py @@ -0,0 +1,35 @@ +from typing import Optional + +from transformers.models.gpt2.configuration_gpt2 import GPT2Config +from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel + +from ..base import Actor + + +class GPTActor(Actor): + """ + GPT Actor model. + + Args: + pretrained (str): Pretrained model name or path. + config (GPT2Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the LoRa layer. + lora_train_bias (str): Bias training strategy for the LoRa layer. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[GPT2Config] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = GPT2LMHeadModel.from_pretrained(pretrained) + elif config is not None: + model = GPT2LMHeadModel(config) + else: + model = GPT2LMHeadModel(GPT2Config()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/gpt/gpt_critic.py b/applications/Chat/coati/models/gpt/gpt_critic.py new file mode 100644 index 000000000000..25bb1ed94de4 --- /dev/null +++ b/applications/Chat/coati/models/gpt/gpt_critic.py @@ -0,0 +1,37 @@ +from typing import Optional + +import torch.nn as nn +from transformers.models.gpt2.configuration_gpt2 import GPT2Config +from transformers.models.gpt2.modeling_gpt2 import GPT2Model + +from ..base import Critic + + +class GPTCritic(Critic): + """ + GPT Critic model. + + Args: + pretrained (str): Pretrained model name or path. + config (GPT2Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the LO-RA decomposition. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[GPT2Config] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = GPT2Model.from_pretrained(pretrained) + elif config is not None: + model = GPT2Model(config) + else: + model = GPT2Model(GPT2Config()) + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.n_embd, 1) + super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/gpt/gpt_lm.py b/applications/Chat/coati/models/gpt/gpt_lm.py new file mode 100644 index 000000000000..23fc13bf23a4 --- /dev/null +++ b/applications/Chat/coati/models/gpt/gpt_lm.py @@ -0,0 +1,35 @@ +from typing import Optional + +from transformers.models.gpt2.configuration_gpt2 import GPT2Config +from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel + +from ..base import LM + + +class GPTLM(LM): + """ + GPT language model. + + Args: + pretrained (str): Pretrained model name or path. + config (GPT2Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the LoRa layer. + lora_train_bias (str): Bias training strategy for the LoRa layer. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[GPT2Config] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = GPT2LMHeadModel.from_pretrained(pretrained) + elif config is not None: + model = GPT2LMHeadModel(config) + else: + model = GPT2LMHeadModel(GPT2Config()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/gpt/gpt_rm.py b/applications/Chat/coati/models/gpt/gpt_rm.py new file mode 100644 index 000000000000..054432e1ce86 --- /dev/null +++ b/applications/Chat/coati/models/gpt/gpt_rm.py @@ -0,0 +1,39 @@ +from typing import Optional + +import torch.nn as nn +from transformers.models.gpt2.configuration_gpt2 import GPT2Config +from transformers.models.gpt2.modeling_gpt2 import GPT2Model + +from ..base import RewardModel + + +class GPTRM(RewardModel): + """ + GPT Reward model. + + Args: + pretrained (str): Pretrained model name or path. + config (GPT2Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the low-rank approximation. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[GPT2Config] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = GPT2Model.from_pretrained(pretrained) + elif config is not None: + model = GPT2Model(config) + else: + model = GPT2Model(GPT2Config()) + if checkpoint: + model.gradient_checkpointing_enable() + + value_head = nn.Linear(model.config.n_embd, 1) + value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.n_embd + 1)) + super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/llama/__init__.py b/applications/Chat/coati/models/llama/__init__.py new file mode 100644 index 000000000000..0d4dada3c9f1 --- /dev/null +++ b/applications/Chat/coati/models/llama/__init__.py @@ -0,0 +1,6 @@ +from .llama_actor import LlamaActor +from .llama_critic import LlamaCritic +from .llama_lm import LlamaLM +from .llama_rm import LlamaRM + +__all__ = ['LlamaActor', 'LlamaCritic', 'LlamaRM', 'LlamaLM'] diff --git a/applications/Chat/coati/models/llama/llama_actor.py b/applications/Chat/coati/models/llama/llama_actor.py new file mode 100644 index 000000000000..2c7adb390d8b --- /dev/null +++ b/applications/Chat/coati/models/llama/llama_actor.py @@ -0,0 +1,38 @@ +from typing import Optional + +import torch +from transformers import AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM + +from ..base import Actor + + +class LlamaActor(Actor): + """ + Llama Actor model. + + Args: + pretrained (str): Pretrained model name or path. + config (LlamaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[LlamaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + + if pretrained is not None: + model = LlamaForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = LlamaForCausalLM(config) + else: + model = LlamaForCausalLM(LlamaConfig()) + + if checkpoint: + model.gradient_checkpointing_enable() + + super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/llama/llama_critic.py b/applications/Chat/coati/models/llama/llama_critic.py new file mode 100644 index 000000000000..cd565031e112 --- /dev/null +++ b/applications/Chat/coati/models/llama/llama_critic.py @@ -0,0 +1,42 @@ +from typing import Optional + +import torch +import torch.nn as nn +from transformers import AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM + +from ..base import Critic + + +class LlamaCritic(Critic): + """ + Llama Critic model. + + Args: + pretrained (str): Pretrained model name or path. + config (LlamaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[LlamaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none', + **kwargs) -> None: + + if pretrained is not None: + model = LlamaForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = LlamaForCausalLM(config) + else: + model = LlamaForCausalLM(LlamaConfig()) + + if checkpoint: + model.gradient_checkpointing_enable() + + value_head = nn.Linear(model.config.hidden_size, 1) + + super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/Chat/coati/models/llama/llama_lm.py b/applications/Chat/coati/models/llama/llama_lm.py new file mode 100644 index 000000000000..181910fb13eb --- /dev/null +++ b/applications/Chat/coati/models/llama/llama_lm.py @@ -0,0 +1,40 @@ +from typing import Optional + +from transformers import LlamaConfig, LlamaForCausalLM + +from ..base import LM + + +class LlamaLM(LM): + """ + Llama language model. + + Args: + pretrained (str): Pretrained model name or path. + config (LlamaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[LlamaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + + if pretrained is not None: + model = LlamaForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = LlamaForCausalLM(config) + else: + model = LlamaForCausalLM(LlamaConfig()) + + if checkpoint: + model.gradient_checkpointing_enable() + + super().__init__(model, lora_rank, lora_train_bias) + + def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): + return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) diff --git a/applications/Chat/coati/models/llama/llama_rm.py b/applications/Chat/coati/models/llama/llama_rm.py new file mode 100644 index 000000000000..f936019d62d2 --- /dev/null +++ b/applications/Chat/coati/models/llama/llama_rm.py @@ -0,0 +1,40 @@ +from typing import Optional + +import torch.nn as nn +from transformers import LlamaConfig, LlamaForCausalLM, LlamaModel + +from ..base import RewardModel + + +class LlamaRM(RewardModel): + """ + Llama Reward model. + + Args: + pretrained (str): Pretrained model name or path. + config (LlamaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[LlamaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + + if pretrained is not None: + model = LlamaModel.from_pretrained(pretrained) + elif config is not None: + model = LlamaModel(config) + else: + model = LlamaModel(LlamaConfig()) + + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.hidden_size, 1) + value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1)) + + super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/lora.py b/applications/Chat/coati/models/lora.py new file mode 100644 index 000000000000..f8f7a1cb5d81 --- /dev/null +++ b/applications/Chat/coati/models/lora.py @@ -0,0 +1,129 @@ +import math +from typing import Optional + +import loralib as lora +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class LoraLinear(lora.LoRALayer, nn.Module): + """Replace in-place ops to out-of-place ops to fit gemini. Convert a torch.nn.Linear to LoraLinear. + """ + + def __init__( + self, + weight: nn.Parameter, + bias: Optional[nn.Parameter], + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0., + fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) + merge_weights: bool = True, + ): + nn.Module.__init__(self) + lora.LoRALayer.__init__(self, + r=r, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + merge_weights=merge_weights) + self.weight = weight + self.bias = bias + + out_features, in_features = weight.shape + self.in_features = in_features + self.out_features = out_features + + self.fan_in_fan_out = fan_in_fan_out + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Parameter(self.weight.new_zeros((r, in_features))) + self.lora_B = nn.Parameter(self.weight.new_zeros((out_features, r))) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + if fan_in_fan_out: + self.weight.data = self.weight.data.T + + def reset_parameters(self): + if hasattr(self, 'lora_A'): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B) + + def train(self, mode: bool = True): + + def T(w): + return w.T if self.fan_in_fan_out else w + + nn.Module.train(self, mode) + if self.merge_weights and self.merged: + # Make sure that the weights are not merged + if self.r > 0: + self.weight.data -= T(self.lora_B @ self.lora_A) * self.scaling + self.merged = False + + def eval(self): + + def T(w): + return w.T if self.fan_in_fan_out else w + + nn.Module.eval(self) + if self.merge_weights and not self.merged: + # Merge the weights and mark it + if self.r > 0: + self.weight.data += T(self.lora_B @ self.lora_A) * self.scaling + delattr(self, 'lora_A') + delattr(self, 'lora_B') + self.merged = True + + def forward(self, x: torch.Tensor): + + def T(w): + return w.T if self.fan_in_fan_out else w + + if self.r > 0 and not self.merged: + result = F.linear(x, T(self.weight), bias=self.bias) + if self.r > 0: + result = result + (self.lora_dropout(x) @ self.lora_A.t() @ self.lora_B.t()) * self.scaling + return result + else: + return F.linear(x, T(self.weight), bias=self.bias) + + +def lora_linear_wrapper(linear: nn.Linear, lora_rank: int) -> LoraLinear: + assert lora_rank <= linear.in_features, f'LoRA rank ({lora_rank}) must be less than or equal to in features ({linear.in_features})' + lora_linear = LoraLinear(linear.weight, linear.bias, r=lora_rank, merge_weights=False) + return lora_linear + + +def convert_to_lora_recursively(module: nn.Module, lora_rank: int) -> None: + for name, child in module.named_children(): + if isinstance(child, nn.Linear): + setattr(module, name, lora_linear_wrapper(child, lora_rank)) + else: + convert_to_lora_recursively(child, lora_rank) + + +class LoRAModule(nn.Module): + """A LoRA module base class. All derived classes should call `convert_to_lora()` at the bottom of `__init__()`. + This calss will convert all torch.nn.Linear layer to LoraLinear layer. + + Args: + lora_rank (int, optional): LoRA rank. 0 means LoRA is not applied. Defaults to 0. + lora_train_bias (str, optional): Whether LoRA train biases. + 'none' means it doesn't train biases. 'all' means it trains all biases. 'lora_only' means it only trains biases of LoRA layers. + Defaults to 'none'. + """ + + def __init__(self, lora_rank: int = 0, lora_train_bias: str = 'none') -> None: + super().__init__() + self.lora_rank = lora_rank + self.lora_train_bias = lora_train_bias + + def convert_to_lora(self) -> None: + if self.lora_rank <= 0: + return + convert_to_lora_recursively(self, self.lora_rank) + lora.mark_only_lora_as_trainable(self, self.lora_train_bias) diff --git a/applications/Chat/coati/models/loss.py b/applications/Chat/coati/models/loss.py new file mode 100644 index 000000000000..7fc437d90fdb --- /dev/null +++ b/applications/Chat/coati/models/loss.py @@ -0,0 +1,117 @@ +from typing import Optional + +import torch +import torch.nn as nn + +from .utils import masked_mean + + +class GPTLMLoss(nn.Module): + """ + GPT Language Model Loss + """ + + def __init__(self): + super().__init__() + self.loss = nn.CrossEntropyLoss() + + def forward(self, logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + return self.loss(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + +class PolicyLoss(nn.Module): + """ + Policy Loss for PPO + """ + + def __init__(self, clip_eps: float = 0.2) -> None: + super().__init__() + self.clip_eps = clip_eps + + def forward(self, + log_probs: torch.Tensor, + old_log_probs: torch.Tensor, + advantages: torch.Tensor, + action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + ratio = (log_probs - old_log_probs).exp() + surr1 = ratio * advantages + surr2 = ratio.clamp(1 - self.clip_eps, 1 + self.clip_eps) * advantages + loss = -torch.min(surr1, surr2) + if action_mask is not None: + loss = masked_mean(loss, action_mask) + loss = loss.mean() + return loss + + +class ValueLoss(nn.Module): + """ + Value Loss for PPO + """ + + def __init__(self, clip_eps: float = 0.4) -> None: + super().__init__() + self.clip_eps = clip_eps + + def forward(self, + values: torch.Tensor, + old_values: torch.Tensor, + reward: torch.Tensor, + action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + values_clipped = old_values + (values - old_values).clamp(-self.clip_eps, self.clip_eps) + surr1 = (values_clipped - reward)**2 + surr2 = (values - reward)**2 + loss = torch.max(surr1, surr2) + loss = loss.mean() + return loss + + +class PPOPtxActorLoss(nn.Module): + """ + To Do: + + PPO-ptx Actor Loss + """ + + def __init__(self, policy_clip_eps: float = 0.2, pretrain_coef: float = 0.0, pretrain_loss_fn=GPTLMLoss()) -> None: + super().__init__() + self.pretrain_coef = pretrain_coef + self.policy_loss_fn = PolicyLoss(clip_eps=policy_clip_eps) + self.pretrain_loss_fn = pretrain_loss_fn + + def forward(self, + log_probs: torch.Tensor, + old_log_probs: torch.Tensor, + advantages: torch.Tensor, + lm_logits: torch.Tensor, + lm_input_ids: torch.Tensor, + action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + policy_loss = self.policy_loss_fn(log_probs, old_log_probs, advantages, action_mask=action_mask) + lm_loss = self.pretrain_loss_fn(lm_logits, lm_input_ids) + return policy_loss + self.pretrain_coef * lm_loss + + +class LogSigLoss(nn.Module): + """ + Pairwise Loss for Reward Model + Details: https://arxiv.org/abs/2203.02155 + """ + + def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor: + probs = torch.sigmoid(chosen_reward - reject_reward) + log_probs = torch.log(probs) + loss = -log_probs.mean() + return loss + + +class LogExpLoss(nn.Module): + """ + Pairwise Loss for Reward Model + Details: https://arxiv.org/abs/2204.05862 + """ + + def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor: + loss = torch.log(1 + torch.exp(reject_reward - chosen_reward)).mean() + return loss diff --git a/applications/Chat/coati/models/opt/__init__.py b/applications/Chat/coati/models/opt/__init__.py new file mode 100644 index 000000000000..3d7a8adbf82e --- /dev/null +++ b/applications/Chat/coati/models/opt/__init__.py @@ -0,0 +1,6 @@ +from .opt_actor import OPTActor +from .opt_critic import OPTCritic +from .opt_lm import OPTLM +from .opt_rm import OPTRM + +__all__ = ['OPTActor', 'OPTCritic', 'OPTRM', 'OPTLM'] diff --git a/applications/Chat/coati/models/opt/opt_actor.py b/applications/Chat/coati/models/opt/opt_actor.py new file mode 100644 index 000000000000..c14e4377ffb2 --- /dev/null +++ b/applications/Chat/coati/models/opt/opt_actor.py @@ -0,0 +1,35 @@ +from typing import Optional + +from transformers.models.opt.configuration_opt import OPTConfig +from transformers.models.opt.modeling_opt import OPTForCausalLM + +from ..base import Actor + + +class OPTActor(Actor): + """ + OPT Actor model. + + Args: + pretrained (str): Pretrained model name or path. + config (OPTConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the low-rank approximation. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[OPTConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = OPTForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = OPTForCausalLM(config) + else: + model = OPTForCausalLM(OPTConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/opt/opt_critic.py b/applications/Chat/coati/models/opt/opt_critic.py new file mode 100644 index 000000000000..fcfebd8a8b03 --- /dev/null +++ b/applications/Chat/coati/models/opt/opt_critic.py @@ -0,0 +1,38 @@ +from typing import Optional + +import torch.nn as nn +from transformers.models.opt.configuration_opt import OPTConfig +from transformers.models.opt.modeling_opt import OPTModel + +from ..base import Critic + + +class OPTCritic(Critic): + """ + OPT Critic model. + + Args: + pretrained (str): Pretrained model name or path. + config (OPTConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the low-rank approximation. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[OPTConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none', + **kwargs) -> None: + if pretrained is not None: + model = OPTModel.from_pretrained(pretrained) + elif config is not None: + model = OPTModel(config) + else: + model = OPTModel(OPTConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.word_embed_proj_dim, 1) + super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/Chat/coati/models/opt/opt_lm.py b/applications/Chat/coati/models/opt/opt_lm.py new file mode 100644 index 000000000000..65d79e1b2307 --- /dev/null +++ b/applications/Chat/coati/models/opt/opt_lm.py @@ -0,0 +1,35 @@ +from typing import Optional + +from transformers.models.opt.configuration_opt import OPTConfig +from transformers.models.opt.modeling_opt import OPTForCausalLM + +from ..base import LM + + +class OPTLM(LM): + """ + OPT language model. + + Args: + pretrained (str): Pretrained model name or path. + config (OPTConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the low-rank approximation. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[OPTConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = OPTForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = OPTForCausalLM(config) + else: + model = OPTForCausalLM(OPTConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/opt/opt_rm.py b/applications/Chat/coati/models/opt/opt_rm.py new file mode 100644 index 000000000000..50fc0dee8568 --- /dev/null +++ b/applications/Chat/coati/models/opt/opt_rm.py @@ -0,0 +1,38 @@ +from typing import Optional + +import torch.nn as nn +from transformers import OPTConfig, OPTModel + +from ..base import RewardModel + + +class OPTRM(RewardModel): + """ + OPT Reward model. + + Args: + pretrained (str): Pretrained model name or path. + config (OPTConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the low-rank approximation. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[OPTConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = OPTModel.from_pretrained(pretrained) + elif config is not None: + model = OPTModel(config) + else: + model = OPTModel(OPTConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + + value_head = nn.Linear(model.config.word_embed_proj_dim, 1) + value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.word_embed_proj_dim + 1)) + super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/utils.py b/applications/Chat/coati/models/utils.py new file mode 100644 index 000000000000..0ff13181fcd2 --- /dev/null +++ b/applications/Chat/coati/models/utils.py @@ -0,0 +1,92 @@ +from typing import Optional, Union + +import loralib as lora +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def compute_approx_kl(log_probs: torch.Tensor, + log_probs_base: torch.Tensor, + action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + """ + Compute the approximate KL divergence between two distributions. + Schulman blog: http://joschu.net/blog/kl-approx.html + + Args: + log_probs: Log probabilities of the new distribution. + log_probs_base: Log probabilities of the base distribution. + action_mask: Mask for actions. + """ + + log_ratio = log_probs - log_probs_base + approx_kl = (log_ratio.exp() - 1) - log_ratio + if action_mask is not None: + approx_kl = masked_mean(approx_kl, action_mask, dim=1) + return approx_kl + approx_kl = approx_kl.mean(dim=1) + return approx_kl + + +def compute_reward(r: Union[torch.Tensor, float], + kl_coef: float, + log_probs: torch.Tensor, + log_probs_base: torch.Tensor, + action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + if kl_coef <= 0.0: + return r + kl = compute_approx_kl(log_probs, log_probs_base, action_mask=action_mask) + reward = r - kl_coef * kl + return reward + + +def log_probs_from_logits(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + log_probs = F.log_softmax(logits, dim=-1) + log_probs_labels = log_probs.gather(dim=-1, index=labels.unsqueeze(-1)) + return log_probs_labels.squeeze(-1) + + +def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor: + tensor = tensor * mask + tensor = tensor.sum(dim=dim) + mask_sum = mask.sum(dim=dim) + mean = tensor / (mask_sum + 1e-8) + return mean + + +def masked_normalize(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1, eps: float = 1e-8) -> torch.Tensor: + tensor = tensor * mask + mean = masked_mean(tensor, mask, dim=dim) + mean_centered = tensor - mean + var = masked_mean(mean_centered**2, mask, dim=dim) + return mean_centered * var.clamp(min=eps).rsqrt() + + +def normalize(tensor: torch.Tensor, dim: int = 0, eps: float = 1e-8) -> torch.Tensor: + mean = tensor.mean(dim) + mean_centered = tensor - mean + var = (mean_centered**2).mean(dim) + norm = mean_centered * var.clamp(min=eps).rsqrt() + return norm + + +def convert_to_lora(model: nn.Module, + input_size: int, + output_size: int, + lora_rank: int = 16, + lora_alpha: int = 1, + lora_dropout: float = 0., + fan_in_fan_out: bool = False, + merge_weights: bool = True): + if lora_rank > min(input_size, output_size): + raise ValueError(f"LoRA rank {lora_rank} must be less or equal than {min(input_size, output_size)}") + + for name, module in model.named_modules(): + if isinstance(module, nn.Linear): + module._modules[name] = lora.Linear(input_size, + output_size, + r=lora_rank, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + fan_in_fan_out=fan_in_fan_out, + merge_weights=merge_weights) diff --git a/applications/Chat/coati/replay_buffer/__init__.py b/applications/Chat/coati/replay_buffer/__init__.py new file mode 100644 index 000000000000..1ebf60382913 --- /dev/null +++ b/applications/Chat/coati/replay_buffer/__init__.py @@ -0,0 +1,4 @@ +from .base import ReplayBuffer +from .naive import NaiveReplayBuffer + +__all__ = ['ReplayBuffer', 'NaiveReplayBuffer'] diff --git a/applications/Chat/coati/replay_buffer/base.py b/applications/Chat/coati/replay_buffer/base.py new file mode 100644 index 000000000000..4c3812461a10 --- /dev/null +++ b/applications/Chat/coati/replay_buffer/base.py @@ -0,0 +1,43 @@ +from abc import ABC, abstractmethod +from typing import Any + +from coati.experience_maker.base import Experience + + +class ReplayBuffer(ABC): + """Replay buffer base class. It stores experience. + + Args: + sample_batch_size (int): Batch size when sampling. + limit (int, optional): Limit of number of experience samples. A number <= 0 means unlimited. Defaults to 0. + """ + + def __init__(self, sample_batch_size: int, limit: int = 0) -> None: + super().__init__() + self.sample_batch_size = sample_batch_size + # limit <= 0 means unlimited + self.limit = limit + + @abstractmethod + def append(self, experience: Experience) -> None: + pass + + @abstractmethod + def clear(self) -> None: + pass + + @abstractmethod + def sample(self) -> Experience: + pass + + @abstractmethod + def __len__(self) -> int: + pass + + @abstractmethod + def __getitem__(self, idx: int) -> Any: + pass + + @abstractmethod + def collate_fn(self, batch: Any) -> Experience: + pass diff --git a/applications/Chat/coati/replay_buffer/naive.py b/applications/Chat/coati/replay_buffer/naive.py new file mode 100644 index 000000000000..938f500643c9 --- /dev/null +++ b/applications/Chat/coati/replay_buffer/naive.py @@ -0,0 +1,57 @@ +import random +from typing import List + +import torch +from coati.experience_maker.base import Experience + +from .base import ReplayBuffer +from .utils import BufferItem, make_experience_batch, split_experience_batch + + +class NaiveReplayBuffer(ReplayBuffer): + """Naive replay buffer class. It stores experience. + + Args: + sample_batch_size (int): Batch size when sampling. + limit (int, optional): Limit of number of experience samples. A number <= 0 means unlimited. Defaults to 0. + cpu_offload (bool, optional): Whether to offload experience to cpu when sampling. Defaults to True. + """ + + def __init__(self, sample_batch_size: int, limit: int = 0, cpu_offload: bool = True) -> None: + super().__init__(sample_batch_size, limit) + self.cpu_offload = cpu_offload + self.target_device = torch.device(f'cuda:{torch.cuda.current_device()}') + # TODO(ver217): add prefetch + self.items: List[BufferItem] = [] + + @torch.no_grad() + def append(self, experience: Experience) -> None: + if self.cpu_offload: + experience.to_device(torch.device('cpu')) + items = split_experience_batch(experience) + self.items.extend(items) + if self.limit > 0: + samples_to_remove = len(self.items) - self.limit + if samples_to_remove > 0: + self.items = self.items[samples_to_remove:] + + def clear(self) -> None: + self.items.clear() + + @torch.no_grad() + def sample(self) -> Experience: + items = random.sample(self.items, self.sample_batch_size) + experience = make_experience_batch(items) + if self.cpu_offload: + experience.to_device(self.target_device) + return experience + + def __len__(self) -> int: + return len(self.items) + + def __getitem__(self, idx: int) -> BufferItem: + return self.items[idx] + + def collate_fn(self, batch) -> Experience: + experience = make_experience_batch(batch) + return experience diff --git a/applications/Chat/coati/replay_buffer/utils.py b/applications/Chat/coati/replay_buffer/utils.py new file mode 100644 index 000000000000..55ddb2ae8191 --- /dev/null +++ b/applications/Chat/coati/replay_buffer/utils.py @@ -0,0 +1,73 @@ +from dataclasses import dataclass +from typing import List, Optional + +import torch +import torch.nn.functional as F +from coati.experience_maker.base import Experience + + +@dataclass +class BufferItem: + """BufferItem is an item of experience data. + + Shapes of each tensor: + sequences: (S) + action_log_probs: (A) + values: (1) + reward: (1) + advatanges: (1) + attention_mask: (S) + action_mask: (A) + + "A" is the number of actions. + """ + sequences: torch.Tensor + action_log_probs: torch.Tensor + values: torch.Tensor + reward: torch.Tensor + advantages: torch.Tensor + attention_mask: Optional[torch.LongTensor] + action_mask: Optional[torch.BoolTensor] + + +def split_experience_batch(experience: Experience) -> List[BufferItem]: + batch_size = experience.sequences.size(0) + batch_kwargs = [{} for _ in range(batch_size)] + keys = ('sequences', 'action_log_probs', 'values', 'reward', 'advantages', 'attention_mask', 'action_mask') + for key in keys: + value = getattr(experience, key) + if isinstance(value, torch.Tensor): + vals = torch.unbind(value) + else: + # None + vals = [value for _ in range(batch_size)] + assert batch_size == len(vals) + for i, v in enumerate(vals): + batch_kwargs[i][key] = v + items = [BufferItem(**kwargs) for kwargs in batch_kwargs] + return items + + +def zero_pad_sequences(sequences: List[torch.Tensor], side: str = 'left') -> torch.Tensor: + assert side in ('left', 'right') + max_len = max(seq.size(0) for seq in sequences) + padded_sequences = [] + for seq in sequences: + pad_len = max_len - seq.size(0) + padding = (pad_len, 0) if side == 'left' else (0, pad_len) + padded_sequences.append(F.pad(seq, padding)) + return torch.stack(padded_sequences, dim=0) + + +def make_experience_batch(items: List[BufferItem]) -> Experience: + kwargs = {} + to_pad_keys = set(('action_log_probs', 'action_mask')) + keys = ('sequences', 'action_log_probs', 'values', 'reward', 'advantages', 'attention_mask', 'action_mask') + for key in keys: + vals = [getattr(item, key) for item in items] + if key in to_pad_keys: + batch_data = zero_pad_sequences(vals) + else: + batch_data = torch.stack(vals, dim=0) + kwargs[key] = batch_data + return Experience(**kwargs) diff --git a/applications/Chat/coati/trainer/__init__.py b/applications/Chat/coati/trainer/__init__.py new file mode 100644 index 000000000000..525b57bf21d3 --- /dev/null +++ b/applications/Chat/coati/trainer/__init__.py @@ -0,0 +1,6 @@ +from .base import Trainer +from .ppo import PPOTrainer +from .rm import RewardModelTrainer +from .sft import SFTTrainer + +__all__ = ['Trainer', 'PPOTrainer', 'RewardModelTrainer', 'SFTTrainer'] diff --git a/applications/Chat/coati/trainer/base.py b/applications/Chat/coati/trainer/base.py new file mode 100644 index 000000000000..610bb5111976 --- /dev/null +++ b/applications/Chat/coati/trainer/base.py @@ -0,0 +1,168 @@ +from abc import ABC, abstractmethod +from typing import Any, Callable, Dict, List, Optional, Union + +import torch +from coati.experience_maker import Experience, ExperienceMaker +from coati.replay_buffer import ReplayBuffer +from torch import Tensor +from torch.utils.data import DistributedSampler +from tqdm import tqdm + +from .callbacks import Callback +from .strategies import Strategy +from .utils import is_rank_0 + + +class Trainer(ABC): + """ + Base class for rlhf trainers. + + Args: + strategy (Strategy):the strategy to use for training + experience_maker (ExperienceMaker): the experience maker to use for produce experience to fullfill replay buffer + replay_buffer (ReplayBuffer): the replay buffer to use for training + experience_batch_size (int, defaults to 8): the batch size to use for experience generation + max_epochs (int, defaults to 1): the number of epochs of training process + tokenizer (Callable, optional): the tokenizer to use for tokenizing the input + sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer + data_loader_pin_memory (bool, defaults to True): whether to pin memory for data loader + callbacks (List[Callback], defaults to []): the callbacks to call during training process + generate_kwargs (dict, optional): the kwargs to use while model generating + """ + + def __init__(self, + strategy: Strategy, + experience_maker: ExperienceMaker, + replay_buffer: ReplayBuffer, + experience_batch_size: int = 8, + max_epochs: int = 1, + tokenizer: Optional[Callable[[Any], dict]] = None, + sample_replay_buffer: bool = False, + dataloader_pin_memory: bool = True, + callbacks: List[Callback] = [], + **generate_kwargs) -> None: + super().__init__() + self.strategy = strategy + self.experience_maker = experience_maker + self.replay_buffer = replay_buffer + self.experience_batch_size = experience_batch_size + self.max_epochs = max_epochs + self.tokenizer = tokenizer + self.generate_kwargs = generate_kwargs + self.sample_replay_buffer = sample_replay_buffer + self.dataloader_pin_memory = dataloader_pin_memory + self.callbacks = callbacks + + @abstractmethod + def training_step(self, experience: Experience) -> Dict[str, Any]: + pass + + def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience: + if isinstance(inputs, Tensor): + return self.experience_maker.make_experience(inputs, **self.generate_kwargs) + elif isinstance(inputs, dict): + return self.experience_maker.make_experience(**inputs, **self.generate_kwargs) + else: + raise ValueError(f'Unsupported input type "{type(inputs)}"') + + def _sample_prompts(self, prompts) -> list: + indices = list(range(len(prompts))) + sampled_indices = self.strategy.experience_sampler.choice(indices, self.experience_batch_size, replace=False) + return [prompts[i] for i in sampled_indices] + + def _learn(self): + # replay buffer may be empty at first, we should rebuild at each training + if not self.sample_replay_buffer: + dataloader = self.strategy.setup_dataloader(self.replay_buffer, self.dataloader_pin_memory) + device = torch.cuda.current_device() + if self.sample_replay_buffer: + pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) + for _ in pbar: + experience = self.replay_buffer.sample() + metrics = self.training_step(experience) + pbar.set_postfix(metrics) + else: + for epoch in range(self.max_epochs): + self._on_learn_epoch_start(epoch) + if isinstance(dataloader.sampler, DistributedSampler): + dataloader.sampler.set_epoch(epoch) + pbar = tqdm(dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0()) + for experience in pbar: + self._on_learn_batch_start() + experience.to_device(device) + metrics = self.training_step(experience) + self._on_learn_batch_end(metrics, experience) + pbar.set_postfix(metrics) + self._on_learn_epoch_end(epoch) + + def fit(self, + prompt_dataloader, + pretrain_dataloader, + num_episodes: int = 50000, + max_timesteps: int = 500, + update_timesteps: int = 5000) -> None: + time = 0 + self.pretrain_dataloader = pretrain_dataloader + self.prompt_dataloader = prompt_dataloader + self._on_fit_start() + for episode in range(num_episodes): + self._on_episode_start(episode) + for timestep in tqdm(range(max_timesteps), + desc=f'Episode [{episode+1}/{num_episodes}]', + disable=not is_rank_0()): + time += 1 + prompts = next(iter(self.prompt_dataloader)) + self._on_make_experience_start() + self.experience_maker.initial_model.to(torch.cuda.current_device()) + self.experience_maker.reward_model.to(torch.cuda.current_device()) + experience = self._make_experience(prompts) + self._on_make_experience_end(experience) + self.replay_buffer.append(experience) + if time % update_timesteps == 0: + self.experience_maker.initial_model.to('cpu') + self.experience_maker.reward_model.to('cpu') + self._learn() + self.replay_buffer.clear() + self._on_episode_end(episode) + self._on_fit_end() + + # TODO(ver217): maybe simplify these code using context + def _on_fit_start(self) -> None: + for callback in self.callbacks: + callback.on_fit_start() + + def _on_fit_end(self) -> None: + for callback in self.callbacks: + callback.on_fit_end() + + def _on_episode_start(self, episode: int) -> None: + for callback in self.callbacks: + callback.on_episode_start(episode) + + def _on_episode_end(self, episode: int) -> None: + for callback in self.callbacks: + callback.on_episode_end(episode) + + def _on_make_experience_start(self) -> None: + for callback in self.callbacks: + callback.on_make_experience_start() + + def _on_make_experience_end(self, experience: Experience) -> None: + for callback in self.callbacks: + callback.on_make_experience_end(experience) + + def _on_learn_epoch_start(self, epoch: int) -> None: + for callback in self.callbacks: + callback.on_learn_epoch_start(epoch) + + def _on_learn_epoch_end(self, epoch: int) -> None: + for callback in self.callbacks: + callback.on_learn_epoch_end(epoch) + + def _on_learn_batch_start(self) -> None: + for callback in self.callbacks: + callback.on_learn_batch_start() + + def _on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: + for callback in self.callbacks: + callback.on_learn_batch_end(metrics, experience) diff --git a/applications/Chat/coati/trainer/callbacks/__init__.py b/applications/Chat/coati/trainer/callbacks/__init__.py new file mode 100644 index 000000000000..9ed0ee6f7640 --- /dev/null +++ b/applications/Chat/coati/trainer/callbacks/__init__.py @@ -0,0 +1,5 @@ +from .base import Callback +from .performance_evaluator import PerformanceEvaluator +from .save_checkpoint import SaveCheckpoint + +__all__ = ['Callback', 'PerformanceEvaluator', 'SaveCheckpoint'] diff --git a/applications/Chat/coati/trainer/callbacks/base.py b/applications/Chat/coati/trainer/callbacks/base.py new file mode 100644 index 000000000000..f5616048855b --- /dev/null +++ b/applications/Chat/coati/trainer/callbacks/base.py @@ -0,0 +1,39 @@ +from abc import ABC + +from coati.experience_maker import Experience + + +class Callback(ABC): + """ + Base callback class. It defines the interface for callbacks. + """ + + def on_fit_start(self) -> None: + pass + + def on_fit_end(self) -> None: + pass + + def on_episode_start(self, episode: int) -> None: + pass + + def on_episode_end(self, episode: int) -> None: + pass + + def on_make_experience_start(self) -> None: + pass + + def on_make_experience_end(self, experience: Experience) -> None: + pass + + def on_learn_epoch_start(self, epoch: int) -> None: + pass + + def on_learn_epoch_end(self, epoch: int) -> None: + pass + + def on_learn_batch_start(self) -> None: + pass + + def on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: + pass diff --git a/applications/Chat/coati/trainer/callbacks/performance_evaluator.py b/applications/Chat/coati/trainer/callbacks/performance_evaluator.py new file mode 100644 index 000000000000..0fc3b077a1d1 --- /dev/null +++ b/applications/Chat/coati/trainer/callbacks/performance_evaluator.py @@ -0,0 +1,133 @@ +from time import time +from typing import Optional + +import torch +import torch.distributed as dist +from coati.experience_maker import Experience + +from .base import Callback + + +def get_world_size() -> int: + if dist.is_initialized(): + return dist.get_world_size() + return 1 + + +def print_rank_0(*args, **kwargs) -> None: + if not dist.is_initialized() or dist.get_rank() == 0: + print(*args, **kwargs) + + +@torch.no_grad() +def all_reduce_mean(x: float, world_size: int) -> float: + if world_size == 1: + return x + tensor = torch.tensor([x], device=torch.cuda.current_device()) + dist.all_reduce(tensor) + tensor = tensor / world_size + return tensor.item() + + +class PerformanceEvaluator(Callback): + """ + Callback for valuate the performance of the model. + Args: + actor_num_params: The number of parameters of the actor model. + critic_num_params: The number of parameters of the critic model. + initial_model_num_params: The number of parameters of the initial model. + reward_model_num_params: The number of parameters of the reward model. + enable_grad_checkpoint: Whether to enable gradient checkpointing. + ignore_episodes: The number of episodes to ignore when calculating the performance. + """ + + def __init__(self, + actor_num_params: int, + critic_num_params: int, + initial_model_num_params: int, + reward_model_num_params: int, + enable_grad_checkpoint: bool = False, + ignore_episodes: int = 0) -> None: + super().__init__() + self.world_size = get_world_size() + self.actor_num_params = actor_num_params + self.critic_num_params = critic_num_params + self.initial_model_num_params = initial_model_num_params + self.reward_model_num_params = reward_model_num_params + self.enable_grad_checkpoint = enable_grad_checkpoint + self.ignore_episodes = ignore_episodes + self.disable: bool = False + + self.make_experience_duration: float = 0. + self.make_experience_start_time: Optional[float] = None + self.make_experience_num_samples: int = 0 + self.make_experience_flop: int = 0 + self.learn_duration: float = 0. + self.learn_start_time: Optional[float] = None + self.learn_num_samples: int = 0 + self.learn_flop: int = 0 + + def on_episode_start(self, episode: int) -> None: + self.disable = self.ignore_episodes > 0 and episode < self.ignore_episodes + + def on_make_experience_start(self) -> None: + if self.disable: + return + self.make_experience_start_time = time() + + def on_make_experience_end(self, experience: Experience) -> None: + if self.disable: + return + self.make_experience_duration += time() - self.make_experience_start_time + + batch_size, seq_len = experience.sequences.shape + + self.make_experience_num_samples += batch_size + + # actor generate + num_actions = experience.action_mask.size(1) + input_len = seq_len - num_actions + total_seq_len = (input_len + seq_len - 1) * num_actions / 2 + self.make_experience_flop += self.actor_num_params * batch_size * total_seq_len * 2 + # actor forward + self.make_experience_flop += self.actor_num_params * batch_size * seq_len * 2 + # critic forward + self.make_experience_flop += self.critic_num_params * batch_size * seq_len * 2 + # initial model forward + self.make_experience_flop += self.initial_model_num_params * batch_size * seq_len * 2 + # reward model forward + self.make_experience_flop += self.reward_model_num_params * batch_size * seq_len * 2 + + def on_learn_batch_start(self) -> None: + if self.disable: + return + self.learn_start_time = time() + + def on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: + if self.disable: + return + self.learn_duration += time() - self.learn_start_time + + batch_size, seq_len = experience.sequences.shape + + self.learn_num_samples += batch_size + + # actor forward-backward, 3 means forward(1) + backward(2) + self.learn_flop += self.actor_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint)) + # critic foward-backward + self.learn_flop += self.critic_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint)) + + def on_fit_end(self) -> None: + avg_make_experience_duration = all_reduce_mean(self.make_experience_duration, self.world_size) + avg_learn_duration = all_reduce_mean(self.learn_duration, self.world_size) + + avg_make_experience_throughput = self.make_experience_num_samples / (avg_make_experience_duration + 1e-12) + avg_make_experience_tflops = self.make_experience_flop / 1e12 / (avg_make_experience_duration + 1e-12) + + avg_learn_throughput = self.learn_num_samples / (avg_learn_duration + 1e-12) + avg_learn_tflops = self.learn_flop / 1e12 / (avg_learn_duration + 1e-12) + + print_rank_0( + f'Making experience throughput: {avg_make_experience_throughput:.3f} samples/sec, TFLOPS: {avg_make_experience_tflops:.3f}' + ) + print_rank_0(f'Learning throughput: {avg_learn_throughput:.3f} samples/sec, TFLOPS: {avg_learn_tflops:.3f}') diff --git a/applications/Chat/coati/trainer/callbacks/save_checkpoint.py b/applications/Chat/coati/trainer/callbacks/save_checkpoint.py new file mode 100644 index 000000000000..d2dcc0dd4c65 --- /dev/null +++ b/applications/Chat/coati/trainer/callbacks/save_checkpoint.py @@ -0,0 +1,75 @@ +import os + +import torch.distributed as dist +from coati.trainer.strategies import ColossalAIStrategy, Strategy +from coati.trainer.utils import is_rank_0 +from torch import nn +from torch.optim import Optimizer + +from .base import Callback + + +class SaveCheckpoint(Callback): + """ + The callback for saving checkpoint for coati. + + Only support saving actor and critic model. + A typical architecture of the saved checkpoint would be: + - checkpoint + - episode_x + - actor.pt + - actor-optim-rank-0.pt + - actor-optim-rank-1.pt + - critic.pt + - critic-optim-rank-0.pt + - critic-optim-rank-1.pt + - ... + + Args: + path(str): the base path you want to save checkpoint, the checkpoint would be saved at `path/checkpoint` + interval(int): the interval episode of saving checkpoint + strategy(Strategy): the strategy used to train + actor(nn.Module): the actor model + critic(nn.Module): the critic model + actor_optim(Optimizer): the optimizer of actor + critic_optim(Optimizer): the optimizer of critic + + """ + + def __init__(self, + path: str, + interval: int, + strategy: Strategy, + actor: nn.Module = None, + critic: nn.Module = None, + actor_optim: Optimizer = None, + critic_optim: Optimizer = None) -> None: + super().__init__() + self.path = os.path.join(path, 'checkpoint') + self.interval = interval + self.strategy = strategy + self.model_dict = {'actor': [actor, actor_optim], 'critic': [critic, critic_optim]} + + def on_episode_end(self, episode: int) -> None: + if (episode + 1) % self.interval != 0: + return + base_path = os.path.join(self.path, f'episode_{episode}') + if not os.path.exists(base_path): + os.makedirs(base_path) + + for model in self.model_dict.keys(): + + # save model + if self.model_dict[model][0] is None: + # saving only optimizer states is meaningless, so it would be skipped + continue + model_path = os.path.join(base_path, f'{model}.pt') + self.strategy.save_model(model=self.model_dict[model][0], path=model_path, only_rank0=True) + + # save optimizer + if self.model_dict[model][1] is None: + continue + only_rank0 = not isinstance(self.strategy, ColossalAIStrategy) + rank = 0 if is_rank_0() else dist.get_rank() + optim_path = os.path.join(base_path, f'{model}-optim-rank-{rank}.pt') + self.strategy.save_optimizer(optimizer=self.model_dict[model][1], path=optim_path, only_rank0=only_rank0) diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py new file mode 100644 index 000000000000..84254d50d7e7 --- /dev/null +++ b/applications/Chat/coati/trainer/ppo.py @@ -0,0 +1,135 @@ +from typing import Any, Callable, Dict, List, Optional + +import torch +import torch.nn as nn +from coati.experience_maker import Experience, NaiveExperienceMaker +from coati.models.base import Actor, Critic +from coati.models.generation_utils import update_model_kwargs_fn +from coati.models.loss import PolicyLoss, ValueLoss +from coati.replay_buffer import NaiveReplayBuffer +from torch.optim import Optimizer +from transformers.tokenization_utils_base import PreTrainedTokenizerBase + +from .base import Trainer +from .callbacks import Callback +from .strategies import Strategy + + +class PPOTrainer(Trainer): + """ + Trainer for PPO algorithm. + + Args: + strategy (Strategy): the strategy to use for training + actor (Actor): the actor model in ppo algorithm + critic (Critic): the critic model in ppo algorithm + reward_model (nn.Module): the reward model in rlhf algorithm to make reward of sentences + initial_model (Actor): the initial model in rlhf algorithm to generate reference logits to limit the update of actor + actor_optim (Optimizer): the optimizer to use for actor model + critic_optim (Optimizer): the optimizer to use for critic model + kl_coef (float, defaults to 0.1): the coefficient of kl divergence loss + train_batch_size (int, defaults to 8): the batch size to use for training + buffer_limit (int, defaults to 0): the max_size limitaiton of replay buffer + buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu + eps_clip (float, defaults to 0.2): the clip coefficient of policy loss + value_clip (float, defaults to 0.4): the clip coefficient of value loss + experience_batch_size (int, defaults to 8): the batch size to use for experience generation + max_epochs (int, defaults to 1): the number of epochs of training process + tokenier (Callable, optional): the tokenizer to use for tokenizing the input + sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer + dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader + callbacks (List[Callback], defaults to []): the callbacks to call during training process + generate_kwargs (dict, optional): the kwargs to use while model generating + """ + + def __init__(self, + strategy: Strategy, + actor: Actor, + critic: Critic, + reward_model: nn.Module, + initial_model: Actor, + actor_optim: Optimizer, + critic_optim: Optimizer, + kl_coef: float = 0.1, + ptx_coef: float = 0.9, + train_batch_size: int = 8, + buffer_limit: int = 0, + buffer_cpu_offload: bool = True, + eps_clip: float = 0.2, + value_clip: float = 0.4, + experience_batch_size: int = 8, + max_epochs: int = 1, + tokenizer: Optional[Callable[[Any], dict]] = None, + sample_replay_buffer: bool = False, + dataloader_pin_memory: bool = True, + callbacks: List[Callback] = [], + **generate_kwargs) -> None: + experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef) + replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload) + generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor) + super().__init__(strategy, experience_maker, replay_buffer, experience_batch_size, max_epochs, tokenizer, + sample_replay_buffer, dataloader_pin_memory, callbacks, **generate_kwargs) + self.actor = actor + self.critic = critic + + self.actor_loss_fn = PolicyLoss(eps_clip) + self.critic_loss_fn = ValueLoss(value_clip) + self.ptx_loss_fn = nn.CrossEntropyLoss(ignore_index=-100) + self.ptx_coef = ptx_coef + self.actor_optim = actor_optim + self.critic_optim = critic_optim + + def training_step(self, experience: Experience) -> Dict[str, float]: + self.actor.train() + self.critic.train() + # policy loss + num_actions = experience.action_mask.size(1) + action_log_probs = self.actor(experience.sequences, num_actions, attention_mask=experience.attention_mask) + actor_loss = self.actor_loss_fn(action_log_probs, + experience.action_log_probs, + experience.advantages, + action_mask=experience.action_mask) + + # ptx loss + if self.ptx_coef != 0: + ptx = next(iter(self.pretrain_dataloader))['input_ids'].to(torch.cuda.current_device()) + label = next(iter(self.pretrain_dataloader))['labels'].to(torch.cuda.current_device())[:, 1:] + attention_mask = next(iter(self.pretrain_dataloader))['attention_mask'].to(torch.cuda.current_device()) + ptx_log_probs = self.actor.get_base_model()(ptx, attention_mask=attention_mask)['logits'][..., :-1, :] + ptx_loss = self.ptx_loss_fn(ptx_log_probs.view(-1, ptx_log_probs.size(-1)), label.view(-1)) + actor_loss = ptx_loss * self.ptx_coef + actor_loss * (1 - self.ptx_coef) + + self.strategy.backward(actor_loss, self.actor, self.actor_optim) + self.strategy.optimizer_step(self.actor_optim) + self.actor_optim.zero_grad() + + # value loss + values = self.critic(experience.sequences, + action_mask=experience.action_mask, + attention_mask=experience.attention_mask) + critic_loss = self.critic_loss_fn(values, + experience.values, + experience.reward, + action_mask=experience.action_mask) + self.strategy.backward(critic_loss, self.critic, self.critic_optim) + self.strategy.optimizer_step(self.critic_optim) + self.critic_optim.zero_grad() + + return {'reward': experience.reward.mean().item()} + + +def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None: + origin_model = strategy._unwrap_actor(actor) + new_kwargs = {**generate_kwargs} + # use huggingface models method directly + if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): + new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation + + if 'update_model_kwargs_fn' not in generate_kwargs: + new_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn + + return new_kwargs + + +def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer) diff --git a/applications/Chat/coati/trainer/rm.py b/applications/Chat/coati/trainer/rm.py new file mode 100644 index 000000000000..0cf09b0410d2 --- /dev/null +++ b/applications/Chat/coati/trainer/rm.py @@ -0,0 +1,135 @@ +from abc import ABC +from datetime import datetime +from typing import Optional + +import pandas as pd +import torch +import torch.distributed as dist +from torch.optim import Optimizer, lr_scheduler +from torch.utils.data import DataLoader, Dataset, DistributedSampler +from tqdm import tqdm +from transformers.tokenization_utils_base import PreTrainedTokenizerBase + +from .strategies import Strategy +from .utils import is_rank_0 + + +class RewardModelTrainer(ABC): + """ + Trainer to use while training reward model. + + Args: + model (torch.nn.Module): the model to train + strategy (Strategy): the strategy to use for training + optim(Optimizer): the optimizer to use for training + loss_fn (callable): the loss function to use for training + train_dataset (Dataset): the dataset to use for training + valid_dataset (Dataset): the dataset to use for validation + eval_dataset (Dataset): the dataset to use for evaluation + batch_size (int, defaults to 1): the batch size while training + max_epochs (int, defaults to 2): the number of epochs to train + """ + + def __init__( + self, + model, + strategy: Strategy, + optim: Optimizer, + loss_fn, + train_dataset: Dataset, + valid_dataset: Dataset, + eval_dataset: Dataset, + batch_size: int = 1, + max_epochs: int = 1, + ) -> None: + super().__init__() + self.strategy = strategy + self.epochs = max_epochs + train_sampler = None + + if dist.is_initialized() and dist.get_world_size() > 1: + train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True) + self.train_dataloader = DataLoader(train_dataset, + shuffle=(train_sampler is None), + sampler=train_sampler, + batch_size=batch_size) + self.valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True) + self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size, shuffle=True) + + self.model = strategy.setup_model(model) + self.loss_fn = loss_fn + self.optimizer = strategy.setup_optimizer(optim, self.model) + self.scheduler = lr_scheduler.CosineAnnealingLR(self.optimizer, self.train_dataloader.__len__() // 100) + + def eval_acc(self, dataloader): + dist = 0 + on = 0 + cnt = 0 + self.model.eval() + with torch.no_grad(): + for chosen_ids, c_mask, reject_ids, r_mask in dataloader: + chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) + c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) + reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) + r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) + chosen_reward = self.model(chosen_ids, attention_mask=c_mask) + reject_reward = self.model(reject_ids, attention_mask=r_mask) + for i in range(len(chosen_reward)): + cnt += 1 + if chosen_reward[i] > reject_reward[i]: + on += 1 + dist += (chosen_reward - reject_reward).mean().item() + dist_mean = dist / len(dataloader) + acc = on / cnt + self.model.train() + return dist_mean, acc + + def fit(self): + time = datetime.now() + epoch_bar = tqdm(range(self.epochs), desc='Train epoch', disable=not is_rank_0()) + for epoch in range(self.epochs): + step_bar = tqdm(range(self.train_dataloader.__len__()), + desc='Train step of epoch %d' % epoch, + disable=not is_rank_0()) + # train + self.model.train() + cnt = 0 + acc = 0 + dist = 0 + for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader: + chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) + c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) + reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) + r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) + chosen_reward = self.model(chosen_ids, attention_mask=c_mask) + reject_reward = self.model(reject_ids, attention_mask=r_mask) + loss = self.loss_fn(chosen_reward, reject_reward) + self.strategy.backward(loss, self.model, self.optimizer) + self.strategy.optimizer_step(self.optimizer) + self.optimizer.zero_grad() + cnt += 1 + if cnt == 100: + self.scheduler.step() + dist, acc = self.eval_acc(self.valid_dataloader) + cnt = 0 + if is_rank_0(): + log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], + columns=['step', 'loss', 'dist', 'acc']) + log.to_csv('log_%s.csv' % time, mode='a', header=False, index=False) + step_bar.update() + step_bar.set_postfix({'dist': dist, 'acc': acc}) + + # eval + dist, acc = self.eval_acc(self.eval_dataloader) + if is_rank_0(): + log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], columns=['step', 'loss', 'dist', 'acc']) + log.to_csv('log.csv', mode='a', header=False, index=False) + epoch_bar.update() + step_bar.set_postfix({'dist': dist, 'acc': acc}) + step_bar.close() + + def save_model(self, + path: str, + only_rank0: bool = False, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + self.strategy.save_model(model=self.model, path=path, only_rank0=only_rank0, tokenizer=tokenizer) diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py new file mode 100644 index 000000000000..8eeffea48bdd --- /dev/null +++ b/applications/Chat/coati/trainer/sft.py @@ -0,0 +1,158 @@ +import math +import time +from abc import ABC +from typing import Optional + +import loralib as lora +import torch +import torch.distributed as dist +import wandb +from coati.models.loss import GPTLMLoss +from torch import nn +from torch.optim import Adam, Optimizer +from torch.optim.lr_scheduler import LambdaLR +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from tqdm import tqdm +from transformers.tokenization_utils_base import PreTrainedTokenizerBase +from transformers.trainer import get_scheduler + +from colossalai.logging import get_dist_logger + +from .strategies import Strategy +from .utils import is_rank_0 + + +class SFTTrainer(ABC): + """ + Trainer to use while training reward model. + + Args: + model (torch.nn.Module): the model to train + strategy (Strategy): the strategy to use for training + optim(Optimizer): the optimizer to use for training + train_dataloader: the dataloader to use for training + eval_dataloader: the dataloader to use for evaluation + batch_size (int, defaults to 1): the batch size while training + max_epochs (int, defaults to 2): the number of epochs to train + optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer + """ + + def __init__( + self, + model, + strategy: Strategy, + optim: Optimizer, + train_dataloader: DataLoader, + eval_dataloader: DataLoader = None, + batch_size: int = 1, + max_epochs: int = 2, + accimulation_steps: int = 8, + ) -> None: + super().__init__() + self.strategy = strategy + self.epochs = max_epochs + self.train_dataloader = train_dataloader + self.eval_dataloader = eval_dataloader + + self.model = strategy.setup_model(model) + if "DDP" in str(self.strategy): + self.model = self.model.module + self.optimizer = strategy.setup_optimizer(optim, self.model) + + self.accimulation_steps = accimulation_steps + num_update_steps_per_epoch = len(train_dataloader) // self.accimulation_steps + max_steps = math.ceil(self.epochs * num_update_steps_per_epoch) + + self.scheduler = get_scheduler("cosine", + self.optimizer, + num_warmup_steps=math.ceil(max_steps * 0.03), + num_training_steps=max_steps) + + def fit(self, logger, log_interval=10): + wandb.init(project="Coati", name=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) + wandb.watch(self.model) + total_loss = 0 + # epoch_bar = tqdm(range(self.epochs), desc='Epochs', disable=not is_rank_0()) + step_bar = tqdm(range(len(self.train_dataloader) // self.accimulation_steps * self.epochs), + desc=f'steps', + disable=not is_rank_0()) + for epoch in range(self.epochs): + + # process_bar = tqdm(range(len(self.train_dataloader)), desc=f'Train process for{epoch}', disable=not is_rank_0()) + # train + self.model.train() + for batch_id, batch in enumerate(self.train_dataloader): + + prompt_ids = batch["input_ids"].to(torch.cuda.current_device()) + p_mask = batch["attention_mask"].to(torch.cuda.current_device()) + labels = batch["labels"].to(torch.cuda.current_device()) + # prompt_ids = prompt_ids.squeeze(1).cuda() + # p_mask = p_mask.squeeze(1).cuda() + # prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels) + + outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels) + + loss = outputs.loss + prompt_logits = outputs.logits + + if loss >= 2.5: + logger.warning(f"batch_id:{batch_id}, abnormal loss: {loss}") + + loss = loss / self.accimulation_steps + + self.strategy.backward(loss, self.model, self.optimizer) + + total_loss += loss.item() + + # gradient accumulation + if (batch_id + 1) % self.accimulation_steps == 0: + self.strategy.optimizer_step(self.optimizer) + self.optimizer.zero_grad() + self.scheduler.step() + wandb.log({ + "loss": total_loss / self.accimulation_steps, + "lr": self.scheduler.get_last_lr()[0], + "epoch": epoch, + "batch_id": batch_id + }) + total_loss = 0 + step_bar.update() + + # if batch_id % log_interval == 0: + # logger.info(f'Train Epoch {epoch}/{self.epochs} Batch {batch_id} Rank {dist.get_rank()} loss {loss.item()}') + # wandb.log({"loss": loss.item()}) + + # process_bar.update() + + # eval + if self.eval_dataloader is not None: + self.model.eval() + with torch.no_grad(): + loss_sum = 0 + num_seen = 0 + for batch in self.eval_dataloader: + prompt_ids = batch["input_ids"].to(torch.cuda.current_device()) + p_mask = batch["attention_mask"].to(torch.cuda.current_device()) + labels = batch["labels"].to(torch.cuda.current_device()) + # prompt_ids = prompt_ids.squeeze(1).cuda() + # p_mask = p_mask.squeeze(1).cuda() + + outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels) + loss = outputs.loss + # prompt_logits = outputs.logits + + loss_sum += loss.item() + num_seen += prompt_ids.size(0) + + loss_mean = loss_sum / num_seen + if dist.get_rank() == 0: + logger.info(f'Eval Epoch {epoch}/{self.epochs} loss {loss_mean}') + + # epoch_bar.update() + + def save_model(self, + path: str, + only_rank0: bool = False, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + self.strategy.save_model(model=self.model, path=path, only_rank0=only_rank0, tokenizer=tokenizer) diff --git a/applications/Chat/coati/trainer/strategies/__init__.py b/applications/Chat/coati/trainer/strategies/__init__.py new file mode 100644 index 000000000000..f258c9b8a873 --- /dev/null +++ b/applications/Chat/coati/trainer/strategies/__init__.py @@ -0,0 +1,6 @@ +from .base import Strategy +from .colossalai import ColossalAIStrategy +from .ddp import DDPStrategy +from .naive import NaiveStrategy + +__all__ = ['Strategy', 'NaiveStrategy', 'DDPStrategy', 'ColossalAIStrategy'] diff --git a/applications/Chat/coati/trainer/strategies/base.py b/applications/Chat/coati/trainer/strategies/base.py new file mode 100644 index 000000000000..7d25138561ea --- /dev/null +++ b/applications/Chat/coati/trainer/strategies/base.py @@ -0,0 +1,136 @@ +from abc import ABC, abstractmethod +from contextlib import nullcontext +from typing import Any, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn as nn +from coati.models.base import LM, Actor, Critic, RewardModel +from coati.replay_buffer import ReplayBuffer +from torch.optim import Optimizer +from torch.utils.data import DataLoader +from transformers.tokenization_utils_base import PreTrainedTokenizerBase + +from .sampler import DistributedSampler + +ModelOptimPair = Tuple[nn.Module, Optimizer] +ModelOrModelOptimPair = Union[nn.Module, ModelOptimPair] + + +class Strategy(ABC): + """ + Base class for training strategies. + """ + + def __init__(self) -> None: + super().__init__() + self.setup_distributed() + + @abstractmethod + def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: Optimizer, **kwargs) -> None: + pass + + @abstractmethod + def optimizer_step(self, optimizer: Optimizer, **kwargs) -> None: + pass + + @abstractmethod + def setup_distributed(self) -> None: + pass + + @abstractmethod + def setup_model(self, model: nn.Module) -> nn.Module: + pass + + @abstractmethod + def setup_optimizer(self, optimizer: Optimizer, model: nn.Module) -> Optimizer: + pass + + @abstractmethod + def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: + pass + + def model_init_context(self): + return nullcontext() + + def prepare( + self, *models_or_model_optim_pairs: ModelOrModelOptimPair + ) -> Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]: + """Prepare models or model-optimizer-pairs based on each strategy. + + Example:: + >>> # when fine-tuning actor and critic + >>> (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare((actor, actor_optim), (critic, critic_optim), reward_model, initial_model) + >>> # or when training reward model + >>> (reward_model, reward_model_optim) = strategy.prepare((reward_model, reward_model_optim)) + >>> # or just inference + >>> actor, critic = strategy.prepare(actor, critic) + + Returns: + Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]: Models or model-optimizer-pairs in the original order. + """ + + def prepare_model(model: nn.Module): + if isinstance(model, Actor): + return Actor(self.setup_model(self._unwrap_model(model))) + return self.setup_model(self._unwrap_model(model)) + + rets = [] + for arg in models_or_model_optim_pairs: + if isinstance(arg, tuple): + assert len(arg) == 2, f'Expect (model, optimizer) pair, got a tuple with size "{len(arg)}"' + model, optimizer = arg + model = prepare_model(model) + optimizer = self.setup_optimizer(optimizer, self._unwrap_model(model)) + rets.append((model, optimizer)) + elif isinstance(arg, nn.Module): + rets.append(prepare_model(arg)) + else: + raise RuntimeError(f'Expect model or (model, optimizer) pair, got {type(arg)}') + + if len(rets) == 1: + return rets[0] + return rets + + @staticmethod + def _unwrap_model(model: nn.Module) -> nn.Module: + """Useful for saving state dict. As actor is wrapped by Actor class again in `prepare()`, we should unwrap it before saving. + + Args: + model (nn.Module): an actor or a critic + """ + if isinstance(model, Actor) or isinstance(model, LM): + return model.model + return model + + @staticmethod + def _unwrap_actor(actor: Actor) -> nn.Module: + """Get `actor.model` from a wrapped (by `prepare()`) actor. Useful for getting original huggingface model. + + Args: + actor (Actor): a wrapped actor + """ + return Strategy._unwrap_model(actor) + + @abstractmethod + def save_model(self, + model: nn.Module, + path: str, + only_rank0: bool = False, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + pass + + @abstractmethod + def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None: + pass + + @abstractmethod + def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: + pass + + @abstractmethod + def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None: + pass + + def setup_sampler(self, dataset) -> DistributedSampler: + return DistributedSampler(dataset, 1, 0) diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py new file mode 100644 index 000000000000..521c536406d1 --- /dev/null +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -0,0 +1,213 @@ +import warnings +from typing import Optional, Union + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.optim as optim +from coati.models.base import LM, Actor, RewardModel +from coati.models.lora import LoraLinear +from torch.optim import Optimizer +from transformers.modeling_utils import PreTrainedModel +from transformers.tokenization_utils_base import PreTrainedTokenizerBase + +import colossalai +from colossalai.logging import get_dist_logger +from colossalai.nn.optimizer import CPUAdam, HybridAdam +from colossalai.nn.parallel import ZeroDDP, zero_model_wrapper, zero_optim_wrapper +from colossalai.nn.parallel.utils import get_static_torch_model +from colossalai.tensor import ProcessGroup, ShardSpec +from colossalai.utils import get_current_device +from colossalai.utils.model.colo_init_context import ColoInitContext + +logger = get_dist_logger(__name__) + +from .base import Strategy +from .ddp import DDPStrategy + + +class ColossalAIStrategy(DDPStrategy): + """ + The strategy for training with ColossalAI. + + Args: + stage(int): The stage to use in ZeRO. Choose in (1, 2, 3) + precision(str): The precision to use. Choose in ('fp32', 'fp16'). Stage 3 only supports fp16. + seed(int): The seed for the random number generator. + shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3. + This is not compativle with `from_pretrained()`. We temporarily disable this and will support it in the future. + placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda') + If it is “cpu”, parameters, gradients and optimizer states will be offloaded to CPU, + If it is “cuda”, they will not be offloaded, which means max CUDA memory will be used. It is the fastest. + pin_memory(bool): Whether to pin the memory for the data loader. Only for ZeRO-3. + force_outputs_fp32(bool): Whether to force the outputs to be fp32. Only for ZeRO-3. + search_range_mb(int): The search range in MB for the chunk size. Only for ZeRO-3. + hidden_dim(optional, int): The hidden dimension for the gemini. Only for ZeRO-3. + min_chunk_size_mb(float): The minimum chunk size in MB. Only for ZeRO-3. + gpu_margin_mem_ratio(float): The margin memory ratio for the GPU. Only for ZeRO-3. + reduce_bugket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2. + overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2. + initial_scale(float): The initial scale for the optimizer. + growth_factor(float): The growth factor for the optimizer. + backoff_factor(float): The backoff factor for the optimizer. + growth_interval(int): The growth interval for the optimizer. + hysteresis(int): The hysteresis for the optimizer. + min_scale(float): The minimum scale for the optimizer. + max_scale(float): The maximum scale for the optimizer. + max_norm(float): The maximum norm for the optimizer. + norm_type(float): The norm type for the optimizer. + + """ + + def __init__( + self, + stage: int = 3, + precision: str = 'fp16', + seed: int = 42, + shard_init: bool = False, # only for stage 3 + placement_policy: str = 'cuda', + pin_memory: bool = True, # only for stage 3 + force_outputs_fp32: bool = False, # only for stage 3 + search_range_mb: int = 32, # only for stage 3 + hidden_dim: Optional[int] = None, # only for stage 3 + min_chunk_size_mb: float = 32, # only for stage 3 + gpu_margin_mem_ratio: float = 0.0, # only for stage 3 + reduce_bucket_size: int = 12 * 1024**2, # only for stage 1&2 + overlap_communication: bool = True, # only for stage 1&2 + initial_scale: float = 2**16, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + min_scale: float = 1, + max_scale: float = 2**32, + max_norm: float = 0.0, + norm_type: float = 2.0) -> None: + super().__init__(seed) + assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy "{placement_policy}"' + assert precision in ('fp32', 'fp16'), f'Unsupported precision "{precision}"' + self.stage = stage + # TODO(ver217): support shard_init when using from_pretrained() + if shard_init: + warnings.warn( + f'Shard init is not supported model.from_pretrained() yet. Please load weights after strategy.prepare()' + ) + if stage == 3 and precision == 'fp32': + warnings.warn(f'Stage 3 only supports fp16. Precision is set to fp16.') + precision = 'fp16' + self.precision = precision + self.shard_init = shard_init + self.gemini_config = dict(device=get_current_device(), + placement_policy=placement_policy, + pin_memory=pin_memory, + force_outputs_fp32=force_outputs_fp32, + strict_ddp_mode=shard_init, + search_range_mb=search_range_mb, + hidden_dim=hidden_dim, + min_chunk_size_mb=min_chunk_size_mb) + if stage == 3: + self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio) + else: + self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size, + overlap_communication=overlap_communication, + cpu_offload=(placement_policy == 'cpu')) + self.optim_kwargs = dict(initial_scale=initial_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + min_scale=min_scale, + max_scale=max_scale, + max_norm=max_norm, + norm_type=norm_type) + + def setup_distributed(self) -> None: + colossalai.launch_from_torch({}, seed=self.seed) + + def model_init_context(self): + if self.stage == 3: + world_size = dist.get_world_size() + shard_pg = ProcessGroup(tp_degree=world_size) if self.shard_init else None + default_dist_spec = ShardSpec([-1], [world_size]) if self.shard_init else None + return ColoInitContext(device=get_current_device(), + dtype=torch.half, + default_pg=shard_pg, + default_dist_spec=default_dist_spec) + return super().model_init_context() + + def setup_model(self, model: nn.Module) -> nn.Module: + + model = zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config) + + if self.stage != 3 and self.precision == 'fp16': + model = model.half() + return model + + def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer: + assert isinstance(optimizer, (CPUAdam, HybridAdam)), f'Unsupported optimizer {type(optimizer)}' + return zero_optim_wrapper(model, optimizer, optim_config=self.zero_optim_config, **self.optim_kwargs) + + def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None: + optimizer.backward(loss) + + def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: + optimizer.step() + + @staticmethod + def _unwrap_actor(actor: Actor) -> nn.Module: + model: Union[nn.Module, ZeroDDP] = Strategy._unwrap_actor(actor) + if isinstance(model, ZeroDDP): + return model.module + return model + + def _unwrap_model(self, model: Union[nn.Module, ZeroDDP]) -> nn.Module: + if isinstance(model, ZeroDDP) and self.stage == 3: + logger.info(f"model type: {type(model)}, get static torch model") + model = get_static_torch_model(model) + logger.info(f"unwrapped_model type: {type(model)}") + + return super()._unwrap_model(model) + + def save_model(self, + model: nn.Module, + path: str, + only_rank0: bool = True, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + + if only_rank0 and dist.get_rank() != 0: + return None + unwrapped_model = self._unwrap_model(model) + # TODO : better way to get torch model from gemini model + # to get torch model from gemini model + + for module in unwrapped_model.modules(): + if isinstance(module, LoraLinear): + module.merge_weights = True + module.eval() + if isinstance(unwrapped_model, RewardModel): + state_dict = unwrapped_model.state_dict() + if only_rank0 and dist.get_rank() != 0: + return + torch.save(state_dict, path) + else: + try: + if isinstance(unwrapped_model, LM): + unwrapped_model = unwrapped_model.model + logger.info(f'Saving model to {path}', ranks=[0]) + unwrapped_model.save_pretrained(path) + logger.info(f'Model saved to {path} Successfully', ranks=[0]) + if tokenizer is not None: + logger.info(f'Saving tokenizer to {path}', ranks=[0]) + tokenizer.save_pretrained(path) + logger.info(f'Tokenizer saved to {path} Successfully', ranks=[0]) + except AttributeError: + state_dict = unwrapped_model.state_dict() + if only_rank0 and dist.get_rank() != 0: + return + torch.save(state_dict, path) + + def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: + if only_rank0: + raise RuntimeError( + f'Optimizer states are sharded when using ColossalAIStrategy. Only rank0 is not supported.') + torch.save(optimizer.state_dict(), path) diff --git a/applications/Chat/coati/trainer/strategies/ddp.py b/applications/Chat/coati/trainer/strategies/ddp.py new file mode 100644 index 000000000000..83cbbe633de9 --- /dev/null +++ b/applications/Chat/coati/trainer/strategies/ddp.py @@ -0,0 +1,93 @@ +import os +import random + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +from coati.models.base import Actor +from coati.models.lora import LoraLinear +from coati.replay_buffer import ReplayBuffer +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.optim import Optimizer +from torch.utils.data import DataLoader + +from .base import Strategy +from .naive import NaiveStrategy +from .sampler import DistributedSampler + + +class DDPStrategy(NaiveStrategy): + """ + Strategy for distributed training using torch.distributed. + """ + + def __init__(self, seed: int = 42) -> None: + self.seed = seed + super().__init__() + + def setup_distributed(self) -> None: + try: + rank = int(os.environ['RANK']) + local_rank = int(os.environ['LOCAL_RANK']) + world_size = int(os.environ['WORLD_SIZE']) + host = os.environ['MASTER_ADDR'] + port = int(os.environ['MASTER_PORT']) + except KeyError as e: + raise RuntimeError( + f"Could not find {e} in the torch environment, visit https://www.colossalai.org/ for more information on launching with torch" + ) + dist.init_process_group('nccl', init_method=f'tcp://[{host}]:{port}', world_size=world_size, rank=rank) + self.set_seed(self.seed) + torch.cuda.set_device(local_rank) + + def set_seed(self, seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + def setup_model(self, model: nn.Module) -> nn.Module: + device = torch.cuda.current_device() + return DDP(model, device_ids=[device]) + + def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: + # DDP only mode, replay buffers on each rank are different. + # sampler = DistributedSampler(replay_buffer, + # num_replicas=dist.get_world_size(), + # rank=dist.get_rank(), + # shuffle=True, + # seed=self.seed, + # drop_last=True) + return DataLoader( + replay_buffer, + batch_size=replay_buffer.sample_batch_size, + # sampler=sampler, + shuffle=True, + drop_last=True, + pin_memory=pin_memory, + collate_fn=replay_buffer.collate_fn) + + @staticmethod + def _unwrap_actor(actor: Actor) -> nn.Module: + model: DDP = Strategy._unwrap_actor(actor) + return model.module + + def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None: + for module in model.modules(): + if isinstance(module, LoraLinear): + module.merge_weights = True + module.eval() + + if only_rank0 and dist.get_rank() != 0: + return + model = model.model.module + state_dict = model.state_dict() + torch.save(state_dict, path) + + def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: + if only_rank0 and dist.get_rank() != 0: + return + super().save_optimizer(optimizer, path, only_rank0) + + def setup_sampler(self, dataset) -> DistributedSampler: + return DistributedSampler(dataset, dist.get_world_size(), dist.get_rank()) diff --git a/applications/Chat/coati/trainer/strategies/naive.py b/applications/Chat/coati/trainer/strategies/naive.py new file mode 100644 index 000000000000..80768d7e649c --- /dev/null +++ b/applications/Chat/coati/trainer/strategies/naive.py @@ -0,0 +1,55 @@ +from typing import Any + +import torch +import torch.nn as nn +import torch.optim as optim +from coati.replay_buffer import ReplayBuffer +from torch.optim import Optimizer +from torch.utils.data import DataLoader + +from .base import Strategy + + +class NaiveStrategy(Strategy): + """ + Strategy for single GPU. No parallelism is used. + """ + + def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None: + loss.backward() + + def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: + optimizer.step() + + def setup_distributed(self) -> None: + pass + + def setup_model(self, model: nn.Module) -> nn.Module: + return model + + def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer: + return optimizer + + def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: + return DataLoader(replay_buffer, + batch_size=replay_buffer.sample_batch_size, + shuffle=True, + drop_last=True, + pin_memory=pin_memory, + collate_fn=replay_buffer.collate_fn) + + def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None: + unwrapped_model = self._unwrap_model(model) + torch.save(unwrapped_model.state_dict(), path) + + def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None: + unwrapped_model = self._unwrap_model(model) + state_dict = torch.load(path, map_location=map_location) + unwrapped_model.load_state_dict(state_dict, strict=strict) + + def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: + torch.save(optimizer.state_dict(), path) + + def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None: + state_dict = torch.load(path, map_location=map_location) + optimizer.load_state_dict(state_dict) diff --git a/applications/Chat/coati/trainer/strategies/sampler.py b/applications/Chat/coati/trainer/strategies/sampler.py new file mode 100644 index 000000000000..d726fa640fa2 --- /dev/null +++ b/applications/Chat/coati/trainer/strategies/sampler.py @@ -0,0 +1,32 @@ +import math + +import numpy as np + + +class DistributedSampler: + + def __init__(self, dataset, num_replicas: int, rank: int) -> None: + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + + if len(self.dataset) % self.num_replicas != 0: + self.num_samples = math.ceil( + (len(self.dataset) - self.num_replicas) / self.num_replicas # type: ignore[arg-type] + ) + else: + self.num_samples = math.ceil(len(self.dataset) / self.num_replicas) + + self.total_size = self.num_samples * self.num_replicas + + indices = list(range(len(self.dataset))) + indices = indices[:self.total_size] + assert len(indices) == self.total_size + # subsample + indices = indices[self.rank:self.total_size:self.num_replicas] + assert len(indices) == self.num_samples + self.indices = indices + + def sample(self, batch_size: int) -> list: + sampled_indices = np.random.choice(self.indices, batch_size, replace=False) + return [self.dataset[idx] for idx in sampled_indices] diff --git a/applications/Chat/coati/trainer/utils.py b/applications/Chat/coati/trainer/utils.py new file mode 100644 index 000000000000..6c9f7f085f8c --- /dev/null +++ b/applications/Chat/coati/trainer/utils.py @@ -0,0 +1,5 @@ +import torch.distributed as dist + + +def is_rank_0() -> bool: + return not dist.is_initialized() or dist.get_rank() == 0 diff --git a/applications/Chat/coati/utils/__init__.py b/applications/Chat/coati/utils/__init__.py new file mode 100644 index 000000000000..e75401d382a8 --- /dev/null +++ b/applications/Chat/coati/utils/__init__.py @@ -0,0 +1,3 @@ +from .tokenizer_utils import prepare_llama_tokenizer_and_embedding, smart_tokenizer_and_embedding_resize + +__all__ = ['smart_tokenizer_and_embedding_resize', 'prepare_llama_tokenizer_and_embedding'] diff --git a/applications/Chat/coati/utils/tokenizer_utils.py b/applications/Chat/coati/utils/tokenizer_utils.py new file mode 100644 index 000000000000..80dcc55fca3e --- /dev/null +++ b/applications/Chat/coati/utils/tokenizer_utils.py @@ -0,0 +1,78 @@ +# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict + +import transformers + +from ..models.llama.llama_lm import LlamaLM + +DEFAULT_PAD_TOKEN = "[PAD]" +DEFAULT_EOS_TOKEN = "" +DEFAULT_BOS_TOKEN = "" +DEFAULT_UNK_TOKEN = "" + + +def prepare_llama_tokenizer_and_embedding( + tokenizer: transformers.PreTrainedTokenizer, + model: transformers.PreTrainedModel, + special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN), +): + """prepare llama tokenizer and embedding. + + """ + + if tokenizer.pad_token is None: + smart_tokenizer_and_embedding_resize( + special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN), + tokenizer=tokenizer, + model=model, + ) + + tokenizer.add_special_tokens({ + "eos_token": DEFAULT_EOS_TOKEN, + "bos_token": DEFAULT_BOS_TOKEN, + "unk_token": DEFAULT_UNK_TOKEN, + }) + + return tokenizer + + +def smart_tokenizer_and_embedding_resize( + tokenizer: transformers.PreTrainedTokenizer, + model: transformers.PreTrainedModel, + special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN), +): + """Resize tokenizer and embedding. + + Note: This is the unoptimized version that may make your embedding size not be divisible by 64. + """ + + if tokenizer.pad_token is None: + num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) + + if isinstance(model, LlamaLM): + model = model.get_base_model() + + model.resize_token_embeddings(len(tokenizer)) + + if num_new_tokens > 0: + input_embeddings = model.get_input_embeddings().weight.data + output_embeddings = model.get_output_embeddings().weight.data + + input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) + output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) + + input_embeddings[-num_new_tokens:] = input_embeddings_avg + output_embeddings[-num_new_tokens:] = output_embeddings_avg diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md new file mode 100644 index 000000000000..60e6d68bdc0f --- /dev/null +++ b/applications/Chat/examples/README.md @@ -0,0 +1,141 @@ +# Examples + +## Install requirements + +```shell +pip install -r requirements.txt +``` + +## Train the reward model (Stage 2) +Use these code to train your reward model. +```shell +# Take naive reward model training with opt-350m as example +python train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy naive +# use colossalai_zero2 +torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy colossalai_zero2 +``` + +### Features and tricks in RM training +- We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)and[rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets. +- We support 2 kinds of loss_function named 'log_sig'(used by OpenAI) and 'log_exp'(used by Anthropic). +- We change the loss to valid_acc and pair_dist to monitor progress during training. +- We add special token to the end of the sequence to get better result. +- We use cosine-reducing lr-scheduler for RM training. +- We set value_head as 1 liner layer and initialize the weight of value_head using N(0，1/(d_model + 1)) distribution. +- We train a Bloom-560m reward model for 1 epoch and find the test acc of the model achieve the performance mentions in [Anthropics paper](https://arxiv.org/abs/2204.05862). + +### Experiment result +Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862): + +
    image + +
    Our training & test result of bloom-560m for 1 epoch: + +
    image + +
    + +## Train with dummy prompt data (Stage 3) + +This script supports 4 kinds of strategies: + +- naive +- ddp +- colossalai_zero2 +- colossalai_gemini + +It uses random generated prompt data. + +Naive strategy only support single GPU training: + +```shell +python train_dummy.py --strategy naive +# display cli help +python train_dummy.py -h +``` + +DDP strategy and ColossalAI strategy support multi GPUs training: + +```shell +# run DDP on 2 GPUs +torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy ddp +# run ColossalAI on 2 GPUs +torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy colossalai_zero2 +``` + +## Train with real prompt data (Stage 3) + +We use [awesome-chatgpt-prompts](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts) as example dataset. It is a small dataset with hundreds of prompts. + +You should download `prompts.csv` first. + +This script also supports 4 strategies. + +```shell +# display cli help +python train_dummy.py -h +# run naive on 1 GPU +python train_prompts.py prompts.csv --strategy naive +# run DDP on 2 GPUs +torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy ddp +# run ColossalAI on 2 GPUs +torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai_zero2 +``` + +## Inference example(After Stage3) +We support naive inference demo after training. +```shell +# inference, using pretrain path to configure model +python inference.py --model_path --model --pretrain +# example +python inference.py --model_path ./actor_checkpoint_prompts.pt --pretrain bigscience/bloom-560m --model bloom +``` + +## Attention +The examples is just a demo for testing our progress of RM and PPO training. + + +#### data +- [x] [rm-static](https://huggingface.co/datasets/Dahoas/rm-static) +- [x] [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) +- [ ] [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) +- [ ] [openai/webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons) +- [ ] [Dahoas/instruct-synthetic-prompt-responses](https://huggingface.co/datasets/Dahoas/instruct-synthetic-prompt-responses) + +## Support Model + +### GPT +- [x] GPT2-S (s) +- [x] GPT2-M (m) +- [x] GPT2-L (l) +- [ ] GPT2-XL (xl) +- [x] GPT2-4B (4b) +- [ ] GPT2-6B (6b) +- [ ] GPT2-8B (8b) +- [ ] GPT2-10B (10b) +- [ ] GPT2-12B (12b) +- [ ] GPT2-15B (15b) +- [ ] GPT2-18B (18b) +- [ ] GPT2-20B (20b) +- [ ] GPT2-24B (24b) +- [ ] GPT2-28B (28b) +- [ ] GPT2-32B (32b) +- [ ] GPT2-36B (36b) +- [ ] GPT2-40B (40b) +- [ ] GPT3 (175b) + +### BLOOM +- [x] [BLOOM-560m](https://huggingface.co/bigscience/bloom-560m) +- [x] [BLOOM-1b1](https://huggingface.co/bigscience/bloom-1b1) +- [x] [BLOOM-3b](https://huggingface.co/bigscience/bloom-3b) +- [x] [BLOOM-7b](https://huggingface.co/bigscience/bloom-7b1) +- [ ] BLOOM-175b + +### OPT +- [x] [OPT-125M](https://huggingface.co/facebook/opt-125m) +- [x] [OPT-350M](https://huggingface.co/facebook/opt-350m) +- [ ] [OPT-1.3B](https://huggingface.co/facebook/opt-1.3b) +- [ ] [OPT-2.7B](https://huggingface.co/facebook/opt-2.7b) +- [ ] [OPT-6.7B](https://huggingface.co/facebook/opt-6.7b) +- [ ] [OPT-13B](https://huggingface.co/facebook/opt-13b) +- [ ] [OPT-30B](https://huggingface.co/facebook/opt-30b) diff --git a/applications/Chat/examples/inference.py b/applications/Chat/examples/inference.py new file mode 100644 index 000000000000..f75950804d2e --- /dev/null +++ b/applications/Chat/examples/inference.py @@ -0,0 +1,59 @@ +import argparse + +import torch +from coati.models.bloom import BLOOMActor +from coati.models.gpt import GPTActor +from coati.models.opt import OPTActor +from transformers import AutoTokenizer +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + + +def eval(args): + # configure model + if args.model == 'gpt2': + actor = GPTActor(pretrained=args.pretrain).to(torch.cuda.current_device()) + elif args.model == 'bloom': + actor = BLOOMActor(pretrained=args.pretrain).to(torch.cuda.current_device()) + elif args.model == 'opt': + actor = OPTActor(pretrained=args.pretrain).to(torch.cuda.current_device()) + else: + raise ValueError(f'Unsupported model "{args.model}"') + + state_dict = torch.load(args.model_path) + actor.model.load_state_dict(state_dict) + + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m') + else: + raise ValueError(f'Unsupported model "{args.model}"') + + actor.eval() + input = args.input + input_ids = tokenizer.encode(input, return_tensors='pt').to(torch.cuda.current_device()) + outputs = actor.generate(input_ids, + max_length=args.max_length, + do_sample=True, + top_k=50, + top_p=0.95, + num_return_sequences=1) + output = tokenizer.batch_decode(outputs[0], skip_special_tokens=True) + print(output) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) + # We suggest to use the pretrained model from HuggingFace, use pretrain to configure model + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--model_path', type=str, default=None) + parser.add_argument('--input', type=str, default='Question: How are you ? Answer:') + parser.add_argument('--max_length', type=int, default=100) + args = parser.parse_args() + eval(args) diff --git a/applications/Chat/examples/requirements.txt b/applications/Chat/examples/requirements.txt new file mode 100644 index 000000000000..40e6edc7ea73 --- /dev/null +++ b/applications/Chat/examples/requirements.txt @@ -0,0 +1,2 @@ +pandas>=1.4.1 +sentencepiece diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh new file mode 100755 index 000000000000..db1d0b64e3b3 --- /dev/null +++ b/applications/Chat/examples/test_ci.sh @@ -0,0 +1,97 @@ +#!/usr/bin/env bash + +set -xue + +if [ -z "$PROMPT_PATH" ]; then + echo "Please set \$PROMPT_PATH to the path to prompts csv." + exit 1 +fi + +BASE=$(realpath $(dirname $0)) + +export OMP_NUM_THREADS=8 + +# install requirements +pip install -r ${BASE}/requirements.txt + +# train dummy +python ${BASE}/train_dummy.py --strategy naive --num_episodes 1 \ + --max_timesteps 2 --update_timesteps 2 \ + --max_epochs 1 --train_batch_size 2 --lora_rank 4 + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ + --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ + --pretrain 'facebook/opt-350m' --model opt --lora_rank 4\ + --save_path ${BASE}/actor_checkpoint_dummy.pt +python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'facebook/opt-350m' --model opt + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ + --strategy ddp --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ + --pretrain 'facebook/opt-350m' --model opt --lora_rank 4\ + --save_path ${BASE}/actor_checkpoint_dummy.pt +python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'facebook/opt-350m' --model opt + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ + --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ + --pretrain 'gpt2' --model gpt2 --lora_rank 4\ + --save_path ${BASE}/actor_checkpoint_dummy.pt +python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'gpt2' --model gpt2 + +rm -rf ${BASE}/actor_checkpoint_dummy.pt + +# train prompts +python ${BASE}/train_prompts.py $PROMPT_PATH --strategy naive --num_episodes 1 \ + --max_timesteps 2 --update_timesteps 2 \ + --max_epochs 1 --train_batch_size 2 --lora_rank 4 + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ + --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ + --pretrain 'facebook/opt-350m' --model opt --lora_rank 4\ + --save_path ${BASE}/actor_checkpoint_prompts.pt +python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'facebook/opt-350m' --model opt + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ + --strategy ddp --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ + --pretrain 'gpt2' --model gpt2 --lora_rank 4\ + --save_path ${BASE}/actor_checkpoint_prompts.pt +python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'gpt2' --model gpt2 + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ + --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ + --pretrain 'gpt2' --model gpt2 --lora_rank 4\ + --save_path ${BASE}/actor_checkpoint_prompts.pt +python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'gpt2' --model gpt2 + +rm -rf ${BASE}/actor_checkpoint_prompts.pt + +# train rm +torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ + --pretrain 'facebook/opt-350m' --model 'opt' \ + --strategy colossalai_zero2 --loss_fn 'log_sig'\ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ + --test True --lora_rank 4 + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ + --pretrain 'gpt2' --model 'gpt2' \ + --strategy colossalai_gemini --loss_fn 'log_exp'\ + --dataset 'Dahoas/rm-static' --test True --lora_rank 4 + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ + --pretrain 'bigscience/bloom-560m' --model 'bloom' \ + --strategy colossalai_zero2 --loss_fn 'log_sig'\ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ + --test True --lora_rank 4 + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ + --pretrain 'microsoft/deberta-v3-large' --model 'deberta' \ + --strategy colossalai_zero2 --loss_fn 'log_sig'\ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ + --test True --lora_rank 4 + +rm -rf ${BASE}/rm_ckpt.pt diff --git a/applications/Chat/examples/train_dummy.py b/applications/Chat/examples/train_dummy.py new file mode 100644 index 000000000000..d944b018de8f --- /dev/null +++ b/applications/Chat/examples/train_dummy.py @@ -0,0 +1,148 @@ +import argparse +from copy import deepcopy + +import torch +from coati.models.base import RewardModel +from coati.models.bloom import BLOOMActor, BLOOMCritic +from coati.models.gpt import GPTActor, GPTCritic +from coati.models.opt import OPTActor, OPTCritic +from coati.trainer import PPOTrainer +from coati.trainer.callbacks import SaveCheckpoint +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from torch.optim import Adam +from transformers import AutoTokenizer, BloomTokenizerFast +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.nn.optimizer import HybridAdam + + +def preprocess_batch(samples): + input_ids = torch.stack(samples) + attention_mask = torch.ones_like(input_ids, dtype=torch.long) + return {'input_ids': input_ids, 'attention_mask': attention_mask} + + +def main(args): + # configure strategy + if args.strategy == 'naive': + strategy = NaiveStrategy() + elif args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + # configure model + with strategy.model_init_context(): + if args.model == 'gpt2': + actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + critic = GPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'bloom': + actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + critic = BLOOMCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'opt': + actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + critic = OPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + else: + raise ValueError(f'Unsupported model "{args.model}"') + + initial_model = deepcopy(actor).to(torch.cuda.current_device()) + reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).to(torch.cuda.current_device()) + + # configure optimizer + if args.strategy.startswith('colossalai'): + actor_optim = HybridAdam(actor.parameters(), lr=5e-6) + critic_optim = HybridAdam(critic.parameters(), lr=5e-6) + else: + actor_optim = Adam(actor.parameters(), lr=5e-6) + critic_optim = Adam(critic.parameters(), lr=5e-6) + + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + else: + raise ValueError(f'Unsupported model "{args.model}"') + + (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( + (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) + + callbacks = [] + if args.save_ckpt_path: + ckpt_callback = SaveCheckpoint( + args.save_ckpt_path, + args.save_ckpt_interval, + strategy, + actor, + critic, + actor_optim, + critic_optim, + ) + callbacks.append(ckpt_callback) + + # configure trainer + + trainer = PPOTrainer(strategy, + actor, + critic, + reward_model, + initial_model, + actor_optim, + critic_optim, + max_epochs=args.max_epochs, + train_batch_size=args.train_batch_size, + tokenizer=preprocess_batch, + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + callbacks=callbacks) + + random_prompts = torch.randint(tokenizer.vocab_size, (1000, 64), device=torch.cuda.current_device()) + trainer.fit(random_prompts, + num_episodes=args.num_episodes, + max_timesteps=args.max_timesteps, + update_timesteps=args.update_timesteps) + + # save model checkpoint after fitting + trainer.save_model(args.save_path, only_rank0=True) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + strategy.save_optimizer(actor_optim, + 'actor_optim_checkpoint_dummy_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', type=str, default='gpt2', choices=['gpt2', 'bloom', 'opt']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--save_path', type=str, default='actor_checkpoint_dummy.pt') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--num_episodes', type=int, default=50) + parser.add_argument('--max_timesteps', type=int, default=10) + parser.add_argument('--update_timesteps', type=int, default=10) + parser.add_argument('--max_epochs', type=int, default=5) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + parser.add_argument('--save_ckpt_path', + type=str, + default=None, + help="path to save checkpoint, None means not to save") + parser.add_argument('--save_ckpt_interval', type=int, default=1, help="the interval of episode to save checkpoint") + args = parser.parse_args() + main(args) diff --git a/applications/Chat/examples/train_dummy.sh b/applications/Chat/examples/train_dummy.sh new file mode 100755 index 000000000000..595da573e2b1 --- /dev/null +++ b/applications/Chat/examples/train_dummy.sh @@ -0,0 +1,18 @@ +set_n_least_used_CUDA_VISIBLE_DEVICES() { + local n=${1:-"9999"} + echo "GPU Memory Usage:" + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ + | tail -n +2 \ + | nl -v 0 \ + | tee /dev/tty \ + | sort -g -k 2 \ + | awk '{print $1}' \ + | head -n $n) + export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') + echo "Now CUDA_VISIBLE_DEVICES is set to:" + echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" +} + +set_n_least_used_CUDA_VISIBLE_DEVICES 2 + +torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy colossalai_zero2 diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py new file mode 100644 index 000000000000..c573f5e6fae8 --- /dev/null +++ b/applications/Chat/examples/train_prompts.py @@ -0,0 +1,199 @@ +import argparse + +import pandas as pd +import torch +import torch.distributed as dist +from coati.dataset import DataCollatorForSupervisedDataset, PromptDataset, SupervisedDataset +from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic +from coati.models.gpt import GPTRM, GPTActor, GPTCritic +from coati.models.llama import LlamaActor +from coati.models.opt import OPTRM, OPTActor, OPTCritic +from coati.trainer import PPOTrainer +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.utils import prepare_llama_tokenizer_and_embedding +from torch.optim import Adam +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer + +from colossalai.nn.optimizer import HybridAdam + + +def main(args): + # configure strategy + if args.strategy == 'naive': + strategy = NaiveStrategy() + elif args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + if args.rm_path is not None: + state_dict = torch.load(args.rm_path, map_location='cpu') + + # configure model + if args.model == 'gpt2': + initial_model = GPTActor(pretrained=args.pretrain) + reward_model = GPTRM(pretrained=args.rm_pretrain) + elif args.model == 'bloom': + initial_model = BLOOMActor(pretrained=args.pretrain) + reward_model = BLOOMRM(pretrained=args.rm_pretrain) + elif args.model == 'opt': + initial_model = OPTActor(pretrained=args.pretrain) + reward_model = OPTRM(pretrained=args.rm_pretrain) + elif args.model == 'llama': + initial_model = LlamaActor(pretrained=args.pretrain) + reward_model = BLOOMRM(pretrained=args.rm_pretrain) + else: + raise ValueError(f'Unsupported model "{args.model}"') + if args.rm_path is not None: + reward_model.load_state_dict(state_dict) + + if args.strategy != 'colossalai_gemini': + initial_model.to(torch.float16).to(torch.cuda.current_device()) + reward_model.to(torch.float16).to(torch.cuda.current_device()) + + with strategy.model_init_context(): + if args.model == 'gpt2': + actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) + critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + elif args.model == 'bloom': + actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank) + critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + elif args.model == 'opt': + actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) + critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + elif args.model == 'llama': + actor = LlamaActor(pretrained=args.pretrain, lora_rank=args.lora_rank) + critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + else: + raise ValueError(f'Unsupported model "{args.model}"') + if args.rm_path is not None: + critic.load_state_dict(state_dict) + del state_dict + + if args.strategy != 'colossalai_gemini': + critic.to(torch.float16).to(torch.cuda.current_device()) + actor.to(torch.float16).to(torch.cuda.current_device()) + + # configure optimizer + if args.strategy.startswith('colossalai'): + actor_optim = HybridAdam(actor.parameters(), lr=1e-7) + critic_optim = HybridAdam(critic.parameters(), lr=1e-7) + else: + actor_optim = Adam(actor.parameters(), lr=1e-7) + critic_optim = Adam(critic.parameters(), lr=1e-7) + + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m') + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + elif args.model == 'llama': + tokenizer = LlamaTokenizer.from_pretrained(args.pretrain) + tokenizer.eos_token = '<\s>' + else: + raise ValueError(f'Unsupported model "{args.model}"') + + if args.model == 'llama': + tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, actor) + else: + tokenizer.pad_token = tokenizer.eos_token + + data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) + + prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_path, max_datasets_size=16384) + if dist.is_initialized() and dist.get_world_size() > 1: + prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True) + prompt_dataloader = DataLoader(prompt_dataset, + shuffle=(prompt_sampler is None), + sampler=prompt_sampler, + batch_size=args.train_batch_size) + + pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384) + if dist.is_initialized() and dist.get_world_size() > 1: + pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True) + pretrain_dataloader = DataLoader(pretrain_dataset, + shuffle=(pretrain_sampler is None), + sampler=pretrain_sampler, + batch_size=args.ptx_batch_size, + collate_fn=data_collator) + + def tokenize_fn(texts): + # MUST padding to max length to ensure inputs of all ranks have the same length + # Different length may lead to hang when using gemini, as different generation steps + batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) + return {k: v.to(torch.cuda.current_device()) for k, v in batch.items()} + + (actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim)) + + # configure trainer + trainer = PPOTrainer( + strategy, + actor, + critic, + reward_model, + initial_model, + actor_optim, + critic_optim, + kl_coef=args.kl_coef, + ptx_coef=args.ptx_coef, + max_epochs=args.max_epochs, + train_batch_size=args.train_batch_size, + experience_batch_size=args.experience_batch_size, + tokenizer=tokenize_fn, + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + ) + + trainer.fit(prompt_dataloader=prompt_dataloader, + pretrain_dataloader=pretrain_dataloader, + num_episodes=args.num_episodes, + max_timesteps=args.max_timesteps, + update_timesteps=args.update_timesteps) + + # save model checkpoint after fitting + trainer.save_model(args.save_path, only_rank0=True, tokenizer=tokenizer) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + strategy.save_optimizer(actor_optim, + 'actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--prompt_path', type=str, default=None, help='path to the prompt dataset') + parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset') + parser.add_argument('--strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive', + help='strategy to use') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--rm_path', type=str, default=None) + parser.add_argument('--rm_pretrain', type=str, default=None) + parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--num_episodes', type=int, default=10) + parser.add_argument('--max_timesteps', type=int, default=10) + parser.add_argument('--update_timesteps', type=int, default=10) + parser.add_argument('--max_epochs', type=int, default=5) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--ptx_batch_size', type=int, default=1) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + parser.add_argument('--kl_coef', type=float, default=0.1) + parser.add_argument('--ptx_coef', type=float, default=0.9) + args = parser.parse_args() + main(args) diff --git a/applications/Chat/examples/train_prompts.sh b/applications/Chat/examples/train_prompts.sh new file mode 100755 index 000000000000..db73ac8e8e85 --- /dev/null +++ b/applications/Chat/examples/train_prompts.sh @@ -0,0 +1,18 @@ +set_n_least_used_CUDA_VISIBLE_DEVICES() { + local n=${1:-"9999"} + echo "GPU Memory Usage:" + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ + | tail -n +2 \ + | nl -v 0 \ + | tee /dev/tty \ + | sort -g -k 2 \ + | awk '{print $1}' \ + | head -n $n) + export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') + echo "Now CUDA_VISIBLE_DEVICES is set to:" + echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" +} + +set_n_least_used_CUDA_VISIBLE_DEVICES 2 + +torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai_zero2 diff --git a/applications/Chat/examples/train_reward_model.py b/applications/Chat/examples/train_reward_model.py new file mode 100644 index 000000000000..729dfa23128f --- /dev/null +++ b/applications/Chat/examples/train_reward_model.py @@ -0,0 +1,160 @@ +import argparse +from random import randint + +import loralib as lora +import torch +from coati.dataset import HhRlhfDataset, RmStaticDataset +from coati.models import LogExpLoss, LogSigLoss +from coati.models.base import RewardModel +from coati.models.bloom import BLOOMRM +from coati.models.deberta import DebertaRM +from coati.models.gpt import GPTRM +from coati.models.llama import LlamaRM +from coati.models.opt import OPTRM +from coati.trainer import RewardModelTrainer +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.utils import prepare_llama_tokenizer_and_embedding +from datasets import load_dataset +from torch.optim import Adam +from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer, LlamaTokenizer +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.nn.optimizer import HybridAdam + + +def train(args): + # configure strategy + if args.strategy == 'naive': + strategy = NaiveStrategy() + elif args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + # configure model + with strategy.model_init_context(): + if args.model == 'bloom': + model = BLOOMRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'opt': + model = OPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'gpt2': + model = GPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'deberta': + model = DebertaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'llama': + model = LlamaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + else: + raise ValueError(f'Unsupported model "{args.model}"') + + if args.model_path is not None: + state_dict = torch.load(args.model_path) + model.load_state_dict(state_dict) + + model = model.to(torch.float16) + + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m') + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + elif args.model == 'deberta': + tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v3-large') + elif args.model == 'llama': + tokenizer = LlamaTokenizer.from_pretrained(args.pretrain) + else: + raise ValueError(f'Unsupported model "{args.model}"') + max_len = args.max_len + + if args.model == 'llama': + tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model) + else: + tokenizer.pad_token = tokenizer.eos_token + + # configure optimizer + if args.strategy.startswith('colossalai'): + optim = HybridAdam(model.parameters(), lr=5e-6) + else: + optim = Adam(model.parameters(), lr=5e-6) + + # configure loss function + if args.loss_fn == 'log_sig': + loss_fn = LogSigLoss() + elif args.loss_fn == 'log_exp': + loss_fn = LogExpLoss() + else: + raise ValueError(f'Unsupported loss function "{args.loss_fn}"') + + # prepare for data and dataset + if args.subset is not None: + data = load_dataset(args.dataset, data_dir=args.subset) + else: + data = load_dataset(args.dataset) + + if args.test: + train_data = data['train'].select(range(100)) + eval_data = data['test'].select(range(10)) + else: + train_data = data['train'] + eval_data = data['test'] + valid_data = data['test'].select((randint(0, len(eval_data) - 1) for _ in range(len(eval_data) // 5))) + + if args.dataset == 'Dahoas/rm-static': + train_dataset = RmStaticDataset(train_data, tokenizer, max_len) + valid_dataset = RmStaticDataset(valid_data, tokenizer, max_len) + eval_dataset = RmStaticDataset(eval_data, tokenizer, max_len) + elif args.dataset == 'Anthropic/hh-rlhf': + train_dataset = HhRlhfDataset(train_data, tokenizer, max_len) + valid_dataset = HhRlhfDataset(valid_data, tokenizer, max_len) + eval_dataset = HhRlhfDataset(eval_data, tokenizer, max_len) + else: + raise ValueError(f'Unsupported dataset "{args.dataset}"') + + trainer = RewardModelTrainer(model=model, + strategy=strategy, + optim=optim, + loss_fn=loss_fn, + train_dataset=train_dataset, + valid_dataset=valid_dataset, + eval_dataset=eval_dataset, + batch_size=args.batch_size, + max_epochs=args.max_epochs) + + trainer.fit() + # save model checkpoint after fitting on only rank0 + trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + strategy.save_optimizer(trainer.optimizer, + 'rm_optim_checkpoint_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta', 'llama'], default='bloom') + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--model_path', type=str, default=None) + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--dataset', + type=str, + choices=['Anthropic/hh-rlhf', 'Dahoas/rm-static'], + default='Dahoas/rm-static') + parser.add_argument('--subset', type=str, default=None) + parser.add_argument('--save_path', type=str, default='rm_ckpt') + parser.add_argument('--max_epochs', type=int, default=1) + parser.add_argument('--batch_size', type=int, default=1) + parser.add_argument('--max_len', type=int, default=512) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + parser.add_argument('--loss_fn', type=str, default='log_sig', choices=['log_sig', 'log_exp']) + parser.add_argument('--test', type=bool, default=False) + args = parser.parse_args() + train(args) diff --git a/applications/Chat/examples/train_rm.sh b/applications/Chat/examples/train_rm.sh new file mode 100755 index 000000000000..4f9f55b6b59a --- /dev/null +++ b/applications/Chat/examples/train_rm.sh @@ -0,0 +1,8 @@ +set_n_least_used_CUDA_VISIBLE_DEVICES 1 + +python train_reward_model.py --pretrain 'microsoft/deberta-v3-large' \ + --model 'deberta' \ + --strategy naive \ + --loss_fn 'log_exp'\ + --save_path 'rmstatic.pt' \ + --test True diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py new file mode 100644 index 000000000000..035d5a1ded1d --- /dev/null +++ b/applications/Chat/examples/train_sft.py @@ -0,0 +1,184 @@ +import argparse +import os + +import loralib as lora +import torch +import torch.distributed as dist +from coati.dataset import DataCollatorForSupervisedDataset, SFTDataset, SupervisedDataset +from coati.models.base import RewardModel +from coati.models.bloom import BLOOMLM +from coati.models.gpt import GPTLM +from coati.models.llama import LlamaLM +from coati.models.opt import OPTLM +from coati.trainer import SFTTrainer +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.utils import prepare_llama_tokenizer_and_embedding +from datasets import load_dataset +from torch.optim import Adam +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from transformers import AutoTokenizer, BloomTokenizerFast +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.logging import get_dist_logger +from colossalai.nn.optimizer import HybridAdam +from colossalai.tensor import ColoParameter + + +def train(args): + # configure strategy + if args.strategy == 'naive': + strategy = NaiveStrategy() + elif args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + # configure model + with strategy.model_init_context(): + if args.model == 'bloom': + model = BLOOMLM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'opt': + model = OPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'gpt2': + model = GPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'llama': + model = LlamaLM(pretrained=args.pretrain, lora_rank=args.lora_rank, + checkpoint=True).to(torch.float16).to(torch.cuda.current_device()) + else: + raise ValueError(f'Unsupported model "{args.model}"') + + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + elif args.model == 'llama': + tokenizer = AutoTokenizer.from_pretrained( + args.pretrain, + padding_side="right", + use_fast=False, + ) + tokenizer.eos_token = '<\s>' + else: + raise ValueError(f'Unsupported model "{args.model}"') + tokenizer.pad_token = tokenizer.eos_token + if args.model == 'llama': + tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model) + + if args.strategy == 'colossalai_gemini': + # this is a hack to deal with the resized embedding + # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatiblity + for name, param in model.named_parameters(): + if not isinstance(param, ColoParameter): + sub_module_name = '.'.join(name.split('.')[:-1]) + weight_name = name.split('.')[-1] + sub_module = model.get_submodule(sub_module_name) + setattr(sub_module, weight_name, ColoParameter(param)) + else: + tokenizer.pad_token = tokenizer.eos_token + + # configure optimizer + if args.strategy.startswith('colossalai'): + optim = HybridAdam(model.parameters(), lr=args.lr, clipping_norm=1.0) + else: + optim = Adam(model.parameters(), lr=args.lr) + + logger = get_dist_logger() + + # configure dataset + if args.dataset == 'yizhongw/self_instruct': + train_data = load_dataset(args.dataset, 'super_natural_instructions', split='train') + eval_data = load_dataset(args.dataset, 'super_natural_instructions', split='test') + + train_dataset = SFTDataset(train_data, tokenizer) + eval_dataset = SFTDataset(eval_data, tokenizer) + + else: + train_dataset = SupervisedDataset(tokenizer=tokenizer, + data_path=args.dataset, + max_datasets_size=args.max_datasets_size) + eval_dataset = None + data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) + + if dist.is_initialized() and dist.get_world_size() > 1: + train_sampler = DistributedSampler(train_dataset, + shuffle=True, + seed=42, + drop_last=True, + rank=dist.get_rank(), + num_replicas=dist.get_world_size()) + if eval_dataset is not None: + eval_sampler = DistributedSampler(eval_dataset, + shuffle=False, + seed=42, + drop_last=False, + rank=dist.get_rank(), + num_replicas=dist.get_world_size()) + else: + train_sampler = None + eval_sampler = None + + train_dataloader = DataLoader(train_dataset, + shuffle=(train_sampler is None), + sampler=train_sampler, + batch_size=args.batch_size, + collate_fn=data_collator, + pin_memory=True) + if eval_dataset is not None: + eval_dataloader = DataLoader(eval_dataset, + shuffle=(eval_sampler is None), + sampler=eval_sampler, + batch_size=args.batch_size, + collate_fn=data_collator, + pin_memory=True) + else: + eval_dataloader = None + + trainer = SFTTrainer(model=model, + strategy=strategy, + optim=optim, + train_dataloader=train_dataloader, + eval_dataloader=eval_dataloader, + batch_size=args.batch_size, + max_epochs=args.max_epochs, + accimulation_steps=args.accimulation_steps) + + trainer.fit(logger=logger, log_interval=args.log_interval) + + # save model checkpoint after fitting on only rank0 + trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + strategy.save_optimizer(trainer.optimizer, + 'rm_optim_checkpoint_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom') + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--dataset', type=str, default=None) + parser.add_argument('--max_datasets_size', type=int, default=None) + parser.add_argument('--save_path', type=str, default='output') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--max_epochs', type=int, default=3) + parser.add_argument('--batch_size', type=int, default=4) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log") + parser.add_argument('--lr', type=float, default=5e-6) + parser.add_argument('--accimulation_steps', type=int, default=8) + args = parser.parse_args() + train(args) diff --git a/applications/Chat/examples/train_sft.sh b/applications/Chat/examples/train_sft.sh new file mode 100755 index 000000000000..73710d1b19f8 --- /dev/null +++ b/applications/Chat/examples/train_sft.sh @@ -0,0 +1,12 @@ +torchrun --standalone --nproc_per_node=4 train_sft.py \ + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2 \ + --log_interval 10 \ + --save_path /path/to/Coati-7B \ + --dataset /path/to/data.json \ + --batch_size 4 \ + --accimulation_steps 8 \ + --lr 2e-5 \ + --max_datasets_size 512 \ + --max_epochs 1 \ diff --git a/applications/Chat/inference/README.md b/applications/Chat/inference/README.md new file mode 100644 index 000000000000..3fb330748a13 --- /dev/null +++ b/applications/Chat/inference/README.md @@ -0,0 +1,111 @@ +# Inference + +We provide an online inference server and a benchmark. We aim to run inference on single GPU, so quantization is essential when using large models. + +We support 8-bit quantization (RTN), which is powered by [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) and [transformers](https://github.com/huggingface/transformers). And 4-bit quantization (GPTQ), which is powered by [gptq](https://github.com/IST-DASLab/gptq) and [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). We also support FP16 inference. + +We only support LLaMA family models now. + +## Choosing precision (quantization) + +**FP16**: Fastest, best output quality, highest memory usage + +**8-bit**: Slow, easier setup (originally supported by transformers), lower output quality (due to RTN), **recommended for first-timers** + +**4-bit**: Faster, lowest memory usage, higher output quality (due to GPTQ), but more difficult setup + +## Hardware requirements for LLaMA + +Tha data is from [LLaMA Int8 4bit ChatBot Guide v2](https://rentry.org/llama-tard-v2). + +### 8-bit + +| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples | +| :---: | :---: | :---: | :---: | :---: | +| LLaMA-7B | 9.2GB | 10GB | 24GB | 3060 12GB, RTX 3080 10GB, RTX 3090 | +| LLaMA-13B | 16.3GB | 20GB | 32GB | RTX 3090 Ti, RTX 4090 | +| LLaMA-30B | 36GB | 40GB | 64GB | A6000 48GB, A100 40GB | +| LLaMA-65B | 74GB | 80GB | 128GB | A100 80GB | + +### 4-bit + +| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples | +| :---: | :---: | :---: | :---: | :---: | +| LLaMA-7B | 3.5GB | 6GB | 16GB | RTX 1660, 2060, AMD 5700xt, RTX 3050, 3060 | +| LLaMA-13B | 6.5GB | 10GB | 32GB | AMD 6900xt, RTX 2060 12GB, 3060 12GB, 3080, A2000 | +| LLaMA-30B | 15.8GB | 20GB | 64GB | RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100 | +| LLaMA-65B | 31.2GB | 40GB | 128GB | A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000, Titan Ada | + +## 8-bit setup + +8-bit quantization is originally supported by the latest [transformers](https://github.com/huggingface/transformers). Please install it from source. + +Please ensure you have downloaded HF-format model weights of LLaMA models. + +Usage: + +```python +from transformers import LlamaForCausalLM + +USE_8BIT = True # use 8-bit quantization; otherwise, use fp16 + +model = LlamaForCausalLM.from_pretrained( + "pretrained/path", + load_in_8bit=USE_8BIT, + torch_dtype=torch.float16, + device_map="auto", + ) +if not USE_8BIT: + model.half() # use fp16 +model.eval() +``` + +**Troubleshooting**: if you get error indicating your CUDA-related libraries not found when loading 8-bit model, you can check whether your `LD_LIBRARY_PATH` is correct. + +E.g. you can set `export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`. + +## 4-bit setup + +Please ensure you have downloaded HF-format model weights of LLaMA models first. + +Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight convertion script. + +After installing this lib, we may convert the original HF-format LLaMA model weights to 4-bit version. + +```shell +CUDA_VISIBLE_DEVICES=0 python llama.py /path/to/pretrained/llama-7b c4 --wbits 4 --groupsize 128 --save llama7b-4bit.pt +``` + +Run this command in your cloned `GPTQ-for-LLaMa` directory, then you will get a 4-bit weight file `llama7b-4bit-128g.pt`. + +**Troubleshooting**: if you get error about `position_ids`, you can checkout to commit `50287c3b9ae4a3b66f6b5127c643ec39b769b155`(`GPTQ-for-LLaMa` repo). + +## Online inference server + +In this directory: + +```shell +export CUDA_VISIBLE_DEVICES=0 +# fp16, will listen on 0.0.0.0:7070 by default +python server.py /path/to/pretrained +# 8-bit, will listen on localhost:8080 +python server.py /path/to/pretrained --quant 8bit --http_host localhost --http_port 8080 +# 4-bit +python server.py /path/to/pretrained --quant 4bit --gptq_checkpoint /path/to/llama7b-4bit-128g.pt --gptq_group_size 128 +``` + +## Benchmark + +In this directory: + +```shell +export CUDA_VISIBLE_DEVICES=0 +# fp16 +python benchmark.py /path/to/pretrained +# 8-bit +python benchmark.py /path/to/pretrained --quant 8bit +# 4-bit +python benchmark.py /path/to/pretrained --quant 4bit --gptq_checkpoint /path/to/llama7b-4bit-128g.pt --gptq_group_size 128 +``` + +This benchmark will record throughput and peak CUDA memory usage. diff --git a/applications/Chat/inference/benchmark.py b/applications/Chat/inference/benchmark.py new file mode 100644 index 000000000000..59cd1eeea2aa --- /dev/null +++ b/applications/Chat/inference/benchmark.py @@ -0,0 +1,132 @@ +# Adapted from https://github.com/tloen/alpaca-lora/blob/main/generate.py + +import argparse +from time import time + +import torch +from llama_gptq import load_quant +from transformers import AutoTokenizer, GenerationConfig, LlamaForCausalLM + + +def generate_prompt(instruction, input=None): + if input: + return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. + +### Instruction: +{instruction} + +### Input: +{input} + +### Response:""" + else: + return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +{instruction} + +### Response:""" + + +@torch.no_grad() +def evaluate( + model, + tokenizer, + instruction, + input=None, + temperature=0.1, + top_p=0.75, + top_k=40, + num_beams=4, + max_new_tokens=128, + **kwargs, +): + prompt = generate_prompt(instruction, input) + inputs = tokenizer(prompt, return_tensors="pt") + input_ids = inputs["input_ids"].cuda() + generation_config = GenerationConfig( + temperature=temperature, + top_p=top_p, + top_k=top_k, + num_beams=num_beams, + **kwargs, + ) + generation_output = model.generate( + input_ids=input_ids, + generation_config=generation_config, + return_dict_in_generate=True, + output_scores=True, + max_new_tokens=max_new_tokens, + do_sample=True, + ) + s = generation_output.sequences[0] + output = tokenizer.decode(s) + n_new_tokens = s.size(0) - input_ids.size(1) + return output.split("### Response:")[1].strip(), n_new_tokens + + +instructions = [ + "Tell me about alpacas.", + "Tell me about the president of Mexico in 2019.", + "Tell me about the king of France in 2019.", + "List all Canadian provinces in alphabetical order.", + "Write a Python program that prints the first 10 Fibonacci numbers.", + "Write a program that prints the numbers from 1 to 100. But for multiples of three print 'Fizz' instead of the number and for the multiples of five print 'Buzz'. For numbers which are multiples of both three and five print 'FizzBuzz'.", + "Tell me five words that rhyme with 'shock'.", + "Translate the sentence 'I have no mouth but I must scream' into Spanish.", + "Count up from 1 to 500.", + # === + "How to play support in legends of league", + "Write a Python program that calculate Fibonacci numbers.", +] +inst = [instructions[0]] * 4 + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + 'pretrained', + help='Path to pretrained model. Can be a local path or a model name from the HuggingFace model hub.') + parser.add_argument('--quant', + choices=['8bit', '4bit'], + default=None, + help='Quantization mode. Default: None (no quantization, fp16).') + parser.add_argument( + '--gptq_checkpoint', + default=None, + help='Path to GPTQ checkpoint. This is only useful when quantization mode is 4bit. Default: None.') + parser.add_argument('--gptq_group_size', + type=int, + default=128, + help='Group size for GPTQ. This is only useful when quantization mode is 4bit. Default: 128.') + args = parser.parse_args() + + if args.quant == '4bit': + assert args.gptq_checkpoint is not None, 'Please specify a GPTQ checkpoint.' + + tokenizer = AutoTokenizer.from_pretrained(args.pretrained) + + if args.quant == '4bit': + model = load_quant(args.pretrained, args.gptq_checkpoint, 4, args.gptq_group_size) + model.cuda() + else: + model = LlamaForCausalLM.from_pretrained( + args.pretrained, + load_in_8bit=(args.quant == '8bit'), + torch_dtype=torch.float16, + device_map="auto", + ) + if args.quant != '8bit': + model.half() # seems to fix bugs for some users. + model.eval() + + total_tokens = 0 + start = time() + for instruction in instructions: + print(f"Instruction: {instruction}") + resp, tokens = evaluate(model, tokenizer, instruction, temparature=0.2, num_beams=1) + total_tokens += tokens + print(f"Response: {resp}") + print('\n----------------------------\n') + duration = time() - start + print(f'Total time: {duration:.3f} s, {total_tokens/duration:.3f} tokens/s') + print(f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.3f} GB') diff --git a/applications/Chat/inference/llama_gptq/__init__.py b/applications/Chat/inference/llama_gptq/__init__.py new file mode 100644 index 000000000000..51c8d6316290 --- /dev/null +++ b/applications/Chat/inference/llama_gptq/__init__.py @@ -0,0 +1,5 @@ +from .loader import load_quant + +__all__ = [ + 'load_quant', +] diff --git a/applications/Chat/inference/llama_gptq/loader.py b/applications/Chat/inference/llama_gptq/loader.py new file mode 100644 index 000000000000..a5c6ac7d1589 --- /dev/null +++ b/applications/Chat/inference/llama_gptq/loader.py @@ -0,0 +1,41 @@ +import torch +import torch.nn as nn +import transformers +from transformers import LlamaConfig, LlamaForCausalLM + +from .model_utils import find_layers +from .quant import make_quant + + +def load_quant(pretrained: str, checkpoint: str, wbits: int, groupsize: int): + config = LlamaConfig.from_pretrained(pretrained) + + def noop(*args, **kwargs): + pass + + torch.nn.init.kaiming_uniform_ = noop + torch.nn.init.uniform_ = noop + torch.nn.init.normal_ = noop + + torch.set_default_dtype(torch.half) + transformers.modeling_utils._init_weights = False + torch.set_default_dtype(torch.half) + model = LlamaForCausalLM(config) + torch.set_default_dtype(torch.float) + model = model.eval() + layers = find_layers(model) + for name in ['lm_head']: + if name in layers: + del layers[name] + make_quant(model, layers, wbits, groupsize) + + print(f'Loading model with {wbits} bits...') + if checkpoint.endswith('.safetensors'): + from safetensors.torch import load_file as safe_load + model.load_state_dict(safe_load(checkpoint)) + else: + model.load_state_dict(torch.load(checkpoint)) + model.seqlen = 2048 + print('Done.') + + return model diff --git a/applications/Chat/inference/llama_gptq/model_utils.py b/applications/Chat/inference/llama_gptq/model_utils.py new file mode 100644 index 000000000000..62db171abb52 --- /dev/null +++ b/applications/Chat/inference/llama_gptq/model_utils.py @@ -0,0 +1,13 @@ +# copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/past/modelutils.py + +import torch +import torch.nn as nn + + +def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''): + if type(module) in layers: + return {name: module} + res = {} + for name1, child in module.named_children(): + res.update(find_layers(child, layers=layers, name=name + '.' + name1 if name != '' else name1)) + return res diff --git a/applications/Chat/inference/llama_gptq/quant.py b/applications/Chat/inference/llama_gptq/quant.py new file mode 100644 index 000000000000..f7d5b7ce4bd8 --- /dev/null +++ b/applications/Chat/inference/llama_gptq/quant.py @@ -0,0 +1,283 @@ +# copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/past/quant.py + +import math + +import numpy as np +import torch +import torch.nn as nn + + +def quantize(x, scale, zero, maxq): + q = torch.clamp(torch.round(x / scale) + zero, 0, maxq) + return scale * (q - zero) + + +class Quantizer(nn.Module): + + def __init__(self, shape=1): + super(Quantizer, self).__init__() + self.register_buffer('maxq', torch.tensor(0)) + self.register_buffer('scale', torch.zeros(shape)) + self.register_buffer('zero', torch.zeros(shape)) + + def configure(self, bits, perchannel=False, sym=True, mse=False, norm=2.4, grid=100, maxshrink=.8): + self.maxq = torch.tensor(2**bits - 1) + self.perchannel = perchannel + self.sym = sym + self.mse = mse + self.norm = norm + self.grid = grid + self.maxshrink = maxshrink + + def find_params(self, x, weight=False): + dev = x.device + self.maxq = self.maxq.to(dev) + + shape = x.shape + if self.perchannel: + if weight: + x = x.flatten(1) + else: + if len(shape) == 4: + x = x.permute([1, 0, 2, 3]) + x = x.flatten(1) + if len(shape) == 3: + x = x.reshape((-1, shape[-1])).t() + if len(shape) == 2: + x = x.t() + else: + x = x.flatten().unsqueeze(0) + + tmp = torch.zeros(x.shape[0], device=dev) + xmin = torch.minimum(x.min(1)[0], tmp) + xmax = torch.maximum(x.max(1)[0], tmp) + + if self.sym: + xmax = torch.maximum(torch.abs(xmin), xmax) + tmp = xmin < 0 + if torch.any(tmp): + xmin[tmp] = -xmax[tmp] + tmp = (xmin == 0) & (xmax == 0) + xmin[tmp] = -1 + xmax[tmp] = +1 + + self.scale = (xmax - xmin) / self.maxq + if self.sym: + self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2) + else: + self.zero = torch.round(-xmin / self.scale) + + if self.mse: + best = torch.full([x.shape[0]], float('inf'), device=dev) + for i in range(int(self.maxshrink * self.grid)): + p = 1 - i / self.grid + xmin1 = p * xmin + xmax1 = p * xmax + scale1 = (xmax1 - xmin1) / self.maxq + zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero + q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq) + q -= x + q.abs_() + q.pow_(self.norm) + err = torch.sum(q, 1) + tmp = err < best + if torch.any(tmp): + best[tmp] = err[tmp] + self.scale[tmp] = scale1[tmp] + self.zero[tmp] = zero1[tmp] + if not self.perchannel: + if weight: + tmp = shape[0] + else: + tmp = shape[1] if len(shape) != 3 else shape[2] + self.scale = self.scale.repeat(tmp) + self.zero = self.zero.repeat(tmp) + + if weight: + shape = [-1] + [1] * (len(shape) - 1) + self.scale = self.scale.reshape(shape) + self.zero = self.zero.reshape(shape) + return + if len(shape) == 4: + self.scale = self.scale.reshape((1, -1, 1, 1)) + self.zero = self.zero.reshape((1, -1, 1, 1)) + if len(shape) == 3: + self.scale = self.scale.reshape((1, 1, -1)) + self.zero = self.zero.reshape((1, 1, -1)) + if len(shape) == 2: + self.scale = self.scale.unsqueeze(0) + self.zero = self.zero.unsqueeze(0) + + def quantize(self, x): + if self.ready(): + return quantize(x, self.scale, self.zero, self.maxq) + return x + + def enabled(self): + return self.maxq > 0 + + def ready(self): + return torch.all(self.scale != 0) + + +try: + import quant_cuda +except: + print('CUDA extension not installed.') + +# Assumes layer is perfectly divisible into 256 * 256 blocks + + +class QuantLinear(nn.Module): + + def __init__(self, bits, groupsize, infeatures, outfeatures): + super().__init__() + if bits not in [2, 3, 4, 8]: + raise NotImplementedError("Only 2,3,4,8 bits are supported.") + self.infeatures = infeatures + self.outfeatures = outfeatures + self.bits = bits + if groupsize != -1 and groupsize < 32 and groupsize != int(math.pow(2, int(math.log2(groupsize)))): + raise NotImplementedError("groupsize supports powers of 2 greater than 32. (e.g. : 32,64,128,etc)") + groupsize = groupsize if groupsize != -1 else infeatures + self.groupsize = groupsize + self.register_buffer( + 'qzeros', torch.zeros((math.ceil(infeatures / groupsize), outfeatures // 256 * (bits * 8)), + dtype=torch.int)) + self.register_buffer('scales', torch.zeros((math.ceil(infeatures / groupsize), outfeatures))) + self.register_buffer('bias', torch.zeros(outfeatures)) + self.register_buffer('qweight', torch.zeros((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int)) + self._initialized_quant_state = False + + def pack(self, linear, scales, zeros): + scales = scales.t().contiguous() + zeros = zeros.t().contiguous() + scale_zeros = zeros * scales + self.scales = scales.clone() + if linear.bias is not None: + self.bias = linear.bias.clone() + + intweight = [] + for idx in range(self.infeatures): + g_idx = idx // self.groupsize + intweight.append( + torch.round((linear.weight.data[:, idx] + scale_zeros[g_idx]) / self.scales[g_idx]).to(torch.int)[:, + None]) + intweight = torch.cat(intweight, dim=1) + intweight = intweight.t().contiguous() + intweight = intweight.numpy().astype(np.uint32) + qweight = np.zeros((intweight.shape[0] // 256 * (self.bits * 8), intweight.shape[1]), dtype=np.uint32) + i = 0 + row = 0 + while row < qweight.shape[0]: + if self.bits in [2, 4, 8]: + for j in range(i, i + (32 // self.bits)): + qweight[row] |= intweight[j] << (self.bits * (j - i)) + i += 32 // self.bits + row += 1 + elif self.bits == 3: + for j in range(i, i + 10): + qweight[row] |= intweight[j] << (3 * (j - i)) + i += 10 + qweight[row] |= intweight[i] << 30 + row += 1 + qweight[row] |= (intweight[i] >> 2) & 1 + i += 1 + for j in range(i, i + 10): + qweight[row] |= intweight[j] << (3 * (j - i) + 1) + i += 10 + qweight[row] |= intweight[i] << 31 + row += 1 + qweight[row] |= (intweight[i] >> 1) & 0x3 + i += 1 + for j in range(i, i + 10): + qweight[row] |= intweight[j] << (3 * (j - i) + 2) + i += 10 + row += 1 + else: + raise NotImplementedError("Only 2,3,4,8 bits are supported.") + + qweight = qweight.astype(np.int32) + self.qweight = torch.from_numpy(qweight) + + zeros -= 1 + zeros = zeros.numpy().astype(np.uint32) + qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 256 * (self.bits * 8)), dtype=np.uint32) + i = 0 + col = 0 + while col < qzeros.shape[1]: + if self.bits in [2, 4, 8]: + for j in range(i, i + (32 // self.bits)): + qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i)) + i += 32 // self.bits + col += 1 + elif self.bits == 3: + for j in range(i, i + 10): + qzeros[:, col] |= zeros[:, j] << (3 * (j - i)) + i += 10 + qzeros[:, col] |= zeros[:, i] << 30 + col += 1 + qzeros[:, col] |= (zeros[:, i] >> 2) & 1 + i += 1 + for j in range(i, i + 10): + qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 1) + i += 10 + qzeros[:, col] |= zeros[:, i] << 31 + col += 1 + qzeros[:, col] |= (zeros[:, i] >> 1) & 0x3 + i += 1 + for j in range(i, i + 10): + qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 2) + i += 10 + col += 1 + else: + raise NotImplementedError("Only 2,3,4,8 bits are supported.") + + qzeros = qzeros.astype(np.int32) + self.qzeros = torch.from_numpy(qzeros) + + def forward(self, x): + intermediate_dtype = torch.float32 + + if not self._initialized_quant_state: + # Do we even have a bias? Check for at least one non-zero element. + if self.bias is not None and bool(torch.any(self.bias != 0)): + # Then make sure it's the right type. + self.bias.data = self.bias.data.to(intermediate_dtype) + else: + self.bias = None + + outshape = list(x.shape) + outshape[-1] = self.outfeatures + x = x.reshape(-1, x.shape[-1]) + if self.bias is None: + y = torch.zeros(x.shape[0], outshape[-1], dtype=intermediate_dtype, device=x.device) + else: + y = self.bias.clone().repeat(x.shape[0], 1) + + output_dtype = x.dtype + x = x.to(intermediate_dtype) + if self.bits == 2: + quant_cuda.vecquant2matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize) + elif self.bits == 3: + quant_cuda.vecquant3matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize) + elif self.bits == 4: + quant_cuda.vecquant4matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize) + elif self.bits == 8: + quant_cuda.vecquant8matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize) + else: + raise NotImplementedError("Only 2,3,4,8 bits are supported.") + y = y.to(output_dtype) + return y.reshape(outshape) + + +def make_quant(module, names, bits, groupsize, name=''): + if isinstance(module, QuantLinear): + return + for attr in dir(module): + tmp = getattr(module, attr) + name1 = name + '.' + attr if name != '' else attr + if name1 in names: + setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features)) + for name1, child in module.named_children(): + make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1) diff --git a/applications/Chat/inference/locustfile.py b/applications/Chat/inference/locustfile.py new file mode 100644 index 000000000000..51cdc68125bb --- /dev/null +++ b/applications/Chat/inference/locustfile.py @@ -0,0 +1,27 @@ +from json import JSONDecodeError + +from locust import HttpUser, task + +samples = [[ + dict( + instruction='Who is the best player in the history of NBA?', + response= + 'The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1' + ), + dict(instruction='continue this talk', response=''), +], [ + dict(instruction='Who is the best player in the history of NBA?', response=''), +]] + + +class GenerationUser(HttpUser): + + @task + def generate(self): + for sample in samples: + data = {'max_new_tokens': 64, 'history': sample} + with self.client.post('/generate', json=data, catch_response=True) as response: + if response.status_code in (200, 406): + response.success() + else: + response.failure('Response wrong') diff --git a/applications/Chat/inference/requirements.txt b/applications/Chat/inference/requirements.txt new file mode 100644 index 000000000000..67a9874e569a --- /dev/null +++ b/applications/Chat/inference/requirements.txt @@ -0,0 +1,10 @@ +fastapi +locustio +numpy +pydantic +safetensors +slowapi +sse_starlette +torch +uvicorn +git+https://github.com/huggingface/transformers diff --git a/applications/Chat/inference/server.py b/applications/Chat/inference/server.py new file mode 100644 index 000000000000..46a8b9a0568a --- /dev/null +++ b/applications/Chat/inference/server.py @@ -0,0 +1,165 @@ +import argparse +import os +from threading import Lock +from typing import Dict, Generator, List, Optional + +import torch +import uvicorn +from fastapi import FastAPI, HTTPException, Request +from fastapi.middleware.cors import CORSMiddleware +from llama_gptq import load_quant +from pydantic import BaseModel, Field +from slowapi import Limiter, _rate_limit_exceeded_handler +from slowapi.errors import RateLimitExceeded +from slowapi.util import get_remote_address +from sse_starlette.sse import EventSourceResponse +from transformers import AutoTokenizer, GenerationConfig, LlamaForCausalLM +from utils import ChatPromptProcessor, Dialogue, LockedIterator, sample_streamingly, update_model_kwargs_fn + +CONTEXT = 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.' +MAX_LEN = 2048 +running_lock = Lock() + + +class GenerationTaskReq(BaseModel): + max_new_tokens: int = Field(gt=0, le=512, example=64) + history: List[Dialogue] = Field(min_items=1) + top_k: Optional[int] = Field(default=None, gt=0, example=50) + top_p: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.5) + temperature: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.7) + + +limiter = Limiter(key_func=get_remote_address) +app = FastAPI() +app.state.limiter = limiter +app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler) + +# set CORS +origin_spec_from_env = os.environ.get('CORS_ORIGIN', None) + +if origin_spec_from_env is not None: + # allow CORS from the specified origins + origins = os.environ['CORS_ORIGIN'].split(',') +else: + # allow CORS from all origins + origins = ["*"] + +app.add_middleware( + CORSMiddleware, + allow_origins=origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +def generate_streamingly(prompt, max_new_tokens, top_k, top_p, temperature): + inputs = {k: v.cuda() for k, v in tokenizer(prompt, return_tensors="pt").items()} + model_kwargs = { + 'max_generate_tokens': max_new_tokens, + 'early_stopping': True, + 'top_k': top_k, + 'top_p': top_p, + 'temperature': temperature, + 'prepare_inputs_fn': model.prepare_inputs_for_generation, + 'update_model_kwargs_fn': update_model_kwargs_fn, + } + is_first_word = True + generator = LockedIterator(sample_streamingly(model, **inputs, **model_kwargs), running_lock) + for output in generator: + output = output.cpu() + tokens = tokenizer.convert_ids_to_tokens(output, skip_special_tokens=True) + current_sub_tokens = [] + for token in tokens: + if token in tokenizer.all_special_tokens: + continue + current_sub_tokens.append(token) + if current_sub_tokens: + out_string = tokenizer.sp_model.decode(current_sub_tokens) + if is_first_word: + out_string = out_string.lstrip() + is_first_word = False + elif current_sub_tokens[0].startswith('▁'): + # whitespace will be ignored by the frontend + out_string = ' ' + out_string + yield out_string + + +async def event_generator(request: Request, generator: Generator): + while True: + if await request.is_disconnected(): + break + try: + yield {'event': 'generate', 'data': next(generator)} + except StopIteration: + yield {'event': 'end', 'data': ''} + break + + +@app.post('/generate/stream') +@limiter.limit('1/second') +def generate(data: GenerationTaskReq, request: Request): + prompt = prompt_processor.preprocess_prompt(data.history, data.max_new_tokens) + event_source = event_generator( + request, generate_streamingly(prompt, data.max_new_tokens, data.top_k, data.top_p, data.temperature)) + return EventSourceResponse(event_source) + + +@app.post('/generate') +@limiter.limit('1/second') +def generate_no_stream(data: GenerationTaskReq, request: Request): + prompt = prompt_processor.preprocess_prompt(data.history, data.max_new_tokens) + inputs = {k: v.cuda() for k, v in tokenizer(prompt, return_tensors="pt").items()} + with running_lock: + output = model.generate(**inputs, **data.dict(exclude={'history'})) + output = output.cpu() + prompt_len = inputs['input_ids'].size(1) + response = output[0, prompt_len:] + out_string = tokenizer.decode(response, skip_special_tokens=True) + return out_string.lstrip() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + 'pretrained', + help='Path to pretrained model. Can be a local path or a model name from the HuggingFace model hub.') + parser.add_argument('--quant', + choices=['8bit', '4bit'], + default=None, + help='Quantization mode. Default: None (no quantization, fp16).') + parser.add_argument( + '--gptq_checkpoint', + default=None, + help='Path to GPTQ checkpoint. This is only useful when quantization mode is 4bit. Default: None.') + parser.add_argument('--gptq_group_size', + type=int, + default=128, + help='Group size for GPTQ. This is only useful when quantization mode is 4bit. Default: 128.') + parser.add_argument('--http_host', default='0.0.0.0') + parser.add_argument('--http_port', type=int, default=7070) + args = parser.parse_args() + + if args.quant == '4bit': + assert args.gptq_checkpoint is not None, 'Please specify a GPTQ checkpoint.' + + tokenizer = AutoTokenizer.from_pretrained(args.pretrained) + prompt_processor = ChatPromptProcessor(tokenizer, CONTEXT, MAX_LEN) + + if args.quant == '4bit': + model = load_quant(args.pretrained, args.gptq_checkpoint, 4, args.gptq_group_size) + model.cuda() + else: + model = LlamaForCausalLM.from_pretrained( + args.pretrained, + load_in_8bit=(args.quant == '8bit'), + torch_dtype=torch.float16, + device_map="auto", + ) + if args.quant != '8bit': + model.half() # seems to fix bugs for some users. + model.eval() + + config = uvicorn.Config(app, host=args.http_host, port=args.http_port) + server = uvicorn.Server(config=config) + server.run() diff --git a/applications/Chat/inference/tests/test_chat_prompt.py b/applications/Chat/inference/tests/test_chat_prompt.py new file mode 100644 index 000000000000..f5737ebe8c09 --- /dev/null +++ b/applications/Chat/inference/tests/test_chat_prompt.py @@ -0,0 +1,56 @@ +import os + +from transformers import AutoTokenizer +from utils import ChatPromptProcessor, Dialogue + +CONTEXT = 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.' +tokenizer = AutoTokenizer.from_pretrained(os.environ['PRETRAINED_PATH']) + +samples = [ + ([ + Dialogue( + instruction='Who is the best player in the history of NBA?', + response= + 'The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1' + ), + Dialogue(instruction='continue this talk', response=''), + ], 128, + 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\nWho is the best player in the history of NBA?\n\n### Response:\nThe best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1\n\n### Instruction:\ncontinue this talk\n\n### Response:\n' + ), + ([ + Dialogue( + instruction='Who is the best player in the history of NBA?', + response= + 'The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1' + ), + Dialogue(instruction='continue this talk', response=''), + ], 200, + 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\ncontinue this talk\n\n### Response:\n' + ), + ([ + Dialogue( + instruction='Who is the best player in the history of NBA?', + response= + 'The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1' + ), + Dialogue(instruction='continue this talk', response=''), + ], 211, + 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\ncontinue this\n\n### Response:\n' + ), + ([ + Dialogue(instruction='Who is the best player in the history of NBA?', response=''), + ], 128, + 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\nWho is the best player in the history of NBA?\n\n### Response:\n' + ), +] + + +def test_chat_prompt_processor(): + processor = ChatPromptProcessor(tokenizer, CONTEXT, 256) + for history, max_new_tokens, result in samples: + prompt = processor.preprocess_prompt(history, max_new_tokens) + assert prompt == result + + +if __name__ == '__main__': + test_chat_prompt_processor() diff --git a/applications/Chat/inference/utils.py b/applications/Chat/inference/utils.py new file mode 100644 index 000000000000..3d04aa57d553 --- /dev/null +++ b/applications/Chat/inference/utils.py @@ -0,0 +1,179 @@ +from threading import Lock +from typing import Any, Callable, Generator, List, Optional + +import torch +import torch.distributed as dist +import torch.nn as nn +from pydantic import BaseModel, Field + +try: + from transformers.generation_logits_process import ( + LogitsProcessorList, + TemperatureLogitsWarper, + TopKLogitsWarper, + TopPLogitsWarper, + ) +except ImportError: + from transformers.generation import LogitsProcessorList, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper + + +def prepare_logits_processor(top_k: Optional[int] = None, + top_p: Optional[float] = None, + temperature: Optional[float] = None) -> LogitsProcessorList: + processor_list = LogitsProcessorList() + if temperature is not None and temperature != 1.0: + processor_list.append(TemperatureLogitsWarper(temperature)) + if top_k is not None and top_k != 0: + processor_list.append(TopKLogitsWarper(top_k)) + if top_p is not None and top_p < 1.0: + processor_list.append(TopPLogitsWarper(top_p)) + return processor_list + + +def _is_sequence_finished(unfinished_sequences: torch.Tensor) -> bool: + if dist.is_initialized() and dist.get_world_size() > 1: + # consider DP + unfinished_sequences = unfinished_sequences.clone() + dist.all_reduce(unfinished_sequences) + return unfinished_sequences.max() == 0 + + +def sample_streamingly(model: nn.Module, + input_ids: torch.Tensor, + max_generate_tokens: int, + early_stopping: bool = False, + eos_token_id: Optional[int] = None, + pad_token_id: Optional[int] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + temperature: Optional[float] = None, + prepare_inputs_fn: Optional[Callable[[torch.Tensor, Any], dict]] = None, + update_model_kwargs_fn: Optional[Callable[[dict, Any], dict]] = None, + **model_kwargs) -> Generator: + + logits_processor = prepare_logits_processor(top_k, top_p, temperature) + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + + for _ in range(max_generate_tokens): + model_inputs = prepare_inputs_fn(input_ids, **model_kwargs) if prepare_inputs_fn is not None else { + 'input_ids': input_ids + } + outputs = model(**model_inputs) + + next_token_logits = outputs['logits'][:, -1, :] + # pre-process distribution + next_token_logits = logits_processor(input_ids, next_token_logits) + # sample + probs = torch.softmax(next_token_logits, dim=-1, dtype=torch.float) + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + + # finished sentences should have their next token be a padding token + if eos_token_id is not None: + if pad_token_id is None: + raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.") + next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences) + + yield next_tokens + + # update generated ids, model inputs for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + if update_model_kwargs_fn is not None: + model_kwargs = update_model_kwargs_fn(outputs, **model_kwargs) + + # if eos_token was found in one sentence, set sentence to finished + if eos_token_id is not None: + unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_token_id).long()) + + # stop when each sentence is finished if early_stopping=True + if early_stopping and _is_sequence_finished(unfinished_sequences): + break + + +def update_model_kwargs_fn(outputs: dict, **model_kwargs) -> dict: + if "past_key_values" in outputs: + model_kwargs["past"] = outputs["past_key_values"] + else: + model_kwargs["past"] = None + + # update token_type_ids with last value + if "token_type_ids" in model_kwargs: + token_type_ids = model_kwargs["token_type_ids"] + model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1) + + return model_kwargs + + +class Dialogue(BaseModel): + instruction: str = Field(min_length=1, example='Count up from 1 to 500.') + response: str = Field(example='') + + +def _format_dialogue(instruction: str, response: str = ''): + return f'\n\n### Instruction:\n{instruction}\n\n### Response:\n{response}' + + +class ChatPromptProcessor: + + def __init__(self, tokenizer, context: str, max_len: int = 2048): + self.tokenizer = tokenizer + self.context = context + self.max_len = max_len + # These will be initialized after the first call of preprocess_prompt() + self.context_len: Optional[int] = None + self.dialogue_placeholder_len: Optional[int] = None + + def preprocess_prompt(self, history: List[Dialogue], max_new_tokens: int) -> str: + if self.context_len is None: + self.context_len = len(self.tokenizer(self.context)['input_ids']) + if self.dialogue_placeholder_len is None: + self.dialogue_placeholder_len = len( + self.tokenizer(_format_dialogue(''), add_special_tokens=False)['input_ids']) + prompt = self.context + # the last dialogue must be in the prompt + last_dialogue = history.pop() + # the response of the last dialogue is empty + assert last_dialogue.response == '' + if len(self.tokenizer(_format_dialogue(last_dialogue.instruction), add_special_tokens=False) + ['input_ids']) + max_new_tokens + self.context_len >= self.max_len: + # to avoid truncate placeholder, apply truncate to the original instruction + instruction_truncated = self.tokenizer(last_dialogue.instruction, + add_special_tokens=False, + truncation=True, + max_length=(self.max_len - max_new_tokens - self.context_len - + self.dialogue_placeholder_len))['input_ids'] + instruction_truncated = self.tokenizer.decode(instruction_truncated).lstrip() + prompt += _format_dialogue(instruction_truncated) + return prompt + + res_len = self.max_len - max_new_tokens - len(self.tokenizer(prompt)['input_ids']) + + rows = [] + for dialogue in history[::-1]: + text = _format_dialogue(dialogue.instruction, dialogue.response) + cur_len = len(self.tokenizer(text, add_special_tokens=False)['input_ids']) + if res_len - cur_len < 0: + break + res_len -= cur_len + rows.insert(0, text) + prompt += ''.join(rows) + _format_dialogue(last_dialogue.instruction) + return prompt + + +class LockedIterator: + + def __init__(self, it, lock: Lock) -> None: + self.lock = lock + self.it = iter(it) + + def __iter__(self): + return self + + def __next__(self): + with self.lock: + return next(self.it) diff --git a/applications/Chat/pytest.ini b/applications/Chat/pytest.ini new file mode 100644 index 000000000000..01e5cd217c5d --- /dev/null +++ b/applications/Chat/pytest.ini @@ -0,0 +1,6 @@ +[pytest] +markers = + cpu: tests which can run on CPU + gpu: tests which requires a single GPU + dist: tests which are run in a multi-GPU or multi-machine environment + experiment: tests for experimental features diff --git a/applications/Chat/requirements-test.txt b/applications/Chat/requirements-test.txt new file mode 100644 index 000000000000..e079f8a6038d --- /dev/null +++ b/applications/Chat/requirements-test.txt @@ -0,0 +1 @@ +pytest diff --git a/applications/Chat/requirements.txt b/applications/Chat/requirements.txt new file mode 100644 index 000000000000..af7ff67861eb --- /dev/null +++ b/applications/Chat/requirements.txt @@ -0,0 +1,13 @@ +transformers>=4.20.1 +tqdm +datasets +loralib +colossalai>=0.2.4 +torch<2.0.0, >=1.12.1 +langchain +tokenizers +fastapi +sse_starlette +wandb +sentencepiece +gpustat diff --git a/applications/Chat/setup.py b/applications/Chat/setup.py new file mode 100644 index 000000000000..a285a6dff4bf --- /dev/null +++ b/applications/Chat/setup.py @@ -0,0 +1,41 @@ +from setuptools import find_packages, setup + + +def fetch_requirements(path): + with open(path, 'r') as fd: + return [r.strip() for r in fd.readlines()] + + +def fetch_readme(): + with open('README.md', encoding='utf-8') as f: + return f.read() + + +def fetch_version(): + with open('version.txt', 'r') as f: + return f.read().strip() + + +setup( + name='coati', + version=fetch_version(), + packages=find_packages(exclude=( + 'tests', + 'benchmarks', + '*.egg-info', + )), + description='Colossal-AI Talking Intelligence', + long_description=fetch_readme(), + long_description_content_type='text/markdown', + license='Apache Software License 2.0', + url='https://github.com/hpcaitech/Coati', + install_requires=fetch_requirements('requirements.txt'), + python_requires='>=3.6', + classifiers=[ + 'Programming Language :: Python :: 3', + 'License :: OSI Approved :: Apache Software License', + 'Environment :: GPU :: NVIDIA CUDA', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: System :: Distributed Computing', + ], +) diff --git a/applications/Chat/tests/__init__.py b/applications/Chat/tests/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/applications/Chat/tests/test_checkpoint.py b/applications/Chat/tests/test_checkpoint.py new file mode 100644 index 000000000000..8c7848525201 --- /dev/null +++ b/applications/Chat/tests/test_checkpoint.py @@ -0,0 +1,98 @@ +import os +import tempfile +from contextlib import nullcontext +from functools import partial + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +from coati.models.gpt import GPTActor +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy +from transformers.models.gpt2.configuration_gpt2 import GPT2Config + +from colossalai.nn.optimizer import HybridAdam +from colossalai.testing import rerun_if_address_is_in_use +from colossalai.utils import free_port + +GPT_CONFIG = GPT2Config(n_embd=128, n_layer=4, n_head=4) + + +def get_data(batch_size: int, seq_len: int = 10) -> dict: + input_ids = torch.randint(0, 50257, (batch_size, seq_len), device='cuda') + attention_mask = torch.ones_like(input_ids) + return dict(input_ids=input_ids, attention_mask=attention_mask) + + +def run_test_checkpoint(strategy): + BATCH_SIZE = 2 + + if strategy == 'ddp': + strategy = DDPStrategy() + elif strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + elif strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{strategy}"') + + with strategy.model_init_context(): + actor = GPTActor(config=GPT_CONFIG).cuda() + + actor_optim = HybridAdam(actor.parameters()) + + actor, actor_optim = strategy.prepare((actor, actor_optim)) + + def run_step(): + data = get_data(BATCH_SIZE) + action_mask = torch.ones_like(data['attention_mask'], dtype=torch.bool) + action_log_probs = actor(data['input_ids'], action_mask.size(1), data['attention_mask']) + loss = action_log_probs.sum() + strategy.backward(loss, actor, actor_optim) + strategy.optimizer_step(actor_optim) + + run_step() + + ctx = tempfile.TemporaryDirectory() if dist.get_rank() == 0 else nullcontext() + + with ctx as dirname: + rank0_dirname = [dirname] + dist.broadcast_object_list(rank0_dirname) + rank0_dirname = rank0_dirname[0] + + model_path = os.path.join(rank0_dirname, 'model.pt') + optim_path = os.path.join(rank0_dirname, f'optim-r{dist.get_rank()}.pt') + + strategy.save_model(actor, model_path, only_rank0=True) + strategy.save_optimizer(actor_optim, optim_path, only_rank0=False) + + dist.barrier() + + strategy.load_model(actor, model_path, strict=False) + strategy.load_optimizer(actor_optim, optim_path) + + dist.barrier() + + run_step() + + +def run_dist(rank, world_size, port, strategy): + os.environ['RANK'] = str(rank) + os.environ['LOCAL_RANK'] = str(rank) + os.environ['WORLD_SIZE'] = str(world_size) + os.environ['MASTER_ADDR'] = 'localhost' + os.environ['MASTER_PORT'] = str(port) + run_test_checkpoint(strategy) + + +@pytest.mark.dist +@pytest.mark.parametrize('world_size', [2]) +@pytest.mark.parametrize('strategy', ['ddp', 'colossalai_zero2', 'colossalai_gemini']) +@rerun_if_address_is_in_use() +def test_checkpoint(world_size, strategy): + run_func = partial(run_dist, world_size=world_size, port=free_port(), strategy=strategy) + mp.spawn(run_func, nprocs=world_size) + + +if __name__ == '__main__': + test_checkpoint(2, 'colossalai_zero2') diff --git a/applications/Chat/tests/test_data.py b/applications/Chat/tests/test_data.py new file mode 100644 index 000000000000..577309a0fceb --- /dev/null +++ b/applications/Chat/tests/test_data.py @@ -0,0 +1,122 @@ +import os +from copy import deepcopy +from functools import partial + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +from coati.experience_maker import NaiveExperienceMaker +from coati.models.base import RewardModel +from coati.models.gpt import GPTActor, GPTCritic +from coati.replay_buffer import NaiveReplayBuffer +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy +from transformers.models.gpt2.configuration_gpt2 import GPT2Config + +from colossalai.testing import rerun_if_address_is_in_use +from colossalai.utils import free_port + +GPT_CONFIG = GPT2Config(n_embd=128, n_layer=4, n_head=4) + + +def get_data(batch_size: int, seq_len: int = 10) -> dict: + input_ids = torch.randint(0, 50257, (batch_size, seq_len), device='cuda') + attention_mask = torch.ones_like(input_ids) + return dict(input_ids=input_ids, attention_mask=attention_mask) + + +def gather_and_equal(tensor: torch.Tensor) -> bool: + world_size = dist.get_world_size() + outputs = [torch.empty_like(tensor) for _ in range(world_size)] + dist.all_gather(outputs, tensor.contiguous()) + for t in outputs[1:]: + if not torch.equal(outputs[0], t): + return False + return True + + +def run_test_data(strategy): + EXPERINCE_BATCH_SIZE = 4 + SAMPLE_BATCH_SIZE = 2 + + if strategy == 'ddp': + strategy = DDPStrategy() + elif strategy == 'colossalai': + strategy = ColossalAIStrategy(placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{strategy}"') + + actor = GPTActor(config=GPT_CONFIG).cuda() + critic = GPTCritic(config=GPT_CONFIG).cuda() + + initial_model = deepcopy(actor) + reward_model = RewardModel(deepcopy(critic.model)).cuda() + + experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model) + replay_buffer = NaiveReplayBuffer(SAMPLE_BATCH_SIZE, cpu_offload=False) + + # experience of all ranks should be the same + for _ in range(2): + data = get_data(EXPERINCE_BATCH_SIZE) + assert gather_and_equal(data['input_ids']) + assert gather_and_equal(data['attention_mask']) + experience = experience_maker.make_experience(**data, + do_sample=True, + max_length=16, + eos_token_id=50256, + pad_token_id=50256) + assert gather_and_equal(experience.sequences) + assert gather_and_equal(experience.action_log_probs) + assert gather_and_equal(experience.values) + assert gather_and_equal(experience.reward) + assert gather_and_equal(experience.advantages) + assert gather_and_equal(experience.action_mask) + assert gather_and_equal(experience.attention_mask) + replay_buffer.append(experience) + + # replay buffer's data should be the same + buffer_size = torch.tensor([len(replay_buffer)], device='cuda') + assert gather_and_equal(buffer_size) + for item in replay_buffer.items: + assert gather_and_equal(item.sequences) + assert gather_and_equal(item.action_log_probs) + assert gather_and_equal(item.values) + assert gather_and_equal(item.reward) + assert gather_and_equal(item.advantages) + assert gather_and_equal(item.action_mask) + assert gather_and_equal(item.attention_mask) + + # dataloader of each rank should have the same size and different batch + dataloader = strategy.setup_dataloader(replay_buffer) + dataloader_size = torch.tensor([len(dataloader)], device='cuda') + assert gather_and_equal(dataloader_size) + for experience in dataloader: + assert not gather_and_equal(experience.sequences) + assert not gather_and_equal(experience.action_log_probs) + assert not gather_and_equal(experience.values) + assert not gather_and_equal(experience.reward) + assert not gather_and_equal(experience.advantages) + # action mask and attention mask may be same + + +def run_dist(rank, world_size, port, strategy): + os.environ['RANK'] = str(rank) + os.environ['LOCAL_RANK'] = str(rank) + os.environ['WORLD_SIZE'] = str(world_size) + os.environ['MASTER_ADDR'] = 'localhost' + os.environ['MASTER_PORT'] = str(port) + run_test_data(strategy) + + +@pytest.mark.skip +@pytest.mark.dist +@pytest.mark.parametrize('world_size', [2]) +@pytest.mark.parametrize('strategy', ['ddp', 'colossalai']) +@rerun_if_address_is_in_use() +def test_data(world_size, strategy): + run_func = partial(run_dist, world_size=world_size, port=free_port(), strategy=strategy) + mp.spawn(run_func, nprocs=world_size) + + +if __name__ == '__main__': + test_data(2, 'colossalai') diff --git a/applications/Chat/version.txt b/applications/Chat/version.txt new file mode 100644 index 000000000000..3eefcb9dd5b3 --- /dev/null +++ b/applications/Chat/version.txt @@ -0,0 +1 @@ +1.0.0 From bb6196e71a8061b9e7ee054b453978c3af24d7d4 Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Tue, 28 Mar 2023 20:29:09 +0800 Subject: [PATCH 042/413] remove chatgpt (#3284) --- applications/ChatGPT/.gitignore | 146 ------------ applications/ChatGPT/LICENSE | 202 ----------------- applications/ChatGPT/README.md | 209 ------------------ applications/ChatGPT/benchmarks/README.md | 94 -------- .../ChatGPT/benchmarks/benchmark_gpt_dummy.py | 184 --------------- .../ChatGPT/benchmarks/benchmark_gpt_dummy.sh | 45 ---- .../benchmarks/benchmark_opt_lora_dummy.py | 179 --------------- applications/ChatGPT/chatgpt/__init__.py | 0 .../ChatGPT/chatgpt/dataset/__init__.py | 5 - .../ChatGPT/chatgpt/dataset/reward_dataset.py | 109 --------- .../ChatGPT/chatgpt/dataset/sft_dataset.py | 168 -------------- applications/ChatGPT/chatgpt/dataset/utils.py | 20 -- .../chatgpt/experience_maker/__init__.py | 4 - .../ChatGPT/chatgpt/experience_maker/base.py | 77 ------- .../ChatGPT/chatgpt/experience_maker/naive.py | 36 --- .../ChatGPT/chatgpt/models/__init__.py | 4 - .../ChatGPT/chatgpt/models/base/__init__.py | 6 - .../ChatGPT/chatgpt/models/base/actor.py | 65 ------ .../ChatGPT/chatgpt/models/base/critic.py | 54 ----- .../ChatGPT/chatgpt/models/base/lm.py | 33 --- .../chatgpt/models/base/reward_model.py | 41 ---- .../ChatGPT/chatgpt/models/bloom/__init__.py | 6 - .../chatgpt/models/bloom/bloom_actor.py | 35 --- .../chatgpt/models/bloom/bloom_critic.py | 38 ---- .../ChatGPT/chatgpt/models/bloom/bloom_lm.py | 36 --- .../ChatGPT/chatgpt/models/bloom/bloom_rm.py | 37 ---- .../chatgpt/models/deberta/__init__.py | 4 - .../chatgpt/models/deberta/deberta_critic.py | 36 --- .../chatgpt/models/deberta/deberta_rm.py | 37 ---- .../ChatGPT/chatgpt/models/generation.py | 146 ------------ .../chatgpt/models/generation_utils.py | 92 -------- .../ChatGPT/chatgpt/models/gpt/__init__.py | 6 - .../ChatGPT/chatgpt/models/gpt/gpt_actor.py | 35 --- .../ChatGPT/chatgpt/models/gpt/gpt_critic.py | 37 ---- .../ChatGPT/chatgpt/models/gpt/gpt_lm.py | 36 --- .../ChatGPT/chatgpt/models/gpt/gpt_rm.py | 39 ---- .../ChatGPT/chatgpt/models/llama/__init__.py | 6 - .../chatgpt/models/llama/llama_actor.py | 38 ---- .../chatgpt/models/llama/llama_critic.py | 42 ---- .../ChatGPT/chatgpt/models/llama/llama_lm.py | 40 ---- .../ChatGPT/chatgpt/models/llama/llama_rm.py | 41 ---- applications/ChatGPT/chatgpt/models/lora.py | 130 ----------- applications/ChatGPT/chatgpt/models/loss.py | 115 ---------- .../ChatGPT/chatgpt/models/opt/__init__.py | 6 - .../ChatGPT/chatgpt/models/opt/opt_actor.py | 35 --- .../ChatGPT/chatgpt/models/opt/opt_critic.py | 38 ---- .../ChatGPT/chatgpt/models/opt/opt_lm.py | 36 --- .../ChatGPT/chatgpt/models/opt/opt_rm.py | 38 ---- applications/ChatGPT/chatgpt/models/utils.py | 92 -------- .../ChatGPT/chatgpt/replay_buffer/__init__.py | 4 - .../ChatGPT/chatgpt/replay_buffer/base.py | 43 ---- .../ChatGPT/chatgpt/replay_buffer/naive.py | 57 ----- .../ChatGPT/chatgpt/replay_buffer/utils.py | 73 ------ .../ChatGPT/chatgpt/trainer/__init__.py | 6 - applications/ChatGPT/chatgpt/trainer/base.py | 162 -------------- .../chatgpt/trainer/callbacks/__init__.py | 5 - .../ChatGPT/chatgpt/trainer/callbacks/base.py | 39 ---- .../callbacks/performance_evaluator.py | 133 ----------- .../trainer/callbacks/save_checkpoint.py | 75 ------- applications/ChatGPT/chatgpt/trainer/ppo.py | 116 ---------- applications/ChatGPT/chatgpt/trainer/rm.py | 120 ---------- applications/ChatGPT/chatgpt/trainer/sft.py | 106 --------- .../chatgpt/trainer/strategies/__init__.py | 6 - .../chatgpt/trainer/strategies/base.py | 131 ----------- .../chatgpt/trainer/strategies/colossalai.py | 190 ---------------- .../ChatGPT/chatgpt/trainer/strategies/ddp.py | 93 -------- .../chatgpt/trainer/strategies/naive.py | 55 ----- .../chatgpt/trainer/strategies/sampler.py | 32 --- applications/ChatGPT/chatgpt/trainer/utils.py | 5 - .../ChatGPT/chatgpt/utils/__init__.py | 3 - .../ChatGPT/chatgpt/utils/tokenizer_utils.py | 80 ------- applications/ChatGPT/examples/README.md | 141 ------------ applications/ChatGPT/examples/inference.py | 59 ----- .../ChatGPT/examples/requirements.txt | 2 - applications/ChatGPT/examples/test_ci.sh | 97 -------- applications/ChatGPT/examples/train_dummy.py | 148 ------------- applications/ChatGPT/examples/train_dummy.sh | 18 -- .../ChatGPT/examples/train_prompts.py | 132 ----------- .../ChatGPT/examples/train_prompts.sh | 18 -- .../ChatGPT/examples/train_reward_model.py | 143 ------------ applications/ChatGPT/examples/train_rm.sh | 8 - applications/ChatGPT/examples/train_sft.py | 143 ------------ applications/ChatGPT/examples/train_sft.sh | 26 --- applications/ChatGPT/pytest.ini | 6 - applications/ChatGPT/requirements-test.txt | 1 - applications/ChatGPT/requirements.txt | 7 - applications/ChatGPT/setup.py | 41 ---- applications/ChatGPT/tests/__init__.py | 0 applications/ChatGPT/tests/test_checkpoint.py | 98 -------- applications/ChatGPT/tests/test_data.py | 122 ---------- applications/ChatGPT/version.txt | 1 - 91 files changed, 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-*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ -docs/.build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# IDE -.idea/ -.vscode/ - -# macos -*.DS_Store -#data/ - -docs/.build - -# pytorch checkpoint -*.pt - -# ignore version.py generated by setup.py -colossalai/version.py diff --git a/applications/ChatGPT/LICENSE b/applications/ChatGPT/LICENSE deleted file mode 100644 index 0528c89ea9ec..000000000000 --- a/applications/ChatGPT/LICENSE +++ /dev/null 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright 2021- HPC-AI Technology Inc. - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/applications/ChatGPT/README.md b/applications/ChatGPT/README.md deleted file mode 100644 index 206ede5f1843..000000000000 --- a/applications/ChatGPT/README.md +++ /dev/null @@ -1,209 +0,0 @@ -# RLHF - Colossal-AI - -## Table of Contents - -- [What is RLHF - Colossal-AI?](#intro) -- [How to Install?](#install) -- [The Plan](#the-plan) -- [How can you partcipate in open source?](#invitation-to-open-source-contribution) ---- -## Intro -Implementation of RLHF (Reinforcement Learning with Human Feedback) powered by Colossal-AI. It supports distributed training and offloading, which can fit extremly large models. More details can be found in the [blog](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt). - -

    - -

    - -## Training process (step 3) -

    - -

    -

    - -

    - - -## Install -```shell -pip install . -``` - -## Usage - -The main entrypoint is `Trainer`. We only support PPO trainer now. We support many training strategies: - -- NaiveStrategy: simplest strategy. Train on single GPU. -- DDPStrategy: use `torch.nn.parallel.DistributedDataParallel`. Train on multi GPUs. -- ColossalAIStrategy: use Gemini and Zero of ColossalAI. It eliminates model duplication on each GPU and supports offload. It's very useful when training large models on multi GPUs. - -Simplest usage: - -```python -from chatgpt.trainer import PPOTrainer -from chatgpt.trainer.strategies import ColossalAIStrategy -from chatgpt.models.gpt import GPTActor, GPTCritic -from chatgpt.models.base import RewardModel -from copy import deepcopy -from colossalai.nn.optimizer import HybridAdam - -strategy = ColossalAIStrategy() - -with strategy.model_init_context(): - # init your model here - # load pretrained gpt2 - actor = GPTActor(pretrained='gpt2') - critic = GPTCritic() - initial_model = deepcopy(actor).cuda() - reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda() - -actor_optim = HybridAdam(actor.parameters(), lr=5e-6) -critic_optim = HybridAdam(critic.parameters(), lr=5e-6) - -# prepare models and optimizers -(actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) - -# load saved model checkpoint after preparing -strategy.load_model(actor, 'actor_checkpoint.pt', strict=False) -# load saved optimizer checkpoint after preparing -strategy.load_optimizer(actor_optim, 'actor_optim_checkpoint.pt') - -trainer = PPOTrainer(strategy, - actor, - critic, - reward_model, - initial_model, - actor_optim, - critic_optim, - ...) - -trainer.fit(dataset, ...) - -# save model checkpoint after fitting on only rank0 -strategy.save_model(actor, 'actor_checkpoint.pt', only_rank0=True) -# save optimizer checkpoint on all ranks -strategy.save_optimizer(actor_optim, 'actor_optim_checkpoint.pt', only_rank0=False) -``` - -For more details, see `examples/`. - -We also support training reward model with true-world data. See `examples/train_reward_model.py`. - -## FAQ - -### How to save/load checkpoint - -To load pretrained model, you can simply use huggingface pretrained models: - -```python -# load OPT-350m pretrained model -actor = OPTActor(pretrained='facebook/opt-350m') -``` - -To save model checkpoint: - -```python -# save model checkpoint on only rank0 -strategy.save_model(actor, 'actor_checkpoint.pt', only_rank0=True) -``` - -This function must be called after `strategy.prepare()`. - -For DDP strategy, model weights are replicated on all ranks. And for ColossalAI strategy, model weights may be sharded, but all-gather will be applied before returning state dict. You can set `only_rank0=True` for both of them, which only saves checkpoint on rank0, to save disk space usage. The checkpoint is float32. - -To save optimizer checkpoint: - -```python -# save optimizer checkpoint on all ranks -strategy.save_optimizer(actor_optim, 'actor_optim_checkpoint.pt', only_rank0=False) -``` - -For DDP strategy, optimizer states are replicated on all ranks. You can set `only_rank0=True`. But for ColossalAI strategy, optimizer states are sharded over all ranks, and no all-gather will be applied. So for ColossalAI strategy, you can only set `only_rank0=False`. That is to say, each rank will save a cehckpoint. When loading, each rank should load the corresponding part. - -Note that different stategy may have different shapes of optimizer checkpoint. - -To load model checkpoint: - -```python -# load saved model checkpoint after preparing -strategy.load_model(actor, 'actor_checkpoint.pt', strict=False) -``` - -To load optimizer checkpoint: - -```python -# load saved optimizer checkpoint after preparing -strategy.load_optimizer(actor_optim, 'actor_optim_checkpoint.pt') -``` - -## The Plan - -- [x] implement PPO fine-tuning -- [x] implement training reward model -- [x] support LoRA -- [x] support inference -- [ ] open source the reward model weight -- [ ] support llama from [facebook](https://github.com/facebookresearch/llama) -- [ ] support BoN(best of N sample) -- [ ] implement PPO-ptx fine-tuning -- [ ] integrate with Ray -- [ ] support more RL paradigms, like Implicit Language Q-Learning (ILQL), -- [ ] support chain of throught by [langchain](https://github.com/hwchase17/langchain) - -### Real-time progress -You will find our progress in github project broad - -[Open ChatGPT](https://github.com/orgs/hpcaitech/projects/17/views/1) - -## Invitation to open-source contribution -Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models from the starting point of replicating ChatGPT! - -You may contact us or participate in the following ways: -1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks! -2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md). -3. Join the Colossal-AI community on -[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w), -and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas. -4. Send your official proposal to email contact@hpcaitech.com - -Thanks so much to all of our amazing contributors! - -## Quick Preview -

    - -

    - -- Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference - -

    - -

    - -- Up to 10.3x growth in model capacity on one GPU -- A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU) - -

    - -

    - -- Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU -- Keep in a sufficiently high running speed - -## Citations - -```bibtex -@article{Hu2021LoRALA, - title = {LoRA: Low-Rank Adaptation of Large Language Models}, - author = {Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Weizhu Chen}, - journal = {ArXiv}, - year = {2021}, - volume = {abs/2106.09685} -} - -@article{ouyang2022training, - title={Training language models to follow instructions with human feedback}, - author={Ouyang, Long and Wu, Jeff and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll L and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others}, - journal={arXiv preprint arXiv:2203.02155}, - year={2022} -} -``` diff --git a/applications/ChatGPT/benchmarks/README.md b/applications/ChatGPT/benchmarks/README.md deleted file mode 100644 index b4e28ba1d764..000000000000 --- a/applications/ChatGPT/benchmarks/README.md +++ /dev/null @@ -1,94 +0,0 @@ -# Benchmarks - -## Benchmark GPT on dummy prompt data - -We provide various GPT models (string in parentheses is the corresponding model name used in this script): - -- GPT2-S (s) -- GPT2-M (m) -- GPT2-L (l) -- GPT2-XL (xl) -- GPT2-4B (4b) -- GPT2-6B (6b) -- GPT2-8B (8b) -- GPT2-10B (10b) -- GPT2-12B (12b) -- GPT2-15B (15b) -- GPT2-18B (18b) -- GPT2-20B (20b) -- GPT2-24B (24b) -- GPT2-28B (28b) -- GPT2-32B (32b) -- GPT2-36B (36b) -- GPT2-40B (40b) -- GPT3 (175b) - -We also provide various training strategies: - -- ddp: torch DDP -- colossalai_gemini: ColossalAI GeminiDDP with `placement_policy="cuda"`, like zero3 -- colossalai_gemini_cpu: ColossalAI GeminiDDP with `placement_policy="cpu"`, like zero3-offload -- colossalai_zero2: ColossalAI zero2 -- colossalai_zero2_cpu: ColossalAI zero2-offload -- colossalai_zero1: ColossalAI zero1 -- colossalai_zero1_cpu: ColossalAI zero1-offload - -We only support `torchrun` to launch now. E.g. - -```shell -# run GPT2-S on single-node single-GPU with min batch size -torchrun --standalone --nproc_per_node 1 benchmark_gpt_dummy.py --model s --strategy ddp --experience_batch_size 1 --train_batch_size 1 -# run GPT2-XL on single-node 4-GPU -torchrun --standalone --nproc_per_node 4 benchmark_gpt_dummy.py --model xl --strategy colossalai_zero2 -# run GPT3 on 8-node 8-GPU -torchrun --nnodes 8 --nproc_per_node 8 \ - --rdzv_id=$JOB_ID --rdzv_backend=c10d --rdzv_endpoint=$HOST_NODE_ADDR \ - benchmark_gpt_dummy.py --model 175b --strategy colossalai_gemini -``` - -> ⚠ Batch sizes in CLI args and outputed throughput/TFLOPS are all values of per GPU. - -In this benchmark, we assume the model architectures/sizes of actor and critic are the same for simplicity. But in practice, to reduce training cost, we may use a smaller critic. - -We also provide a simple shell script to run a set of benchmarks. But it only supports benchmark on single node. However, it's easy to run on multi-nodes by modifying launch command in this script. - -Usage: - -```shell -# run for GPUS=(1 2 4 8) x strategy=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256) -./benchmark_gpt_dummy.sh -# run for GPUS=2 x strategy=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256) -./benchmark_gpt_dummy.sh 2 -# run for GPUS=2 x strategy=ddp x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256) -./benchmark_gpt_dummy.sh 2 ddp -# run for GPUS=2 x strategy=ddp x model=l x batch_size=(1 2 4 8 16 32 64 128 256) -./benchmark_gpt_dummy.sh 2 ddp l -``` - -## Benchmark OPT with LoRA on dummy prompt data - -We provide various OPT models (string in parentheses is the corresponding model name used in this script): - -- OPT-125M (125m) -- OPT-350M (350m) -- OPT-700M (700m) -- OPT-1.3B (1.3b) -- OPT-2.7B (2.7b) -- OPT-3.5B (3.5b) -- OPT-5.5B (5.5b) -- OPT-6.7B (6.7b) -- OPT-10B (10b) -- OPT-13B (13b) - -We only support `torchrun` to launch now. E.g. - -```shell -# run OPT-125M with no lora (lora_rank=0) on single-node single-GPU with min batch size -torchrun --standalone --nproc_per_node 1 benchmark_opt_lora_dummy.py --model 125m --strategy ddp --experience_batch_size 1 --train_batch_size 1 --lora_rank 0 -# run OPT-350M with lora_rank=4 on single-node 4-GPU -torchrun --standalone --nproc_per_node 4 benchmark_opt_lora_dummy.py --model 350m --strategy colossalai_zero2 --lora_rank 4 -``` - -> ⚠ Batch sizes in CLI args and outputed throughput/TFLOPS are all values of per GPU. - -In this benchmark, we assume the model architectures/sizes of actor and critic are the same for simplicity. But in practice, to reduce training cost, we may use a smaller critic. diff --git a/applications/ChatGPT/benchmarks/benchmark_gpt_dummy.py b/applications/ChatGPT/benchmarks/benchmark_gpt_dummy.py deleted file mode 100644 index 5ee65763b936..000000000000 --- a/applications/ChatGPT/benchmarks/benchmark_gpt_dummy.py +++ /dev/null @@ -1,184 +0,0 @@ -import argparse -from copy import deepcopy - -import torch -import torch.distributed as dist -import torch.nn as nn -from chatgpt.models.base import RewardModel -from chatgpt.models.gpt import GPTActor, GPTCritic -from chatgpt.trainer import PPOTrainer -from chatgpt.trainer.callbacks import PerformanceEvaluator -from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, Strategy -from torch.optim import Adam -from transformers.models.gpt2.configuration_gpt2 import GPT2Config -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - - -def get_model_numel(model: nn.Module, strategy: Strategy) -> int: - numel = sum(p.numel() for p in model.parameters()) - if isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3 and strategy.shard_init: - numel *= dist.get_world_size() - return numel - - -def preprocess_batch(samples) -> dict: - input_ids = torch.stack(samples) - attention_mask = torch.ones_like(input_ids, dtype=torch.long) - return {'input_ids': input_ids, 'attention_mask': attention_mask} - - -def print_rank_0(*args, **kwargs) -> None: - if dist.get_rank() == 0: - print(*args, **kwargs) - - -def print_model_numel(model_dict: dict) -> None: - B = 1024**3 - M = 1024**2 - K = 1024 - outputs = '' - for name, numel in model_dict.items(): - outputs += f'{name}: ' - if numel >= B: - outputs += f'{numel / B:.2f} B\n' - elif numel >= M: - outputs += f'{numel / M:.2f} M\n' - elif numel >= K: - outputs += f'{numel / K:.2f} K\n' - else: - outputs += f'{numel}\n' - print_rank_0(outputs) - - -def get_gpt_config(model_name: str) -> GPT2Config: - model_map = { - 's': GPT2Config(), - 'm': GPT2Config(n_embd=1024, n_layer=24, n_head=16), - 'l': GPT2Config(n_embd=1280, n_layer=36, n_head=20), - 'xl': GPT2Config(n_embd=1600, n_layer=48, n_head=25), - '2b': GPT2Config(n_embd=2048, n_layer=40, n_head=16), - '4b': GPT2Config(n_embd=2304, n_layer=64, n_head=16), - '6b': GPT2Config(n_embd=4096, n_layer=30, n_head=16), - '8b': GPT2Config(n_embd=4096, n_layer=40, n_head=16), - '10b': GPT2Config(n_embd=4096, n_layer=50, n_head=16), - '12b': GPT2Config(n_embd=4096, n_layer=60, n_head=16), - '15b': GPT2Config(n_embd=4096, n_layer=78, n_head=16), - '18b': GPT2Config(n_embd=4096, n_layer=90, n_head=16), - '20b': GPT2Config(n_embd=8192, n_layer=25, n_head=16), - '24b': GPT2Config(n_embd=8192, n_layer=30, n_head=16), - '28b': GPT2Config(n_embd=8192, n_layer=35, n_head=16), - '32b': GPT2Config(n_embd=8192, n_layer=40, n_head=16), - '36b': GPT2Config(n_embd=8192, n_layer=45, n_head=16), - '40b': GPT2Config(n_embd=8192, n_layer=50, n_head=16), - '175b': GPT2Config(n_positions=2048, n_embd=12288, n_layer=96, n_head=96), - } - try: - return model_map[model_name] - except KeyError: - raise ValueError(f'Unknown model "{model_name}"') - - -def main(args): - if args.strategy == 'ddp': - strategy = DDPStrategy() - elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) - elif args.strategy == 'colossalai_gemini_cpu': - strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) - elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - elif args.strategy == 'colossalai_zero2_cpu': - strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') - elif args.strategy == 'colossalai_zero1': - strategy = ColossalAIStrategy(stage=1, placement_policy='cuda') - elif args.strategy == 'colossalai_zero1_cpu': - strategy = ColossalAIStrategy(stage=1, placement_policy='cpu') - else: - raise ValueError(f'Unsupported strategy "{args.strategy}"') - - model_config = get_gpt_config(args.model) - - with strategy.model_init_context(): - actor = GPTActor(config=model_config).cuda() - critic = GPTCritic(config=model_config).cuda() - - initial_model = deepcopy(actor).cuda() - reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda() - - actor_numel = get_model_numel(actor, strategy) - critic_numel = get_model_numel(critic, strategy) - initial_model_numel = get_model_numel(initial_model, strategy) - reward_model_numel = get_model_numel(reward_model, strategy) - print_model_numel({ - 'Actor': actor_numel, - 'Critic': critic_numel, - 'Initial model': initial_model_numel, - 'Reward model': reward_model_numel - }) - performance_evaluator = PerformanceEvaluator(actor_numel, - critic_numel, - initial_model_numel, - reward_model_numel, - enable_grad_checkpoint=False, - ignore_episodes=1) - - if args.strategy.startswith('colossalai'): - actor_optim = HybridAdam(actor.parameters(), lr=5e-6) - critic_optim = HybridAdam(critic.parameters(), lr=5e-6) - else: - actor_optim = Adam(actor.parameters(), lr=5e-6) - critic_optim = Adam(critic.parameters(), lr=5e-6) - - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) - - trainer = PPOTrainer(strategy, - actor, - critic, - reward_model, - initial_model, - actor_optim, - critic_optim, - max_epochs=args.max_epochs, - train_batch_size=args.train_batch_size, - experience_batch_size=args.experience_batch_size, - tokenizer=preprocess_batch, - max_length=512, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - callbacks=[performance_evaluator]) - - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 400), device=torch.cuda.current_device()) - trainer.fit(random_prompts, - num_episodes=args.num_episodes, - max_timesteps=args.max_timesteps, - update_timesteps=args.update_timesteps) - - print_rank_0(f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.2f} GB') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--model', default='s') - parser.add_argument('--strategy', - choices=[ - 'ddp', 'colossalai_gemini', 'colossalai_gemini_cpu', 'colossalai_zero2', - 'colossalai_zero2_cpu', 'colossalai_zero1', 'colossalai_zero1_cpu' - ], - default='ddp') - parser.add_argument('--num_episodes', type=int, default=3) - parser.add_argument('--max_timesteps', type=int, default=8) - parser.add_argument('--update_timesteps', type=int, default=8) - parser.add_argument('--max_epochs', type=int, default=3) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - args = parser.parse_args() - main(args) diff --git a/applications/ChatGPT/benchmarks/benchmark_gpt_dummy.sh b/applications/ChatGPT/benchmarks/benchmark_gpt_dummy.sh deleted file mode 100755 index d70f8872570a..000000000000 --- a/applications/ChatGPT/benchmarks/benchmark_gpt_dummy.sh +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env bash -# Usage: $0 -set -xu - -BASE=$(realpath $(dirname $0)) - - -PY_SCRIPT=${BASE}/benchmark_gpt_dummy.py -export OMP_NUM_THREADS=8 - -function tune_batch_size() { - # we found when experience batch size is equal to train batch size - # peak CUDA memory usage of making experience phase is less than or equal to that of training phase - # thus, experience batch size can be larger than or equal to train batch size - for bs in 1 2 4 8 16 32 64 128 256; do - torchrun --standalone --nproc_per_node $1 $PY_SCRIPT --model $2 --strategy $3 --experience_batch_size $bs --train_batch_size $bs || return 1 - done -} - -if [ $# -eq 0 ]; then - num_gpus=(1 2 4 8) -else - num_gpus=($1) -fi - -if [ $# -le 1 ]; then - strategies=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") -else - strategies=($2) -fi - -if [ $# -le 2 ]; then - models=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") -else - models=($3) -fi - - -for num_gpu in ${num_gpus[@]}; do - for strategy in ${strategies[@]}; do - for model in ${models[@]}; do - tune_batch_size $num_gpu $model $strategy || break - done - done -done diff --git a/applications/ChatGPT/benchmarks/benchmark_opt_lora_dummy.py b/applications/ChatGPT/benchmarks/benchmark_opt_lora_dummy.py deleted file mode 100644 index 207edbca94b5..000000000000 --- a/applications/ChatGPT/benchmarks/benchmark_opt_lora_dummy.py +++ /dev/null @@ -1,179 +0,0 @@ -import argparse -from copy import deepcopy - -import torch -import torch.distributed as dist -import torch.nn as nn -from chatgpt.models.base import RewardModel -from chatgpt.models.opt import OPTActor, OPTCritic -from chatgpt.trainer import PPOTrainer -from chatgpt.trainer.callbacks import PerformanceEvaluator -from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, Strategy -from torch.optim import Adam -from transformers import AutoTokenizer -from transformers.models.opt.configuration_opt import OPTConfig - -from colossalai.nn.optimizer import HybridAdam - - -def get_model_numel(model: nn.Module, strategy: Strategy) -> int: - numel = sum(p.numel() for p in model.parameters()) - if isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3 and strategy.shard_init: - numel *= dist.get_world_size() - return numel - - -def preprocess_batch(samples) -> dict: - input_ids = torch.stack(samples) - attention_mask = torch.ones_like(input_ids, dtype=torch.long) - return {'input_ids': input_ids, 'attention_mask': attention_mask} - - -def print_rank_0(*args, **kwargs) -> None: - if dist.get_rank() == 0: - print(*args, **kwargs) - - -def print_model_numel(model_dict: dict) -> None: - B = 1024**3 - M = 1024**2 - K = 1024 - outputs = '' - for name, numel in model_dict.items(): - outputs += f'{name}: ' - if numel >= B: - outputs += f'{numel / B:.2f} B\n' - elif numel >= M: - outputs += f'{numel / M:.2f} M\n' - elif numel >= K: - outputs += f'{numel / K:.2f} K\n' - else: - outputs += f'{numel}\n' - print_rank_0(outputs) - - -def get_gpt_config(model_name: str) -> OPTConfig: - model_map = { - '125m': OPTConfig.from_pretrained('facebook/opt-125m'), - '350m': OPTConfig(hidden_size=1024, ffn_dim=4096, num_hidden_layers=24, num_attention_heads=16), - '700m': OPTConfig(hidden_size=1280, ffn_dim=5120, num_hidden_layers=36, num_attention_heads=20), - '1.3b': OPTConfig.from_pretrained('facebook/opt-1.3b'), - '2.7b': OPTConfig.from_pretrained('facebook/opt-2.7b'), - '3.5b': OPTConfig(hidden_size=3072, ffn_dim=12288, num_hidden_layers=32, num_attention_heads=32), - '5.5b': OPTConfig(hidden_size=3840, ffn_dim=15360, num_hidden_layers=32, num_attention_heads=32), - '6.7b': OPTConfig.from_pretrained('facebook/opt-6.7b'), - '10b': OPTConfig(hidden_size=5120, ffn_dim=20480, num_hidden_layers=32, num_attention_heads=32), - '13b': OPTConfig.from_pretrained('facebook/opt-13b'), - } - try: - return model_map[model_name] - except KeyError: - raise ValueError(f'Unknown model "{model_name}"') - - -def main(args): - if args.strategy == 'ddp': - strategy = DDPStrategy() - elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) - elif args.strategy == 'colossalai_gemini_cpu': - strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) - elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - elif args.strategy == 'colossalai_zero2_cpu': - strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') - elif args.strategy == 'colossalai_zero1': - strategy = ColossalAIStrategy(stage=1, placement_policy='cuda') - elif args.strategy == 'colossalai_zero1_cpu': - strategy = ColossalAIStrategy(stage=1, placement_policy='cpu') - else: - raise ValueError(f'Unsupported strategy "{args.strategy}"') - - torch.cuda.set_per_process_memory_fraction(args.cuda_mem_frac) - - model_config = get_gpt_config(args.model) - - with strategy.model_init_context(): - actor = OPTActor(config=model_config, lora_rank=args.lora_rank).cuda() - critic = OPTCritic(config=model_config, lora_rank=args.lora_rank).cuda() - - initial_model = deepcopy(actor).cuda() - reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda() - - actor_numel = get_model_numel(actor, strategy) - critic_numel = get_model_numel(critic, strategy) - initial_model_numel = get_model_numel(initial_model, strategy) - reward_model_numel = get_model_numel(reward_model, strategy) - print_model_numel({ - 'Actor': actor_numel, - 'Critic': critic_numel, - 'Initial model': initial_model_numel, - 'Reward model': reward_model_numel - }) - performance_evaluator = PerformanceEvaluator(actor_numel, - critic_numel, - initial_model_numel, - reward_model_numel, - enable_grad_checkpoint=False, - ignore_episodes=1) - - if args.strategy.startswith('colossalai'): - actor_optim = HybridAdam(actor.parameters(), lr=5e-6) - critic_optim = HybridAdam(critic.parameters(), lr=5e-6) - else: - actor_optim = Adam(actor.parameters(), lr=5e-6) - critic_optim = Adam(critic.parameters(), lr=5e-6) - - tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m') - tokenizer.pad_token = tokenizer.eos_token - - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) - - trainer = PPOTrainer(strategy, - actor, - critic, - reward_model, - initial_model, - actor_optim, - critic_optim, - max_epochs=args.max_epochs, - train_batch_size=args.train_batch_size, - experience_batch_size=args.experience_batch_size, - tokenizer=preprocess_batch, - max_length=512, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - callbacks=[performance_evaluator]) - - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 400), device=torch.cuda.current_device()) - trainer.fit(random_prompts, - num_episodes=args.num_episodes, - max_timesteps=args.max_timesteps, - update_timesteps=args.update_timesteps) - - print_rank_0(f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.2f} GB') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--model', default='125m') - parser.add_argument('--strategy', - choices=[ - 'ddp', 'colossalai_gemini', 'colossalai_gemini_cpu', 'colossalai_zero2', - 'colossalai_zero2_cpu', 'colossalai_zero1', 'colossalai_zero1_cpu' - ], - default='ddp') - parser.add_argument('--num_episodes', type=int, default=3) - parser.add_argument('--max_timesteps', type=int, default=8) - parser.add_argument('--update_timesteps', type=int, default=8) - parser.add_argument('--max_epochs', type=int, default=3) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - parser.add_argument('--lora_rank', type=int, default=4) - parser.add_argument('--cuda_mem_frac', type=float, default=1.0) - args = parser.parse_args() - main(args) diff --git a/applications/ChatGPT/chatgpt/__init__.py b/applications/ChatGPT/chatgpt/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/applications/ChatGPT/chatgpt/dataset/__init__.py b/applications/ChatGPT/chatgpt/dataset/__init__.py deleted file mode 100644 index df484f46d24c..000000000000 --- a/applications/ChatGPT/chatgpt/dataset/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .reward_dataset import RmStaticDataset, HhRlhfDataset -from .utils import is_rank_0 -from .sft_dataset import SFTDataset, AlpacaDataset, AlpacaDataCollator - -__all__ = ['RmStaticDataset', 'HhRlhfDataset','is_rank_0', 'SFTDataset', 'AlpacaDataset', 'AlpacaDataCollator'] diff --git a/applications/ChatGPT/chatgpt/dataset/reward_dataset.py b/applications/ChatGPT/chatgpt/dataset/reward_dataset.py deleted file mode 100644 index 9ee13490b893..000000000000 --- a/applications/ChatGPT/chatgpt/dataset/reward_dataset.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Callable - -from torch.utils.data import Dataset -from tqdm import tqdm - -from .utils import is_rank_0 - -# Dahaos/rm-static -class RmStaticDataset(Dataset): - """ - Dataset for reward model - - Args: - dataset: dataset for reward model - tokenizer: tokenizer for reward model - max_length: max length of input - special_token: special token at the end of sentence - """ - - def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None: - super().__init__() - self.chosen = [] - self.reject = [] - if special_token is None: - self.end_token = tokenizer.eos_token - else: - self.end_token = special_token - for data in tqdm(dataset, disable=not is_rank_0()): - prompt = data['prompt'] - - chosen = prompt + data['chosen'] + self.end_token - chosen_token = tokenizer(chosen, - max_length=max_length, - padding="max_length", - truncation=True, - return_tensors="pt") - self.chosen.append({ - "input_ids": chosen_token['input_ids'], - "attention_mask": chosen_token['attention_mask'] - }) - - reject = prompt + data['rejected'] + self.end_token - reject_token = tokenizer(reject, - max_length=max_length, - padding="max_length", - truncation=True, - return_tensors="pt") - self.reject.append({ - "input_ids": reject_token['input_ids'], - "attention_mask": reject_token['attention_mask'] - }) - - def __len__(self): - length = len(self.chosen) - return length - - def __getitem__(self, idx): - return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][ - "input_ids"], self.reject[idx]["attention_mask"] - -# Anthropic/hh-rlhf -class HhRlhfDataset(Dataset): - """ - Dataset for reward model - - Args: - dataset: dataset for reward model - tokenizer: tokenizer for reward model - max_length: max length of input - special_token: special token at the end of sentence - """ - def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None: - super().__init__() - self.chosen = [] - self.reject = [] - if special_token is None: - self.end_token = tokenizer.eos_token - else: - self.end_token = special_token - for data in tqdm(dataset, disable=not is_rank_0()): - chosen = data['chosen'] + self.end_token - chosen_token = tokenizer(chosen, - max_length=max_length, - padding="max_length", - truncation=True, - return_tensors="pt") - self.chosen.append({ - "input_ids": chosen_token['input_ids'], - "attention_mask": chosen_token['attention_mask'] - }) - - reject = data['rejected'] + self.end_token - reject_token = tokenizer(reject, - max_length=max_length, - padding="max_length", - truncation=True, - return_tensors="pt") - self.reject.append({ - "input_ids": reject_token['input_ids'], - "attention_mask": reject_token['attention_mask'] - }) - - def __len__(self): - length = len(self.chosen) - return length - - def __getitem__(self, idx): - return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][ - "input_ids"], self.reject[idx]["attention_mask"] diff --git a/applications/ChatGPT/chatgpt/dataset/sft_dataset.py b/applications/ChatGPT/chatgpt/dataset/sft_dataset.py deleted file mode 100644 index 5a5d37f695f3..000000000000 --- a/applications/ChatGPT/chatgpt/dataset/sft_dataset.py +++ /dev/null @@ -1,168 +0,0 @@ -# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import copy -from dataclasses import dataclass, field -from typing import Callable, Dict, Sequence -import random -from torch.utils.data import Dataset -import torch.distributed as dist -from tqdm import tqdm -import torch - -from .utils import is_rank_0, jload - -import transformers -from colossalai.logging import get_dist_logger - -logger = get_dist_logger() - -IGNORE_INDEX = -100 -PROMPT_DICT = { - "prompt_input": ( - "Below is an instruction that describes a task, paired with an input that provides further context. " - "Write a response that appropriately completes the request.\n\n" - "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:" - ), - "prompt_no_input": ( - "Below is an instruction that describes a task. " - "Write a response that appropriately completes the request.\n\n" - "### Instruction:\n{instruction}\n\n### Response:" - ), -} - -class SFTDataset(Dataset): - """ - Dataset for sft model - - Args: - dataset: dataset for supervised model - tokenizer: tokenizer for supervised model - max_length: max length of input - """ - - def __init__(self, dataset, tokenizer: Callable, max_length: int=512) -> None: - super().__init__() - # self.prompts = [] - self.input_ids = [] - - for data in tqdm(dataset, disable=not is_rank_0()): - prompt = data['prompt'] + data['completion'] + "<|endoftext|>" - prompt_token = tokenizer(prompt, - max_length=max_length, - padding="max_length", - truncation=True, - return_tensors="pt") - - # self.prompts.append(prompt_token)s - self.input_ids.append(prompt_token) - self.labels = copy.deepcopy(self.input_ids) - - def __len__(self): - length = len(self.prompts) - return length - - def __getitem__(self, idx): - # dict(input_ids=self.input_ids[i], labels=self.labels[i]) - return dict(input_ids=self.input_ids[i], labels=self.labels[i]) - # return dict(self.prompts[idx], self.prompts[idx]) - - -def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: - """Tokenize a list of strings.""" - tokenized_list = [ - tokenizer( - text, - return_tensors="pt", - padding="longest", - max_length=tokenizer.model_max_length, - truncation=True, - ) - for text in strings - ] - input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] - input_ids_lens = labels_lens = [ - tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list - ] - return dict( - input_ids=input_ids, - labels=labels, - input_ids_lens=input_ids_lens, - labels_lens=labels_lens, - ) - -def preprocess( - sources: Sequence[str], - targets: Sequence[str], - tokenizer: transformers.PreTrainedTokenizer, -) -> Dict: - """Preprocess the data by tokenizing.""" - examples = [s + t for s, t in zip(sources, targets)] - examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)] - input_ids = examples_tokenized["input_ids"] - labels = copy.deepcopy(input_ids) - for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): - label[:source_len] = IGNORE_INDEX - return dict(input_ids=input_ids, labels=labels) - -class AlpacaDataset(Dataset): - """Dataset for supervised fine-tuning.""" - - def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, max_length: int=None): - super(AlpacaDataset, self).__init__() - logger.info("Loading data...") - list_data_dict = jload(data_path) - logger.info(f"Loaded {len(list_data_dict)} examples.") - - if max_length is not None: - logger.info(f"Truncating data to max length {max_length}...") - list_data_dict = [example for example in list_data_dict if len(example["input"]) <= max_length] - - logger.info("Formatting inputs...") - prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] - sources = [ - prompt_input.format_map(example) if example.get("input", "") != "" else prompt_no_input.format_map(example) - for example in list_data_dict - ] - targets = [f"{example['output']}{tokenizer.eos_token}" for example in list_data_dict] - - logger.info("Tokenizing inputs... This may take some time...") - data_dict = preprocess(sources, targets, tokenizer) - - self.input_ids = data_dict["input_ids"] - self.labels = data_dict["labels"] - - def __len__(self): - return len(self.input_ids) - - def __getitem__(self, i) -> Dict[str, torch.Tensor]: - return dict(input_ids=self.input_ids[i], labels=self.labels[i]) - -@dataclass -class AlpacaDataCollator(object): - """Collate examples for supervised fine-tuning.""" - - tokenizer: transformers.PreTrainedTokenizer - - def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: - input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels")) - input_ids = torch.nn.utils.rnn.pad_sequence( - input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id - ) - labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) - return dict( - input_ids=input_ids, - labels=labels, - attention_mask=input_ids.ne(self.tokenizer.pad_token_id), - ) diff --git a/applications/ChatGPT/chatgpt/dataset/utils.py b/applications/ChatGPT/chatgpt/dataset/utils.py deleted file mode 100644 index 0e88cc8c39b4..000000000000 --- a/applications/ChatGPT/chatgpt/dataset/utils.py +++ /dev/null @@ -1,20 +0,0 @@ -import io -import json - -import torch.distributed as dist - - -def is_rank_0() -> bool: - return not dist.is_initialized() or dist.get_rank() == 0 - -def _make_r_io_base(f, mode: str): - if not isinstance(f, io.IOBase): - f = open(f, mode=mode) - return f - -def jload(f, mode="r"): - """Load a .json file into a dictionary.""" - f = _make_r_io_base(f, mode) - jdict = json.load(f) - f.close() - return jdict \ No newline at end of file diff --git a/applications/ChatGPT/chatgpt/experience_maker/__init__.py b/applications/ChatGPT/chatgpt/experience_maker/__init__.py deleted file mode 100644 index 39ca7576b227..000000000000 --- a/applications/ChatGPT/chatgpt/experience_maker/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .base import Experience, ExperienceMaker -from .naive import NaiveExperienceMaker - -__all__ = ['Experience', 'ExperienceMaker', 'NaiveExperienceMaker'] diff --git a/applications/ChatGPT/chatgpt/experience_maker/base.py b/applications/ChatGPT/chatgpt/experience_maker/base.py deleted file mode 100644 index f3640fc1e496..000000000000 --- a/applications/ChatGPT/chatgpt/experience_maker/base.py +++ /dev/null @@ -1,77 +0,0 @@ -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import Optional - -import torch -import torch.nn as nn -from chatgpt.models.base import Actor - - -@dataclass -class Experience: - """Experience is a batch of data. - These data should have the the sequence length and number of actions. - Left padding for sequences is applied. - - Shapes of each tensor: - sequences: (B, S) - action_log_probs: (B, A) - values: (B) - reward: (B) - advatanges: (B) - attention_mask: (B, S) - action_mask: (B, A) - - "A" is the number of actions. - """ - sequences: torch.Tensor - action_log_probs: torch.Tensor - values: torch.Tensor - reward: torch.Tensor - advantages: torch.Tensor - attention_mask: Optional[torch.LongTensor] - action_mask: Optional[torch.BoolTensor] - - @torch.no_grad() - def to_device(self, device: torch.device) -> None: - self.sequences = self.sequences.to(device) - self.action_log_probs = self.action_log_probs.to(device) - self.values = self.values.to(device) - self.reward = self.reward.to(device) - self.advantages = self.advantages.to(device) - if self.attention_mask is not None: - self.attention_mask = self.attention_mask.to(device) - if self.action_mask is not None: - self.action_mask = self.action_mask.to(device) - - def pin_memory(self): - self.sequences = self.sequences.pin_memory() - self.action_log_probs = self.action_log_probs.pin_memory() - self.values = self.values.pin_memory() - self.reward = self.reward.pin_memory() - self.advantages = self.advantages.pin_memory() - if self.attention_mask is not None: - self.attention_mask = self.attention_mask.pin_memory() - if self.action_mask is not None: - self.action_mask = self.action_mask.pin_memory() - return self - - -class ExperienceMaker(ABC): - - def __init__(self, - actor: Actor, - critic: nn.Module, - reward_model: nn.Module, - initial_model: Actor, - kl_coef: float = 0.1) -> None: - super().__init__() - self.actor = actor - self.critic = critic - self.reward_model = reward_model - self.initial_model = initial_model - self.kl_coef = kl_coef - - @abstractmethod - def make_experience(self, input_ids: torch.Tensor, **generate_kwargs) -> Experience: - pass diff --git a/applications/ChatGPT/chatgpt/experience_maker/naive.py b/applications/ChatGPT/chatgpt/experience_maker/naive.py deleted file mode 100644 index 64835cfa1918..000000000000 --- a/applications/ChatGPT/chatgpt/experience_maker/naive.py +++ /dev/null @@ -1,36 +0,0 @@ -import torch -from chatgpt.models.utils import compute_reward, normalize - -from .base import Experience, ExperienceMaker - - -class NaiveExperienceMaker(ExperienceMaker): - """ - Naive experience maker. - """ - - @torch.no_grad() - def make_experience(self, input_ids: torch.Tensor, **generate_kwargs) -> Experience: - self.actor.eval() - self.critic.eval() - self.initial_model.eval() - self.reward_model.eval() - - sequences, attention_mask, action_mask = self.actor.generate(input_ids, - return_action_mask=True, - **generate_kwargs) - num_actions = action_mask.size(1) - - action_log_probs = self.actor(sequences, num_actions, attention_mask) - base_action_log_probs = self.initial_model(sequences, num_actions, attention_mask) - value = self.critic(sequences, action_mask, attention_mask) - r = self.reward_model(sequences, attention_mask) - - reward = compute_reward(r, self.kl_coef, action_log_probs, base_action_log_probs, action_mask=action_mask) - - advantage = reward - value - # TODO(ver217): maybe normalize adv - if advantage.ndim == 1: - advantage = advantage.unsqueeze(-1) - - return Experience(sequences, action_log_probs, value, reward, advantage, attention_mask, action_mask) diff --git a/applications/ChatGPT/chatgpt/models/__init__.py b/applications/ChatGPT/chatgpt/models/__init__.py deleted file mode 100644 index b274188a21df..000000000000 --- a/applications/ChatGPT/chatgpt/models/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .base import Actor, Critic, RewardModel -from .loss import PolicyLoss, PPOPtxActorLoss, ValueLoss, LogSigLoss, LogExpLoss - -__all__ = ['Actor', 'Critic', 'RewardModel', 'PolicyLoss', 'ValueLoss', 'PPOPtxActorLoss', 'LogSigLoss', 'LogExpLoss'] diff --git a/applications/ChatGPT/chatgpt/models/base/__init__.py b/applications/ChatGPT/chatgpt/models/base/__init__.py deleted file mode 100644 index 7c7b1ceba257..000000000000 --- a/applications/ChatGPT/chatgpt/models/base/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .actor import Actor -from .critic import Critic -from .reward_model import RewardModel -from .lm import LM - -__all__ = ['Actor', 'Critic', 'RewardModel', 'LM'] diff --git a/applications/ChatGPT/chatgpt/models/base/actor.py b/applications/ChatGPT/chatgpt/models/base/actor.py deleted file mode 100644 index a364f879a850..000000000000 --- a/applications/ChatGPT/chatgpt/models/base/actor.py +++ /dev/null @@ -1,65 +0,0 @@ -from typing import Optional, Tuple, Union - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ..generation import generate -from ..lora import LoRAModule -from ..utils import log_probs_from_logits - - -class Actor(LoRAModule): - """ - Actor model base class. - - Args: - model (nn.Module): Actor Model. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = 'none') -> None: - super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias) - self.model = model - self.convert_to_lora() - - @torch.no_grad() - def generate( - self, - input_ids: torch.Tensor, - return_action_mask: bool = True, - **kwargs - ) -> Union[Tuple[torch.LongTensor, torch.LongTensor], Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor]]: - sequences = generate(self.model, input_ids, **kwargs) - attention_mask = None - pad_token_id = kwargs.get('pad_token_id', None) - if pad_token_id is not None: - attention_mask = sequences.not_equal(pad_token_id).to(dtype=torch.long, device=sequences.device) - if not return_action_mask: - return sequences, attention_mask, None - input_len = input_ids.size(1) - eos_token_id = kwargs.get('eos_token_id', None) - if eos_token_id is None: - action_mask = torch.ones_like(sequences, dtype=torch.bool) - else: - # left padding may be applied, only mask action - action_mask = (sequences[:, input_len:] == eos_token_id).cumsum(dim=-1) == 0 - action_mask = F.pad(action_mask, (1 + input_len, -1), value=True) # include eos token and input - action_mask[:, :input_len] = False - action_mask = action_mask[:, 1:] - return sequences, attention_mask, action_mask[:, -(sequences.size(1) - input_len):] - - def forward(self, - sequences: torch.LongTensor, - num_actions: int, - attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - """Returns action log probs - """ - output = self.model(sequences, attention_mask=attention_mask) - logits = output['logits'] - log_probs = log_probs_from_logits(logits[:, :-1, :], sequences[:, 1:]) - return log_probs[:, -num_actions:] - - def get_base_model(self): - return self.model \ No newline at end of file diff --git a/applications/ChatGPT/chatgpt/models/base/critic.py b/applications/ChatGPT/chatgpt/models/base/critic.py deleted file mode 100644 index e68a743a7762..000000000000 --- a/applications/ChatGPT/chatgpt/models/base/critic.py +++ /dev/null @@ -1,54 +0,0 @@ -from typing import Optional - -import torch -import torch.nn as nn - -from ..lora import LoRAModule -from ..utils import masked_mean - - -class Critic(LoRAModule): - """ - Critic model base class. - - Args: - model (nn.Module): Critic model. - value_head (nn.Module): Value head to get value. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__( - self, - model: nn.Module, - value_head: nn.Module, - lora_rank: int = 0, - lora_train_bias: str = 'none', - use_action_mask: bool = False, - ) -> None: - - super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias) - self.model = model - self.value_head = value_head - self.use_action_mask = use_action_mask - self.convert_to_lora() - - def forward(self, - sequences: torch.LongTensor, - action_mask: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - outputs = self.model(sequences, attention_mask=attention_mask) - last_hidden_states = outputs['last_hidden_state'] - - values = self.value_head(last_hidden_states).squeeze(-1) - - if action_mask is not None and self.use_action_mask: - num_actions = action_mask.size(1) - prompt_mask = attention_mask[:, :-num_actions] - values = values[:, :-num_actions] - value = masked_mean(values, prompt_mask, dim=1) - return value - - values = values[:, :-1] - value = values.mean(dim=1) - return value diff --git a/applications/ChatGPT/chatgpt/models/base/lm.py b/applications/ChatGPT/chatgpt/models/base/lm.py deleted file mode 100644 index b6bd7aff8315..000000000000 --- a/applications/ChatGPT/chatgpt/models/base/lm.py +++ /dev/null @@ -1,33 +0,0 @@ -from typing import Optional, Tuple, Union - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ..generation import generate -from .actor import Actor - - -class LM(Actor): - """ - Language model base class. - - Args: - model (nn.Module): Language Model. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = 'none') -> None: - super().__init__(model=model, lora_rank=lora_rank, lora_train_bias=lora_train_bias) - - def forward(self, - sequences: torch.LongTensor, - attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - """Returns output log probs - """ - output = self.model(sequences, attention_mask=attention_mask) - logits = output['logits'] - log_probs = F.log_softmax(logits, dim=-1) - return log_probs - diff --git a/applications/ChatGPT/chatgpt/models/base/reward_model.py b/applications/ChatGPT/chatgpt/models/base/reward_model.py deleted file mode 100644 index ce8c0a1d3568..000000000000 --- a/applications/ChatGPT/chatgpt/models/base/reward_model.py +++ /dev/null @@ -1,41 +0,0 @@ -from typing import Optional - -import torch -import torch.nn as nn - -from ..lora import LoRAModule - - -class RewardModel(LoRAModule): - """ - Reward model base class. - - Args: - model (nn.Module): Reward model. - value_head (nn.Module): Value head to get reward score. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - model: nn.Module, - value_head: Optional[nn.Module] = None, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias) - self.model = model - self.convert_to_lora() - - if value_head is not None: - if value_head.out_features != 1: - raise ValueError("The value head of reward model's output dim should be 1!") - self.value_head = value_head - else: - self.value_head = nn.Linear(model.config.n_embd, 1) - - def forward(self, sequences: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - outputs = self.model(sequences, attention_mask=attention_mask) - last_hidden_states = outputs['last_hidden_state'] - values = self.value_head(last_hidden_states)[:, :-1] - value = values.mean(dim=1).squeeze(1) # ensure shape is (B) - return value diff --git a/applications/ChatGPT/chatgpt/models/bloom/__init__.py b/applications/ChatGPT/chatgpt/models/bloom/__init__.py deleted file mode 100644 index 7d6d7753bb9a..000000000000 --- a/applications/ChatGPT/chatgpt/models/bloom/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .bloom_actor import BLOOMActor -from .bloom_critic import BLOOMCritic -from .bloom_rm import BLOOMRM -from .bloom_lm import BLOOMLM - -__all__ = ['BLOOMActor', 'BLOOMCritic', 'BLOOMRM', 'BLOOMLM'] diff --git a/applications/ChatGPT/chatgpt/models/bloom/bloom_actor.py b/applications/ChatGPT/chatgpt/models/bloom/bloom_actor.py deleted file mode 100644 index d7577f096493..000000000000 --- a/applications/ChatGPT/chatgpt/models/bloom/bloom_actor.py +++ /dev/null @@ -1,35 +0,0 @@ -from typing import Optional - -import torch -from transformers import BloomConfig, BloomForCausalLM, BloomModel - -from ..base import Actor - - -class BLOOMActor(Actor): - """ - BLOOM Actor model. - - Args: - pretrained (str): Pretrained model name or path. - config (BloomConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: str = None, - config: Optional[BloomConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = BloomForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = BloomForCausalLM(config) - else: - model = BloomForCausalLM(BloomConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/bloom/bloom_critic.py b/applications/ChatGPT/chatgpt/models/bloom/bloom_critic.py deleted file mode 100644 index a32fb2e102f9..000000000000 --- a/applications/ChatGPT/chatgpt/models/bloom/bloom_critic.py +++ /dev/null @@ -1,38 +0,0 @@ -from typing import Optional - -import torch -import torch.nn as nn -from transformers import BloomConfig, BloomForCausalLM, BloomModel - -from ..base import Critic - - -class BLOOMCritic(Critic): - """ - BLOOM Critic model. - - Args: - pretrained (str): Pretrained model name or path. - config (BloomConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: str = None, - config: Optional[BloomConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none', - **kwargs) -> None: - if pretrained is not None: - model = BloomModel.from_pretrained(pretrained) - elif config is not None: - model = BloomModel(config) - else: - model = BloomModel(BloomConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - value_head = nn.Linear(model.config.hidden_size, 1) - super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/ChatGPT/chatgpt/models/bloom/bloom_lm.py b/applications/ChatGPT/chatgpt/models/bloom/bloom_lm.py deleted file mode 100644 index 81e17f27c11a..000000000000 --- a/applications/ChatGPT/chatgpt/models/bloom/bloom_lm.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Optional - -import torch -from transformers import BloomConfig, BloomForCausalLM, BloomModel - -from ..base import LM - - -class BLOOMLM(LM): - """ - BLOOM language model. - - Args: - pretrained (str): Pretrained model name or path. - config (BloomConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: str = None, - config: Optional[BloomConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = BloomForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = BloomForCausalLM(config) - else: - model = BloomForCausalLM(BloomConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) - diff --git a/applications/ChatGPT/chatgpt/models/bloom/bloom_rm.py b/applications/ChatGPT/chatgpt/models/bloom/bloom_rm.py deleted file mode 100644 index 2dba227ff7d0..000000000000 --- a/applications/ChatGPT/chatgpt/models/bloom/bloom_rm.py +++ /dev/null @@ -1,37 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers import BloomConfig, BloomForCausalLM, BloomModel - -from ..base import RewardModel - - -class BLOOMRM(RewardModel): - """ - BLOOM Reward model. - - Args: - pretrained (str): Pretrained model name or path. - config (BloomConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: str = None, - config: Optional[BloomConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = BloomModel.from_pretrained(pretrained) - elif config is not None: - model = BloomModel(config) - else: - model = BloomModel(BloomConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - value_head = nn.Linear(model.config.hidden_size, 1) - value_head.weight.data.normal_(mean=0.0, std=1/(model.config.hidden_size + 1)) - super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/deberta/__init__.py b/applications/ChatGPT/chatgpt/models/deberta/__init__.py deleted file mode 100644 index b66888f34fd0..000000000000 --- a/applications/ChatGPT/chatgpt/models/deberta/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .deberta_critic import DebertaCritic -from .deberta_rm import DebertaRM - -__all__ = ['DebertaCritic', 'DebertaRM'] diff --git a/applications/ChatGPT/chatgpt/models/deberta/deberta_critic.py b/applications/ChatGPT/chatgpt/models/deberta/deberta_critic.py deleted file mode 100644 index e84c1dbd8380..000000000000 --- a/applications/ChatGPT/chatgpt/models/deberta/deberta_critic.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers import DebertaV2Config, DebertaV2Model - -from ..base import Critic - - -class DebertaCritic(Critic): - """ - Deberta Critic model. - - Args: - pretrained (str): Pretrained model name or path. - config (DebertaV2Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the LO-RA decomposition. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[DebertaV2Config] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = DebertaV2Model.from_pretrained(pretrained) - elif config is not None: - model = DebertaV2Model(config) - else: - model = DebertaV2Model(DebertaV2Config()) - if checkpoint: - model.gradient_checkpointing_enable() - value_head = nn.Linear(model.config.hidden_size, 1) - super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/deberta/deberta_rm.py b/applications/ChatGPT/chatgpt/models/deberta/deberta_rm.py deleted file mode 100644 index 2448c879ec85..000000000000 --- a/applications/ChatGPT/chatgpt/models/deberta/deberta_rm.py +++ /dev/null @@ -1,37 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers import DebertaV2Config, DebertaV2Model - -from ..base import RewardModel - - -class DebertaRM(RewardModel): - """ - Deberta Reward model. - - Args: - pretrained (str): Pretrained model name or path. - config (DebertaV2Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the LO-RA decomposition. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: str = None, - config: Optional[DebertaV2Config] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = DebertaV2Model.from_pretrained(pretrained) - elif config is not None: - model = DebertaV2Model(config) - else: - model = DebertaV2Model(DebertaV2Config()) - if checkpoint: - model.gradient_checkpointing_enable() - value_head = nn.Linear(model.config.hidden_size, 1) - value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1)) - super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/generation.py b/applications/ChatGPT/chatgpt/models/generation.py deleted file mode 100644 index eb30c36d0f84..000000000000 --- a/applications/ChatGPT/chatgpt/models/generation.py +++ /dev/null @@ -1,146 +0,0 @@ -from typing import Any, Callable, Optional - -import torch -import torch.distributed as dist -import torch.nn as nn - -try: - from transformers.generation_logits_process import ( - LogitsProcessorList, - TemperatureLogitsWarper, - TopKLogitsWarper, - TopPLogitsWarper, - ) -except ImportError: - from transformers.generation import LogitsProcessorList, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper - - -def prepare_logits_processor(top_k: Optional[int] = None, - top_p: Optional[float] = None, - temperature: Optional[float] = None) -> LogitsProcessorList: - processor_list = LogitsProcessorList() - if temperature is not None and temperature != 1.0: - processor_list.append(TemperatureLogitsWarper(temperature)) - if top_k is not None and top_k != 0: - processor_list.append(TopKLogitsWarper(top_k)) - if top_p is not None and top_p < 1.0: - processor_list.append(TopPLogitsWarper(top_p)) - return processor_list - - -def _is_sequence_finished(unfinished_sequences: torch.Tensor) -> bool: - if dist.is_initialized() and dist.get_world_size() > 1: - # consider DP - unfinished_sequences = unfinished_sequences.clone() - dist.all_reduce(unfinished_sequences) - return unfinished_sequences.max() == 0 - - -def sample(model: nn.Module, - input_ids: torch.Tensor, - max_length: int, - early_stopping: bool = False, - eos_token_id: Optional[int] = None, - pad_token_id: Optional[int] = None, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - temperature: Optional[float] = None, - prepare_inputs_fn: Optional[Callable[[torch.Tensor, Any], dict]] = None, - update_model_kwargs_fn: Optional[Callable[[dict, Any], dict]] = None, - **model_kwargs) -> torch.Tensor: - if input_ids.size(1) >= max_length: - return input_ids - - logits_processor = prepare_logits_processor(top_k, top_p, temperature) - unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) - - for _ in range(input_ids.size(1), max_length): - model_inputs = prepare_inputs_fn(input_ids, **model_kwargs) if prepare_inputs_fn is not None else { - 'input_ids': input_ids - } - outputs = model(**model_inputs) - - next_token_logits = outputs['logits'][:, -1, :] - # pre-process distribution - next_token_logits = logits_processor(input_ids, next_token_logits) - # sample - probs = torch.softmax(next_token_logits, dim=-1, dtype=torch.float) - next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) - - # finished sentences should have their next token be a padding token - if eos_token_id is not None: - if pad_token_id is None: - raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.") - next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences) - - # update generated ids, model inputs for next step - input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) - if update_model_kwargs_fn is not None: - model_kwargs = update_model_kwargs_fn(outputs, **model_kwargs) - - # if eos_token was found in one sentence, set sentence to finished - if eos_token_id is not None: - unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_token_id).long()) - - # stop when each sentence is finished if early_stopping=True - if early_stopping and _is_sequence_finished(unfinished_sequences): - break - - return input_ids - - -def generate(model: nn.Module, - input_ids: torch.Tensor, - max_length: int, - num_beams: int = 1, - do_sample: bool = True, - early_stopping: bool = False, - eos_token_id: Optional[int] = None, - pad_token_id: Optional[int] = None, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - temperature: Optional[float] = None, - prepare_inputs_fn: Optional[Callable[[torch.Tensor, Any], dict]] = None, - update_model_kwargs_fn: Optional[Callable[[dict, Any], dict]] = None, - **model_kwargs) -> torch.Tensor: - """Generate token sequence. The returned sequence is input_ids + generated_tokens. - - Args: - model (nn.Module): model - input_ids (torch.Tensor): input sequence - max_length (int): max length of the returned sequence - num_beams (int, optional): number of beams. Defaults to 1. - do_sample (bool, optional): whether to do sample. Defaults to True. - early_stopping (bool, optional): if True, the sequence length may be smaller than max_length due to finding eos. Defaults to False. - eos_token_id (Optional[int], optional): end of sequence token id. Defaults to None. - pad_token_id (Optional[int], optional): pad token id. Defaults to None. - top_k (Optional[int], optional): the number of highest probability vocabulary tokens to keep for top-k-filtering. Defaults to None. - top_p (Optional[float], optional): If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. Defaults to None. - temperature (Optional[float], optional): The value used to module the next token probabilities. Defaults to None. - prepare_inputs_fn (Optional[Callable[[torch.Tensor, Any], dict]], optional): Function to preprocess model inputs. Arguments of this function should be input_ids and model_kwargs. Defaults to None. - update_model_kwargs_fn (Optional[Callable[[dict, Any], dict]], optional): Function to update model_kwargs based on outputs. Arguments of this function should be outputs and model_kwargs. Defaults to None. - """ - is_greedy_gen_mode = ((num_beams == 1) and do_sample is False) - is_sample_gen_mode = ((num_beams == 1) and do_sample is True) - is_beam_gen_mode = ((num_beams > 1) and do_sample is False) - if is_greedy_gen_mode: - # run greedy search - raise NotImplementedError - elif is_sample_gen_mode: - # run sample - return sample(model, - input_ids, - max_length, - early_stopping=early_stopping, - eos_token_id=eos_token_id, - pad_token_id=pad_token_id, - top_k=top_k, - top_p=top_p, - temperature=temperature, - prepare_inputs_fn=prepare_inputs_fn, - update_model_kwargs_fn=update_model_kwargs_fn, - **model_kwargs) - elif is_beam_gen_mode: - raise NotImplementedError - else: - raise ValueError("Unsupported generation mode") diff --git a/applications/ChatGPT/chatgpt/models/generation_utils.py b/applications/ChatGPT/chatgpt/models/generation_utils.py deleted file mode 100644 index c7bc1b383fb9..000000000000 --- a/applications/ChatGPT/chatgpt/models/generation_utils.py +++ /dev/null @@ -1,92 +0,0 @@ -from typing import Optional - -import torch - - -def gpt_prepare_inputs_fn(input_ids: torch.Tensor, past: Optional[torch.Tensor] = None, **kwargs) -> dict: - token_type_ids = kwargs.get("token_type_ids", None) - # only last token for inputs_ids if past is defined in kwargs - if past: - input_ids = input_ids[:, -1].unsqueeze(-1) - if token_type_ids is not None: - token_type_ids = token_type_ids[:, -1].unsqueeze(-1) - - attention_mask = kwargs.get("attention_mask", None) - position_ids = kwargs.get("position_ids", None) - - if attention_mask is not None and position_ids is None: - # create position_ids on the fly for batch generation - position_ids = attention_mask.long().cumsum(-1) - 1 - position_ids.masked_fill_(attention_mask == 0, 1) - if past: - position_ids = position_ids[:, -1].unsqueeze(-1) - else: - position_ids = None - return { - "input_ids": input_ids, - "past_key_values": past, - "use_cache": kwargs.get("use_cache"), - "position_ids": position_ids, - "attention_mask": attention_mask, - "token_type_ids": token_type_ids, - } - - -def update_model_kwargs_fn(outputs: dict, **model_kwargs) -> dict: - if "past_key_values" in outputs: - model_kwargs["past"] = outputs["past_key_values"] - else: - model_kwargs["past"] = None - - # update token_type_ids with last value - if "token_type_ids" in model_kwargs: - token_type_ids = model_kwargs["token_type_ids"] - model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1) - - # update attention mask - if "attention_mask" in model_kwargs: - attention_mask = model_kwargs["attention_mask"] - model_kwargs["attention_mask"] = torch.cat( - [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1) - - return model_kwargs - - -def opt_prepare_inputs_fn(input_ids: torch.Tensor, - past: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - **kwargs) -> dict: - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_ids.shape) - - if past: - input_ids = input_ids[:, -1:] - # first step, decoder_cached_states are empty - return { - "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed - "attention_mask": attention_mask, - "past_key_values": past, - "use_cache": use_cache, - } - - -def bloom_prepare_inputs_fn(input_ids: torch.Tensor, - past: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - **kwargs) -> dict: - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_ids.shape) - - if past: - input_ids = input_ids[:, -1:] - # first step, decoder_cached_states are empty - return { - "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed - "attention_mask": attention_mask, - "past_key_values": past, - "use_cache": use_cache, - } diff --git a/applications/ChatGPT/chatgpt/models/gpt/__init__.py b/applications/ChatGPT/chatgpt/models/gpt/__init__.py deleted file mode 100644 index c6ae05113cc0..000000000000 --- a/applications/ChatGPT/chatgpt/models/gpt/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .gpt_actor import GPTActor -from .gpt_critic import GPTCritic -from .gpt_rm import GPTRM -from .gpt_lm import GPTLM - -__all__ = ['GPTActor', 'GPTCritic', 'GPTRM', 'GPTLM'] diff --git a/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py deleted file mode 100644 index 6a53ad40b817..000000000000 --- a/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py +++ /dev/null @@ -1,35 +0,0 @@ -from typing import Optional - -from transformers.models.gpt2.configuration_gpt2 import GPT2Config -from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel - -from ..base import Actor - - -class GPTActor(Actor): - """ - GPT Actor model. - - Args: - pretrained (str): Pretrained model name or path. - config (GPT2Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the LoRa layer. - lora_train_bias (str): Bias training strategy for the LoRa layer. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[GPT2Config] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = GPT2LMHeadModel.from_pretrained(pretrained) - elif config is not None: - model = GPT2LMHeadModel(config) - else: - model = GPT2LMHeadModel(GPT2Config()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py deleted file mode 100644 index 25bb1ed94de4..000000000000 --- a/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py +++ /dev/null @@ -1,37 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers.models.gpt2.configuration_gpt2 import GPT2Config -from transformers.models.gpt2.modeling_gpt2 import GPT2Model - -from ..base import Critic - - -class GPTCritic(Critic): - """ - GPT Critic model. - - Args: - pretrained (str): Pretrained model name or path. - config (GPT2Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the LO-RA decomposition. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[GPT2Config] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = GPT2Model.from_pretrained(pretrained) - elif config is not None: - model = GPT2Model(config) - else: - model = GPT2Model(GPT2Config()) - if checkpoint: - model.gradient_checkpointing_enable() - value_head = nn.Linear(model.config.n_embd, 1) - super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/gpt/gpt_lm.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_lm.py deleted file mode 100644 index 5740c80d3e77..000000000000 --- a/applications/ChatGPT/chatgpt/models/gpt/gpt_lm.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Optional - -from transformers.models.gpt2.configuration_gpt2 import GPT2Config -from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel - -from ..base import LM - - -class GPTLM(LM): - """ - GPT language model. - - Args: - pretrained (str): Pretrained model name or path. - config (GPT2Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the LoRa layer. - lora_train_bias (str): Bias training strategy for the LoRa layer. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[GPT2Config] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = GPT2LMHeadModel.from_pretrained(pretrained) - elif config is not None: - model = GPT2LMHeadModel(config) - else: - model = GPT2LMHeadModel(GPT2Config()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) - diff --git a/applications/ChatGPT/chatgpt/models/gpt/gpt_rm.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_rm.py deleted file mode 100644 index 19d673de6825..000000000000 --- a/applications/ChatGPT/chatgpt/models/gpt/gpt_rm.py +++ /dev/null @@ -1,39 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers.models.gpt2.configuration_gpt2 import GPT2Config -from transformers.models.gpt2.modeling_gpt2 import GPT2Model - -from ..base import RewardModel - - -class GPTRM(RewardModel): - """ - GPT Reward model. - - Args: - pretrained (str): Pretrained model name or path. - config (GPT2Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the low-rank approximation. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[GPT2Config] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = GPT2Model.from_pretrained(pretrained) - elif config is not None: - model = GPT2Model(config) - else: - model = GPT2Model(GPT2Config()) - if checkpoint: - model.gradient_checkpointing_enable() - - value_head = nn.Linear(model.config.n_embd, 1) - value_head.weight.data.normal_(mean=0.0, std=1/(model.config.n_embd + 1)) - super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/llama/__init__.py b/applications/ChatGPT/chatgpt/models/llama/__init__.py deleted file mode 100644 index 3edb51e14376..000000000000 --- a/applications/ChatGPT/chatgpt/models/llama/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .llama_actor import LlamaActor -from .llama_critic import LlamaCritic -from .llama_rm import LlamaRM -from .llama_lm import LlamaLM - -__all__ = ['LlamaActor', 'LlamaCritic', 'LlamaRM', 'LlamaLM'] diff --git a/applications/ChatGPT/chatgpt/models/llama/llama_actor.py b/applications/ChatGPT/chatgpt/models/llama/llama_actor.py deleted file mode 100644 index 2c7adb390d8b..000000000000 --- a/applications/ChatGPT/chatgpt/models/llama/llama_actor.py +++ /dev/null @@ -1,38 +0,0 @@ -from typing import Optional - -import torch -from transformers import AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM - -from ..base import Actor - - -class LlamaActor(Actor): - """ - Llama Actor model. - - Args: - pretrained (str): Pretrained model name or path. - config (LlamaConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[LlamaConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - - if pretrained is not None: - model = LlamaForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = LlamaForCausalLM(config) - else: - model = LlamaForCausalLM(LlamaConfig()) - - if checkpoint: - model.gradient_checkpointing_enable() - - super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/llama/llama_critic.py b/applications/ChatGPT/chatgpt/models/llama/llama_critic.py deleted file mode 100644 index cd565031e112..000000000000 --- a/applications/ChatGPT/chatgpt/models/llama/llama_critic.py +++ /dev/null @@ -1,42 +0,0 @@ -from typing import Optional - -import torch -import torch.nn as nn -from transformers import AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM - -from ..base import Critic - - -class LlamaCritic(Critic): - """ - Llama Critic model. - - Args: - pretrained (str): Pretrained model name or path. - config (LlamaConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[LlamaConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none', - **kwargs) -> None: - - if pretrained is not None: - model = LlamaForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = LlamaForCausalLM(config) - else: - model = LlamaForCausalLM(LlamaConfig()) - - if checkpoint: - model.gradient_checkpointing_enable() - - value_head = nn.Linear(model.config.hidden_size, 1) - - super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/ChatGPT/chatgpt/models/llama/llama_lm.py b/applications/ChatGPT/chatgpt/models/llama/llama_lm.py deleted file mode 100644 index 5a1a88e0d253..000000000000 --- a/applications/ChatGPT/chatgpt/models/llama/llama_lm.py +++ /dev/null @@ -1,40 +0,0 @@ -from typing import Optional - -from transformers import LlamaConfig, LlamaForCausalLM - -from ..base import LM - - -class LlamaLM(LM): - """ - Llama language model. - - Args: - pretrained (str): Pretrained model name or path. - config (LlamaConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[LlamaConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - - if pretrained is not None: - model = LlamaForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = LlamaForCausalLM(config) - else: - model = LlamaForCausalLM(LlamaConfig()) - - if checkpoint: - model.gradient_checkpointing_enable() - - super().__init__(model, lora_rank, lora_train_bias) - - def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): - return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) diff --git a/applications/ChatGPT/chatgpt/models/llama/llama_rm.py b/applications/ChatGPT/chatgpt/models/llama/llama_rm.py deleted file mode 100644 index 81fa22d1969d..000000000000 --- a/applications/ChatGPT/chatgpt/models/llama/llama_rm.py +++ /dev/null @@ -1,41 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers import LlamaConfig, LlamaForCausalLM - -from ..base import RewardModel - - -class LlamaRM(RewardModel): - """ - Llama Reward model. - - Args: - pretrained (str): Pretrained model name or path. - config (LlamaConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[LlamaConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - - if pretrained is not None: - model = LlamaForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = LlamaForCausalLM(config) - else: - model = LlamaForCausalLM(LlamaConfig()) - - if checkpoint: - model.gradient_checkpointing_enable() - - value_head = nn.Linear(model.config.hidden_size, 1) - value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1)) - - super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/lora.py b/applications/ChatGPT/chatgpt/models/lora.py deleted file mode 100644 index 9c19f472d726..000000000000 --- a/applications/ChatGPT/chatgpt/models/lora.py +++ /dev/null @@ -1,130 +0,0 @@ -import math -from typing import Optional - -import loralib as lora -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class LoraLinear(lora.LoRALayer, nn.Module): - """Replace in-place ops to out-of-place ops to fit gemini. Convert a torch.nn.Linear to LoraLinear. - """ - - def __init__( - self, - weight: nn.Parameter, - bias: Optional[nn.Parameter], - r: int = 0, - lora_alpha: int = 1, - lora_dropout: float = 0., - fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) - merge_weights: bool = True, - ): - nn.Module.__init__(self) - lora.LoRALayer.__init__(self, - r=r, - lora_alpha=lora_alpha, - lora_dropout=lora_dropout, - merge_weights=merge_weights) - self.weight = weight - self.bias = bias - - out_features, in_features = weight.shape - self.in_features = in_features - self.out_features = out_features - - self.fan_in_fan_out = fan_in_fan_out - # Actual trainable parameters - if r > 0: - self.lora_A = nn.Parameter(self.weight.new_zeros((r, in_features))) - self.lora_B = nn.Parameter(self.weight.new_zeros((out_features, r))) - self.scaling = self.lora_alpha / self.r - # Freezing the pre-trained weight matrix - self.weight.requires_grad = False - self.reset_parameters() - if fan_in_fan_out: - self.weight.data = self.weight.data.T - - def reset_parameters(self): - if hasattr(self, 'lora_A'): - # initialize A the same way as the default for nn.Linear and B to zero - nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5)) - nn.init.zeros_(self.lora_B) - - def train(self, mode: bool = True): - - def T(w): - return w.T if self.fan_in_fan_out else w - - nn.Module.train(self, mode) - if self.merge_weights and self.merged: - # Make sure that the weights are not merged - if self.r > 0: - self.weight.data -= T(self.lora_B @ self.lora_A) * self.scaling - self.merged = False - - def eval(self): - - def T(w): - return w.T if self.fan_in_fan_out else w - - nn.Module.eval(self) - if self.merge_weights and not self.merged: - # Merge the weights and mark it - if self.r > 0: - self.weight.data += T(self.lora_B @ self.lora_A) * self.scaling - delattr(self, 'lora_A') - delattr(self, 'lora_B') - self.merged = True - - def forward(self, x: torch.Tensor): - - def T(w): - return w.T if self.fan_in_fan_out else w - - if self.r > 0 and not self.merged: - result = F.linear(x, T(self.weight), bias=self.bias) - if self.r > 0: - result = result + (self.lora_dropout(x) @ self.lora_A.t() @ self.lora_B.t()) * self.scaling - return result - else: - return F.linear(x, T(self.weight), bias=self.bias) - - -def lora_linear_wrapper(linear: nn.Linear, lora_rank: int) -> LoraLinear: - assert lora_rank <= linear.in_features, f'LoRA rank ({lora_rank}) must be less than or equal to in features ({linear.in_features})' - lora_linear = LoraLinear(linear.weight, linear.bias, r=lora_rank, merge_weights=False) - return lora_linear - - -def convert_to_lora_recursively(module: nn.Module, lora_rank: int) -> None: - for name, child in module.named_children(): - if isinstance(child, nn.Linear): - setattr(module, name, lora_linear_wrapper(child, lora_rank)) - else: - convert_to_lora_recursively(child, lora_rank) - - -class LoRAModule(nn.Module): - """A LoRA module base class. All derived classes should call `convert_to_lora()` at the bottom of `__init__()`. - This calss will convert all torch.nn.Linear layer to LoraLinear layer. - - Args: - lora_rank (int, optional): LoRA rank. 0 means LoRA is not applied. Defaults to 0. - lora_train_bias (str, optional): Whether LoRA train biases. - 'none' means it doesn't train biases. 'all' means it trains all biases. 'lora_only' means it only trains biases of LoRA layers. - Defaults to 'none'. - """ - - def __init__(self, lora_rank: int = 0, lora_train_bias: str = 'none') -> None: - super().__init__() - self.lora_rank = lora_rank - self.lora_train_bias = lora_train_bias - - def convert_to_lora(self) -> None: - if self.lora_rank <= 0: - return - convert_to_lora_recursively(self, self.lora_rank) - lora.mark_only_lora_as_trainable(self, self.lora_train_bias) - diff --git a/applications/ChatGPT/chatgpt/models/loss.py b/applications/ChatGPT/chatgpt/models/loss.py deleted file mode 100644 index c5b1ccc93228..000000000000 --- a/applications/ChatGPT/chatgpt/models/loss.py +++ /dev/null @@ -1,115 +0,0 @@ -from typing import Optional - -import torch -import torch.nn as nn - -from .utils import masked_mean - - -class GPTLMLoss(nn.Module): - """ - GPT Language Model Loss - """ - - def __init__(self): - super().__init__() - self.loss = nn.CrossEntropyLoss() - - def forward(self, logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - return self.loss(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) - - -class PolicyLoss(nn.Module): - """ - Policy Loss for PPO - """ - - def __init__(self, clip_eps: float = 0.2) -> None: - super().__init__() - self.clip_eps = clip_eps - - def forward(self, - log_probs: torch.Tensor, - old_log_probs: torch.Tensor, - advantages: torch.Tensor, - action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - ratio = (log_probs - old_log_probs).exp() - surr1 = ratio * advantages - surr2 = ratio.clamp(1 - self.clip_eps, 1 + self.clip_eps) * advantages - loss = -torch.min(surr1, surr2) - if action_mask is not None: - loss = masked_mean(loss, action_mask) - loss = loss.mean() - return loss - - -class ValueLoss(nn.Module): - """ - Value Loss for PPO - """ - - def __init__(self, clip_eps: float = 0.4) -> None: - super().__init__() - self.clip_eps = clip_eps - - def forward(self, - values: torch.Tensor, - old_values: torch.Tensor, - reward: torch.Tensor, - action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - values_clipped = old_values + (values - old_values).clamp(-self.clip_eps, self.clip_eps) - surr1 = (values_clipped - reward)**2 - surr2 = (values - reward)**2 - loss = torch.max(surr1, surr2) - loss = loss.mean() - return loss - - -class PPOPtxActorLoss(nn.Module): - """ - To Do: - - PPO-ptx Actor Loss - """ - - def __init__(self, policy_clip_eps: float = 0.2, pretrain_coef: float = 0.0, pretrain_loss_fn=GPTLMLoss()) -> None: - super().__init__() - self.pretrain_coef = pretrain_coef - self.policy_loss_fn = PolicyLoss(clip_eps=policy_clip_eps) - self.pretrain_loss_fn = pretrain_loss_fn - - def forward(self, - log_probs: torch.Tensor, - old_log_probs: torch.Tensor, - advantages: torch.Tensor, - lm_logits: torch.Tensor, - lm_input_ids: torch.Tensor, - action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - policy_loss = self.policy_loss_fn(log_probs, old_log_probs, advantages, action_mask=action_mask) - lm_loss = self.pretrain_loss_fn(lm_logits, lm_input_ids) - return policy_loss + self.pretrain_coef * lm_loss - - -class LogSigLoss(nn.Module): - """ - Pairwise Loss for Reward Model - Details: https://arxiv.org/abs/2203.02155 - """ - def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor: - probs = torch.sigmoid(chosen_reward - reject_reward) - log_probs = torch.log(probs) - loss = -log_probs.mean() - return loss - - -class LogExpLoss(nn.Module): - """ - Pairwise Loss for Reward Model - Details: https://arxiv.org/abs/2204.05862 - """ - def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor: - loss = torch.log(1 + torch.exp(reject_reward - chosen_reward)).mean() - return loss diff --git a/applications/ChatGPT/chatgpt/models/opt/__init__.py b/applications/ChatGPT/chatgpt/models/opt/__init__.py deleted file mode 100644 index fccec3bdff99..000000000000 --- a/applications/ChatGPT/chatgpt/models/opt/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .opt_actor import OPTActor -from .opt_critic import OPTCritic -from .opt_rm import OPTRM -from .opt_lm import OPTLM - -__all__ = ['OPTActor', 'OPTCritic', 'OPTRM', 'OPTLM'] diff --git a/applications/ChatGPT/chatgpt/models/opt/opt_actor.py b/applications/ChatGPT/chatgpt/models/opt/opt_actor.py deleted file mode 100644 index c14e4377ffb2..000000000000 --- a/applications/ChatGPT/chatgpt/models/opt/opt_actor.py +++ /dev/null @@ -1,35 +0,0 @@ -from typing import Optional - -from transformers.models.opt.configuration_opt import OPTConfig -from transformers.models.opt.modeling_opt import OPTForCausalLM - -from ..base import Actor - - -class OPTActor(Actor): - """ - OPT Actor model. - - Args: - pretrained (str): Pretrained model name or path. - config (OPTConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the low-rank approximation. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[OPTConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = OPTForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = OPTForCausalLM(config) - else: - model = OPTForCausalLM(OPTConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/opt/opt_critic.py b/applications/ChatGPT/chatgpt/models/opt/opt_critic.py deleted file mode 100644 index fcfebd8a8b03..000000000000 --- a/applications/ChatGPT/chatgpt/models/opt/opt_critic.py +++ /dev/null @@ -1,38 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers.models.opt.configuration_opt import OPTConfig -from transformers.models.opt.modeling_opt import OPTModel - -from ..base import Critic - - -class OPTCritic(Critic): - """ - OPT Critic model. - - Args: - pretrained (str): Pretrained model name or path. - config (OPTConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the low-rank approximation. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[OPTConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none', - **kwargs) -> None: - if pretrained is not None: - model = OPTModel.from_pretrained(pretrained) - elif config is not None: - model = OPTModel(config) - else: - model = OPTModel(OPTConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - value_head = nn.Linear(model.config.word_embed_proj_dim, 1) - super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/ChatGPT/chatgpt/models/opt/opt_lm.py b/applications/ChatGPT/chatgpt/models/opt/opt_lm.py deleted file mode 100644 index 35bfe198a225..000000000000 --- a/applications/ChatGPT/chatgpt/models/opt/opt_lm.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Optional - -from transformers.models.opt.configuration_opt import OPTConfig -from transformers.models.opt.modeling_opt import OPTForCausalLM - -from ..base import LM - - -class OPTLM(LM): - """ - OPT language model. - - Args: - pretrained (str): Pretrained model name or path. - config (OPTConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the low-rank approximation. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[OPTConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = OPTForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = OPTForCausalLM(config) - else: - model = OPTForCausalLM(OPTConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) - diff --git a/applications/ChatGPT/chatgpt/models/opt/opt_rm.py b/applications/ChatGPT/chatgpt/models/opt/opt_rm.py deleted file mode 100644 index ef7f0fb16fd1..000000000000 --- a/applications/ChatGPT/chatgpt/models/opt/opt_rm.py +++ /dev/null @@ -1,38 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers import OPTConfig, OPTModel - -from ..base import RewardModel - - -class OPTRM(RewardModel): - """ - OPT Reward model. - - Args: - pretrained (str): Pretrained model name or path. - config (OPTConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the low-rank approximation. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[OPTConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = OPTModel.from_pretrained(pretrained) - elif config is not None: - model = OPTModel(config) - else: - model = OPTModel(OPTConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - - value_head = nn.Linear(model.config.word_embed_proj_dim, 1) - value_head.weight.data.normal_(mean=0.0, std=1/(model.config.word_embed_proj_dim + 1)) - super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/ChatGPT/chatgpt/models/utils.py b/applications/ChatGPT/chatgpt/models/utils.py deleted file mode 100644 index 0ff13181fcd2..000000000000 --- a/applications/ChatGPT/chatgpt/models/utils.py +++ /dev/null @@ -1,92 +0,0 @@ -from typing import Optional, Union - -import loralib as lora -import torch -import torch.nn as nn -import torch.nn.functional as F - - -def compute_approx_kl(log_probs: torch.Tensor, - log_probs_base: torch.Tensor, - action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - """ - Compute the approximate KL divergence between two distributions. - Schulman blog: http://joschu.net/blog/kl-approx.html - - Args: - log_probs: Log probabilities of the new distribution. - log_probs_base: Log probabilities of the base distribution. - action_mask: Mask for actions. - """ - - log_ratio = log_probs - log_probs_base - approx_kl = (log_ratio.exp() - 1) - log_ratio - if action_mask is not None: - approx_kl = masked_mean(approx_kl, action_mask, dim=1) - return approx_kl - approx_kl = approx_kl.mean(dim=1) - return approx_kl - - -def compute_reward(r: Union[torch.Tensor, float], - kl_coef: float, - log_probs: torch.Tensor, - log_probs_base: torch.Tensor, - action_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - if kl_coef <= 0.0: - return r - kl = compute_approx_kl(log_probs, log_probs_base, action_mask=action_mask) - reward = r - kl_coef * kl - return reward - - -def log_probs_from_logits(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: - log_probs = F.log_softmax(logits, dim=-1) - log_probs_labels = log_probs.gather(dim=-1, index=labels.unsqueeze(-1)) - return log_probs_labels.squeeze(-1) - - -def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor: - tensor = tensor * mask - tensor = tensor.sum(dim=dim) - mask_sum = mask.sum(dim=dim) - mean = tensor / (mask_sum + 1e-8) - return mean - - -def masked_normalize(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1, eps: float = 1e-8) -> torch.Tensor: - tensor = tensor * mask - mean = masked_mean(tensor, mask, dim=dim) - mean_centered = tensor - mean - var = masked_mean(mean_centered**2, mask, dim=dim) - return mean_centered * var.clamp(min=eps).rsqrt() - - -def normalize(tensor: torch.Tensor, dim: int = 0, eps: float = 1e-8) -> torch.Tensor: - mean = tensor.mean(dim) - mean_centered = tensor - mean - var = (mean_centered**2).mean(dim) - norm = mean_centered * var.clamp(min=eps).rsqrt() - return norm - - -def convert_to_lora(model: nn.Module, - input_size: int, - output_size: int, - lora_rank: int = 16, - lora_alpha: int = 1, - lora_dropout: float = 0., - fan_in_fan_out: bool = False, - merge_weights: bool = True): - if lora_rank > min(input_size, output_size): - raise ValueError(f"LoRA rank {lora_rank} must be less or equal than {min(input_size, output_size)}") - - for name, module in model.named_modules(): - if isinstance(module, nn.Linear): - module._modules[name] = lora.Linear(input_size, - output_size, - r=lora_rank, - lora_alpha=lora_alpha, - lora_dropout=lora_dropout, - fan_in_fan_out=fan_in_fan_out, - merge_weights=merge_weights) diff --git a/applications/ChatGPT/chatgpt/replay_buffer/__init__.py b/applications/ChatGPT/chatgpt/replay_buffer/__init__.py deleted file mode 100644 index 1ebf60382913..000000000000 --- a/applications/ChatGPT/chatgpt/replay_buffer/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .base import ReplayBuffer -from .naive import NaiveReplayBuffer - -__all__ = ['ReplayBuffer', 'NaiveReplayBuffer'] diff --git a/applications/ChatGPT/chatgpt/replay_buffer/base.py b/applications/ChatGPT/chatgpt/replay_buffer/base.py deleted file mode 100644 index 5036b09045c4..000000000000 --- a/applications/ChatGPT/chatgpt/replay_buffer/base.py +++ /dev/null @@ -1,43 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Any - -from chatgpt.experience_maker.base import Experience - - -class ReplayBuffer(ABC): - """Replay buffer base class. It stores experience. - - Args: - sample_batch_size (int): Batch size when sampling. - limit (int, optional): Limit of number of experience samples. A number <= 0 means unlimited. Defaults to 0. - """ - - def __init__(self, sample_batch_size: int, limit: int = 0) -> None: - super().__init__() - self.sample_batch_size = sample_batch_size - # limit <= 0 means unlimited - self.limit = limit - - @abstractmethod - def append(self, experience: Experience) -> None: - pass - - @abstractmethod - def clear(self) -> None: - pass - - @abstractmethod - def sample(self) -> Experience: - pass - - @abstractmethod - def __len__(self) -> int: - pass - - @abstractmethod - def __getitem__(self, idx: int) -> Any: - pass - - @abstractmethod - def collate_fn(self, batch: Any) -> Experience: - pass diff --git a/applications/ChatGPT/chatgpt/replay_buffer/naive.py b/applications/ChatGPT/chatgpt/replay_buffer/naive.py deleted file mode 100644 index 3fc53da65bff..000000000000 --- a/applications/ChatGPT/chatgpt/replay_buffer/naive.py +++ /dev/null @@ -1,57 +0,0 @@ -import random -from typing import List - -import torch -from chatgpt.experience_maker.base import Experience - -from .base import ReplayBuffer -from .utils import BufferItem, make_experience_batch, split_experience_batch - - -class NaiveReplayBuffer(ReplayBuffer): - """Naive replay buffer class. It stores experience. - - Args: - sample_batch_size (int): Batch size when sampling. - limit (int, optional): Limit of number of experience samples. A number <= 0 means unlimited. Defaults to 0. - cpu_offload (bool, optional): Whether to offload experience to cpu when sampling. Defaults to True. - """ - - def __init__(self, sample_batch_size: int, limit: int = 0, cpu_offload: bool = True) -> None: - super().__init__(sample_batch_size, limit) - self.cpu_offload = cpu_offload - self.target_device = torch.device(f'cuda:{torch.cuda.current_device()}') - # TODO(ver217): add prefetch - self.items: List[BufferItem] = [] - - @torch.no_grad() - def append(self, experience: Experience) -> None: - if self.cpu_offload: - experience.to_device(torch.device('cpu')) - items = split_experience_batch(experience) - self.items.extend(items) - if self.limit > 0: - samples_to_remove = len(self.items) - self.limit - if samples_to_remove > 0: - self.items = self.items[samples_to_remove:] - - def clear(self) -> None: - self.items.clear() - - @torch.no_grad() - def sample(self) -> Experience: - items = random.sample(self.items, self.sample_batch_size) - experience = make_experience_batch(items) - if self.cpu_offload: - experience.to_device(self.target_device) - return experience - - def __len__(self) -> int: - return len(self.items) - - def __getitem__(self, idx: int) -> BufferItem: - return self.items[idx] - - def collate_fn(self, batch) -> Experience: - experience = make_experience_batch(batch) - return experience diff --git a/applications/ChatGPT/chatgpt/replay_buffer/utils.py b/applications/ChatGPT/chatgpt/replay_buffer/utils.py deleted file mode 100644 index 752f16704771..000000000000 --- a/applications/ChatGPT/chatgpt/replay_buffer/utils.py +++ /dev/null @@ -1,73 +0,0 @@ -from dataclasses import dataclass -from typing import List, Optional - -import torch -import torch.nn.functional as F -from chatgpt.experience_maker.base import Experience - - -@dataclass -class BufferItem: - """BufferItem is an item of experience data. - - Shapes of each tensor: - sequences: (S) - action_log_probs: (A) - values: (1) - reward: (1) - advatanges: (1) - attention_mask: (S) - action_mask: (A) - - "A" is the number of actions. - """ - sequences: torch.Tensor - action_log_probs: torch.Tensor - values: torch.Tensor - reward: torch.Tensor - advantages: torch.Tensor - attention_mask: Optional[torch.LongTensor] - action_mask: Optional[torch.BoolTensor] - - -def split_experience_batch(experience: Experience) -> List[BufferItem]: - batch_size = experience.sequences.size(0) - batch_kwargs = [{} for _ in range(batch_size)] - keys = ('sequences', 'action_log_probs', 'values', 'reward', 'advantages', 'attention_mask', 'action_mask') - for key in keys: - value = getattr(experience, key) - if isinstance(value, torch.Tensor): - vals = torch.unbind(value) - else: - # None - vals = [value for _ in range(batch_size)] - assert batch_size == len(vals) - for i, v in enumerate(vals): - batch_kwargs[i][key] = v - items = [BufferItem(**kwargs) for kwargs in batch_kwargs] - return items - - -def zero_pad_sequences(sequences: List[torch.Tensor], side: str = 'left') -> torch.Tensor: - assert side in ('left', 'right') - max_len = max(seq.size(0) for seq in sequences) - padded_sequences = [] - for seq in sequences: - pad_len = max_len - seq.size(0) - padding = (pad_len, 0) if side == 'left' else (0, pad_len) - padded_sequences.append(F.pad(seq, padding)) - return torch.stack(padded_sequences, dim=0) - - -def make_experience_batch(items: List[BufferItem]) -> Experience: - kwargs = {} - to_pad_keys = set(('action_log_probs', 'action_mask')) - keys = ('sequences', 'action_log_probs', 'values', 'reward', 'advantages', 'attention_mask', 'action_mask') - for key in keys: - vals = [getattr(item, key) for item in items] - if key in to_pad_keys: - batch_data = zero_pad_sequences(vals) - else: - batch_data = torch.stack(vals, dim=0) - kwargs[key] = batch_data - return Experience(**kwargs) diff --git a/applications/ChatGPT/chatgpt/trainer/__init__.py b/applications/ChatGPT/chatgpt/trainer/__init__.py deleted file mode 100644 index 525b57bf21d3..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .base import Trainer -from .ppo import PPOTrainer -from .rm import RewardModelTrainer -from .sft import SFTTrainer - -__all__ = ['Trainer', 'PPOTrainer', 'RewardModelTrainer', 'SFTTrainer'] diff --git a/applications/ChatGPT/chatgpt/trainer/base.py b/applications/ChatGPT/chatgpt/trainer/base.py deleted file mode 100644 index a2419a35b6cd..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/base.py +++ /dev/null @@ -1,162 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Any, Callable, Dict, List, Optional, Union - -import torch -from chatgpt.experience_maker import Experience, ExperienceMaker -from chatgpt.replay_buffer import ReplayBuffer -from torch import Tensor -from torch.utils.data import DistributedSampler -from tqdm import tqdm - -from .callbacks import Callback -from .strategies import Strategy -from .utils import is_rank_0 - - -class Trainer(ABC): - """ - Base class for rlhf trainers. - - Args: - strategy (Strategy):the strategy to use for training - experience_maker (ExperienceMaker): the experience maker to use for produce experience to fullfill replay buffer - replay_buffer (ReplayBuffer): the replay buffer to use for training - experience_batch_size (int, defaults to 8): the batch size to use for experience generation - max_epochs (int, defaults to 1): the number of epochs of training process - tokenizer (Callable, optional): the tokenizer to use for tokenizing the input - sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer - data_loader_pin_memory (bool, defaults to True): whether to pin memory for data loader - callbacks (List[Callback], defaults to []): the callbacks to call during training process - generate_kwargs (dict, optional): the kwargs to use while model generating - """ - - def __init__(self, - strategy: Strategy, - experience_maker: ExperienceMaker, - replay_buffer: ReplayBuffer, - experience_batch_size: int = 8, - max_epochs: int = 1, - tokenizer: Optional[Callable[[Any], dict]] = None, - sample_replay_buffer: bool = False, - dataloader_pin_memory: bool = True, - callbacks: List[Callback] = [], - **generate_kwargs) -> None: - super().__init__() - self.strategy = strategy - self.experience_maker = experience_maker - self.replay_buffer = replay_buffer - self.experience_batch_size = experience_batch_size - self.max_epochs = max_epochs - self.tokenizer = tokenizer - self.generate_kwargs = generate_kwargs - self.sample_replay_buffer = sample_replay_buffer - self.dataloader_pin_memory = dataloader_pin_memory - self.callbacks = callbacks - - @abstractmethod - def training_step(self, experience: Experience) -> Dict[str, Any]: - pass - - def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience: - if isinstance(inputs, Tensor): - return self.experience_maker.make_experience(inputs, **self.generate_kwargs) - elif isinstance(inputs, dict): - return self.experience_maker.make_experience(**inputs, **self.generate_kwargs) - else: - raise ValueError(f'Unsupported input type "{type(inputs)}"') - - def _sample_prompts(self, prompts) -> list: - indices = list(range(len(prompts))) - sampled_indices = self.strategy.experience_sampler.choice(indices, self.experience_batch_size, replace=False) - return [prompts[i] for i in sampled_indices] - - def _learn(self): - # replay buffer may be empty at first, we should rebuild at each training - if not self.sample_replay_buffer: - dataloader = self.strategy.setup_dataloader(self.replay_buffer, self.dataloader_pin_memory) - device = torch.cuda.current_device() - if self.sample_replay_buffer: - pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) - for _ in pbar: - experience = self.replay_buffer.sample() - metrics = self.training_step(experience) - pbar.set_postfix(metrics) - else: - for epoch in range(self.max_epochs): - self._on_learn_epoch_start(epoch) - if isinstance(dataloader.sampler, DistributedSampler): - dataloader.sampler.set_epoch(epoch) - pbar = tqdm(dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0()) - for experience in pbar: - self._on_learn_batch_start() - experience.to_device(device) - metrics = self.training_step(experience) - self._on_learn_batch_end(metrics, experience) - pbar.set_postfix(metrics) - self._on_learn_epoch_end(epoch) - - def fit(self, prompts, num_episodes: int = 50000, max_timesteps: int = 500, update_timesteps: int = 5000) -> None: - time = 0 - sampler = self.strategy.setup_sampler(prompts) - self._on_fit_start() - for episode in range(num_episodes): - self._on_episode_start(episode) - for timestep in tqdm(range(max_timesteps), - desc=f'Episode [{episode+1}/{num_episodes}]', - disable=not is_rank_0()): - time += 1 - rand_prompts = sampler.sample(self.experience_batch_size) - if self.tokenizer is not None: - inputs = self.tokenizer(rand_prompts) - else: - inputs = rand_prompts - self._on_make_experience_start() - experience = self._make_experience(inputs) - self._on_make_experience_end(experience) - self.replay_buffer.append(experience) - if time % update_timesteps == 0: - self._learn() - self.replay_buffer.clear() - self._on_episode_end(episode) - self._on_fit_end() - - # TODO(ver217): maybe simplify these code using context - def _on_fit_start(self) -> None: - for callback in self.callbacks: - callback.on_fit_start() - - def _on_fit_end(self) -> None: - for callback in self.callbacks: - callback.on_fit_end() - - def _on_episode_start(self, episode: int) -> None: - for callback in self.callbacks: - callback.on_episode_start(episode) - - def _on_episode_end(self, episode: int) -> None: - for callback in self.callbacks: - callback.on_episode_end(episode) - - def _on_make_experience_start(self) -> None: - for callback in self.callbacks: - callback.on_make_experience_start() - - def _on_make_experience_end(self, experience: Experience) -> None: - for callback in self.callbacks: - callback.on_make_experience_end(experience) - - def _on_learn_epoch_start(self, epoch: int) -> None: - for callback in self.callbacks: - callback.on_learn_epoch_start(epoch) - - def _on_learn_epoch_end(self, epoch: int) -> None: - for callback in self.callbacks: - callback.on_learn_epoch_end(epoch) - - def _on_learn_batch_start(self) -> None: - for callback in self.callbacks: - callback.on_learn_batch_start() - - def _on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: - for callback in self.callbacks: - callback.on_learn_batch_end(metrics, experience) diff --git a/applications/ChatGPT/chatgpt/trainer/callbacks/__init__.py b/applications/ChatGPT/chatgpt/trainer/callbacks/__init__.py deleted file mode 100644 index 9ed0ee6f7640..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/callbacks/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .base import Callback -from .performance_evaluator import PerformanceEvaluator -from .save_checkpoint import SaveCheckpoint - -__all__ = ['Callback', 'PerformanceEvaluator', 'SaveCheckpoint'] diff --git a/applications/ChatGPT/chatgpt/trainer/callbacks/base.py b/applications/ChatGPT/chatgpt/trainer/callbacks/base.py deleted file mode 100644 index 0b01345f7872..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/callbacks/base.py +++ /dev/null @@ -1,39 +0,0 @@ -from abc import ABC - -from chatgpt.experience_maker import Experience - - -class Callback(ABC): - """ - Base callback class. It defines the interface for callbacks. - """ - - def on_fit_start(self) -> None: - pass - - def on_fit_end(self) -> None: - pass - - def on_episode_start(self, episode: int) -> None: - pass - - def on_episode_end(self, episode: int) -> None: - pass - - def on_make_experience_start(self) -> None: - pass - - def on_make_experience_end(self, experience: Experience) -> None: - pass - - def on_learn_epoch_start(self, epoch: int) -> None: - pass - - def on_learn_epoch_end(self, epoch: int) -> None: - pass - - def on_learn_batch_start(self) -> None: - pass - - def on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: - pass diff --git a/applications/ChatGPT/chatgpt/trainer/callbacks/performance_evaluator.py b/applications/ChatGPT/chatgpt/trainer/callbacks/performance_evaluator.py deleted file mode 100644 index faa38af1b84e..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/callbacks/performance_evaluator.py +++ /dev/null @@ -1,133 +0,0 @@ -from time import time -from typing import Optional - -import torch -import torch.distributed as dist -from chatgpt.experience_maker import Experience - -from .base import Callback - - -def get_world_size() -> int: - if dist.is_initialized(): - return dist.get_world_size() - return 1 - - -def print_rank_0(*args, **kwargs) -> None: - if not dist.is_initialized() or dist.get_rank() == 0: - print(*args, **kwargs) - - -@torch.no_grad() -def all_reduce_mean(x: float, world_size: int) -> float: - if world_size == 1: - return x - tensor = torch.tensor([x], device=torch.cuda.current_device()) - dist.all_reduce(tensor) - tensor = tensor / world_size - return tensor.item() - - -class PerformanceEvaluator(Callback): - """ - Callback for valuate the performance of the model. - Args: - actor_num_params: The number of parameters of the actor model. - critic_num_params: The number of parameters of the critic model. - initial_model_num_params: The number of parameters of the initial model. - reward_model_num_params: The number of parameters of the reward model. - enable_grad_checkpoint: Whether to enable gradient checkpointing. - ignore_episodes: The number of episodes to ignore when calculating the performance. - """ - - def __init__(self, - actor_num_params: int, - critic_num_params: int, - initial_model_num_params: int, - reward_model_num_params: int, - enable_grad_checkpoint: bool = False, - ignore_episodes: int = 0) -> None: - super().__init__() - self.world_size = get_world_size() - self.actor_num_params = actor_num_params - self.critic_num_params = critic_num_params - self.initial_model_num_params = initial_model_num_params - self.reward_model_num_params = reward_model_num_params - self.enable_grad_checkpoint = enable_grad_checkpoint - self.ignore_episodes = ignore_episodes - self.disable: bool = False - - self.make_experience_duration: float = 0. - self.make_experience_start_time: Optional[float] = None - self.make_experience_num_samples: int = 0 - self.make_experience_flop: int = 0 - self.learn_duration: float = 0. - self.learn_start_time: Optional[float] = None - self.learn_num_samples: int = 0 - self.learn_flop: int = 0 - - def on_episode_start(self, episode: int) -> None: - self.disable = self.ignore_episodes > 0 and episode < self.ignore_episodes - - def on_make_experience_start(self) -> None: - if self.disable: - return - self.make_experience_start_time = time() - - def on_make_experience_end(self, experience: Experience) -> None: - if self.disable: - return - self.make_experience_duration += time() - self.make_experience_start_time - - batch_size, seq_len = experience.sequences.shape - - self.make_experience_num_samples += batch_size - - # actor generate - num_actions = experience.action_mask.size(1) - input_len = seq_len - num_actions - total_seq_len = (input_len + seq_len - 1) * num_actions / 2 - self.make_experience_flop += self.actor_num_params * batch_size * total_seq_len * 2 - # actor forward - self.make_experience_flop += self.actor_num_params * batch_size * seq_len * 2 - # critic forward - self.make_experience_flop += self.critic_num_params * batch_size * seq_len * 2 - # initial model forward - self.make_experience_flop += self.initial_model_num_params * batch_size * seq_len * 2 - # reward model forward - self.make_experience_flop += self.reward_model_num_params * batch_size * seq_len * 2 - - def on_learn_batch_start(self) -> None: - if self.disable: - return - self.learn_start_time = time() - - def on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: - if self.disable: - return - self.learn_duration += time() - self.learn_start_time - - batch_size, seq_len = experience.sequences.shape - - self.learn_num_samples += batch_size - - # actor forward-backward, 3 means forward(1) + backward(2) - self.learn_flop += self.actor_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint)) - # critic foward-backward - self.learn_flop += self.critic_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint)) - - def on_fit_end(self) -> None: - avg_make_experience_duration = all_reduce_mean(self.make_experience_duration, self.world_size) - avg_learn_duration = all_reduce_mean(self.learn_duration, self.world_size) - - avg_make_experience_throughput = self.make_experience_num_samples / (avg_make_experience_duration + 1e-12) - avg_make_experience_tflops = self.make_experience_flop / 1e12 / (avg_make_experience_duration + 1e-12) - - avg_learn_throughput = self.learn_num_samples / (avg_learn_duration + 1e-12) - avg_learn_tflops = self.learn_flop / 1e12 / (avg_learn_duration + 1e-12) - - print_rank_0( - f'Making experience throughput: {avg_make_experience_throughput:.3f} samples/sec, TFLOPS: {avg_make_experience_tflops:.3f}' - ) - print_rank_0(f'Learning throughput: {avg_learn_throughput:.3f} samples/sec, TFLOPS: {avg_learn_tflops:.3f}') diff --git a/applications/ChatGPT/chatgpt/trainer/callbacks/save_checkpoint.py b/applications/ChatGPT/chatgpt/trainer/callbacks/save_checkpoint.py deleted file mode 100644 index 8f2beb12db22..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/callbacks/save_checkpoint.py +++ /dev/null @@ -1,75 +0,0 @@ -import os - -import torch.distributed as dist -from chatgpt.trainer.strategies import ColossalAIStrategy, Strategy -from chatgpt.trainer.utils import is_rank_0 -from torch import nn -from torch.optim import Optimizer - -from .base import Callback - - -class SaveCheckpoint(Callback): - """ - The callback for saving checkpoint for chatgpt. - - Only support saving actor and critic model. - A typical architecture of the saved checkpoint would be: - - checkpoint - - episode_x - - actor.pt - - actor-optim-rank-0.pt - - actor-optim-rank-1.pt - - critic.pt - - critic-optim-rank-0.pt - - critic-optim-rank-1.pt - - ... - - Args: - path(str): the base path you want to save checkpoint, the checkpoint would be saved at `path/checkpoint` - interval(int): the interval episode of saving checkpoint - strategy(Strategy): the strategy used to train - actor(nn.Module): the actor model - critic(nn.Module): the critic model - actor_optim(Optimizer): the optimizer of actor - critic_optim(Optimizer): the optimizer of critic - - """ - - def __init__(self, - path: str, - interval: int, - strategy: Strategy, - actor: nn.Module = None, - critic: nn.Module = None, - actor_optim: Optimizer = None, - critic_optim: Optimizer = None) -> None: - super().__init__() - self.path = os.path.join(path, 'checkpoint') - self.interval = interval - self.strategy = strategy - self.model_dict = {'actor': [actor, actor_optim], 'critic': [critic, critic_optim]} - - def on_episode_end(self, episode: int) -> None: - if (episode + 1) % self.interval != 0: - return - base_path = os.path.join(self.path, f'episode_{episode}') - if not os.path.exists(base_path): - os.makedirs(base_path) - - for model in self.model_dict.keys(): - - # save model - if self.model_dict[model][0] is None: - # saving only optimizer states is meaningless, so it would be skipped - continue - model_path = os.path.join(base_path, f'{model}.pt') - self.strategy.save_model(model=self.model_dict[model][0], path=model_path, only_rank0=True) - - # save optimizer - if self.model_dict[model][1] is None: - continue - only_rank0 = not isinstance(self.strategy, ColossalAIStrategy) - rank = 0 if is_rank_0() else dist.get_rank() - optim_path = os.path.join(base_path, f'{model}-optim-rank-{rank}.pt') - self.strategy.save_optimizer(optimizer=self.model_dict[model][1], path=optim_path, only_rank0=only_rank0) diff --git a/applications/ChatGPT/chatgpt/trainer/ppo.py b/applications/ChatGPT/chatgpt/trainer/ppo.py deleted file mode 100644 index dacab4784039..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/ppo.py +++ /dev/null @@ -1,116 +0,0 @@ -from typing import Any, Callable, Dict, List, Optional - -import torch.nn as nn -from chatgpt.experience_maker import Experience, NaiveExperienceMaker -from chatgpt.models.base import Actor, Critic -from chatgpt.models.generation_utils import update_model_kwargs_fn -from chatgpt.models.loss import PolicyLoss, ValueLoss -from chatgpt.replay_buffer import NaiveReplayBuffer -from torch.optim import Optimizer - -from .base import Trainer -from .callbacks import Callback -from .strategies import Strategy - - -class PPOTrainer(Trainer): - """ - Trainer for PPO algorithm. - - Args: - strategy (Strategy): the strategy to use for training - actor (Actor): the actor model in ppo algorithm - critic (Critic): the critic model in ppo algorithm - reward_model (nn.Module): the reward model in rlhf algorithm to make reward of sentences - initial_model (Actor): the initial model in rlhf algorithm to generate reference logits to limit the update of actor - actor_optim (Optimizer): the optimizer to use for actor model - critic_optim (Optimizer): the optimizer to use for critic model - kl_coef (float, defaults to 0.1): the coefficient of kl divergence loss - train_batch_size (int, defaults to 8): the batch size to use for training - buffer_limit (int, defaults to 0): the max_size limitaiton of replay buffer - buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu - eps_clip (float, defaults to 0.2): the clip coefficient of policy loss - value_clip (float, defaults to 0.4): the clip coefficient of value loss - experience_batch_size (int, defaults to 8): the batch size to use for experience generation - max_epochs (int, defaults to 1): the number of epochs of training process - tokenier (Callable, optional): the tokenizer to use for tokenizing the input - sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer - dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader - callbacks (List[Callback], defaults to []): the callbacks to call during training process - generate_kwargs (dict, optional): the kwargs to use while model generating - """ - - def __init__(self, - strategy: Strategy, - actor: Actor, - critic: Critic, - reward_model: nn.Module, - initial_model: Actor, - actor_optim: Optimizer, - critic_optim: Optimizer, - kl_coef: float = 0.1, - train_batch_size: int = 8, - buffer_limit: int = 0, - buffer_cpu_offload: bool = True, - eps_clip: float = 0.2, - value_clip: float = 0.4, - experience_batch_size: int = 8, - max_epochs: int = 1, - tokenizer: Optional[Callable[[Any], dict]] = None, - sample_replay_buffer: bool = False, - dataloader_pin_memory: bool = True, - callbacks: List[Callback] = [], - **generate_kwargs) -> None: - experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef) - replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload) - generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor) - super().__init__(strategy, experience_maker, replay_buffer, experience_batch_size, max_epochs, tokenizer, - sample_replay_buffer, dataloader_pin_memory, callbacks, **generate_kwargs) - self.actor = actor - self.critic = critic - - self.actor_loss_fn = PolicyLoss(eps_clip) - self.critic_loss_fn = ValueLoss(value_clip) - - self.actor_optim = actor_optim - self.critic_optim = critic_optim - - def training_step(self, experience: Experience) -> Dict[str, float]: - self.actor.train() - self.critic.train() - - num_actions = experience.action_mask.size(1) - action_log_probs = self.actor(experience.sequences, num_actions, attention_mask=experience.attention_mask) - actor_loss = self.actor_loss_fn(action_log_probs, - experience.action_log_probs, - experience.advantages, - action_mask=experience.action_mask) - self.strategy.backward(actor_loss, self.actor, self.actor_optim) - self.strategy.optimizer_step(self.actor_optim) - self.actor_optim.zero_grad() - - values = self.critic(experience.sequences, - action_mask=experience.action_mask, - attention_mask=experience.attention_mask) - critic_loss = self.critic_loss_fn(values, - experience.values, - experience.reward, - action_mask=experience.action_mask) - self.strategy.backward(critic_loss, self.critic, self.critic_optim) - self.strategy.optimizer_step(self.critic_optim) - self.critic_optim.zero_grad() - - return {'actor_loss': actor_loss.item(), 'critic_loss': critic_loss.item()} - - -def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None: - origin_model = strategy._unwrap_actor(actor) - new_kwargs = {**generate_kwargs} - # use huggingface models method directly - if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): - new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation - - if 'update_model_kwargs_fn' not in generate_kwargs: - new_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn - - return new_kwargs diff --git a/applications/ChatGPT/chatgpt/trainer/rm.py b/applications/ChatGPT/chatgpt/trainer/rm.py deleted file mode 100644 index 7fa87a64968b..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/rm.py +++ /dev/null @@ -1,120 +0,0 @@ -from abc import ABC -import pandas as pd -import loralib as lora -import torch -from datetime import datetime -from torch.optim import Optimizer, lr_scheduler -from torch.utils.data import DataLoader, Dataset -from tqdm import tqdm - -from .strategies import Strategy -from .utils import is_rank_0 - - -class RewardModelTrainer(ABC): - """ - Trainer to use while training reward model. - - Args: - model (torch.nn.Module): the model to train - strategy (Strategy): the strategy to use for training - optim(Optimizer): the optimizer to use for training - loss_fn (callable): the loss function to use for training - train_dataset (Dataset): the dataset to use for training - valid_dataset (Dataset): the dataset to use for validation - eval_dataset (Dataset): the dataset to use for evaluation - batch_size (int, defaults to 1): the batch size while training - max_epochs (int, defaults to 2): the number of epochs to train - """ - - def __init__( - self, - model, - strategy: Strategy, - optim: Optimizer, - loss_fn, - train_dataset: Dataset, - valid_dataset: Dataset, - eval_dataset: Dataset, - batch_size: int = 1, - max_epochs: int = 1, - ) -> None: - super().__init__() - self.strategy = strategy - self.epochs = max_epochs - self.train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) - self.valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True) - self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size, shuffle=True) - - self.model = strategy.setup_model(model) - self.loss_fn = loss_fn - self.optimizer = strategy.setup_optimizer(optim, self.model) - self.scheduler = lr_scheduler.CosineAnnealingLR(self.optimizer, self.train_dataloader.__len__()//100) - - - def eval_acc(self, dataloader): - dist = 0 - on = 0 - cnt = 0 - self.model.eval() - with torch.no_grad(): - for chosen_ids, c_mask, reject_ids, r_mask in dataloader: - chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) - c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) - reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) - r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) - chosen_reward = self.model(chosen_ids, attention_mask=c_mask) - reject_reward = self.model(reject_ids, attention_mask=r_mask) - for i in range(len(chosen_reward)): - cnt += 1 - if chosen_reward[i] > reject_reward[i]: - on += 1 - dist += (chosen_reward - reject_reward).mean().item() - dist_mean = dist / len(dataloader) - acc = on / cnt - self.model.train() - return dist_mean, acc - - - def fit(self): - time = datetime.now() - epoch_bar = tqdm(range(self.epochs), desc='Train epoch', disable=not is_rank_0()) - for epoch in range(self.epochs): - step_bar = tqdm(range(self.train_dataloader.__len__()), - desc='Train step of epoch %d' % epoch, - disable=not is_rank_0()) - # train - self.model.train() - cnt = 0 - acc = 0 - dist = 0 - for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader: - chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) - c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) - reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) - r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) - chosen_reward = self.model(chosen_ids, attention_mask=c_mask) - reject_reward = self.model(reject_ids, attention_mask=r_mask) - loss = self.loss_fn(chosen_reward, reject_reward) - self.strategy.backward(loss, self.model, self.optimizer) - self.strategy.optimizer_step(self.optimizer) - self.optimizer.zero_grad() - cnt += 1 - if cnt == 100: - self.scheduler.step() - dist, acc = self.eval_acc(self.valid_dataloader) - cnt = 0 - if is_rank_0(): - log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], columns=['step', 'loss', 'dist', 'acc']) - log.to_csv('log_%s.csv' % time, mode='a', header=False, index=False) - step_bar.update() - step_bar.set_postfix({'dist': dist, 'acc': acc}) - - # eval - dist, acc = self.eval_acc(self.eval_dataloader) - if is_rank_0(): - log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], columns=['step', 'loss', 'dist', 'acc']) - log.to_csv('log.csv', mode='a', header=False, index=False) - epoch_bar.update() - step_bar.set_postfix({'dist': dist, 'acc': acc}) - step_bar.close() diff --git a/applications/ChatGPT/chatgpt/trainer/sft.py b/applications/ChatGPT/chatgpt/trainer/sft.py deleted file mode 100644 index d524ded3e825..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/sft.py +++ /dev/null @@ -1,106 +0,0 @@ -from abc import ABC -from typing import Optional -import loralib as lora -import torch -from chatgpt.models.loss import GPTLMLoss -from torch.optim import Adam, Optimizer -from torch.utils.data import DataLoader -from torch.utils.data.distributed import DistributedSampler -from tqdm import tqdm -import torch.distributed as dist -from .strategies import Strategy -from .utils import is_rank_0 -from colossalai.logging import get_dist_logger - - -class SFTTrainer(ABC): - """ - Trainer to use while training reward model. - - Args: - model (torch.nn.Module): the model to train - strategy (Strategy): the strategy to use for training - optim(Optimizer): the optimizer to use for training - train_dataloader: the dataloader to use for training - eval_dataloader: the dataloader to use for evaluation - batch_size (int, defaults to 1): the batch size while training - max_epochs (int, defaults to 2): the number of epochs to train - optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer - """ - - def __init__( - self, - model, - strategy: Strategy, - optim: Optimizer, - train_dataloader: DataLoader, - eval_dataloader: DataLoader = None, - sampler: Optional[DistributedSampler] = None, - batch_size: int = 1, - max_epochs: int = 2, - ) -> None: - super().__init__() - self.strategy = strategy - self.epochs = max_epochs - self.sampler = sampler - - self.train_dataloader = train_dataloader - self.eval_dataloader = eval_dataloader - - self.model = strategy.setup_model(model) - if "DDP" in str(self.strategy): - self.model = self.model.module - self.loss_fn = GPTLMLoss() - self.optimizer = strategy.setup_optimizer(optim, self.model) - - def fit(self, logger, use_lora, log_interval=10): - epoch_bar = tqdm(range(self.epochs), desc='Train epoch', disable=not is_rank_0()) - for epoch in range(self.epochs): - if isinstance(self.sampler, DistributedSampler): - self.sampler.set_epoch(epoch) - # train - self.model.train() - for batch_id, batch in enumerate(self.train_dataloader): - prompt_ids = batch["input_ids"].to(torch.cuda.current_device()) - p_mask = batch["attention_mask"].to(torch.cuda.current_device()) - labels = batch["labels"].to(torch.cuda.current_device()) - # prompt_ids = prompt_ids.squeeze(1).cuda() - # p_mask = p_mask.squeeze(1).cuda() - # prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels) - outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels) - loss = outputs.loss - prompt_logits = outputs.logits - - # loss = self.loss_fn(prompt_logits, labels) - self.strategy.backward(loss, self.model, self.optimizer) - self.strategy.optimizer_step(self.optimizer) - self.optimizer.zero_grad() - if batch_id % log_interval == 0: - logger.info(f'Train Epoch {epoch}/{self.epochs} Batch {batch_id} Rank {dist.get_rank()} loss {loss.item()}') - - # eval - if self.eval_dataloader is not None: - self.model.eval() - with torch.no_grad(): - loss_sum = 0 - num_seen = 0 - for batch in self.eval_dataloader: - prompt_ids = batch["input_ids"].to(torch.cuda.current_device()) - p_mask = batch["attention_mask"].to(torch.cuda.current_device()) - labels = batch["labels"].to(torch.cuda.current_device()) - # prompt_ids = prompt_ids.squeeze(1).cuda() - # p_mask = p_mask.squeeze(1).cuda() - - outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels) - loss = outputs.loss - # prompt_logits = outputs.logits - - loss_sum += loss.item() - num_seen += prompt_ids.size(0) - - loss_mean = loss_sum / num_seen - if dist.get_rank() == 0: - logger.info(f'Eval Epoch {epoch}/{self.epochs} loss {loss_mean}') - - epoch_bar.update() - diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/__init__.py b/applications/ChatGPT/chatgpt/trainer/strategies/__init__.py deleted file mode 100644 index f258c9b8a873..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/strategies/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .base import Strategy -from .colossalai import ColossalAIStrategy -from .ddp import DDPStrategy -from .naive import NaiveStrategy - -__all__ = ['Strategy', 'NaiveStrategy', 'DDPStrategy', 'ColossalAIStrategy'] diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/base.py b/applications/ChatGPT/chatgpt/trainer/strategies/base.py deleted file mode 100644 index 4347c08b4333..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/strategies/base.py +++ /dev/null @@ -1,131 +0,0 @@ -from abc import ABC, abstractmethod -from contextlib import nullcontext -from typing import Any, List, Tuple, Union - -import numpy as np -import torch -import torch.nn as nn -from chatgpt.models.base import Actor, Critic, RewardModel -from chatgpt.replay_buffer import ReplayBuffer -from torch.optim import Optimizer -from torch.utils.data import DataLoader - -from .sampler import DistributedSampler - -ModelOptimPair = Tuple[nn.Module, Optimizer] -ModelOrModelOptimPair = Union[nn.Module, ModelOptimPair] - - -class Strategy(ABC): - """ - Base class for training strategies. - """ - - def __init__(self) -> None: - super().__init__() - self.setup_distributed() - - @abstractmethod - def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: Optimizer, **kwargs) -> None: - pass - - @abstractmethod - def optimizer_step(self, optimizer: Optimizer, **kwargs) -> None: - pass - - @abstractmethod - def setup_distributed(self) -> None: - pass - - @abstractmethod - def setup_model(self, model: nn.Module) -> nn.Module: - pass - - @abstractmethod - def setup_optimizer(self, optimizer: Optimizer, model: nn.Module) -> Optimizer: - pass - - @abstractmethod - def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: - pass - - def model_init_context(self): - return nullcontext() - - def prepare( - self, *models_or_model_optim_pairs: ModelOrModelOptimPair - ) -> Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]: - """Prepare models or model-optimizer-pairs based on each strategy. - - Example:: - >>> # when fine-tuning actor and critic - >>> (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare((actor, actor_optim), (critic, critic_optim), reward_model, initial_model) - >>> # or when training reward model - >>> (reward_model, reward_model_optim) = strategy.prepare((reward_model, reward_model_optim)) - >>> # or just inference - >>> actor, critic = strategy.prepare(actor, critic) - - Returns: - Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]: Models or model-optimizer-pairs in the original order. - """ - - def prepare_model(model: nn.Module): - if isinstance(model, Actor): - return Actor(self.setup_model(self._unwrap_model(model))) - return self.setup_model(self._unwrap_model(model)) - - rets = [] - for arg in models_or_model_optim_pairs: - if isinstance(arg, tuple): - assert len(arg) == 2, f'Expect (model, optimizer) pair, got a tuple with size "{len(arg)}"' - model, optimizer = arg - model = prepare_model(model) - optimizer = self.setup_optimizer(optimizer, self._unwrap_model(model)) - rets.append((model, optimizer)) - elif isinstance(arg, nn.Module): - rets.append(prepare_model(arg)) - else: - raise RuntimeError(f'Expect model or (model, optimizer) pair, got {type(arg)}') - - if len(rets) == 1: - return rets[0] - return rets - - @staticmethod - def _unwrap_model(model: nn.Module) -> nn.Module: - """Useful for saving state dict. As actor is wrapped by Actor class again in `prepare()`, we should unwrap it before saving. - - Args: - model (nn.Module): an actor or a critic - """ - if isinstance(model, Actor): - return model.model - return model - - @staticmethod - def _unwrap_actor(actor: Actor) -> nn.Module: - """Get `actor.model` from a wrapped (by `prepare()`) actor. Useful for getting original huggingface model. - - Args: - actor (Actor): a wrapped actor - """ - return Strategy._unwrap_model(actor) - - @abstractmethod - def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None: - pass - - @abstractmethod - def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None: - pass - - @abstractmethod - def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: - pass - - @abstractmethod - def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None: - pass - - def setup_sampler(self, dataset) -> DistributedSampler: - return DistributedSampler(dataset, 1, 0) diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py deleted file mode 100644 index 0a7c9173283c..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py +++ /dev/null @@ -1,190 +0,0 @@ -import warnings -from typing import Optional, Union - -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.optim as optim -from chatgpt.models.base import Actor -from chatgpt.models.lora import LoraLinear -from torch.optim import Optimizer - -from transformers.modeling_utils import PreTrainedModel -from transformers.tokenization_utils_base import PreTrainedTokenizerBase - -import colossalai -from colossalai.nn.optimizer import CPUAdam, HybridAdam -from colossalai.nn.parallel import ZeroDDP, zero_model_wrapper, zero_optim_wrapper -from colossalai.nn.parallel.utils import get_static_torch_model -from colossalai.tensor import ProcessGroup, ShardSpec -from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext - -from .base import Strategy -from .ddp import DDPStrategy - - -class ColossalAIStrategy(DDPStrategy): - """ - The strategy for training with ColossalAI. - - Args: - stage(int): The stage to use in ZeRO. Choose in (1, 2, 3) - precision(str): The precision to use. Choose in ('fp32', 'fp16'). Stage 3 only supports fp16. - seed(int): The seed for the random number generator. - shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3. - This is not compativle with `from_pretrained()`. We temporarily disable this and will support it in the future. - placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda') - If it is “cpu”, parameters, gradients and optimizer states will be offloaded to CPU, - If it is “cuda”, they will not be offloaded, which means max CUDA memory will be used. It is the fastest. - pin_memory(bool): Whether to pin the memory for the data loader. Only for ZeRO-3. - force_outputs_fp32(bool): Whether to force the outputs to be fp32. Only for ZeRO-3. - search_range_mb(int): The search range in MB for the chunk size. Only for ZeRO-3. - hidden_dim(optional, int): The hidden dimension for the gemini. Only for ZeRO-3. - min_chunk_size_mb(float): The minimum chunk size in MB. Only for ZeRO-3. - gpu_margin_mem_ratio(float): The margin memory ratio for the GPU. Only for ZeRO-3. - reduce_bugket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2. - overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2. - initial_scale(float): The initial scale for the optimizer. - growth_factor(float): The growth factor for the optimizer. - backoff_factor(float): The backoff factor for the optimizer. - growth_interval(int): The growth interval for the optimizer. - hysteresis(int): The hysteresis for the optimizer. - min_scale(float): The minimum scale for the optimizer. - max_scale(float): The maximum scale for the optimizer. - max_norm(float): The maximum norm for the optimizer. - norm_type(float): The norm type for the optimizer. - - """ - - def __init__( - self, - stage: int = 3, - precision: str = 'fp16', - seed: int = 42, - shard_init: bool = False, # only for stage 3 - placement_policy: str = 'cuda', - pin_memory: bool = True, # only for stage 3 - force_outputs_fp32: bool = False, # only for stage 3 - search_range_mb: int = 32, # only for stage 3 - hidden_dim: Optional[int] = None, # only for stage 3 - min_chunk_size_mb: float = 32, # only for stage 3 - gpu_margin_mem_ratio: float = 0.0, # only for stage 3 - reduce_bucket_size: int = 12 * 1024**2, # only for stage 1&2 - overlap_communication: bool = True, # only for stage 1&2 - initial_scale: float = 2**16, - growth_factor: float = 2, - backoff_factor: float = 0.5, - growth_interval: int = 1000, - hysteresis: int = 2, - min_scale: float = 1, - max_scale: float = 2**32, - max_norm: float = 0.0, - norm_type: float = 2.0) -> None: - super().__init__(seed) - assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy "{placement_policy}"' - assert precision in ('fp32', 'fp16'), f'Unsupported precision "{precision}"' - self.stage = stage - # TODO(ver217): support shard_init when using from_pretrained() - if shard_init: - warnings.warn( - f'Shard init is not supported model.from_pretrained() yet. Please load weights after strategy.prepare()' - ) - if stage == 3 and precision == 'fp32': - warnings.warn(f'Stage 3 only supports fp16. Precision is set to fp16.') - precision = 'fp16' - self.precision = precision - self.shard_init = shard_init - self.gemini_config = dict(device=get_current_device(), - placement_policy=placement_policy, - pin_memory=pin_memory, - force_outputs_fp32=force_outputs_fp32, - strict_ddp_mode=shard_init, - search_range_mb=search_range_mb, - hidden_dim=hidden_dim, - min_chunk_size_mb=min_chunk_size_mb) - if stage == 3: - self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio) - else: - self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size, - overlap_communication=overlap_communication, - cpu_offload=(placement_policy == 'cpu')) - self.optim_kwargs = dict(initial_scale=initial_scale, - growth_factor=growth_factor, - backoff_factor=backoff_factor, - growth_interval=growth_interval, - hysteresis=hysteresis, - min_scale=min_scale, - max_scale=max_scale, - max_norm=max_norm, - norm_type=norm_type) - - def setup_distributed(self) -> None: - colossalai.launch_from_torch({}, seed=self.seed) - - def model_init_context(self): - if self.stage == 3: - world_size = dist.get_world_size() - shard_pg = ProcessGroup(tp_degree=world_size) if self.shard_init else None - default_dist_spec = ShardSpec([-1], [world_size]) if self.shard_init else None - return ColoInitContext(device=get_current_device(), - dtype=torch.half, - default_pg=shard_pg, - default_dist_spec=default_dist_spec) - return super().model_init_context() - - def setup_model(self, model: nn.Module) -> nn.Module: - model = zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config) - if self.stage != 3 and self.precision == 'fp16': - model = model.half() - return model - - def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer: - assert isinstance(optimizer, (CPUAdam, HybridAdam)), f'Unsupported optimizer {type(optimizer)}' - return zero_optim_wrapper(model, optimizer, optim_config=self.zero_optim_config, **self.optim_kwargs) - - def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None: - optimizer.backward(loss) - - def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: - optimizer.step() - - @staticmethod - def _unwrap_actor(actor: Actor) -> nn.Module: - model: Union[nn.Module, ZeroDDP] = Strategy._unwrap_actor(actor) - if isinstance(model, ZeroDDP): - return model.module - return model - - def save_model(self, model: nn.Module, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - unwrapped_model = self._unwrap_model(model) - # TODO : better way to get torch model from gemini model - # to get torch model from gemini model - if isinstance(unwrapped_model, ZeroDDP): - state_dict = unwrapped_model.state_dict() - unwrapped_model = get_static_torch_model(unwrapped_model) - if only_rank0 and dist.get_rank() != 0: - return - unwrapped_model.load_state_dict(state_dict) - # merge lora_weights into weights - for module in unwrapped_model.modules(): - if isinstance(module, LoraLinear): - module.merge_weights = True - module.eval() - # get state_dict and save - - if not isinstance(self.model, PreTrainedModel): - state_dict = unwrapped_model.state_dict() - if only_rank0 and dist.get_rank() != 0: - return - torch.save(state_dict, path) - else: - self.model.save_pretrained(path) - if tokenizer is not None: - tokenizer.save_pretrained(path) - - def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: - if only_rank0: - raise RuntimeError( - f'Optimizer states are sharded when using ColossalAIStrategy. Only rank0 is not supported.') - torch.save(optimizer.state_dict(), path) diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/ddp.py b/applications/ChatGPT/chatgpt/trainer/strategies/ddp.py deleted file mode 100644 index c9f92c12fe0a..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/strategies/ddp.py +++ /dev/null @@ -1,93 +0,0 @@ -import os -import random - -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -from chatgpt.models.base import Actor -from chatgpt.models.lora import LoraLinear -from chatgpt.replay_buffer import ReplayBuffer -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.optim import Optimizer -from torch.utils.data import DataLoader - -from .base import Strategy -from .naive import NaiveStrategy -from .sampler import DistributedSampler - - -class DDPStrategy(NaiveStrategy): - """ - Strategy for distributed training using torch.distributed. - """ - - def __init__(self, seed: int = 42) -> None: - self.seed = seed - super().__init__() - - def setup_distributed(self) -> None: - try: - rank = int(os.environ['RANK']) - local_rank = int(os.environ['LOCAL_RANK']) - world_size = int(os.environ['WORLD_SIZE']) - host = os.environ['MASTER_ADDR'] - port = int(os.environ['MASTER_PORT']) - except KeyError as e: - raise RuntimeError( - f"Could not find {e} in the torch environment, visit https://www.colossalai.org/ for more information on launching with torch" - ) - dist.init_process_group('nccl', init_method=f'tcp://[{host}]:{port}', world_size=world_size, rank=rank) - self.set_seed(self.seed) - torch.cuda.set_device(local_rank) - - def set_seed(self, seed: int) -> None: - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - - def setup_model(self, model: nn.Module) -> nn.Module: - device = torch.cuda.current_device() - return DDP(model, device_ids=[device]) - - def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: - # DDP only mode, replay buffers on each rank are different. - # sampler = DistributedSampler(replay_buffer, - # num_replicas=dist.get_world_size(), - # rank=dist.get_rank(), - # shuffle=True, - # seed=self.seed, - # drop_last=True) - return DataLoader( - replay_buffer, - batch_size=replay_buffer.sample_batch_size, - # sampler=sampler, - shuffle=True, - drop_last=True, - pin_memory=pin_memory, - collate_fn=replay_buffer.collate_fn) - - @staticmethod - def _unwrap_actor(actor: Actor) -> nn.Module: - model: DDP = Strategy._unwrap_actor(actor) - return model.module - - def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None: - for module in model.modules(): - if isinstance(module, LoraLinear): - module.merge_weights=True - module.eval() - - if only_rank0 and dist.get_rank() != 0: - return - model = model.model.module - state_dict = model.state_dict() - torch.save(state_dict, path) - - def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: - if only_rank0 and dist.get_rank() != 0: - return - super().save_optimizer(optimizer, path, only_rank0) - - def setup_sampler(self, dataset) -> DistributedSampler: - return DistributedSampler(dataset, dist.get_world_size(), dist.get_rank()) diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/naive.py b/applications/ChatGPT/chatgpt/trainer/strategies/naive.py deleted file mode 100644 index 99b8d6635394..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/strategies/naive.py +++ /dev/null @@ -1,55 +0,0 @@ -from typing import Any - -import torch -import torch.nn as nn -import torch.optim as optim -from chatgpt.replay_buffer import ReplayBuffer -from torch.optim import Optimizer -from torch.utils.data import DataLoader - -from .base import Strategy - - -class NaiveStrategy(Strategy): - """ - Strategy for single GPU. No parallelism is used. - """ - - def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None: - loss.backward() - - def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: - optimizer.step() - - def setup_distributed(self) -> None: - pass - - def setup_model(self, model: nn.Module) -> nn.Module: - return model - - def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer: - return optimizer - - def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: - return DataLoader(replay_buffer, - batch_size=replay_buffer.sample_batch_size, - shuffle=True, - drop_last=True, - pin_memory=pin_memory, - collate_fn=replay_buffer.collate_fn) - - def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None: - unwrapped_model = self._unwrap_model(model) - torch.save(unwrapped_model.state_dict(), path) - - def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None: - unwrapped_model = self._unwrap_model(model) - state_dict = torch.load(path, map_location=map_location) - unwrapped_model.load_state_dict(state_dict, strict=strict) - - def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: - torch.save(optimizer.state_dict(), path) - - def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None: - state_dict = torch.load(path, map_location=map_location) - optimizer.load_state_dict(state_dict) diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/sampler.py b/applications/ChatGPT/chatgpt/trainer/strategies/sampler.py deleted file mode 100644 index d726fa640fa2..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/strategies/sampler.py +++ /dev/null @@ -1,32 +0,0 @@ -import math - -import numpy as np - - -class DistributedSampler: - - def __init__(self, dataset, num_replicas: int, rank: int) -> None: - self.dataset = dataset - self.num_replicas = num_replicas - self.rank = rank - - if len(self.dataset) % self.num_replicas != 0: - self.num_samples = math.ceil( - (len(self.dataset) - self.num_replicas) / self.num_replicas # type: ignore[arg-type] - ) - else: - self.num_samples = math.ceil(len(self.dataset) / self.num_replicas) - - self.total_size = self.num_samples * self.num_replicas - - indices = list(range(len(self.dataset))) - indices = indices[:self.total_size] - assert len(indices) == self.total_size - # subsample - indices = indices[self.rank:self.total_size:self.num_replicas] - assert len(indices) == self.num_samples - self.indices = indices - - def sample(self, batch_size: int) -> list: - sampled_indices = np.random.choice(self.indices, batch_size, replace=False) - return [self.dataset[idx] for idx in sampled_indices] diff --git a/applications/ChatGPT/chatgpt/trainer/utils.py b/applications/ChatGPT/chatgpt/trainer/utils.py deleted file mode 100644 index 6c9f7f085f8c..000000000000 --- a/applications/ChatGPT/chatgpt/trainer/utils.py +++ /dev/null @@ -1,5 +0,0 @@ -import torch.distributed as dist - - -def is_rank_0() -> bool: - return not dist.is_initialized() or dist.get_rank() == 0 diff --git a/applications/ChatGPT/chatgpt/utils/__init__.py b/applications/ChatGPT/chatgpt/utils/__init__.py deleted file mode 100644 index 8f526d7efdad..000000000000 --- a/applications/ChatGPT/chatgpt/utils/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .tokenizer_utils import smart_tokenizer_and_embedding_resize, prepare_llama_tokenizer_and_embedding - -__all__ = ['smart_tokenizer_and_embedding_resize', 'prepare_llama_tokenizer_and_embedding'] \ No newline at end of file diff --git a/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py b/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py deleted file mode 100644 index 9cfae61ebeda..000000000000 --- a/applications/ChatGPT/chatgpt/utils/tokenizer_utils.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Dict - -import transformers - -from ..models.llama.llama_lm import LlamaLM - -DEFAULT_PAD_TOKEN = "[PAD]" -DEFAULT_EOS_TOKEN = "    " -DEFAULT_BOS_TOKEN = "" -DEFAULT_UNK_TOKEN = "    " - -def prepare_llama_tokenizer_and_embedding( - tokenizer: transformers.PreTrainedTokenizer, - model: transformers.PreTrainedModel, - special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN), -): - """prepare llama tokenizer and embedding. - - """ - - if tokenizer.pad_token is None: - smart_tokenizer_and_embedding_resize( - special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN), - tokenizer=tokenizer, - model=model, - ) - - tokenizer.add_special_tokens( - { - "eos_token": DEFAULT_EOS_TOKEN, - "bos_token": DEFAULT_BOS_TOKEN, - "unk_token": DEFAULT_UNK_TOKEN, - } - ) - - return tokenizer - - -def smart_tokenizer_and_embedding_resize( - tokenizer: transformers.PreTrainedTokenizer, - model: transformers.PreTrainedModel, - special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN), -): - """Resize tokenizer and embedding. - - Note: This is the unoptimized version that may make your embedding size not be divisible by 64. - """ - - if tokenizer.pad_token is None: - num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) - - if isinstance(model, LlamaLM): - model = model.get_base_model() - - model.resize_token_embeddings(len(tokenizer)) - - if num_new_tokens > 0: - input_embeddings = model.get_input_embeddings().weight.data - output_embeddings = model.get_output_embeddings().weight.data - - input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) - output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) - - input_embeddings[-num_new_tokens:] = input_embeddings_avg - output_embeddings[-num_new_tokens:] = output_embeddings_avg - \ No newline at end of file diff --git a/applications/ChatGPT/examples/README.md b/applications/ChatGPT/examples/README.md deleted file mode 100644 index 203e4b4950bd..000000000000 --- a/applications/ChatGPT/examples/README.md +++ /dev/null @@ -1,141 +0,0 @@ -# Examples - -## Install requirements - -```shell -pip install -r requirements.txt -``` - -## Train the reward model (Stage 2) -Use these code to train your reward model. -```shell -# Take naive reward model training with opt-350m as example -python train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy naive -# use colossalai_zero2 -torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy colossalai_zero2 -``` - -### Features and tricks in RM training -- We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) and [rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets. -- We support 2 kinds of loss_function named 'log_sig'(used by OpenAI) and 'log_exp'(used by Anthropic). -- We change the loss to valid_acc and pair_dist to monitor progress during training. -- We add special token to the end of the sequence to get better result. -- We use cosine-reducing lr-scheduler for RM training. -- We set value_head as 1 liner layer and initialize the weight of value_head using N(0，1/(d_model + 1)) distribution. -- We train a Bloom-560m reward model for 1 epoch and find the test acc of the model achieve the performance mentions in [Anthropics paper](https://arxiv.org/abs/2204.05862). - -### Experiment result -Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862): - -
    image - -
    Our training & test result of bloom-560m for 1 epoch: - -
    image - -
    - -## Train with dummy prompt data (Stage 3) - -This script supports 4 kinds of strategies: - -- naive -- ddp -- colossalai_zero2 -- colossalai_gemini - -It uses random generated prompt data. - -Naive strategy only support single GPU training: - -```shell -python train_dummy.py --strategy naive -# display cli help -python train_dummy.py -h -``` - -DDP strategy and ColossalAI strategy support multi GPUs training: - -```shell -# run DDP on 2 GPUs -torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy ddp -# run ColossalAI on 2 GPUs -torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy colossalai_zero2 -``` - -## Train with real prompt data (Stage 3) - -We use [awesome-chatgpt-prompts](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts) as example dataset. It is a small dataset with hundreds of prompts. - -You should download `prompts.csv` first. - -This script also supports 4 strategies. - -```shell -# display cli help -python train_dummy.py -h -# run naive on 1 GPU -python train_prompts.py prompts.csv --strategy naive -# run DDP on 2 GPUs -torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy ddp -# run ColossalAI on 2 GPUs -torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai_zero2 -``` - -## Inference example(After Stage3) -We support naive inference demo after training. -```shell -# inference, using pretrain path to configure model -python inference.py --model_path --model --pretrain -# example -python inference.py --model_path ./actor_checkpoint_prompts.pt --pretrain bigscience/bloom-560m --model bloom -``` - -## Attention -The examples is just a demo for testing our progress of RM and PPO training. - - -#### data -- [x] [rm-static](https://huggingface.co/datasets/Dahoas/rm-static) -- [x] [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) -- [ ] [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) -- [ ] [openai/webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons) -- [ ] [Dahoas/instruct-synthetic-prompt-responses](https://huggingface.co/datasets/Dahoas/instruct-synthetic-prompt-responses) - -## Support Model - -### GPT -- [x] GPT2-S (s) -- [x] GPT2-M (m) -- [x] GPT2-L (l) -- [ ] GPT2-XL (xl) -- [x] GPT2-4B (4b) -- [ ] GPT2-6B (6b) -- [ ] GPT2-8B (8b) -- [ ] GPT2-10B (10b) -- [ ] GPT2-12B (12b) -- [ ] GPT2-15B (15b) -- [ ] GPT2-18B (18b) -- [ ] GPT2-20B (20b) -- [ ] GPT2-24B (24b) -- [ ] GPT2-28B (28b) -- [ ] GPT2-32B (32b) -- [ ] GPT2-36B (36b) -- [ ] GPT2-40B (40b) -- [ ] GPT3 (175b) - -### BLOOM -- [x] [BLOOM-560m](https://huggingface.co/bigscience/bloom-560m) -- [x] [BLOOM-1b1](https://huggingface.co/bigscience/bloom-1b1) -- [x] [BLOOM-3b](https://huggingface.co/bigscience/bloom-3b) -- [x] [BLOOM-7b](https://huggingface.co/bigscience/bloom-7b1) -- [ ] BLOOM-175b - -### OPT -- [x] [OPT-125M](https://huggingface.co/facebook/opt-125m) -- [x] [OPT-350M](https://huggingface.co/facebook/opt-350m) -- [ ] [OPT-1.3B](https://huggingface.co/facebook/opt-1.3b) -- [ ] [OPT-2.7B](https://huggingface.co/facebook/opt-2.7b) -- [ ] [OPT-6.7B](https://huggingface.co/facebook/opt-6.7b) -- [ ] [OPT-13B](https://huggingface.co/facebook/opt-13b) -- [ ] [OPT-30B](https://huggingface.co/facebook/opt-30b) diff --git a/applications/ChatGPT/examples/inference.py b/applications/ChatGPT/examples/inference.py deleted file mode 100644 index 08885c33b194..000000000000 --- a/applications/ChatGPT/examples/inference.py +++ /dev/null @@ -1,59 +0,0 @@ -import argparse - -import torch -from chatgpt.models.bloom import BLOOMActor -from chatgpt.models.gpt import GPTActor -from chatgpt.models.opt import OPTActor -from transformers import AutoTokenizer -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - - -def eval(args): - # configure model - if args.model == 'gpt2': - actor = GPTActor(pretrained=args.pretrain).to(torch.cuda.current_device()) - elif args.model == 'bloom': - actor = BLOOMActor(pretrained=args.pretrain).to(torch.cuda.current_device()) - elif args.model == 'opt': - actor = OPTActor(pretrained=args.pretrain).to(torch.cuda.current_device()) - else: - raise ValueError(f'Unsupported model "{args.model}"') - - state_dict = torch.load(args.model_path) - actor.model.load_state_dict(state_dict) - - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m') - else: - raise ValueError(f'Unsupported model "{args.model}"') - - actor.eval() - input = args.input - input_ids = tokenizer.encode(input, return_tensors='pt').to(torch.cuda.current_device()) - outputs = actor.generate(input_ids, - max_length=args.max_length, - do_sample=True, - top_k=50, - top_p=0.95, - num_return_sequences=1) - output = tokenizer.batch_decode(outputs[0], skip_special_tokens=True) - print(output) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) - # We suggest to use the pretrained model from HuggingFace, use pretrain to configure model - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--model_path', type=str, default=None) - parser.add_argument('--input', type=str, default='Question: How are you ? Answer:') - parser.add_argument('--max_length', type=int, default=100) - args = parser.parse_args() - eval(args) diff --git a/applications/ChatGPT/examples/requirements.txt b/applications/ChatGPT/examples/requirements.txt deleted file mode 100644 index 40e6edc7ea73..000000000000 --- a/applications/ChatGPT/examples/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -pandas>=1.4.1 -sentencepiece diff --git a/applications/ChatGPT/examples/test_ci.sh b/applications/ChatGPT/examples/test_ci.sh deleted file mode 100755 index 1d05c4c58341..000000000000 --- a/applications/ChatGPT/examples/test_ci.sh +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env bash - -set -xue - -if [ -z "$PROMPT_PATH" ]; then - echo "Please set \$PROMPT_PATH to the path to prompts csv." - exit 1 -fi - -BASE=$(realpath $(dirname $0)) - -export OMP_NUM_THREADS=8 - -# install requirements -pip install -r ${BASE}/requirements.txt - -# train dummy -python ${BASE}/train_dummy.py --strategy naive --num_episodes 1 \ - --max_timesteps 2 --update_timesteps 2 \ - --max_epochs 1 --train_batch_size 2 --lora_rank 4 - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ - --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'facebook/opt-350m' --model opt --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_dummy.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'facebook/opt-350m' --model opt - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ - --strategy ddp --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'facebook/opt-350m' --model opt --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_dummy.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'facebook/opt-350m' --model opt - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'gpt2' --model gpt2 --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_dummy.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'gpt2' --model gpt2 - -rm -rf ${BASE}/actor_checkpoint_dummy.pt - -# train prompts -python ${BASE}/train_prompts.py $PROMPT_PATH --strategy naive --num_episodes 1 \ - --max_timesteps 2 --update_timesteps 2 \ - --max_epochs 1 --train_batch_size 2 --lora_rank 4 - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'facebook/opt-350m' --model opt --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_prompts.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'facebook/opt-350m' --model opt - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ - --strategy ddp --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'gpt2' --model gpt2 --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_prompts.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'gpt2' --model gpt2 - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ - --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'gpt2' --model gpt2 --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_prompts.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'gpt2' --model gpt2 - -rm -rf ${BASE}/actor_checkpoint_prompts.pt - -# train rm -torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'facebook/opt-350m' --model 'opt' \ - --strategy colossalai_zero2 --loss_fn 'log_sig'\ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ - --test True --lora_rank 4 - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'gpt2' --model 'gpt2' \ - --strategy colossalai_gemini --loss_fn 'log_exp'\ - --dataset 'Dahoas/rm-static' --test True --lora_rank 4 - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'bigscience/bloom-560m' --model 'bloom' \ - --strategy colossalai_zero2 --loss_fn 'log_sig'\ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ - --test True --lora_rank 4 - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'microsoft/deberta-v3-large' --model 'deberta' \ - --strategy colossalai_zero2 --loss_fn 'log_sig'\ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ - --test True --lora_rank 4 - -rm -rf ${BASE}/rm_ckpt.pt diff --git a/applications/ChatGPT/examples/train_dummy.py b/applications/ChatGPT/examples/train_dummy.py deleted file mode 100644 index c0ebf8f9b7b6..000000000000 --- a/applications/ChatGPT/examples/train_dummy.py +++ /dev/null @@ -1,148 +0,0 @@ -import argparse -from copy import deepcopy - -import torch -from chatgpt.models.base import RewardModel -from chatgpt.models.bloom import BLOOMActor, BLOOMCritic -from chatgpt.models.gpt import GPTActor, GPTCritic -from chatgpt.models.opt import OPTActor, OPTCritic -from chatgpt.trainer import PPOTrainer -from chatgpt.trainer.callbacks import SaveCheckpoint -from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - - -def preprocess_batch(samples): - input_ids = torch.stack(samples) - attention_mask = torch.ones_like(input_ids, dtype=torch.long) - return {'input_ids': input_ids, 'attention_mask': attention_mask} - - -def main(args): - # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': - strategy = DDPStrategy() - elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) - elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - else: - raise ValueError(f'Unsupported strategy "{args.strategy}"') - - # configure model - with strategy.model_init_context(): - if args.model == 'gpt2': - actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = GPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'bloom': - actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = BLOOMCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'opt': - actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = OPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - else: - raise ValueError(f'Unsupported model "{args.model}"') - - initial_model = deepcopy(actor).to(torch.cuda.current_device()) - reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).to(torch.cuda.current_device()) - - # configure optimizer - if args.strategy.startswith('colossalai'): - actor_optim = HybridAdam(actor.parameters(), lr=5e-6) - critic_optim = HybridAdam(critic.parameters(), lr=5e-6) - else: - actor_optim = Adam(actor.parameters(), lr=5e-6) - critic_optim = Adam(critic.parameters(), lr=5e-6) - - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - else: - raise ValueError(f'Unsupported model "{args.model}"') - - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) - - callbacks = [] - if args.save_ckpt_path: - ckpt_callback = SaveCheckpoint( - args.save_ckpt_path, - args.save_ckpt_interval, - strategy, - actor, - critic, - actor_optim, - critic_optim, - ) - callbacks.append(ckpt_callback) - - # configure trainer - - trainer = PPOTrainer(strategy, - actor, - critic, - reward_model, - initial_model, - actor_optim, - critic_optim, - max_epochs=args.max_epochs, - train_batch_size=args.train_batch_size, - tokenizer=preprocess_batch, - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - callbacks=callbacks) - - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 64), device=torch.cuda.current_device()) - trainer.fit(random_prompts, - num_episodes=args.num_episodes, - max_timesteps=args.max_timesteps, - update_timesteps=args.update_timesteps) - - # save model checkpoint after fitting - strategy.save_model(actor, args.save_path, only_rank0=True) - # save optimizer checkpoint on all ranks - if args.need_optim_ckpt: - strategy.save_optimizer(actor_optim, - 'actor_optim_checkpoint_dummy_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--model', type=str, default='gpt2', choices=['gpt2', 'bloom', 'opt']) - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--save_path', type=str, default='actor_checkpoint_dummy.pt') - parser.add_argument('--need_optim_ckpt', type=bool, default=False) - parser.add_argument('--num_episodes', type=int, default=50) - parser.add_argument('--max_timesteps', type=int, default=10) - parser.add_argument('--update_timesteps', type=int, default=10) - parser.add_argument('--max_epochs', type=int, default=5) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") - parser.add_argument('--save_ckpt_path', - type=str, - default=None, - help="path to save checkpoint, None means not to save") - parser.add_argument('--save_ckpt_interval', type=int, default=1, help="the interval of episode to save checkpoint") - args = parser.parse_args() - main(args) diff --git a/applications/ChatGPT/examples/train_dummy.sh b/applications/ChatGPT/examples/train_dummy.sh deleted file mode 100755 index 595da573e2b1..000000000000 --- a/applications/ChatGPT/examples/train_dummy.sh +++ /dev/null @@ -1,18 +0,0 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES() { - local n=${1:-"9999"} - echo "GPU Memory Usage:" - local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ - | tail -n +2 \ - | nl -v 0 \ - | tee /dev/tty \ - | sort -g -k 2 \ - | awk '{print $1}' \ - | head -n $n) - export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') - echo "Now CUDA_VISIBLE_DEVICES is set to:" - echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" -} - -set_n_least_used_CUDA_VISIBLE_DEVICES 2 - -torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy colossalai_zero2 diff --git a/applications/ChatGPT/examples/train_prompts.py b/applications/ChatGPT/examples/train_prompts.py deleted file mode 100644 index 8f48a11c33e8..000000000000 --- a/applications/ChatGPT/examples/train_prompts.py +++ /dev/null @@ -1,132 +0,0 @@ -import argparse -from copy import deepcopy - -import pandas as pd -import torch -from chatgpt.models.base import RewardModel -from chatgpt.models.bloom import BLOOMActor, BLOOMCritic -from chatgpt.models.gpt import GPTActor, GPTCritic -from chatgpt.models.opt import OPTActor, OPTCritic -from chatgpt.trainer import PPOTrainer -from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - - -def main(args): - # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': - strategy = DDPStrategy() - elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) - elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - else: - raise ValueError(f'Unsupported strategy "{args.strategy}"') - - # configure model - with strategy.model_init_context(): - if args.model == 'gpt2': - actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = GPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'bloom': - actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = BLOOMCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'opt': - actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = OPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - else: - raise ValueError(f'Unsupported model "{args.model}"') - - initial_model = deepcopy(actor) - reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).to(torch.cuda.current_device()) - - # configure optimizer - if args.strategy.startswith('colossalai'): - actor_optim = HybridAdam(actor.parameters(), lr=5e-6) - critic_optim = HybridAdam(critic.parameters(), lr=5e-6) - else: - actor_optim = Adam(actor.parameters(), lr=5e-6) - critic_optim = Adam(critic.parameters(), lr=5e-6) - - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - else: - raise ValueError(f'Unsupported model "{args.model}"') - - dataset = pd.read_csv(args.prompt_path)['prompt'] - - def tokenize_fn(texts): - # MUST padding to max length to ensure inputs of all ranks have the same length - # Different length may lead to hang when using gemini, as different generation steps - batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) - return {k: v.cuda() for k, v in batch.items()} - - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) - - # configure trainer - trainer = PPOTrainer( - strategy, - actor, - critic, - reward_model, - initial_model, - actor_optim, - critic_optim, - max_epochs=args.max_epochs, - train_batch_size=args.train_batch_size, - experience_batch_size=args.experience_batch_size, - tokenizer=tokenize_fn, - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - ) - - trainer.fit(dataset, - num_episodes=args.num_episodes, - max_timesteps=args.max_timesteps, - update_timesteps=args.update_timesteps) - # save model checkpoint after fitting - strategy.save_model(actor, args.save_path, only_rank0=True) - # save optimizer checkpoint on all ranks - if args.need_optim_ckpt: - strategy.save_optimizer(actor_optim, - 'actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('prompt_path') - parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') - parser.add_argument('--need_optim_ckpt', type=bool, default=False) - parser.add_argument('--num_episodes', type=int, default=10) - parser.add_argument('--max_timesteps', type=int, default=10) - parser.add_argument('--update_timesteps', type=int, default=10) - parser.add_argument('--max_epochs', type=int, default=5) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") - args = parser.parse_args() - main(args) diff --git a/applications/ChatGPT/examples/train_prompts.sh b/applications/ChatGPT/examples/train_prompts.sh deleted file mode 100755 index db73ac8e8e85..000000000000 --- a/applications/ChatGPT/examples/train_prompts.sh +++ /dev/null @@ -1,18 +0,0 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES() { - local n=${1:-"9999"} - echo "GPU Memory Usage:" - local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ - | tail -n +2 \ - | nl -v 0 \ - | tee /dev/tty \ - | sort -g -k 2 \ - | awk '{print $1}' \ - | head -n $n) - export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') - echo "Now CUDA_VISIBLE_DEVICES is set to:" - echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" -} - -set_n_least_used_CUDA_VISIBLE_DEVICES 2 - -torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai_zero2 diff --git a/applications/ChatGPT/examples/train_reward_model.py b/applications/ChatGPT/examples/train_reward_model.py deleted file mode 100644 index a9c844b7b1f8..000000000000 --- a/applications/ChatGPT/examples/train_reward_model.py +++ /dev/null @@ -1,143 +0,0 @@ -import argparse - -import loralib as lora -import torch -from chatgpt.dataset import HhRlhfDataset, RmStaticDataset -from chatgpt.models import LogSigLoss, LogExpLoss -from chatgpt.models.base import RewardModel -from chatgpt.models.bloom import BLOOMRM -from chatgpt.models.gpt import GPTRM -from chatgpt.models.opt import OPTRM -from chatgpt.models.deberta import DebertaRM -from chatgpt.trainer import RewardModelTrainer -from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from datasets import load_dataset -from random import randint -from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - -def train(args): - # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': - strategy = DDPStrategy() - elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') - elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - else: - raise ValueError(f'Unsupported strategy "{args.strategy}"') - - # configure model - with strategy.model_init_context(): - if args.model == 'bloom': - model = BLOOMRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'opt': - model = OPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'gpt2': - model = GPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'deberta': - model = DebertaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - else: - raise ValueError(f'Unsupported model "{args.model}"') - - if args.model_path is not None: - state_dict = torch.load(args.model_path) - model.load_state_dict(state_dict) - - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m') - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - elif args.model == 'deberta': - tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v3-large') - else: - raise ValueError(f'Unsupported model "{args.model}"') - max_len = args.max_len - - # configure optimizer - if args.strategy.startswith('colossalai'): - optim = HybridAdam(model.parameters(), lr=1.5e-5) - else: - optim = Adam(model.parameters(), lr=1.5e-5) - - # configure loss function - if args.loss_fn == 'log_sig': - loss_fn = LogSigLoss() - elif args.loss_fn == 'log_exp': - loss_fn = LogExpLoss() - else: - raise ValueError(f'Unsupported loss function "{args.loss_fn}"') - - # prepare for data and dataset - if args.subset is not None: - data = load_dataset(args.dataset, data_dir=args.subset) - else: - data = load_dataset(args.dataset) - - if args.test: - train_data = data['train'].select(range(100)) - eval_data = data['test'].select(range(10)) - else: - train_data = data['train'] - eval_data = data['test'] - valid_data = data['test'].select((randint(0, len(eval_data) - 1) for _ in range(len(eval_data)//10))) - - if args.dataset == 'Dahoas/rm-static': - train_dataset = RmStaticDataset(train_data, tokenizer, max_len) - valid_dataset = RmStaticDataset(valid_data, tokenizer, max_len) - eval_dataset = RmStaticDataset(eval_data, tokenizer, max_len) - elif args.dataset == 'Anthropic/hh-rlhf': - train_dataset = HhRlhfDataset(train_data, tokenizer, max_len) - valid_dataset = HhRlhfDataset(valid_data, tokenizer, max_len) - eval_dataset = HhRlhfDataset(eval_data, tokenizer, max_len) - else: - raise ValueError(f'Unsupported dataset "{args.dataset}"') - - trainer = RewardModelTrainer(model=model, - strategy=strategy, - optim=optim, - loss_fn = loss_fn, - train_dataset=train_dataset, - valid_dataset=valid_dataset, - eval_dataset=eval_dataset, - batch_size=args.batch_size, - max_epochs=args.max_epochs) - - trainer.fit() - # save model checkpoint after fitting on only rank0 - strategy.save_model(trainer.model, args.save_path, only_rank0=True) - # save optimizer checkpoint on all ranks - if args.need_optim_ckpt: - strategy.save_optimizer(trainer.optimizer, 'rm_optim_checkpoint_%d.pt' % (torch.cuda.current_device()), only_rank0=False) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta'], default='bloom') - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--model_path', type=str, default=None) - parser.add_argument('--need_optim_ckpt', type=bool, default=False) - parser.add_argument('--dataset', type=str, - choices=['Anthropic/hh-rlhf', 'Dahoas/rm-static'], - default='Dahoas/rm-static') - parser.add_argument('--subset', type=str, default=None) - parser.add_argument('--save_path', type=str, default='rm_ckpt.pt') - parser.add_argument('--max_epochs', type=int, default=1) - parser.add_argument('--batch_size', type=int, default=1) - parser.add_argument('--max_len', type=int, default=512) - parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") - parser.add_argument('--loss_fn', type=str, default='log_sig', choices=['log_sig', 'log_exp']) - parser.add_argument('--test', type=bool, default=False) - args = parser.parse_args() - train(args) diff --git a/applications/ChatGPT/examples/train_rm.sh b/applications/ChatGPT/examples/train_rm.sh deleted file mode 100755 index 4f9f55b6b59a..000000000000 --- a/applications/ChatGPT/examples/train_rm.sh +++ /dev/null @@ -1,8 +0,0 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES 1 - -python train_reward_model.py --pretrain 'microsoft/deberta-v3-large' \ - --model 'deberta' \ - --strategy naive \ - --loss_fn 'log_exp'\ - --save_path 'rmstatic.pt' \ - --test True diff --git a/applications/ChatGPT/examples/train_sft.py b/applications/ChatGPT/examples/train_sft.py deleted file mode 100644 index ffbf89ccd9bc..000000000000 --- a/applications/ChatGPT/examples/train_sft.py +++ /dev/null @@ -1,143 +0,0 @@ -import argparse - -import loralib as lora -import torch -import torch.distributed as dist -from torch.utils.data.distributed import DistributedSampler -from chatgpt.dataset import SFTDataset, AlpacaDataset, AlpacaDataCollator -from chatgpt.models.base import RewardModel -from chatgpt.models.bloom import BLOOMLM -from chatgpt.models.gpt import GPTLM -from chatgpt.models.opt import OPTLM -from chatgpt.models.llama import LlamaLM -from chatgpt.trainer import SFTTrainer -from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from chatgpt.utils import prepare_llama_tokenizer_and_embedding -from datasets import load_dataset -from torch.optim import Adam -from torch.utils.data import DataLoader -from transformers import AutoTokenizer, BloomTokenizerFast -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam -from colossalai.logging import get_dist_logger - - -def train(args): - # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': - strategy = DDPStrategy() - elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') - elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - else: - raise ValueError(f'Unsupported strategy "{args.strategy}"') - - # configure model - with strategy.model_init_context(): - if args.model == 'bloom': - model = BLOOMLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() - elif args.model == 'opt': - model = OPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() - elif args.model == 'gpt2': - model = GPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() - elif args.model == 'llama': - model = LlamaLM(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda() - else: - raise ValueError(f'Unsupported model "{args.model}"') - - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - elif args.model == 'llama': - tokenizer = AutoTokenizer.from_pretrained( - args.pretrain, - padding_side="right", - use_fast=False, - ) - else: - raise ValueError(f'Unsupported model "{args.model}"') - - if args.model == 'llama': - tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model) - else: - tokenizer.pad_token = tokenizer.eos_token - - max_len = 512 - - # configure optimizer - if args.strategy.startswith('colossalai'): - optim = HybridAdam(model.parameters(), lr=5e-5) - else: - optim = Adam(model.parameters(), lr=5e-5) - - logger = get_dist_logger() - - # configure dataset - if args.dataset == 'yizhongw/self_instruct': - train_data = load_dataset(args.dataset, 'super_natural_instructions', split='train') - eval_data = load_dataset(args.dataset, 'super_natural_instructions', split='test') - - train_dataset = SFTDataset(train_data, tokenizer, max_len) - eval_dataset = SFTDataset(eval_data, tokenizer, max_len) - - elif 'alpaca' in args.dataset: - train_dataset = AlpacaDataset(tokenizer=tokenizer, data_path=args.dataset) - eval_dataset = None - data_collator = AlpacaDataCollator(tokenizer=tokenizer) - - if dist.is_initialized() and dist.get_world_size() > 1: - train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True) - if eval_dataset is not None: - eval_sampler = DistributedSampler(eval_dataset, shuffle=False, seed=42, drop_last=False) - else: - train_sampler = None - eval_sampler = None - - train_dataloader = DataLoader(train_dataset, shuffle=(train_sampler is None), sampler=train_sampler, batch_size=args.batch_size, collate_fn=data_collator) - if eval_dataset is not None: - eval_dataloader = DataLoader(eval_dataset, shuffle=(eval_sampler is None), sampler=eval_sampler, batch_size=args.batch_size, collate_fn=data_collator) - else: - eval_dataloader = None - - trainer = SFTTrainer(model=model, - strategy=strategy, - optim=optim, - train_dataloader=train_dataloader, - eval_dataloader=eval_dataloader, - batch_size=args.batch_size, - max_epochs=args.max_epochs) - - trainer.fit(logger=logger, use_lora=args.lora_rank, log_interval=args.log_interval) - - # save model checkpoint after fitting on only rank0 - strategy.save_model(model, 'sft_checkpoint.pt', only_rank0=True) - # save optimizer checkpoint on all ranks - strategy.save_optimizer(optim, 'sft_optim_checkpoint_%d.pt' % (torch.cuda.current_device()), only_rank0=False) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom') - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--dataset', type=str, default='yizhongw/self_instruct') - parser.add_argument('--save_path', type=str, default='sft_ckpt.pth') - parser.add_argument('--max_epochs', type=int, default=1) - parser.add_argument('--batch_size', type=int, default=4) - parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") - parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log") - args = parser.parse_args() - train(args) - diff --git a/applications/ChatGPT/examples/train_sft.sh b/applications/ChatGPT/examples/train_sft.sh deleted file mode 100755 index 1b85e83b6880..000000000000 --- a/applications/ChatGPT/examples/train_sft.sh +++ /dev/null @@ -1,26 +0,0 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES() { - local n=${1:-"9999"} - echo "GPU Memory Usage:" - local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ - | tail -n +2 \ - | nl -v 0 \ - | tee /dev/tty \ - | sort -g -k 2 \ - | awk '{print $1}' \ - | head -n $n) - export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') - echo "Now CUDA_VISIBLE_DEVICES is set to:" - echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" -} - -set_n_least_used_CUDA_VISIBLE_DEVICES 8 - -#torchrun --standalone --nproc_per_node=2 train_sft.py --pretrain 'bigscience/bloomz-560m' --model 'bloom' --strategy colossalai_zero2 --log_interval 10 -#torchrun --standalone --nproc_per_node=8 train_sft.py --model 'gpt2' --strategy colossalai_zero2 --batch_size 1 --log_interval 10 -torchrun --standalone --nproc_per_node=8 train_sft.py \ - --pretrain "/data/personal/nus-mql/LLAMA-7B" \ - --model 'llama' \ - --strategy colossalai_zero2 \ - --log_interval 10 \ - --save_path /data/personal/nus-mql/Coati-7B \ - --dataset /data/personal/nus-mql/stanford_alpaca/alpaca_data.json diff --git a/applications/ChatGPT/pytest.ini b/applications/ChatGPT/pytest.ini deleted file mode 100644 index 01e5cd217c5d..000000000000 --- a/applications/ChatGPT/pytest.ini +++ /dev/null @@ -1,6 +0,0 @@ -[pytest] -markers = - cpu: tests which can run on CPU - gpu: tests which requires a single GPU - dist: tests which are run in a multi-GPU or multi-machine environment - experiment: tests for experimental features diff --git a/applications/ChatGPT/requirements-test.txt b/applications/ChatGPT/requirements-test.txt deleted file mode 100644 index e079f8a6038d..000000000000 --- a/applications/ChatGPT/requirements-test.txt +++ /dev/null @@ -1 +0,0 @@ -pytest diff --git a/applications/ChatGPT/requirements.txt b/applications/ChatGPT/requirements.txt deleted file mode 100644 index 3469111925ff..000000000000 --- a/applications/ChatGPT/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -transformers>=4.20.1 -tqdm -datasets -loralib -colossalai>=0.2.4 -torch==1.12.1 -langchain diff --git a/applications/ChatGPT/setup.py b/applications/ChatGPT/setup.py deleted file mode 100644 index deec10e0c841..000000000000 --- a/applications/ChatGPT/setup.py +++ /dev/null @@ -1,41 +0,0 @@ -from setuptools import find_packages, setup - - -def fetch_requirements(path): - with open(path, 'r') as fd: - return [r.strip() for r in fd.readlines()] - - -def fetch_readme(): - with open('README.md', encoding='utf-8') as f: - return f.read() - - -def fetch_version(): - with open('version.txt', 'r') as f: - return f.read().strip() - - -setup( - name='chatgpt', - version=fetch_version(), - packages=find_packages(exclude=( - 'tests', - 'benchmarks', - '*.egg-info', - )), - description='A RLFH implementation (ChatGPT) powered by ColossalAI', - long_description=fetch_readme(), - long_description_content_type='text/markdown', - license='Apache Software License 2.0', - url='https://github.com/hpcaitech/ChatGPT', - install_requires=fetch_requirements('requirements.txt'), - python_requires='>=3.6', - classifiers=[ - 'Programming Language :: Python :: 3', - 'License :: OSI Approved :: Apache Software License', - 'Environment :: GPU :: NVIDIA CUDA', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - 'Topic :: System :: Distributed Computing', - ], -) diff --git a/applications/ChatGPT/tests/__init__.py b/applications/ChatGPT/tests/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/applications/ChatGPT/tests/test_checkpoint.py b/applications/ChatGPT/tests/test_checkpoint.py deleted file mode 100644 index 1bbd133f76d3..000000000000 --- a/applications/ChatGPT/tests/test_checkpoint.py +++ /dev/null @@ -1,98 +0,0 @@ -import os -import tempfile -from contextlib import nullcontext -from functools import partial - -import pytest -import torch -import torch.distributed as dist -import torch.multiprocessing as mp -from chatgpt.models.gpt import GPTActor -from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy -from transformers.models.gpt2.configuration_gpt2 import GPT2Config - -from colossalai.nn.optimizer import HybridAdam -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port - -GPT_CONFIG = GPT2Config(n_embd=128, n_layer=4, n_head=4) - - -def get_data(batch_size: int, seq_len: int = 10) -> dict: - input_ids = torch.randint(0, 50257, (batch_size, seq_len), device='cuda') - attention_mask = torch.ones_like(input_ids) - return dict(input_ids=input_ids, attention_mask=attention_mask) - - -def run_test_checkpoint(strategy): - BATCH_SIZE = 2 - - if strategy == 'ddp': - strategy = DDPStrategy() - elif strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) - elif strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - else: - raise ValueError(f'Unsupported strategy "{strategy}"') - - with strategy.model_init_context(): - actor = GPTActor(config=GPT_CONFIG).cuda() - - actor_optim = HybridAdam(actor.parameters()) - - actor, actor_optim = strategy.prepare((actor, actor_optim)) - - def run_step(): - data = get_data(BATCH_SIZE) - action_mask = torch.ones_like(data['attention_mask'], dtype=torch.bool) - action_log_probs = actor(data['input_ids'], action_mask.size(1), data['attention_mask']) - loss = action_log_probs.sum() - strategy.backward(loss, actor, actor_optim) - strategy.optimizer_step(actor_optim) - - run_step() - - ctx = tempfile.TemporaryDirectory() if dist.get_rank() == 0 else nullcontext() - - with ctx as dirname: - rank0_dirname = [dirname] - dist.broadcast_object_list(rank0_dirname) - rank0_dirname = rank0_dirname[0] - - model_path = os.path.join(rank0_dirname, 'model.pt') - optim_path = os.path.join(rank0_dirname, f'optim-r{dist.get_rank()}.pt') - - strategy.save_model(actor, model_path, only_rank0=True) - strategy.save_optimizer(actor_optim, optim_path, only_rank0=False) - - dist.barrier() - - strategy.load_model(actor, model_path, strict=False) - strategy.load_optimizer(actor_optim, optim_path) - - dist.barrier() - - run_step() - - -def run_dist(rank, world_size, port, strategy): - os.environ['RANK'] = str(rank) - os.environ['LOCAL_RANK'] = str(rank) - os.environ['WORLD_SIZE'] = str(world_size) - os.environ['MASTER_ADDR'] = 'localhost' - os.environ['MASTER_PORT'] = str(port) - run_test_checkpoint(strategy) - - -@pytest.mark.dist -@pytest.mark.parametrize('world_size', [2]) -@pytest.mark.parametrize('strategy', ['ddp', 'colossalai_zero2', 'colossalai_gemini']) -@rerun_if_address_is_in_use() -def test_checkpoint(world_size, strategy): - run_func = partial(run_dist, world_size=world_size, port=free_port(), strategy=strategy) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_checkpoint(2, 'colossalai_zero2') diff --git a/applications/ChatGPT/tests/test_data.py b/applications/ChatGPT/tests/test_data.py deleted file mode 100644 index 3d8fe912cb27..000000000000 --- a/applications/ChatGPT/tests/test_data.py +++ /dev/null @@ -1,122 +0,0 @@ -import os -from copy import deepcopy -from functools import partial - -import pytest -import torch -import torch.distributed as dist -import torch.multiprocessing as mp -from chatgpt.experience_maker import NaiveExperienceMaker -from chatgpt.models.base import RewardModel -from chatgpt.models.gpt import GPTActor, GPTCritic -from chatgpt.replay_buffer import NaiveReplayBuffer -from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy -from transformers.models.gpt2.configuration_gpt2 import GPT2Config - -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port - -GPT_CONFIG = GPT2Config(n_embd=128, n_layer=4, n_head=4) - - -def get_data(batch_size: int, seq_len: int = 10) -> dict: - input_ids = torch.randint(0, 50257, (batch_size, seq_len), device='cuda') - attention_mask = torch.ones_like(input_ids) - return dict(input_ids=input_ids, attention_mask=attention_mask) - - -def gather_and_equal(tensor: torch.Tensor) -> bool: - world_size = dist.get_world_size() - outputs = [torch.empty_like(tensor) for _ in range(world_size)] - dist.all_gather(outputs, tensor.contiguous()) - for t in outputs[1:]: - if not torch.equal(outputs[0], t): - return False - return True - - -def run_test_data(strategy): - EXPERINCE_BATCH_SIZE = 4 - SAMPLE_BATCH_SIZE = 2 - - if strategy == 'ddp': - strategy = DDPStrategy() - elif strategy == 'colossalai': - strategy = ColossalAIStrategy(placement_policy='cuda') - else: - raise ValueError(f'Unsupported strategy "{strategy}"') - - actor = GPTActor(config=GPT_CONFIG).cuda() - critic = GPTCritic(config=GPT_CONFIG).cuda() - - initial_model = deepcopy(actor) - reward_model = RewardModel(deepcopy(critic.model)).cuda() - - experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model) - replay_buffer = NaiveReplayBuffer(SAMPLE_BATCH_SIZE, cpu_offload=False) - - # experience of all ranks should be the same - for _ in range(2): - data = get_data(EXPERINCE_BATCH_SIZE) - assert gather_and_equal(data['input_ids']) - assert gather_and_equal(data['attention_mask']) - experience = experience_maker.make_experience(**data, - do_sample=True, - max_length=16, - eos_token_id=50256, - pad_token_id=50256) - assert gather_and_equal(experience.sequences) - assert gather_and_equal(experience.action_log_probs) - assert gather_and_equal(experience.values) - assert gather_and_equal(experience.reward) - assert gather_and_equal(experience.advantages) - assert gather_and_equal(experience.action_mask) - assert gather_and_equal(experience.attention_mask) - replay_buffer.append(experience) - - # replay buffer's data should be the same - buffer_size = torch.tensor([len(replay_buffer)], device='cuda') - assert gather_and_equal(buffer_size) - for item in replay_buffer.items: - assert gather_and_equal(item.sequences) - assert gather_and_equal(item.action_log_probs) - assert gather_and_equal(item.values) - assert gather_and_equal(item.reward) - assert gather_and_equal(item.advantages) - assert gather_and_equal(item.action_mask) - assert gather_and_equal(item.attention_mask) - - # dataloader of each rank should have the same size and different batch - dataloader = strategy.setup_dataloader(replay_buffer) - dataloader_size = torch.tensor([len(dataloader)], device='cuda') - assert gather_and_equal(dataloader_size) - for experience in dataloader: - assert not gather_and_equal(experience.sequences) - assert not gather_and_equal(experience.action_log_probs) - assert not gather_and_equal(experience.values) - assert not gather_and_equal(experience.reward) - assert not gather_and_equal(experience.advantages) - # action mask and attention mask may be same - - -def run_dist(rank, world_size, port, strategy): - os.environ['RANK'] = str(rank) - os.environ['LOCAL_RANK'] = str(rank) - os.environ['WORLD_SIZE'] = str(world_size) - os.environ['MASTER_ADDR'] = 'localhost' - os.environ['MASTER_PORT'] = str(port) - run_test_data(strategy) - - -@pytest.mark.skip -@pytest.mark.dist -@pytest.mark.parametrize('world_size', [2]) -@pytest.mark.parametrize('strategy', ['ddp', 'colossalai']) -@rerun_if_address_is_in_use() -def test_data(world_size, strategy): - run_func = partial(run_dist, world_size=world_size, port=free_port(), strategy=strategy) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_data(2, 'colossalai') diff --git a/applications/ChatGPT/version.txt b/applications/ChatGPT/version.txt deleted file mode 100644 index 3eefcb9dd5b3..000000000000 --- a/applications/ChatGPT/version.txt +++ /dev/null @@ -1 +0,0 @@ -1.0.0 From 4905b21b945bd3a5ac72c0ece112392cbc5e7096 Mon Sep 17 00:00:00 2001 From: ver217 Date: Tue, 28 Mar 2023 21:20:28 +0800 Subject: [PATCH 043/413] [coati] fix inference output (#3285) * [coati] fix inference requirements * [coati] add output postprocess * [coati] update inference readme * [coati] fix inference requirements --- applications/Chat/inference/README.md | 6 ++++++ applications/Chat/inference/requirements.txt | 4 +++- applications/Chat/inference/server.py | 4 ++-- applications/Chat/inference/utils.py | 8 ++++++++ 4 files changed, 19 insertions(+), 3 deletions(-) diff --git a/applications/Chat/inference/README.md b/applications/Chat/inference/README.md index 3fb330748a13..6c23bc73cd60 100644 --- a/applications/Chat/inference/README.md +++ b/applications/Chat/inference/README.md @@ -36,6 +36,12 @@ Tha data is from [LLaMA Int8 4bit ChatBot Guide v2](https://rentry.org/llama-tar | LLaMA-30B | 15.8GB | 20GB | 64GB | RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100 | | LLaMA-65B | 31.2GB | 40GB | 128GB | A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000, Titan Ada | +## General setup + +```shell +pip install -r requirements.txt +``` + ## 8-bit setup 8-bit quantization is originally supported by the latest [transformers](https://github.com/huggingface/transformers). Please install it from source. diff --git a/applications/Chat/inference/requirements.txt b/applications/Chat/inference/requirements.txt index 67a9874e569a..7b0ac18a3b36 100644 --- a/applications/Chat/inference/requirements.txt +++ b/applications/Chat/inference/requirements.txt @@ -1,5 +1,5 @@ fastapi -locustio +locust numpy pydantic safetensors @@ -8,3 +8,5 @@ sse_starlette torch uvicorn git+https://github.com/huggingface/transformers +accelerate +bitsandbytes diff --git a/applications/Chat/inference/server.py b/applications/Chat/inference/server.py index 46a8b9a0568a..bfcd89264296 100644 --- a/applications/Chat/inference/server.py +++ b/applications/Chat/inference/server.py @@ -17,7 +17,7 @@ from utils import ChatPromptProcessor, Dialogue, LockedIterator, sample_streamingly, update_model_kwargs_fn CONTEXT = 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.' -MAX_LEN = 2048 +MAX_LEN = 512 running_lock = Lock() @@ -116,7 +116,7 @@ def generate_no_stream(data: GenerationTaskReq, request: Request): prompt_len = inputs['input_ids'].size(1) response = output[0, prompt_len:] out_string = tokenizer.decode(response, skip_special_tokens=True) - return out_string.lstrip() + return prompt_processor.postprocess_output(out_string) if __name__ == '__main__': diff --git a/applications/Chat/inference/utils.py b/applications/Chat/inference/utils.py index 3d04aa57d553..a01983de35d3 100644 --- a/applications/Chat/inference/utils.py +++ b/applications/Chat/inference/utils.py @@ -1,3 +1,4 @@ +import re from threading import Lock from typing import Any, Callable, Generator, List, Optional @@ -118,6 +119,9 @@ def _format_dialogue(instruction: str, response: str = ''): return f'\n\n### Instruction:\n{instruction}\n\n### Response:\n{response}' +STOP_PAT = re.compile(r'(###|instruction:).*', flags=(re.I | re.S)) + + class ChatPromptProcessor: def __init__(self, tokenizer, context: str, max_len: int = 2048): @@ -164,6 +168,10 @@ def preprocess_prompt(self, history: List[Dialogue], max_new_tokens: int) -> str prompt += ''.join(rows) + _format_dialogue(last_dialogue.instruction) return prompt + def postprocess_output(self, output: str) -> str: + output = STOP_PAT.sub('', output) + return output.strip() + class LockedIterator: From 1f7d9afbf83e85cc6dce0bae02744e12c9f3564a Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Tue, 28 Mar 2023 23:07:15 +0800 Subject: [PATCH 044/413] add example (#3286) --- applications/Chat/README.md | 70 ++++++++++++++++++++++++ applications/Chat/assets/Phd.png | Bin 0 -> 279686 bytes applications/Chat/assets/chemical.png | Bin 0 -> 314106 bytes applications/Chat/assets/economy.png | Bin 0 -> 399451 bytes applications/Chat/assets/game.png | Bin 0 -> 413026 bytes applications/Chat/assets/physical.png | Bin 0 -> 175468 bytes applications/Chat/assets/quick_sort.png | Bin 0 -> 177264 bytes applications/Chat/assets/regex.png | Bin 0 -> 41401 bytes applications/Chat/assets/table.png | Bin 0 -> 118910 bytes applications/Chat/assets/tex.png | Bin 0 -> 290812 bytes applications/Chat/assets/travel.png | Bin 0 -> 235307 bytes applications/Chat/assets/writing.png | Bin 0 -> 234135 bytes 12 files changed, 70 insertions(+) create mode 100644 applications/Chat/assets/Phd.png create mode 100644 applications/Chat/assets/chemical.png create mode 100644 applications/Chat/assets/economy.png create mode 100644 applications/Chat/assets/game.png create mode 100644 applications/Chat/assets/physical.png create mode 100644 applications/Chat/assets/quick_sort.png create mode 100644 applications/Chat/assets/regex.png create mode 100644 applications/Chat/assets/table.png create mode 100644 applications/Chat/assets/tex.png create mode 100644 applications/Chat/assets/travel.png create mode 100644 applications/Chat/assets/writing.png diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 731005ab25c3..cdf718cd7130 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -18,6 +18,8 @@ - [Stage2 - Training reward model](#stage2---training-reward-model) - [Stage3 - Training model with reinforcement learning by human feedback](#stage3---training-model-with-reinforcement-learning-by-human-feedback) - [Coati7B examples](#coati7b-examples) + - [Generation](#generation) + - [Open QA](#open-qa) - [FAQ](#faq) - [How to save/load checkpoint](#how-to-saveload-checkpoint) - [The Plan](#the-plan) @@ -77,6 +79,7 @@ pip install . ### Supervised datasets collection we colllected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo +[InstructionWild](https://github.com/XueFuzhao/InstructionWild) Here is how we collected the data

    @@ -143,6 +146,73 @@ We also support training reward model with true-world data. See `examples/train_ ## Coati7B examples +### Generation + +

    E-mail + +![phd](assets/Phd.png) +
    + +
    coding + +![sort](assets/quick_sort.png) + +
    + +
    regex + +![regex](assets/regex.png) + +
    + +
    Tex + +![tex](assets/tex.png) + +
    + +
    writing + +![writing](assets/writing.png) + +
    + +
    Table + +![Table](assets/table.png) + +
    + +### Open QA +
    Game + +![Game](assets/game.png) + +
    + +
    Travel + +![Travel](assets/travel.png) + +
    + +
    Physical + +![Physical](assets/Physical.png) + +
    + +
    Chemical + +![Chemical](assets/chemical.png) + +
    + +
    Economy + +![Economy](assets/economy.png) + +
    ## FAQ diff --git a/applications/Chat/assets/Phd.png b/applications/Chat/assets/Phd.png new file mode 100644 index 0000000000000000000000000000000000000000..344d9db3067294c630ff3341059cb3fb9bc3aa75 GIT binary patch literal 279686 zcmagFWmua{7d0B(9g4dYTHM{GxR>Hk+}*W6p)C}rI23m%4#A~31b26Lg2TC==Xt;L zoqy+7a^;#N_sr~>z4lsbe^*nH!$2iL1%W^q@*ku%Kp+I*E!;O`Sl|V*ewYme3I@qb zOK5p#9A_eVYyDm9FUqKC%a2gmrz++~W2BjHng3&aRPWX1S*df}wo#vF)WZ0VO4&D# zLh@~x_y-7dWePHKo8k5gTT-na{(pY)PO9^{imUUV zFZ6Z5*Mp{gFFM9h|NT8A25igM$P67Mw%D%{pZ^)~<=gX>3J>2& zHUSi6&*C5Z(!lxeA4kDqDX<(w;vis`rs?8w>0Lo; zQ(@4TO9{r1R21%i{tuHbBS2r0v5zW?vyeG1@Dq(w83)N$VZK^7-GB3YRmHA@cMe2O z#&;_sKM*xbdAq%*gh#bxIxWEvx`6WUAWS-B>RDBuTr|as|lge`~eX`TOqzTP8Q#&3v!9 zS%&utyC3B|H1zMk%es{f*IllPA)nI>bA4{{qr?f zOlrPK`a~lo1GBi-_1}WOI8ecJliw|`C+T?C ztCCMYz$p(F|LpIU7>HVYTY^R^`<_{ME}vR@P{Q4M=)Voe1ThvcERK4W3RIwR%+s@S530I)R-1|hl);yJg2WK(coTSk$GW|(L>ZeQSn3bv z-)usH38~@De_G<>o_9XWxXNM|3n~xKF5*$|-%|OD>z39Zs=S7xi^s;5l*h$Z`H*gi 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--- applications/Chat/README.md | 30 +++++++++++----------- applications/Chat/assets/Phd.png | Bin 279686 -> 0 bytes applications/Chat/assets/chemical.png | Bin 314106 -> 0 bytes applications/Chat/assets/data-collect.png | Bin 410749 -> 0 bytes applications/Chat/assets/economy.png | Bin 399451 -> 0 bytes applications/Chat/assets/game.png | Bin 413026 -> 0 bytes applications/Chat/assets/logo_coati.png | Bin 655366 -> 0 bytes applications/Chat/assets/physical.png | Bin 175468 -> 0 bytes applications/Chat/assets/quick_sort.png | Bin 177264 -> 0 bytes applications/Chat/assets/regex.png | Bin 41401 -> 0 bytes applications/Chat/assets/stage-3.jpeg | Bin 378888 -> 0 bytes applications/Chat/assets/table.png | Bin 118910 -> 0 bytes applications/Chat/assets/tex.png | Bin 290812 -> 0 bytes applications/Chat/assets/travel.png | Bin 235307 -> 0 bytes applications/Chat/assets/writing.png | Bin 234135 -> 0 bytes 15 files changed, 15 insertions(+), 15 deletions(-) delete mode 100644 applications/Chat/assets/Phd.png delete mode 100644 applications/Chat/assets/chemical.png delete mode 100644 applications/Chat/assets/data-collect.png delete mode 100644 applications/Chat/assets/economy.png delete mode 100644 applications/Chat/assets/game.png delete mode 100644 applications/Chat/assets/logo_coati.png delete mode 100644 applications/Chat/assets/physical.png delete mode 100644 applications/Chat/assets/quick_sort.png delete mode 100644 applications/Chat/assets/regex.png delete mode 100644 applications/Chat/assets/stage-3.jpeg delete mode 100644 applications/Chat/assets/table.png delete mode 100644 applications/Chat/assets/tex.png delete mode 100644 applications/Chat/assets/travel.png delete mode 100644 applications/Chat/assets/writing.png diff --git a/applications/Chat/README.md b/applications/Chat/README.md index cdf718cd7130..8885d9731844 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -1,6 +1,6 @@

    Coati - ColossalAI Talking Intelligence - +

    @@ -60,7 +60,7 @@ You can experience the performance of Coati7B on this page. ### Install the environment ```shell -conda creat -n coati +conda create -n coati conda activate coati pip install . ``` @@ -83,7 +83,7 @@ we colllected 104K bilingual dataset of Chinese and English, and you can find th Here is how we collected the data

    - +

    ### Stage1 - Supervised instructs tuning @@ -127,7 +127,7 @@ torchrun --standalone --nproc_per_node=4 train_reward_model.py Stage3 uses reinforcement learning algorithm, which is the most complex part of the training process:

    - +

    you can run the `examples/train_prompts.sh` to start training PPO with human feedback @@ -150,67 +150,67 @@ We also support training reward model with true-world data. See `examples/train_
    E-mail -![phd](assets/Phd.png) +![phd](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/Phd.png)
    coding -![sort](assets/quick_sort.png) +![sort](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/quick_sort.png)
    regex -![regex](assets/regex.png) +![regex](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/regex.png)
    Tex -![tex](assets/tex.png) +![tex](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/tex.png)
    writing -![writing](assets/writing.png) +![writing](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/writing.png)
    Table -![Table](assets/table.png) +![Table](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/table.png)
    ### Open QA
    Game -![Game](assets/game.png) +![Game](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/game.png)
    Travel -![Travel](assets/travel.png) +![Travel](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/travel.png)
    Physical -![Physical](assets/Physical.png) +![Physical](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/Physical.png)
    Chemical -![Chemical](assets/chemical.png) +![Chemical](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/chemical.png)
    Economy -![Economy](assets/economy.png) +![Economy](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/economy.png)
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z-@%N}Yc`bDaPf{w|9rO(b+SL>c7ELgB+fmSsr%1;_fU+5F0o~f9;6;BJ;}=m4 zje4vOGG6aG7fsEw1+^>=SnNQ)d(yVm44vjm#y;!6WaHkqw+Ocao*+;f7|D+95JMG=hnC2++^M?R#+I@xF zFhz9Fc!}2fZV$x)!08e@KbFOjFWm1eEcjG_tMbG0Ay{ZZDS6S`mxS^{?lAAe_URV8 z|HjjBw9WAm!wu0DIJ{~CsRMRXTB$44ug#tpNA801`EOdYXcuhNjENEaZ7(IJYuyw~ zO^2h>`NCizh#2h|#SEyxS%eSft`OL{?`6OOupxA1-P&|x&gFZ)MbrDP?S3Lo!pF&x z((A?|x~FnwXOG4g$wbHNu`X9Dn+AQ!)al z{z!$R&l~lcE+cxkoApW9>kdz!VntS3DO{k3lKA>YnCPw4ss}JZUEx{2KpJWpvJ$CB zJ$>?slX*L^PdpT6{wEmuPzx~luFtC2 z+`{JhrpEz{t(y79EatS7d*~F5W zRl?{XhqeDI{!1~a;-lh8+K+$I#-}3T@NEuAFTI)gy5KxRma^d)R{_3%w)ICjZZbVneBDj{FEj4 zp}+ropuNR4lDh4|P2nGX)5iI`^S%p>iMEqhL^1ynE!u=hA#F=>K`ZWn>;!|86!-jN zY=GwzCI>(Ns8-HQ_`ll^mNK7rdk4sn8@K*7#MS@r vE&$&~`5#f|e>b&p|9|*^JK+Zp?(bh+KCu)19HQ{=|CExD7cUjn5BUE8b`JWr From c5484281aace3a5762c8ceefb4aa0371585e3c38 Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Wed, 29 Mar 2023 00:38:36 +0800 Subject: [PATCH 046/413] [ColossalChat]add cite for datasets (#3292) --- applications/Chat/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 8885d9731844..cf54e7e7f273 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -332,6 +332,15 @@ The Phd student [Zangwei Zheng](https://github.com/zhengzangw) and [Xue Fuzhao]( journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } + +@misc{instructionwild, + author = {Fuzhao Xue and Zangwei Zheng and Yang You }, + title = {Instruction in the Wild: A User-based Instruction Dataset}, + year = {2023}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/XueFuzhao/InstructionWild}}, +} ``` ## Licenses From a88ed0f83a1d98fe6f7b9064551201773ac9c99c Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Wed, 29 Mar 2023 00:53:23 +0800 Subject: [PATCH 047/413] add limit (#3293) --- applications/Chat/README.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index cf54e7e7f273..80e1f36579d6 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -20,6 +20,8 @@ - [Coati7B examples](#coati7b-examples) - [Generation](#generation) - [Open QA](#open-qa) + - [Limitation for LLaMA-finetuned models](#limitation-for-llama-finetuned-models) + - [Limitation of dataset](#limitation-of-dataset) - [FAQ](#faq) - [How to save/load checkpoint](#how-to-saveload-checkpoint) - [The Plan](#the-plan) @@ -214,6 +216,19 @@ We also support training reward model with true-world data. See `examples/train_ +### Limitation for LLaMA-finetuned models +- Both Alpaca and ColossalChat are based on LLaMA. It is hard to compensate for the missing knowledge in the pre-training stage. +- Lack of counting ability: Cannot count the number of items in a list. +- Lack of Logics (reasoning and calculation) +- Tend to repeat the last sentence (fail to produce the end token). +- Poor multilingual results: LLaMA is mainly trained on English datasets (Generation performs better than QA). +### Limitation of dataset +- Lack of summarization ability: No such instructions in finetune datasets. +- Lack of multi-turn chat: No such instructions in finetune datasets +- Lack of self-recognition: No such instructions in finetune datasets +- Lack of Safety: + - When the input contains fake facts, the model makes up false facts and explanations. + - Cannot abide by OpenAI's policy: When generating prompts from OpenAI API, it always abides by its policy. So no violation case is in the datasets. ## FAQ ### How to save/load checkpoint From ce2cafae7613be2b805ebec18c2db5e398c50ca9 Mon Sep 17 00:00:00 2001 From: ver217 Date: Wed, 29 Mar 2023 01:18:45 +0800 Subject: [PATCH 048/413] [coati] add repetition_penalty for inference (#3294) --- applications/Chat/inference/server.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/applications/Chat/inference/server.py b/applications/Chat/inference/server.py index bfcd89264296..f67b78a084f4 100644 --- a/applications/Chat/inference/server.py +++ b/applications/Chat/inference/server.py @@ -27,6 +27,7 @@ class GenerationTaskReq(BaseModel): top_k: Optional[int] = Field(default=None, gt=0, example=50) top_p: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.5) temperature: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.7) + repetition_penalty: Optional[float] = Field(default=None, gt=1.0, example=1.2) limiter = Limiter(key_func=get_remote_address) @@ -55,6 +56,7 @@ class GenerationTaskReq(BaseModel): def generate_streamingly(prompt, max_new_tokens, top_k, top_p, temperature): inputs = {k: v.cuda() for k, v in tokenizer(prompt, return_tensors="pt").items()} + #TODO(ver217): streaming generation does not support repetition_penalty now model_kwargs = { 'max_generate_tokens': max_new_tokens, 'early_stopping': True, From 73b542a12466aa4906670562cc3ee7c2a7c729d2 Mon Sep 17 00:00:00 2001 From: ver217 Date: Wed, 29 Mar 2023 02:14:35 +0800 Subject: [PATCH 049/413] [coati] inference supports profanity check (#3295) --- applications/Chat/inference/server.py | 17 ++++++++++++++--- applications/Chat/inference/utils.py | 16 +++++++++++++++- 2 files changed, 29 insertions(+), 4 deletions(-) diff --git a/applications/Chat/inference/server.py b/applications/Chat/inference/server.py index f67b78a084f4..b4627299397e 100644 --- a/applications/Chat/inference/server.py +++ b/applications/Chat/inference/server.py @@ -14,7 +14,7 @@ from slowapi.util import get_remote_address from sse_starlette.sse import EventSourceResponse from transformers import AutoTokenizer, GenerationConfig, LlamaForCausalLM -from utils import ChatPromptProcessor, Dialogue, LockedIterator, sample_streamingly, update_model_kwargs_fn +from utils import ChatPromptProcessor, Dialogue, LockedIterator, sample_streamingly, update_model_kwargs_fn, load_json CONTEXT = 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.' MAX_LEN = 512 @@ -111,6 +111,8 @@ def generate(data: GenerationTaskReq, request: Request): @limiter.limit('1/second') def generate_no_stream(data: GenerationTaskReq, request: Request): prompt = prompt_processor.preprocess_prompt(data.history, data.max_new_tokens) + if prompt_processor.has_censored_words(prompt): + return prompt_processor.SAFE_RESPONSE inputs = {k: v.cuda() for k, v in tokenizer(prompt, return_tensors="pt").items()} with running_lock: output = model.generate(**inputs, **data.dict(exclude={'history'})) @@ -118,7 +120,10 @@ def generate_no_stream(data: GenerationTaskReq, request: Request): prompt_len = inputs['input_ids'].size(1) response = output[0, prompt_len:] out_string = tokenizer.decode(response, skip_special_tokens=True) - return prompt_processor.postprocess_output(out_string) + out_string = prompt_processor.postprocess_output(out_string) + if prompt_processor.has_censored_words(out_string): + return prompt_processor.SAFE_RESPONSE + return out_string if __name__ == '__main__': @@ -140,13 +145,19 @@ def generate_no_stream(data: GenerationTaskReq, request: Request): help='Group size for GPTQ. This is only useful when quantization mode is 4bit. Default: 128.') parser.add_argument('--http_host', default='0.0.0.0') parser.add_argument('--http_port', type=int, default=7070) + parser.add_argument('--profanity_file', default=None, help='Path to profanity words list. It should be a JSON file containing a list of words.') args = parser.parse_args() if args.quant == '4bit': assert args.gptq_checkpoint is not None, 'Please specify a GPTQ checkpoint.' tokenizer = AutoTokenizer.from_pretrained(args.pretrained) - prompt_processor = ChatPromptProcessor(tokenizer, CONTEXT, MAX_LEN) + + if args.profanity_file is not None: + censored_words = load_json(args.profanity_file) + else: + censored_words = [] + prompt_processor = ChatPromptProcessor(tokenizer, CONTEXT, MAX_LEN, censored_words=censored_words) if args.quant == '4bit': model = load_quant(args.pretrained, args.gptq_checkpoint, 4, args.gptq_group_size) diff --git a/applications/Chat/inference/utils.py b/applications/Chat/inference/utils.py index a01983de35d3..1bb0e82ba966 100644 --- a/applications/Chat/inference/utils.py +++ b/applications/Chat/inference/utils.py @@ -1,6 +1,7 @@ import re from threading import Lock from typing import Any, Callable, Generator, List, Optional +import json import torch import torch.distributed as dist @@ -123,11 +124,16 @@ def _format_dialogue(instruction: str, response: str = ''): class ChatPromptProcessor: + SAFE_RESPONSE = 'The input/response contains inappropriate content, please rephrase your prompt.' - def __init__(self, tokenizer, context: str, max_len: int = 2048): + def __init__(self, tokenizer, context: str, max_len: int = 2048, censored_words: List[str]=[]): self.tokenizer = tokenizer self.context = context self.max_len = max_len + if len(censored_words) > 0: + self.censored_pat = re.compile(f'({"|".join(map(re.escape, censored_words))})', flags=re.I) + else: + self.censored_pat = None # These will be initialized after the first call of preprocess_prompt() self.context_len: Optional[int] = None self.dialogue_placeholder_len: Optional[int] = None @@ -172,6 +178,10 @@ def postprocess_output(self, output: str) -> str: output = STOP_PAT.sub('', output) return output.strip() + def has_censored_words(self, text: str) -> bool: + if self.censored_pat is None: + return False + return self.censored_pat.search(text) is not None class LockedIterator: @@ -185,3 +195,7 @@ def __iter__(self): def __next__(self): with self.lock: return next(self.it) + +def load_json(path: str): + with open(path) as f: + return json.load(f) \ No newline at end of file From c8b723d6c2caed371dfe5e3670d08adc7b7d5def Mon Sep 17 00:00:00 2001 From: BlueRum <70618399+ht-zhou@users.noreply.github.com> Date: Wed, 29 Mar 2023 02:32:17 +0800 Subject: [PATCH 050/413] [chat]Update Readme (#3296) * Update README.md * Update README.md * Update README.md * update example readme --- applications/Chat/README.md | 68 ++++++++-- applications/Chat/examples/README.md | 193 +++++++++++++++++---------- 2 files changed, 180 insertions(+), 81 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 80e1f36579d6..c7553041a287 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -17,6 +17,7 @@ - [Stage1 - Supervised instructs tuning](#stage1---supervised-instructs-tuning) - [Stage2 - Training reward model](#stage2---training-reward-model) - [Stage3 - Training model with reinforcement learning by human feedback](#stage3---training-model-with-reinforcement-learning-by-human-feedback) + - [Inference - After Training](#inference---after-training) - [Coati7B examples](#coati7b-examples) - [Generation](#generation) - [Open QA](#open-qa) @@ -129,22 +130,68 @@ torchrun --standalone --nproc_per_node=4 train_reward_model.py Stage3 uses reinforcement learning algorithm, which is the most complex part of the training process:

    - +

    you can run the `examples/train_prompts.sh` to start training PPO with human feedback ``` -torchrun --standalone --nproc_per_node=4 train_prompts.py prompts.csv \ - --pretrain "/path/to/LLaMa-7B/" \ - --model 'llama' \ - --strategy colossalai_zero2 +torchrun --standalone --nproc_per_node=4 train_prompts.py \ + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2 \ + --prompt_path /path/to/your/prompt_dataset \ + --pretrain_dataset /path/to/your/pretrain_dataset \ + --rm_pretrain /your/pretrain/rm/defination \ + --rm_path /your/rm/model/path ``` +For more details, see [`examples/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples). -For more details, see `examples/`. +### Inference - After Training +#### 8-bit setup -We also support training reward model with true-world data. See `examples/train_reward_model.py`. +8-bit quantization is originally supported by the latest [transformers](https://github.com/huggingface/transformers). Please install it from source. + +Please ensure you have downloaded HF-format model weights of LLaMA models. + +Usage: + +```python +from transformers import LlamaForCausalLM +USE_8BIT = True # use 8-bit quantization; otherwise, use fp16 +model = LlamaForCausalLM.from_pretrained( + "pretrained/path", + load_in_8bit=USE_8BIT, + torch_dtype=torch.float16, + device_map="auto", + ) +if not USE_8BIT: + model.half() # use fp16 +model.eval() +``` + +**Troubleshooting**: if you get error indicating your CUDA-related libraries not found when loading 8-bit model, you can check whether your `LD_LIBRARY_PATH` is correct. + +E.g. you can set `export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`. + +#### 4-bit setup + +Please ensure you have downloaded HF-format model weights of LLaMA models first. + +Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight convertion script. + +After installing this lib, we may convert the original HF-format LLaMA model weights to 4-bit version. + +```shell +CUDA_VISIBLE_DEVICES=0 python llama.py /path/to/pretrained/llama-7b c4 --wbits 4 --groupsize 128 --save llama7b-4bit.pt +``` + +Run this command in your cloned `GPTQ-for-LLaMa` directory, then you will get a 4-bit weight file `llama7b-4bit-128g.pt`. + +**Troubleshooting**: if you get error about `position_ids`, you can checkout to commit `50287c3b9ae4a3b66f6b5127c643ec39b769b155`(`GPTQ-for-LLaMa` repo). + +For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference). ## Coati7B examples @@ -200,7 +247,7 @@ We also support training reward model with true-world data. See `examples/train_
    Physical -![Physical](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/Physical.png) +![Physical](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/physical.png)
    @@ -216,12 +263,15 @@ We also support training reward model with true-world data. See `examples/train_ +You can find more examples in this [repo](https://github.com/XueFuzhao/InstructionWild/blob/main/compare.md). + ### Limitation for LLaMA-finetuned models - Both Alpaca and ColossalChat are based on LLaMA. It is hard to compensate for the missing knowledge in the pre-training stage. - Lack of counting ability: Cannot count the number of items in a list. - Lack of Logics (reasoning and calculation) - Tend to repeat the last sentence (fail to produce the end token). - Poor multilingual results: LLaMA is mainly trained on English datasets (Generation performs better than QA). + ### Limitation of dataset - Lack of summarization ability: No such instructions in finetune datasets. - Lack of multi-turn chat: No such instructions in finetune datasets @@ -229,6 +279,7 @@ We also support training reward model with true-world data. See `examples/train_ - Lack of Safety: - When the input contains fake facts, the model makes up false facts and explanations. - Cannot abide by OpenAI's policy: When generating prompts from OpenAI API, it always abides by its policy. So no violation case is in the datasets. + ## FAQ ### How to save/load checkpoint @@ -262,7 +313,6 @@ trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) - [x] implement training reward model - [x] support LoRA - [x] support inference -- [x] open source the reward model weight - [x] support llama from [facebook](https://github.com/facebookresearch/llama) - [x] implement PPO-ptx fine-tuning - [ ] integrate with Ray diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 60e6d68bdc0f..95404368cd38 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -6,15 +6,64 @@ pip install -r requirements.txt ``` -## Train the reward model (Stage 2) -Use these code to train your reward model. -```shell -# Take naive reward model training with opt-350m as example -python train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy naive -# use colossalai_zero2 -torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy colossalai_zero2 +## Supervised datasets collection + +We colllected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo +[InstructionWild](https://github.com/XueFuzhao/InstructionWild). + +The following pic shows how we collected the data. +

    + +

    + +## Stage1 - Supervised instructs tuning + +Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned earlier to fine-tune the model. + +You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning. + +You can also use the following cmd to start a supervised instructs fine-tuning with your own settings. ``` +torchrun --standalone --nproc_per_node=4 train_sft.py \ + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2 \ + --log_interval 10 \ + --save_path /path/to/Coati-7B \ + --dataset /path/to/data.json \ + --batch_size 4 \ + --accimulation_steps 8 \ + --lr 2e-5 \ + --max_datasets_size 512 \ + --max_epochs 1 \ +``` +### Arg List +- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive' +- --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' +- --pretrain: pretrain model, type=str, default=None +- --max_datasets_size: the max size of dataset, type=int, default=None +- --save_path: path to save the model, type=str, default='output' +- --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False +- --max_epochs: max epochs for training, type=int, default=3 +- --batch_size: batch size while training, type=int, default=4 +- --lora_rank: low-rank adaptation matrices rank, type=int, default=0 +- --log_interval: how many steps to log, type=int, default=100 + +## Stage2 - Training reward model + +We train a reward model in stage 2, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model. + +You can run the `examples/train_rm.sh` to start a reward model training. +You can also use the following cmd to start training a reward model. +``` +torchrun --standalone --nproc_per_node=4 train_reward_model.py + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2 \ + --loss_fn 'log_exp'\ + --save_path 'rmstatic.pt' \ +``` ### Features and tricks in RM training - We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)and[rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets. - We support 2 kinds of loss_function named 'log_sig'(used by OpenAI) and 'log_exp'(used by Anthropic). @@ -27,73 +76,79 @@ torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain "faceb ### Experiment result Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862): -

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    image +
    image
    Our training & test result of bloom-560m for 1 epoch: -
    image - -
    +
    image -## Train with dummy prompt data (Stage 3) +
    We also train the reward model based on LLaMA-7B, which reaches the ACC of 72.06% after 1 epoch, performing almost the same as Anthropic's best RM. -This script supports 4 kinds of strategies: +### Arg List +- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive' +- --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' +- --pretrain: pretrain model, type=str, default=None +- --model_path: the path of rm model(if continue to train), type=str, default=None +- --save_path: path to save the model, type=str, default='output' +- --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False +- --max_epochs: max epochs for training, type=int, default=3 +- --dataset: dataset name, type=str, choices=['Anthropic/hh-rlhf', 'Dahoas/rm-static'] +- --subset: subset of the dataset, type=str, default=None +- --batch_size: batch size while training, type=int, default=4 +- --lora_rank: low-rank adaptation matrices rank, type=int, default=0 +- --loss_func: which kind of loss function, choices=['log_sig', 'log_exp'] +- --max_len: max sentence length for generation, type=int, default=512 +- --test: whether is only tesing, if it's ture, the dataset will be small -- naive -- ddp -- colossalai_zero2 -- colossalai_gemini +## Stage3 - Training model using prompts with RL -It uses random generated prompt data. +Stage3 uses reinforcement learning algorithm, which is the most complex part of the training process, as shown below: -Naive strategy only support single GPU training: +

    + +

    -```shell -python train_dummy.py --strategy naive -# display cli help -python train_dummy.py -h -``` +You can run the `examples/train_prompts.sh` to start PPO training. +You can also use the cmd following to start PPO training. -DDP strategy and ColossalAI strategy support multi GPUs training: - -```shell -# run DDP on 2 GPUs -torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy ddp -# run ColossalAI on 2 GPUs -torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy colossalai_zero2 ``` - -## Train with real prompt data (Stage 3) - -We use [awesome-chatgpt-prompts](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts) as example dataset. It is a small dataset with hundreds of prompts. - -You should download `prompts.csv` first. - -This script also supports 4 strategies. - -```shell -# display cli help -python train_dummy.py -h -# run naive on 1 GPU -python train_prompts.py prompts.csv --strategy naive -# run DDP on 2 GPUs -torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy ddp -# run ColossalAI on 2 GPUs -torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai_zero2 +torchrun --standalone --nproc_per_node=4 train_prompts.py \ + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2 \ + --prompt_path /path/to/your/prompt_dataset \ + --pretrain_dataset /path/to/your/pretrain_dataset \ + --rm_pretrain /your/pretrain/rm/defination \ + --rm_path /your/rm/model/path ``` +### Arg List +- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive' +- --model: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' +- --pretrain: pretrain model, type=str, default=None +- --rm_pretrain: pretrain model for reward model, type=str, default=None +- --rm_path: the path of rm model, type=str, default=None +- --save_path: path to save the model, type=str, default='output' +- --prompt_path: path of the prompt dataset, type=str, default=None +- --pretrain_dataset: path of the ptx dataset, type=str, default=None +- --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False +- --num_episodes: num of episodes for training, type=int, default=10 +- --max_epochs: max epochs for training in one episode, type=int, default=5 +- --max_timesteps: max episodes in one batch, type=int, default=10 +- --update_timesteps: timesteps to update, type=int, default=10 +- --train_batch_size: batch size while training, type=int, default=8 +- --ptx_batch_size: batch size to compute ptx loss, type=int, default=1 +- --experience_batch_size: batch size to make experience, type=int, default=8 +- --lora_rank: low-rank adaptation matrices rank, type=int, default=0 +- --kl_coef: kl_coef using for computing reward, type=float, default=0.1 +- --ptx_coef: ptx_coef using for computing policy loss, type=float, default=0.9 + +## Inference example - After Stage3 +We support different inference options, including int8 and int4 quantization. +For details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference). -## Inference example(After Stage3) -We support naive inference demo after training. -```shell -# inference, using pretrain path to configure model -python inference.py --model_path --model --pretrain -# example -python inference.py --model_path ./actor_checkpoint_prompts.pt --pretrain bigscience/bloom-560m --model bloom -``` ## Attention -The examples is just a demo for testing our progress of RM and PPO training. - +The examples are demos for the whole training process.You need to change the hyper-parameters to reach great performance. #### data - [x] [rm-static](https://huggingface.co/datasets/Dahoas/rm-static) @@ -111,25 +166,13 @@ The examples is just a demo for testing our progress of RM and PPO training. - [ ] GPT2-XL (xl) - [x] GPT2-4B (4b) - [ ] GPT2-6B (6b) -- [ ] GPT2-8B (8b) -- [ ] GPT2-10B (10b) -- [ ] GPT2-12B (12b) -- [ ] GPT2-15B (15b) -- [ ] GPT2-18B (18b) -- [ ] GPT2-20B (20b) -- [ ] GPT2-24B (24b) -- [ ] GPT2-28B (28b) -- [ ] GPT2-32B (32b) -- [ ] GPT2-36B (36b) -- [ ] GPT2-40B (40b) -- [ ] GPT3 (175b) ### BLOOM - [x] [BLOOM-560m](https://huggingface.co/bigscience/bloom-560m) - [x] [BLOOM-1b1](https://huggingface.co/bigscience/bloom-1b1) - [x] [BLOOM-3b](https://huggingface.co/bigscience/bloom-3b) - [x] [BLOOM-7b](https://huggingface.co/bigscience/bloom-7b1) -- [ ] BLOOM-175b +- [ ] [BLOOM-175b](https://huggingface.co/bigscience/bloom) ### OPT - [x] [OPT-125M](https://huggingface.co/facebook/opt-125m) @@ -139,3 +182,9 @@ The examples is just a demo for testing our progress of RM and PPO training. - [ ] [OPT-6.7B](https://huggingface.co/facebook/opt-6.7b) - [ ] [OPT-13B](https://huggingface.co/facebook/opt-13b) - [ ] [OPT-30B](https://huggingface.co/facebook/opt-30b) + +### [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) +- [x] LLaMA-7B +- [x] LLaMA-13B +- [ ] LLaMA-33B +- [ ] LLaMA-65B From 682af6139671030c5b4da69246197627f189c404 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Wed, 29 Mar 2023 02:35:10 +0800 Subject: [PATCH 051/413] [doc] add ColossalChat (#3297) * [doc] add ColossalChat --- README.md | 21 +++++++++++++++------ docs/README-zh-Hans.md | 20 ++++++++++++++------ 2 files changed, 29 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 3098d72b4591..77c3471d9d25 100644 --- a/README.md +++ b/README.md @@ -66,7 +66,7 @@
  • Colossal-AI for Real World Applications @@ -214,22 +214,31 @@ Please visit our [documentation](https://www.colossalai.org/) and [examples](htt

    (back to top)

    ## Colossal-AI in the Real World -### ChatGPT -A low-cost [ChatGPT](https://openai.com/blog/chatgpt/) equivalent implementation process. [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/ChatGPT) [[blog]](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) -

    + +### ColossalChat + +

    + +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[blog]](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) [[demo]](https://chat.colossalai.org) + +

    - Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference -

    +

    - Up to 10.3x growth in model capacity on one GPU - A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU) -

    +

    diff --git a/docs/README-zh-Hans.md b/docs/README-zh-Hans.md index 81c45abfd833..4be923eca024 100644 --- a/docs/README-zh-Hans.md +++ b/docs/README-zh-Hans.md @@ -66,7 +66,7 @@
  • Colossal-AI 成功案例 @@ -212,22 +212,30 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的

    (返回顶端)

    ## Colossal-AI 成功案例 -### ChatGPT -低成本复现[ChatGPT](https://openai.com/blog/chatgpt/)完整流程 [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/ChatGPT) [[博客]](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) -

    +### ColossalChat + +

    + +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[博客]](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) [[在线样例]](https://chat.colossalai.org) + +

    - 最高可提升单机训练速度7.73倍，单卡推理速度1.42倍 -

    +

    - 单卡模型容量最多提升10.3倍 - 最小demo训练流程最低仅需1.62GB显存 (任意消费级GPU) -

    +

    From 5134ad5d1abf95fe63a72452953f894b9630ea93 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 29 Mar 2023 02:35:40 +0800 Subject: [PATCH 052/413] [format] applied code formatting on changed files in pull request 3296 (#3298) Co-authored-by: github-actions --- applications/Chat/README.md | 2 +- applications/Chat/examples/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index c7553041a287..3e431ca9e8c2 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -17,7 +17,7 @@ - [Stage1 - Supervised instructs tuning](#stage1---supervised-instructs-tuning) - [Stage2 - Training reward model](#stage2---training-reward-model) - [Stage3 - Training model with reinforcement learning by human feedback](#stage3---training-model-with-reinforcement-learning-by-human-feedback) - - [Inference - After Training](#inference---after-training) + - [Inference - After Training](#inference---after-training) - [Coati7B examples](#coati7b-examples) - [Generation](#generation) - [Open QA](#open-qa) diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 95404368cd38..56d8cbb15396 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -100,7 +100,7 @@ Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862): - --max_len: max sentence length for generation, type=int, default=512 - --test: whether is only tesing, if it's ture, the dataset will be small -## Stage3 - Training model using prompts with RL +## Stage3 - Training model using prompts with RL Stage3 uses reinforcement learning algorithm, which is the most complex part of the training process, as shown below: From 62f715613161899198c9468d6cdbcaa3d04c157f Mon Sep 17 00:00:00 2001 From: ver217 Date: Wed, 29 Mar 2023 04:26:35 +0800 Subject: [PATCH 053/413] [coati] fix inference profanity check (#3299) --- applications/Chat/inference/requirements.txt | 1 + applications/Chat/inference/utils.py | 11 +++++------ 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/applications/Chat/inference/requirements.txt b/applications/Chat/inference/requirements.txt index 7b0ac18a3b36..511fe1a4f1f3 100644 --- a/applications/Chat/inference/requirements.txt +++ b/applications/Chat/inference/requirements.txt @@ -10,3 +10,4 @@ uvicorn git+https://github.com/huggingface/transformers accelerate bitsandbytes +jieba \ No newline at end of file diff --git a/applications/Chat/inference/utils.py b/applications/Chat/inference/utils.py index 1bb0e82ba966..37944be70a3b 100644 --- a/applications/Chat/inference/utils.py +++ b/applications/Chat/inference/utils.py @@ -2,6 +2,7 @@ from threading import Lock from typing import Any, Callable, Generator, List, Optional import json +import jieba import torch import torch.distributed as dist @@ -130,10 +131,7 @@ def __init__(self, tokenizer, context: str, max_len: int = 2048, censored_words: self.tokenizer = tokenizer self.context = context self.max_len = max_len - if len(censored_words) > 0: - self.censored_pat = re.compile(f'({"|".join(map(re.escape, censored_words))})', flags=re.I) - else: - self.censored_pat = None + self.censored_words = set([word.lower() for word in censored_words]) # These will be initialized after the first call of preprocess_prompt() self.context_len: Optional[int] = None self.dialogue_placeholder_len: Optional[int] = None @@ -179,9 +177,10 @@ def postprocess_output(self, output: str) -> str: return output.strip() def has_censored_words(self, text: str) -> bool: - if self.censored_pat is None: + if len(self.censored_words) == 0: return False - return self.censored_pat.search(text) is not None + intersection = set(jieba.cut(text.lower())) & self.censored_words + return len(intersection) > 0 class LockedIterator: From 8257e1055d5ceff97eb4516c02ee525d0d80ddc8 Mon Sep 17 00:00:00 2001 From: BlueRum <70618399+ht-zhou@users.noreply.github.com> Date: Wed, 29 Mar 2023 08:44:16 +0800 Subject: [PATCH 054/413] [chat]polish prompts training (#3300) * polish train_prompts * polish readme --- applications/Chat/examples/README.md | 1 + applications/Chat/examples/train_prompts.py | 93 ++++++++++++--------- 2 files changed, 55 insertions(+), 39 deletions(-) diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 56d8cbb15396..49401ec30db5 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -125,6 +125,7 @@ torchrun --standalone --nproc_per_node=4 train_prompts.py \ - --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive' - --model: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' - --pretrain: pretrain model, type=str, default=None +- --rm_model: reward model type, type=str, choices=['gpt2', 'bloom', 'opt', 'llama'], default=None - --rm_pretrain: pretrain model for reward model, type=str, default=None - --rm_path: the path of rm model, type=str, default=None - --save_path: path to save the model, type=str, default='output' diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index c573f5e6fae8..ff3cf78dc383 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -1,20 +1,19 @@ import argparse - import pandas as pd import torch import torch.distributed as dist -from coati.dataset import DataCollatorForSupervisedDataset, PromptDataset, SupervisedDataset -from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic -from coati.models.gpt import GPTRM, GPTActor, GPTCritic -from coati.models.llama import LlamaActor -from coati.models.opt import OPTRM, OPTActor, OPTCritic +from coati.models.bloom import BLOOMActor, BLOOMRM, BLOOMCritic +from coati.models.gpt import GPTActor, GPTRM, GPTCritic +from coati.models.opt import OPTActor, OPTRM, OPTCritic +from coati.models.llama import LlamaActor, LlamaRM, LlamaCritic from coati.trainer import PPOTrainer from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from coati.utils import prepare_llama_tokenizer_and_embedding from torch.optim import Adam from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler -from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer +from transformers import AutoTokenizer, BloomTokenizerFast, LlamaTokenizer, GPT2Tokenizer +from coati.dataset import SupervisedDataset, DataCollatorForSupervisedDataset, PromptDataset +from coati.utils import prepare_llama_tokenizer_and_embedding from colossalai.nn.optimizer import HybridAdam @@ -38,44 +37,66 @@ def main(args): # configure model if args.model == 'gpt2': initial_model = GPTActor(pretrained=args.pretrain) - reward_model = GPTRM(pretrained=args.rm_pretrain) elif args.model == 'bloom': initial_model = BLOOMActor(pretrained=args.pretrain) - reward_model = BLOOMRM(pretrained=args.rm_pretrain) elif args.model == 'opt': initial_model = OPTActor(pretrained=args.pretrain) - reward_model = OPTRM(pretrained=args.rm_pretrain) elif args.model == 'llama': initial_model = LlamaActor(pretrained=args.pretrain) + else: + raise ValueError(f'Unsupported actor model "{args.model}"') + + if args.rm_model == None: + rm_model_name = args.model + else: + rm_model_name = args.rm_model + + if rm_model_name == 'gpt2': + reward_model = GPTRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'bloom': reward_model = BLOOMRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'opt': + reward_model = OPTRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'llama': + reward_model = LlamaRM(pretrained=args.rm_pretrain) else: - raise ValueError(f'Unsupported model "{args.model}"') + raise ValueError(f'Unsupported reward model "{rm_model_name}"') + + if args.rm_path is not None: reward_model.load_state_dict(state_dict) - + if args.strategy != 'colossalai_gemini': - initial_model.to(torch.float16).to(torch.cuda.current_device()) - reward_model.to(torch.float16).to(torch.cuda.current_device()) - + initial_model.to(torch.float16).to(torch.cuda.current_device()) + reward_model.to(torch.float16).to(torch.cuda.current_device()) + with strategy.model_init_context(): if args.model == 'gpt2': actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) - critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) elif args.model == 'bloom': actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank) - critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) elif args.model == 'opt': actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) - critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) elif args.model == 'llama': actor = LlamaActor(pretrained=args.pretrain, lora_rank=args.lora_rank) + else: + raise ValueError(f'Unsupported actor model "{args.model}"') + + if rm_model_name == 'gpt2': + critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + elif rm_model_name == 'bloom': critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + elif rm_model_name == 'opt': + critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + elif rm_model_name == 'llama': + critic = LlamaCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) else: - raise ValueError(f'Unsupported model "{args.model}"') + raise ValueError(f'Unsupported reward model "{rm_model_name}"') + if args.rm_path is not None: critic.load_state_dict(state_dict) del state_dict - + if args.strategy != 'colossalai_gemini': critic.to(torch.float16).to(torch.cuda.current_device()) actor.to(torch.float16).to(torch.cuda.current_device()) @@ -100,38 +121,32 @@ def main(args): tokenizer.eos_token = '<\s>' else: raise ValueError(f'Unsupported model "{args.model}"') - + if args.model == 'llama': tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, actor) else: tokenizer.pad_token = tokenizer.eos_token - + data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) - + prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_path, max_datasets_size=16384) if dist.is_initialized() and dist.get_world_size() > 1: prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True) - prompt_dataloader = DataLoader(prompt_dataset, - shuffle=(prompt_sampler is None), - sampler=prompt_sampler, - batch_size=args.train_batch_size) - + prompt_dataloader = DataLoader(prompt_dataset, shuffle=(prompt_sampler is None), sampler=prompt_sampler, batch_size=args.train_batch_size) + pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384) if dist.is_initialized() and dist.get_world_size() > 1: pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True) - pretrain_dataloader = DataLoader(pretrain_dataset, - shuffle=(pretrain_sampler is None), - sampler=pretrain_sampler, - batch_size=args.ptx_batch_size, - collate_fn=data_collator) - + pretrain_dataloader = DataLoader(pretrain_dataset, shuffle=(pretrain_sampler is None), sampler=pretrain_sampler, batch_size=args.ptx_batch_size, collate_fn=data_collator) + def tokenize_fn(texts): # MUST padding to max length to ensure inputs of all ranks have the same length # Different length may lead to hang when using gemini, as different generation steps batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) return {k: v.to(torch.cuda.current_device()) for k, v in batch.items()} - - (actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim)) + + (actor, actor_optim), (critic, critic_optim) = strategy.prepare( + (actor, actor_optim), (critic, critic_optim)) # configure trainer trainer = PPOTrainer( @@ -177,10 +192,10 @@ def tokenize_fn(texts): parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset') parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive', - help='strategy to use') + default='naive', help='strategy to use') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--rm_model', default=None, choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--rm_path', type=str, default=None) parser.add_argument('--rm_pretrain', type=str, default=None) parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts') From e235a246730ec727a6d182ce5c92c1c4870f278f Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 29 Mar 2023 08:47:00 +0800 Subject: [PATCH 055/413] [application] updated the README (#3301) * [application] updated the README * polish code --- applications/Chat/README.md | 23 ++++++++++++++++++----- applications/README.md | 12 ++++++++++++ 2 files changed, 30 insertions(+), 5 deletions(-) create mode 100644 applications/README.md diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 3e431ca9e8c2..2f1771bc9d37 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -1,13 +1,14 @@

    - Coati - ColossalAI Talking Intelligence - + +
    + ColossalChat

    ## Table of Contents - [Table of Contents](#table-of-contents) -- [What is Coati ?](#what-is-coati-) +- [What is ColossalChat and Coati ?](#what-is-colossalchat-and-coati-) - [Online demo](#online-demo) - [Install](#install) - [Install the environment](#install-the-environment) @@ -18,6 +19,8 @@ - [Stage2 - Training reward model](#stage2---training-reward-model) - [Stage3 - Training model with reinforcement learning by human feedback](#stage3---training-model-with-reinforcement-learning-by-human-feedback) - [Inference - After Training](#inference---after-training) + - [8-bit setup](#8-bit-setup) + - [4-bit setup](#4-bit-setup) - [Coati7B examples](#coati7b-examples) - [Generation](#generation) - [Open QA](#open-qa) @@ -33,9 +36,13 @@ - [Citations](#citations) - [Licenses](#licenses) --- -## What is Coati ? +## What is ColossalChat and Coati ? -Coati is a large language model developed by Colossal-AI, which is also a unified large language model framework that has implemented the following functions +ColossalChat is the project to implement LLM with RLHF, powered by the Colossal-AI project. + +Coati stands for `ColossalAI Talking Intelligence`. It is the name for the module implemented in this project and is also the name of the large language model developed by the ColossalChat project. + +The Coati package provides a unified large language model framework that has implemented the following functions - Supports comprehensive large-model training acceleration capabilities for ColossalAI, without requiring knowledge of complex distributed training algorithms - Supervised datasets collection - Supervised insturcts fine-tuning @@ -45,18 +52,24 @@ Coati is a large language model developed by Colossal-AI, which is also a unifie - Fast model deploying - Perfectly integration with the Hugging Face ecosystem, high degree of model customization +**As Colossa-AI is undergoing some major updates, this project will be actively maintained to stay in line with the Colossal-AI project.** + More details can be found in the [blog](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt).

    +Image source: https://openai.com/blog/chatgpt

    + ## Online demo You can experience the performance of Coati7B on this page. [chat.colossalai.org](https://chat.colossalai.org/) +Due to resource constraints, we will only provide this service from 29th Mar 2023 to 5 April 2023. However, we have provided the inference code in the [inference](./inference/) folder. The WebUI will be open-sourced soon as well. + > Warning: Due to model and dataset size limitations, Coati is just a baby model, Coati7B may output incorrect information and lack the ability for multi-turn dialogue. There is still significant room for improvement. ## Install diff --git a/applications/README.md b/applications/README.md new file mode 100644 index 000000000000..b6bde313e54d --- /dev/null +++ b/applications/README.md @@ -0,0 +1,12 @@ +# Applications + +This directory contains the applications that are powered by Colossal-AI. + +The list of applications include: + +- [X] [Chatbot](./Chat/README.md) +- [ ] Stable Diffusion +- [ ] Dreambooth + + +> Please note that the `Chatbot` application is migrated from the original `ChatGPT` folder. From 31c78f2be3272a9a4062fe78eca34b3847a0c900 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Wed, 29 Mar 2023 09:27:55 +0800 Subject: [PATCH 056/413] [doc] add ColossalChat news (#3304) * [doc] add ColossalChat news * [doc] add ColossalChat news --- README.md | 5 +++-- applications/Chat/README.md | 18 +++++++++++------- docs/README-zh-Hans.md | 5 +++-- 3 files changed, 17 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 77c3471d9d25..1a7e4e7edbc9 100644 --- a/README.md +++ b/README.md @@ -25,8 +25,9 @@
    • ## Latest News +* [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) * [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs) -* [2023/02] [Open source solution replicates ChatGPT training process! Ready to go with only 1.6GB GPU memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) +* [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) * [2023/01] [Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism](https://medium.com/pytorch/latest-colossal-ai-boasts-novel-automatic-parallelism-and-offers-savings-up-to-46x-for-stable-1453b48f3f02) * [2022/11] [Diffusion Pretraining and Hardware Fine-Tuning Can Be Almost 7X Cheaper](https://www.hpc-ai.tech/blog/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper) * [2022/10] [Use a Laptop to Analyze 90% of Proteins, With a Single-GPU Inference Sequence Exceeding 10,000](https://www.hpc-ai.tech/blog/use-a-laptop-to-analyze-90-of-proteins-with-a-single-gpu-inference-sequence-exceeding) @@ -223,7 +224,7 @@ Please visit our [documentation](https://www.colossalai.org/) and [examples](htt
    -[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[blog]](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) [[demo]](https://chat.colossalai.org) +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[demo]](https://chat.colossalai.org)

    diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 2f1771bc9d37..f69f32ac4a40 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -38,7 +38,7 @@ --- ## What is ColossalChat and Coati ? -ColossalChat is the project to implement LLM with RLHF, powered by the Colossal-AI project. +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) is the project to implement LLM with RLHF, powered by the [Colossal-AI](https://github.com/hpcaitech/ColossalAI) project. Coati stands for `ColossalAI Talking Intelligence`. It is the name for the module implemented in this project and is also the name of the large language model developed by the ColossalChat project. @@ -52,16 +52,20 @@ The Coati package provides a unified large language model framework that has imp - Fast model deploying - Perfectly integration with the Hugging Face ecosystem, high degree of model customization -**As Colossa-AI is undergoing some major updates, this project will be actively maintained to stay in line with the Colossal-AI project.** +

    +

    + +

    + Image source: https://openai.com/blog/chatgpt +
    -More details can be found in the [blog](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt). +**As Colossa-AI is undergoing some major updates, this project will be actively maintained to stay in line with the Colossal-AI project.** -

    - -Image source: https://openai.com/blog/chatgpt -

    +More details can be found in the latest news. +* [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +* [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) ## Online demo You can experience the performance of Coati7B on this page. diff --git a/docs/README-zh-Hans.md b/docs/README-zh-Hans.md index 4be923eca024..4d29ae156e5e 100644 --- a/docs/README-zh-Hans.md +++ b/docs/README-zh-Hans.md @@ -24,8 +24,9 @@
    ## 新闻 +* [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) * [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs) -* [2023/02] [Open source solution replicates ChatGPT training process! Ready to go with only 1.6GB GPU memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) +* [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) * [2023/01] [Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism](https://medium.com/pytorch/latest-colossal-ai-boasts-novel-automatic-parallelism-and-offers-savings-up-to-46x-for-stable-1453b48f3f02) * [2022/11] [Diffusion Pretraining and Hardware Fine-Tuning Can Be Almost 7X Cheaper](https://www.hpc-ai.tech/blog/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper) * [2022/10] [Use a Laptop to Analyze 90% of Proteins, With a Single-GPU Inference Sequence Exceeding 10,000](https://www.hpc-ai.tech/blog/use-a-laptop-to-analyze-90-of-proteins-with-a-single-gpu-inference-sequence-exceeding) @@ -220,7 +221,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
    -[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[博客]](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) [[在线样例]](https://chat.colossalai.org) +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[博客]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[在线样例]](https://chat.colossalai.org)

    From cb413ccf28a82602eedfeebb3be5224aa02486dc Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 29 Mar 2023 09:28:24 +0800 Subject: [PATCH 057/413] [format] applied code formatting on changed files in pull request 3300 (#3302) Co-authored-by: github-actions --- applications/Chat/examples/train_prompts.py | 65 ++++++++++++--------- 1 file changed, 36 insertions(+), 29 deletions(-) diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index ff3cf78dc383..6643796d7a8b 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -1,19 +1,20 @@ import argparse + import pandas as pd import torch import torch.distributed as dist -from coati.models.bloom import BLOOMActor, BLOOMRM, BLOOMCritic -from coati.models.gpt import GPTActor, GPTRM, GPTCritic -from coati.models.opt import OPTActor, OPTRM, OPTCritic -from coati.models.llama import LlamaActor, LlamaRM, LlamaCritic +from coati.dataset import DataCollatorForSupervisedDataset, PromptDataset, SupervisedDataset +from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic +from coati.models.gpt import GPTRM, GPTActor, GPTCritic +from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM +from coati.models.opt import OPTRM, OPTActor, OPTCritic from coati.trainer import PPOTrainer from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.utils import prepare_llama_tokenizer_and_embedding from torch.optim import Adam from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler -from transformers import AutoTokenizer, BloomTokenizerFast, LlamaTokenizer, GPT2Tokenizer -from coati.dataset import SupervisedDataset, DataCollatorForSupervisedDataset, PromptDataset -from coati.utils import prepare_llama_tokenizer_and_embedding +from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer from colossalai.nn.optimizer import HybridAdam @@ -45,12 +46,12 @@ def main(args): initial_model = LlamaActor(pretrained=args.pretrain) else: raise ValueError(f'Unsupported actor model "{args.model}"') - + if args.rm_model == None: rm_model_name = args.model else: rm_model_name = args.rm_model - + if rm_model_name == 'gpt2': reward_model = GPTRM(pretrained=args.rm_pretrain) elif rm_model_name == 'bloom': @@ -61,15 +62,14 @@ def main(args): reward_model = LlamaRM(pretrained=args.rm_pretrain) else: raise ValueError(f'Unsupported reward model "{rm_model_name}"') - - + if args.rm_path is not None: reward_model.load_state_dict(state_dict) - + if args.strategy != 'colossalai_gemini': - initial_model.to(torch.float16).to(torch.cuda.current_device()) - reward_model.to(torch.float16).to(torch.cuda.current_device()) - + initial_model.to(torch.float16).to(torch.cuda.current_device()) + reward_model.to(torch.float16).to(torch.cuda.current_device()) + with strategy.model_init_context(): if args.model == 'gpt2': actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) @@ -81,7 +81,7 @@ def main(args): actor = LlamaActor(pretrained=args.pretrain, lora_rank=args.lora_rank) else: raise ValueError(f'Unsupported actor model "{args.model}"') - + if rm_model_name == 'gpt2': critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) elif rm_model_name == 'bloom': @@ -92,11 +92,11 @@ def main(args): critic = LlamaCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) else: raise ValueError(f'Unsupported reward model "{rm_model_name}"') - + if args.rm_path is not None: critic.load_state_dict(state_dict) del state_dict - + if args.strategy != 'colossalai_gemini': critic.to(torch.float16).to(torch.cuda.current_device()) actor.to(torch.float16).to(torch.cuda.current_device()) @@ -121,32 +121,38 @@ def main(args): tokenizer.eos_token = '<\s>' else: raise ValueError(f'Unsupported model "{args.model}"') - + if args.model == 'llama': tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, actor) else: tokenizer.pad_token = tokenizer.eos_token - + data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) - + prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_path, max_datasets_size=16384) if dist.is_initialized() and dist.get_world_size() > 1: prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True) - prompt_dataloader = DataLoader(prompt_dataset, shuffle=(prompt_sampler is None), sampler=prompt_sampler, batch_size=args.train_batch_size) - + prompt_dataloader = DataLoader(prompt_dataset, + shuffle=(prompt_sampler is None), + sampler=prompt_sampler, + batch_size=args.train_batch_size) + pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384) if dist.is_initialized() and dist.get_world_size() > 1: pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True) - pretrain_dataloader = DataLoader(pretrain_dataset, shuffle=(pretrain_sampler is None), sampler=pretrain_sampler, batch_size=args.ptx_batch_size, collate_fn=data_collator) - + pretrain_dataloader = DataLoader(pretrain_dataset, + shuffle=(pretrain_sampler is None), + sampler=pretrain_sampler, + batch_size=args.ptx_batch_size, + collate_fn=data_collator) + def tokenize_fn(texts): # MUST padding to max length to ensure inputs of all ranks have the same length # Different length may lead to hang when using gemini, as different generation steps batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) return {k: v.to(torch.cuda.current_device()) for k, v in batch.items()} - - (actor, actor_optim), (critic, critic_optim) = strategy.prepare( - (actor, actor_optim), (critic, critic_optim)) + + (actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim)) # configure trainer trainer = PPOTrainer( @@ -192,7 +198,8 @@ def tokenize_fn(texts): parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset') parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive', help='strategy to use') + default='naive', + help='strategy to use') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--rm_model', default=None, choices=['gpt2', 'bloom', 'opt', 'llama']) From a0b374925bbd23578bee9ada8807e9c9d30b98e5 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 29 Mar 2023 10:15:56 +0800 Subject: [PATCH 058/413] [release] v0.2.8 (#3305) --- version.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/version.txt b/version.txt index b0032849c80b..a45be4627678 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.2.7 +0.2.8 From b512893637c540abef45b15a1b249835b875a736 Mon Sep 17 00:00:00 2001 From: BlueRum <70618399+ht-zhou@users.noreply.github.com> Date: Wed, 29 Mar 2023 10:25:50 +0800 Subject: [PATCH 059/413] Polish readme link (#3306) --- applications/Chat/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index f69f32ac4a40..3c4844977013 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -280,7 +280,7 @@ For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tre -You can find more examples in this [repo](https://github.com/XueFuzhao/InstructionWild/blob/main/compare.md). +You can find more examples in this [repo](https://github.com/XueFuzhao/InstructionWild/blob/main/comparison.md). ### Limitation for LLaMA-finetuned models - Both Alpaca and ColossalChat are based on LLaMA. It is hard to compensate for the missing knowledge in the pre-training stage. From 0fbadce79c812935107c7c415e1c359885d1bcfe Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Wed, 29 Mar 2023 11:04:30 +0800 Subject: [PATCH 060/413] [doc] added authors to the chat application (#3307) --- applications/Chat/README.md | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 3c4844977013..f0abbf0397b9 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -377,9 +377,16 @@ Thanks so much to all of our amazing contributors! ## Authors -Coati is developed by ColossalAI Team: [Fazzie](https://fazzie-key.cool/about/index.html), [FrankLeeeee](https://github.com/FrankLeeeee), [BlueRum](https://github.com/ht-zhou), [ver217](https://github.com/ver217) - -The Phd student [Zangwei Zheng](https://github.com/zhengzangw) and [Xue Fuzhao](https://github.com/XueFuzhao) also contributed a lot to this project. +Coati is developed by ColossalAI Team: +- [Fazzie](https://fazzie-key.cool/about/index.html) +- [FrankLeeeee](https://github.com/FrankLeeeee) +- [BlueRum](https://github.com/ht-zhou) +- [ver217](https://github.com/ver217) +- [ofey404](https://github.com/ofey404) + +The Phd student from [(HPC-AI) Lab](https://ai.comp.nus.edu.sg/) also contributed a lot to this project. +- [Zangwei Zheng](https://github.com/zhengzangw) +- [Xue Fuzhao](https://github.com/XueFuzhao) ## Citations From 204ca2f09afe8717134a8fb56f1946c72d7b18d6 Mon Sep 17 00:00:00 2001 From: dayellow <49357110+dayellow@users.noreply.github.com> Date: Mon, 27 Mar 2023 18:43:43 +0800 Subject: [PATCH 061/413] [NFC] polish colossalai/fx/profiler/experimental/profiler_module/embedding.py code style (#3256) Co-authored-by: Minghao Huang --- .../fx/profiler/experimental/profiler_module/embedding.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/colossalai/fx/profiler/experimental/profiler_module/embedding.py b/colossalai/fx/profiler/experimental/profiler_module/embedding.py index dca6f9453af3..a1ade5d3ad93 100644 --- a/colossalai/fx/profiler/experimental/profiler_module/embedding.py +++ b/colossalai/fx/profiler/experimental/profiler_module/embedding.py @@ -1,5 +1,7 @@ from typing import Tuple + import torch + from ..registry import meta_profiler_module @@ -8,4 +10,4 @@ def torch_nn_embedding(self: torch.nn.Embedding, input: torch.Tensor) -> Tuple[i # nn.Embedding is a dictionary lookup, so technically it has 0 FLOPs. (https://discuss.pytorch.org/t/correct-way-to-calculate-flops-in-model/67198/6) flops = 0 macs = 0 - return flops, macs \ No newline at end of file + return flops, macs From 1ff7d5bfa57a1fc9a2f6630e5c93664f1039d569 Mon Sep 17 00:00:00 2001 From: LuGY <74758262+Gy-Lu@users.noreply.github.com> Date: Mon, 27 Mar 2023 18:47:44 +0800 Subject: [PATCH 062/413] [NFC] polish colossalai/engine/gradient_handler/_moe_gradient_handler.py (#3260) --- .../gradient_handler/_moe_gradient_handler.py | 91 ++++++++++--------- 1 file changed, 46 insertions(+), 45 deletions(-) diff --git a/colossalai/engine/gradient_handler/_moe_gradient_handler.py b/colossalai/engine/gradient_handler/_moe_gradient_handler.py index 02cea5e67a12..b499345d4e18 100644 --- a/colossalai/engine/gradient_handler/_moe_gradient_handler.py +++ b/colossalai/engine/gradient_handler/_moe_gradient_handler.py @@ -1,45 +1,46 @@ -from colossalai.core import global_context as gpc -from colossalai.registry import GRADIENT_HANDLER -from colossalai.utils.moe import get_moe_epsize_param_dict -from ._base_gradient_handler import BaseGradientHandler -from ...context.parallel_mode import ParallelMode -from .utils import bucket_allreduce -from colossalai.context.moe_context import MOE_CONTEXT - - -@GRADIENT_HANDLER.register_module -class MoeGradientHandler(BaseGradientHandler): - """A helper class to handle all-reduce operations in a data parallel group and - moe model parallel. A all-reduce collective communication will be operated in - :func:`handle_gradient` among a data parallel group. - For better performance, it bucketizes the gradients of all parameters that are - the same type to improve the efficiency of communication. - - Args: - model (Module): Model where the gradients accumulate. - optimizer (Optimizer): Optimizer for updating the parameters. - """ - - def __init__(self, model, optimizer=None): - super().__init__(model, optimizer) - - def handle_gradient(self): - """A method running an all-reduce operation in a data parallel group. - Then running an all-reduce operation for all parameters in experts - across moe model parallel group - """ - global_data = gpc.data_parallel_size - - if global_data > 1: - epsize_param_dict = get_moe_epsize_param_dict(self._model) - - # epsize is 1, indicating the params are replicated among processes in data parallelism - # use the ParallelMode.DATA to get data parallel group - # reduce gradients for all parameters in data parallelism - if 1 in epsize_param_dict: - bucket_allreduce(param_list=epsize_param_dict[1], group=gpc.get_group(ParallelMode.DATA)) - - for ep_size in epsize_param_dict: - if ep_size != 1 and ep_size != MOE_CONTEXT.world_size: - bucket_allreduce(param_list=epsize_param_dict[ep_size], - group=MOE_CONTEXT.parallel_info_dict[ep_size].dp_group) +from colossalai.context.moe_context import MOE_CONTEXT +from colossalai.core import global_context as gpc +from colossalai.registry import GRADIENT_HANDLER +from colossalai.utils.moe import get_moe_epsize_param_dict + +from ...context.parallel_mode import ParallelMode +from ._base_gradient_handler import BaseGradientHandler +from .utils import bucket_allreduce + + +@GRADIENT_HANDLER.register_module +class MoeGradientHandler(BaseGradientHandler): + """A helper class to handle all-reduce operations in a data parallel group and + moe model parallel. A all-reduce collective communication will be operated in + :func:`handle_gradient` among a data parallel group. + For better performance, it bucketizes the gradients of all parameters that are + the same type to improve the efficiency of communication. + + Args: + model (Module): Model where the gradients accumulate. + optimizer (Optimizer): Optimizer for updating the parameters. + """ + + def __init__(self, model, optimizer=None): + super().__init__(model, optimizer) + + def handle_gradient(self): + """A method running an all-reduce operation in a data parallel group. + Then running an all-reduce operation for all parameters in experts + across moe model parallel group + """ + global_data = gpc.data_parallel_size + + if global_data > 1: + epsize_param_dict = get_moe_epsize_param_dict(self._model) + + # epsize is 1, indicating the params are replicated among processes in data parallelism + # use the ParallelMode.DATA to get data parallel group + # reduce gradients for all parameters in data parallelism + if 1 in epsize_param_dict: + bucket_allreduce(param_list=epsize_param_dict[1], group=gpc.get_group(ParallelMode.DATA)) + + for ep_size in epsize_param_dict: + if ep_size != 1 and ep_size != MOE_CONTEXT.world_size: + bucket_allreduce(param_list=epsize_param_dict[ep_size], + group=MOE_CONTEXT.parallel_info_dict[ep_size].dp_group) From 488f37048c994229ecbaab5cd7fc0f5d2493ea7a Mon Sep 17 00:00:00 2001 From: jiangmingyan <37931082+jiangmingyan@users.noreply.github.com> Date: Mon, 27 Mar 2023 18:48:16 +0800 Subject: [PATCH 063/413] [NFC] polish colossalai/global_variables.py code style (#3259) Co-authored-by: luchen --- colossalai/global_variables.py | 112 ++++++++++++++++----------------- 1 file changed, 56 insertions(+), 56 deletions(-) diff --git a/colossalai/global_variables.py b/colossalai/global_variables.py index e3575ea12ad0..61b31965e2e6 100644 --- a/colossalai/global_variables.py +++ b/colossalai/global_variables.py @@ -1,56 +1,56 @@ -from typing import Optional - - -class TensorParallelEnv(object): - _instance = None - - def __new__(cls, *args, **kwargs): - if cls._instance is None: - cls._instance = object.__new__(cls, *args, **kwargs) - return cls._instance - - def __init__(self, *args, **kwargs): - self.load(*args, **kwargs) - - def load(self, - mode: Optional[str] = None, - vocab_parallel: bool = False, - parallel_input_1d: bool = False, - summa_dim: int = None, - tesseract_dim: int = None, - tesseract_dep: int = None, - depth_3d: int = None, - input_group_3d=None, - weight_group_3d=None, - output_group_3d=None, - input_x_weight_group_3d=None, - output_x_weight_group_3d=None): - self.mode = mode - self.vocab_parallel = vocab_parallel - self.parallel_input_1d = parallel_input_1d - self.summa_dim = summa_dim - self.tesseract_dim = tesseract_dim - self.tesseract_dep = tesseract_dep - self.depth_3d = depth_3d - self.input_group_3d = input_group_3d - self.weight_group_3d = weight_group_3d - self.output_group_3d = output_group_3d - self.input_x_weight_group_3d = input_x_weight_group_3d - self.output_x_weight_group_3d = output_x_weight_group_3d - - def save(self): - return dict(mode=self.mode, - vocab_parallel=self.vocab_parallel, - parallel_input_1d=self.parallel_input_1d, - summa_dim=self.summa_dim, - tesseract_dim=self.tesseract_dim, - tesseract_dep=self.tesseract_dep, - depth_3d=self.depth_3d, - input_group_3d=self.input_group_3d, - weight_group_3d=self.weight_group_3d, - output_group_3d=self.output_group_3d, - input_x_weight_group_3d=self.input_x_weight_group_3d, - output_x_weight_group_3d=self.output_x_weight_group_3d) - - -tensor_parallel_env = TensorParallelEnv() +from typing import Optional + + +class TensorParallelEnv(object): + _instance = None + + def __new__(cls, *args, **kwargs): + if cls._instance is None: + cls._instance = object.__new__(cls, *args, **kwargs) + return cls._instance + + def __init__(self, *args, **kwargs): + self.load(*args, **kwargs) + + def load(self, + mode: Optional[str] = None, + vocab_parallel: bool = False, + parallel_input_1d: bool = False, + summa_dim: int = None, + tesseract_dim: int = None, + tesseract_dep: int = None, + depth_3d: int = None, + input_group_3d=None, + weight_group_3d=None, + output_group_3d=None, + input_x_weight_group_3d=None, + output_x_weight_group_3d=None): + self.mode = mode + self.vocab_parallel = vocab_parallel + self.parallel_input_1d = parallel_input_1d + self.summa_dim = summa_dim + self.tesseract_dim = tesseract_dim + self.tesseract_dep = tesseract_dep + self.depth_3d = depth_3d + self.input_group_3d = input_group_3d + self.weight_group_3d = weight_group_3d + self.output_group_3d = output_group_3d + self.input_x_weight_group_3d = input_x_weight_group_3d + self.output_x_weight_group_3d = output_x_weight_group_3d + + def save(self): + return dict(mode=self.mode, + vocab_parallel=self.vocab_parallel, + parallel_input_1d=self.parallel_input_1d, + summa_dim=self.summa_dim, + tesseract_dim=self.tesseract_dim, + tesseract_dep=self.tesseract_dep, + depth_3d=self.depth_3d, + input_group_3d=self.input_group_3d, + weight_group_3d=self.weight_group_3d, + output_group_3d=self.output_group_3d, + input_x_weight_group_3d=self.input_x_weight_group_3d, + output_x_weight_group_3d=self.output_x_weight_group_3d) + + +tensor_parallel_env = TensorParallelEnv() From 00778abc481edd6abacd7c3a12720749f3500a6e Mon Sep 17 00:00:00 2001 From: CsRic <59389055+CsRic@users.noreply.github.com> Date: Mon, 27 Mar 2023 22:03:29 +0800 Subject: [PATCH 064/413] [NFC] polish colossalai/fx/passes/split_module.py code style (#3263) Co-authored-by: csric --- colossalai/fx/passes/split_module.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/colossalai/fx/passes/split_module.py b/colossalai/fx/passes/split_module.py index bc257edc8c89..9bc4bf1f5c42 100644 --- a/colossalai/fx/passes/split_module.py +++ b/colossalai/fx/passes/split_module.py @@ -1,9 +1,10 @@ +import inspect +from typing import Any, Callable, Dict, List, Optional + import torch -from torch.fx.graph_module import GraphModule -from typing import Callable, List, Dict, Any, Optional -from torch.fx._compatibility import compatibility from packaging import version -import inspect +from torch.fx._compatibility import compatibility +from torch.fx.graph_module import GraphModule @compatibility(is_backward_compatible=True) @@ -38,7 +39,7 @@ def split_module( m: GraphModule, root_m: torch.nn.Module, split_callback: Callable[[torch.fx.node.Node], int], - merge_output = False, + merge_output=False, ): """ Adapted from https://github.com/pytorch/pytorch/blob/master/torch/fx/passes/split_module.py @@ -132,10 +133,8 @@ def record_cross_partition_use(def_node: torch.fx.node.Node, use_partition.inputs.setdefault(def_node.name) if def_partition_name is not None: use_partition.partitions_dependent_on.setdefault(def_partition_name) - - def record_output( - def_node: torch.fx.node.Node, use_node: Optional[torch.fx.node.Node] - ): # noqa: B950 + + def record_output(def_node: torch.fx.node.Node, use_node: Optional[torch.fx.node.Node]): # noqa: B950 def_partition_name = getattr(def_node, "_fx_partition", None) use_partition_name = getattr(use_node, "_fx_partition", None) if def_partition_name != use_partition_name: @@ -291,7 +290,7 @@ def record_output( for partition_name in sorted_partitions: partition = partitions[partition_name] - + new_gm = torch.fx.graph_module.GraphModule(base_mod_attrs, base_mod_graph) return new_gm From c4a226b7294683805e3f375a903f3ca40c5fb119 Mon Sep 17 00:00:00 2001 From: Camille Zhong <44392324+Camille7777@users.noreply.github.com> Date: Mon, 27 Mar 2023 22:03:46 +0800 Subject: [PATCH 065/413] [NFC] polish tensor_placement_policy.py code style (#3265) --- colossalai/gemini/tensor_placement_policy.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/colossalai/gemini/tensor_placement_policy.py b/colossalai/gemini/tensor_placement_policy.py index cfcfb385667c..0e575254c0b6 100644 --- a/colossalai/gemini/tensor_placement_policy.py +++ b/colossalai/gemini/tensor_placement_policy.py @@ -1,15 +1,15 @@ +import functools from abc import ABC, abstractmethod from time import time -from typing import List, Optional +from typing import List, Optional, Type + import torch -from colossalai.utils import get_current_device -from colossalai.utils.memory import colo_device_memory_capacity -from colossalai.gemini.tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage -from colossalai.gemini.stateful_tensor import StatefulTensor from colossalai.gemini.memory_tracer import MemStatsCollector -from typing import Type -import functools +from colossalai.gemini.stateful_tensor import StatefulTensor +from colossalai.gemini.tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage +from colossalai.utils import get_current_device +from colossalai.utils.memory import colo_device_memory_capacity class TensorPlacementPolicy(ABC): From d58fa705b2519a4e2c908aa9893d5111652d2480 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Tue, 28 Mar 2023 10:30:30 +0800 Subject: [PATCH 066/413] [NFC] polish code style (#3268) Co-authored-by: Yuanchen Xu --- colossalai/nn/_ops/_utils.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/colossalai/nn/_ops/_utils.py b/colossalai/nn/_ops/_utils.py index 56bb5f465184..24877bbb552f 100644 --- a/colossalai/nn/_ops/_utils.py +++ b/colossalai/nn/_ops/_utils.py @@ -1,12 +1,11 @@ -import torch -from typing import Union, Optional, List -from colossalai.tensor import ColoTensor +from typing import List, Optional, Union + import torch import torch.distributed as dist -from colossalai.global_variables import tensor_parallel_env as env +from colossalai.global_variables import tensor_parallel_env as env from colossalai.nn.layer.utils import divide -from colossalai.tensor import ProcessGroup, ColoTensorSpec +from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup GeneralTensor = Union[ColoTensor, torch.Tensor] Number = Union[int, float] @@ -135,7 +134,7 @@ def backward(ctx, grad_output): class _SplitForwardGatherBackward(torch.autograd.Function): """ Split the input and keep only the corresponding chuck to the rank. - + Args: input_: input matrix. process_group: parallel mode. From 4cadb25b96d3fef31e27a83e0fbede7f5bcb9153 Mon Sep 17 00:00:00 2001 From: CZYCW Date: Tue, 28 Mar 2023 10:31:02 +0800 Subject: [PATCH 067/413] [NFC] policy colossalai/fx/proxy.py code style (#3269) --- colossalai/fx/proxy.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/colossalai/fx/proxy.py b/colossalai/fx/proxy.py index 06272c48f852..7317072c6298 100644 --- a/colossalai/fx/proxy.py +++ b/colossalai/fx/proxy.py @@ -1,7 +1,9 @@ import operator +from typing import Any, List, Union + import torch -from torch.fx.proxy import Proxy, Attribute -from typing import List, Union, Any +from torch.fx.proxy import Attribute, Proxy + from colossalai.fx.tracer.meta_patch import meta_patched_function __all__ = ['ColoProxy'] From 6b3bb2c2490dd84e3610e3dc006b9b0234a866b6 Mon Sep 17 00:00:00 2001 From: Xuanlei Zhao <43881818+oahzxl@users.noreply.github.com> Date: Tue, 28 Mar 2023 10:43:28 +0800 Subject: [PATCH 068/413] [NFC] polish code style (#3273) --- colossalai/fx/passes/passes_for_gpt2_test.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/colossalai/fx/passes/passes_for_gpt2_test.py b/colossalai/fx/passes/passes_for_gpt2_test.py index f98fcd686ea4..abc1a089e9a9 100644 --- a/colossalai/fx/passes/passes_for_gpt2_test.py +++ b/colossalai/fx/passes/passes_for_gpt2_test.py @@ -1,14 +1,15 @@ +import inspect +from typing import Any, Callable, Dict, List, Optional + import torch -from torch.fx.graph_module import GraphModule -from typing import Callable, List, Dict, Any, Optional -from torch.fx._compatibility import compatibility from packaging import version +from torch.fx._compatibility import compatibility +from torch.fx.graph_module import GraphModule +from torch.fx.node import Node + +from colossalai.fx.passes.adding_split_node_pass import balanced_split_pass, pipe_split from colossalai.fx.passes.meta_info_prop import TensorMetadata -import inspect -from typing import List from colossalai.fx.passes.split_module import Partition -from colossalai.fx.passes.adding_split_node_pass import pipe_split, balanced_split_pass -from torch.fx.node import Node def customized_split_pass_for_gpt2(gm: torch.fx.GraphModule, pp_size: int, partition_list: List[int]): From 4b95464994260063f519c40f35df8fd87a7256fe Mon Sep 17 00:00:00 2001 From: lucasliunju Date: Tue, 28 Mar 2023 10:44:04 +0800 Subject: [PATCH 069/413] [NFC] polish colossalai/amp/__init__.py code style (#3272) --- colossalai/amp/__init__.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/colossalai/amp/__init__.py b/colossalai/amp/__init__.py index 16da81f23898..963215476b6b 100644 --- a/colossalai/amp/__init__.py +++ b/colossalai/amp/__init__.py @@ -1,14 +1,16 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from .amp_type import AMP_TYPE -from colossalai.context import Config import torch.nn as nn -from torch.optim import Optimizer from torch.nn.modules.loss import _Loss -from .torch_amp import convert_to_torch_amp +from torch.optim import Optimizer + +from colossalai.context import Config + +from .amp_type import AMP_TYPE from .apex_amp import convert_to_apex_amp from .naive_amp import convert_to_naive_amp +from .torch_amp import convert_to_torch_amp __all__ = ['convert_to_amp', 'convert_to_naive_amp', 'convert_to_apex_amp', 'convert_to_torch_amp', 'AMP_TYPE'] From 196d4696d0ef5360b1d8b7f99d8124d768e4e075 Mon Sep 17 00:00:00 2001 From: Tong Li Date: Tue, 28 Mar 2023 11:23:38 +0800 Subject: [PATCH 070/413] [NFC] polish colossalai/nn/_ops/addmm.py code style (#3274) --- colossalai/nn/_ops/addmm.py | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/colossalai/nn/_ops/addmm.py b/colossalai/nn/_ops/addmm.py index fe2eb0c999a1..660b48a71d57 100644 --- a/colossalai/nn/_ops/addmm.py +++ b/colossalai/nn/_ops/addmm.py @@ -1,9 +1,9 @@ import torch + +from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ReplicaSpec, ShardSpec, distspec from colossalai.tensor.op_wrapper import colo_op_impl -from colossalai.tensor import ComputePattern, ComputePattern, ComputeSpec, ColoTensor -from colossalai.tensor import distspec, ColoTensorSpec, ShardSpec, ReplicaSpec -from ._utils import GeneralTensor, Number, convert_to_colo_tensor -from ._utils import reduce_input, reduce_grad + +from ._utils import GeneralTensor, Number, convert_to_colo_tensor, reduce_grad, reduce_input def colo_addmm_1Drow(input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTensor, beta: Number, @@ -69,9 +69,13 @@ def colo_addmm(input_tensor: GeneralTensor, if not mat2.has_compute_spec(): # No Model Parallel Applied assert mat2.is_replicate(), 'Invalid mat2 spec for native addmm op' assert input_tensor.is_replicate(), 'Invalid input spec for native addmm op' - ret_tensor = ColoTensor.from_torch_tensor( - tensor=torch.addmm(input_tensor, mat1, mat2, beta=beta, alpha=alpha, **kargs), - spec=ColoTensorSpec(mat2.get_process_group())) + ret_tensor = ColoTensor.from_torch_tensor(tensor=torch.addmm(input_tensor, + mat1, + mat2, + beta=beta, + alpha=alpha, + **kargs), + spec=ColoTensorSpec(mat2.get_process_group())) elif mat2.has_compute_pattern(ComputePattern.TP1D): # Single Model Parallel Applied if mat2.is_shard_1drow() and input_tensor.is_replicate(): mode = 'row' From 1168b50e3387e9440af6cbbd1e085696ad4e5831 Mon Sep 17 00:00:00 2001 From: Zirui Zhu Date: Tue, 28 Mar 2023 14:31:38 +0800 Subject: [PATCH 071/413] [NFC] polish colossalai/engine/schedule/_pipeline_schedule_v2.py code style (#3275) --- colossalai/engine/schedule/_pipeline_schedule_v2.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/colossalai/engine/schedule/_pipeline_schedule_v2.py b/colossalai/engine/schedule/_pipeline_schedule_v2.py index 50a87aafad02..28c58bd82b5c 100644 --- a/colossalai/engine/schedule/_pipeline_schedule_v2.py +++ b/colossalai/engine/schedule/_pipeline_schedule_v2.py @@ -1,11 +1,12 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from typing import Tuple, Iterable +from typing import Iterable, Tuple -from colossalai import engine -import colossalai.communication.p2p_v2 as comm import torch.cuda + +import colossalai.communication.p2p_v2 as comm +from colossalai import engine from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.utils.cuda import get_current_device @@ -35,7 +36,7 @@ def pack_return_tensors(return_tensors): class PipelineScheduleV2(PipelineSchedule): """Derived class of PipelineSchedule, the only difference is that forward_backward_step is reconstructed with p2p_v2 - + Args: num_microbatches (int): The number of microbatches. data_process_func (Callable, optional): @@ -43,9 +44,9 @@ class PipelineScheduleV2(PipelineSchedule): tensor_shape (torch.Size, optional): Specified shape in pipeline communication. scatter_gather_tensors (bool, optional): If set to `True`, communication will be reduced over pipeline when using 1D tensor parallelization. - + Example: - + # this shows an example of customized data_process_func def data_process_func(stage_output, dataloader_output): output1, output2 = stage_output From 8af977f2230af1182a60ceec78a37bf1169765fe Mon Sep 17 00:00:00 2001 From: Arsmart1 <49458769+Arsmart1@users.noreply.github.com> Date: Tue, 28 Mar 2023 14:32:07 +0800 Subject: [PATCH 072/413] [NFC] polish colossalai/context/parallel_context.py code style (#3276) --- colossalai/context/parallel_context.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/colossalai/context/parallel_context.py b/colossalai/context/parallel_context.py index dd12dad6d347..0cd533fdef1a 100644 --- a/colossalai/context/parallel_context.py +++ b/colossalai/context/parallel_context.py @@ -10,15 +10,16 @@ import numpy as np import torch import torch.distributed as dist + from colossalai.constants import ALLOWED_MODES, INITIALIZER_MAPPING from colossalai.context.config import Config +from colossalai.context.singleton_meta import SingletonMeta from colossalai.global_variables import tensor_parallel_env as env from colossalai.logging import get_dist_logger from colossalai.registry import DIST_GROUP_INITIALIZER from .parallel_mode import ParallelMode from .random import add_seed, get_seeds, set_mode -from colossalai.context.singleton_meta import SingletonMeta class ParallelContext(metaclass=SingletonMeta): From 94eec1c5ade1bd03dcb8690400ab72751fcc32d5 Mon Sep 17 00:00:00 2001 From: Sze-qq <68757353+Sze-qq@users.noreply.github.com> Date: Tue, 28 Mar 2023 15:55:09 +0800 Subject: [PATCH 073/413] [NFC] polish colossalai/engine/gradient_accumulation/_gradient_accumulation.py code style (#3277) Co-authored-by: siqi --- .../gradient_accumulation/_gradient_accumulation.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/colossalai/engine/gradient_accumulation/_gradient_accumulation.py b/colossalai/engine/gradient_accumulation/_gradient_accumulation.py index 89c28c3be87a..cf66be1cd821 100644 --- a/colossalai/engine/gradient_accumulation/_gradient_accumulation.py +++ b/colossalai/engine/gradient_accumulation/_gradient_accumulation.py @@ -1,21 +1,22 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from typing import Union +from typing import Any, Iterable, Tuple, Union + import torch.nn as nn from torch import Tensor -from typing import Iterable, Any, Tuple -from colossalai.nn.optimizer import ColossalaiOptimizer from torch.nn.parallel.distributed import DistributedDataParallel from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler from torch.utils.data import DataLoader -from colossalai.utils import conditional_context + from colossalai.engine import BaseGradientHandler +from colossalai.nn.optimizer import ColossalaiOptimizer +from colossalai.utils import conditional_context class GradAccumOptimizer(ColossalaiOptimizer): - """A wrapper for the optimizer to enable gradient accumulation by skipping the steps + """A wrapper for the optimizer to enable gradient accumulation by skipping the steps before accumulation size is reached. Args: @@ -161,7 +162,7 @@ def __next__(self) -> Union[Tensor, Tuple[Tensor]]: class GradAccumLrSchedulerByStep(_LRScheduler): - """A wrapper for the LR scheduler to enable gradient accumulation by skipping the steps + """A wrapper for the LR scheduler to enable gradient accumulation by skipping the steps before accumulation size is reached. Args: From 964a28678f9bd1f71cc139e8ab514a74be4cfecf Mon Sep 17 00:00:00 2001 From: "Kai Wang (Victor Kai)" <37533040+kaiwang960112@users.noreply.github.com> Date: Tue, 28 Mar 2023 18:04:53 +0800 Subject: [PATCH 074/413] [NFC] polish initializer_3d.py code style (#3279) --- .../context/process_group_initializer/initializer_3d.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/colossalai/context/process_group_initializer/initializer_3d.py b/colossalai/context/process_group_initializer/initializer_3d.py index b752b8f45654..1ed8eec86efc 100644 --- a/colossalai/context/process_group_initializer/initializer_3d.py +++ b/colossalai/context/process_group_initializer/initializer_3d.py @@ -4,6 +4,7 @@ import math import torch.distributed as dist + from colossalai.global_variables import tensor_parallel_env as env from colossalai.registry import DIST_GROUP_INITIALIZER @@ -213,7 +214,8 @@ def init_dist_group(self): for h in range(self.num_group): for k in range(self.depth): ranks = [ - h * self.depth**3 + i + self.depth * (j + self.depth * k) for j in range(self.depth) + h * self.depth**3 + i + self.depth * (j + self.depth * k) + for j in range(self.depth) for i in range(self.depth) ] group = dist.new_group(ranks) @@ -266,7 +268,8 @@ def init_dist_group(self): for h in range(self.num_group): for j in range(self.depth): ranks = [ - h * self.depth**3 + i + self.depth * (j + self.depth * k) for k in range(self.depth) + h * self.depth**3 + i + self.depth * (j + self.depth * k) + for k in range(self.depth) for i in range(self.depth) ] group = dist.new_group(ranks) From 1bed38ef37c6edd4ddc9c935218382305c5f9438 Mon Sep 17 00:00:00 2001 From: Ziheng Qin <37519855+henryqin1997@users.noreply.github.com> Date: Tue, 28 Mar 2023 11:36:11 -0700 Subject: [PATCH 075/413] [NFC] polish colossalai/cli/benchmark/models.py code style (#3290) --- colossalai/cli/benchmark/models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/colossalai/cli/benchmark/models.py b/colossalai/cli/benchmark/models.py index 38ea54188b8c..f8fd1c41a059 100644 --- a/colossalai/cli/benchmark/models.py +++ b/colossalai/cli/benchmark/models.py @@ -1,4 +1,5 @@ import torch + import colossalai.nn as col_nn From 1ce9d0c53102f49ec81e08602c0855ff0816c00f Mon Sep 17 00:00:00 2001 From: RichardoLuo <50363844+RichardoLuo@users.noreply.github.com> Date: Wed, 29 Mar 2023 02:36:29 +0800 Subject: [PATCH 076/413] [NFC] polish initializer_data.py code style (#3287) --- .../context/process_group_initializer/initializer_data.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/colossalai/context/process_group_initializer/initializer_data.py b/colossalai/context/process_group_initializer/initializer_data.py index 0b8b0d91fcb9..9715ebff7f00 100644 --- a/colossalai/context/process_group_initializer/initializer_data.py +++ b/colossalai/context/process_group_initializer/initializer_data.py @@ -4,8 +4,9 @@ from torch import distributed as dist from colossalai.registry import DIST_GROUP_INITIALIZER -from .process_group_initializer import ProcessGroupInitializer + from ..parallel_mode import ParallelMode +from .process_group_initializer import ProcessGroupInitializer @DIST_GROUP_INITIALIZER.register_module From 64350029fecb167db415c82ad41cbd8190dde9e5 Mon Sep 17 00:00:00 2001 From: Xu Kai Date: Wed, 29 Mar 2023 02:45:11 -0500 Subject: [PATCH 077/413] [NFC] polish colossalai/gemini/paramhooks/_param_hookmgr.py code style --- colossalai/gemini/paramhooks/_param_hookmgr.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/colossalai/gemini/paramhooks/_param_hookmgr.py b/colossalai/gemini/paramhooks/_param_hookmgr.py index ee57cb46a90d..84f32be358e3 100644 --- a/colossalai/gemini/paramhooks/_param_hookmgr.py +++ b/colossalai/gemini/paramhooks/_param_hookmgr.py @@ -1,6 +1,7 @@ +import functools from typing import Callable, List + import torch -import functools class BaseParamHookMgr(object): From ad285e165608a0e34c3b5992e8c2c8eb2d023986 Mon Sep 17 00:00:00 2001 From: Michelle <97082656+MichelleMa8@users.noreply.github.com> Date: Wed, 29 Mar 2023 17:53:32 +0800 Subject: [PATCH 078/413] [NFC] polish colossalai/fx/tracer/_tracer_utils.py (#3323) * [NFC] polish colossalai/engine/schedule/_pipeline_schedule.py code style * [NFC] polish colossalai/fx/tracer/_tracer_utils.py code style --------- Co-authored-by: Qianran Ma --- colossalai/fx/tracer/_tracer_utils.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/colossalai/fx/tracer/_tracer_utils.py b/colossalai/fx/tracer/_tracer_utils.py index 0ec49a90a133..e160497a7444 100644 --- a/colossalai/fx/tracer/_tracer_utils.py +++ b/colossalai/fx/tracer/_tracer_utils.py @@ -1,6 +1,8 @@ -from typing import List, Union, Any -from ..proxy import ColoProxy, ColoAttribute +from typing import Any, List, Union + import torch + +from ..proxy import ColoAttribute, ColoProxy from .meta_patch import meta_patched_function, meta_patched_module __all__ = ['is_element_in_list', 'extract_meta'] From 15a74da79c93a3c55ba88851df32438c73b1e08c Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Thu, 30 Mar 2023 11:45:01 +0800 Subject: [PATCH 079/413] [doc] add Intel cooperation news (#3333) * [doc] add Intel cooperation news * [doc] add Intel cooperation news --- README.md | 1 + docs/README-zh-Hans.md | 1 + 2 files changed, 2 insertions(+) diff --git a/README.md b/README.md index 1a7e4e7edbc9..2752027a683f 100644 --- a/README.md +++ b/README.md @@ -26,6 +26,7 @@ ## Latest News * [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +* [2023/03] [Intel and Colossal-AI Partner to Deliver Cost-Efficient Open-Source Solution for Protein Folding Structure Prediction](https://www.hpc-ai.tech/blog/intel-habana) * [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs) * [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) * [2023/01] [Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism](https://medium.com/pytorch/latest-colossal-ai-boasts-novel-automatic-parallelism-and-offers-savings-up-to-46x-for-stable-1453b48f3f02) diff --git a/docs/README-zh-Hans.md b/docs/README-zh-Hans.md index 4d29ae156e5e..3630e8539a8b 100644 --- a/docs/README-zh-Hans.md +++ b/docs/README-zh-Hans.md @@ -25,6 +25,7 @@ ## 新闻 * [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +* [2023/03] [Intel and Colossal-AI Partner to Deliver Cost-Efficient Open-Source Solution for Protein Folding Structure Prediction](https://www.hpc-ai.tech/blog/intel-habana) * [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs) * [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) * [2023/01] [Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism](https://medium.com/pytorch/latest-colossal-ai-boasts-novel-automatic-parallelism-and-offers-savings-up-to-46x-for-stable-1453b48f3f02) From fbd2a9e05b44b3e95df35518a8f09e9105313358 Mon Sep 17 00:00:00 2001 From: YuliangLiu0306 Date: Thu, 30 Mar 2023 11:22:20 +0800 Subject: [PATCH 080/413] [hotfix] meta_tensor_compatibility_with_torch2 --- colossalai/fx/_compatibility.py | 2 -- colossalai/fx/profiler/opcount.py | 3 ++- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/colossalai/fx/_compatibility.py b/colossalai/fx/_compatibility.py index 6caad920d2ae..0444a4816273 100644 --- a/colossalai/fx/_compatibility.py +++ b/colossalai/fx/_compatibility.py @@ -14,9 +14,7 @@ from . import _meta_regist_13 META_COMPATIBILITY = True elif TORCH_MAJOR == 2: - from . import _meta_regist_13 META_COMPATIBILITY = True - raise UserWarning("Colossalai is not tested with torch2.0 yet!!!") def compatibility(is_backward_compatible: bool = False) -> Callable: diff --git a/colossalai/fx/profiler/opcount.py b/colossalai/fx/profiler/opcount.py index 407a6bed5200..ba090a2ec51b 100644 --- a/colossalai/fx/profiler/opcount.py +++ b/colossalai/fx/profiler/opcount.py @@ -223,7 +223,8 @@ def zero_flop_jit(*args): return 0 -if version.parse(torch.__version__) >= version.parse('1.12.0'): +if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( + torch.__version__) < version.parse('2.0.0'): flop_mapping = { # gemm, gemv and dot aten.mm.default: matmul_flop_jit, From 82132f4e3d393ad2671222ea78fbf2535534a3d8 Mon Sep 17 00:00:00 2001 From: Andrew Date: Wed, 29 Mar 2023 23:18:37 -0700 Subject: [PATCH 081/413] [chat] correcting a few obvious typos and grammars errors (#3338) --- applications/Chat/README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index f0abbf0397b9..8f22084953ba 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -45,12 +45,12 @@ Coati stands for `ColossalAI Talking Intelligence`. It is the name for the modul The Coati package provides a unified large language model framework that has implemented the following functions - Supports comprehensive large-model training acceleration capabilities for ColossalAI, without requiring knowledge of complex distributed training algorithms - Supervised datasets collection -- Supervised insturcts fine-tuning +- Supervised instructions fine-tuning - Training reward model - Reinforcement learning with human feedback - Quantization inference - Fast model deploying -- Perfectly integration with the Hugging Face ecosystem, high degree of model customization +- Perfectly integrated with the Hugging Face ecosystem, a high degree of model customization

    @@ -98,7 +98,7 @@ pip install . ### Supervised datasets collection -we colllected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo +we collected 104K bilingual datasets of Chinese and English, and you can find the datasets in this repo [InstructionWild](https://github.com/XueFuzhao/InstructionWild) Here is how we collected the data @@ -188,17 +188,17 @@ if not USE_8BIT: model.eval() ``` -**Troubleshooting**: if you get error indicating your CUDA-related libraries not found when loading 8-bit model, you can check whether your `LD_LIBRARY_PATH` is correct. +**Troubleshooting**: if you get errors indicating your CUDA-related libraries are not found when loading the 8-bit model, you can check whether your `LD_LIBRARY_PATH` is correct. E.g. you can set `export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`. #### 4-bit setup -Please ensure you have downloaded HF-format model weights of LLaMA models first. +Please ensure you have downloaded the HF-format model weights of LLaMA models first. -Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight convertion script. +Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight conversion scripts. -After installing this lib, we may convert the original HF-format LLaMA model weights to 4-bit version. +After installing this lib, we may convert the original HF-format LLaMA model weights to a 4-bit version. ```shell CUDA_VISIBLE_DEVICES=0 python llama.py /path/to/pretrained/llama-7b c4 --wbits 4 --groupsize 128 --save llama7b-4bit.pt @@ -206,7 +206,7 @@ CUDA_VISIBLE_DEVICES=0 python llama.py /path/to/pretrained/llama-7b c4 --wbits 4 Run this command in your cloned `GPTQ-for-LLaMa` directory, then you will get a 4-bit weight file `llama7b-4bit-128g.pt`. -**Troubleshooting**: if you get error about `position_ids`, you can checkout to commit `50287c3b9ae4a3b66f6b5127c643ec39b769b155`(`GPTQ-for-LLaMa` repo). +**Troubleshooting**: if you get errors about `position_ids`, you can checkout to commit `50287c3b9ae4a3b66f6b5127c643ec39b769b155`(`GPTQ-for-LLaMa` repo). For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference). @@ -334,7 +334,7 @@ trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) - [x] implement PPO-ptx fine-tuning - [ ] integrate with Ray - [ ] support more RL paradigms, like Implicit Language Q-Learning (ILQL), -- [ ] support chain of throught by [langchain](https://github.com/hwchase17/langchain) +- [ ] support chain-of-thought by [langchain](https://github.com/hwchase17/langchain) ### Real-time progress You will find our progress in github project broad From 198a74b9fdfb4fccd33ef621acaceed45f75445c Mon Sep 17 00:00:00 2001 From: yuxuan-lou <83441848+yuxuan-lou@users.noreply.github.com> Date: Thu, 30 Mar 2023 14:19:26 +0800 Subject: [PATCH 082/413] [NFC] polish colossalai/context/random/__init__.py code style (#3327) --- colossalai/context/random/__init__.py | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/colossalai/context/random/__init__.py b/colossalai/context/random/__init__.py index 422c3676c09d..d64b993257c1 100644 --- a/colossalai/context/random/__init__.py +++ b/colossalai/context/random/__init__.py @@ -1,5 +1,16 @@ -from ._helper import (seed, set_mode, with_seed, add_seed, get_seeds, get_states, get_current_mode, set_seed_states, - sync_states, moe_set_seed, reset_seeds) +from ._helper import ( + add_seed, + get_current_mode, + get_seeds, + get_states, + moe_set_seed, + reset_seeds, + seed, + set_mode, + set_seed_states, + sync_states, + with_seed, +) __all__ = [ 'seed', 'set_mode', 'with_seed', 'add_seed', 'get_seeds', 'get_states', 'get_current_mode', 'set_seed_states', From 8706a8c66c5473e24acc42a2ef03eb5367f7b07a Mon Sep 17 00:00:00 2001 From: Ofey Chan Date: Thu, 30 Mar 2023 14:19:39 +0800 Subject: [PATCH 083/413] [NFC] polish colossalai/engine/gradient_handler/__init__.py code style (#3329) --- colossalai/engine/gradient_handler/__init__.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/colossalai/engine/gradient_handler/__init__.py b/colossalai/engine/gradient_handler/__init__.py index 6177da69ba5b..2dea768bad7e 100644 --- a/colossalai/engine/gradient_handler/__init__.py +++ b/colossalai/engine/gradient_handler/__init__.py @@ -1,10 +1,9 @@ from ._base_gradient_handler import BaseGradientHandler from ._data_parallel_gradient_handler import DataParallelGradientHandler -from ._zero_gradient_handler import ZeROGradientHandler -from ._sequence_parallel_gradient_handler import SequenceParallelGradientHandler -from ._pipeline_parallel_gradient_handler import PipelineSharedModuleGradientHandler from ._moe_gradient_handler import MoeGradientHandler +from ._pipeline_parallel_gradient_handler import PipelineSharedModuleGradientHandler from ._sequence_parallel_gradient_handler import SequenceParallelGradientHandler +from ._zero_gradient_handler import ZeROGradientHandler __all__ = [ 'BaseGradientHandler', 'DataParallelGradientHandler', 'ZeROGradientHandler', 'PipelineSharedModuleGradientHandler', From e78a1e949af12dd76d4a2858b6d3c9a6a03bfe34 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E3=82=A2=E3=83=9E=E3=83=87=E3=82=A6=E3=82=B9?= Date: Thu, 30 Mar 2023 15:25:24 +0800 Subject: [PATCH 084/413] fix torch 2.0 compatibility (#3346) --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e32b3ecda063..4e4f35edb2d9 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -8,4 +8,4 @@ click fabric contexttimer ninja -torch>=1.11,<2.0 +torch>=1.11 From fee2af861078181e18a059800ba09a4edfb311f0 Mon Sep 17 00:00:00 2001 From: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com> Date: Thu, 30 Mar 2023 17:47:24 +0800 Subject: [PATCH 085/413] [autoparallel] adapt autoparallel with new analyzer (#3261) * [autoparallel] adapt autoparallel with new analyzer * fix all node handler tests * polish * polish --- .../_subclasses/_meta_registration.py | 5 +- colossalai/_analyzer/fx/passes/shape_prop.py | 58 +++++- .../_analyzer/fx/tracer/bias_addition.py | 114 ++++------- .../auto_parallel/meta_profiler/metainfo.py | 24 ++- .../passes/runtime_preparation_pass.py | 11 +- .../tensor_shard/node_handler/bmm_handler.py | 5 +- .../strategy/matmul_strategy_generator.py | 4 - colossalai/fx/_meta_regist_12.py | 2 +- .../test_node_handler/test_addmm_handler.py | 11 +- .../test_batch_norm_handler.py | 10 +- .../test_bias_linear_function_node.py | 14 +- .../test_bias_linear_module_node.py | 14 +- .../test_binary_elementwise_handler.py | 12 +- .../test_node_handler/test_bmm_handler.py | 25 ++- .../test_node_handler/test_conv_handler.py | 13 +- .../test_default_reshape_handler.py | 23 ++- .../test_embedding_handler.py | 15 +- .../test_node_handler/test_getattr_handler.py | 12 +- .../test_node_handler/test_getitem_handler.py | 24 ++- .../test_layer_norm_handler.py | 10 +- .../test_node_handler/test_linear_handler.py | 21 ++- .../test_node_handler/test_matmul_handler.py | 16 +- .../test_norm_pooling_handler.py | 10 +- .../test_node_handler/test_output_handler.py | 14 +- .../test_permute_and_transpose_handler.py | 27 +-- .../test_placeholder_handler.py | 14 +- .../test_node_handler/test_shard_option.py | 18 +- .../test_node_handler/test_softmax_handler.py | 17 +- .../test_node_handler/test_split_handler.py | 27 +-- .../test_node_handler/test_sum_handler.py | 178 +++++++++--------- .../test_tensor_constructor.py | 12 +- .../test_unary_element_wise_handler.py | 24 +-- .../test_node_handler/test_view_handler.py | 24 +-- .../test_node_handler/test_where_handler.py | 27 +-- .../test_node_handler/utils.py | 14 +- .../test_pipeline/test_topo/test_topo.py | 18 +- 36 files changed, 481 insertions(+), 386 deletions(-) diff --git a/colossalai/_analyzer/_subclasses/_meta_registration.py b/colossalai/_analyzer/_subclasses/_meta_registration.py index 2af7e05399af..4b1fd28e982f 100644 --- a/colossalai/_analyzer/_subclasses/_meta_registration.py +++ b/colossalai/_analyzer/_subclasses/_meta_registration.py @@ -446,10 +446,7 @@ def meta_index_Tensor(self, indices): @register_meta(aten.embedding_dense_backward.default) def meta_embedding_dense_backward(grad_output: torch.Tensor, indices: torch.Tensor, num_weights, padding_idx, scale_grad_by_freq): - return new((num_weights, grad_output.size(-1)), - dtype=grad_output.dtype, - device=grad_output.device, - layout=grad_output.layout) + return new((num_weights, grad_output.size(-1)), dtype=grad_output.dtype, layout=grad_output.layout) # ============================== Dropout =========================================== # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Dropout.cpp diff --git a/colossalai/_analyzer/fx/passes/shape_prop.py b/colossalai/_analyzer/fx/passes/shape_prop.py index ab3e1a4d6a3d..b3859e250ac8 100644 --- a/colossalai/_analyzer/fx/passes/shape_prop.py +++ b/colossalai/_analyzer/fx/passes/shape_prop.py @@ -51,7 +51,10 @@ def _normalize_tuple(x): def _current_device(module): - return next(module.parameters()).device + try: + return next(module.parameters()).device + except StopIteration: + return torch.device('cpu') @compatibility(is_backward_compatible=False) @@ -120,15 +123,18 @@ def _convert_meta(t: torch.Tensor): return t.to('meta') if isinstance(elem, MetaTensor): + if getattr(self, '_is_param', False): + return torch.nn.Parameter(_convert_meta(elem._tensor)) return _convert_meta(elem._tensor) elif isinstance(elem, torch.Tensor): + if isinstance(elem, torch.nn.Parameter): + return torch.nn.Parameter(_convert_meta(elem)) return _convert_meta(elem) else: return elem - # unwrap_fn = lambda elem: elem._tensor if isinstance(elem, MetaTensor) else elem is_pure_tensor = lambda elem: isinstance(elem, MetaTensor) and not isinstance(elem, torch.nn.Parameter) n_info = MetaInfo(n) n_info.outputs = _normalize_tuple(r) @@ -149,7 +155,11 @@ def _convert_meta(t: torch.Tensor): n_info.inputs = tuple(v for v in args if is_pure_tensor(v)) + \ tuple(v for v in kwargs.values() if is_pure_tensor(v)) - n._meta_data = tree_map(unwrap_fn, _normalize_tuple(r)) # align with SPMD + # align with SPMD + if isinstance(r, (tuple, list)): + n._meta_data = tree_map(unwrap_fn, _normalize_tuple(r)) + else: + n._meta_data = unwrap_fn(r) n_info.global_ctx = self.global_hook.ctx n_info.curr_ctx = self.global_hook.ctx.copy() @@ -175,10 +185,48 @@ def call_function(self, target: 'Target', args: Tuple[Any, ...], kwargs: Dict[st Return Any: The value returned by the function invocation """ + convert_to_param = False + if target in (torch.transpose, torch.reshape) and isinstance(args[0], torch.nn.parameter.Parameter): + convert_to_param = True if target in self._custom_dispatch_func: - return self._custom_dispatch_func[target](*args, **kwargs) + res = self._custom_dispatch_func[target](*args, **kwargs) + else: + res = super().call_function(target, args, kwargs) + if convert_to_param: + return torch.nn.Parameter(res) + else: + return res + + def call_method(self, target: 'Target', args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> Any: + """ + Execute a ``call_method`` node and return the result. + + Args: + target (Target): The call target for this node. See + `Node `__ for + details on semantics + args (Tuple): Tuple of positional args for this invocation + kwargs (Dict): Dict of keyword arguments for this invocation + + Return + Any: The value returned by the method invocation + """ + # args[0] is the `self` object for this method call + self_obj, *args_tail = args + + target_method = getattr(self_obj.__class__, target) + + convert_to_parameter = False + if target_method in (torch.Tensor.view, torch.Tensor.transpose) and isinstance( + args[0], torch.nn.parameter.Parameter): + convert_to_parameter = True + # Execute the method and return the result + assert isinstance(target, str) + res = getattr(self_obj, target)(*args_tail, **kwargs) + if convert_to_parameter: + return torch.nn.Parameter(res) else: - return super().call_function(target, args, kwargs) + return res def propagate(self, *args, device=None): """ diff --git a/colossalai/_analyzer/fx/tracer/bias_addition.py b/colossalai/_analyzer/fx/tracer/bias_addition.py index 1e75b47ca5b0..495678501664 100644 --- a/colossalai/_analyzer/fx/tracer/bias_addition.py +++ b/colossalai/_analyzer/fx/tracer/bias_addition.py @@ -21,111 +21,69 @@ def linear_impl(input, weight, bias=None): @register_tracer_impl(F.conv1d, name='_bias_addition_impl') -def conv1d_impl(input, weight, bias=None, stride=_single(1), padding=_single(0), dilation=_single(1), groups=1): +def conv1d_impl(input, weight, **kwargs): + bias = getattr(kwargs, 'bias', None) if bias is None: - return F.conv1d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + return F.conv1d(input, weight, **kwargs) else: - return F.conv1d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape( - (-1, 1)) + new_kwargs = kwargs + new_kwargs['bias'] = None + return F.conv1d(input, weight, **kwargs) + bias.reshape((-1, 1)) @register_tracer_impl(F.conv2d, name='_bias_addition_impl') -def conv2d_impl(input, weight, bias=None, stride=_pair(1), padding=_pair(0), dilation=_pair(1), groups=1): +def conv2d_impl(input, weight, **kwargs): + bias = getattr(kwargs, 'bias', None) if bias is None: - return F.conv2d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + return F.conv2d(input, weight, **kwargs) else: - return F.conv2d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape( - (-1, 1, 1)) + new_kwargs = kwargs + new_kwargs['bias'] = None + return F.conv2d(input, weight, **kwargs) + bias.reshape((-1, 1, 1)) @register_tracer_impl(F.conv3d, name='_bias_addition_impl') -def conv3d_impl(input, weight, bias=None, stride=_triple(1), padding=_triple(0), dilation=_triple(1), groups=1): +def conv3d_impl(input, weight, **kwargs): + bias = getattr(kwargs, 'bias', None) if bias is None: - return F.conv3d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + return F.conv3d(input, weight, **kwargs) else: - return F.conv3d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape( - (-1, 1, 1, 1)) + new_kwargs = kwargs + new_kwargs['bias'] = None + return F.conv3d(input, weight, **new_kwargs) + bias.reshape((-1, 1, 1, 1)) @register_tracer_impl(F.conv_transpose1d, name='_bias_addition_impl') -def conv_transpose1d_impl(input, - weight, - bias=None, - stride=_single(1), - padding=_single(0), - output_padding=_single(0), - groups=1, - dilation=_single(1)): +def conv_transpose1d_impl(input, weight, **kwargs): + bias = getattr(kwargs, 'bias', None) if bias is None: - return F.conv_transpose1d(input, - weight, - stride=stride, - padding=padding, - output_padding=output_padding, - groups=groups, - dilation=dilation) + return F.conv_transpose1d(input, weight, **kwargs) else: - return F.conv_transpose1d(input, - weight, - stride=stride, - padding=padding, - output_padding=output_padding, - groups=groups, - dilation=dilation) + bias.reshape((-1, 1)) + new_kwargs = kwargs + new_kwargs['bias'] = None + return F.conv_transpose1d(input, weight, **new_kwargs) + bias.reshape((-1, 1)) @register_tracer_impl(F.conv_transpose2d, name='_bias_addition_impl') -def conv_transpose2d_impl(input, - weight, - bias=None, - stride=_pair(1), - padding=_pair(0), - output_padding=_pair(0), - groups=1, - dilation=_pair(1)): +def conv_transpose2d_impl(input, weight, **kwargs): + bias = getattr(kwargs, 'bias', None) if bias is None: - return F.conv_transpose2d(input, - weight, - stride=stride, - padding=padding, - output_padding=output_padding, - groups=groups, - dilation=dilation) + return F.conv_transpose2d(input, weight, **kwargs) else: - return F.conv_transpose2d(input, - weight, - stride=stride, - padding=padding, - output_padding=output_padding, - groups=groups, - dilation=dilation) + bias.reshape((-1, 1, 1)) + new_kwargs = kwargs + new_kwargs['bias'] = None + return F.conv_transpose2d(input, weight, **new_kwargs) + bias.reshape((-1, 1, 1)) @register_tracer_impl(F.conv_transpose3d, name='_bias_addition_impl') -def conv_transpose3d_impl(input, - weight, - bias=None, - stride=_triple(1), - padding=_triple(0), - output_padding=_triple(0), - groups=1, - dilation=_triple(1)): +def conv_transpose3d_impl(input, weight, **kwargs): + bias = getattr(kwargs, 'bias', None) if bias is None: - return F.conv_transpose3d(input, - weight, - stride=stride, - padding=padding, - output_padding=output_padding, - groups=groups, - dilation=dilation) + return F.conv_transpose3d(input, weight, **kwargs) else: - return F.conv_transpose3d(input, - weight, - stride=stride, - padding=padding, - output_padding=output_padding, - groups=groups, - dilation=dilation) + bias.reshape((-1, 1, 1, 1)) + new_kwargs = kwargs + new_kwargs['bias'] = None + return F.conv_transpose3d(input, weight, **new_kwargs) + bias.reshape((-1, 1, 1, 1)) @register_tracer_impl(torch.addmm, name='_bias_addition_impl') diff --git a/colossalai/auto_parallel/meta_profiler/metainfo.py b/colossalai/auto_parallel/meta_profiler/metainfo.py index 218187768a7b..44b1882e06cc 100644 --- a/colossalai/auto_parallel/meta_profiler/metainfo.py +++ b/colossalai/auto_parallel/meta_profiler/metainfo.py @@ -70,14 +70,28 @@ def target(self, target: Callable) -> None: if self._strategy is not None and self._target is not None: self.compute_metainfo() - def compute_sharded_opdata(self, operation_data: OperationData, sharding_spec: ShardingSpec) -> torch.Tensor: + def compute_sharded_opdata(self, operation_data: OperationData, sharding_spec: ShardingSpec): """ Compute sharded opdata based on the given data and sharding spec. """ - return OperationData(name=operation_data.name, - data=torch.zeros(sharding_spec.get_sharded_shape_per_device(), device="meta"), - type=operation_data.type, - logical_shape=operation_data.logical_shape) + + if isinstance(sharding_spec, ShardingSpec): + op_data = OperationData(name=operation_data.name, + data=torch.zeros(sharding_spec.get_sharded_shape_per_device(), device="meta"), + type=operation_data.type, + logical_shape=operation_data.logical_shape) + elif isinstance(sharding_spec, (list, tuple)): + data = operation_data.data + assert isinstance(data, (list, tuple)), f"Data Should be list or tuple, but got {type(data)}." + assert len(data) == len(sharding_spec), f"Length of data and sharding spec should be the same." + sharded_data = [] + for d, s in zip(data, sharding_spec): + sharded_data.append(torch.zeros(s.get_sharded_shape_per_device(), device="meta")) + op_data = OperationData(name=operation_data.name, data=sharded_data, type=operation_data.type) + else: + raise ValueError(f"Sharding spec should be ShardingSpec or list, but got {type(sharding_spec)}.") + + return op_data def compute_metainfo(self): """ diff --git a/colossalai/auto_parallel/passes/runtime_preparation_pass.py b/colossalai/auto_parallel/passes/runtime_preparation_pass.py index e63bfdfe730c..3be3084222fe 100644 --- a/colossalai/auto_parallel/passes/runtime_preparation_pass.py +++ b/colossalai/auto_parallel/passes/runtime_preparation_pass.py @@ -387,12 +387,13 @@ def module_params_sharding_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMes # This stream is created for overlaping the communication and computation. reduction_stream = torch.cuda.Stream() - def _add_hook_for_grad_communication(node, param): + def _add_hook_for_grad_communication(node, param, name=None): comm_actions = node.best_strategy.communication_actions - def _filter_param_to_hook(node, op_data, comm_action): - if node.op == 'call_module' and op_data.type == OperationDataType.PARAM and op_data.name == param.name and comm_action.comm_type == CommType.HOOK: + def _filter_param_to_hook(node, op_data, comm_action, name): + + if node.op == 'call_module' and op_data.type == OperationDataType.PARAM and op_data.name == name and comm_action.comm_type == CommType.HOOK: return True if node.op == 'get_attr' and isinstance( node._meta_data, torch.nn.parameter.Parameter) and comm_action.comm_type == CommType.HOOK: @@ -402,7 +403,7 @@ def _filter_param_to_hook(node, op_data, comm_action): for operation_data, comm_action in comm_actions.items(): comm_spec_to_use = comm_action.comm_spec # register hook to the parameters - if _filter_param_to_hook(node, operation_data, comm_action): + if _filter_param_to_hook(node, operation_data, comm_action, name=name): def wrapper(param, comm_spec, stream, overlap): @@ -442,7 +443,7 @@ def _shard_param(param, target_sharding_spec): param = _shard_param(param, target_sharding_spec) setattr(target_module, name, param) - _add_hook_for_grad_communication(node, param) + _add_hook_for_grad_communication(node, param, name) sharded_buffer_dict = {} # apply the sharding spec of buffers diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/bmm_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/bmm_handler.py index 9e1d958e15ab..da2b733c9f7a 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/bmm_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/bmm_handler.py @@ -81,7 +81,10 @@ def get_operation_data_mapping(self) -> Dict[str, OperationData]: def get_strategy_generator(self) -> List[StrategyGenerator]: op_data_mapping = self.get_operation_data_mapping() generators = [] - generators.append(BatchedMatMulStrategyGenerator(op_data_mapping, self.device_mesh)) + generator = BatchedMatMulStrategyGenerator(op_data_mapping, self.device_mesh) + # addbmm will shrink the first batch dim + generator.squeeze_batch_dim = True + generators.append(generator) return generators def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, List[ShardingStrategy]]: diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py index 5d70e131d1e9..1ce5a08f2d6b 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py @@ -776,10 +776,6 @@ def validate(self) -> bool: bias_op_data = self.op_data['bias'] assert bias_op_data.data.dim() < 3 and len(bias_op_data.logical_shape) == 2 - if self.op_data['output'].data.dim() == 2: - # addbmm will shrink the first batch dim - self.squeeze_batch_dim = True - def update_compute_cost(self, strategy: ShardingStrategy) -> ShardingStrategy: fwd_compute_cost = self.op_data['input'].data.shape[-1] * reduce(operator.mul, self.op_data['output'].data.shape) diff --git a/colossalai/fx/_meta_regist_12.py b/colossalai/fx/_meta_regist_12.py index 153214447223..52e8d63ae543 100644 --- a/colossalai/fx/_meta_regist_12.py +++ b/colossalai/fx/_meta_regist_12.py @@ -386,7 +386,7 @@ def meta_local_scalar_dense(self: torch.Tensor): @register_meta(aten.where.self) def meta_where_self(condition: torch.Tensor, self: torch.Tensor, other: torch.Tensor): result_type = torch.result_type(self, other) - return torch.empty_like(self, dtype=result_type) + return torch.empty_like(condition + self + other, dtype=result_type) @register_meta(aten.index.Tensor) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py index aa5a57474335..35f12ce83af2 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py @@ -1,22 +1,20 @@ -from faulthandler import disable from functools import partial -from xml.dom import WrongDocumentErr import pytest import torch import torch.multiprocessing as mp import torch.nn as nn -from typing_extensions import Self +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( - OperationData, OperationDataType, ShardingStrategy, StrategiesVector, ) from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import parameterize, rerun_if_address_is_in_use @@ -96,7 +94,7 @@ def check_addmm_function_handler(rank, input_shape, model_cls, world_size, port) meta_arg_names=meta_arg_names, node_type='bias_module') - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] # %m1 : torch.Tensor [#users=1] = placeholder[target=m1] @@ -109,6 +107,7 @@ def check_addmm_function_handler(rank, input_shape, model_cls, world_size, port) # return add graph = tracer.trace(model, meta_args=meta_args_for_tracer) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args_for_tracer.values()) # [input_1, m1, m2, addmm, output] node_list = list(graph.nodes) linear_node = node_list[4] diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py index 0ab70abffb4c..2069b5e8a4de 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py @@ -5,10 +5,12 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.batch_norm_handler import BatchNormModuleHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -38,13 +40,15 @@ def check_bn_module_handler(rank, world_size, port): strategy_number=strategy_number, input_args=[input], meta_arg_names=['input']) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] # %_0 : [#users=1] = call_module[target=0](args = (%input_1,), kwargs = {}) # return _0 - graph = tracer.trace(model, meta_args={"input": torch.rand(4, 16, 64, 64).to('meta')}) + meta_args = {"input": torch.rand(4, 16, 64, 64).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) bn_mod_node = list(graph.nodes)[1] strategies_vector = StrategiesVector(bn_mod_node) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py index 162d1fbba295..dca5f6e227fa 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py @@ -1,14 +1,14 @@ -from faulthandler import disable from functools import partial -from xml.dom import WrongDocumentErr import pytest import torch import torch.multiprocessing as mp import torch.nn as nn import torch.nn.functional as F -from typing_extensions import Self +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler, LinearModuleHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( OperationData, @@ -17,12 +17,10 @@ StrategiesVector, ) from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize from colossalai.utils import free_port from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -66,7 +64,7 @@ def check_linear_module_handler(rank, world_size, port): meta_arg_names=meta_arg_names, node_type='bias_module') - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %x : torch.Tensor [#users=1] = placeholder[target=x] # %weight : [#users=1] = get_attr[target=weight] @@ -74,8 +72,10 @@ def check_linear_module_handler(rank, world_size, port): # %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%x, %weight), kwargs = {}) # %add : [#users=1] = call_function[target=operator.add](args = (%linear, %bias), kwargs = {}) # return add - graph = tracer.trace(model, meta_args={"x": torch.rand(4, 4, 4, 16).to('meta')}) + meta_args = {"x": torch.rand(4, 4, 4, 16).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) linear_mod_node = list(graph.nodes)[3] strategies_vector = StrategiesVector(linear_mod_node) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py index c5c3f378197e..14d4a73fb4f8 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py @@ -1,13 +1,13 @@ -from faulthandler import disable from functools import partial -from xml.dom import WrongDocumentErr import pytest import torch import torch.multiprocessing as mp import torch.nn as nn -from typing_extensions import Self +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler, LinearModuleHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( OperationData, @@ -16,12 +16,10 @@ StrategiesVector, ) from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize from colossalai.utils import free_port from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -62,9 +60,11 @@ def check_linear_module_handler(rank, bias, world_size, port): meta_arg_names=meta_arg_names, node_type='bias_module') - tracer = ColoTracer() - graph = tracer.trace(model, meta_args={"x": torch.rand(4, 4, 4, 16).to('meta')}) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {"x": torch.rand(4, 4, 4, 16).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) linear_mod_node = list(graph.nodes)[3] strategies_vector = StrategiesVector(linear_mod_node) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py index 50385c0450a8..2414749f60a4 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py @@ -5,10 +5,12 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import BinaryElementwiseHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -52,10 +54,11 @@ def forward(self, x1, x2): input_args=input_args, meta_arg_names=meta_arg_names) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) meta_args = {'x1': torch.rand(4, 4).to('meta'), 'x2': torch.rand([4] * other_dim).to('meta')} graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) op_node = list(graph.nodes)[2] strategies_vector = StrategiesVector(op_node) @@ -172,12 +175,11 @@ def check_binary_elementwise_handler_with_int(rank, op, other_dim, model_cls, wo strategy_number=strategy_number, input_args=input_args, meta_arg_names=meta_arg_names) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) meta_args = {'x1': torch.rand(4, 4).to('meta')} graph = tracer.trace(model, meta_args=meta_args) - print(graph) - # assert False gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) if model_cls == BEOpModelWithNodeConst: op_node = list(graph.nodes)[2] diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py index 02c7e0671149..34c20c1ac0fe 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py @@ -5,10 +5,12 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import BMMFunctionHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -52,13 +54,11 @@ def check_2d_device_mesh(rank, module, world_size, port): strategy_number=strategy_number, input_args=input_args, meta_arg_names=meta_arg_names) - tracer = ColoTracer() - graph = tracer.trace(model, - meta_args={ - "x1": torch.rand(4, 8, 16).to('meta'), - 'x2': torch.rand(4, 16, 8).to('meta') - }) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {'x1': torch.rand(4, 8, 16).to('meta'), 'x2': torch.rand(4, 16, 8).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) linear_mod_node = list(graph.nodes)[2] strategies_vector = StrategiesVector(linear_mod_node) @@ -147,13 +147,11 @@ def check_1d_device_mesh(rank, module, world_size, port): strategy_number=strategy_number, input_args=input_args, meta_arg_names=meta_arg_names) - tracer = ColoTracer() - graph = tracer.trace(model, - meta_args={ - "x1": torch.rand(4, 8, 16).to('meta'), - 'x2': torch.rand(4, 16, 8).to('meta') - }) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {'x1': torch.rand(4, 8, 16).to('meta'), 'x2': torch.rand(4, 16, 8).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) linear_mod_node = list(graph.nodes)[2] strategies_vector = StrategiesVector(linear_mod_node) @@ -205,6 +203,7 @@ def check_1d_device_mesh(rank, module, world_size, port): @run_on_environment_flag(name='AUTO_PARALLEL') @parameterize('module', [BMMTensorMethodModule, BMMTorchFunctionModule]) +@parameterize('module', [BMMTensorMethodModule, BMMTorchFunctionModule]) @pytest.mark.dist @rerun_if_address_is_in_use() def test_bmm_handler(module): diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py index 2acd015c8f59..fe1a0d726db0 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py @@ -5,10 +5,12 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler, ConvModuleHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -41,9 +43,11 @@ def check_conv_module_handler(rank, bias, world_size, port): strategy_number=strategy_number, input_args=[input], meta_arg_names=['input']) - tracer = ColoTracer() - graph = tracer.trace(model, meta_args={"input": torch.rand(4, 4, 64, 64).to('meta')}) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {'input': torch.rand(4, 4, 64, 64).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) conv_mod_node = list(graph.nodes)[1] strategies_vector = StrategiesVector(conv_mod_node) @@ -178,7 +182,7 @@ def check_conv_function_handler(rank, bias, world_size, port): meta_arg_names=meta_arg_names, input_kwargs=input_kwargs) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] # %others : torch.Tensor [#users=1] = placeholder[target=others] @@ -189,6 +193,7 @@ def check_conv_function_handler(rank, bias, world_size, port): meta_args['bias'] = torch.rand(16).to('meta') graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) if bias: conv_mod_node = list(graph.nodes)[3] diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_default_reshape_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_default_reshape_handler.py index ea7c2b729635..8e5b7512ca0e 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_default_reshape_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_default_reshape_handler.py @@ -1,11 +1,13 @@ import torch import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import DefaultReshapeHandler from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.testing.pytest_wrapper import run_on_environment_flag @@ -23,19 +25,20 @@ def forward(self, input, other): @run_on_environment_flag(name='AUTO_PARALLEL') def test_reshape_handler(): model = ReshapeModel() - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] # %other : torch.Tensor [#users=1] = placeholder[target=other] # %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%input_1, %other), kwargs = {}) # %view : [#users=1] = call_method[target=view](args = (%conv2d, 2, -1), kwargs = {}) # return view - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(4, 4, 64, 64).to('meta'), - "other": torch.rand(4, 16, 3, 3).to('meta'), - }) + meta_args = { + "input": torch.rand(4, 4, 64, 64).to('meta'), + "other": torch.rand(16, 4, 3, 3).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) @@ -67,13 +70,13 @@ def test_reshape_handler(): assert mapping['input'].name == "conv2d" assert mapping['input'].data.is_meta - assert mapping['input'].data.shape == torch.Size([4, 4, 62, 62]) + assert mapping['input'].data.shape == torch.Size([4, 16, 62, 62]) assert mapping['input'].type == OperationDataType.ARG - assert mapping['input'].logical_shape == torch.Size([4, 4, 62, 62]) + assert mapping['input'].logical_shape == torch.Size([4, 16, 62, 62]) assert mapping['output'].name == "view" assert mapping['output'].data.is_meta - assert mapping['output'].data.shape == torch.Size([2, 30752]) + assert mapping['output'].data.shape == torch.Size([2, 123008]) assert mapping['output'].type == OperationDataType.OUTPUT # reshape handler is a following strategy handler, so the number of strategies is equal to the predecessor node. diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py index 5bce383dd0ab..a61d2ed5c108 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py @@ -5,13 +5,15 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.embedding_handler import ( EmbeddingFunctionHandler, EmbeddingModuleHandler, ) from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -60,9 +62,11 @@ def check_embedding_module_handler(rank, world_size, port): input_args=[input], meta_arg_names=['input']) - tracer = ColoTracer() - graph = tracer.trace(model, meta_args={"input": torch.rand(4, 16, 16).to('meta')}) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {"input": torch.randint(NUM_EMBEDDINGS, (4, 16, 16)).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) embedding_node = list(graph.nodes)[1] strategies_vector = StrategiesVector(embedding_node) @@ -171,18 +175,19 @@ def check_embedding_function_handler(rank, world_size, port): input_args=input_args, meta_arg_names=meta_arg_names, input_kwargs=input_kwargs) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] # %others : torch.Tensor [#users=1] = placeholder[target=others] # %embedding : [#users=1] = call_function[target=torch.nn.functional.embedding](args = (%input_1, %others), kwargs = {padding_idx: None, max_norm: None, norm_type: 2.0, scale_grad_by_freq: False, sparse: False}) # return embedding meta_args = { - "input": torch.rand(4, 16, 16).to('meta'), + "input": torch.randint(NUM_EMBEDDINGS, (4, 16, 16)).to('meta'), "others": torch.rand(NUM_EMBEDDINGS, EMBEDDING_DIMS).to('meta') } graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) embedding_node = list(graph.nodes)[2] strategies_vector = StrategiesVector(embedding_node) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py index 681e93a5fe16..fb611330946a 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py @@ -1,10 +1,13 @@ +import pytest import torch import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.getattr_handler import GetattrHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer class GetattrModel(nn.Module): @@ -18,15 +21,18 @@ def forward(self, input): return weight +@pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') def test_getattr_handler(): model = GetattrModel() - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=0] = placeholder[target=input] # %conv_weight : [#users=1] = get_attr[target=conv.weight] # return conv_weight - graph = tracer.trace(model, meta_args={'input': torch.rand(4, 4, 64, 64).to('meta')}) + meta_args = {'input': torch.rand(4, 4, 64, 64).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py index c72d2a6a80e8..9a29808ebb31 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py @@ -5,13 +5,15 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.default_reshape_handler import DefaultReshapeHandler from colossalai.auto_parallel.tensor_shard.node_handler.getitem_handler import GetItemHandler from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler from colossalai.auto_parallel.tensor_shard.node_handler.placeholder_handler import PlaceholderHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.fx.tracer.meta_patch.patched_module import linear from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers @@ -58,15 +60,15 @@ def check_getitem_from_tensor_handler(rank, getitem_index, world_size, port): meta_arg_names=['input', 'other'], node_type='following') - tracer = ColoTracer() - - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(8, 16, 64, 32).to('meta'), - "other": torch.rand(64, 32).to('meta'), - }) + tracer = ColoTracer(bias_addition_split=True) + meta_args = { + "input": torch.rand(8, 16, 64, 32).to('meta'), + "other": torch.rand(64, 32).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *list(meta_args.values())) linear_mod_node = list(graph.nodes)[2] getitem_mod_node = list(graph.nodes)[3] getitem_strategies_vector = StrategiesVector(getitem_mod_node) @@ -129,10 +131,12 @@ def test_getitem_from_tuple_handler(): # %split : [#users=1] = call_function[target=torch.functional.split](args = (%conv2d, 2), kwargs = {dim: 0}) # %getitem : [#users=1] = call_function[target=operator.getitem](args = (%split, 1), kwargs = {}) # return getitem - graph = tracer.trace(model, meta_args={ + meta_args = { "input": torch.rand(4, 4, 64, 64).to('meta'), - }) + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_layer_norm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_layer_norm_handler.py index f4d0063fd6b6..edd7bae6c979 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_layer_norm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_layer_norm_handler.py @@ -5,10 +5,12 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.layer_norm_handler import LayerNormModuleHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.fx.tracer.meta_patch.patched_module import linear from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers @@ -40,13 +42,15 @@ def check_ln_module_handler(rank, world_size, port): strategy_number=strategy_number, input_args=input_args, meta_arg_names=meta_arg_names) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] # %_0 : [#users=1] = call_module[target=0](args = (%input_1,), kwargs = {}) # return _0 - graph = tracer.trace(model, meta_args={"input": torch.rand(4, 16).to('meta')}) + meta_args = {"input": torch.rand(4, 16).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) ln_mod_node = list(graph.nodes)[1] strategies_vector = StrategiesVector(ln_mod_node) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py index 18afacf56b8e..bec5c3dc5e28 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py @@ -5,6 +5,9 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler, LinearModuleHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( OperationData, @@ -13,7 +16,6 @@ StrategiesVector, ) from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -49,9 +51,11 @@ def check_linear_module_handler(rank, bias, input_shape, world_size, port): input_args=input_args, meta_arg_names=meta_arg_names) - tracer = ColoTracer() - graph = tracer.trace(model, meta_args={"input": torch.rand(input_shape).to('meta')}) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {"input": torch.rand(input_shape).cuda()} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) linear_mod_node = list(graph.nodes)[1] strategies_vector = StrategiesVector(linear_mod_node) @@ -196,13 +200,12 @@ def check_linear_function_handler(rank, bias, input_shape, world_size, port): input_args=input_args, meta_arg_names=meta_arg_names) - tracer = ColoTracer() - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(input_shape).to('meta'), - 'others': torch.rand(32, 16).to('meta') - }) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {'input': torch.rand(input_shape).to('meta'), 'others': torch.rand(32, 16).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) + if bias: linear_func_node = list(graph.nodes)[3] else: diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_matmul_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_matmul_handler.py index 91b3ae27d599..46c3ff4434d7 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_matmul_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_matmul_handler.py @@ -2,6 +2,9 @@ import torch import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.matmul_handler import ( MatMulHandler, MatMulType, @@ -15,7 +18,6 @@ StrategiesVector, ) from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.testing.utils import parameterize @@ -57,9 +59,11 @@ def test_matmul_node_handler(tensor_shapes): model = MatMulModule() - tracer = ColoTracer() - graph = tracer.trace(model, meta_args={"x1": x1.to('meta'), 'x2': x2.to('meta')}) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {"x1": x1.to('meta'), 'x2': x2.to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) print(graph) @@ -124,7 +128,6 @@ def test_matmul_node_handler(tensor_shapes): input_sharding_spec = strategy.get_sharding_spec_by_name('x1') other_sharding_spec = strategy.get_sharding_spec_by_name('x2') output_sharding_spec = strategy.get_sharding_spec_by_name('matmul') - if matmul_type == MatMulType.DOT: # dot product will produce a scaler # results should fulfill: @@ -159,7 +162,10 @@ def test_matmul_node_handler(tensor_shapes): if len(other_shape) > 1: assert other_sharding_spec.sharding_sequence[-1] == output_sharding_spec.sharding_sequence[-1] if len(input_shape) > 1: - assert input_sharding_spec.sharding_sequence[-2] == output_sharding_spec.sharding_sequence[-2] + if len(other_shape) == 1: + assert input_sharding_spec.sharding_sequence[-2] == output_sharding_spec.sharding_sequence[-1] + else: + assert input_sharding_spec.sharding_sequence[-2] == output_sharding_spec.sharding_sequence[-2] if len(other_shape) > 2: assert other_sharding_spec.sharding_sequence[-2] == input_sharding_spec.sharding_sequence[-1] diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py index f219bc2f3976..aacc7d9aeb64 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py @@ -2,10 +2,12 @@ import torch import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.normal_pooling_handler import NormPoolingHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.fx.tracer.meta_patch.patched_module import linear from colossalai.testing.pytest_wrapper import run_on_environment_flag @@ -13,14 +15,16 @@ @run_on_environment_flag(name='AUTO_PARALLEL') def test_norm_pool_handler(): model = nn.Sequential(nn.MaxPool2d(4, padding=1).to('meta')) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] # %_0 : [#users=1] = call_module[target=0](args = (%input_1,), kwargs = {}) # return _0 - graph = tracer.trace(model, meta_args={"input": torch.rand(4, 4, 64, 64).to('meta')}) + meta_args = {"input": torch.rand(4, 4, 64, 64).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py index 26376c429ebc..5efbb4f5f6a4 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py @@ -1,10 +1,13 @@ +import pytest import torch import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.output_handler import OutputHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -18,19 +21,20 @@ def forward(self, x): return x, y +@pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') @parameterize('output_option', ['distributed', 'replicated']) @rerun_if_address_is_in_use() def test_output_handler(output_option): model = OutputModel() - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %x : torch.Tensor [#users=2] = placeholder[target=x] # %mul : [#users=1] = call_function[target=operator.mul](args = (%x, 2), kwargs = {}) # return (x, mul) - graph = tracer.trace(model, meta_args={ - "x": torch.rand(4, 4, 64, 64).to('meta'), - }) + meta_args = {'x': torch.rand(4, 4, 64, 64).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_permute_and_transpose_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_permute_and_transpose_handler.py index af03481d830e..0a5ad3e3523d 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_permute_and_transpose_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_permute_and_transpose_handler.py @@ -5,12 +5,14 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import PermuteHandler, TransposeHandler from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -88,7 +90,7 @@ def check_view_handler(rank, call_function, reshape_dims, model_cls, world_size, input_args=[input, other], meta_arg_names=['input', 'other'], node_type='following') - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) if model_cls.__name__ == 'ConvReshapeModel': # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] @@ -96,11 +98,11 @@ def check_view_handler(rank, call_function, reshape_dims, model_cls, world_size, # %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%input_1, %other), kwargs = {bias: None}) # %permute : [#users=1] = call_function[target=torch.permute](args = (%conv2d, (0, 2, 1, 3)), kwargs = {}) # return permute - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(8, 8, 66, 66).to('meta'), - "other": torch.rand(16, 8, 3, 3).to('meta'), - }) + meta_args = { + 'input': torch.rand(8, 8, 66, 66).to('meta'), + 'other': torch.rand(16, 8, 3, 3).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) if model_cls.__name__ == 'LinearReshapeModel': # graph(): @@ -109,13 +111,14 @@ def check_view_handler(rank, call_function, reshape_dims, model_cls, world_size, # %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %other), kwargs = {bias: None}) # %permute : [#users=1] = call_method[target=view](args = (%linear, 32, 4, 32, 32, 4), kwargs = {}) # return permute - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(8, 16, 64, 32).to('meta'), - "other": torch.rand(64, 32).to('meta'), - }) + meta_args = { + 'input': torch.rand(8, 16, 64, 32).to('meta'), + 'other': torch.rand(64, 32).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) previous_mod_node = list(graph.nodes)[2] reshape_node = list(graph.nodes)[3] diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_placeholder_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_placeholder_handler.py index 9bc453a27cdc..5e8fb51edbff 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_placeholder_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_placeholder_handler.py @@ -1,10 +1,13 @@ +import pytest import torch import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.placeholder_handler import PlaceholderHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -17,18 +20,21 @@ def forward(self, input): return input +@pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') @parameterize('placeholder_option', ['distributed', 'replicated']) @rerun_if_address_is_in_use() def test_placeholder_handler(placeholder_option): model = PlaceholderModel() - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] # return input_1 - graph = tracer.trace(model, meta_args={ + meta_args = { "input": torch.rand(4, 4, 64, 64).to('meta'), - }) + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py index f6895d92ab03..e589fff996c6 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py @@ -1,17 +1,15 @@ -from functools import partial - import torch import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler from colossalai.auto_parallel.tensor_shard.options import ShardOption from colossalai.auto_parallel.tensor_shard.sharding_strategy import StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer -from colossalai.testing import parameterize from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize class LinearModel(nn.Module): @@ -30,13 +28,11 @@ def check_shard_option(shard_option): mesh_shape = (2, 2) device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - tracer = ColoTracer() - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(4, 4, 4, 16).to('meta'), - 'others': torch.rand(32, 16).to('meta') - }) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {'input': torch.rand(4, 4, 4, 16).to('meta'), 'others': torch.rand(32, 16).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) linear_func_node = list(graph.nodes)[2] strategies_vector = StrategiesVector(linear_func_node) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py index c43ee292bedf..db463a4e9d6a 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py @@ -6,11 +6,13 @@ import torch.nn as nn import torch.nn.functional as F +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler from colossalai.auto_parallel.tensor_shard.node_handler.softmax_handler import SoftmaxHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -54,7 +56,7 @@ def check_split_handler(rank, softmax_dim, model_cls, world_size, port): input_args=[input, other], meta_arg_names=['input', 'other'], node_type='following') - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] @@ -62,13 +64,14 @@ def check_split_handler(rank, softmax_dim, model_cls, world_size, port): # %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %other), kwargs = {bias: None}) # %softmax : [#users=1] = call_method[target=split](args = (%linear,), kwargs = {}) # return split - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(8, 16, 64, 32).to('meta'), - "other": torch.rand(64, 32).to('meta'), - }) + meta_args = { + 'input': torch.rand(8, 16, 64, 32).to('meta'), + 'other': torch.rand(64, 32).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) previous_mod_node = list(graph.nodes)[2] split_node = list(graph.nodes)[3] diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py index 044aef19d38d..db59ea60ef4b 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py @@ -5,12 +5,14 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import SplitHandler from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -76,7 +78,7 @@ def check_split_handler(rank, split_size, split_dim, model_cls, world_size, port input_args=[input, other], meta_arg_names=['input', 'other'], node_type='following') - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) if model_cls.__name__ == 'ConvSplitModel': # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] @@ -84,11 +86,11 @@ def check_split_handler(rank, split_size, split_dim, model_cls, world_size, port # %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%input_1, %other), kwargs = {}) # %split : [#users=1] = call_method[target=split](args = (%conv2d,), kwargs = {}) # return split - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(8, 8, 66, 66).to('meta'), - "other": torch.rand(16, 8, 3, 3).to('meta'), - }) + meta_args = { + 'input': torch.rand(8, 8, 66, 66).to('meta'), + 'other': torch.rand(16, 8, 3, 3).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) if model_cls.__name__ == 'LinearSplitModel': # graph(): @@ -97,13 +99,14 @@ def check_split_handler(rank, split_size, split_dim, model_cls, world_size, port # %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %other), kwargs = {bias: None}) # %split : [#users=1] = call_method[target=split](args = (%linear,), kwargs = {}) # return split - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(8, 16, 64, 32).to('meta'), - "other": torch.rand(64, 32).to('meta'), - }) + meta_args = { + 'input': torch.rand(8, 16, 64, 32).to('meta'), + 'other': torch.rand(64, 32).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) previous_mod_node = list(graph.nodes)[2] split_node = list(graph.nodes)[3] diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py index 5fda4de1a101..add51d73f2a4 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py @@ -5,12 +5,13 @@ import torch.multiprocessing as mp import torch.nn as nn -from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler from colossalai.auto_parallel.tensor_shard.node_handler.sum_handler import SumHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -58,7 +59,7 @@ def check_sum_handler(rank, sum_dims, keepdim, world_size, port): meta_arg_names=['input', 'other'], node_type='following') - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] @@ -66,12 +67,13 @@ def check_sum_handler(rank, sum_dims, keepdim, world_size, port): # %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %other), kwargs = {bias: None}) # %sum_1 : [#users=1] = call_function[target=torch.sum](args = (%linear,), kwargs = {}) # return sum_1 - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(8, 16, 64, 32).to('meta'), - "other": torch.rand(64, 32).to('meta'), - }) + meta_args = { + "input": torch.rand(8, 16, 64, 32).to('meta'), + "other": torch.rand(64, 32).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) previous_mod_node = list(graph.nodes)[2] sum_node = list(graph.nodes)[3] @@ -116,107 +118,107 @@ def check_sum_handler(rank, sum_dims, keepdim, world_size, port): # check strategy name if sum_dims == (0, 2) and keepdim == False: - assert '[R, R, R, S1] -> [R, S1]_0' in strategy_name_list - assert '[R, S0, R, S1] -> [S0, S1]_1' in strategy_name_list - assert '[R, R, R, S1] -> [R, S1]_2' in strategy_name_list - assert '[R, R, R, S0] -> [R, S0]_3' in strategy_name_list - assert '[R, S1, R, S0] -> [S1, S0]_4' in strategy_name_list - assert '[R, R, R, S0] -> [R, S0]_5' in strategy_name_list - assert '[R, R, R, R] -> [R, R]_6' in strategy_name_list - assert '[R, S0, R, R] -> [S0, R]_7' in strategy_name_list + assert '[R, R, R, R] -> [R, R]_0' in strategy_name_list + assert '[R, S01, R, R] -> [S01, R]_1' in strategy_name_list + assert '[R, R, R, R] -> [R, R]_2' in strategy_name_list + assert '[R, R, R, R] -> [R, R]_3' in strategy_name_list + assert '[R, R, R, S01] -> [R, S01]_4' in strategy_name_list + assert '[R, R, R, S1] -> [R, S1]_5' in strategy_name_list + assert '[R, R, R, S0] -> [R, S0]_6' in strategy_name_list + assert '[R, R, R, R] -> [R, R]_7' in strategy_name_list assert '[R, R, R, R] -> [R, R]_8' in strategy_name_list - assert '[R, R, R, R] -> [R, R]_9' in strategy_name_list - assert '[R, S1, R, R] -> [S1, R]_10' in strategy_name_list - assert '[R, R, R, R] -> [R, R]_11' in strategy_name_list - assert '[R, R, R, S1] -> [R, S1]_12' in strategy_name_list - assert '[R, R, R, S0] -> [R, S0]_13' in strategy_name_list - assert '[R, R, R, R] -> [R, R]_14' in strategy_name_list - assert '[R, R, R, R] -> [R, R]_15' in strategy_name_list + assert '[R, R, R, S0] -> [R, S0]_9' in strategy_name_list + assert '[R, R, R, S1] -> [R, S1]_10' in strategy_name_list + assert '[R, R, R, S1] -> [R, S1]_11' in strategy_name_list + assert '[R, S0, R, S1] -> [S0, S1]_12' in strategy_name_list + assert '[R, R, R, S1] -> [R, S1]_13' in strategy_name_list + assert '[R, R, R, S0] -> [R, S0]_14' in strategy_name_list + assert '[R, S1, R, S0] -> [S1, S0]_15' in strategy_name_list assert '[R, R, R, S0] -> [R, S0]_16' in strategy_name_list - assert '[R, R, R, S1] -> [R, S1]_17' in strategy_name_list - assert '[R, R, R, R] -> [R, R]_18' in strategy_name_list - assert '[R, S01, R, R] -> [S01, R]_19' in strategy_name_list + assert '[R, R, R, R] -> [R, R]_17' in strategy_name_list + assert '[R, S0, R, R] -> [S0, R]_18' in strategy_name_list + assert '[R, R, R, R] -> [R, R]_19' in strategy_name_list assert '[R, R, R, R] -> [R, R]_20' in strategy_name_list - assert '[R, R, R, R] -> [R, R]_21' in strategy_name_list - assert '[R, R, R, S01] -> [R, S01]_22' in strategy_name_list + assert '[R, S1, R, R] -> [S1, R]_21' in strategy_name_list + assert '[R, R, R, R] -> [R, R]_22' in strategy_name_list assert '[R, R, R, R] -> [R, R]_23' in strategy_name_list if sum_dims == (0, 2) and keepdim == True: - assert '[R, R, R, S1] -> [R, R, R, S1]_0' in strategy_name_list - assert '[R, S0, R, S1] -> [R, S0, R, S1]_1' in strategy_name_list - assert '[R, R, R, S1] -> [R, R, R, S1]_2' in strategy_name_list - assert '[R, R, R, S0] -> [R, R, R, S0]_3' in strategy_name_list - assert '[R, S1, R, S0] -> [R, S1, R, S0]_4' in strategy_name_list - assert '[R, R, R, S0] -> [R, R, R, S0]_5' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_6' in strategy_name_list - assert '[R, S0, R, R] -> [R, S0, R, R]_7' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_0' in strategy_name_list + assert '[R, S01, R, R] -> [R, S01, R, R]_1' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_2' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_3' in strategy_name_list + assert '[R, R, R, S01] -> [R, R, R, S01]_4' in strategy_name_list + assert '[R, R, R, S1] -> [R, R, R, S1]_5' in strategy_name_list + assert '[R, R, R, S0] -> [R, R, R, S0]_6' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_7' in strategy_name_list assert '[R, R, R, R] -> [R, R, R, R]_8' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_9' in strategy_name_list - assert '[R, S1, R, R] -> [R, S1, R, R]_10' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_11' in strategy_name_list - assert '[R, R, R, S1] -> [R, R, R, S1]_12' in strategy_name_list - assert '[R, R, R, S0] -> [R, R, R, S0]_13' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_14' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_15' in strategy_name_list + assert '[R, R, R, S0] -> [R, R, R, S0]_9' in strategy_name_list + assert '[R, R, R, S1] -> [R, R, R, S1]_10' in strategy_name_list + assert '[R, R, R, S1] -> [R, R, R, S1]_11' in strategy_name_list + assert '[R, S0, R, S1] -> [R, S0, R, S1]_12' in strategy_name_list + assert '[R, R, R, S1] -> [R, R, R, S1]_13' in strategy_name_list + assert '[R, R, R, S0] -> [R, R, R, S0]_14' in strategy_name_list + assert '[R, S1, R, S0] -> [R, S1, R, S0]_15' in strategy_name_list assert '[R, R, R, S0] -> [R, R, R, S0]_16' in strategy_name_list - assert '[R, R, R, S1] -> [R, R, R, S1]_17' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_18' in strategy_name_list - assert '[R, S01, R, R] -> [R, S01, R, R]_19' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_17' in strategy_name_list + assert '[R, S0, R, R] -> [R, S0, R, R]_18' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_19' in strategy_name_list assert '[R, R, R, R] -> [R, R, R, R]_20' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_21' in strategy_name_list - assert '[R, R, R, S01] -> [R, R, R, S01]_22' in strategy_name_list + assert '[R, S1, R, R] -> [R, S1, R, R]_21' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_22' in strategy_name_list assert '[R, R, R, R] -> [R, R, R, R]_23' in strategy_name_list if sum_dims == 1 and keepdim == False: - assert '[S0, R, R, S1] -> [S0, R, S1]_0' in strategy_name_list - assert '[R, R, R, S1] -> [R, R, S1]_1' in strategy_name_list - assert '[R, R, S0, S1] -> [R, S0, S1]_2' in strategy_name_list - assert '[S1, R, R, S0] -> [S1, R, S0]_3' in strategy_name_list - assert '[R, R, R, S0] -> [R, R, S0]_4' in strategy_name_list - assert '[R, R, S1, S0] -> [R, S1, S0]_5' in strategy_name_list - assert '[S0, R, R, R] -> [S0, R, R]_6' in strategy_name_list + assert '[S01, R, R, R] -> [S01, R, R]_0' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R]_1' in strategy_name_list + assert '[R, R, S01, R] -> [R, S01, R]_2' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R]_3' in strategy_name_list + assert '[R, R, R, S01] -> [R, R, S01]_4' in strategy_name_list + assert '[R, R, R, S1] -> [R, R, S1]_5' in strategy_name_list + assert '[R, R, R, S0] -> [R, R, S0]_6' in strategy_name_list assert '[R, R, R, R] -> [R, R, R]_7' in strategy_name_list - assert '[R, R, S0, R] -> [R, S0, R]_8' in strategy_name_list - assert '[S1, R, R, R] -> [S1, R, R]_9' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R]_10' in strategy_name_list - assert '[R, R, S1, R] -> [R, S1, R]_11' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R]_8' in strategy_name_list + assert '[R, R, R, S0] -> [R, R, S0]_9' in strategy_name_list + assert '[R, R, R, S1] -> [R, R, S1]_10' in strategy_name_list + assert '[S0, R, R, S1] -> [S0, R, S1]_11' in strategy_name_list assert '[R, R, R, S1] -> [R, R, S1]_12' in strategy_name_list - assert '[R, R, R, S0] -> [R, R, S0]_13' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R]_14' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R]_15' in strategy_name_list - assert '[R, R, R, S0] -> [R, R, S0]_16' in strategy_name_list - assert '[R, R, R, S1] -> [R, R, S1]_17' in strategy_name_list - assert '[S01, R, R, R] -> [S01, R, R]_18' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R]_19' in strategy_name_list - assert '[R, R, S01, R] -> [R, S01, R]_20' in strategy_name_list + assert '[R, R, S0, S1] -> [R, S0, S1]_13' in strategy_name_list + assert '[S1, R, R, S0] -> [S1, R, S0]_14' in strategy_name_list + assert '[R, R, R, S0] -> [R, R, S0]_15' in strategy_name_list + assert '[R, R, S1, S0] -> [R, S1, S0]_16' in strategy_name_list + assert '[S0, R, R, R] -> [S0, R, R]_17' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R]_18' in strategy_name_list + assert '[R, R, S0, R] -> [R, S0, R]_19' in strategy_name_list + assert '[S1, R, R, R] -> [S1, R, R]_20' in strategy_name_list assert '[R, R, R, R] -> [R, R, R]_21' in strategy_name_list - assert '[R, R, R, S01] -> [R, R, S01]_22' in strategy_name_list + assert '[R, R, S1, R] -> [R, S1, R]_22' in strategy_name_list assert '[R, R, R, R] -> [R, R, R]_23' in strategy_name_list if sum_dims == 1 and keepdim == True: - assert '[S0, R, R, S1] -> [S0, R, R, S1]_0' in strategy_name_list - assert '[R, R, R, S1] -> [R, R, R, S1]_1' in strategy_name_list - assert '[R, R, S0, S1] -> [R, R, S0, S1]_2' in strategy_name_list - assert '[S1, R, R, S0] -> [S1, R, R, S0]_3' in strategy_name_list - assert '[R, R, R, S0] -> [R, R, R, S0]_4' in strategy_name_list - assert '[R, R, S1, S0] -> [R, R, S1, S0]_5' in strategy_name_list - assert '[S0, R, R, R] -> [S0, R, R, R]_6' in strategy_name_list + assert '[S01, R, R, R] -> [S01, R, R, R]_0' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_1' in strategy_name_list + assert '[R, R, S01, R] -> [R, R, S01, R]_2' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_3' in strategy_name_list + assert '[R, R, R, S01] -> [R, R, R, S01]_4' in strategy_name_list + assert '[R, R, R, S1] -> [R, R, R, S1]_5' in strategy_name_list + assert '[R, R, R, S0] -> [R, R, R, S0]_6' in strategy_name_list assert '[R, R, R, R] -> [R, R, R, R]_7' in strategy_name_list - assert '[R, R, S0, R] -> [R, R, S0, R]_8' in strategy_name_list - assert '[S1, R, R, R] -> [S1, R, R, R]_9' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_10' in strategy_name_list - assert '[R, R, S1, R] -> [R, R, S1, R]_11' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_8' in strategy_name_list + assert '[R, R, R, S0] -> [R, R, R, S0]_9' in strategy_name_list + assert '[R, R, R, S1] -> [R, R, R, S1]_10' in strategy_name_list + assert '[S0, R, R, S1] -> [S0, R, R, S1]_11' in strategy_name_list assert '[R, R, R, S1] -> [R, R, R, S1]_12' in strategy_name_list - assert '[R, R, R, S0] -> [R, R, R, S0]_13' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_14' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_15' in strategy_name_list - assert '[R, R, R, S0] -> [R, R, R, S0]_16' in strategy_name_list - assert '[R, R, R, S1] -> [R, R, R, S1]_17' in strategy_name_list - assert '[S01, R, R, R] -> [S01, R, R, R]_18' in strategy_name_list - assert '[R, R, R, R] -> [R, R, R, R]_19' in strategy_name_list - assert '[R, R, S01, R] -> [R, R, S01, R]_20' in strategy_name_list + assert '[R, R, S0, S1] -> [R, R, S0, S1]_13' in strategy_name_list + assert '[S1, R, R, S0] -> [S1, R, R, S0]_14' in strategy_name_list + assert '[R, R, R, S0] -> [R, R, R, S0]_15' in strategy_name_list + assert '[R, R, S1, S0] -> [R, R, S1, S0]_16' in strategy_name_list + assert '[S0, R, R, R] -> [S0, R, R, R]_17' in strategy_name_list + assert '[R, R, R, R] -> [R, R, R, R]_18' in strategy_name_list + assert '[R, R, S0, R] -> [R, R, S0, R]_19' in strategy_name_list + assert '[S1, R, R, R] -> [S1, R, R, R]_20' in strategy_name_list assert '[R, R, R, R] -> [R, R, R, R]_21' in strategy_name_list - assert '[R, R, R, S01] -> [R, R, R, S01]_22' in strategy_name_list + assert '[R, R, S1, R] -> [R, R, S1, R]_22' in strategy_name_list assert '[R, R, R, R] -> [R, R, R, R]_23' in strategy_name_list diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py index de35fe256ac7..f54b208c3380 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py @@ -1,10 +1,12 @@ import torch import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.tensor_constructor_handler import TensorConstructorHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.testing.pytest_wrapper import run_on_environment_flag @@ -22,7 +24,7 @@ def forward(self, x): @run_on_environment_flag(name='AUTO_PARALLEL') def test_where_handler(): model = TensorConstructorModel() - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %x : torch.Tensor [#users=2] = placeholder[target=x] # %size : [#users=1] = call_method[target=size](args = (%x,), kwargs = {}) @@ -30,10 +32,10 @@ def test_where_handler(): # %arange : [#users=1] = call_function[target=torch.arange](args = (%getitem,), kwargs = {}) # %add : [#users=1] = call_function[target=operator.add](args = (%x, %arange), kwargs = {}) # return add - graph = tracer.trace(model, meta_args={ - "x": torch.rand(10).to('meta'), - }) + meta_args = {'x': torch.rand(10).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_unary_element_wise_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_unary_element_wise_handler.py index a861cb7f57f0..bd88089734a7 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_unary_element_wise_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_unary_element_wise_handler.py @@ -1,12 +1,13 @@ import torch import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler from colossalai.auto_parallel.tensor_shard.node_handler.unary_elementwise_handler import UnaryElementwiseHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer -from colossalai.fx.tracer.meta_patch.patched_module import linear from colossalai.testing.pytest_wrapper import run_on_environment_flag @@ -25,19 +26,20 @@ def forward(self, input, other): @run_on_environment_flag(name='AUTO_PARALLEL') def test_elementwise_handler(): model = ReLuModel() - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] # %other : torch.Tensor [#users=1] = placeholder[target=other] # %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%input_1, %other), kwargs = {}) # %act : [#users=1] = call_module[target=act](args = (%conv2d,), kwargs = {}) # return act - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(4, 4, 64, 64).to('meta'), - "other": torch.rand(4, 16, 3, 3).to('meta'), - }) + meta_args = { + 'input': torch.rand(4, 4, 64, 64).to('meta'), + 'other': torch.rand(16, 4, 3, 3).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) @@ -69,13 +71,13 @@ def test_elementwise_handler(): assert mapping['input'].name == "conv2d" assert mapping['input'].data.is_meta - assert mapping['input'].data.shape == torch.Size([4, 4, 62, 62]) + assert mapping['input'].data.shape == torch.Size([4, 16, 62, 62]) assert mapping['input'].type == OperationDataType.ARG - assert mapping['input'].logical_shape == torch.Size([4, 4, 62, 62]) + assert mapping['input'].logical_shape == torch.Size([4, 16, 62, 62]) assert mapping['output'].name == "act" assert mapping['output'].data.is_meta - assert mapping['output'].data.shape == torch.Size([4, 4, 62, 62]) + assert mapping['output'].data.shape == torch.Size([4, 16, 62, 62]) assert mapping['output'].type == OperationDataType.OUTPUT # getitem is a following strategy handler, so the number of strategies is equal to the predecessor node. diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py index 8a96ac0d66f0..300e8f94e7fe 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py @@ -5,12 +5,14 @@ import torch.multiprocessing as mp import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.node_handler import ViewHandler from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use @@ -74,7 +76,7 @@ def check_view_handler(rank, tgt_shape, model_cls, world_size, port): input_args=[input, other], meta_arg_names=['input', 'other'], node_type='following') - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) if model_cls.__name__ == 'ConvViewModel': # graph(): # %input_1 : torch.Tensor [#users=1] = placeholder[target=input] @@ -82,11 +84,8 @@ def check_view_handler(rank, tgt_shape, model_cls, world_size, port): # %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%input_1, %other), kwargs = {}) # %view : [#users=1] = call_method[target=view](args = (%conv2d, 2, -1), kwargs = {}) # return view - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(8, 8, 66, 66).to('meta'), - "other": torch.rand(16, 8, 3, 3).to('meta'), - }) + meta_args = {'input': torch.rand(8, 8, 66, 66).to('meta'), 'other': torch.rand(16, 8, 3, 3).to('meta')} + graph = tracer.trace(model, meta_args=meta_args) if model_cls.__name__ == 'LinearViewModel': # graph(): @@ -95,13 +94,14 @@ def check_view_handler(rank, tgt_shape, model_cls, world_size, port): # %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %other), kwargs = {bias: None}) # %view : [#users=1] = call_method[target=view](args = (%linear, 32, 4, 32, 32, 4), kwargs = {}) # return view - graph = tracer.trace(model, - meta_args={ - "input": torch.rand(8, 16, 64, 32).to('meta'), - "other": torch.rand(64, 32).to('meta'), - }) + meta_args = { + 'input': torch.rand(8, 16, 64, 32).to('meta'), + 'other': torch.rand(64, 32).to('meta'), + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) previous_mod_node = list(graph.nodes)[2] view_node = list(graph.nodes)[3] diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py index 9838e2eb01c6..c150ebd90053 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py @@ -1,12 +1,13 @@ +import pytest import torch import torch.nn as nn -from colossalai.auto_parallel.tensor_shard.node_handler.where_handler import \ - WhereHandler -from colossalai.auto_parallel.tensor_shard.sharding_strategy import (OperationData, OperationDataType, StrategiesVector) +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer +from colossalai.auto_parallel.tensor_shard.node_handler.where_handler import WhereHandler +from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer -from colossalai.fx.tracer.meta_patch.patched_module import linear class ConvModel(nn.Module): @@ -19,22 +20,24 @@ def forward(self, condition, x, y): return output +@pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') def test_where_handler(): model = ConvModel() - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) # graph(): # %condition : torch.Tensor [#users=1] = placeholder[target=condition] # %x : torch.Tensor [#users=1] = placeholder[target=x] # %y : torch.Tensor [#users=1] = placeholder[target=y] # %where : [#users=1] = call_function[target=torch.where](args = (%condition, %x, %y), kwargs = {}) # return where - graph = tracer.trace(model, - meta_args={ - "condition": torch.rand(4, 4, 64, 64).to('meta'), - "x": torch.rand(4, 1, 64, 64).to('meta'), - "y": torch.rand(1, 4, 64, 64).to('meta') - }) + meta_args = { + 'condition': torch.rand(4, 4, 64, 64).to('meta'), + 'x': torch.rand(4, 1, 64, 64).to('meta'), + 'y': torch.rand(1, 4, 64, 64).to('meta') + } + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(model, graph) + shape_prop_pass(gm, *meta_args.values()) physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/utils.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/utils.py index 0cdfdbc9d0cd..28a8bbd9a4c1 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/utils.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/utils.py @@ -4,6 +4,9 @@ import torch from torch.fx import GraphModule +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass from colossalai.auto_parallel.tensor_shard.options import SolverOptions @@ -11,7 +14,6 @@ from colossalai.auto_parallel.tensor_shard.solver.cost_graph import CostGraph from colossalai.auto_parallel.tensor_shard.solver.solver import Solver from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.tracer.tracer import ColoTracer from colossalai.tensor.shape_consistency import to_global from colossalai.testing.comparison import assert_close @@ -79,14 +81,16 @@ def numerical_test_for_node_strategy(model: torch.nn.Module, model_to_shard, args_to_shard, kwargs_to_shard = _build_model_to_compare(model, input_args, input_kwargs, grad_to_shard_dict) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) input_sample = {} for input_arg, meta_arg_name in zip(input_args, meta_arg_names): - input_sample[meta_arg_name] = torch.rand(input_arg.shape).to('meta') + input_sample[meta_arg_name] = torch.empty(input_arg.shape, dtype=input_arg.dtype).to('meta') for meta_kwarg_name, input_kwarg in input_kwargs.items(): - input_sample[meta_kwarg_name] = torch.rand(input_kwarg.shape).to('meta') + input_sample[meta_kwarg_name] = torch.empty(input_kwarg.shape, dtype=input_kwarg.dtype).to('meta') graph = tracer.trace(root=model_to_shard, meta_args=input_sample) - gm = GraphModule(model_to_shard, graph, model_to_shard.__class__.__name__) + gm = ColoGraphModule(model_to_shard, graph, model_to_shard.__class__.__name__) + shape_prop_pass(gm, *input_sample.values()) + solver_options = SolverOptions() strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options) strategies_constructor.build_strategies_and_cost() diff --git a/tests/test_fx/test_pipeline/test_topo/test_topo.py b/tests/test_fx/test_pipeline/test_topo/test_topo.py index 75c74870523c..16da56250dc3 100644 --- a/tests/test_fx/test_pipeline/test_topo/test_topo.py +++ b/tests/test_fx/test_pipeline/test_topo/test_topo.py @@ -1,11 +1,13 @@ import pytest import torch import transformers -from topo_utils import split_model_and_get_DAG, check_topo, MLP +from topo_utils import MLP, check_topo, split_model_and_get_DAG BATCH_SIZE = 1 SEQ_LENGHT = 16 + +@pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') def test_opt(): MODEL_LIST = [ MLP, @@ -13,7 +15,10 @@ def test_opt(): ] CONFIGS = [ - {'dim': 10, 'layers': 12}, + { + 'dim': 10, + 'layers': 12 + }, transformers.OPTConfig(vocab_size=100, hidden_size=128, num_hidden_layers=4, num_attention_heads=4), ] @@ -21,15 +26,15 @@ def data_gen_MLP(): x = torch.zeros((16, 10)) kwargs = dict(x=x) return kwargs - + def data_gen_OPT(): input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64) attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64) kwargs = dict(input_ids=input_ids, attention_mask=attention_mask) return kwargs - + DATAGEN = [ - data_gen_MLP, + data_gen_MLP, data_gen_OPT, ] @@ -39,5 +44,6 @@ def data_gen_OPT(): # print(f'{top_mod=}\n----\n{topo=}') check_topo(top_mod, topo) + if __name__ == '__main__': - test_opt() \ No newline at end of file + test_opt() From 1a1d68b053942d9f97c9eb96f1ae4475dc52167b Mon Sep 17 00:00:00 2001 From: HELSON Date: Fri, 31 Mar 2023 09:20:33 +0800 Subject: [PATCH 086/413] [moe] add checkpoint for moe models (#3354) * [moe] add checkpoint for moe models * [hotfix] fix bugs in unit test --- colossalai/nn/layer/moe/__init__.py | 19 +- colossalai/nn/layer/moe/checkpoint.py | 40 +++ colossalai/nn/layer/moe/experts.py | 375 ++++++++++++----------- colossalai/nn/layer/moe/layers.py | 413 +++++++++++++------------- tests/test_moe/test_moe_checkpoint.py | 54 ++++ 5 files changed, 517 insertions(+), 384 deletions(-) create mode 100644 colossalai/nn/layer/moe/checkpoint.py create mode 100644 tests/test_moe/test_moe_checkpoint.py diff --git a/colossalai/nn/layer/moe/__init__.py b/colossalai/nn/layer/moe/__init__.py index 2a51344c31a4..05333fe965f1 100644 --- a/colossalai/nn/layer/moe/__init__.py +++ b/colossalai/nn/layer/moe/__init__.py @@ -1,9 +1,10 @@ -from .experts import Experts, FFNExperts, TPExperts -from .layers import MoeLayer, MoeModule -from .routers import MoeRouter, Top1Router, Top2Router -from .utils import NormalNoiseGenerator, UniformNoiseGenerator, build_ffn_experts - -__all__ = [ - 'Experts', 'FFNExperts', 'TPExperts', 'Top1Router', 'Top2Router', 'MoeLayer', 'NormalNoiseGenerator', - 'UniformNoiseGenerator', 'build_ffn_experts', 'MoeModule', 'MoeRouter' -] +from .checkpoint import load_moe_model, save_moe_model +from .experts import Experts, FFNExperts, TPExperts +from .layers import MoeLayer, MoeModule +from .routers import MoeRouter, Top1Router, Top2Router +from .utils import NormalNoiseGenerator, UniformNoiseGenerator, build_ffn_experts + +__all__ = [ + 'Experts', 'FFNExperts', 'TPExperts', 'Top1Router', 'Top2Router', 'MoeLayer', 'NormalNoiseGenerator', + 'UniformNoiseGenerator', 'build_ffn_experts', 'MoeModule', 'MoeRouter', 'save_moe_model', 'load_moe_model' +] diff --git a/colossalai/nn/layer/moe/checkpoint.py b/colossalai/nn/layer/moe/checkpoint.py new file mode 100644 index 000000000000..efda1f22252d --- /dev/null +++ b/colossalai/nn/layer/moe/checkpoint.py @@ -0,0 +1,40 @@ +import torch +import torch.distributed as dist +import torch.nn as nn + +from .experts import MoeExperts + + +def save_moe_model(model: nn.Module, save_path: str): + state_dict = model.state_dict() + if dist.get_rank() == 0: + torch.save(state_dict, save_path) + dist.barrier() + + +def load_moe_model(model: nn.Module, load_path: str): + state_dict = torch.load(load_path) + + for prefix, module in model.named_modules(): + if prefix.endswith('.moe_layer.experts'): + # this module should be an Experts instance + assert isinstance(module, MoeExperts) + + ep_rank = dist.get_rank(module.dist_info.ep_group) + num_local = module.num_local_experts + for i in range(num_local): + expert_id = ep_rank * num_local + i + for name, _ in module.experts[i].named_parameters(): + cur_key = f'{prefix}.experts.{i}.{name}' + param_key = f'{prefix}.experts.{expert_id}.{name}' + load_param = state_dict[param_key] + state_dict[cur_key] = load_param + + for name, _ in module.experts[0].named_parameters(): + pop_pre = f'{prefix}.experts.' + pop_suf = f'.{name}' + for i in range(num_local, module.num_total_experts): + pop_key = f'{pop_pre}{i}{pop_suf}' + state_dict.pop(pop_key) + + model.load_state_dict(state_dict) diff --git a/colossalai/nn/layer/moe/experts.py b/colossalai/nn/layer/moe/experts.py index 055afded9a20..4fb9ad332c24 100644 --- a/colossalai/nn/layer/moe/experts.py +++ b/colossalai/nn/layer/moe/experts.py @@ -1,172 +1,203 @@ -import math - -import torch -import torch.nn as nn -from colossalai.context import ParallelMode, seed -from colossalai.utils import get_current_device -from colossalai.context.moe_context import MOE_CONTEXT -from colossalai.zero.init_ctx import no_shard_zero_decrator -from typing import Type - - -class MoeExperts(nn.Module): - """Basic class for experts in MoE. It stores what kind of communication expersts use - to exchange tokens, how many experts in a single GPU and parallel information such as - expert parallel size, data parallel size and their distributed communication groups. - """ - - def __init__(self, comm_name: str, num_experts: int): - super().__init__() - assert comm_name in {"all_to_all", "all_gather"}, \ - "This kind of communication has not been implemented yet.\n Please use Experts build function." - self.comm_name = comm_name - # Get the configuration of experts' deployment and parallel information from moe contex - self.num_local_experts, self.dist_info = MOE_CONTEXT.get_info(num_experts) - - -@no_shard_zero_decrator(is_replicated=False) -class Experts(MoeExperts): - """A wrapper class to create experts. It will create E experts across the - moe model parallel group, where E is the number of experts. Every expert - is a instence of the class, 'expert' in initialization parameters. - - Args: - expert_cls (:class:`torch.nn.Module`): The class of all experts - num_experts (int): The number of experts - expert_args: Args used to initialize experts, the args could be found in corresponding expert class - """ - - def __init__(self, expert_cls: Type[nn.Module], num_experts: int, **expert_args): - super().__init__("all_to_all", num_experts) - - # Use seed to make every expert different from others - with seed(ParallelMode.TENSOR): - self.experts = nn.ModuleList([expert_cls(**expert_args) for _ in range(self.num_local_experts)]) - - # Attach parallel information for all parameters in Experts - for exp in self.experts: - for param in exp.parameters(): - param.__setattr__('moe_info', self.dist_info) - - def forward(self, inputs: torch.Tensor): - # Split inputs for each expert - expert_input = torch.chunk(inputs, self.num_local_experts, dim=1) - expert_output = [] - - # Get outputs from each expert - for i in range(self.num_local_experts): - expert_output.append(self.experts[i](expert_input[i])) - - # Concatenate all outputs together - output = torch.cat(expert_output, dim=1).contiguous() - return output - - -class FFNExperts(MoeExperts): - """Use torch.bmm to speed up for multiple experts. - """ - - def __init__(self, num_experts: int, d_model: int, d_ff: int, activation=None, drop_rate: float = 0): - super().__init__("all_to_all", num_experts) - - self.w1 = nn.Parameter(torch.empty(self.num_local_experts, d_model, d_ff, device=get_current_device())) - self.b1 = nn.Parameter(torch.empty(self.num_local_experts, 1, d_ff, device=get_current_device())) - - self.w2 = nn.Parameter(torch.empty(self.num_local_experts, d_ff, d_model, device=get_current_device())) - self.b2 = nn.Parameter(torch.empty(self.num_local_experts, 1, d_model, device=get_current_device())) - - s1 = math.sqrt(0.1 / d_model) - s2 = math.sqrt(0.1 / d_ff) - - with seed(ParallelMode.TENSOR): - nn.init.trunc_normal_(self.w1, std=s1) - nn.init.trunc_normal_(self.b1, std=s1) - nn.init.trunc_normal_(self.w2, std=s2) - nn.init.trunc_normal_(self.b2, std=s2) - - self.act = nn.GELU() if activation is None else activation - self.drop = nn.Dropout(p=drop_rate) - - for param in self.parameters(): - param.__setattr__('moe_info', self.dist_info) - - def forward(self, inputs): # inputs [g, el, c, h] - - el = inputs.size(1) - h = inputs.size(-1) - - inputs = inputs.transpose(0, 1) - inshape = inputs.shape - inputs = inputs.reshape(el, -1, h) - - out_ff = torch.baddbmm(self.b1, inputs, self.w1) - out_act = self.act(out_ff) - with seed(ParallelMode.TENSOR): - out_inter = self.drop(out_act) - - out_model = torch.baddbmm(self.b2, out_inter, self.w2) - with seed(ParallelMode.TENSOR): - outputs = self.drop(out_model) # outputs [el, gc, h] - - outputs = outputs.reshape(inshape) - outputs = outputs.transpose(0, 1).contiguous() - return outputs - - -class TPExperts(MoeExperts): - """Use tensor parallelism to split each expert evenly, which can deploy experts in - case that the number of experts can't be divied by maximum expert parallel size or - maximum expert parallel size can't be divied by the number of experts. - """ - - def __init__(self, num_experts: int, d_model: int, d_ff: int, activation=None, drop_rate: float = 0): - super().__init__("all_gather", MOE_CONTEXT.max_ep_size) - - assert d_ff % MOE_CONTEXT.max_ep_size == 0, \ - "d_ff should be divied by maximum expert parallel size" - - p_ff = d_ff // MOE_CONTEXT.max_ep_size - - self.w1 = nn.Parameter(torch.empty(num_experts, d_model, p_ff, device=get_current_device())) - self.b1 = nn.Parameter(torch.empty(num_experts, 1, p_ff, device=get_current_device())) - - self.w2 = nn.Parameter(torch.empty(num_experts, p_ff, d_model, device=get_current_device())) - self.b2 = nn.Parameter(torch.empty(num_experts, 1, d_model, device=get_current_device())) - - s1 = math.sqrt(0.1 / d_model) - s2 = math.sqrt(0.1 / d_ff) - - with seed(ParallelMode.TENSOR): - nn.init.trunc_normal_(self.w1, std=s1) - nn.init.trunc_normal_(self.b1, std=s1) - nn.init.trunc_normal_(self.w2, std=s2) - - nn.init.trunc_normal_(self.b2, std=s2) - - self.act = nn.GELU() if activation is None else activation - self.drop = nn.Dropout(p=drop_rate) - - self.w1.__setattr__('moe_info', self.dist_info) - self.w2.__setattr__('moe_info', self.dist_info) - self.b1.__setattr__('moe_info', self.dist_info) - - def forward(self, inputs): # inputs [g, e, c, h] - - e = inputs.size(1) - h = inputs.size(-1) - - inputs = inputs.transpose(0, 1) - inshape = inputs.shape - inputs = inputs.reshape(e, -1, h) - - out_ff = torch.baddbmm(self.b1, inputs, self.w1) - out_act = self.act(out_ff) - with seed(ParallelMode.TENSOR): - out_inter = self.drop(out_act) - - out_model = torch.baddbmm(self.b2, out_inter, self.w2) - outputs = self.drop(out_model) # outputs [e, gc, h] - - outputs = outputs.reshape(inshape) - outputs = outputs.transpose(0, 1).contiguous() - return outputs # outputs [g, e, c, h] +import math +from copy import deepcopy +from typing import Type + +import torch +import torch.distributed as dist +import torch.nn as nn + +from colossalai.context import ParallelMode, seed +from colossalai.context.moe_context import MOE_CONTEXT +from colossalai.utils import get_current_device +from colossalai.zero.init_ctx import no_shard_zero_decrator + + +class MoeExperts(nn.Module): + """Basic class for experts in MoE. It stores what kind of communication expersts use + to exchange tokens, how many experts in a single GPU and parallel information such as + expert parallel size, data parallel size and their distributed communication groups. + """ + + def __init__(self, comm_name: str, num_experts: int): + super().__init__() + assert comm_name in {"all_to_all", "all_gather"}, \ + "This kind of communication has not been implemented yet.\n Please use Experts build function." + self.comm_name = comm_name + self.num_total_experts = num_experts + # Get the configuration of experts' deployment and parallel information from moe contex + self.num_local_experts, self.dist_info = MOE_CONTEXT.get_info(num_experts) + + +@no_shard_zero_decrator(is_replicated=False) +class Experts(MoeExperts): + """A wrapper class to create experts. It will create E experts across the + moe model parallel group, where E is the number of experts. Every expert + is a instence of the class, 'expert' in initialization parameters. + + Args: + expert_cls (:class:`torch.nn.Module`): The class of all experts + num_experts (int): The number of experts + expert_args: Args used to initialize experts, the args could be found in corresponding expert class + """ + + def __init__(self, expert_cls: Type[nn.Module], num_experts: int, **expert_args): + super().__init__("all_to_all", num_experts) + + # Use seed to make every expert different from others + with seed(ParallelMode.TENSOR): + self.experts = nn.ModuleList([expert_cls(**expert_args) for _ in range(self.num_local_experts)]) + + # Attach parallel information for all parameters in Experts + for exp in self.experts: + for param in exp.parameters(): + param.__setattr__('moe_info', self.dist_info) + + def forward(self, inputs: torch.Tensor): + # Split inputs for each expert + expert_input = torch.chunk(inputs, self.num_local_experts, dim=1) + expert_output = [] + + # Get outputs from each expert + for i in range(self.num_local_experts): + expert_output.append(self.experts[i](expert_input[i])) + + # Concatenate all outputs together + output = torch.cat(expert_output, dim=1).contiguous() + return output + + def state_dict(self, destination=None, prefix='', keep_vars=False): + assert keep_vars == False, "Only support keep_vars=False now" + dp_rank = dist.get_rank(self.dist_info.dp_group) + ep_rank = dist.get_rank(self.dist_info.ep_group) + submodule_dict = dict() + example_submodule = None + for name, subm in self.experts.named_modules(): + if subm is self.experts: + continue + module_number = self.num_local_experts * ep_rank + int(name) + submodule_dict[module_number] = subm + example_submodule = subm + + if dp_rank == 0: + local_prefix = prefix + 'experts.' + buffer_module = deepcopy(example_submodule) + for i in range(self.num_total_experts): + source_rank = i // self.num_local_experts + current_prefix = local_prefix + str(i) + '.' + comm_module = submodule_dict.get(i, buffer_module) + for name, param in comm_module.named_parameters(): + dist.broadcast(param.data, src=source_rank, group=self.dist_info.ep_group) + if ep_rank == 0: + destination[current_prefix + name] = param.data.cpu() + + dist.barrier() + + +class FFNExperts(MoeExperts): + """Use torch.bmm to speed up for multiple experts. + """ + + def __init__(self, num_experts: int, d_model: int, d_ff: int, activation=None, drop_rate: float = 0): + super().__init__("all_to_all", num_experts) + + self.w1 = nn.Parameter(torch.empty(self.num_local_experts, d_model, d_ff, device=get_current_device())) + self.b1 = nn.Parameter(torch.empty(self.num_local_experts, 1, d_ff, device=get_current_device())) + + self.w2 = nn.Parameter(torch.empty(self.num_local_experts, d_ff, d_model, device=get_current_device())) + self.b2 = nn.Parameter(torch.empty(self.num_local_experts, 1, d_model, device=get_current_device())) + + s1 = math.sqrt(0.1 / d_model) + s2 = math.sqrt(0.1 / d_ff) + + with seed(ParallelMode.TENSOR): + nn.init.trunc_normal_(self.w1, std=s1) + nn.init.trunc_normal_(self.b1, std=s1) + nn.init.trunc_normal_(self.w2, std=s2) + nn.init.trunc_normal_(self.b2, std=s2) + + self.act = nn.GELU() if activation is None else activation + self.drop = nn.Dropout(p=drop_rate) + + for param in self.parameters(): + param.__setattr__('moe_info', self.dist_info) + + def forward(self, inputs): # inputs [g, el, c, h] + + el = inputs.size(1) + h = inputs.size(-1) + + inputs = inputs.transpose(0, 1) + inshape = inputs.shape + inputs = inputs.reshape(el, -1, h) + + out_ff = torch.baddbmm(self.b1, inputs, self.w1) + out_act = self.act(out_ff) + with seed(ParallelMode.TENSOR): + out_inter = self.drop(out_act) + + out_model = torch.baddbmm(self.b2, out_inter, self.w2) + with seed(ParallelMode.TENSOR): + outputs = self.drop(out_model) # outputs [el, gc, h] + + outputs = outputs.reshape(inshape) + outputs = outputs.transpose(0, 1).contiguous() + return outputs + + +class TPExperts(MoeExperts): + """Use tensor parallelism to split each expert evenly, which can deploy experts in + case that the number of experts can't be divied by maximum expert parallel size or + maximum expert parallel size can't be divied by the number of experts. + """ + + def __init__(self, num_experts: int, d_model: int, d_ff: int, activation=None, drop_rate: float = 0): + super().__init__("all_gather", MOE_CONTEXT.max_ep_size) + + assert d_ff % MOE_CONTEXT.max_ep_size == 0, \ + "d_ff should be divied by maximum expert parallel size" + + p_ff = d_ff // MOE_CONTEXT.max_ep_size + + self.w1 = nn.Parameter(torch.empty(num_experts, d_model, p_ff, device=get_current_device())) + self.b1 = nn.Parameter(torch.empty(num_experts, 1, p_ff, device=get_current_device())) + + self.w2 = nn.Parameter(torch.empty(num_experts, p_ff, d_model, device=get_current_device())) + self.b2 = nn.Parameter(torch.empty(num_experts, 1, d_model, device=get_current_device())) + + s1 = math.sqrt(0.1 / d_model) + s2 = math.sqrt(0.1 / d_ff) + + with seed(ParallelMode.TENSOR): + nn.init.trunc_normal_(self.w1, std=s1) + nn.init.trunc_normal_(self.b1, std=s1) + nn.init.trunc_normal_(self.w2, std=s2) + + nn.init.trunc_normal_(self.b2, std=s2) + + self.act = nn.GELU() if activation is None else activation + self.drop = nn.Dropout(p=drop_rate) + + self.w1.__setattr__('moe_info', self.dist_info) + self.w2.__setattr__('moe_info', self.dist_info) + self.b1.__setattr__('moe_info', self.dist_info) + + def forward(self, inputs): # inputs [g, e, c, h] + + e = inputs.size(1) + h = inputs.size(-1) + + inputs = inputs.transpose(0, 1) + inshape = inputs.shape + inputs = inputs.reshape(e, -1, h) + + out_ff = torch.baddbmm(self.b1, inputs, self.w1) + out_act = self.act(out_ff) + with seed(ParallelMode.TENSOR): + out_inter = self.drop(out_act) + + out_model = torch.baddbmm(self.b2, out_inter, self.w2) + outputs = self.drop(out_model) # outputs [e, gc, h] + + outputs = outputs.reshape(inshape) + outputs = outputs.transpose(0, 1).contiguous() + return outputs # outputs [g, e, c, h] diff --git a/colossalai/nn/layer/moe/layers.py b/colossalai/nn/layer/moe/layers.py index 259f53f1adf5..0969eb818229 100644 --- a/colossalai/nn/layer/moe/layers.py +++ b/colossalai/nn/layer/moe/layers.py @@ -1,203 +1,210 @@ -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -from colossalai.context.moe_context import MOE_CONTEXT -from colossalai.utils import get_current_device -from colossalai.nn.layer.moe._operation import COL_MOE_KERNEL_FLAG, AllToAll, AllGather, \ - ReduceScatter, MoeDispatch, MoeCombine -from colossalai.nn.layer.moe.experts import MoeExperts, Experts -from colossalai.nn.layer.moe.utils import UniformNoiseGenerator, NormalNoiseGenerator -from colossalai.nn.layer.moe.routers import MoeRouter, Top1Router, Top2Router -from colossalai.zero.init_ctx import no_shard_zero_context, no_shard_zero_decrator -from typing import Optional, Type, Tuple - - -@no_shard_zero_decrator(is_replicated=True) -class MoeLayer(nn.Module): - """A MoE layer, that puts its input tensor to its gate and uses the output logits - to router all tokens, is mainly used to exchange all tokens for every expert across - the moe tensor group by all to all comunication. Then it will get the output of all - experts and exchange the output. At last returns the output of the moe system. - - Args: - dim_model (int): Dimension of model. - num_experts (int): The number of experts. - router (MoeRouter): Instance of router used in routing. - experts (MoeExperts): Instance of experts generated by Expert. - """ - - def __init__(self, dim_model: int, num_experts: int, router: MoeRouter, experts: MoeExperts): - super().__init__() - self.d_model = dim_model - self.num_experts = num_experts - self.gate_weight = torch.nn.Parameter(torch.empty(num_experts, dim_model)) - self.router: MoeRouter = router - self.experts: MoeExperts = experts - self.use_kernel = True if COL_MOE_KERNEL_FLAG and MOE_CONTEXT.use_kernel_optim else False - self.ep_group = experts.dist_info.ep_group - self.ep_size = experts.dist_info.ep_size - self.num_local_experts = experts.num_local_experts - - nn.init.trunc_normal_(self.gate_weight, std=math.sqrt(0.1 / dim_model)) - - def a2a_process(self, dispatch_data: torch.Tensor): - expert_input = AllToAll.apply(dispatch_data, self.ep_group) - input_shape = expert_input.shape - expert_input = expert_input.reshape(self.ep_size, self.num_local_experts, -1, self.d_model) - expert_output = self.experts(expert_input) - expert_output = expert_output.reshape(input_shape) - expert_output = AllToAll.apply(expert_output, self.ep_group) - return expert_output - - def tp_process(self, dispatch_data: torch.Tensor): - expert_in = AllGather.apply(dispatch_data, self.ep_group) - expert_out = self.experts(expert_in) - expert_out = ReduceScatter.apply(expert_out, self.ep_group) - return expert_out - - def forward(self, inputs: torch.Tensor) -> Tuple: - # reshape the input tokens - tokens = inputs.reshape(-1, self.d_model) - - # the data type of the inputs in the gating should be fp32 - fp32_input = tokens.to(torch.float) - fp32_weight = self.gate_weight.to(torch.float) - gate_output = F.linear(fp32_input, fp32_weight) - - # the result from the router - route_result_list = self.router(inputs=gate_output, use_kernel=self.use_kernel, ep_group=self.ep_group) - - if self.use_kernel: - dispatch_data = MoeDispatch.apply(tokens, *route_result_list[1:]) - dispatch_data = dispatch_data.reshape(self.num_experts, -1, self.d_model) - else: - sec_mask_f = route_result_list[1].type_as(inputs) - dispatch_data = torch.matmul(sec_mask_f.permute(1, 2, 0), tokens) - - # dispatch_data [e, c, h] - if self.experts.comm_name == "all_to_all": - expert_output = self.a2a_process(dispatch_data) - elif self.experts.comm_name == "all_gather": - expert_output = self.tp_process(dispatch_data) - else: - raise NotImplementedError("This kind of communication has not been implemented yet.\n Please use Experts " - "build function.") - # expert_output [e, c, h] - if self.use_kernel: - expert_output = expert_output.reshape(-1, self.d_model) - ans = MoeCombine.apply(expert_output, *route_result_list) - else: - combine_weights = route_result_list[0].type_as(inputs) - combine_weights = combine_weights.view(combine_weights.shape[0], -1) - expert_output = expert_output.view(-1, expert_output.shape[-1]) - ans = torch.matmul(combine_weights, expert_output) - - ans = ans.reshape(inputs.shape) - l_aux = self.router.pop_routing_loss() - return ans, l_aux - - -class MoeModule(nn.Module): - """A class for users to create MoE modules in their models. - - Args: - dim_model (int): Hidden dimension of training model - num_experts (int): The number experts - top_k (int, optional): The number of experts for dispatchment of each token - capacity_factor_train (float, optional): Capacity factor in routing during training - capacity_factor_eval (float, optional): Capacity factor in routing during evaluation - min_capacity (int, optional): The minimum number of the capacity of each expert - noisy_policy (str, optional): The policy of noisy function. Now we have 'Jitter' and 'Gaussian'. - 'Jitter' can be found in `Switch Transformer paper`_. - 'Gaussian' can be found in `ViT-MoE paper`_. - drop_tks (bool, optional): Whether drops tokens in evaluation - use_residual (bool, optional): Makes this MoE layer a Residual MoE. - More information can be found in `Microsoft paper`_. - residual_instance (nn.Module, optional): The instance of residual module in Resiual MoE - expert_instance (MoeExperts, optional): The instance of experts module in MoeLayer - expert_cls (Type[nn.Module], optional): The class of each expert when no instance is given - expert_args (optional): The args of expert when no instance is given - - .. _Switch Transformer paper: - https://arxiv.org/abs/2101.03961 - .. _ViT-MoE paper: - https://arxiv.org/abs/2106.05974 - .. _Microsoft paper: - https://arxiv.org/abs/2201.05596 - """ - - def __init__(self, - dim_model: int, - num_experts: int, - top_k: int = 1, - capacity_factor_train: float = 1.25, - capacity_factor_eval: float = 2.0, - min_capacity: int = 4, - noisy_policy: Optional[str] = None, - drop_tks: bool = True, - use_residual: bool = False, - residual_instance: Optional[nn.Module] = None, - expert_instance: Optional[MoeExperts] = None, - expert_cls: Optional[Type[nn.Module]] = None, - **expert_args): - super().__init__() - - noisy_func = None - if noisy_policy is not None: - if noisy_policy == 'Jitter': - noisy_func = UniformNoiseGenerator() - elif noisy_policy == 'Gaussian': - noisy_func = NormalNoiseGenerator(num_experts) - else: - raise NotImplementedError("Unsupported input noisy policy") - - if top_k == 1: - moe_router_cls = Top1Router - elif top_k == 2: - moe_router_cls = Top2Router - else: - raise NotImplementedError("top_k > 2 is not supported yet") - - self.moe_router = moe_router_cls(capacity_factor_train=capacity_factor_train, - capacity_factor_eval=capacity_factor_eval, - min_capacity=min_capacity, - noisy_func=noisy_func, - drop_tks=drop_tks) - self.use_residual = use_residual - if use_residual: - if residual_instance is not None: - self.residual_module = residual_instance - else: - assert expert_cls is not None, \ - "Expert class can't be None when residual instance is not given" - self.residual_module = expert_cls(**expert_args) - - with no_shard_zero_context(): - self.residual_combine = nn.Linear(dim_model, 2, device=get_current_device()) - - if expert_instance is not None: - self.experts = expert_instance - else: - assert expert_cls is not None, \ - "Expert class can't be None when experts instance is not given" - self.experts = Experts(expert_cls, num_experts, **expert_args) - - self.moe_layer = MoeLayer(dim_model=dim_model, - num_experts=num_experts, - router=self.moe_router, - experts=self.experts) - - def forward(self, inputs: torch.Tensor): - moe_output, l_aux = self.moe_layer(inputs) - - if self.use_residual: - residual_output = self.residual_module(inputs) - combine_coef = self.residual_combine(inputs) - combine_coef = F.softmax(combine_coef, dim=-1) - output = moe_output * combine_coef[..., 0:1] + residual_output * combine_coef[..., 1:] - else: - output = moe_output - - return output, l_aux +import math +from typing import Optional, Tuple, Type + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from colossalai.context.moe_context import MOE_CONTEXT +from colossalai.nn.layer.moe._operation import ( + COL_MOE_KERNEL_FLAG, + AllGather, + AllToAll, + MoeCombine, + MoeDispatch, + ReduceScatter, +) +from colossalai.nn.layer.moe.experts import Experts, MoeExperts +from colossalai.nn.layer.moe.routers import MoeRouter, Top1Router, Top2Router +from colossalai.nn.layer.moe.utils import NormalNoiseGenerator, UniformNoiseGenerator +from colossalai.utils import get_current_device +from colossalai.zero.init_ctx import no_shard_zero_context, no_shard_zero_decrator + + +@no_shard_zero_decrator(is_replicated=True) +class MoeLayer(nn.Module): + """A MoE layer, that puts its input tensor to its gate and uses the output logits + to router all tokens, is mainly used to exchange all tokens for every expert across + the moe tensor group by all to all comunication. Then it will get the output of all + experts and exchange the output. At last returns the output of the moe system. + + Args: + dim_model (int): Dimension of model. + num_experts (int): The number of experts. + router (MoeRouter): Instance of router used in routing. + experts (MoeExperts): Instance of experts generated by Expert. + """ + + def __init__(self, dim_model: int, num_experts: int, router: MoeRouter, experts: MoeExperts): + super().__init__() + self.d_model = dim_model + self.num_experts = num_experts + self.gate_weight = torch.nn.Parameter(torch.empty(num_experts, dim_model)) + self.router: MoeRouter = router + self.experts: MoeExperts = experts + self.use_kernel = True if COL_MOE_KERNEL_FLAG and MOE_CONTEXT.use_kernel_optim else False + self.ep_group = experts.dist_info.ep_group + self.ep_size = experts.dist_info.ep_size + self.num_local_experts = experts.num_local_experts + + nn.init.trunc_normal_(self.gate_weight, std=math.sqrt(0.1 / dim_model)) + + def a2a_process(self, dispatch_data: torch.Tensor): + expert_input = AllToAll.apply(dispatch_data, self.ep_group) + input_shape = expert_input.shape + expert_input = expert_input.reshape(self.ep_size, self.num_local_experts, -1, self.d_model) + expert_output = self.experts(expert_input) + expert_output = expert_output.reshape(input_shape) + expert_output = AllToAll.apply(expert_output, self.ep_group) + return expert_output + + def tp_process(self, dispatch_data: torch.Tensor): + expert_in = AllGather.apply(dispatch_data, self.ep_group) + expert_out = self.experts(expert_in) + expert_out = ReduceScatter.apply(expert_out, self.ep_group) + return expert_out + + def forward(self, inputs: torch.Tensor) -> Tuple: + # reshape the input tokens + tokens = inputs.reshape(-1, self.d_model) + + # the data type of the inputs in the gating should be fp32 + fp32_input = tokens.to(torch.float) + fp32_weight = self.gate_weight.to(torch.float) + gate_output = F.linear(fp32_input, fp32_weight) + + # the result from the router + route_result_list = self.router(inputs=gate_output, use_kernel=self.use_kernel, ep_group=self.ep_group) + + if self.use_kernel: + dispatch_data = MoeDispatch.apply(tokens, *route_result_list[1:]) + dispatch_data = dispatch_data.reshape(self.num_experts, -1, self.d_model) + else: + sec_mask_f = route_result_list[1].type_as(inputs) + dispatch_data = torch.matmul(sec_mask_f.permute(1, 2, 0), tokens) + + # dispatch_data [e, c, h] + if self.experts.comm_name == "all_to_all": + expert_output = self.a2a_process(dispatch_data) + elif self.experts.comm_name == "all_gather": + expert_output = self.tp_process(dispatch_data) + else: + raise NotImplementedError("This kind of communication has not been implemented yet.\n Please use Experts " + "build function.") + # expert_output [e, c, h] + if self.use_kernel: + expert_output = expert_output.reshape(-1, self.d_model) + ans = MoeCombine.apply(expert_output, *route_result_list) + else: + combine_weights = route_result_list[0].type_as(inputs) + combine_weights = combine_weights.view(combine_weights.shape[0], -1) + expert_output = expert_output.view(-1, expert_output.shape[-1]) + ans = torch.matmul(combine_weights, expert_output) + + ans = ans.reshape(inputs.shape) + l_aux = self.router.pop_routing_loss() + return ans, l_aux + + +class MoeModule(nn.Module): + """A class for users to create MoE modules in their models. + + Args: + dim_model (int): Hidden dimension of training model + num_experts (int): The number experts + top_k (int, optional): The number of experts for dispatchment of each token + capacity_factor_train (float, optional): Capacity factor in routing during training + capacity_factor_eval (float, optional): Capacity factor in routing during evaluation + min_capacity (int, optional): The minimum number of the capacity of each expert + noisy_policy (str, optional): The policy of noisy function. Now we have 'Jitter' and 'Gaussian'. + 'Jitter' can be found in `Switch Transformer paper`_. + 'Gaussian' can be found in `ViT-MoE paper`_. + drop_tks (bool, optional): Whether drops tokens in evaluation + use_residual (bool, optional): Makes this MoE layer a Residual MoE. + More information can be found in `Microsoft paper`_. + residual_instance (nn.Module, optional): The instance of residual module in Resiual MoE + expert_instance (MoeExperts, optional): The instance of experts module in MoeLayer + expert_cls (Type[nn.Module], optional): The class of each expert when no instance is given + expert_args (optional): The args of expert when no instance is given + + .. _Switch Transformer paper: + https://arxiv.org/abs/2101.03961 + .. _ViT-MoE paper: + https://arxiv.org/abs/2106.05974 + .. _Microsoft paper: + https://arxiv.org/abs/2201.05596 + """ + + def __init__(self, + dim_model: int, + num_experts: int, + top_k: int = 1, + capacity_factor_train: float = 1.25, + capacity_factor_eval: float = 2.0, + min_capacity: int = 4, + noisy_policy: Optional[str] = None, + drop_tks: bool = True, + use_residual: bool = False, + residual_instance: Optional[nn.Module] = None, + expert_instance: Optional[MoeExperts] = None, + expert_cls: Optional[Type[nn.Module]] = None, + **expert_args): + super().__init__() + + noisy_func = None + if noisy_policy is not None: + if noisy_policy == 'Jitter': + noisy_func = UniformNoiseGenerator() + elif noisy_policy == 'Gaussian': + noisy_func = NormalNoiseGenerator(num_experts) + else: + raise NotImplementedError("Unsupported input noisy policy") + + if top_k == 1: + moe_router_cls = Top1Router + elif top_k == 2: + moe_router_cls = Top2Router + else: + raise NotImplementedError("top_k > 2 is not supported yet") + + self.moe_router = moe_router_cls(capacity_factor_train=capacity_factor_train, + capacity_factor_eval=capacity_factor_eval, + min_capacity=min_capacity, + noisy_func=noisy_func, + drop_tks=drop_tks) + self.use_residual = use_residual + if use_residual: + if residual_instance is not None: + self.residual_module = residual_instance + else: + assert expert_cls is not None, \ + "Expert class can't be None when residual instance is not given" + self.residual_module = expert_cls(**expert_args) + + with no_shard_zero_context(): + self.residual_combine = nn.Linear(dim_model, 2, device=get_current_device()) + + if expert_instance is not None: + my_experts = expert_instance + else: + assert expert_cls is not None, \ + "Expert class can't be None when experts instance is not given" + my_experts = Experts(expert_cls, num_experts, **expert_args) + + self.moe_layer = MoeLayer(dim_model=dim_model, + num_experts=num_experts, + router=self.moe_router, + experts=my_experts) + + def forward(self, inputs: torch.Tensor): + moe_output, l_aux = self.moe_layer(inputs) + + if self.use_residual: + residual_output = self.residual_module(inputs) + combine_coef = self.residual_combine(inputs) + combine_coef = F.softmax(combine_coef, dim=-1) + output = moe_output * combine_coef[..., 0:1] + residual_output * combine_coef[..., 1:] + else: + output = moe_output + + return output, l_aux diff --git a/tests/test_moe/test_moe_checkpoint.py b/tests/test_moe/test_moe_checkpoint.py new file mode 100644 index 000000000000..f99e74ea55c1 --- /dev/null +++ b/tests/test_moe/test_moe_checkpoint.py @@ -0,0 +1,54 @@ +import os +from functools import partial + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +import colossalai +from colossalai.context import MOE_CONTEXT +from colossalai.nn.layer.moe import load_moe_model, save_moe_model +from colossalai.testing import parameterize, rerun_if_address_is_in_use +from colossalai.utils import free_port, get_current_device +from colossalai.utils.model.colo_init_context import ColoInitContext +from tests.test_moe.test_moe_zero_init import MoeModel +from tests.test_tensor.common_utils import debug_print +from tests.test_zero.common import CONFIG + + +def exam_moe_checkpoint(): + with ColoInitContext(device=get_current_device()): + model = MoeModel(checkpoint=True) + save_moe_model(model, 'temp_path.pth') + + with ColoInitContext(device=get_current_device()): + other_model = MoeModel(checkpoint=True) + load_moe_model(other_model, 'temp_path.pth') + + state_0 = model.state_dict() + state_1 = other_model.state_dict() + for k, v in state_0.items(): + u = state_1.get(k) + assert torch.equal(u.data, v.data) + + if dist.get_rank() == 0: + os.remove('temp_path.pth') + + +def _run_dist(rank, world_size, port): + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + MOE_CONTEXT.setup(seed=42) + exam_moe_checkpoint() + + +@pytest.mark.dist +@pytest.mark.parametrize("world_size", [2, 4]) +@rerun_if_address_is_in_use() +def test_moe_checkpoint(world_size): + run_func = partial(_run_dist, world_size=world_size, port=free_port()) + mp.spawn(run_func, nprocs=world_size) + + +if __name__ == '__main__': + test_moe_checkpoint(world_size=4) From 5f2e34e6c96506a8d09f7d9b8db5e92f0e85f61d Mon Sep 17 00:00:00 2001 From: ver217 Date: Fri, 31 Mar 2023 16:06:13 +0800 Subject: [PATCH 087/413] [booster] implement Gemini plugin (#3352) * [booster] add gemini plugin * [booster] update docstr * [booster] gemini plugin add coloparam convertor * [booster] fix coloparam convertor * [booster] fix gemini plugin device * [booster] add gemini plugin test * [booster] gemini plugin ignore sync bn * [booster] skip some model * [booster] skip some model * [booster] modify test world size * [booster] modify test world size * [booster] skip test --- colossalai/booster/plugin/__init__.py | 3 +- colossalai/booster/plugin/gemini_plugin.py | 338 ++++++++++++++++++ .../test_plugin/test_gemini_plugin.py | 150 ++++++++ 3 files changed, 490 insertions(+), 1 deletion(-) create mode 100644 colossalai/booster/plugin/gemini_plugin.py create mode 100644 tests/test_booster/test_plugin/test_gemini_plugin.py diff --git a/colossalai/booster/plugin/__init__.py b/colossalai/booster/plugin/__init__.py index 3328fe2b9627..8e09b6cb281d 100644 --- a/colossalai/booster/plugin/__init__.py +++ b/colossalai/booster/plugin/__init__.py @@ -1,4 +1,5 @@ +from .gemini_plugin import GeminiPlugin from .plugin_base import Plugin from .torch_ddp_plugin import TorchDDPPlugin -__all__ = ['Plugin', 'TorchDDPPlugin'] +__all__ = ['Plugin', 'TorchDDPPlugin', 'GeminiPlugin'] diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py new file mode 100644 index 000000000000..c3c9d007d44f --- /dev/null +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -0,0 +1,338 @@ +import random +import warnings +from typing import Callable, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +from torch import Tensor +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO +from colossalai.cluster import DistCoordinator +from colossalai.gemini.memory_tracer import MemStats +from colossalai.interface import ModelWrapper, OptimizerWrapper +from colossalai.nn.parallel import GeminiDDP, zero_model_wrapper, zero_optim_wrapper +from colossalai.tensor.colo_parameter import ColoParameter +from colossalai.utils import get_current_device +from colossalai.utils.model.colo_init_context import _convert_to_coloparam + +from .plugin_base import Plugin + +__all__ = ['GeminiPlugin'] + + +def convert_to_colo_param(module: nn.Module) -> None: + """Convert module's paramters to ColoParameter. This is a workaround and will be deprecated when lazy init is compatible with Gemini. + + Args: + module (nn.Module): Module to be converted. + """ + converted_modules = set() # handle shared modules + converted_params = dict() # record mapping between (torch.Tensor, ColoTensor) to distinguish the same reference + + def convert_recursively(m: nn.Module): + for child in m.children(): + if child not in converted_modules: + converted_modules.add(child) + convert_recursively(child) + + for name, p in m.named_parameters(recurse=False): + assert not isinstance(p, ColoParameter) + if p in converted_params: + target = converted_params[p] + else: + target = _convert_to_coloparam(p, p.device, p.dtype) + converted_params[p] = target + setattr(m, name, target) + target.shared_param_modules.append(m) + + convert_recursively(module) + + # optimizer should replace params in group as well. This attr should be deleted after replacing to avoid memory leak + module._converted_params = converted_params + + +def replace_param_in_group(optimizer: Optimizer, converted_params: dict) -> None: + """Replace param in optimizer's group with converted ColoParameter. + + Args: + optimizer (Optimizer): Optimizer to be replaced. + converted_params (dict): Mapping between (torch.Tensor, ColoTensor). + """ + for group in optimizer.param_groups: + for i, p in enumerate(group['params']): + if p in converted_params: + group['params'][i] = converted_params[p] + + +class GeminiCheckpointIO(GeneralCheckpointIO): + + def __init__(self) -> None: + super().__init__() + self.coordinator = DistCoordinator() + + def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool = True): + """ + Load model from checkpoint with automatic unwrapping. + """ + # the model should be unwrapped in self.load_model via ModelWrapper.unwrap + return super().load_unsharded_model(model, checkpoint, strict=strict) + + def save_unsharded_model(self, model: GeminiDDP, checkpoint: str): + """ + Save model to checkpoint but only on master process. + """ + # the model should be unwrapped in self.load_model via ModelWrapper.unwrap + # as there is communication when get state dict, this must be called on all processes + state_dict = model.state_dict(only_rank_0=True) + if self.coordinator.is_master(): + self.save_checkpoint(state_dict, checkpoint) + + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str): + """ + Save optimizer to checkpoint but only on master process. + """ + # TODO(ver217): optimizer state dict is sharded + super().save_unsharded_optimizer(optimizer, checkpoint) + + def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + """ + Save model to checkpoint but only on master process. + """ + if self.coordinator.is_master(): + super().save_lr_scheduler(lr_scheduler, checkpoint) + + +class GeminiModel(ModelWrapper): + + def __init__(self, module: nn.Module, gemini_config: dict) -> None: + super().__init__(module) + # TODO(ver217): only support Gemini now + convert_to_colo_param(module) + self.module = zero_model_wrapper(module, zero_stage=3, gemini_config=gemini_config) + + def unwrap(self): + # as save/load state dict is coupled with the GeminiDDP, we only return GeminiDDP model + return self.module + + +class GeminiOptimizer(OptimizerWrapper): + + def __init__(self, module: GeminiDDP, optimizer: Optimizer, zero_optim_config: dict, optim_kwargs: dict) -> None: + replace_param_in_group(optimizer, module.module._converted_params) + del module.module._converted_params + optimizer = zero_optim_wrapper(module, optimizer, optim_config=zero_optim_config, **optim_kwargs) + super().__init__(optimizer) + + def backward(self, loss: Tensor, *args, **kwargs): + self.optim.backward(loss) + + def clip_grad_by_norm(self, + max_norm: Union[float, int], + norm_type: Union[float, int] = 2, + error_if_nonfinite: bool = False, + *args, + **kwargs) -> Tensor: + warnings.warn(f'Gemini controls grad clipping by itself, so you should not use clip_grad_by_norm') + + def clip_grad_by_value(self, clip_value: float, *args, **kwargs) -> None: + raise NotImplementedError('Gemini does not support clip_grad_by_value') + + +class GeminiPlugin(Plugin): + """ + Plugin for Gemini. + + Example: + >>> from colossalai.booster import Booster + >>> from colossalai.booster.plugin import GeminiPlugin + >>> + >>> model, train_dataset, optimizer, criterion = ... + >>> plugin = GeminiPlugin() + + >>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8) + >>> booster = Booster(plugin=plugin) + >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion) + + Args: + device (torch.device): device to place the model. + placement_policy (str, optional): "cpu", "cuda", "auto". Defaults to "cpu". + pin_memory (bool, optional): use pin memory on CPU. Defaults to False. + force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False. + strict_ddp_mode (bool, optional): use strict ddp mode (only use dp without other parallelism). Defaults to False. + search_range_mb (int, optional): chunk size searching range in MegaByte. Defaults to 32. + hidden_dim (int, optional): the hidden dimension of DNN. + Users can provide this argument to speed up searching. + If users do not know this argument before training, it is ok. We will use a default value 1024. + min_chunk_size_mb (float, optional): the minimum chunk size in MegaByte. + If the aggregate size of parameters is still samller than the minimum chunk size, + all parameters will be compacted into one small chunk. + memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer. + gpu_margin_mem_ratio (float, optional): The ratio of GPU remaining memory (after the first forward-backward) + which will be used when using hybrid CPU optimizer. + This argument is meaningless when `placement_policy` of `GeminiManager` is not "auto". + Defaults to 0.0. + initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32. + min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1. + growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2. + backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5. + growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000. + hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2. + max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32. + max_norm (float, optional): max_norm used for `clip_grad_norm`. You should notice that you shall not do + clip_grad_norm by yourself when using ZeRO DDP. The ZeRO optimizer will take care of clip_grad_norm. + norm_type (float, optional): norm_type used for `clip_grad_norm`. + """ + + def __init__( + self, + device: Optional[torch.device] = None, + placement_policy: str = "cpu", + pin_memory: bool = False, + force_outputs_fp32: bool = False, + strict_ddp_mode: bool = False, + search_range_mb: int = 32, + hidden_dim: Optional[int] = None, + min_chunk_size_mb: float = 32, + memstats: Optional[MemStats] = None, + gpu_margin_mem_ratio: float = 0.0, + initial_scale: float = 2**32, + min_scale: float = 1, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + max_scale: float = 2**32, + max_norm: float = 0.0, + norm_type: float = 2.0, + ) -> None: + + assert dist.is_initialized( + ), 'torch.distributed is not initialized, please use colossalai.launch to create the distributed environment' + self.rank = dist.get_rank() + self.world_size = dist.get_world_size() + self.gemini_config = dict( + device=(device or get_current_device()), + placement_policy=placement_policy, + pin_memory=pin_memory, + force_outputs_fp32=force_outputs_fp32, + strict_ddp_mode=strict_ddp_mode, + search_range_mb=search_range_mb, + hidden_dim=hidden_dim, + min_chunk_size_mb=min_chunk_size_mb, + memstats=memstats, + ) + self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio,) + self.optim_kwargs = dict(initial_scale=initial_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + min_scale=min_scale, + max_scale=max_scale, + max_norm=max_norm, + norm_type=norm_type) + + def support_no_sync(self) -> bool: + return False + + def control_precision(self) -> bool: + return True + + def supported_precisions(self) -> List[str]: + return ['fp16'] + + def control_device(self) -> bool: + return True + + def supported_devices(self) -> List[str]: + return ['cuda'] + + def prepare_train_dataloader(self, + dataset, + batch_size, + shuffle=False, + seed=1024, + drop_last=False, + pin_memory=False, + num_workers=0, + **kwargs): + r""" + Prepare a dataloader for distributed training. The dataloader will be wrapped by + `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. + + Note: + 1. Evaluation datasets should not be passed to this function. + + Args: + dataset (`torch.utils.data.Dataset`): The dataset to be loaded. + shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. + seed (int, optional): Random worker seed for sampling, defaults to 1024. + add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. + drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size + is not divisible by the batch size. If False and the size of dataset is not divisible by + the batch size, then the last batch will be smaller, defaults to False. + pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. + num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. + kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in + `DataLoader `_. + + Returns: + :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. + """ + _kwargs = kwargs.copy() + sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle) + + # Deterministic dataloader + def seed_worker(worker_id): + worker_seed = seed + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + random.seed(worker_seed) + + return DataLoader(dataset, + batch_size=batch_size, + sampler=sampler, + worker_init_fn=seed_worker, + drop_last=drop_last, + pin_memory=pin_memory, + num_workers=num_workers, + **_kwargs) + + def configure( + self, + model: nn.Module, + optimizer: Optimizer, + criterion: Callable = None, + dataloader: DataLoader = None, + lr_scheduler: LRScheduler = None, + ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + + if not isinstance(model, ModelWrapper): + # convert model to sync bn + # FIXME(ver217): gemini does not support sync bn + # In torch/nn/modules/_functions.py, line 22, ``mean, invstd = torch.batch_norm_stats(input, eps)`` will get fp32 mean and invstd even though the input is fp16. + # This inconsistency of dtype will cause the error. + # We have two possible solutions: + # 1. keep batch norm always in fp32. This is hard for gemini, as it use chunks. + # 2. patch sync bn or write a new on. This is relatively easy, but we need to test it. + # model = nn.SyncBatchNorm.convert_sync_batchnorm(model, None) + + # wrap the model with Gemini + model = GeminiModel(model, self.gemini_config) + + if not isinstance(optimizer, OptimizerWrapper): + optimizer = GeminiOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs) + + return model, optimizer, criterion, dataloader, lr_scheduler + + def control_checkpoint_io(self) -> bool: + return True + + def get_checkpoint_io(self) -> CheckpointIO: + return GeminiCheckpointIO() diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py new file mode 100644 index 000000000000..7a0d4a15d53a --- /dev/null +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -0,0 +1,150 @@ +from functools import partial + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin +from colossalai.nn.optimizer import HybridAdam +from colossalai.tensor.colo_parameter import ColoParameter +from colossalai.testing import rerun_if_address_is_in_use +from colossalai.utils import free_port +from tests.kit.model_zoo import model_zoo + + +def check_gemini_plugin(early_stop: bool = True): + """check gemini plugin over model zoo + + Args: + early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True. + """ + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, max_norm=1.0, initial_scale=2**5) + booster = Booster(plugin=plugin) + + passed_models = [] + failed_info = {} # (model_name, error) pair + + for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + # These models lead to CUDA error + if name in ('diffusers_auto_encoder_kl', 'diffusers_vq_model', 'diffusers_unet2d_model', 'timm_resmlp', + 'timm_gmixer_12_224', 'timm_gmlp_b16_224', 'timm_mixer_b16_224', 'timm_convnext'): + continue + # These models are not compatible with gemini + if name in [ + 'diffusers_clip_vision_model', + 'timm_resnet', + 'timm_beit', + 'timm_beitv2', + 'timm_eca_nfnet', + 'timm_efficientformer', + 'timm_hrnet_w18_small', + 'timm_nf_ecaresnet101', + 'timm_nf_regnet_b0', + 'timm_skresnet18', + 'timm_wide_resnet50_2', + 'timm_convit', + 'timm_dm_nfnet', + 'timm_swin_transformer', + 'torchaudio_conformer', + 'torchaudio_deepspeech', + 'torchaudio_wavernn', + 'torchaudio_tacotron', + 'deepfm_interactionarch', + 'deepfm_simpledeepfmnn', + 'dlrm', + 'dlrm_interactionarch', + 'torchvision_googlenet', + 'torchvision_inception_v3', + 'torchvision_mobilenet_v3_small', + 'torchvision_resnet18', + 'torchvision_resnext50_32x4d', + 'torchvision_wide_resnet50_2', + 'torchvision_vit_b_16', + 'torchvision_convnext_base', + 'torchvision_swin_s', + 'transformers_albert', + 'transformers_albert_for_pretraining', + 'transformers_bert', + 'transformers_bert_for_pretraining', + 'transformers_gpt_double_heads', + 'torchaudio_hubert_base', + ]: + continue + try: + model = model_fn() + optimizer = HybridAdam(model.parameters(), lr=1e-3) + criterion = lambda x: x.mean() + data = data_gen_fn() + + data = { + k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v + for k, v in data.items() + } + + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + for n, p in model.named_parameters(): + assert isinstance(p, ColoParameter), f'{n} is not a ColoParameter' + + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + + booster.backward(loss, optimizer) + optimizer.step() + passed_models.append(name) + except Exception as e: + failed_info[name] = e + if early_stop: + raise e + if dist.get_rank() == 0: + print(f'Passed models({len(passed_models)}): {passed_models}\n\n') + print(f'Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n') + assert len(failed_info) == 0, '\n'.join([f'{k}: {v}' for k, v in failed_info.items()]) + + +def check_dataloader_sharding(): + plugin = GeminiPlugin() + + # create a custom dasetset with 0 to 10 + dataset = torch.utils.data.TensorDataset(torch.arange(0, 10)) + train_dataloader = plugin.prepare_train_dataloader(dataset, batch_size=2) + + # get the first batch of data + batch = next(iter(train_dataloader))[0].cuda() + is_rank_0 = dist.get_rank() == 0 + + if is_rank_0: + batch_to_compare = batch.clone() + else: + batch_to_compare = batch + # pass to the rank 1 value to rank 0 + dist.broadcast(batch_to_compare, src=1) + + # compare on rank 0 + if is_rank_0: + assert not torch.equal(batch, + batch_to_compare), 'Same number was found across ranks but expected it to be different' + + +def run_dist(rank, world_size, port, early_stop: bool = True): + # init dist env + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + check_dataloader_sharding() + check_gemini_plugin(early_stop=early_stop) + + +@pytest.mark.skip(reason='Skip gemini plugin test due to OOM') +@rerun_if_address_is_in_use() +def test_gemini_plugin(early_stop: bool = True): + world_size = 2 + run_func = partial(run_dist, world_size=world_size, port=free_port(), early_stop=early_stop) + mp.spawn(run_func, nprocs=world_size) + + +if __name__ == '__main__': + test_gemini_plugin(early_stop=False) From 51cd2fec57990c27e2169c723404c2df333fa0c6 Mon Sep 17 00:00:00 2001 From: Jan Roudaut Date: Fri, 31 Mar 2023 17:32:44 +0200 Subject: [PATCH 088/413] Typofix: malformed `xformers` version (#3384) s/0.12.0/0.0.12/ --- examples/images/diffusion/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/images/diffusion/README.md b/examples/images/diffusion/README.md index 3f9690500130..c05cc6604e25 100644 --- a/examples/images/diffusion/README.md +++ b/examples/images/diffusion/README.md @@ -82,7 +82,7 @@ CUDA_EXT=1 pip install . #### Step 3:Accelerate with flash attention by xformers(Optional) -Notice that xformers will accelerate the training process in cost of extra disk space. The suitable version of xformers for this training process is 0.12.0. You can download xformers directly via pip. For more release versions, feel free to check its official website: [XFormers](./https://pypi.org/project/xformers/) +Notice that xformers will accelerate the training process in cost of extra disk space. The suitable version of xformers for this training process is 0.0.12. You can download xformers directly via pip. For more release versions, feel free to check its official website: [XFormers](./https://pypi.org/project/xformers/) ``` pip install xformers==0.0.12 From dd367ce79539972d38f9afb307bdd2a7df470501 Mon Sep 17 00:00:00 2001 From: Jan Roudaut Date: Sat, 1 Apr 2023 17:09:40 +0200 Subject: [PATCH 089/413] [doc] polish diffusion example (#3386) * [examples/images/diffusion]: README.md: typo fixes * Update README.md * Grammar fixes * Reformulated "Step 3" (xformers) introduction to the cost => at the cost + reworded pip availability. --- examples/images/diffusion/README.md | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/examples/images/diffusion/README.md b/examples/images/diffusion/README.md index c05cc6604e25..0c7f42ded318 100644 --- a/examples/images/diffusion/README.md +++ b/examples/images/diffusion/README.md @@ -37,7 +37,7 @@ This project is in rapid development. ## Installation -### Option #1: install from source +### Option #1: Install from source #### Step 1: Requirements To begin with, make sure your operating system has the cuda version suitable for this exciting training session, which is cuda11.6/11.8. For your convience, we have set up the rest of packages here. You can create and activate a suitable [conda](https://conda.io/) environment named `ldm` : @@ -54,11 +54,11 @@ conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit pip install transformers diffusers invisible-watermark ``` -#### Step 2:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website +#### Step 2: Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website You can install the latest version (0.2.7) from our official website or from source. Notice that the suitable version for this training is colossalai(0.2.5), which stands for torch(1.12.1). -##### Download suggested verision for this training +##### Download suggested version for this training ``` pip install colossalai==0.2.5 @@ -80,9 +80,9 @@ cd ColossalAI CUDA_EXT=1 pip install . ``` -#### Step 3:Accelerate with flash attention by xformers(Optional) +#### Step 3: Accelerate with flash attention by xformers (Optional) -Notice that xformers will accelerate the training process in cost of extra disk space. The suitable version of xformers for this training process is 0.0.12. You can download xformers directly via pip. For more release versions, feel free to check its official website: [XFormers](./https://pypi.org/project/xformers/) +Notice that xformers will accelerate the training process at the cost of extra disk space. The suitable version of xformers for this training process is 0.0.12, which can be downloaded directly via pip. For more release versions, feel free to check its official website: [XFormers](https://pypi.org/project/xformers/) ``` pip install xformers==0.0.12 @@ -120,7 +120,7 @@ docker run --rm \ /bin/bash ######################## -# Insider Container # +# Inside a Container # ######################## # Once you have entered the docker container, go to the stable diffusion directory for training cd examples/images/diffusion/ @@ -132,14 +132,14 @@ bash train_colossalai.sh ``` It is important for you to configure your volume mapping in order to get the best training experience. -1. **Mandatory**, mount your prepared data to `/data/scratch` via `-v :/data/scratch`, where you need to replace `` with the actual data path on your machine. Notice that within docker we need to transform Win expresison into Linuxd, e.g. C:\User\Desktop into /c/User/Desktop. +1. **Mandatory**, mount your prepared data to `/data/scratch` via `-v :/data/scratch`, where you need to replace `` with the actual data path on your machine. Notice that within docker we need to transform the Windows path to a Linux one, e.g. `C:\User\Desktop` into `/mnt/c/User/Desktop`. 2. **Recommended**, store the downloaded model weights to your host machine instead of the container directory via `-v :/root/.cache/huggingface`, where you need to replace the `` with the actual path. In this way, you don't have to repeatedly download the pretrained weights for every `docker run`. 3. **Optional**, if you encounter any problem stating that shared memory is insufficient inside container, please add `-v /dev/shm:/dev/shm` to your `docker run` command. ## Download the model checkpoint from pretrained -### stable-diffusion-v2-base(Recommand) +### stable-diffusion-v2-base (Recommended) ``` wget https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt @@ -182,12 +182,12 @@ python main.py --logdir /tmp/ --train --base configs/train_colossalai.yaml --ckp ### Training config -You can change the trainging config in the yaml file +You can change the training config in the yaml file - devices: device number used for training, default = 8 - max_epochs: max training epochs, default = 2 - precision: the precision type used in training, default = 16 (fp16), you must use fp16 if you want to apply colossalai -- placement_policy: the training strategy supported by Colossal AI, defult = 'cuda', which refers to loading all the parameters into cuda memory. On the other hand, 'cpu' refers to 'cpu offload' strategy while 'auto' enables 'Gemini', both featured by Colossal AI. +- placement_policy: the training strategy supported by Colossal AI, default = 'cuda', which refers to loading all the parameters into cuda memory. On the other hand, 'cpu' refers to 'cpu offload' strategy while 'auto' enables 'Gemini', both featured by Colossal AI. - more information about the configuration of ColossalAIStrategy can be found [here](https://pytorch-lightning.readthedocs.io/en/latest/advanced/model_parallel.html#colossal-ai) @@ -202,7 +202,8 @@ python main.py --logdir /tmp/ -t -b configs/Teyvat/train_colossalai_teyvat.yaml ``` ## Inference -you can get yout training last.ckpt and train config.yaml in your `--logdir`, and run by + +You can get your training last.ckpt and train config.yaml in your `--logdir`, and run by ``` python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms --outdir ./output \ From 94c24d94447405bdd5e1f3ded997137f38147329 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Chris=20Sundstr=C3=B6m?= Date: Sun, 2 Apr 2023 16:00:57 +0200 Subject: [PATCH 090/413] Improve grammar and punctuation (#3398) Minor changes to improve grammar and punctuation. --- README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 2752027a683f..65c8ae166608 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![logo](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/colossal-ai_logo_vertical.png)](https://www.colossalai.org/) - Colossal-AI: Making large AI models cheaper, faster and more accessible + Colossal-AI: Making large AI models cheaper, faster, and more accessible

    Paper | Documentation | @@ -115,7 +115,7 @@ distributed training and inference in a few lines. - [PatrickStar](https://arxiv.org/abs/2108.05818) - Friendly Usage - - Parallelism based on configuration file + - Parallelism based on the configuration file - Inference - [Energon-AI](https://github.com/hpcaitech/EnergonAI) @@ -129,7 +129,7 @@ distributed training and inference in a few lines.

    -- Save 50% GPU resources, and 10.7% acceleration +- Save 50% GPU resources and 10.7% acceleration ### GPT-2 @@ -151,7 +151,7 @@ distributed training and inference in a few lines. ### OPT -- [Open Pretrained Transformer (OPT)](https://github.com/facebookresearch/metaseq), a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because public pretrained model weights. +- [Open Pretrained Transformer (OPT)](https://github.com/facebookresearch/metaseq), a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because of public pre-trained model weights. - 45% speedup fine-tuning OPT at low cost in lines. [[Example]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/opt) [[Online Serving]](https://colossalai.org/docs/advanced_tutorials/opt_service) Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) for more details. @@ -245,7 +245,7 @@ Please visit our [documentation](https://www.colossalai.org/) and [examples](htt

    - Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU -- Keep in a sufficiently high running speed +- Keep at a sufficiently high running speed

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    @@ -304,7 +304,7 @@ Requirements: - Python >= 3.7 - CUDA >= 11.0 -If you encounter any problem about installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository. +If you encounter any problem with installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository. ### Install from PyPI @@ -322,9 +322,9 @@ However, if you want to build the PyTorch extensions during installation, you ca CUDA_EXT=1 pip install colossalai ``` -**Otherwise, CUDA kernels will be built during runtime when you actually need it.** +**Otherwise, CUDA kernels will be built during runtime when you actually need them.** -We also keep release the nightly version to PyPI on a weekly basis. This allows you to access the unreleased features and bug fixes in the main branch. +We also keep releasing the nightly version to PyPI every week. This allows you to access the unreleased features and bug fixes in the main branch. Installation can be made via ```bash @@ -333,7 +333,7 @@ pip install colossalai-nightly ### Download From Source -> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. :) +> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problems. :) ```shell git clone https://github.com/hpcaitech/ColossalAI.git @@ -423,6 +423,6 @@ To cite this project, you can use the following BibTeX citation. } ``` -Colossal-AI has been accepted as official tutorials by top conference [SC](https://sc22.supercomputing.org/), [AAAI](https://aaai.org/Conferences/AAAI-23/), [PPoPP](https://ppopp23.sigplan.org/), [CVPR](https://cvpr2023.thecvf.com/), [ISC](https://www.isc-hpc.com/), etc. +Colossal-AI has been accepted as official tutorial by top conferences [SC](https://sc22.supercomputing.org/), [AAAI](https://aaai.org/Conferences/AAAI-23/), [PPoPP](https://ppopp23.sigplan.org/), [CVPR](https://cvpr2023.thecvf.com/), [ISC](https://www.isc-hpc.com/), etc.

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    From 30412866e0c6860de0787cd9c5e9a5bfffdc712c Mon Sep 17 00:00:00 2001 From: Camille Zhong <44392324+Camille7777@users.noreply.github.com> Date: Mon, 3 Apr 2023 10:11:03 +0800 Subject: [PATCH 091/413] [chatgpt] add pre-trained model RoBERTa for RLHF stage 2 & 3 (#3223) * Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) * Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. * Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci * add test for reward model training * Update test_ci.sh * Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. * Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) * Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. * Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci * Update test_ci.sh * Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. * update roberta with coati --- .../Chat/coati/models/roberta/__init__.py | 5 +++ .../coati/models/roberta/roberta_actor.py | 35 +++++++++++++++++ .../coati/models/roberta/roberta_critic.py | 38 ++++++++++++++++++ .../Chat/coati/models/roberta/roberta_rm.py | 39 +++++++++++++++++++ applications/Chat/examples/inference.py | 9 ++++- applications/Chat/examples/test_ci.sh | 20 ++++++++++ applications/Chat/examples/train_dummy.py | 10 ++++- applications/Chat/examples/train_prompts.py | 17 ++++++-- .../Chat/examples/train_reward_model.py | 9 ++++- 9 files changed, 173 insertions(+), 9 deletions(-) create mode 100644 applications/Chat/coati/models/roberta/__init__.py create mode 100644 applications/Chat/coati/models/roberta/roberta_actor.py create mode 100644 applications/Chat/coati/models/roberta/roberta_critic.py create mode 100644 applications/Chat/coati/models/roberta/roberta_rm.py diff --git a/applications/Chat/coati/models/roberta/__init__.py b/applications/Chat/coati/models/roberta/__init__.py new file mode 100644 index 000000000000..0f4a8de067b1 --- /dev/null +++ b/applications/Chat/coati/models/roberta/__init__.py @@ -0,0 +1,5 @@ +from .roberta_actor import RoBERTaActor +from .roberta_critic import RoBERTaCritic +from .roberta_rm import RoBERTaRM + +__all__ = ['RoBERTaActor', 'RoBERTaCritic', 'RoBERTaRM'] \ No newline at end of file diff --git a/applications/Chat/coati/models/roberta/roberta_actor.py b/applications/Chat/coati/models/roberta/roberta_actor.py new file mode 100644 index 000000000000..e35fa6eb19a8 --- /dev/null +++ b/applications/Chat/coati/models/roberta/roberta_actor.py @@ -0,0 +1,35 @@ +from typing import Optional + +from transformers.models.roberta.configuration_roberta import RobertaConfig +from transformers.models.roberta.modeling_roberta import RobertaForCausalLM + +from ..base import Actor + +class RoBERTaActor(Actor): + """ + RoBERTa Actor model. + + Args: + pretrained (str): Pretrained model name or path. + config (RoBERTaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the low-rank approximation. + lora_train_bias (str): LoRA bias training mode. + """ + + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[RobertaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = RobertaForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = RobertaForCausalLM(config) + else: + model = RobertaForCausalLM(RobertaConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/roberta/roberta_critic.py b/applications/Chat/coati/models/roberta/roberta_critic.py new file mode 100644 index 000000000000..c8dc0d9e14f2 --- /dev/null +++ b/applications/Chat/coati/models/roberta/roberta_critic.py @@ -0,0 +1,38 @@ +from typing import Optional + +import torch.nn as nn +from transformers.models.roberta.configuration_roberta import RobertaConfig +from transformers.models.roberta.modeling_roberta import RobertaModel + +from ..base import Critic + + +class RoBERTaCritic(Critic): + """ + RoBERTa Critic model. + + Args: + pretrained (str): Pretrained model name or path. + config (RoBERTa Config): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the low-rank approximation. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[RobertaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none', + **kwargs) -> None: + if pretrained is not None: + model = RobertaModel.from_pretrained(pretrained, add_pooling_layer=False) + elif config is not None: + model = RobertaModel(config) + else: + model = RobertaModel(RobertaConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + value_head = nn.Linear(model.config.hidden_size, 1) + super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/Chat/coati/models/roberta/roberta_rm.py b/applications/Chat/coati/models/roberta/roberta_rm.py new file mode 100644 index 000000000000..77075052978b --- /dev/null +++ b/applications/Chat/coati/models/roberta/roberta_rm.py @@ -0,0 +1,39 @@ +from typing import Optional + +import torch.nn as nn +from transformers import RobertaConfig, RobertaModel + + +from ..base import RewardModel + + +class RoBERTaRM(RewardModel): + """ + RoBERTa Reward model. + + Args: + pretrained (str): Pretrained model name or path. + config (RoBERTaConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): Rank of the low-rank approximation. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: Optional[str] = None, + config: Optional[RobertaConfig] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = RobertaModel.from_pretrained(pretrained, add_pooling_layer=False) + elif config is not None: + model = RobertaModel(config) + else: + model = RobertaModel(RobertaConfig()) + if checkpoint: + model.gradient_checkpointing_enable() + + value_head = nn.Linear(model.config.hidden_size, 1) + value_head.weight.data.normal_(mean=0.0, std=1/(model.config.hidden_size + 1)) + super().__init__(model, value_head, lora_rank, lora_train_bias) \ No newline at end of file diff --git a/applications/Chat/examples/inference.py b/applications/Chat/examples/inference.py index f75950804d2e..ae59d91c1822 100644 --- a/applications/Chat/examples/inference.py +++ b/applications/Chat/examples/inference.py @@ -4,7 +4,8 @@ from coati.models.bloom import BLOOMActor from coati.models.gpt import GPTActor from coati.models.opt import OPTActor -from transformers import AutoTokenizer +from coati.models.roberta import RoBERTaActor +from transformers import AutoTokenizer, RobertaTokenizer from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer @@ -16,6 +17,8 @@ def eval(args): actor = BLOOMActor(pretrained=args.pretrain).to(torch.cuda.current_device()) elif args.model == 'opt': actor = OPTActor(pretrained=args.pretrain).to(torch.cuda.current_device()) + elif args.model == 'roberta': + actor = RoBERTaActor(pretrained=args.pretrain).to(torch.cuda.current_device()) else: raise ValueError(f'Unsupported model "{args.model}"') @@ -31,6 +34,8 @@ def eval(args): tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m') + elif args.model == 'roberta': + tokenizer = RobertaTokenizer.from_pretrained("roberta-base") else: raise ValueError(f'Unsupported model "{args.model}"') @@ -49,7 +54,7 @@ def eval(args): if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'roberta']) # We suggest to use the pretrained model from HuggingFace, use pretrain to configure model parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--model_path', type=str, default=None) diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index db1d0b64e3b3..64cf68a0a13f 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -40,6 +40,13 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ --save_path ${BASE}/actor_checkpoint_dummy.pt python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'gpt2' --model gpt2 +torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ + --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ + --pretrain 'roberta-base' --model roberta --lora_rank 4\ + --save_path ${BASE}/actor_checkpoint_dummy.pt +python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'roberta-base' --model roberta + rm -rf ${BASE}/actor_checkpoint_dummy.pt # train prompts @@ -68,6 +75,13 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ --save_path ${BASE}/actor_checkpoint_prompts.pt python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'gpt2' --model gpt2 +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ + --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ + --pretrain 'roberta-base' --model roberta --lora_rank 4\ + --save_path ${BASE}/actor_checkpoint_prompts.pt +python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'roberta-base' --model roberta + rm -rf ${BASE}/actor_checkpoint_prompts.pt # train rm @@ -94,4 +108,10 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ --test True --lora_rank 4 +torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ + --pretrain 'roberta-base' --model 'roberta' \ + --strategy colossalai_zero2 --loss_fn 'log_exp'\ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ + --test True --lora_rank 4 + rm -rf ${BASE}/rm_ckpt.pt diff --git a/applications/Chat/examples/train_dummy.py b/applications/Chat/examples/train_dummy.py index d944b018de8f..4ac7ace44803 100644 --- a/applications/Chat/examples/train_dummy.py +++ b/applications/Chat/examples/train_dummy.py @@ -6,11 +6,12 @@ from coati.models.bloom import BLOOMActor, BLOOMCritic from coati.models.gpt import GPTActor, GPTCritic from coati.models.opt import OPTActor, OPTCritic +from coati.models.roberta import RoBERTaActor, RoBERTaCritic from coati.trainer import PPOTrainer from coati.trainer.callbacks import SaveCheckpoint from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast +from transformers import AutoTokenizer, BloomTokenizerFast, RobertaTokenizer from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer from colossalai.nn.optimizer import HybridAdam @@ -46,6 +47,9 @@ def main(args): elif args.model == 'opt': actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) critic = OPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'roberta': + actor = RoBERTaActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + critic = RoBERTaCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) else: raise ValueError(f'Unsupported model "{args.model}"') @@ -69,6 +73,8 @@ def main(args): tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + elif args.model == 'roberta': + tokenizer = RobertaTokenizer.from_pretrained("roberta-base") else: raise ValueError(f'Unsupported model "{args.model}"') @@ -128,7 +134,7 @@ def main(args): parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive') - parser.add_argument('--model', type=str, default='gpt2', choices=['gpt2', 'bloom', 'opt']) + parser.add_argument('--model', type=str, default='gpt2', choices=['gpt2', 'bloom', 'opt', 'roberta']) parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--save_path', type=str, default='actor_checkpoint_dummy.pt') parser.add_argument('--need_optim_ckpt', type=bool, default=False) diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index 6643796d7a8b..5ded6d8432ed 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -8,13 +8,14 @@ from coati.models.gpt import GPTRM, GPTActor, GPTCritic from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM from coati.models.opt import OPTRM, OPTActor, OPTCritic +from coati.models.roberta import RoBERTaRM, RoBERTaActor, RoBERTaCritic from coati.trainer import PPOTrainer from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy from coati.utils import prepare_llama_tokenizer_and_embedding from torch.optim import Adam from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler -from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer +from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer, RobertaTokenizer from colossalai.nn.optimizer import HybridAdam @@ -44,6 +45,8 @@ def main(args): initial_model = OPTActor(pretrained=args.pretrain) elif args.model == 'llama': initial_model = LlamaActor(pretrained=args.pretrain) + elif args.model == 'roberta': + initial_model = RoBERTaActor(pretrained=args.pretrain) else: raise ValueError(f'Unsupported actor model "{args.model}"') @@ -60,6 +63,8 @@ def main(args): reward_model = OPTRM(pretrained=args.rm_pretrain) elif rm_model_name == 'llama': reward_model = LlamaRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'roberta': + reward_model = RoBERTaRM(pretrained=args.rm_pretrain) else: raise ValueError(f'Unsupported reward model "{rm_model_name}"') @@ -79,6 +84,8 @@ def main(args): actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) elif args.model == 'llama': actor = LlamaActor(pretrained=args.pretrain, lora_rank=args.lora_rank) + elif args.model == 'roberta': + actor = RoBERTaActor(pretrained=args.pretrain, lora_rank=args.lora_rank) else: raise ValueError(f'Unsupported actor model "{args.model}"') @@ -90,6 +97,8 @@ def main(args): critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) elif rm_model_name == 'llama': critic = LlamaCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + elif rm_model_name == 'roberta': + critic = RoBERTaCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) else: raise ValueError(f'Unsupported reward model "{rm_model_name}"') @@ -119,6 +128,8 @@ def main(args): elif args.model == 'llama': tokenizer = LlamaTokenizer.from_pretrained(args.pretrain) tokenizer.eos_token = '<\s>' + elif args.model == 'roberta': + tokenizer = RobertaTokenizer.from_pretrained("roberta-base") else: raise ValueError(f'Unsupported model "{args.model}"') @@ -200,9 +211,9 @@ def tokenize_fn(texts): choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive', help='strategy to use') - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama', 'roberta']) parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--rm_model', default=None, choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--rm_model', default=None, choices=['gpt2', 'bloom', 'opt', 'llama', 'roberta']) parser.add_argument('--rm_path', type=str, default=None) parser.add_argument('--rm_pretrain', type=str, default=None) parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts') diff --git a/applications/Chat/examples/train_reward_model.py b/applications/Chat/examples/train_reward_model.py index 729dfa23128f..aa1b51dea7f9 100644 --- a/applications/Chat/examples/train_reward_model.py +++ b/applications/Chat/examples/train_reward_model.py @@ -11,12 +11,13 @@ from coati.models.gpt import GPTRM from coati.models.llama import LlamaRM from coati.models.opt import OPTRM +from coati.models.roberta import RoBERTaRM from coati.trainer import RewardModelTrainer from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer, LlamaTokenizer +from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer, LlamaTokenizer, RobertaTokenizer from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer from colossalai.nn.optimizer import HybridAdam @@ -47,6 +48,8 @@ def train(args): model = DebertaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) elif args.model == 'llama': model = LlamaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + elif args.model == 'roberta': + model = RoBERTaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) else: raise ValueError(f'Unsupported model "{args.model}"') @@ -67,6 +70,8 @@ def train(args): tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v3-large') elif args.model == 'llama': tokenizer = LlamaTokenizer.from_pretrained(args.pretrain) + elif args.model == 'roberta': + tokenizer = RobertaTokenizer.from_pretrained("roberta-base") else: raise ValueError(f'Unsupported model "{args.model}"') max_len = args.max_len @@ -140,7 +145,7 @@ def train(args): parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive') - parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta', 'llama'], default='bloom') + parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta', 'llama', 'roberta'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--model_path', type=str, default=None) parser.add_argument('--need_optim_ckpt', type=bool, default=False) From 638a07a7f9b504e6c9781e9aa2a9b6c5e9dc49ed Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 3 Apr 2023 17:12:22 +0800 Subject: [PATCH 092/413] [test] fixed gemini plugin test (#3411) * [test] fixed gemini plugin test * polish code * polish code --- .../offload/base_offload_module.py | 16 ++--- .../auto_parallel/offload/mem_optimize.py | 21 +++--- colossalai/auto_parallel/offload/runtime.py | 59 +++++++++-------- colossalai/auto_parallel/offload/util.py | 33 ++++++---- .../test_offload/test_perf.py | 64 +++++++++---------- .../test_plugin/test_gemini_plugin.py | 60 ++++++----------- .../test_zero/low_level_zero/test_zero1_2.py | 2 + 7 files changed, 124 insertions(+), 131 deletions(-) diff --git a/colossalai/auto_parallel/offload/base_offload_module.py b/colossalai/auto_parallel/offload/base_offload_module.py index 59cea4ece266..3a32f0722a1b 100644 --- a/colossalai/auto_parallel/offload/base_offload_module.py +++ b/colossalai/auto_parallel/offload/base_offload_module.py @@ -1,10 +1,11 @@ -from typing import Optional, Set from functools import partial +from typing import Optional, Set + import torch import torch.nn as nn -from colossalai.nn.parallel.data_parallel import _cast_float from colossalai.gemini.tensor_utils import free_storage +from colossalai.nn.parallel.data_parallel import _cast_float from .region_manager import RegionManager from .util import GlobalRuntimeInfo @@ -20,10 +21,7 @@ class BaseOffloadModule: is_sync (bool): synchronous mode or not. """ - def __init__(self, - model: nn.Module, - region_manager: RegionManager, - is_sync=True): + def __init__(self, model: nn.Module, region_manager: RegionManager, is_sync=True): self.model = model self.region_manager = region_manager @@ -69,8 +67,8 @@ def _post_backward(self): for p in self.model.parameters(): p.grad = None - GlobalRuntimeInfo.fwd_prefetch_event_map.clear() - GlobalRuntimeInfo.bwd_prefetch_event_map.clear() + GlobalRuntimeInfo().fwd_prefetch_event_map.clear() + GlobalRuntimeInfo().bwd_prefetch_event_map.clear() def grad_handle(self, p, grad): empty_grad = torch.empty_like(grad) @@ -82,7 +80,7 @@ def grad_handle(self, p, grad): self.overflow_counter += region.has_inf_or_nan master_stream = torch.cuda.current_stream() with torch.cuda.stream(self.grad_offload_stream): - GlobalRuntimeInfo.d2h_stream.wait_stream(master_stream) + GlobalRuntimeInfo().d2h_stream.wait_stream(master_stream) region.move_grad_to_cpu() return empty_grad diff --git a/colossalai/auto_parallel/offload/mem_optimize.py b/colossalai/auto_parallel/offload/mem_optimize.py index 02778696a106..d56166dea982 100644 --- a/colossalai/auto_parallel/offload/mem_optimize.py +++ b/colossalai/auto_parallel/offload/mem_optimize.py @@ -1,4 +1,5 @@ from typing import Dict + import torch import torch.fx from torch.fx import GraphModule @@ -7,10 +8,11 @@ from colossalai.fx import ColoTracer, is_compatible_with_meta from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from .region_manager import RegionManager -from .runtime import runtime_syn_offload_apply_pass, runtime_asyn_offload_apply_pass from .base_offload_module import BaseOffloadModule -from .util import compute_max_param_mem, compute_total_param_mem, compute_act_peak_mem, GlobalRuntimeInfo +from .region_manager import RegionManager +from .runtime import runtime_asyn_offload_apply_pass, runtime_syn_offload_apply_pass +from .util import GlobalRuntimeInfo, compute_act_peak_mem, compute_max_param_mem, compute_total_param_mem + def memory_optimize(model: torch.nn.Module, inps: Dict[str, torch.Tensor], @@ -29,13 +31,14 @@ def memory_optimize(model: torch.nn.Module, region_manager = RegionManager(graph, solver_name=solver_name, memory_budget=memory_budget) region_manager._build_regions() - GlobalRuntimeInfo.region_list = region_manager.region_list + GlobalRuntimeInfo().region_list = region_manager.region_list - act_peak_mem = compute_act_peak_mem(region_manager.region_list) / 1024 ** 2 - max_param_mem = compute_max_param_mem(region_manager.region_list) / 1024 ** 2 - total_param_mem = compute_total_param_mem(region_manager.region_list) / 1024 ** 2 + act_peak_mem = compute_act_peak_mem(region_manager.region_list) / 1024**2 + max_param_mem = compute_max_param_mem(region_manager.region_list) / 1024**2 + total_param_mem = compute_total_param_mem(region_manager.region_list) / 1024**2 print( - f"act_peak_mem={act_peak_mem:.3f} MB | max_param_mem={max_param_mem:.3f} MB | total_param_mem={total_param_mem:.3f}") + f"act_peak_mem={act_peak_mem:.3f} MB | max_param_mem={max_param_mem:.3f} MB | total_param_mem={total_param_mem:.3f}" + ) if solver_name == 'syn': gm = runtime_syn_offload_apply_pass(gm, region_manager.region_list) @@ -45,5 +48,5 @@ def memory_optimize(model: torch.nn.Module, raise TypeError(f"Unknown solver name {solver_name}!") gm.recompile() - optimized_model = BaseOffloadModule(gm, region_manager, solver_name=='syn') + optimized_model = BaseOffloadModule(gm, region_manager, solver_name == 'syn') return optimized_model diff --git a/colossalai/auto_parallel/offload/runtime.py b/colossalai/auto_parallel/offload/runtime.py index 91c7945bd65f..764ac608826b 100644 --- a/colossalai/auto_parallel/offload/runtime.py +++ b/colossalai/auto_parallel/offload/runtime.py @@ -1,4 +1,5 @@ from typing import List + import torch from torch.fx.node import Node @@ -23,13 +24,13 @@ def forward(ctx, input_, fwd_info, bwd_info): ctx.bwd_info = bwd_info d2h_rid = fwd_info.get('d2h_rid', None) if d2h_rid is not None: - free_region = GlobalRuntimeInfo.region_list[d2h_rid] + free_region = GlobalRuntimeInfo().region_list[d2h_rid] assert isinstance(free_region, Region) free_region.free_cuda_data() h2d_rid = fwd_info.get('h2d_rid', None) if h2d_rid is not None: - h2d_region = GlobalRuntimeInfo.region_list[h2d_rid] + h2d_region = GlobalRuntimeInfo().region_list[h2d_rid] assert isinstance(h2d_region, Region) h2d_region.move_param_to_cuda() @@ -40,7 +41,7 @@ def backward(ctx, grad_output): h2d_rid = ctx.bwd_info.get('h2d_rid', None) if h2d_rid is not None: - pref_region = GlobalRuntimeInfo.region_list[h2d_rid] + pref_region = GlobalRuntimeInfo().region_list[h2d_rid] assert isinstance(pref_region, Region) pref_region.move_param_to_cuda() @@ -65,23 +66,22 @@ def forward(ctx, input_, fwd_info, bwd_info): sync_rid = fwd_info.get('sync_rid', None) if sync_rid is not None: - prefetch_event = GlobalRuntimeInfo.fwd_prefetch_event_map.get( - sync_rid, None) + prefetch_event = GlobalRuntimeInfo().fwd_prefetch_event_map.get(sync_rid, None) if prefetch_event: prefetch_event.wait() h2d_rid = fwd_info.get('h2d_rid', None) if h2d_rid is not None: - pref_region = GlobalRuntimeInfo.region_list[h2d_rid] + pref_region = GlobalRuntimeInfo().region_list[h2d_rid] assert isinstance(pref_region, Region) master_stream = torch.cuda.current_stream() - with torch.cuda.stream(GlobalRuntimeInfo.h2d_stream): - GlobalRuntimeInfo.h2d_stream.wait_stream(master_stream) + with torch.cuda.stream(GlobalRuntimeInfo().h2d_stream): + GlobalRuntimeInfo().h2d_stream.wait_stream(master_stream) pref_region.move_param_to_cuda() prefetch_event = torch.cuda.Event() - prefetch_event.record(GlobalRuntimeInfo.h2d_stream) - GlobalRuntimeInfo.fwd_prefetch_event_map[h2d_rid] = prefetch_event + prefetch_event.record(GlobalRuntimeInfo().h2d_stream) + GlobalRuntimeInfo().fwd_prefetch_event_map[h2d_rid] = prefetch_event return input_ @@ -90,10 +90,9 @@ def backward(ctx, grad_output): sync_rid = ctx.bwd_info.get('sync_rid', None) if sync_rid is not None: - wait_region = GlobalRuntimeInfo.region_list[sync_rid] + wait_region = GlobalRuntimeInfo().region_list[sync_rid] assert isinstance(wait_region, Region) - prefetch_event = GlobalRuntimeInfo.bwd_prefetch_event_map.get( - sync_rid, None) + prefetch_event = GlobalRuntimeInfo().bwd_prefetch_event_map.get(sync_rid, None) if prefetch_event: prefetch_event.wait() else: @@ -101,16 +100,16 @@ def backward(ctx, grad_output): h2d_rid = ctx.bwd_info.get('h2d_rid', None) if h2d_rid is not None: - pref_region = GlobalRuntimeInfo.region_list[h2d_rid] + pref_region = GlobalRuntimeInfo().region_list[h2d_rid] assert isinstance(pref_region, Region) master_stream = torch.cuda.current_stream() - with torch.cuda.stream(GlobalRuntimeInfo.h2d_stream): - GlobalRuntimeInfo.h2d_stream.wait_stream(master_stream) + with torch.cuda.stream(GlobalRuntimeInfo().h2d_stream): + GlobalRuntimeInfo().h2d_stream.wait_stream(master_stream) pref_region.move_param_to_cuda() prefetch_event = torch.cuda.Event() - prefetch_event.record(GlobalRuntimeInfo.h2d_stream) - GlobalRuntimeInfo.bwd_prefetch_event_map[h2d_rid] = prefetch_event + prefetch_event.record(GlobalRuntimeInfo().h2d_stream) + GlobalRuntimeInfo().bwd_prefetch_event_map[h2d_rid] = prefetch_event return grad_output, None, None @@ -129,6 +128,7 @@ def convert_fwd_upload_bwd_offload_to_action(tensor, fwd_info, bwd_info): ret = SynPreFwdPostBwdOP.apply(tensor, fwd_info, bwd_info) return ret + def convert_fwd_prefetch_bwd_offload_to_action(tensor, fwd_info, bwd_info): ''' Convert Prefetch and Offload operation into runtime action. @@ -189,7 +189,8 @@ def runtime_syn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[R if fwd_info or bwd_info: with mod_graph.inserting_after(last_inp_node): - new_node = mod_graph.create_node('call_function', convert_fwd_upload_bwd_offload_to_action, + new_node = mod_graph.create_node('call_function', + convert_fwd_upload_bwd_offload_to_action, args=(last_inp_node, fwd_info, bwd_info)) replace_node_users(last_inp_node, new_node) @@ -206,11 +207,11 @@ def runtime_asyn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[ # upload parameters of the first region last_inp_node = tuple(mod_graph.nodes)[0] - first_region_with_p = [ - region for region in region_list if region.param_size][0] + first_region_with_p = [region for region in region_list if region.param_size][0] fwd_info = {"h2d_rid": first_region_with_p.r_id} with mod_graph.inserting_after(last_inp_node): - upload_apply_node = mod_graph.create_node('call_function', convert_fwd_upload_bwd_offload_to_action, + upload_apply_node = mod_graph.create_node('call_function', + convert_fwd_upload_bwd_offload_to_action, args=(last_inp_node, fwd_info, {})) replace_node_users(last_inp_node, upload_apply_node) last_inp_node = upload_apply_node @@ -225,19 +226,20 @@ def runtime_asyn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[ fwd_info['h2d_rid'] = fwd_prefetch_region.r_id # forward offload - if r_idx > 0 and region_list[r_idx-1].need_offload: + if r_idx > 0 and region_list[r_idx - 1].need_offload: fwd_info['d2h_rid'] = r_idx - 1 bwd_info = {} # backward prefetch - if r_idx > 0 and region_list[r_idx-1].need_offload: + if r_idx > 0 and region_list[r_idx - 1].need_offload: bwd_info['sync_rid'] = r_idx - 1 - if r_idx > 0 and region_list[r_idx-1].bwd_prefetch_region: - bwd_info['h2d_rid'] = region_list[r_idx-1].bwd_prefetch_region.r_id + if r_idx > 0 and region_list[r_idx - 1].bwd_prefetch_region: + bwd_info['h2d_rid'] = region_list[r_idx - 1].bwd_prefetch_region.r_id if fwd_info or bwd_info: with mod_graph.inserting_after(last_inp_node): - new_node = mod_graph.create_node('call_function', convert_fwd_prefetch_bwd_offload_to_action, + new_node = mod_graph.create_node('call_function', + convert_fwd_prefetch_bwd_offload_to_action, args=(last_inp_node, fwd_info, bwd_info)) replace_node_users(last_inp_node, new_node) @@ -246,7 +248,8 @@ def runtime_asyn_offload_apply_pass(gm: torch.fx.GraphModule, region_list: List[ if region.bwd_prefetch_region: bwd_info = {'h2d_rid': region.bwd_prefetch_region.r_id} with mod_graph.inserting_after(last_inp_node): - new_node = mod_graph.create_node('call_function', convert_fwd_prefetch_bwd_offload_to_action, + new_node = mod_graph.create_node('call_function', + convert_fwd_prefetch_bwd_offload_to_action, args=(last_inp_node, {}, bwd_info)) replace_node_users(last_inp_node, new_node) # gm.graph.print_tabular() diff --git a/colossalai/auto_parallel/offload/util.py b/colossalai/auto_parallel/offload/util.py index a99c4eb20225..6b010512cc9c 100644 --- a/colossalai/auto_parallel/offload/util.py +++ b/colossalai/auto_parallel/offload/util.py @@ -1,6 +1,9 @@ from dataclasses import dataclass from typing import List + import torch + +from colossalai.context.singleton_meta import SingletonMeta from colossalai.fx.profiler import calculate_fwd_out, calculate_fwd_tmp from .region import Region @@ -12,6 +15,7 @@ class NodeInfo: runtime_fwd_mem: float = 0 runtime_bwd_mem: float = 0 + class NvDevicePower: """ NVIDIA GPU computing performance (TFLOPs). @@ -30,12 +34,14 @@ class NvDevicePower: A100_FP32 = 19.5 -class GlobalRuntimeInfo: - h2d_stream = torch.cuda.Stream() - d2h_stream = torch.cuda.Stream() - fwd_prefetch_event_map = {} - bwd_prefetch_event_map = {} - region_list = [] +class GlobalRuntimeInfo(metaclass=SingletonMeta): + + def __init__(self): + self.h2d_stream = torch.cuda.Stream() + self.d2h_stream = torch.cuda.Stream() + self.fwd_prefetch_event_map = {} + self.bwd_prefetch_event_map = {} + self.region_list = [] def compute_act_peak_mem(region_list: List[Region]) -> float: @@ -70,21 +76,24 @@ def compute_act_peak_mem(region_list: List[Region]) -> float: return act_peak_mem + def compute_max_param_mem(region_list: List[Region]) -> float: return max(region.param_size for region in region_list) + def compute_total_param_mem(region_list: List[Region]) -> float: return sum(region.param_size for region in region_list if region.r_id <= region.shared_rid) + def requires_upload_p_in_fwd(shared_reg: Region): - return (shared_reg.r_id >= shared_reg.shared_rid) or ( - shared_reg.r_id < shared_reg.shared_rid and shared_reg.need_offload) + return (shared_reg.r_id >= shared_reg.shared_rid) or (shared_reg.r_id < shared_reg.shared_rid + and shared_reg.need_offload) + def requires_release_p_in_bwd(shared_reg: Region): - return (shared_reg.r_id >= shared_reg.shared_rid) or ( - shared_reg.r_id < shared_reg.shared_rid and shared_reg.need_offload) + return (shared_reg.r_id >= shared_reg.shared_rid) or (shared_reg.r_id < shared_reg.shared_rid + and shared_reg.need_offload) + def requires_offload_g_in_bwd(region: Region): return region.param_size and (region.r_id <= region.shared_rid) - - diff --git a/tests/test_auto_parallel/test_offload/test_perf.py b/tests/test_auto_parallel/test_offload/test_perf.py index d569570f4b7d..17bf9cb87f51 100644 --- a/tests/test_auto_parallel/test_offload/test_perf.py +++ b/tests/test_auto_parallel/test_offload/test_perf.py @@ -1,46 +1,44 @@ import time -import pytest from functools import partial +import pytest import torch -from torch.utils._pytree import tree_map import torch.multiprocessing as mp +from torch.utils._pytree import tree_map import colossalai -from colossalai.nn.optimizer import HybridAdam -from colossalai.fx.profiler import parameter_size -from colossalai.utils.model.colo_init_context import ColoInitContext -from colossalai.utils import free_port, get_current_device -from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper from colossalai.auto_parallel.offload.amp_optimizer import AMPOptimizer from colossalai.auto_parallel.offload.mem_optimize import memory_optimize from colossalai.auto_parallel.offload.solver import NOT_NVML +from colossalai.fx.profiler import parameter_size +from colossalai.nn.optimizer import HybridAdam +from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper from colossalai.testing import parameterize - -from tests.test_tensor.common_utils import set_seed +from colossalai.utils import free_port, get_current_device +from colossalai.utils.model.colo_init_context import ColoInitContext from tests.test_auto_parallel.test_offload.model_utils import * +from tests.test_tensor.common_utils import set_seed @parameterize('model_name', ['gpt2_']) @parameterize('memory_budget', [5000]) @parameterize('solver_name', ['asyn']) -def exam_fwd_bwd( - model_name: str, - memory_budget: float, - solver_name: str -): +def exam_fwd_bwd(model_name: str, memory_budget: float, solver_name: str): # build model get_components_func = non_distributed_component_funcs.get_callable(model_name) model_builder, data_gen = get_components_func() - label = torch.randint(low=0, high=128, size=(64, 8,), device=get_current_device()) + label = torch.randint(low=0, high=128, size=( + 64, + 8, + ), device=get_current_device()) criterion = LMLoss() set_seed(42) start_time = time.time() model = model_builder() model.train() - param_size = parameter_size(model) / 1024 ** 2 / 2 + param_size = parameter_size(model) / 1024**2 / 2 init_time = time.time() - start_time print(f"init_param_size={param_size:.3f} MB | init_model_time={init_time:.3f} s") @@ -92,13 +90,11 @@ def exam_fwd_bwd( torch.cuda.synchronize() exec_time = sum(sorted(time_list)[:5]) / 5 - runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024 ** 2 - runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024 ** 2 + runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024**2 + runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024**2 print(f'gemini | model_name: {model_name}') - print( - f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB ' - f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|' - ) + print(f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB ' + f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|') print(time_list) del data_args @@ -129,22 +125,26 @@ def exam_fwd_bwd( torch.cuda.synchronize() exec_time = sum(sorted(time_list)[:5]) / 5 - runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024 ** 2 - runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024 ** 2 + runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024**2 + runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024**2 print(f'solver_name: {solver_name} | model_name: {model_name}') - print( - f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB ' - f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|' - ) + print(f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB ' + f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|') print(time_list) -@pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed') -def test_perf(rank, world_size, port): + +def run_dist(rank, world_size, port): config = {} colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') exam_fwd_bwd() -if __name__ == '__main__': - run_func = partial(test_perf, world_size=1, port=free_port()) +@pytest.mark.skip("this test failed") +@pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed') +def test_perf(): + run_func = partial(run_dist, world_size=1, port=free_port()) mp.spawn(run_func, nprocs=1) + + +if __name__ == '__main__': + test_perf() diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index 7a0d4a15d53a..169983a76110 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -21,9 +21,6 @@ def check_gemini_plugin(early_stop: bool = True): Args: early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True. """ - plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, max_norm=1.0, initial_scale=2**5) - booster = Booster(plugin=plugin) - passed_models = [] failed_info = {} # (model_name, error) pair @@ -34,46 +31,23 @@ def check_gemini_plugin(early_stop: bool = True): continue # These models are not compatible with gemini if name in [ - 'diffusers_clip_vision_model', - 'timm_resnet', - 'timm_beit', - 'timm_beitv2', - 'timm_eca_nfnet', - 'timm_efficientformer', - 'timm_hrnet_w18_small', - 'timm_nf_ecaresnet101', - 'timm_nf_regnet_b0', - 'timm_skresnet18', - 'timm_wide_resnet50_2', - 'timm_convit', - 'timm_dm_nfnet', - 'timm_swin_transformer', - 'torchaudio_conformer', - 'torchaudio_deepspeech', - 'torchaudio_wavernn', - 'torchaudio_tacotron', - 'deepfm_interactionarch', - 'deepfm_simpledeepfmnn', - 'dlrm', - 'dlrm_interactionarch', - 'torchvision_googlenet', - 'torchvision_inception_v3', - 'torchvision_mobilenet_v3_small', - 'torchvision_resnet18', - 'torchvision_resnext50_32x4d', - 'torchvision_wide_resnet50_2', - 'torchvision_vit_b_16', - 'torchvision_convnext_base', - 'torchvision_swin_s', - 'transformers_albert', - 'transformers_albert_for_pretraining', - 'transformers_bert', - 'transformers_bert_for_pretraining', - 'transformers_gpt_double_heads', - 'torchaudio_hubert_base', + 'diffusers_clip_vision_model', 'timm_resnet', 'timm_beit', 'timm_beitv2', 'timm_eca_nfnet', + 'timm_efficientformer', 'timm_hrnet_w18_small', 'timm_nf_ecaresnet101', 'timm_nf_regnet_b0', + 'timm_skresnet18', 'timm_wide_resnet50_2', 'timm_convit', 'timm_dm_nfnet', 'timm_swin_transformer', + 'torchaudio_conformer', 'torchaudio_deepspeech', 'torchaudio_wavernn', 'torchaudio_tacotron', + 'deepfm_interactionarch', 'deepfm_simpledeepfmnn', 'dlrm', 'dlrm_interactionarch', + 'torchvision_googlenet', 'torchvision_inception_v3', 'torchvision_mobilenet_v3_small', + 'torchvision_resnet18', 'torchvision_resnext50_32x4d', 'torchvision_wide_resnet50_2', + 'torchvision_vit_b_16', 'torchvision_convnext_base', 'torchvision_swin_s', 'transformers_albert', + 'transformers_albert_for_pretraining', 'transformers_bert', 'transformers_bert_for_pretraining', + 'transformers_gpt_double_heads', 'torchaudio_hubert_base', 'torchaudio_wav2vec2_base', + 'transformers_t5_for_conditional_generation', 'transformers_t5', 'transformers_t5_encoder_model' ]: continue + try: + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, max_norm=1.0, initial_scale=2**5) + booster = Booster(plugin=plugin) model = model_fn() optimizer = HybridAdam(model.parameters(), lr=1e-3) criterion = lambda x: x.mean() @@ -97,10 +71,15 @@ def check_gemini_plugin(early_stop: bool = True): booster.backward(loss, optimizer) optimizer.step() passed_models.append(name) + + del booster, plugin, model, optimizer, criterion, data, output, loss except Exception as e: failed_info[name] = e if early_stop: raise e + + torch.cuda.empty_cache() + if dist.get_rank() == 0: print(f'Passed models({len(passed_models)}): {passed_models}\n\n') print(f'Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n') @@ -138,7 +117,6 @@ def run_dist(rank, world_size, port, early_stop: bool = True): check_gemini_plugin(early_stop=early_stop) -@pytest.mark.skip(reason='Skip gemini plugin test due to OOM') @rerun_if_address_is_in_use() def test_gemini_plugin(early_stop: bool = True): world_size = 2 diff --git a/tests/test_zero/low_level_zero/test_zero1_2.py b/tests/test_zero/low_level_zero/test_zero1_2.py index 930b6129174e..ed76e0171fb4 100644 --- a/tests/test_zero/low_level_zero/test_zero1_2.py +++ b/tests/test_zero/low_level_zero/test_zero1_2.py @@ -9,6 +9,7 @@ from torch.testing import assert_close import colossalai +from colossalai.testing import rerun_if_address_is_in_use from colossalai.testing.random import seed_all from colossalai.utils import free_port from colossalai.zero import LowLevelZeroOptimizer @@ -176,6 +177,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist +@rerun_if_address_is_in_use() def test_zero_1_2(): world_size = 2 run_func = partial(run_dist, world_size=world_size, port=free_port()) From b09adff724c2bbded1c71cc51a707f736a0e2899 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Tue, 4 Apr 2023 09:46:23 +0800 Subject: [PATCH 093/413] [chat]fix sft training for bloom, gpt and opt (#3418) fix sft training for bloom, gpt and opt --- applications/Chat/coati/models/bloom/bloom_lm.py | 3 +++ applications/Chat/coati/models/gpt/gpt_lm.py | 3 +++ applications/Chat/coati/models/opt/opt_lm.py | 3 +++ 3 files changed, 9 insertions(+) diff --git a/applications/Chat/coati/models/bloom/bloom_lm.py b/applications/Chat/coati/models/bloom/bloom_lm.py index 628af2e341a2..e4184fcd0d9c 100644 --- a/applications/Chat/coati/models/bloom/bloom_lm.py +++ b/applications/Chat/coati/models/bloom/bloom_lm.py @@ -33,3 +33,6 @@ def __init__(self, if checkpoint: model.gradient_checkpointing_enable() super().__init__(model, lora_rank, lora_train_bias) + + def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): + return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) diff --git a/applications/Chat/coati/models/gpt/gpt_lm.py b/applications/Chat/coati/models/gpt/gpt_lm.py index 23fc13bf23a4..c558d7e9ea8d 100644 --- a/applications/Chat/coati/models/gpt/gpt_lm.py +++ b/applications/Chat/coati/models/gpt/gpt_lm.py @@ -33,3 +33,6 @@ def __init__(self, if checkpoint: model.gradient_checkpointing_enable() super().__init__(model, lora_rank, lora_train_bias) + + def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): + return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) diff --git a/applications/Chat/coati/models/opt/opt_lm.py b/applications/Chat/coati/models/opt/opt_lm.py index 65d79e1b2307..47afae847f13 100644 --- a/applications/Chat/coati/models/opt/opt_lm.py +++ b/applications/Chat/coati/models/opt/opt_lm.py @@ -33,3 +33,6 @@ def __init__(self, if checkpoint: model.gradient_checkpointing_enable() super().__init__(model, lora_rank, lora_train_bias) + + def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): + return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) From 26b7aac0be10fb83692e197ca326f8b67c1c990b Mon Sep 17 00:00:00 2001 From: ver217 Date: Tue, 4 Apr 2023 13:48:16 +0800 Subject: [PATCH 094/413] [zero] reorganize zero/gemini folder structure (#3424) * [zero] refactor low-level zero folder structure * [zero] fix legacy zero import path * [zero] fix legacy zero import path * [zero] remove useless import * [zero] refactor gemini folder structure * [zero] refactor gemini folder structure * [zero] refactor legacy zero import path * [zero] refactor gemini folder structure * [zero] refactor gemini folder structure * [zero] refactor gemini folder structure * [zero] refactor legacy zero import path * [zero] fix test import path * [zero] fix test * [zero] fix circular import * [zero] update import --- .../coati/trainer/strategies/colossalai.py | 9 +- .../offload/base_offload_module.py | 2 +- colossalai/auto_parallel/offload/region.py | 15 +- colossalai/booster/plugin/gemini_plugin.py | 6 +- colossalai/engine/_base_engine.py | 2 +- .../engine/schedule/_pipeline_schedule.py | 2 +- colossalai/gemini/__init__.py | 9 - colossalai/initialize.py | 5 +- colossalai/nn/layer/moe/experts.py | 2 +- colossalai/nn/layer/moe/layers.py | 2 +- colossalai/nn/optimizer/gemini_optimizer.py | 15 - colossalai/nn/parallel/__init__.py | 8 +- colossalai/nn/parallel/data_parallel.py | 525 +--------------- colossalai/nn/parallel/gemini_parallel.py | 63 -- colossalai/zero/__init__.py | 57 +- colossalai/zero/gemini/__init__.py | 11 + .../{ => zero}/gemini/chunk/__init__.py | 0 colossalai/{ => zero}/gemini/chunk/chunk.py | 0 colossalai/{ => zero}/gemini/chunk/manager.py | 3 +- .../{ => zero}/gemini/chunk/search_utils.py | 2 +- colossalai/{ => zero}/gemini/chunk/utils.py | 5 +- .../gemini}/colo_init_context.py | 5 +- colossalai/zero/gemini/gemini_ddp.py | 590 ++++++++++++++++++ .../zero/{utils => gemini}/gemini_hook.py | 4 +- colossalai/{ => zero}/gemini/gemini_mgr.py | 6 +- .../gemini/gemini_optimizer.py} | 14 +- .../gemini/memory_tracer/__init__.py | 0 .../memory_tracer/chunk_memstats_collector.py | 4 +- .../gemini/memory_tracer/memory_monitor.py | 0 .../gemini/memory_tracer/memory_stats.py | 2 +- .../memory_tracer/memstats_collector.py | 13 +- .../memory_tracer/param_runtime_order.py | 0 .../memory_tracer/runtime_mem_tracer.py | 9 +- .../static_memstats_collector.py | 2 +- .../{ => zero}/gemini/memory_tracer/utils.py | 0 .../{ => zero}/gemini/placement_policy.py | 5 +- .../{nn/parallel => zero/gemini}/utils.py | 5 +- colossalai/zero/legacy/__init__.py | 44 ++ colossalai/zero/legacy/gemini/__init__.py | 9 + .../legacy}/gemini/gemini_context.py | 0 .../legacy}/gemini/ophooks/__init__.py | 0 .../gemini/ophooks/_shard_grad_ophook.py | 0 .../gemini/ophooks/_shard_param_ophook.py | 1 + .../gemini/ophooks/runtime_mem_tracer_hook.py | 4 +- .../{ => zero/legacy}/gemini/ophooks/utils.py | 0 .../legacy}/gemini/paramhooks/__init__.py | 0 .../gemini/paramhooks/_param_hookmgr.py | 0 .../legacy}/gemini/stateful_tensor.py | 8 +- .../legacy}/gemini/stateful_tensor_mgr.py | 15 +- .../legacy}/gemini/tensor_placement_policy.py | 7 +- .../{ => zero/legacy}/gemini/tensor_utils.py | 6 +- .../zero/{ => legacy}/init_ctx/__init__.py | 0 .../{ => legacy}/init_ctx/init_context.py | 8 +- .../zero/{ => legacy}/shard_utils/__init__.py | 0 .../shard_utils/base_shard_strategy.py | 3 +- .../bucket_tensor_shard_strategy.py | 11 +- .../zero/{ => legacy}/shard_utils/commons.py | 4 +- .../shard_utils/tensor_shard_strategy.py | 11 +- .../{ => legacy}/sharded_model/__init__.py | 2 +- .../zero/{ => legacy}/sharded_model/_utils.py | 6 +- .../sharded_model/reduce_scatter.py | 0 .../sharded_model/sharded_model_v2.py | 20 +- .../zero/{ => legacy}/sharded_model/utils.py | 5 +- .../sharded_model}/zero_hook.py | 10 +- .../zero/legacy/sharded_optim/__init__.py | 3 + .../sharded_optim/sharded_optim_v2.py | 10 +- .../zero/legacy/sharded_param/__init__.py | 4 + .../sharded_param/sharded_param.py | 12 +- .../sharded_param/sharded_tensor.py | 3 +- colossalai/zero/low_level/__init__.py | 3 + .../{sharded_optim => low_level}/_utils.py | 0 .../bookkeeping/__init__.py | 0 .../bookkeeping/base_store.py | 0 .../bookkeeping/bucket_store.py | 0 .../bookkeeping/gradient_store.py | 0 .../bookkeeping/parameter_store.py | 0 .../bookkeeping/tensor_bucket.py | 0 .../low_level_optim.py | 0 colossalai/zero/sharded_optim/__init__.py | 4 - colossalai/zero/sharded_param/__init__.py | 4 - colossalai/zero/utils/__init__.py | 3 - .../zero_wrapper.py => zero/wrapper.py} | 6 +- docs/source/en/features/nvme_offload.md | 2 +- docs/source/zh-Hans/features/nvme_offload.md | 2 +- examples/images/dreambooth/debug.py | 2 +- .../dreambooth/train_dreambooth_colossalai.py | 5 +- .../train_dreambooth_colossalai_lora.py | 5 +- examples/images/vit/test_vit.py | 2 +- examples/images/vit/train.py | 2 +- examples/language/bert/train_bert_demo.py | 3 +- .../language/gpt/gemini/train_gpt_demo.py | 3 +- examples/language/opt/train_gemini_opt.py | 20 +- examples/language/palm/train.py | 8 +- .../roberta/pretraining/run_pretraining.py | 138 ++-- examples/tutorial/opt/opt/run_clm.py | 22 +- .../test_offload/test_perf.py | 3 +- .../test_compatibility_with_gemini.py | 3 +- tests/test_ddp/test_ddp_ignore_params.py | 8 +- tests/test_ddp/test_ddp_state_dict.py | 15 +- tests/test_gemini/test_gemini_manager.py | 146 ++--- tests/test_gemini/test_param_op.py | 2 +- tests/test_gemini/test_runtime_mem_tracer.py | 4 +- tests/test_gemini/update/test_chunk_mgrv2.py | 2 +- tests/test_gemini/update/test_chunkv2.py | 4 +- tests/test_gemini/update/test_fwd_bwd.py | 8 +- .../test_gemini/update/test_gemini_use_rmt.py | 10 +- .../update/test_get_torch_model.py | 5 +- tests/test_gemini/update/test_grad_clip.py | 8 +- tests/test_gemini/update/test_inference.py | 8 +- tests/test_gemini/update/test_optim.py | 8 +- tests/test_gemini/update/test_search.py | 4 +- .../update/test_zeroddp_state_dict.py | 7 +- .../update/test_zerooptim_state_dict.py | 8 +- tests/test_moe/test_moe_checkpoint.py | 2 +- tests/test_moe/test_moe_colo_init.py | 123 ++-- tests/test_moe/test_moe_zero_init.py | 226 ++++--- tests/test_moe/test_moe_zero_model.py | 10 +- tests/test_moe/test_moe_zero_optim.py | 12 +- tests/test_optimizer/test_cpu_adam.py | 2 +- .../test_optimizer/test_fused_adam_kernel.py | 2 +- tests/test_optimizer/test_hybrid_adam.py | 4 +- tests/test_tensor/model/test_gpt2.py | 2 +- tests/test_tensor/model/test_model.py | 2 +- tests/test_tensor/model/test_module_spec.py | 2 +- tests/test_tensor/test_context.py | 2 +- tests/test_tensor/test_tp_with_zero.py | 6 +- tests/test_utils/test_colo_checkpoint.py | 26 +- tests/test_utils/test_commons.py | 13 +- .../test_zero_gradient_clippling.py | 13 +- tests/test_zero/common.py | 5 +- .../low_level_zero/test_zero_init.py | 3 +- .../test_zero/low_level_zero/test_zero_tp.py | 3 +- tests/test_zero/test_found_inf.py | 144 ++--- tests/test_zero/test_init_context.py | 6 +- tests/test_zero/test_shard_model_v2.py | 10 +- tests/test_zero/test_shard_param.py | 11 +- .../test_sharded_optim_state_dict.py | 25 +- tests/test_zero/test_sharded_optim_v2.py | 12 +- .../test_sharded_optim_with_sync_bn.py | 9 +- tests/test_zero/test_state_dict.py | 14 +- tests/test_zero/test_tensor_utils.py | 25 +- tests/test_zero/test_zero_engine.py | 14 +- 142 files changed, 1427 insertions(+), 1396 deletions(-) delete mode 100644 colossalai/gemini/__init__.py delete mode 100644 colossalai/nn/optimizer/gemini_optimizer.py delete mode 100644 colossalai/nn/parallel/gemini_parallel.py create mode 100644 colossalai/zero/gemini/__init__.py rename colossalai/{ => zero}/gemini/chunk/__init__.py (100%) rename colossalai/{ => zero}/gemini/chunk/chunk.py (100%) rename colossalai/{ => zero}/gemini/chunk/manager.py (99%) rename colossalai/{ => zero}/gemini/chunk/search_utils.py (98%) rename colossalai/{ => zero}/gemini/chunk/utils.py (91%) rename colossalai/{utils/model => zero/gemini}/colo_init_context.py (97%) create mode 100644 colossalai/zero/gemini/gemini_ddp.py rename colossalai/zero/{utils => gemini}/gemini_hook.py (95%) rename colossalai/{ => zero}/gemini/gemini_mgr.py (97%) rename colossalai/{nn/optimizer/zero_optimizer.py => zero/gemini/gemini_optimizer.py} (97%) rename colossalai/{ => zero}/gemini/memory_tracer/__init__.py (100%) rename colossalai/{ => zero}/gemini/memory_tracer/chunk_memstats_collector.py (91%) rename colossalai/{ => zero}/gemini/memory_tracer/memory_monitor.py (100%) rename colossalai/{ => zero}/gemini/memory_tracer/memory_stats.py (98%) rename colossalai/{ => zero}/gemini/memory_tracer/memstats_collector.py (92%) rename colossalai/{ => zero}/gemini/memory_tracer/param_runtime_order.py (100%) rename colossalai/{ => zero}/gemini/memory_tracer/runtime_mem_tracer.py (95%) rename colossalai/{ => zero}/gemini/memory_tracer/static_memstats_collector.py (98%) rename colossalai/{ => zero}/gemini/memory_tracer/utils.py (100%) rename colossalai/{ => zero}/gemini/placement_policy.py (98%) rename colossalai/{nn/parallel => zero/gemini}/utils.py (97%) create mode 100644 colossalai/zero/legacy/__init__.py create mode 100644 colossalai/zero/legacy/gemini/__init__.py rename colossalai/{ => zero/legacy}/gemini/gemini_context.py (100%) rename colossalai/{ => zero/legacy}/gemini/ophooks/__init__.py (100%) rename colossalai/{ => zero/legacy}/gemini/ophooks/_shard_grad_ophook.py (100%) rename colossalai/{ => zero/legacy}/gemini/ophooks/_shard_param_ophook.py (99%) rename colossalai/{ => zero/legacy}/gemini/ophooks/runtime_mem_tracer_hook.py (96%) rename colossalai/{ => zero/legacy}/gemini/ophooks/utils.py (100%) rename colossalai/{ => zero/legacy}/gemini/paramhooks/__init__.py (100%) rename colossalai/{ => zero/legacy}/gemini/paramhooks/_param_hookmgr.py (100%) rename colossalai/{ => zero/legacy}/gemini/stateful_tensor.py (97%) rename colossalai/{ => zero/legacy}/gemini/stateful_tensor_mgr.py (94%) rename colossalai/{ => zero/legacy}/gemini/tensor_placement_policy.py (96%) rename colossalai/{ => zero/legacy}/gemini/tensor_utils.py (97%) rename colossalai/zero/{ => legacy}/init_ctx/__init__.py (100%) rename colossalai/zero/{ => legacy}/init_ctx/init_context.py (97%) rename colossalai/zero/{ => legacy}/shard_utils/__init__.py (100%) rename colossalai/zero/{ => legacy}/shard_utils/base_shard_strategy.py (87%) rename colossalai/zero/{ => legacy}/shard_utils/bucket_tensor_shard_strategy.py (89%) rename colossalai/zero/{ => legacy}/shard_utils/commons.py (95%) rename colossalai/zero/{ => legacy}/shard_utils/tensor_shard_strategy.py (86%) rename colossalai/zero/{ => legacy}/sharded_model/__init__.py (61%) rename colossalai/zero/{ => legacy}/sharded_model/_utils.py (95%) rename colossalai/zero/{ => legacy}/sharded_model/reduce_scatter.py (100%) rename colossalai/zero/{ => legacy}/sharded_model/sharded_model_v2.py (97%) rename colossalai/zero/{ => legacy}/sharded_model/utils.py (91%) rename colossalai/zero/{utils => legacy/sharded_model}/zero_hook.py (92%) create mode 100644 colossalai/zero/legacy/sharded_optim/__init__.py rename colossalai/zero/{ => legacy}/sharded_optim/sharded_optim_v2.py (97%) create mode 100644 colossalai/zero/legacy/sharded_param/__init__.py rename colossalai/zero/{ => legacy}/sharded_param/sharded_param.py (93%) rename colossalai/zero/{ => legacy}/sharded_param/sharded_tensor.py (92%) create mode 100644 colossalai/zero/low_level/__init__.py rename colossalai/zero/{sharded_optim => low_level}/_utils.py (100%) rename colossalai/zero/{sharded_optim => low_level}/bookkeeping/__init__.py (100%) rename colossalai/zero/{sharded_optim => low_level}/bookkeeping/base_store.py (100%) rename colossalai/zero/{sharded_optim => low_level}/bookkeeping/bucket_store.py (100%) rename colossalai/zero/{sharded_optim => low_level}/bookkeeping/gradient_store.py (100%) rename colossalai/zero/{sharded_optim => low_level}/bookkeeping/parameter_store.py (100%) rename colossalai/zero/{sharded_optim => low_level}/bookkeeping/tensor_bucket.py (100%) rename colossalai/zero/{sharded_optim => low_level}/low_level_optim.py (100%) delete mode 100644 colossalai/zero/sharded_optim/__init__.py delete mode 100644 colossalai/zero/sharded_param/__init__.py delete mode 100644 colossalai/zero/utils/__init__.py rename colossalai/{nn/parallel/zero_wrapper.py => zero/wrapper.py} (95%) diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index 521c536406d1..ba85ba76d4b1 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -14,17 +14,16 @@ import colossalai from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import CPUAdam, HybridAdam -from colossalai.nn.parallel import ZeroDDP, zero_model_wrapper, zero_optim_wrapper -from colossalai.nn.parallel.utils import get_static_torch_model from colossalai.tensor import ProcessGroup, ShardSpec from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext - -logger = get_dist_logger(__name__) +from colossalai.zero import ColoInitContext, ZeroDDP, zero_model_wrapper, zero_optim_wrapper +from colossalai.zero.gemini.utils import get_static_torch_model from .base import Strategy from .ddp import DDPStrategy +logger = get_dist_logger(__name__) + class ColossalAIStrategy(DDPStrategy): """ diff --git a/colossalai/auto_parallel/offload/base_offload_module.py b/colossalai/auto_parallel/offload/base_offload_module.py index 3a32f0722a1b..d0c328e134ff 100644 --- a/colossalai/auto_parallel/offload/base_offload_module.py +++ b/colossalai/auto_parallel/offload/base_offload_module.py @@ -4,8 +4,8 @@ import torch import torch.nn as nn -from colossalai.gemini.tensor_utils import free_storage from colossalai.nn.parallel.data_parallel import _cast_float +from colossalai.zero.legacy.gemini.tensor_utils import free_storage from .region_manager import RegionManager from .util import GlobalRuntimeInfo diff --git a/colossalai/auto_parallel/offload/region.py b/colossalai/auto_parallel/offload/region.py index e6907cc4b81d..9a2f558c3145 100644 --- a/colossalai/auto_parallel/offload/region.py +++ b/colossalai/auto_parallel/offload/region.py @@ -1,7 +1,10 @@ -from typing import List, Dict, Tuple +from typing import Dict, List, Tuple + import torch from torch.fx import Node -from colossalai.gemini.tensor_utils import alloc_storage, free_storage + +from colossalai.zero.legacy.gemini.tensor_utils import alloc_storage, free_storage + class Region: """ @@ -52,15 +55,13 @@ def init_param_data(self, pre_alloc_tensor: torch.Tensor = None): Map the parameters in the region to a contiguous memory space. """ - self.fp16_data = torch.zeros( - self.param_num, dtype=torch.half, device='cuda') + self.fp16_data = torch.zeros(self.param_num, dtype=torch.half, device='cuda') offset = 0 for param in self.fp16_params: param.data = param.data.cuda() p_num = param.data.numel() self.fp16_data[offset:offset + p_num].copy_(param.data.flatten()) - param.data = self.fp16_data[offset:offset + - p_num].view(param.data.shape) + param.data = self.fp16_data[offset:offset + p_num].view(param.data.shape) self.param_to_range[param] = (offset, offset + p_num) offset += p_num @@ -141,4 +142,4 @@ def split(self, cut_node_idx: int, cut_param_idx: int): def __update_params_ptr(self) -> None: for param in self.fp16_params: begin, end = self.param_to_range[param] - param.data = self.fp16_data[begin:end].view(param.data.shape) \ No newline at end of file + param.data = self.fp16_data[begin:end].view(param.data.shape) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index c3c9d007d44f..3c6e539ba972 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -14,12 +14,12 @@ from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO from colossalai.cluster import DistCoordinator -from colossalai.gemini.memory_tracer import MemStats from colossalai.interface import ModelWrapper, OptimizerWrapper -from colossalai.nn.parallel import GeminiDDP, zero_model_wrapper, zero_optim_wrapper from colossalai.tensor.colo_parameter import ColoParameter from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import _convert_to_coloparam +from colossalai.zero import GeminiDDP, zero_model_wrapper, zero_optim_wrapper +from colossalai.zero.gemini.colo_init_context import _convert_to_coloparam +from colossalai.zero.gemini.memory_tracer import MemStats from .plugin_base import Plugin diff --git a/colossalai/engine/_base_engine.py b/colossalai/engine/_base_engine.py index 59d8e1058652..ff8979d82401 100644 --- a/colossalai/engine/_base_engine.py +++ b/colossalai/engine/_base_engine.py @@ -10,8 +10,8 @@ from colossalai.engine.gradient_handler import BaseGradientHandler from colossalai.engine.schedule import BaseSchedule, InterleavedPipelineSchedule, NonPipelineSchedule, PipelineSchedule -from colossalai.gemini.ophooks import BaseOpHook, register_ophooks_recursively from colossalai.logging import get_dist_logger +from colossalai.zero.legacy.gemini import BaseOpHook, register_ophooks_recursively class Engine: diff --git a/colossalai/engine/schedule/_pipeline_schedule.py b/colossalai/engine/schedule/_pipeline_schedule.py index 712ae8242409..38175fe0941c 100644 --- a/colossalai/engine/schedule/_pipeline_schedule.py +++ b/colossalai/engine/schedule/_pipeline_schedule.py @@ -157,7 +157,7 @@ def load_micro_batch(self): return self._move_to_device(mciro_batch_data) def pre_processing(self, engine): - from colossalai.zero.sharded_model.sharded_model_v2 import ShardedModelV2 + from colossalai.zero.legacy import ShardedModelV2 # TODO: remove this after testing new zero with pipeline parallelism model = engine.model diff --git a/colossalai/gemini/__init__.py b/colossalai/gemini/__init__.py deleted file mode 100644 index 7a5a44ebb1ef..000000000000 --- a/colossalai/gemini/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -from .chunk import ChunkManager, TensorInfo, TensorState, search_chunk_configuration -from .gemini_mgr import GeminiManager -from .stateful_tensor_mgr import StatefulTensorMgr -from .tensor_placement_policy import TensorPlacementPolicyFactory - -__all__ = [ - 'StatefulTensorMgr', 'TensorPlacementPolicyFactory', 'GeminiManager', 'TensorInfo', 'TensorState', 'ChunkManager', - 'search_chunk_configuration' -] diff --git a/colossalai/initialize.py b/colossalai/initialize.py index f3719dcb47b3..5d3f3e5530cb 100644 --- a/colossalai/initialize.py +++ b/colossalai/initialize.py @@ -29,13 +29,12 @@ PipelineSchedule, get_tensor_shape, ) -from colossalai.gemini.ophooks import BaseOpHook from colossalai.logging import get_dist_logger from colossalai.nn.optimizer.colossalai_optimizer import ColossalaiOptimizer from colossalai.utils import get_current_device, is_using_ddp, is_using_pp, is_using_sequence, sync_model_param from colossalai.utils.moe import sync_moe_model_param -from colossalai.zero import convert_to_zero_v2 -from colossalai.zero.sharded_optim.sharded_optim_v2 import ShardedOptimizerV2 +from colossalai.zero.legacy import ShardedOptimizerV2, convert_to_zero_v2 +from colossalai.zero.legacy.gemini.ophooks import BaseOpHook def get_default_parser(): diff --git a/colossalai/nn/layer/moe/experts.py b/colossalai/nn/layer/moe/experts.py index 4fb9ad332c24..2e5d9e6e79a9 100644 --- a/colossalai/nn/layer/moe/experts.py +++ b/colossalai/nn/layer/moe/experts.py @@ -9,7 +9,7 @@ from colossalai.context import ParallelMode, seed from colossalai.context.moe_context import MOE_CONTEXT from colossalai.utils import get_current_device -from colossalai.zero.init_ctx import no_shard_zero_decrator +from colossalai.zero.legacy.init_ctx import no_shard_zero_decrator class MoeExperts(nn.Module): diff --git a/colossalai/nn/layer/moe/layers.py b/colossalai/nn/layer/moe/layers.py index 0969eb818229..b90d1f0bfcc6 100644 --- a/colossalai/nn/layer/moe/layers.py +++ b/colossalai/nn/layer/moe/layers.py @@ -18,7 +18,7 @@ from colossalai.nn.layer.moe.routers import MoeRouter, Top1Router, Top2Router from colossalai.nn.layer.moe.utils import NormalNoiseGenerator, UniformNoiseGenerator from colossalai.utils import get_current_device -from colossalai.zero.init_ctx import no_shard_zero_context, no_shard_zero_decrator +from colossalai.zero.legacy.init_ctx import no_shard_zero_context, no_shard_zero_decrator @no_shard_zero_decrator(is_replicated=True) diff --git a/colossalai/nn/optimizer/gemini_optimizer.py b/colossalai/nn/optimizer/gemini_optimizer.py deleted file mode 100644 index 31d161612600..000000000000 --- a/colossalai/nn/optimizer/gemini_optimizer.py +++ /dev/null @@ -1,15 +0,0 @@ -from typing import Any - -import torch - -from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer - -__all__ = ['GeminiAdamOptimizer'] - - -class GeminiAdamOptimizer(ZeroOptimizer): - - def __init__(self, model: torch.nn.Module, **defaults: Any) -> None: - optimizer = HybridAdam(model.parameters(), **defaults) - super().__init__(optimizer, model, **defaults) diff --git a/colossalai/nn/parallel/__init__.py b/colossalai/nn/parallel/__init__.py index 2afc8f18c36f..17e010f478c9 100644 --- a/colossalai/nn/parallel/__init__.py +++ b/colossalai/nn/parallel/__init__.py @@ -1,5 +1,5 @@ -from .data_parallel import ColoDDP, ZeroDDP -from .gemini_parallel import GeminiDDP -from .zero_wrapper import zero_model_wrapper, zero_optim_wrapper +from .data_parallel import ColoDDP -__all__ = ['ColoDDP', 'ZeroDDP', 'GeminiDDP', 'zero_model_wrapper', 'zero_optim_wrapper'] +__all__ = [ + 'ColoDDP', +] diff --git a/colossalai/nn/parallel/data_parallel.py b/colossalai/nn/parallel/data_parallel.py index a9d001bd0a9c..f839d6b28444 100644 --- a/colossalai/nn/parallel/data_parallel.py +++ b/colossalai/nn/parallel/data_parallel.py @@ -1,31 +1,14 @@ -import itertools from collections import OrderedDict from functools import partial -from typing import Dict, Iterable, List, Optional, Set +from typing import Iterable, Optional, Set import torch import torch.distributed as dist -import torch.nn as nn -from colossalai.gemini.chunk import Chunk, ChunkManager, TensorState -from colossalai.gemini.gemini_mgr import GeminiManager -from colossalai.gemini.memory_tracer import OrderedParamGenerator -from colossalai.logging import get_dist_logger -from colossalai.nn.parallel.utils import get_temp_total_chunk_on_cuda from colossalai.tensor import ProcessGroup as ColoProcessGroup -from colossalai.tensor import ReplicaSpec -from colossalai.tensor.colo_parameter import ColoParameter, ColoTensor, ColoTensorSpec -from colossalai.tensor.param_op_hook import ColoParamOpHookManager -from colossalai.utils import get_current_device, is_ddp_ignored -from colossalai.zero.utils.gemini_hook import GeminiZeROHook +from colossalai.utils import is_ddp_ignored from .reducer import Reducer -from .utils import get_static_torch_model - -try: - from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX, _IncompatibleKeys -except ImportError: - _EXTRA_STATE_KEY_SUFFIX = '_extra_state' def free_storage(data: torch.Tensor) -> None: @@ -189,507 +172,3 @@ def state_dict(self, destination=None, prefix='', keep_vars=False): def load_state_dict(self, state_dict: 'OrderedDict[str, torch.Tensor]', strict: bool = True): return self.module.load_state_dict(state_dict, strict) - - -class ZeroDDP(ColoDDP): - """ZeRO DDP for ColoTensor. - Warning: Nested ZeroDDP is not supported now. - It is designed to be used with ChunkManager and GeminiManager. - For more details, see the API reference of ``ChunkManager`` and ``GeminiManager``. - - Args: - module (torch.nn.Module): Module to apply ZeRO-DP. - gemini_manager (GeminiManager): Manages the chunk manager and heterogeneous momery space. - For more details, see the API reference of ``GeminiManager``. - pin_memory (bool): Chunks on CPU Memory use pin-memory. - force_outputs_fp32 (bool): If set to True, outputs will be fp32. Otherwise, outputs will be fp16. - Defaults to False. - strict_ddp_mode (bool): If set to True, there is no tensor sharding, each tensor is replicated. - Defaults to False. Users can set it to True, when they clearly know that they only need DDP. - """ - - def __init__(self, - module: torch.nn.Module, - gemini_manager: GeminiManager, - pin_memory: bool = False, - force_outputs_fp32: bool = False, - strict_ddp_mode: bool = False) -> None: - super().__init__(module, process_group=ColoProcessGroup()) - self.gemini_manager = gemini_manager - self.chunk_manager: ChunkManager = gemini_manager.chunk_manager - self.force_outputs_fp32 = force_outputs_fp32 - self.param_op_hook = GeminiZeROHook(gemini_manager) - self.fp32_params: List[ColoTensor] = list() - self.fp16_params: List[ColoParameter] = list() - self.overflow_counter = 0 - self.grads_device: Dict[torch.Tensor, torch.device] = dict() - self.param2name: Dict[nn.Parameter, str] = dict() - self.name2param: Dict[str, nn.Parameter] = dict() - - self._cast_buffers() - self._logger = get_dist_logger() - - if self.gemini_manager._premade_memstats_: - # build chunk in param runtime visited order. - param_order = self.gemini_manager.memstats()._param_runtime_order - else: - # build chunk in param initialized order. - # Note: in this way, it can not get filter unused params during runtime. - param_order = OrderedParamGenerator() - for p in module.parameters(): - param_order.append(p) - - self._init_chunks(param_order=param_order, - strict_ddp_mode=strict_ddp_mode, - cpu_offload=self.gemini_manager.policy_name != 'cuda', - pin_memory=pin_memory) - - for name, param in module.named_parameters(): - self.param2name[param] = name - for m_name, m_var in module.named_modules(): - for p_name, p_var in m_var.named_parameters(recurse=False): - param_name = m_name + '.' + p_name if m_name else p_name - self.name2param[param_name] = p_var - - def _post_forward(self): - """This function is only triggered for inference. - """ - access_list = list(self.chunk_manager.accessed_chunks) - # we need to scatter all accessed chunks and move them to their original places - for chunk in access_list: - if chunk.keep_gathered: - self.chunk_manager.fake_release_chunk(chunk) - else: - assert chunk.can_release - self.chunk_manager.release_chunk(chunk) - first_param = next(iter(chunk.tensors_info)) - self.chunk_manager.move_chunk(chunk, self.grads_device[first_param]) - assert self.chunk_manager.accessed_mem == 0 - # reset all recorded attributes - self.gemini_manager.reset_attributes() - - def forward(self, *args, **kwargs): - # check whether we are in a inference mode - grad_flag = torch.is_grad_enabled() - if not grad_flag: - assert not self.gemini_manager.need_warmup or not self.gemini_manager.is_warmup( - ), "You should run a completed iteration as your warmup iter" - - args, kwargs = _cast_float(args, torch.half), _cast_float(kwargs, torch.half) - self.module.zero_grad(set_to_none=True) - self.gemini_manager.pre_iter(*args) - with ColoParamOpHookManager.use_hooks(self.param_op_hook): - outputs = self.module(*args, **kwargs) - # scatter chunks in the inference mode - if not grad_flag: - self._post_forward() - - if self.force_outputs_fp32: - return _cast_float(outputs, torch.float) - return outputs - - def _setup_grads_ptr(self): - for p in self.module.parameters(): - if is_ddp_ignored(p): - continue - p.grad = None - - def _pre_backward(self): - # set a visit label for all parameters - # the label is used to check whether the parameter is correctly reduced - for param in self.param2name: - if not is_ddp_ignored(param): - setattr(param, "_gemini_reduced", False) - - def _post_backward(self): - if self.chunk_manager.accessed_mem != 0: - error_params = ["Reduction failed at followed parameters:"] - for param in self.param2name: - if not is_ddp_ignored(param) and not getattr(param, "_gemini_reduced"): - error_params.append(self.param2name[param]) - error_str = "\n\t".join(error_params) - raise RuntimeError("ZERO DDP error: the synchronization of gradients doesn't exit properly.", - "The most possible reason is that the model is not compatible with ZeroDDP.\n", - f"{error_str}") - self._setup_grads_ptr() - self._logger.debug( - f'comp cuda demand time: {self.gemini_manager._comp_cuda_demand_time}, layout time: {self.gemini_manager._layout_time}, evict time: {self.gemini_manager._evict_time}, CPU->CUDA vol: {self.gemini_manager._h2d_volume}B, CUDA->CPU vol: {self.gemini_manager._d2h_volume}' - ) - self.gemini_manager.post_iter() - - def backward(self, loss: torch.Tensor): - self._pre_backward() - with self.param_op_hook.switch_to_backward(), ColoParamOpHookManager.use_hooks(self.param_op_hook): - loss.backward() - self._post_backward() - - def backward_by_grad(self, tensor, grad): - with self.param_op_hook.switch_to_backward(), ColoParamOpHookManager.use_hooks(self.param_op_hook): - torch.autograd.backward(tensor, grad) - self._post_backward() - - def grad_handle(self, p, grad): - empty_grad = torch.empty_like(grad) - free_storage(empty_grad) - with torch._C.DisableTorchFunction(): - chunk = self.chunk_manager.get_chunk(p) - if chunk.tensors_info[p].state != TensorState.HOLD_AFTER_BWD: - raise RuntimeError(f"Parameter `{self.param2name[p]}` failed at the gradient reduction. " - "Some unsupported torch function is operated upon this parameter.") - self.chunk_manager.trans_tensor_state(p, TensorState.READY_FOR_REDUCE) - chunk.copy_tensor_to_chunk_slice(p, grad) - reduced = self.chunk_manager.reduce_chunk(chunk) - if reduced: - if chunk.is_gathered: - chunk.cuda_global_chunk.div_(chunk.pg_size) - else: - chunk.cuda_shard.div_(chunk.pg_size) - # check overflow elements - self.overflow_counter += chunk.has_inf_or_nan - # record l2 norm for gradient clipping - if chunk.l2_norm_flag: - chunk.set_l2_norm() - self.chunk_manager.move_chunk(chunk, self.grads_device[p], force_copy=True) - return empty_grad - - def zero_grad(self, set_to_none: bool = False) -> None: - self.module.zero_grad(set_to_none=True) - - def set_chunk_grad_device(self, chunk: Chunk, device: torch.device) -> None: - for tensor in chunk.get_tensors(): - self.grads_device[tensor] = device - - def state_dict(self, destination=None, prefix='', keep_vars=False, only_rank_0: bool = True): - """Returns a dictionary containing a whole state of the module. - - Both parameters and persistent buffers (e.g. running averages) are included. - Keys are corresponding parameter and buffer names. - Parameters and buffers set to ``None`` are not included. - - Warning: The non strict state dict would ignore the parameters if the tensors of the parameters - are shared with other parameters which have been included in the dictionary. - When you need to load the state dict, you should set the argument `strict` to False. - - Returns: - dict: - a dictionary containing a whole state of the module - """ - if destination is None: - destination = OrderedDict() - destination._metadata = OrderedDict() - destination._metadata[prefix[:-1]] = local_metadata = dict(version=self._version) - self._save_to_state_dict(destination, prefix, keep_vars, only_rank_0) - - for hook in self._state_dict_hooks.values(): - hook_result = hook(self, destination, prefix, local_metadata) - if hook_result is not None: - destination = hook_result - return destination - - def _get_param_to_save_data(self, param_list: List[torch.nn.Parameter], only_rank_0: bool) -> Dict: - """ - get param content from chunks. - - Args: - param_list (_type_): a list of torch.nn.Parameters - only_rank_0 (_type_): _description_ - - Returns: - Dict: a dict whose key is param name and value is param with correct payload - """ - # save parameters - param_to_save_data = dict() - chunk_list = self.chunk_manager.get_chunks(param_list) - for chunk in chunk_list: - temp_chunk = get_temp_total_chunk_on_cuda(chunk) - - for tensor, tensor_info in chunk.tensors_info.items(): - record_tensor = torch.empty([0]) - record_flag = (not only_rank_0) | (dist.get_rank(chunk.torch_pg) == 0) - if record_flag: - record_tensor = temp_chunk[tensor_info.offset:tensor_info.end].view(tensor.shape).cpu() - - assert tensor not in param_to_save_data - param_to_save_data[tensor] = record_tensor - - del temp_chunk - return param_to_save_data - - def _save_to_state_dict(self, destination, prefix, keep_vars, only_rank_0=True): - r"""Saves module state to `destination` dictionary, containing a state - of the module, but not its descendants. This is called on every - submodule in :meth:`~torch.nn.Module.state_dict`. - - In rare cases, subclasses can achieve class-specific behavior by - overriding this method with custom logic. - - Args: - destination (dict): a dict where state will be stored - prefix (str): the prefix for parameters and buffers used in this - module - """ - assert keep_vars is False, "`state_dict` with parameter, `keep_vars=True`, is not supported now." - - # get copies of fp32 parameters in CPU - param_to_save_data = self._get_param_to_save_data(self.fp32_params, only_rank_0) - # get the mapping between copies and fp16 parameters - p_mapping = dict() - for p, fp32_p in zip(self.fp16_params, self.fp32_params): - name = self.param2name[p] - assert fp32_p in param_to_save_data, "Parameter '{}' is neglected in the chunk list".format(name) - record_parameter = param_to_save_data[fp32_p] - p_mapping[p] = record_parameter - for name, param in self.name2param.items(): - if param is not None: - if is_ddp_ignored(param): - # deal with ddp ignored parameters - destination[prefix + name] = param if keep_vars else param.detach() - else: - destination[prefix + name] = p_mapping[param] - del p_mapping - del param_to_save_data - - # save all buffers - for name, buf in self.named_buffers(): - if buf is not None and name not in self._non_persistent_buffers_set: - destination[prefix + name] = buf if keep_vars else buf.detach() - # save extra states - extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX - if getattr(self.__class__, "get_extra_state", - torch.nn.Module.get_extra_state) is not torch.nn.Module.get_extra_state: - destination[extra_state_key] = self.get_extra_state() - - def load_state_dict(self, state_dict: 'OrderedDict[str, torch.Tensor]', strict: bool = True): - r"""Copies parameters and buffers from :attr:`state_dict` into - this module and its descendants. If :attr:`strict` is ``True``, then - the keys of :attr:`state_dict` must exactly match the keys returned - by this module's :meth:`~torch.nn.Module.state_dict` function. - - Args: - state_dict (dict): a dict containing parameters and - persistent buffers. - strict (bool, optional): whether to strictly enforce that the keys - in :attr:`state_dict` match the keys returned by this module's - :meth:`~torch.nn.Module.state_dict` function. Default: ``True`` - - Returns: - ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields: - * **missing_keys** is a list of str containing the missing keys - * **unexpected_keys** is a list of str containing the unexpected keys - - Note: - If a parameter or buffer is registered as ``None`` and its corresponding key - exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a - ``RuntimeError``. - """ - missing_keys: List[str] = [] - unexpected_keys: List[str] = [] - error_msgs: List[str] = [] - - # copy state_dict so _load_from_state_dict can modify it - metadata = getattr(state_dict, '_metadata', None) - state_dict = state_dict.copy() - if metadata is not None: - # mypy isn't aware that "_metadata" exists in state_dict - state_dict._metadata = metadata # type: ignore[attr-defined] - - prefix = '' - local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) - self._load_from_state_dict(state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) - - if strict: - if len(unexpected_keys) > 0: - error_msgs.insert( - 0, 'Unexpected key(s) in state_dict: {}. '.format(', '.join( - '"{}"'.format(k) for k in unexpected_keys))) - if len(missing_keys) > 0: - error_msgs.insert( - 0, 'Missing key(s) in state_dict: {}. '.format(', '.join('"{}"'.format(k) for k in missing_keys))) - - if len(error_msgs) > 0: - raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( - self.__class__.__name__, "\n\t".join(error_msgs))) - return _IncompatibleKeys(missing_keys, unexpected_keys) - - def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, - error_msgs): - r"""Copies parameters and buffers from :attr:`state_dict` into only - this module, but not its descendants. This is called on every submodule - in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this - module in input :attr:`state_dict` is provided as :attr:`local_metadata`. - For state dicts without metadata, :attr:`local_metadata` is empty. - Subclasses can achieve class-specific backward compatible loading using - the version number at `local_metadata.get("version", None)`. - - .. note:: - :attr:`state_dict` is not the same object as the input - :attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. So - it can be modified. - - Args: - state_dict (dict): a dict containing parameters and - persistent buffers. - prefix (str): the prefix for parameters and buffers used in this - module - local_metadata (dict): a dict containing the metadata for this module. - See - strict (bool): whether to strictly enforce that the keys in - :attr:`state_dict` with :attr:`prefix` match the names of - parameters and buffers in this module - missing_keys (list of str): if ``strict=True``, add missing keys to - this list - unexpected_keys (list of str): if ``strict=True``, add unexpected - keys to this list - error_msgs (list of str): error messages should be added to this - list, and will be reported together in - :meth:`~torch.nn.Module.load_state_dict` - """ - for hook in self._load_state_dict_pre_hooks.values(): - hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) - - persistent_buffers = {k: v for k, v in self.named_buffers() if k not in self._non_persistent_buffers_set} - local_name_params = itertools.chain(self.named_parameters(), persistent_buffers.items()) - local_state = {k: v for k, v in local_name_params if v is not None} - - def load(param_name, dest_tensor, copy_func): - state_key = prefix + param_name - if state_key in state_dict: - input_param = state_dict[state_key] - # Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+ - if len(dest_tensor.shape) == 0 and len(input_param.shape) == 1: - input_param = input_param[0] - if input_param.shape != dest_tensor.shape: - # local shape should match the one in checkpoint - error_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, ' - 'the shape in current model is {}.'.format(state_key, input_param.shape, - dest_tensor.shape)) - return - try: - with torch.no_grad(): - copy_func(input_param) - except Exception as ex: - error_msgs.append('While copying the parameter named "{}", ' - 'whose dimensions in the model are {} and ' - 'whose dimensions in the checkpoint are {}, ' - 'an exception occurred : {}.'.format(state_key, dest_tensor.size(), - input_param.size(), ex.args)) - elif strict: - missing_keys.append(state_key) - - def load_fp32_parameter(chunk_slice, data): - chunk_slice.copy_(data.flatten()) - - for name, param in self.named_parameters(): - if is_ddp_ignored(param): - # deal with ddp ignored parameters - load(name, param, param.copy_) - - fp32_to_name = dict() - for p, fp32_p in zip(self.fp16_params, self.fp32_params): - if p is not None: - name = self.param2name[p] - fp32_to_name[fp32_p] = name - - chunk_list = self.chunk_manager.get_chunks(self.fp32_params) - for chunk in chunk_list: - temp_chunk = get_temp_total_chunk_on_cuda(chunk) - - for tensor, tensor_info in chunk.tensors_info.items(): - parameter_name = fp32_to_name[tensor] - parameter_slice = temp_chunk[tensor_info.offset:tensor_info.end] - load(parameter_name, tensor, partial(load_fp32_parameter, parameter_slice)) - - if chunk.is_gathered: - chunk.cuda_global_chunk.copy_(temp_chunk) - elif chunk.cuda_shard is not None: - chunk.cuda_shard.copy_(temp_chunk[chunk.shard_begin:chunk.shard_end]) - else: - chunk.cpu_shard.copy_(temp_chunk[chunk.shard_begin:chunk.shard_end]) - - del temp_chunk - - for chunk_32 in chunk_list: - chunk_16 = chunk_32.paired_chunk - assert chunk_16 is not None - chunk_16.optim_update() - - for name, buf in persistent_buffers.items(): - if buf is not None: - load(name, buf, buf.copy_) - - extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX - if getattr(self.__class__, "set_extra_state", - torch.nn.Module.set_extra_state) is not torch.nn.Module.set_extra_state: - if extra_state_key in state_dict: - self.set_extra_state(state_dict[extra_state_key]) - elif strict: - missing_keys.append(extra_state_key) - elif strict and (extra_state_key in state_dict): - unexpected_keys.append(extra_state_key) - - if strict: - for key in state_dict.keys(): - if key.startswith(prefix) and key != extra_state_key: - input_name = key[len(prefix):] - if input_name not in local_state: - unexpected_keys.append(key) - - def _init_chunks(self, param_order, strict_ddp_mode: bool, cpu_offload: bool, pin_memory: bool): - ddp_pg = ColoProcessGroup() - for p in param_order.generate(): - assert isinstance(p, ColoParameter) - - # gather sharded parameters in the strict ddp mode - if strict_ddp_mode: - if not p.is_replicate(): - p.set_dist_spec(ReplicaSpec()) - p.set_process_group(pg=ddp_pg) - - # ignore the parameters with no gradient - if not p.requires_grad: - self.set_params_to_ignore([p]) - - # move ignored parameters to CUDA - if is_ddp_ignored(p): - p.data = p.data.to(device=get_current_device(), dtype=torch.float16) - continue - - # create a fp32 parameter - fp32_data = p.data.float() - fp32_p = ColoTensor(fp32_data, spec=ColoTensorSpec(p.process_group)) - # create a fp16 parameter - p.data = p.data.half() - - # register the fp16 parameter and fp32 parameter in the chunk manager - dp_world_size = p.process_group.dp_world_size() - self.chunk_manager.register_tensor(tensor=p, - group_type='fp16_param', - config_key=dp_world_size, - cpu_offload=cpu_offload, - pin_memory=pin_memory) - self.chunk_manager.register_tensor(tensor=fp32_p, - group_type='fp32_param', - config_key=dp_world_size, - cpu_offload=cpu_offload, - pin_memory=pin_memory) - - self.fp16_params.append(p) - self.fp32_params.append(fp32_p) - self.grads_device[p] = self.gemini_manager.default_device - - self.chunk_manager.close_all_groups() - - for p, fp32_p in zip(self.fp16_params, self.fp32_params): - chunk_16 = self.chunk_manager.get_chunk(p) - chunk_32 = self.chunk_manager.get_chunk(fp32_p) - chunk_32.init_pair(chunk_16) - - # keep gathered chunks are in CUDA - if chunk_16.keep_gathered: - self.grads_device[p] = get_current_device() - - def _cast_buffers(self): - for buffer in self.module.buffers(): - buffer.data = buffer.cuda() - if torch.is_floating_point(buffer): - buffer.data = buffer.half() diff --git a/colossalai/nn/parallel/gemini_parallel.py b/colossalai/nn/parallel/gemini_parallel.py deleted file mode 100644 index 2c6e15d91736..000000000000 --- a/colossalai/nn/parallel/gemini_parallel.py +++ /dev/null @@ -1,63 +0,0 @@ -from typing import Optional - -import torch - -from colossalai.gemini.chunk import init_chunk_manager -from colossalai.gemini.gemini_mgr import GeminiManager -from colossalai.gemini.memory_tracer import MemStats - -from .data_parallel import ZeroDDP - - -class GeminiDDP(ZeroDDP): - - def __init__(self, - module: torch.nn.Module, - device: torch.device, - placement_policy: str = "cpu", - pin_memory: bool = False, - force_outputs_fp32: bool = False, - strict_ddp_mode: bool = False, - search_range_mb: int = 32, - hidden_dim: Optional[int] = None, - min_chunk_size_mb: float = 32, - memstats: Optional[MemStats] = None) -> None: - """ - A torch.Module warpper using ZeRO-DP and Genimi. - ZeRO is for parallel. Gemini is for memory management. - WARNING: The class will modify the module inline! - - Example: - model is initialized under the context of ColoInitContext - >>> model = GeminiDDP(model, torch.cuda.current_device(), "cuda") - >>> logits = model(x) - >>> loss = criterion(logits, labels) - >>> model.backward(loss) - - Args: - module (torch.nn.Module): the model to be wrapped. - device (torch.device): device to place the model. - placement_policy (str, optional): "cpu", "cuda", "auto". Defaults to "cpu". - pin_memory (bool, optional): use pin memory on CPU. Defaults to False. - force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False. - search_range_mb (int, optional): chunk size searching range in MegaByte. Defaults to 32. - hidden_dim (int, optional): the hidden dimension of DNN. - Users can provide this argument to speed up searching. - If users do not know this argument before training, it is ok. We will use a default value 1024. - min_chunk_size_mb (float, optional): the minimum chunk size in MegaByte. - If the aggregate size of parameters is still samller than the minimum chunk size, - all parameters will be compacted into one small chunk. - memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer. - """ - # some ugly hotfix for the compatibility with Lightning - if search_range_mb is None: - search_range_mb = 32 - - chunk_manager = init_chunk_manager(model=module, - init_device=device, - hidden_dim=hidden_dim, - search_range_mb=search_range_mb, - min_chunk_size_mb=min_chunk_size_mb, - strict_ddp_flag=strict_ddp_mode) - gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats) - super().__init__(module, gemini_manager, pin_memory, force_outputs_fp32, strict_ddp_mode) diff --git a/colossalai/zero/__init__.py b/colossalai/zero/__init__.py index 098ccbb45c5a..3465079e4fbb 100644 --- a/colossalai/zero/__init__.py +++ b/colossalai/zero/__init__.py @@ -1,41 +1,16 @@ -from typing import Tuple - -import torch -import torch.nn as nn - -from colossalai.logging import get_dist_logger -from colossalai.zero.sharded_model.sharded_model_v2 import ShardedModelV2 -from colossalai.zero.sharded_optim import LowLevelZeroOptimizer, ShardedOptimizerV2 - -from ..nn.optimizer.zero_optimizer import ZeroOptimizer - - -def convert_to_zero_v2(model: nn.Module, optimizer: torch.optim.Optimizer, model_config, - optimizer_config) -> Tuple[ShardedModelV2, ShardedOptimizerV2]: - """ - A helper function to integrate the model and optimizer with ZeRO optimizer and off-loading - - :param model: Your model object - :type model: :class:`torch.nn.Module` - :param optimizer_config: Your optimizer object - :type optimizer_config: :class:`dict` - - :return: (model, optimizer) - :rtype: Tuple - """ - - logger = get_dist_logger('convert_to_zero_v2') - - logger.info(f'optimizer_config is {optimizer_config}', ranks=[0]) - if optimizer_config is None: - optimizer_config = dict() - logger.info(f'model_config is {model_config}', ranks=[0]) - if model_config is None: - model_config = dict() - - zero_model = ShardedModelV2(model, **model_config) - zero_optimizer = ShardedOptimizerV2(zero_model, optimizer, **optimizer_config) - return zero_model, zero_optimizer - - -__all__ = ['convert_to_zero_v2', 'LowLevelZeroOptimizer', 'ShardedModelV2', 'ShardedOptimizerV2', 'ZeroOptimizer'] +from .gemini import ( + ColoInitContext, + GeminiAdamOptimizer, + GeminiDDP, + ZeroDDP, + ZeroOptimizer, + get_static_torch_model, + post_process_colo_init_ctx, +) +from .low_level import LowLevelZeroOptimizer +from .wrapper import zero_model_wrapper, zero_optim_wrapper + +__all__ = [ + 'ZeroDDP', 'GeminiDDP', 'ZeroOptimizer', 'GeminiAdamOptimizer', 'zero_model_wrapper', 'zero_optim_wrapper', + 'LowLevelZeroOptimizer', 'ColoInitContext', 'post_process_colo_init_ctx', 'get_static_torch_model' +] diff --git a/colossalai/zero/gemini/__init__.py b/colossalai/zero/gemini/__init__.py new file mode 100644 index 000000000000..60f85ca2f540 --- /dev/null +++ b/colossalai/zero/gemini/__init__.py @@ -0,0 +1,11 @@ +from .chunk import ChunkManager, TensorInfo, TensorState, search_chunk_configuration +from .colo_init_context import ColoInitContext, post_process_colo_init_ctx +from .gemini_ddp import GeminiDDP, ZeroDDP +from .gemini_mgr import GeminiManager +from .gemini_optimizer import GeminiAdamOptimizer, ZeroOptimizer +from .utils import get_static_torch_model + +__all__ = [ + 'GeminiManager', 'TensorInfo', 'TensorState', 'ChunkManager', 'search_chunk_configuration', 'ZeroDDP', 'GeminiDDP', + 'get_static_torch_model', 'GeminiAdamOptimizer', 'ZeroOptimizer', 'ColoInitContext', 'post_process_colo_init_ctx' +] diff --git a/colossalai/gemini/chunk/__init__.py b/colossalai/zero/gemini/chunk/__init__.py similarity index 100% rename from colossalai/gemini/chunk/__init__.py rename to colossalai/zero/gemini/chunk/__init__.py diff --git a/colossalai/gemini/chunk/chunk.py b/colossalai/zero/gemini/chunk/chunk.py similarity index 100% rename from colossalai/gemini/chunk/chunk.py rename to colossalai/zero/gemini/chunk/chunk.py diff --git a/colossalai/gemini/chunk/manager.py b/colossalai/zero/gemini/chunk/manager.py similarity index 99% rename from colossalai/gemini/chunk/manager.py rename to colossalai/zero/gemini/chunk/manager.py index 2fa65c970316..d85df0b00476 100644 --- a/colossalai/gemini/chunk/manager.py +++ b/colossalai/zero/gemini/chunk/manager.py @@ -3,10 +3,11 @@ import torch -from colossalai.gemini.chunk import Chunk, ChunkFullError, TensorState from colossalai.tensor import ColoTensor from colossalai.utils import get_current_device +from .chunk import Chunk, ChunkFullError, TensorState + class ChunkManager: """ diff --git a/colossalai/gemini/chunk/search_utils.py b/colossalai/zero/gemini/chunk/search_utils.py similarity index 98% rename from colossalai/gemini/chunk/search_utils.py rename to colossalai/zero/gemini/chunk/search_utils.py index fe9650721d74..a69b782ead2e 100644 --- a/colossalai/gemini/chunk/search_utils.py +++ b/colossalai/zero/gemini/chunk/search_utils.py @@ -5,9 +5,9 @@ import torch.distributed as dist import torch.nn as nn -from colossalai.gemini.memory_tracer import MemStats, OrderedParamGenerator from colossalai.tensor import ColoParameter from colossalai.utils import is_ddp_ignored +from colossalai.zero.gemini.memory_tracer import MemStats, OrderedParamGenerator def _filter_exlarge_params(model: nn.Module, size_dict: Dict[int, List[int]]) -> None: diff --git a/colossalai/gemini/chunk/utils.py b/colossalai/zero/gemini/chunk/utils.py similarity index 91% rename from colossalai/gemini/chunk/utils.py rename to colossalai/zero/gemini/chunk/utils.py index 83512b8e0ee5..283f74203592 100644 --- a/colossalai/gemini/chunk/utils.py +++ b/colossalai/zero/gemini/chunk/utils.py @@ -5,10 +5,11 @@ import torch.distributed as dist import torch.nn as nn -from colossalai.gemini.chunk import ChunkManager -from colossalai.gemini.chunk.search_utils import search_chunk_configuration from colossalai.utils import is_ddp_ignored +from .manager import ChunkManager +from .search_utils import search_chunk_configuration + def safe_div(a, b): if a == 0: diff --git a/colossalai/utils/model/colo_init_context.py b/colossalai/zero/gemini/colo_init_context.py similarity index 97% rename from colossalai/utils/model/colo_init_context.py rename to colossalai/zero/gemini/colo_init_context.py index 87ae413a2a8a..5937ee9eff9a 100644 --- a/colossalai/utils/model/colo_init_context.py +++ b/colossalai/zero/gemini/colo_init_context.py @@ -3,10 +3,8 @@ import torch from torch import nn -from colossalai.nn.parallel.layers import ColoEmbedding, ColoLinear, register_colo_module from colossalai.tensor import ColoParameter, ColoTensor, ProcessGroup - -from .utils import InsertPostInitMethodToModuleSubClasses +from colossalai.utils.model.utils import InsertPostInitMethodToModuleSubClasses # find named_params includes replica @@ -89,6 +87,7 @@ def __init__(self, self._default_dist_spec = default_dist_spec def _register_colo_modules(self): + from colossalai.nn.parallel.layers import ColoEmbedding, ColoLinear, register_colo_module register_colo_module(torch.nn.Linear, ColoLinear()) register_colo_module(torch.nn.Embedding, ColoEmbedding()) diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py new file mode 100644 index 000000000000..50f1b1ef1ccc --- /dev/null +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -0,0 +1,590 @@ +import itertools +from collections import OrderedDict +from functools import partial +from typing import Dict, List, Optional + +import torch +import torch.distributed as dist +import torch.nn as nn + +from colossalai.logging import get_dist_logger +from colossalai.nn.parallel.data_parallel import ColoDDP, _cast_float, free_storage +from colossalai.tensor import ProcessGroup as ColoProcessGroup +from colossalai.tensor import ReplicaSpec +from colossalai.tensor.colo_parameter import ColoParameter, ColoTensor, ColoTensorSpec +from colossalai.tensor.param_op_hook import ColoParamOpHookManager +from colossalai.utils import get_current_device, is_ddp_ignored + +from .chunk import Chunk, ChunkManager, TensorState, init_chunk_manager +from .gemini_hook import GeminiZeROHook +from .gemini_mgr import GeminiManager +from .memory_tracer import MemStats, OrderedParamGenerator +from .utils import get_temp_total_chunk_on_cuda + +try: + from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX, _IncompatibleKeys +except ImportError: + _EXTRA_STATE_KEY_SUFFIX = '_extra_state' + +__all__ = [ + 'ZeroDDP', + 'GeminiDDP', +] + + +class ZeroDDP(ColoDDP): + """ZeRO DDP for ColoTensor. + Warning: Nested ZeroDDP is not supported now. + It is designed to be used with ChunkManager and GeminiManager. + For more details, see the API reference of ``ChunkManager`` and ``GeminiManager``. + + Args: + module (torch.nn.Module): Module to apply ZeRO-DP. + gemini_manager (GeminiManager): Manages the chunk manager and heterogeneous momery space. + For more details, see the API reference of ``GeminiManager``. + pin_memory (bool): Chunks on CPU Memory use pin-memory. + force_outputs_fp32 (bool): If set to True, outputs will be fp32. Otherwise, outputs will be fp16. + Defaults to False. + strict_ddp_mode (bool): If set to True, there is no tensor sharding, each tensor is replicated. + Defaults to False. Users can set it to True, when they clearly know that they only need DDP. + """ + + def __init__(self, + module: torch.nn.Module, + gemini_manager: GeminiManager, + pin_memory: bool = False, + force_outputs_fp32: bool = False, + strict_ddp_mode: bool = False) -> None: + super().__init__(module, process_group=ColoProcessGroup()) + self.gemini_manager = gemini_manager + self.chunk_manager: ChunkManager = gemini_manager.chunk_manager + self.force_outputs_fp32 = force_outputs_fp32 + self.param_op_hook = GeminiZeROHook(gemini_manager) + self.fp32_params: List[ColoTensor] = list() + self.fp16_params: List[ColoParameter] = list() + self.overflow_counter = 0 + self.grads_device: Dict[torch.Tensor, torch.device] = dict() + self.param2name: Dict[nn.Parameter, str] = dict() + self.name2param: Dict[str, nn.Parameter] = dict() + + self._cast_buffers() + self._logger = get_dist_logger() + + if self.gemini_manager._premade_memstats_: + # build chunk in param runtime visited order. + param_order = self.gemini_manager.memstats()._param_runtime_order + else: + # build chunk in param initialized order. + # Note: in this way, it can not get filter unused params during runtime. + param_order = OrderedParamGenerator() + for p in module.parameters(): + param_order.append(p) + + self._init_chunks(param_order=param_order, + strict_ddp_mode=strict_ddp_mode, + cpu_offload=self.gemini_manager.policy_name != 'cuda', + pin_memory=pin_memory) + + for name, param in module.named_parameters(): + self.param2name[param] = name + for m_name, m_var in module.named_modules(): + for p_name, p_var in m_var.named_parameters(recurse=False): + param_name = m_name + '.' + p_name if m_name else p_name + self.name2param[param_name] = p_var + + def _post_forward(self): + """This function is only triggered for inference. + """ + access_list = list(self.chunk_manager.accessed_chunks) + # we need to scatter all accessed chunks and move them to their original places + for chunk in access_list: + if chunk.keep_gathered: + self.chunk_manager.fake_release_chunk(chunk) + else: + assert chunk.can_release + self.chunk_manager.release_chunk(chunk) + first_param = next(iter(chunk.tensors_info)) + self.chunk_manager.move_chunk(chunk, self.grads_device[first_param]) + assert self.chunk_manager.accessed_mem == 0 + # reset all recorded attributes + self.gemini_manager.reset_attributes() + + def forward(self, *args, **kwargs): + # check whether we are in a inference mode + grad_flag = torch.is_grad_enabled() + if not grad_flag: + assert not self.gemini_manager.need_warmup or not self.gemini_manager.is_warmup( + ), "You should run a completed iteration as your warmup iter" + + args, kwargs = _cast_float(args, torch.half), _cast_float(kwargs, torch.half) + self.module.zero_grad(set_to_none=True) + self.gemini_manager.pre_iter(*args) + with ColoParamOpHookManager.use_hooks(self.param_op_hook): + outputs = self.module(*args, **kwargs) + # scatter chunks in the inference mode + if not grad_flag: + self._post_forward() + + if self.force_outputs_fp32: + return _cast_float(outputs, torch.float) + return outputs + + def _setup_grads_ptr(self): + for p in self.module.parameters(): + if is_ddp_ignored(p): + continue + p.grad = None + + def _pre_backward(self): + # set a visit label for all parameters + # the label is used to check whether the parameter is correctly reduced + for param in self.param2name: + if not is_ddp_ignored(param): + setattr(param, "_gemini_reduced", False) + + def _post_backward(self): + if self.chunk_manager.accessed_mem != 0: + error_params = ["Reduction failed at followed parameters:"] + for param in self.param2name: + if not is_ddp_ignored(param) and not getattr(param, "_gemini_reduced"): + error_params.append(self.param2name[param]) + error_str = "\n\t".join(error_params) + raise RuntimeError("ZERO DDP error: the synchronization of gradients doesn't exit properly.", + "The most possible reason is that the model is not compatible with ZeroDDP.\n", + f"{error_str}") + self._setup_grads_ptr() + self._logger.debug( + f'comp cuda demand time: {self.gemini_manager._comp_cuda_demand_time}, layout time: {self.gemini_manager._layout_time}, evict time: {self.gemini_manager._evict_time}, CPU->CUDA vol: {self.gemini_manager._h2d_volume}B, CUDA->CPU vol: {self.gemini_manager._d2h_volume}' + ) + self.gemini_manager.post_iter() + + def backward(self, loss: torch.Tensor): + self._pre_backward() + with self.param_op_hook.switch_to_backward(), ColoParamOpHookManager.use_hooks(self.param_op_hook): + loss.backward() + self._post_backward() + + def backward_by_grad(self, tensor, grad): + with self.param_op_hook.switch_to_backward(), ColoParamOpHookManager.use_hooks(self.param_op_hook): + torch.autograd.backward(tensor, grad) + self._post_backward() + + def grad_handle(self, p, grad): + empty_grad = torch.empty_like(grad) + free_storage(empty_grad) + with torch._C.DisableTorchFunction(): + chunk = self.chunk_manager.get_chunk(p) + if chunk.tensors_info[p].state != TensorState.HOLD_AFTER_BWD: + raise RuntimeError(f"Parameter `{self.param2name[p]}` failed at the gradient reduction. " + "Some unsupported torch function is operated upon this parameter.") + self.chunk_manager.trans_tensor_state(p, TensorState.READY_FOR_REDUCE) + chunk.copy_tensor_to_chunk_slice(p, grad) + reduced = self.chunk_manager.reduce_chunk(chunk) + if reduced: + if chunk.is_gathered: + chunk.cuda_global_chunk.div_(chunk.pg_size) + else: + chunk.cuda_shard.div_(chunk.pg_size) + # check overflow elements + self.overflow_counter += chunk.has_inf_or_nan + # record l2 norm for gradient clipping + if chunk.l2_norm_flag: + chunk.set_l2_norm() + self.chunk_manager.move_chunk(chunk, self.grads_device[p], force_copy=True) + return empty_grad + + def zero_grad(self, set_to_none: bool = False) -> None: + self.module.zero_grad(set_to_none=True) + + def set_chunk_grad_device(self, chunk: Chunk, device: torch.device) -> None: + for tensor in chunk.get_tensors(): + self.grads_device[tensor] = device + + def state_dict(self, destination=None, prefix='', keep_vars=False, only_rank_0: bool = True): + """Returns a dictionary containing a whole state of the module. + + Both parameters and persistent buffers (e.g. running averages) are included. + Keys are corresponding parameter and buffer names. + Parameters and buffers set to ``None`` are not included. + + Warning: The non strict state dict would ignore the parameters if the tensors of the parameters + are shared with other parameters which have been included in the dictionary. + When you need to load the state dict, you should set the argument `strict` to False. + + Returns: + dict: + a dictionary containing a whole state of the module + """ + if destination is None: + destination = OrderedDict() + destination._metadata = OrderedDict() + destination._metadata[prefix[:-1]] = local_metadata = dict(version=self._version) + self._save_to_state_dict(destination, prefix, keep_vars, only_rank_0) + + for hook in self._state_dict_hooks.values(): + hook_result = hook(self, destination, prefix, local_metadata) + if hook_result is not None: + destination = hook_result + return destination + + def _get_param_to_save_data(self, param_list: List[torch.nn.Parameter], only_rank_0: bool) -> Dict: + """ + get param content from chunks. + + Args: + param_list (_type_): a list of torch.nn.Parameters + only_rank_0 (_type_): _description_ + + Returns: + Dict: a dict whose key is param name and value is param with correct payload + """ + # save parameters + param_to_save_data = dict() + chunk_list = self.chunk_manager.get_chunks(param_list) + for chunk in chunk_list: + temp_chunk = get_temp_total_chunk_on_cuda(chunk) + + for tensor, tensor_info in chunk.tensors_info.items(): + record_tensor = torch.empty([0]) + record_flag = (not only_rank_0) | (dist.get_rank(chunk.torch_pg) == 0) + if record_flag: + record_tensor = temp_chunk[tensor_info.offset:tensor_info.end].view(tensor.shape).cpu() + + assert tensor not in param_to_save_data + param_to_save_data[tensor] = record_tensor + + del temp_chunk + return param_to_save_data + + def _save_to_state_dict(self, destination, prefix, keep_vars, only_rank_0=True): + r"""Saves module state to `destination` dictionary, containing a state + of the module, but not its descendants. This is called on every + submodule in :meth:`~torch.nn.Module.state_dict`. + + In rare cases, subclasses can achieve class-specific behavior by + overriding this method with custom logic. + + Args: + destination (dict): a dict where state will be stored + prefix (str): the prefix for parameters and buffers used in this + module + """ + assert keep_vars is False, "`state_dict` with parameter, `keep_vars=True`, is not supported now." + + # get copies of fp32 parameters in CPU + param_to_save_data = self._get_param_to_save_data(self.fp32_params, only_rank_0) + # get the mapping between copies and fp16 parameters + p_mapping = dict() + for p, fp32_p in zip(self.fp16_params, self.fp32_params): + name = self.param2name[p] + assert fp32_p in param_to_save_data, "Parameter '{}' is neglected in the chunk list".format(name) + record_parameter = param_to_save_data[fp32_p] + p_mapping[p] = record_parameter + for name, param in self.name2param.items(): + if param is not None: + if is_ddp_ignored(param): + # deal with ddp ignored parameters + destination[prefix + name] = param if keep_vars else param.detach() + else: + destination[prefix + name] = p_mapping[param] + del p_mapping + del param_to_save_data + + # save all buffers + for name, buf in self.named_buffers(): + if buf is not None and name not in self._non_persistent_buffers_set: + destination[prefix + name] = buf if keep_vars else buf.detach() + # save extra states + extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX + if getattr(self.__class__, "get_extra_state", + torch.nn.Module.get_extra_state) is not torch.nn.Module.get_extra_state: + destination[extra_state_key] = self.get_extra_state() + + def load_state_dict(self, state_dict: 'OrderedDict[str, torch.Tensor]', strict: bool = True): + r"""Copies parameters and buffers from :attr:`state_dict` into + this module and its descendants. If :attr:`strict` is ``True``, then + the keys of :attr:`state_dict` must exactly match the keys returned + by this module's :meth:`~torch.nn.Module.state_dict` function. + + Args: + state_dict (dict): a dict containing parameters and + persistent buffers. + strict (bool, optional): whether to strictly enforce that the keys + in :attr:`state_dict` match the keys returned by this module's + :meth:`~torch.nn.Module.state_dict` function. Default: ``True`` + + Returns: + ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields: + * **missing_keys** is a list of str containing the missing keys + * **unexpected_keys** is a list of str containing the unexpected keys + + Note: + If a parameter or buffer is registered as ``None`` and its corresponding key + exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a + ``RuntimeError``. + """ + missing_keys: List[str] = [] + unexpected_keys: List[str] = [] + error_msgs: List[str] = [] + + # copy state_dict so _load_from_state_dict can modify it + metadata = getattr(state_dict, '_metadata', None) + state_dict = state_dict.copy() + if metadata is not None: + # mypy isn't aware that "_metadata" exists in state_dict + state_dict._metadata = metadata # type: ignore[attr-defined] + + prefix = '' + local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) + self._load_from_state_dict(state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) + + if strict: + if len(unexpected_keys) > 0: + error_msgs.insert( + 0, 'Unexpected key(s) in state_dict: {}. '.format(', '.join( + '"{}"'.format(k) for k in unexpected_keys))) + if len(missing_keys) > 0: + error_msgs.insert( + 0, 'Missing key(s) in state_dict: {}. '.format(', '.join('"{}"'.format(k) for k in missing_keys))) + + if len(error_msgs) > 0: + raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( + self.__class__.__name__, "\n\t".join(error_msgs))) + return _IncompatibleKeys(missing_keys, unexpected_keys) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, + error_msgs): + r"""Copies parameters and buffers from :attr:`state_dict` into only + this module, but not its descendants. This is called on every submodule + in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this + module in input :attr:`state_dict` is provided as :attr:`local_metadata`. + For state dicts without metadata, :attr:`local_metadata` is empty. + Subclasses can achieve class-specific backward compatible loading using + the version number at `local_metadata.get("version", None)`. + + .. note:: + :attr:`state_dict` is not the same object as the input + :attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. So + it can be modified. + + Args: + state_dict (dict): a dict containing parameters and + persistent buffers. + prefix (str): the prefix for parameters and buffers used in this + module + local_metadata (dict): a dict containing the metadata for this module. + See + strict (bool): whether to strictly enforce that the keys in + :attr:`state_dict` with :attr:`prefix` match the names of + parameters and buffers in this module + missing_keys (list of str): if ``strict=True``, add missing keys to + this list + unexpected_keys (list of str): if ``strict=True``, add unexpected + keys to this list + error_msgs (list of str): error messages should be added to this + list, and will be reported together in + :meth:`~torch.nn.Module.load_state_dict` + """ + for hook in self._load_state_dict_pre_hooks.values(): + hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) + + persistent_buffers = {k: v for k, v in self.named_buffers() if k not in self._non_persistent_buffers_set} + local_name_params = itertools.chain(self.named_parameters(), persistent_buffers.items()) + local_state = {k: v for k, v in local_name_params if v is not None} + + def load(param_name, dest_tensor, copy_func): + state_key = prefix + param_name + if state_key in state_dict: + input_param = state_dict[state_key] + # Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+ + if len(dest_tensor.shape) == 0 and len(input_param.shape) == 1: + input_param = input_param[0] + if input_param.shape != dest_tensor.shape: + # local shape should match the one in checkpoint + error_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, ' + 'the shape in current model is {}.'.format(state_key, input_param.shape, + dest_tensor.shape)) + return + try: + with torch.no_grad(): + copy_func(input_param) + except Exception as ex: + error_msgs.append('While copying the parameter named "{}", ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}, ' + 'an exception occurred : {}.'.format(state_key, dest_tensor.size(), + input_param.size(), ex.args)) + elif strict: + missing_keys.append(state_key) + + def load_fp32_parameter(chunk_slice, data): + chunk_slice.copy_(data.flatten()) + + for name, param in self.named_parameters(): + if is_ddp_ignored(param): + # deal with ddp ignored parameters + load(name, param, param.copy_) + + fp32_to_name = dict() + for p, fp32_p in zip(self.fp16_params, self.fp32_params): + if p is not None: + name = self.param2name[p] + fp32_to_name[fp32_p] = name + + chunk_list = self.chunk_manager.get_chunks(self.fp32_params) + for chunk in chunk_list: + temp_chunk = get_temp_total_chunk_on_cuda(chunk) + + for tensor, tensor_info in chunk.tensors_info.items(): + parameter_name = fp32_to_name[tensor] + parameter_slice = temp_chunk[tensor_info.offset:tensor_info.end] + load(parameter_name, tensor, partial(load_fp32_parameter, parameter_slice)) + + if chunk.is_gathered: + chunk.cuda_global_chunk.copy_(temp_chunk) + elif chunk.cuda_shard is not None: + chunk.cuda_shard.copy_(temp_chunk[chunk.shard_begin:chunk.shard_end]) + else: + chunk.cpu_shard.copy_(temp_chunk[chunk.shard_begin:chunk.shard_end]) + + del temp_chunk + + for chunk_32 in chunk_list: + chunk_16 = chunk_32.paired_chunk + assert chunk_16 is not None + chunk_16.optim_update() + + for name, buf in persistent_buffers.items(): + if buf is not None: + load(name, buf, buf.copy_) + + extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX + if getattr(self.__class__, "set_extra_state", + torch.nn.Module.set_extra_state) is not torch.nn.Module.set_extra_state: + if extra_state_key in state_dict: + self.set_extra_state(state_dict[extra_state_key]) + elif strict: + missing_keys.append(extra_state_key) + elif strict and (extra_state_key in state_dict): + unexpected_keys.append(extra_state_key) + + if strict: + for key in state_dict.keys(): + if key.startswith(prefix) and key != extra_state_key: + input_name = key[len(prefix):] + if input_name not in local_state: + unexpected_keys.append(key) + + def _init_chunks(self, param_order, strict_ddp_mode: bool, cpu_offload: bool, pin_memory: bool): + ddp_pg = ColoProcessGroup() + for p in param_order.generate(): + assert isinstance(p, ColoParameter) + + # gather sharded parameters in the strict ddp mode + if strict_ddp_mode: + if not p.is_replicate(): + p.set_dist_spec(ReplicaSpec()) + p.set_process_group(pg=ddp_pg) + + # ignore the parameters with no gradient + if not p.requires_grad: + self.set_params_to_ignore([p]) + + # move ignored parameters to CUDA + if is_ddp_ignored(p): + p.data = p.data.to(device=get_current_device(), dtype=torch.float16) + continue + + # create a fp32 parameter + fp32_data = p.data.float() + fp32_p = ColoTensor(fp32_data, spec=ColoTensorSpec(p.process_group)) + # create a fp16 parameter + p.data = p.data.half() + + # register the fp16 parameter and fp32 parameter in the chunk manager + dp_world_size = p.process_group.dp_world_size() + self.chunk_manager.register_tensor(tensor=p, + group_type='fp16_param', + config_key=dp_world_size, + cpu_offload=cpu_offload, + pin_memory=pin_memory) + self.chunk_manager.register_tensor(tensor=fp32_p, + group_type='fp32_param', + config_key=dp_world_size, + cpu_offload=cpu_offload, + pin_memory=pin_memory) + + self.fp16_params.append(p) + self.fp32_params.append(fp32_p) + self.grads_device[p] = self.gemini_manager.default_device + + self.chunk_manager.close_all_groups() + + for p, fp32_p in zip(self.fp16_params, self.fp32_params): + chunk_16 = self.chunk_manager.get_chunk(p) + chunk_32 = self.chunk_manager.get_chunk(fp32_p) + chunk_32.init_pair(chunk_16) + + # keep gathered chunks are in CUDA + if chunk_16.keep_gathered: + self.grads_device[p] = get_current_device() + + def _cast_buffers(self): + for buffer in self.module.buffers(): + buffer.data = buffer.cuda() + if torch.is_floating_point(buffer): + buffer.data = buffer.half() + + +class GeminiDDP(ZeroDDP): + + def __init__(self, + module: torch.nn.Module, + device: torch.device, + placement_policy: str = "cpu", + pin_memory: bool = False, + force_outputs_fp32: bool = False, + strict_ddp_mode: bool = False, + search_range_mb: int = 32, + hidden_dim: Optional[int] = None, + min_chunk_size_mb: float = 32, + memstats: Optional[MemStats] = None) -> None: + """ + A torch.Module warpper using ZeRO-DP and Genimi. + ZeRO is for parallel. Gemini is for memory management. + WARNING: The class will modify the module inline! + + Example: + model is initialized under the context of ColoInitContext + >>> model = GeminiDDP(model, torch.cuda.current_device(), "cuda") + >>> logits = model(x) + >>> loss = criterion(logits, labels) + >>> model.backward(loss) + + Args: + module (torch.nn.Module): the model to be wrapped. + device (torch.device): device to place the model. + placement_policy (str, optional): "cpu", "cuda", "auto". Defaults to "cpu". + pin_memory (bool, optional): use pin memory on CPU. Defaults to False. + force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False. + search_range_mb (int, optional): chunk size searching range in MegaByte. Defaults to 32. + hidden_dim (int, optional): the hidden dimension of DNN. + Users can provide this argument to speed up searching. + If users do not know this argument before training, it is ok. We will use a default value 1024. + min_chunk_size_mb (float, optional): the minimum chunk size in MegaByte. + If the aggregate size of parameters is still samller than the minimum chunk size, + all parameters will be compacted into one small chunk. + memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer. + """ + # some ugly hotfix for the compatibility with Lightning + if search_range_mb is None: + search_range_mb = 32 + + chunk_manager = init_chunk_manager(model=module, + init_device=device, + hidden_dim=hidden_dim, + search_range_mb=search_range_mb, + min_chunk_size_mb=min_chunk_size_mb, + strict_ddp_flag=strict_ddp_mode) + gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats) + super().__init__(module, gemini_manager, pin_memory, force_outputs_fp32, strict_ddp_mode) diff --git a/colossalai/zero/utils/gemini_hook.py b/colossalai/zero/gemini/gemini_hook.py similarity index 95% rename from colossalai/zero/utils/gemini_hook.py rename to colossalai/zero/gemini/gemini_hook.py index bddc307a0504..dbc2924858e6 100644 --- a/colossalai/zero/utils/gemini_hook.py +++ b/colossalai/zero/gemini/gemini_hook.py @@ -5,10 +5,10 @@ import torch -from colossalai.gemini import TensorState -from colossalai.gemini.gemini_mgr import GeminiManager from colossalai.tensor.param_op_hook import ColoParamOpHook from colossalai.utils import is_ddp_ignored +from colossalai.zero.gemini import TensorState +from colossalai.zero.gemini.gemini_mgr import GeminiManager class TrainingPhase(Enum): diff --git a/colossalai/gemini/gemini_mgr.py b/colossalai/zero/gemini/gemini_mgr.py similarity index 97% rename from colossalai/gemini/gemini_mgr.py rename to colossalai/zero/gemini/gemini_mgr.py index 72a5e4a7f19b..c38e6eff840d 100644 --- a/colossalai/gemini/gemini_mgr.py +++ b/colossalai/zero/gemini/gemini_mgr.py @@ -4,10 +4,8 @@ import torch -from colossalai.gemini.chunk import Chunk, ChunkManager -from colossalai.gemini.memory_tracer import MemStats - -from .memory_tracer import ChunkMemStatsCollector +from .chunk import Chunk, ChunkManager +from .memory_tracer import ChunkMemStatsCollector, MemStats from .placement_policy import PlacementPolicyFactory diff --git a/colossalai/nn/optimizer/zero_optimizer.py b/colossalai/zero/gemini/gemini_optimizer.py similarity index 97% rename from colossalai/nn/optimizer/zero_optimizer.py rename to colossalai/zero/gemini/gemini_optimizer.py index 422ebb7a3944..8e0237ddc7bc 100644 --- a/colossalai/nn/optimizer/zero_optimizer.py +++ b/colossalai/zero/gemini/gemini_optimizer.py @@ -10,12 +10,15 @@ from torch.optim import Optimizer from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler -from colossalai.gemini.chunk import Chunk, ChunkManager from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import ColossalaiOptimizer, CPUAdam, FusedAdam, HybridAdam -from colossalai.nn.parallel.data_parallel import ZeroDDP from colossalai.utils import disposable, get_current_device, is_ddp_ignored +from .chunk import Chunk, ChunkManager +from .gemini_ddp import ZeroDDP + +__all__ = ['ZeroOptimizer', 'GeminiAdamOptimizer'] + _AVAIL_OPTIM_LIST = {FusedAdam, CPUAdam, HybridAdam} @@ -316,3 +319,10 @@ def get_range_pair(local_chunk: Chunk, local_param: Parameter): fake_params_list.append(fake_param) group['params'] = fake_params_list + + +class GeminiAdamOptimizer(ZeroOptimizer): + + def __init__(self, model: torch.nn.Module, **defaults: Any) -> None: + optimizer = HybridAdam(model.parameters(), **defaults) + super().__init__(optimizer, model, **defaults) diff --git a/colossalai/gemini/memory_tracer/__init__.py b/colossalai/zero/gemini/memory_tracer/__init__.py similarity index 100% rename from colossalai/gemini/memory_tracer/__init__.py rename to colossalai/zero/gemini/memory_tracer/__init__.py diff --git a/colossalai/gemini/memory_tracer/chunk_memstats_collector.py b/colossalai/zero/gemini/memory_tracer/chunk_memstats_collector.py similarity index 91% rename from colossalai/gemini/memory_tracer/chunk_memstats_collector.py rename to colossalai/zero/gemini/memory_tracer/chunk_memstats_collector.py index 1a5b6bf525be..f5eb05b4f22a 100644 --- a/colossalai/gemini/memory_tracer/chunk_memstats_collector.py +++ b/colossalai/zero/gemini/memory_tracer/chunk_memstats_collector.py @@ -1,10 +1,10 @@ from typing import Optional -from colossalai.gemini.chunk import ChunkManager -from colossalai.gemini.memory_tracer import MemStats from colossalai.utils import get_current_device from colossalai.utils.memory import colo_device_memory_capacity +from colossalai.zero.gemini.chunk import ChunkManager +from .memory_stats import MemStats from .memstats_collector import MemStatsCollector diff --git a/colossalai/gemini/memory_tracer/memory_monitor.py b/colossalai/zero/gemini/memory_tracer/memory_monitor.py similarity index 100% rename from colossalai/gemini/memory_tracer/memory_monitor.py rename to colossalai/zero/gemini/memory_tracer/memory_monitor.py diff --git a/colossalai/gemini/memory_tracer/memory_stats.py b/colossalai/zero/gemini/memory_tracer/memory_stats.py similarity index 98% rename from colossalai/gemini/memory_tracer/memory_stats.py rename to colossalai/zero/gemini/memory_tracer/memory_stats.py index 84fa00fb9361..9a45034ee27e 100644 --- a/colossalai/gemini/memory_tracer/memory_stats.py +++ b/colossalai/zero/gemini/memory_tracer/memory_stats.py @@ -2,7 +2,7 @@ import torch -from colossalai.gemini.memory_tracer import OrderedParamGenerator +from .param_runtime_order import OrderedParamGenerator class MemStats(object): diff --git a/colossalai/gemini/memory_tracer/memstats_collector.py b/colossalai/zero/gemini/memory_tracer/memstats_collector.py similarity index 92% rename from colossalai/gemini/memory_tracer/memstats_collector.py rename to colossalai/zero/gemini/memory_tracer/memstats_collector.py index d939da6eb4cf..0694be48550a 100644 --- a/colossalai/gemini/memory_tracer/memstats_collector.py +++ b/colossalai/zero/gemini/memory_tracer/memstats_collector.py @@ -1,12 +1,7 @@ import time -from typing import List, Optional - -import torch - -from colossalai.gemini.memory_tracer import SyncCudaMemoryMonitor -from colossalai.gemini.stateful_tensor import StatefulTensor -from colossalai.utils.memory import colo_device_memory_used +from typing import Optional +from .memory_monitor import SyncCudaMemoryMonitor from .memory_stats import MemStats @@ -49,7 +44,7 @@ def next_period_non_model_data_usage(self, device_type: str) -> int: assert self._step_total > 0, 'Cannot get mem stats info before collection phase.' assert len(self._memstats.non_model_data_list(device_type)) > self._step_idx, \ f"{len(self._memstats.non_model_data_list(device_type))} should be > than step idx {self._step_idx}, "\ - f"step total {self._step_total}" + f"step total {self._step_total}" next_non_model_data = self._memstats.non_model_data_list(device_type)[self._step_idx] self._step_idx = (self._step_idx + 1) % self._step_total return next_non_model_data @@ -75,6 +70,8 @@ def record_model_data_volume(self) -> None: Sampling model data statistics. """ if self._start_flag and not self.use_outside_memstats: + from colossalai.zero.legacy.gemini import StatefulTensor + # The following code work for ZeroInitContext, which is deprecated in v0.1.12 cuda_mem = StatefulTensor.GST_MGR.total_mem['cuda'] self._memstats.record_max_cuda_model_data(cuda_mem) diff --git a/colossalai/gemini/memory_tracer/param_runtime_order.py b/colossalai/zero/gemini/memory_tracer/param_runtime_order.py similarity index 100% rename from colossalai/gemini/memory_tracer/param_runtime_order.py rename to colossalai/zero/gemini/memory_tracer/param_runtime_order.py diff --git a/colossalai/gemini/memory_tracer/runtime_mem_tracer.py b/colossalai/zero/gemini/memory_tracer/runtime_mem_tracer.py similarity index 95% rename from colossalai/gemini/memory_tracer/runtime_mem_tracer.py rename to colossalai/zero/gemini/memory_tracer/runtime_mem_tracer.py index a643751da7e2..0c9eac8b63e3 100644 --- a/colossalai/gemini/memory_tracer/runtime_mem_tracer.py +++ b/colossalai/zero/gemini/memory_tracer/runtime_mem_tracer.py @@ -1,9 +1,14 @@ import torch.nn -from colossalai.gemini.memory_tracer import MemStats -from colossalai.gemini.ophooks.runtime_mem_tracer_hook import GradMemStats, GradMemTracerHook, ParamMemTracerHook from colossalai.nn.parallel.data_parallel import _cast_float from colossalai.tensor.param_op_hook import ColoParamOpHookManager +from colossalai.zero.legacy.gemini.ophooks.runtime_mem_tracer_hook import ( + GradMemStats, + GradMemTracerHook, + ParamMemTracerHook, +) + +from .memory_stats import MemStats __all__ = ['RuntimeMemTracer'] diff --git a/colossalai/gemini/memory_tracer/static_memstats_collector.py b/colossalai/zero/gemini/memory_tracer/static_memstats_collector.py similarity index 98% rename from colossalai/gemini/memory_tracer/static_memstats_collector.py rename to colossalai/zero/gemini/memory_tracer/static_memstats_collector.py index 3209881e100c..b8f9a095f422 100644 --- a/colossalai/gemini/memory_tracer/static_memstats_collector.py +++ b/colossalai/zero/gemini/memory_tracer/static_memstats_collector.py @@ -6,7 +6,7 @@ from colossalai.fx.passes.meta_info_prop import MetaInfoProp from colossalai.fx.profiler import calculate_fwd_out, calculate_fwd_tmp, is_compatible_with_meta -from colossalai.gemini.chunk import ChunkManager +from colossalai.zero.gemini.chunk import ChunkManager if is_compatible_with_meta(): from colossalai.fx.profiler import MetaTensor diff --git a/colossalai/gemini/memory_tracer/utils.py b/colossalai/zero/gemini/memory_tracer/utils.py similarity index 100% rename from colossalai/gemini/memory_tracer/utils.py rename to colossalai/zero/gemini/memory_tracer/utils.py diff --git a/colossalai/gemini/placement_policy.py b/colossalai/zero/gemini/placement_policy.py similarity index 98% rename from colossalai/gemini/placement_policy.py rename to colossalai/zero/gemini/placement_policy.py index fed1cc2985ff..84a868872f88 100644 --- a/colossalai/gemini/placement_policy.py +++ b/colossalai/zero/gemini/placement_policy.py @@ -5,11 +5,12 @@ import torch -from colossalai.gemini.chunk import Chunk, ChunkManager -from colossalai.gemini.memory_tracer import ChunkMemStatsCollector from colossalai.utils import get_current_device from colossalai.utils.memory import colo_device_memory_capacity +from .chunk import Chunk, ChunkManager +from .memory_tracer import ChunkMemStatsCollector + class PlacementPolicy(ABC): need_mem_stats: bool = False diff --git a/colossalai/nn/parallel/utils.py b/colossalai/zero/gemini/utils.py similarity index 97% rename from colossalai/nn/parallel/utils.py rename to colossalai/zero/gemini/utils.py index 08fdb6026e38..e52b5b836b0b 100644 --- a/colossalai/nn/parallel/utils.py +++ b/colossalai/zero/gemini/utils.py @@ -6,9 +6,10 @@ import torch.distributed as dist import torch.nn as nn -from colossalai.gemini.chunk import Chunk from colossalai.utils import get_current_device +from .chunk import Chunk + def get_temp_total_chunk_on_cuda(chunk: Chunk): if chunk.is_gathered: @@ -77,7 +78,7 @@ def get_static_torch_model(zero_ddp_model, Returns: torch.nn.Module: a static torch model used for saving checkpoints or numeric checks """ - from colossalai.nn.parallel import ZeroDDP + from colossalai.zero.gemini.gemini_ddp import ZeroDDP assert isinstance(zero_ddp_model, ZeroDDP) state_dict = zero_ddp_model.state_dict(only_rank_0=only_rank_0) diff --git a/colossalai/zero/legacy/__init__.py b/colossalai/zero/legacy/__init__.py new file mode 100644 index 000000000000..35570a1f539a --- /dev/null +++ b/colossalai/zero/legacy/__init__.py @@ -0,0 +1,44 @@ +from typing import Tuple + +import torch +import torch.nn as nn + +from colossalai.logging import get_dist_logger + +from .init_ctx import ZeroInitContext, no_shard_zero_context, no_shard_zero_decrator +from .sharded_model import ShardedModelV2 +from .sharded_optim import ShardedOptimizerV2 + + +def convert_to_zero_v2(model: nn.Module, optimizer: torch.optim.Optimizer, model_config, + optimizer_config) -> Tuple[ShardedModelV2, ShardedOptimizerV2]: + """ + A helper function to integrate the model and optimizer with ZeRO optimizer and off-loading + + :param model: Your model object + :type model: :class:`torch.nn.Module` + :param optimizer_config: Your optimizer object + :type optimizer_config: :class:`dict` + + :return: (model, optimizer) + :rtype: Tuple + """ + + logger = get_dist_logger('convert_to_zero_v2') + + logger.info(f'optimizer_config is {optimizer_config}', ranks=[0]) + if optimizer_config is None: + optimizer_config = dict() + logger.info(f'model_config is {model_config}', ranks=[0]) + if model_config is None: + model_config = dict() + + zero_model = ShardedModelV2(model, **model_config) + zero_optimizer = ShardedOptimizerV2(zero_model, optimizer, **optimizer_config) + return zero_model, zero_optimizer + + +__all__ = [ + 'convert_to_zero_v2', 'ShardedModelV2', 'ShardedOptimizerV2', 'ZeroInitContext', 'no_shard_zero_context', + 'no_shard_zero_decrator' +] diff --git a/colossalai/zero/legacy/gemini/__init__.py b/colossalai/zero/legacy/gemini/__init__.py new file mode 100644 index 000000000000..754ae9bc0044 --- /dev/null +++ b/colossalai/zero/legacy/gemini/__init__.py @@ -0,0 +1,9 @@ +from .ophooks import BaseOpHook, register_ophooks_recursively +from .stateful_tensor import StatefulTensor +from .stateful_tensor_mgr import StatefulTensorMgr +from .tensor_placement_policy import AutoTensorPlacementPolicy, CPUTensorPlacementPolicy, CUDATensorPlacementPolicy + +__all__ = [ + 'StatefulTensorMgr', 'StatefulTensor', 'CPUTensorPlacementPolicy', 'CUDATensorPlacementPolicy', + 'AutoTensorPlacementPolicy', 'register_ophooks_recursively', 'BaseOpHook' +] diff --git a/colossalai/gemini/gemini_context.py b/colossalai/zero/legacy/gemini/gemini_context.py similarity index 100% rename from colossalai/gemini/gemini_context.py rename to colossalai/zero/legacy/gemini/gemini_context.py diff --git a/colossalai/gemini/ophooks/__init__.py b/colossalai/zero/legacy/gemini/ophooks/__init__.py similarity index 100% rename from colossalai/gemini/ophooks/__init__.py rename to colossalai/zero/legacy/gemini/ophooks/__init__.py diff --git a/colossalai/gemini/ophooks/_shard_grad_ophook.py b/colossalai/zero/legacy/gemini/ophooks/_shard_grad_ophook.py similarity index 100% rename from colossalai/gemini/ophooks/_shard_grad_ophook.py rename to colossalai/zero/legacy/gemini/ophooks/_shard_grad_ophook.py diff --git a/colossalai/gemini/ophooks/_shard_param_ophook.py b/colossalai/zero/legacy/gemini/ophooks/_shard_param_ophook.py similarity index 99% rename from colossalai/gemini/ophooks/_shard_param_ophook.py rename to colossalai/zero/legacy/gemini/ophooks/_shard_param_ophook.py index 57f76970cc86..80736d14085e 100644 --- a/colossalai/gemini/ophooks/_shard_param_ophook.py +++ b/colossalai/zero/legacy/gemini/ophooks/_shard_param_ophook.py @@ -1,4 +1,5 @@ import torch + from colossalai.registry import OPHOOKS from . import BaseOpHook diff --git a/colossalai/gemini/ophooks/runtime_mem_tracer_hook.py b/colossalai/zero/legacy/gemini/ophooks/runtime_mem_tracer_hook.py similarity index 96% rename from colossalai/gemini/ophooks/runtime_mem_tracer_hook.py rename to colossalai/zero/legacy/gemini/ophooks/runtime_mem_tracer_hook.py index 6d0df4e615ca..f40d6ced1ee0 100644 --- a/colossalai/gemini/ophooks/runtime_mem_tracer_hook.py +++ b/colossalai/zero/legacy/gemini/ophooks/runtime_mem_tracer_hook.py @@ -5,9 +5,9 @@ import torch -from colossalai.gemini.memory_tracer import MemStats, SyncCudaMemoryMonitor -from colossalai.gemini.tensor_utils import alloc_storage, free_storage from colossalai.tensor.param_op_hook import ColoParamOpHook +from colossalai.zero.gemini.memory_tracer import MemStats, SyncCudaMemoryMonitor +from colossalai.zero.legacy.gemini.tensor_utils import alloc_storage, free_storage class TrainingPhase(Enum): diff --git a/colossalai/gemini/ophooks/utils.py b/colossalai/zero/legacy/gemini/ophooks/utils.py similarity index 100% rename from colossalai/gemini/ophooks/utils.py rename to colossalai/zero/legacy/gemini/ophooks/utils.py diff --git a/colossalai/gemini/paramhooks/__init__.py b/colossalai/zero/legacy/gemini/paramhooks/__init__.py similarity index 100% rename from colossalai/gemini/paramhooks/__init__.py rename to colossalai/zero/legacy/gemini/paramhooks/__init__.py diff --git a/colossalai/gemini/paramhooks/_param_hookmgr.py b/colossalai/zero/legacy/gemini/paramhooks/_param_hookmgr.py similarity index 100% rename from colossalai/gemini/paramhooks/_param_hookmgr.py rename to colossalai/zero/legacy/gemini/paramhooks/_param_hookmgr.py diff --git a/colossalai/gemini/stateful_tensor.py b/colossalai/zero/legacy/gemini/stateful_tensor.py similarity index 97% rename from colossalai/gemini/stateful_tensor.py rename to colossalai/zero/legacy/gemini/stateful_tensor.py index 18fc8fd14d3c..1619ae40798d 100644 --- a/colossalai/gemini/stateful_tensor.py +++ b/colossalai/zero/legacy/gemini/stateful_tensor.py @@ -1,9 +1,9 @@ from enum import Enum -from typing import Optional +from typing import Optional, Union + import torch -from typing import Union -from colossalai.gemini.gemini_context import GeminiMemoryManager +from .gemini_context import GeminiMemoryManager def sizeof_tensor(tensor: torch.Tensor): @@ -19,7 +19,7 @@ class TensorState(Enum): class StatefulTensor(object): - """A Structure stores a Torch Tensor and labeled states. + """A Structure stores a Torch Tensor and labeled states. Inspired from the paper: PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management diff --git a/colossalai/gemini/stateful_tensor_mgr.py b/colossalai/zero/legacy/gemini/stateful_tensor_mgr.py similarity index 94% rename from colossalai/gemini/stateful_tensor_mgr.py rename to colossalai/zero/legacy/gemini/stateful_tensor_mgr.py index c300f9bffc89..3b37444b0fe0 100644 --- a/colossalai/gemini/stateful_tensor_mgr.py +++ b/colossalai/zero/legacy/gemini/stateful_tensor_mgr.py @@ -1,13 +1,16 @@ import functools -import torch import types -from colossalai.utils.cuda import get_current_device -from colossalai.gemini.tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage -from colossalai.gemini.stateful_tensor import StatefulTensor, TensorState -from colossalai.gemini.tensor_placement_policy import TensorPlacementPolicy +from time import time from typing import List + +import torch + from colossalai.logging import get_dist_logger -from time import time +from colossalai.utils.cuda import get_current_device + +from .stateful_tensor import StatefulTensor, TensorState +from .tensor_placement_policy import TensorPlacementPolicy +from .tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage class StatefulTensorMgr(object): diff --git a/colossalai/gemini/tensor_placement_policy.py b/colossalai/zero/legacy/gemini/tensor_placement_policy.py similarity index 96% rename from colossalai/gemini/tensor_placement_policy.py rename to colossalai/zero/legacy/gemini/tensor_placement_policy.py index 0e575254c0b6..165ae51fee60 100644 --- a/colossalai/gemini/tensor_placement_policy.py +++ b/colossalai/zero/legacy/gemini/tensor_placement_policy.py @@ -5,11 +5,12 @@ import torch -from colossalai.gemini.memory_tracer import MemStatsCollector -from colossalai.gemini.stateful_tensor import StatefulTensor -from colossalai.gemini.tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage from colossalai.utils import get_current_device from colossalai.utils.memory import colo_device_memory_capacity +from colossalai.zero.gemini.memory_tracer import MemStatsCollector + +from .stateful_tensor import StatefulTensor +from .tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage class TensorPlacementPolicy(ABC): diff --git a/colossalai/gemini/tensor_utils.py b/colossalai/zero/legacy/gemini/tensor_utils.py similarity index 97% rename from colossalai/gemini/tensor_utils.py rename to colossalai/zero/legacy/gemini/tensor_utils.py index bcc159f9954a..b7f23e0253fd 100644 --- a/colossalai/gemini/tensor_utils.py +++ b/colossalai/zero/legacy/gemini/tensor_utils.py @@ -1,6 +1,8 @@ +from typing import Tuple, Union + import torch -from colossalai.gemini.stateful_tensor import StatefulTensor -from typing import Union, Tuple + +from .stateful_tensor import StatefulTensor def is_storage_empty(tensor: torch.Tensor) -> bool: diff --git a/colossalai/zero/init_ctx/__init__.py b/colossalai/zero/legacy/init_ctx/__init__.py similarity index 100% rename from colossalai/zero/init_ctx/__init__.py rename to colossalai/zero/legacy/init_ctx/__init__.py diff --git a/colossalai/zero/init_ctx/init_context.py b/colossalai/zero/legacy/init_ctx/init_context.py similarity index 97% rename from colossalai/zero/init_ctx/init_context.py rename to colossalai/zero/legacy/init_ctx/init_context.py index b40b69962cf7..f8be0ca4f3fc 100644 --- a/colossalai/zero/init_ctx/init_context.py +++ b/colossalai/zero/legacy/init_ctx/init_context.py @@ -13,10 +13,10 @@ from colossalai.core import global_context as gpc from colossalai.logging import get_dist_logger from colossalai.utils.model.utils import InsertPostInitMethodToModuleSubClasses -from colossalai.zero.shard_utils import BaseShardStrategy -from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16 -from colossalai.zero.sharded_model.sharded_model_v2 import ShardedModelV2 -from colossalai.zero.sharded_param import ShardedParamV2 +from colossalai.zero.legacy.shard_utils import BaseShardStrategy +from colossalai.zero.legacy.sharded_model._utils import cast_tensor_to_fp16 +from colossalai.zero.legacy.sharded_model.sharded_model_v2 import ShardedModelV2 +from colossalai.zero.legacy.sharded_param import ShardedParamV2 @dataclass diff --git a/colossalai/zero/shard_utils/__init__.py b/colossalai/zero/legacy/shard_utils/__init__.py similarity index 100% rename from colossalai/zero/shard_utils/__init__.py rename to colossalai/zero/legacy/shard_utils/__init__.py diff --git a/colossalai/zero/shard_utils/base_shard_strategy.py b/colossalai/zero/legacy/shard_utils/base_shard_strategy.py similarity index 87% rename from colossalai/zero/shard_utils/base_shard_strategy.py rename to colossalai/zero/legacy/shard_utils/base_shard_strategy.py index 7c2f4c9f6659..7ca951091640 100644 --- a/colossalai/zero/shard_utils/base_shard_strategy.py +++ b/colossalai/zero/legacy/shard_utils/base_shard_strategy.py @@ -2,7 +2,8 @@ from typing import List, Optional import torch.distributed as dist -from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor + +from colossalai.zero.legacy.sharded_param.sharded_tensor import ShardedTensor class BaseShardStrategy(ABC): diff --git a/colossalai/zero/shard_utils/bucket_tensor_shard_strategy.py b/colossalai/zero/legacy/shard_utils/bucket_tensor_shard_strategy.py similarity index 89% rename from colossalai/zero/shard_utils/bucket_tensor_shard_strategy.py rename to colossalai/zero/legacy/shard_utils/bucket_tensor_shard_strategy.py index a7bd7cf538e7..11297bf6d62c 100644 --- a/colossalai/zero/shard_utils/bucket_tensor_shard_strategy.py +++ b/colossalai/zero/legacy/shard_utils/bucket_tensor_shard_strategy.py @@ -2,17 +2,18 @@ import torch import torch.distributed as dist -from colossalai.utils import get_current_device -from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor from torch._utils import _flatten_dense_tensors as flatten +from colossalai.utils import get_current_device +from colossalai.zero.legacy.sharded_param.sharded_tensor import ShardedTensor + from .tensor_shard_strategy import TensorShardStrategy class BucketTensorShardStrategy(TensorShardStrategy): - """Use the same shard scheme as `TensorShardStrategy`'s, but it gathers tensors of a sub-module together, - which will fully utilize network bandwidth. - It is especially useful when sub-module contains bias, + """Use the same shard scheme as `TensorShardStrategy`'s, but it gathers tensors of a sub-module together, + which will fully utilize network bandwidth. + It is especially useful when sub-module contains bias, since we cannot utilize network bandwidth well if we only gather a bias tensor (bias is usaully small). """ diff --git a/colossalai/zero/shard_utils/commons.py b/colossalai/zero/legacy/shard_utils/commons.py similarity index 95% rename from colossalai/zero/shard_utils/commons.py rename to colossalai/zero/legacy/shard_utils/commons.py index 71cef44c177f..bf5ae325caf4 100644 --- a/colossalai/zero/shard_utils/commons.py +++ b/colossalai/zero/legacy/shard_utils/commons.py @@ -1,7 +1,7 @@ -import torch -import torch.nn.functional as F from typing import Tuple +import torch + def get_shard(tensor: torch.Tensor, rank: int, world_size: int) -> Tuple[torch.Tensor, int]: """Return the local shard of a full tensor.""" diff --git a/colossalai/zero/shard_utils/tensor_shard_strategy.py b/colossalai/zero/legacy/shard_utils/tensor_shard_strategy.py similarity index 86% rename from colossalai/zero/shard_utils/tensor_shard_strategy.py rename to colossalai/zero/legacy/shard_utils/tensor_shard_strategy.py index 5bdd95400d82..d1df4803b820 100644 --- a/colossalai/zero/shard_utils/tensor_shard_strategy.py +++ b/colossalai/zero/legacy/shard_utils/tensor_shard_strategy.py @@ -2,11 +2,12 @@ import torch import torch.distributed as dist + from colossalai.utils import get_current_device -from colossalai.zero.shard_utils import BaseShardStrategy -from colossalai.zero.shard_utils.commons import get_shard -from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor -from colossalai.gemini.tensor_utils import colo_model_data_tensor_move_inline +from colossalai.zero.legacy.gemini.tensor_utils import colo_model_data_tensor_move_inline +from colossalai.zero.legacy.shard_utils import BaseShardStrategy +from colossalai.zero.legacy.shard_utils.commons import get_shard +from colossalai.zero.legacy.sharded_param.sharded_tensor import ShardedTensor class TensorShardStrategy(BaseShardStrategy): @@ -27,7 +28,7 @@ def _shard_tensor(self, t: ShardedTensor, process_group: Optional[dist.ProcessGr Args: t (ShardedTensor): a tensor to be sharded. - process_group (Optional[dist.ProcessGroup], optional): the process group among which tensor shards. + process_group (Optional[dist.ProcessGroup], optional): the process group among which tensor shards. Defaults to None. """ if t.is_sharded: diff --git a/colossalai/zero/sharded_model/__init__.py b/colossalai/zero/legacy/sharded_model/__init__.py similarity index 61% rename from colossalai/zero/sharded_model/__init__.py rename to colossalai/zero/legacy/sharded_model/__init__.py index 725179295c60..93120bdc34b4 100644 --- a/colossalai/zero/sharded_model/__init__.py +++ b/colossalai/zero/legacy/sharded_model/__init__.py @@ -1,3 +1,3 @@ from .sharded_model_v2 import ShardedModelV2 -__all__ = ['ShardedModelV2'] \ No newline at end of file +__all__ = ['ShardedModelV2'] diff --git a/colossalai/zero/sharded_model/_utils.py b/colossalai/zero/legacy/sharded_model/_utils.py similarity index 95% rename from colossalai/zero/sharded_model/_utils.py rename to colossalai/zero/legacy/sharded_model/_utils.py index 85a3ab73dd1b..2bd01531a78f 100644 --- a/colossalai/zero/sharded_model/_utils.py +++ b/colossalai/zero/legacy/sharded_model/_utils.py @@ -1,9 +1,9 @@ -from typing import Any, Callable, List, Tuple +from typing import Any, Callable, List, Tuple, Union import torch import torch.nn.functional as F -from typing import Union -from colossalai.gemini.stateful_tensor import StatefulTensor + +from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor def get_gradient_predivide_factor(world_size: int) -> float: diff --git a/colossalai/zero/sharded_model/reduce_scatter.py b/colossalai/zero/legacy/sharded_model/reduce_scatter.py similarity index 100% rename from colossalai/zero/sharded_model/reduce_scatter.py rename to colossalai/zero/legacy/sharded_model/reduce_scatter.py diff --git a/colossalai/zero/sharded_model/sharded_model_v2.py b/colossalai/zero/legacy/sharded_model/sharded_model_v2.py similarity index 97% rename from colossalai/zero/sharded_model/sharded_model_v2.py rename to colossalai/zero/legacy/sharded_model/sharded_model_v2.py index 12e8f65d4a35..edd2cc8e68fe 100644 --- a/colossalai/zero/sharded_model/sharded_model_v2.py +++ b/colossalai/zero/legacy/sharded_model/sharded_model_v2.py @@ -13,19 +13,18 @@ from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc -from colossalai.gemini.memory_tracer import MemStatsCollector, StaticMemStatsCollector -from colossalai.gemini.ophooks import register_ophooks_recursively -from colossalai.gemini.paramhooks import BaseParamHookMgr -from colossalai.gemini.stateful_tensor import TensorState -from colossalai.gemini.stateful_tensor_mgr import StatefulTensorMgr -from colossalai.gemini.tensor_placement_policy import TensorPlacementPolicy, TensorPlacementPolicyFactory -from colossalai.gemini.tensor_utils import colo_model_data_move_to_cpu from colossalai.logging import get_dist_logger from colossalai.utils import disposable, get_current_device from colossalai.utils.memory import colo_device_memory_capacity -from colossalai.zero.shard_utils import BaseShardStrategy -from colossalai.zero.sharded_model.reduce_scatter import ReduceScatterBucketer -from colossalai.zero.utils import ZeroHook +from colossalai.zero.gemini.memory_tracer import MemStatsCollector, StaticMemStatsCollector +from colossalai.zero.legacy.gemini.ophooks import register_ophooks_recursively +from colossalai.zero.legacy.gemini.paramhooks import BaseParamHookMgr +from colossalai.zero.legacy.gemini.stateful_tensor import TensorState +from colossalai.zero.legacy.gemini.stateful_tensor_mgr import StatefulTensorMgr +from colossalai.zero.legacy.gemini.tensor_placement_policy import TensorPlacementPolicy, TensorPlacementPolicyFactory +from colossalai.zero.legacy.gemini.tensor_utils import colo_model_data_move_to_cpu +from colossalai.zero.legacy.shard_utils import BaseShardStrategy +from colossalai.zero.legacy.sharded_model.reduce_scatter import ReduceScatterBucketer from ._utils import ( cast_float_arguments, @@ -35,6 +34,7 @@ free_storage, get_gradient_predivide_factor, ) +from .zero_hook import ZeroHook try: from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX diff --git a/colossalai/zero/sharded_model/utils.py b/colossalai/zero/legacy/sharded_model/utils.py similarity index 91% rename from colossalai/zero/sharded_model/utils.py rename to colossalai/zero/legacy/sharded_model/utils.py index 69f5a23ac920..08806e78ea3b 100644 --- a/colossalai/zero/sharded_model/utils.py +++ b/colossalai/zero/legacy/sharded_model/utils.py @@ -1,7 +1,8 @@ +import copy + import torch -from colossalai.zero.sharded_model import ShardedModelV2 -import copy +from colossalai.zero.legacy.sharded_model import ShardedModelV2 def col_model_deepcopy(sharded_model: ShardedModelV2, other_model: torch.nn.Module): diff --git a/colossalai/zero/utils/zero_hook.py b/colossalai/zero/legacy/sharded_model/zero_hook.py similarity index 92% rename from colossalai/zero/utils/zero_hook.py rename to colossalai/zero/legacy/sharded_model/zero_hook.py index 87bf2c0f5086..50f4bdfc775d 100644 --- a/colossalai/zero/utils/zero_hook.py +++ b/colossalai/zero/legacy/sharded_model/zero_hook.py @@ -3,14 +3,14 @@ import torch import torch.distributed as dist -from colossalai.gemini.memory_tracer import MemStatsCollector -from colossalai.gemini.ophooks import BaseOpHook -from colossalai.gemini.stateful_tensor import TensorState -from colossalai.gemini.stateful_tensor_mgr import StatefulTensorMgr from colossalai.logging import get_dist_logger from colossalai.registry import OPHOOKS from colossalai.utils import get_current_device -from colossalai.zero.shard_utils import BaseShardStrategy +from colossalai.zero.gemini.memory_tracer import MemStatsCollector +from colossalai.zero.legacy.gemini.ophooks import BaseOpHook +from colossalai.zero.legacy.gemini.stateful_tensor import TensorState +from colossalai.zero.legacy.gemini.stateful_tensor_mgr import StatefulTensorMgr +from colossalai.zero.legacy.shard_utils import BaseShardStrategy @OPHOOKS.register_module diff --git a/colossalai/zero/legacy/sharded_optim/__init__.py b/colossalai/zero/legacy/sharded_optim/__init__.py new file mode 100644 index 000000000000..b71a70aeffa4 --- /dev/null +++ b/colossalai/zero/legacy/sharded_optim/__init__.py @@ -0,0 +1,3 @@ +from .sharded_optim_v2 import ShardedOptimizerV2 + +__all__ = ['ShardedOptimizerV2'] diff --git a/colossalai/zero/sharded_optim/sharded_optim_v2.py b/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py similarity index 97% rename from colossalai/zero/sharded_optim/sharded_optim_v2.py rename to colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py index 43a0b7d76107..7ce1c056f583 100644 --- a/colossalai/zero/sharded_optim/sharded_optim_v2.py +++ b/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py @@ -14,13 +14,13 @@ from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc -from colossalai.gemini.stateful_tensor import StatefulTensor, TensorState -from colossalai.gemini.tensor_placement_policy import AutoTensorPlacementPolicy -from colossalai.gemini.tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import ColossalaiOptimizer -from colossalai.zero.sharded_model import ShardedModelV2 -from colossalai.zero.sharded_model._utils import cast_tensor_to_fp32 +from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor, TensorState +from colossalai.zero.legacy.gemini.tensor_placement_policy import AutoTensorPlacementPolicy +from colossalai.zero.legacy.gemini.tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage +from colossalai.zero.legacy.sharded_model import ShardedModelV2 +from colossalai.zero.legacy.sharded_model._utils import cast_tensor_to_fp32 class OptimState(Enum): diff --git a/colossalai/zero/legacy/sharded_param/__init__.py b/colossalai/zero/legacy/sharded_param/__init__.py new file mode 100644 index 000000000000..47e2ce2fa0e0 --- /dev/null +++ b/colossalai/zero/legacy/sharded_param/__init__.py @@ -0,0 +1,4 @@ +from .sharded_param import ShardedParamV2 +from .sharded_tensor import ShardedTensor + +__all__ = ['ShardedTensor', 'ShardedParamV2'] diff --git a/colossalai/zero/sharded_param/sharded_param.py b/colossalai/zero/legacy/sharded_param/sharded_param.py similarity index 93% rename from colossalai/zero/sharded_param/sharded_param.py rename to colossalai/zero/legacy/sharded_param/sharded_param.py index db0f2d149431..4bcc4b62104a 100644 --- a/colossalai/zero/sharded_param/sharded_param.py +++ b/colossalai/zero/legacy/sharded_param/sharded_param.py @@ -1,9 +1,11 @@ +from typing import List, Optional, Tuple + import torch -from typing import Optional, Tuple -from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor -from colossalai.gemini.tensor_utils import colo_tensor_mem_usage -from colossalai.gemini.stateful_tensor import StatefulTensor, TensorState -from typing import List + +from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor, TensorState +from colossalai.zero.legacy.gemini.tensor_utils import colo_tensor_mem_usage + +from .sharded_tensor import ShardedTensor EMPTY_TENSOR_DICT = {} diff --git a/colossalai/zero/sharded_param/sharded_tensor.py b/colossalai/zero/legacy/sharded_param/sharded_tensor.py similarity index 92% rename from colossalai/zero/sharded_param/sharded_tensor.py rename to colossalai/zero/legacy/sharded_param/sharded_tensor.py index 77f4aec30f32..af60312600f2 100644 --- a/colossalai/zero/sharded_param/sharded_tensor.py +++ b/colossalai/zero/legacy/sharded_param/sharded_tensor.py @@ -1,5 +1,6 @@ import torch -from colossalai.gemini.stateful_tensor import StatefulTensor, TensorState + +from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor, TensorState class ShardedTensor(StatefulTensor): diff --git a/colossalai/zero/low_level/__init__.py b/colossalai/zero/low_level/__init__.py new file mode 100644 index 000000000000..ae3c1de3a5bc --- /dev/null +++ b/colossalai/zero/low_level/__init__.py @@ -0,0 +1,3 @@ +from .low_level_optim import LowLevelZeroOptimizer + +__all__ = ['LowLevelZeroOptimizer'] diff --git a/colossalai/zero/sharded_optim/_utils.py b/colossalai/zero/low_level/_utils.py similarity index 100% rename from colossalai/zero/sharded_optim/_utils.py rename to colossalai/zero/low_level/_utils.py diff --git a/colossalai/zero/sharded_optim/bookkeeping/__init__.py b/colossalai/zero/low_level/bookkeeping/__init__.py similarity index 100% rename from colossalai/zero/sharded_optim/bookkeeping/__init__.py rename to colossalai/zero/low_level/bookkeeping/__init__.py diff --git a/colossalai/zero/sharded_optim/bookkeeping/base_store.py b/colossalai/zero/low_level/bookkeeping/base_store.py similarity index 100% rename from colossalai/zero/sharded_optim/bookkeeping/base_store.py rename to colossalai/zero/low_level/bookkeeping/base_store.py diff --git a/colossalai/zero/sharded_optim/bookkeeping/bucket_store.py b/colossalai/zero/low_level/bookkeeping/bucket_store.py similarity index 100% rename from colossalai/zero/sharded_optim/bookkeeping/bucket_store.py rename to colossalai/zero/low_level/bookkeeping/bucket_store.py diff --git a/colossalai/zero/sharded_optim/bookkeeping/gradient_store.py b/colossalai/zero/low_level/bookkeeping/gradient_store.py similarity index 100% rename from colossalai/zero/sharded_optim/bookkeeping/gradient_store.py rename to colossalai/zero/low_level/bookkeeping/gradient_store.py diff --git a/colossalai/zero/sharded_optim/bookkeeping/parameter_store.py b/colossalai/zero/low_level/bookkeeping/parameter_store.py similarity index 100% rename from colossalai/zero/sharded_optim/bookkeeping/parameter_store.py rename to colossalai/zero/low_level/bookkeeping/parameter_store.py diff --git a/colossalai/zero/sharded_optim/bookkeeping/tensor_bucket.py b/colossalai/zero/low_level/bookkeeping/tensor_bucket.py similarity index 100% rename from colossalai/zero/sharded_optim/bookkeeping/tensor_bucket.py rename to colossalai/zero/low_level/bookkeeping/tensor_bucket.py diff --git a/colossalai/zero/sharded_optim/low_level_optim.py b/colossalai/zero/low_level/low_level_optim.py similarity index 100% rename from colossalai/zero/sharded_optim/low_level_optim.py rename to colossalai/zero/low_level/low_level_optim.py diff --git a/colossalai/zero/sharded_optim/__init__.py b/colossalai/zero/sharded_optim/__init__.py deleted file mode 100644 index 30c26fb75f30..000000000000 --- a/colossalai/zero/sharded_optim/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .low_level_optim import LowLevelZeroOptimizer -from .sharded_optim_v2 import ShardedOptimizerV2 - -__all__ = ['ShardedOptimizerV2', 'LowLevelZeroOptimizer'] diff --git a/colossalai/zero/sharded_param/__init__.py b/colossalai/zero/sharded_param/__init__.py deleted file mode 100644 index 5642a504acf7..000000000000 --- a/colossalai/zero/sharded_param/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor -from colossalai.zero.sharded_param.sharded_param import ShardedParamV2 - -__all__ = ['ShardedTensor', 'ShardedParamV2'] diff --git a/colossalai/zero/utils/__init__.py b/colossalai/zero/utils/__init__.py deleted file mode 100644 index c4e687228957..000000000000 --- a/colossalai/zero/utils/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .zero_hook import ZeroHook - -__all__ = ['ZeroHook'] \ No newline at end of file diff --git a/colossalai/nn/parallel/zero_wrapper.py b/colossalai/zero/wrapper.py similarity index 95% rename from colossalai/nn/parallel/zero_wrapper.py rename to colossalai/zero/wrapper.py index be8d1da7c24e..4553249e271d 100644 --- a/colossalai/nn/parallel/zero_wrapper.py +++ b/colossalai/zero/wrapper.py @@ -4,7 +4,7 @@ import torch import torch.nn as nn -from .gemini_parallel import GeminiDDP +from .gemini import GeminiDDP def zero_model_wrapper(model: nn.Module, zero_stage: int = 1, gemini_config: Optional[Dict] = None): @@ -99,11 +99,11 @@ def zero_optim_wrapper(model: nn.Module, config_dict['max_scale'] = max_scale if zero_stage in [1, 2]: - from colossalai.zero.sharded_optim.low_level_optim import LowLevelZeroOptimizer + from colossalai.zero.low_level import LowLevelZeroOptimizer config_dict['partition_grad'] = zero_stage == 2 config_dict['clip_grad_norm'] = max_norm return LowLevelZeroOptimizer(optimizer, **config_dict) else: - from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer + from colossalai.zero.gemini.gemini_optimizer import ZeroOptimizer config_dict['clipping_norm'] = max_norm return ZeroOptimizer(optimizer, model, **config_dict) diff --git a/docs/source/en/features/nvme_offload.md b/docs/source/en/features/nvme_offload.md index 2933c3db6c58..38d2c4af904c 100644 --- a/docs/source/en/features/nvme_offload.md +++ b/docs/source/en/features/nvme_offload.md @@ -78,7 +78,7 @@ from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel import colossalai from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper +from colossalai.zero import zero_model_wrapper, zero_optim_wrapper from colossalai.utils.model.colo_init_context import ColoInitContext ``` diff --git a/docs/source/zh-Hans/features/nvme_offload.md b/docs/source/zh-Hans/features/nvme_offload.md index f33474efaa78..fd75ed1f5b3e 100644 --- a/docs/source/zh-Hans/features/nvme_offload.md +++ b/docs/source/zh-Hans/features/nvme_offload.md @@ -77,7 +77,7 @@ from transformers.models.gpt2.configuration_gpt2 import GPT2Config from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel import colossalai from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper +from colossalai.zero import zero_model_wrapper, zero_optim_wrapper from colossalai.utils.model.colo_init_context import ColoInitContext ``` diff --git a/examples/images/dreambooth/debug.py b/examples/images/dreambooth/debug.py index c4adb48230be..33219b2caa29 100644 --- a/examples/images/dreambooth/debug.py +++ b/examples/images/dreambooth/debug.py @@ -5,7 +5,7 @@ from diffusers import AutoencoderKL import colossalai -from colossalai.utils.model.colo_init_context import ColoInitContext, post_process_colo_init_ctx +from colossalai.zero import ColoInitContext, post_process_colo_init_ctx path = "/data/scratch/diffuser/stable-diffusion-v1-4" diff --git a/examples/images/dreambooth/train_dreambooth_colossalai.py b/examples/images/dreambooth/train_dreambooth_colossalai.py index 5c4c86bc7073..e6159e1058b9 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai.py @@ -21,10 +21,9 @@ from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer -from colossalai.nn.parallel.utils import get_static_torch_model from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, GeminiAdamOptimizer +from colossalai.zero.gemini import get_static_torch_model disable_existing_loggers() logger = get_dist_logger() diff --git a/examples/images/dreambooth/train_dreambooth_colossalai_lora.py b/examples/images/dreambooth/train_dreambooth_colossalai_lora.py index 3d789ae2ce0f..1b2fc778d5ed 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai_lora.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai_lora.py @@ -23,10 +23,9 @@ from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer -from colossalai.nn.parallel.utils import get_static_torch_model from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, GeminiAdamOptimizer +from colossalai.zero.gemini import get_static_torch_model disable_existing_loggers() logger = get_dist_logger() diff --git a/examples/images/vit/test_vit.py b/examples/images/vit/test_vit.py index 90f2475b885e..6a587e1df96a 100644 --- a/examples/images/vit/test_vit.py +++ b/examples/images/vit/test_vit.py @@ -18,7 +18,7 @@ from colossalai.testing import rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext def set_seed(seed): diff --git a/examples/images/vit/train.py b/examples/images/vit/train.py index 0b4489244368..b42cf2bedc6b 100644 --- a/examples/images/vit/train.py +++ b/examples/images/vit/train.py @@ -19,7 +19,7 @@ from colossalai.nn.parallel.data_parallel import ColoDDP from colossalai.tensor import ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup, ShardSpec from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext def init_1d_row_for_linear_weight_spec(model, world_size: int): diff --git a/examples/language/bert/train_bert_demo.py b/examples/language/bert/train_bert_demo.py index b690ff787d01..9a0278b2c711 100644 --- a/examples/language/bert/train_bert_demo.py +++ b/examples/language/bert/train_bert_demo.py @@ -12,10 +12,9 @@ import colossalai from colossalai.logging import disable_existing_loggers, get_dist_logger from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, zero_model_wrapper, zero_optim_wrapper CAI_VERSION = colossalai.__version__ diff --git a/examples/language/gpt/gemini/train_gpt_demo.py b/examples/language/gpt/gemini/train_gpt_demo.py index f46226bce2b5..b2a7fa36d021 100644 --- a/examples/language/gpt/gemini/train_gpt_demo.py +++ b/examples/language/gpt/gemini/train_gpt_demo.py @@ -13,10 +13,9 @@ import colossalai from colossalai.logging import disable_existing_loggers, get_dist_logger from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, zero_model_wrapper, zero_optim_wrapper CAI_VERSION = colossalai.__version__ diff --git a/examples/language/opt/train_gemini_opt.py b/examples/language/opt/train_gemini_opt.py index 4993ce25db17..4874f831c2ec 100755 --- a/examples/language/opt/train_gemini_opt.py +++ b/examples/language/opt/train_gemini_opt.py @@ -34,12 +34,9 @@ import colossalai from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer -from colossalai.nn.parallel import GeminiDDP -from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext - from colossalai.tensor import ProcessGroup, ShardSpec +from colossalai.utils import get_current_device +from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, GeminiDDP def get_data(batch_size, seq_len, vocab_size): @@ -179,13 +176,15 @@ def main(): # build model if args.model_name_or_path is None: logger.info("Train a new model from scratch", ranks=[0]) - with ColoInitContext(device=init_dev, dtype=torch.half, + with ColoInitContext(device=init_dev, + dtype=torch.half, default_dist_spec=default_dist_spec, default_pg=shard_pg): model = OPTForCausalLM(config) else: logger.info("Finetune a pre-trained model", ranks=[0]) - with ColoInitContext(device=init_dev, dtype=torch.half, + with ColoInitContext(device=init_dev, + dtype=torch.half, default_dist_spec=default_dist_spec, default_pg=shard_pg): model = OPTForCausalLM.from_pretrained(args.model_name_or_path, @@ -198,8 +197,11 @@ def main(): numel = sum([p.numel() for p in model.parameters()]) PLACEMENT_POLICY = 'cpu' - model = GeminiDDP(model, device=get_current_device(), placement_policy=PLACEMENT_POLICY, - pin_memory=True, strict_ddp_mode=args.shardinit) + model = GeminiDDP(model, + device=get_current_device(), + placement_policy=PLACEMENT_POLICY, + pin_memory=True, + strict_ddp_mode=args.shardinit) optimizer = GeminiAdamOptimizer(model, lr=args.learning_rate, initial_scale=2**14, gpu_margin_mem_ratio=0.0) SEQ_LEN = 1024 diff --git a/examples/language/palm/train.py b/examples/language/palm/train.py index 2f012780da77..7923e4fc855d 100644 --- a/examples/language/palm/train.py +++ b/examples/language/palm/train.py @@ -15,11 +15,9 @@ import colossalai from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer -from colossalai.nn.parallel import ZeroDDP from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec from colossalai.utils import MultiTimer, get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, ZeroDDP # constants @@ -127,7 +125,7 @@ def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: return model -## Parameter Sharding Strategies for Tensor Parallelism +# Parameter Sharding Strategies for Tensor Parallelism def split_param_single_dim_tp1d(dim: int, param: ColoParameter, pg: ProcessGroup): spec = (ShardSpec([dim], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) param.set_tensor_spec(*spec) @@ -232,7 +230,7 @@ def __len__(self): tensor_parallelize(model, pg) model = gemini_zero_dpp(model, pg, args.placement) - #optimizer + # optimizer #optimizer = GeminiAdamOptimizer(model, lr=1e-7, initial_scale=2**5) optimizer = GeminiAdamOptimizer(model, lr=LEARNING_RATE, initial_scale=2**5) diff --git a/examples/language/roberta/pretraining/run_pretraining.py b/examples/language/roberta/pretraining/run_pretraining.py index 9840a122cbc4..eef7bb6ad5cd 100644 --- a/examples/language/roberta/pretraining/run_pretraining.py +++ b/examples/language/roberta/pretraining/run_pretraining.py @@ -1,69 +1,67 @@ -import colossalai import math +import os +import time +from functools import partial + import torch -from colossalai.context import ParallelMode -from colossalai.core import global_context as gpc -import colossalai.nn as col_nn from arguments import parse_args -from pretrain_utils import get_model, get_optimizer, get_lr_scheduler, save_ckpt -from utils.exp_util import get_tflops, get_mem_info, throughput_calculator, log_args -from utils.global_vars import set_global_variables, get_timers, get_tensorboard_writer -from utils.logger import Logger from evaluation import evaluate from loss import LossForPretraining - -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import TensorShardStrategy -from colossalai.zero.sharded_model import ShardedModelV2 -from colossalai.zero.sharded_optim import ShardedOptimizerV2 from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider +from pretrain_utils import get_lr_scheduler, get_model, get_optimizer, save_ckpt from tqdm import tqdm -import os -import time -from functools import partial - from transformers import AutoTokenizer +from utils.exp_util import get_mem_info, get_tflops, log_args, throughput_calculator +from utils.global_vars import get_tensorboard_writer, get_timers, set_global_variables +from utils.logger import Logger -from colossalai.gemini import ChunkManager, GeminiManager -from colossalai.utils.model.colo_init_context import ColoInitContext -from colossalai.utils import get_current_device +import colossalai +import colossalai.nn as col_nn +from colossalai.context import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.nn.optimizer import HybridAdam from colossalai.nn.parallel import ZeroDDP -from colossalai.zero import ZeroOptimizer from colossalai.tensor import ProcessGroup -from colossalai.nn.optimizer import HybridAdam +from colossalai.utils import get_current_device +from colossalai.zero import ZeroOptimizer +from colossalai.zero.gemini import ChunkManager, ColoInitContext, GeminiManager +from colossalai.zero.legacy import ShardedModelV2, ShardedOptimizerV2, ZeroInitContext +from colossalai.zero.legacy.shard_utils import TensorShardStrategy def main(): args = parse_args() launch_time = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime()) - + tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path) os.environ['CUDA_LAUNCH_BLOCKING'] = '1' - + logger = Logger(os.path.join(args.log_path, launch_time), cuda=torch.cuda.is_available(), debug=args.vscode_debug) - + if args.vscode_debug: colossalai.launch(config={}, - rank=args.rank, - world_size=args.world_size, - host=args.host, - port=args.port, - backend=args.backend) + rank=args.rank, + world_size=args.world_size, + host=args.host, + port=args.port, + backend=args.backend) args.local_rank = -1 args.log_interval = 1 else: - colossalai.launch_from_torch(args.colossal_config) #args.colossal_config + colossalai.launch_from_torch(args.colossal_config) # args.colossal_config args.local_rank = int(os.environ["LOCAL_RANK"]) - logger.info(f'launch_from_torch, world size: {torch.distributed.get_world_size()} | ' + - f'ParallelMode.MODEL: {ParallelMode.MODEL} | ParallelMode.DATA: {ParallelMode.DATA} | ParallelMode.TENSOR: {ParallelMode.TENSOR}') + logger.info( + f'launch_from_torch, world size: {torch.distributed.get_world_size()} | ' + + f'ParallelMode.MODEL: {ParallelMode.MODEL} | ParallelMode.DATA: {ParallelMode.DATA} | ParallelMode.TENSOR: {ParallelMode.TENSOR}' + ) log_args(logger, args) args.tokenizer = tokenizer args.logger = logger set_global_variables(launch_time, args.tensorboard_path) - + use_zero = hasattr(gpc.config, 'zero') world_size = torch.distributed.get_world_size() @@ -71,8 +69,8 @@ def main(): if use_zero: shard_strategy = TensorShardStrategy() with ZeroInitContext(target_device=torch.cuda.current_device(), shard_strategy=shard_strategy, - shard_param=True): - + shard_param=True): + config, model, numel = get_model(args, logger) # model = ShardedModelV2(model, shard_strategy, tensor_placement_policy='cpu', reuse_fp16_shard=True) else: @@ -82,9 +80,10 @@ def main(): os.mkdir(os.path.join(args.ckpt_path, launch_time)) logger.info(f'Model numel: {numel}') - + get_tflops_func = partial(get_tflops, numel, args.train_micro_batch_size_per_gpu, args.max_seq_length) - steps_per_epoch = 144003367 // world_size // args.train_micro_batch_size_per_gpu // args.gradient_accumulation_steps // args.refresh_bucket_size #len(dataloader) + # len(dataloader) + steps_per_epoch = 144003367 // world_size // args.train_micro_batch_size_per_gpu // args.gradient_accumulation_steps // args.refresh_bucket_size total_steps = steps_per_epoch * args.epoch # build optimizer and lr_scheduler @@ -98,18 +97,23 @@ def main(): o_l_state_dict['lr_scheduler']['last_epoch'] = o_l_state_dict['lr_scheduler']['last_epoch'] - 1 optimizer = get_optimizer(model, lr=args.lr) optimizer.load_state_dict(o_l_state_dict['optimizer']) - lr_scheduler = get_lr_scheduler(optimizer, total_steps=total_steps, last_epoch=o_l_state_dict['lr_scheduler']['last_epoch']) #o_l_state_dict['lr_scheduler']['last_epoch'] + # o_l_state_dict['lr_scheduler']['last_epoch'] + lr_scheduler = get_lr_scheduler(optimizer, + total_steps=total_steps, + last_epoch=o_l_state_dict['lr_scheduler']['last_epoch']) for state in optimizer.state.values(): for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.cuda(f"cuda:{torch.cuda.current_device()}") # if you want delete the above three code, have to move the model to gpu, because in optimizer.step() lr_scheduler.load_state_dict(o_l_state_dict['lr_scheduler']) - + start_epoch = o_l_state_dict['epoch'] start_shard = o_l_state_dict['shard'] + 1 # global_step = o_l_state_dict['global_step'] + 1 - logger.info(f'resume from epoch {start_epoch} shard {start_shard} step {lr_scheduler.last_epoch} lr {lr_scheduler.get_last_lr()[0]}') + logger.info( + f'resume from epoch {start_epoch} shard {start_shard} step {lr_scheduler.last_epoch} lr {lr_scheduler.get_last_lr()[0]}' + ) else: optimizer = get_optimizer(model, lr=args.lr) lr_scheduler = get_lr_scheduler(optimizer, total_steps=total_steps, last_epoch=-1) @@ -124,12 +128,11 @@ def main(): # initialize with colossalai engine, _, _, lr_scheduelr = colossalai.initialize(model=model, - optimizer=optimizer, - criterion=criterion, - lr_scheduler=lr_scheduler) - + optimizer=optimizer, + criterion=criterion, + lr_scheduler=lr_scheduler) + logger.info(get_mem_info(prefix='After init model, ')) - best_loss = None eval_loss = 0 @@ -146,13 +149,16 @@ def main(): dataset_iterator, total_length = pretrain_dataset_provider.get_shard(shard) # pretrain_dataset_provider.prefetch_shard(shard + 1) # may cause cpu memory overload if torch.distributed.get_rank() == 0: - iterator_data = tqdm(enumerate(dataset_iterator), total=(total_length // args.train_micro_batch_size_per_gpu // world_size), colour='cyan', smoothing=1) + iterator_data = tqdm(enumerate(dataset_iterator), + total=(total_length // args.train_micro_batch_size_per_gpu // world_size), + colour='cyan', + smoothing=1) else: iterator_data = enumerate(dataset_iterator) engine.train() - - for step, batch_data in iterator_data: + + for step, batch_data in iterator_data: # batch_data = pretrain_dataset_provider.get_batch(batch_index) input_ids = batch_data[0].cuda(f"cuda:{torch.cuda.current_device()}") @@ -162,7 +168,7 @@ def main(): # nsp_label = batch_data[5].cuda() output = engine(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) - + loss = engine.criterion(output.logits, mlm_label) pretrain_dataset_provider.prefetch_batch() @@ -172,14 +178,15 @@ def main(): engine.step() lr_scheduelr.step() engine.zero_grad() - + global_step += 1 if global_step % args.log_interval == 0 and global_step != 0 \ - and torch.distributed.get_rank() == 0: + and torch.distributed.get_rank() == 0: elapsed_time = timers('interval_time').elapsed(reset=False) elapsed_time_per_iteration = elapsed_time / global_step - samples_per_sec, tflops, approx_parameters_in_billions = throughput_calculator(numel, args, config, elapsed_time, global_step, world_size) + samples_per_sec, tflops, approx_parameters_in_billions = throughput_calculator( + numel, args, config, elapsed_time, global_step, world_size) cur_loss = train_loss / args.log_interval current_lr = lr_scheduelr.get_last_lr()[0] @@ -189,12 +196,13 @@ def main(): if args.wandb: tensorboard_log = get_tensorboard_writer() - tensorboard_log.log_train({ - 'lr': current_lr, - 'loss': cur_loss, - 'ppl': math.exp(cur_loss), - 'mins_batch': elapsed_time_per_iteration - }, global_step) + tensorboard_log.log_train( + { + 'lr': current_lr, + 'loss': cur_loss, + 'ppl': math.exp(cur_loss), + 'mins_batch': elapsed_time_per_iteration + }, global_step) train_loss = 0 @@ -202,12 +210,14 @@ def main(): logger.info('*' * 100) eval_loss += evaluate(engine, args, logger, global_step) - save_ckpt(engine.model, optimizer, lr_scheduelr, os.path.join(args.ckpt_path, launch_time, f'epoch-{epoch}_shard-{shard}_' + launch_time), epoch, shard, global_step) - - + save_ckpt(engine.model, optimizer, lr_scheduelr, + os.path.join(args.ckpt_path, launch_time, f'epoch-{epoch}_shard-{shard}_' + launch_time), epoch, + shard, global_step) + eval_loss /= len(os.listdir(args.data_path_prefix)) - logger.info(f'epoch {epoch} | shard_length {len(os.listdir(args.data_path_prefix))} | elapsed_time: {timers("epoch_time").elapsed() / 60 :.3f} mins' + \ - f'eval_loss: {eval_loss} | ppl: {math.exp(eval_loss)}') + logger.info( + f'epoch {epoch} | shard_length {len(os.listdir(args.data_path_prefix))} | elapsed_time: {timers("epoch_time").elapsed() / 60 :.3f} mins' + + f'eval_loss: {eval_loss} | ppl: {math.exp(eval_loss)}') logger.info('-' * 100) if args.wandb and torch.distributed.get_rank() == 0: tensorboard_log = get_tensorboard_writer() diff --git a/examples/tutorial/opt/opt/run_clm.py b/examples/tutorial/opt/opt/run_clm.py index c4f576cb18aa..e618b4d66957 100755 --- a/examples/tutorial/opt/opt/run_clm.py +++ b/examples/tutorial/opt/opt/run_clm.py @@ -30,24 +30,13 @@ import datasets import torch import torch.distributed as dist +import transformers from accelerate.utils import set_seed from context import barrier_context from datasets import load_dataset from packaging import version from torch.utils.data import DataLoader from tqdm.auto import tqdm - -import colossalai -import transformers -from colossalai.context import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer -from colossalai.nn.parallel import ZeroDDP -from colossalai.tensor import ProcessGroup -from colossalai.utils import get_current_device, get_dataloader -from colossalai.utils.model.colo_init_context import ColoInitContext from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, @@ -61,6 +50,15 @@ ) from transformers.utils.versions import require_version +import colossalai +from colossalai.context import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.logging import disable_existing_loggers, get_dist_logger +from colossalai.nn.optimizer import HybridAdam +from colossalai.tensor import ProcessGroup +from colossalai.utils import get_current_device, get_dataloader +from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer + require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) diff --git a/tests/test_auto_parallel/test_offload/test_perf.py b/tests/test_auto_parallel/test_offload/test_perf.py index 17bf9cb87f51..c925843fb2b6 100644 --- a/tests/test_auto_parallel/test_offload/test_perf.py +++ b/tests/test_auto_parallel/test_offload/test_perf.py @@ -12,10 +12,9 @@ from colossalai.auto_parallel.offload.solver import NOT_NVML from colossalai.fx.profiler import parameter_size from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper from colossalai.testing import parameterize from colossalai.utils import free_port, get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, zero_model_wrapper, zero_optim_wrapper from tests.test_auto_parallel.test_offload.model_utils import * from tests.test_tensor.common_utils import set_seed diff --git a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py index 760401c3f2c2..9879ae461848 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py @@ -11,12 +11,11 @@ from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper from colossalai.tensor.process_group import ProcessGroup from colossalai.testing import assert_close, rerun_if_address_is_in_use from colossalai.testing.pytest_wrapper import run_on_environment_flag from colossalai.utils import free_port, get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext, post_process_colo_init_ctx +from colossalai.zero import ColoInitContext, post_process_colo_init_ctx, zero_model_wrapper, zero_optim_wrapper class MLP(torch.nn.Module): diff --git a/tests/test_ddp/test_ddp_ignore_params.py b/tests/test_ddp/test_ddp_ignore_params.py index 679c8b0f6afe..2ad20f6bec72 100644 --- a/tests/test_ddp/test_ddp_ignore_params.py +++ b/tests/test_ddp/test_ddp_ignore_params.py @@ -10,14 +10,14 @@ import torch.multiprocessing as mp import colossalai -from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration -from colossalai.gemini.gemini_mgr import GeminiManager -from colossalai.nn.parallel import ColoDDP, ZeroDDP +from colossalai.nn.parallel import ColoDDP from colossalai.tensor import ProcessGroup from colossalai.testing import rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, ZeroDDP +from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager def set_seed(seed): diff --git a/tests/test_ddp/test_ddp_state_dict.py b/tests/test_ddp/test_ddp_state_dict.py index f229364c6eb1..bd4742ff2cc9 100644 --- a/tests/test_ddp/test_ddp_state_dict.py +++ b/tests/test_ddp/test_ddp_state_dict.py @@ -1,18 +1,19 @@ import copy +from collections import OrderedDict +from functools import partial import pytest -import colossalai import torch import torch.multiprocessing as mp + +import colossalai +from colossalai.nn.parallel import ColoDDP +from colossalai.tensor import ColoParameter, ProcessGroup from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils.cuda import get_current_device from colossalai.utils import free_port -from colossalai.utils.model.colo_init_context import ColoInitContext -from functools import partial +from colossalai.utils.cuda import get_current_device +from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs -from colossalai.nn.parallel import ColoDDP -from collections import OrderedDict -from colossalai.tensor import ProcessGroup, ColoParameter def check_state_dict_equal(state_dict: OrderedDict, other_state_dict: OrderedDict): diff --git a/tests/test_gemini/test_gemini_manager.py b/tests/test_gemini/test_gemini_manager.py index 0c138f101f75..aee9432532c6 100644 --- a/tests/test_gemini/test_gemini_manager.py +++ b/tests/test_gemini/test_gemini_manager.py @@ -1,73 +1,73 @@ -import pytest -import torch - -from colossalai.gemini.stateful_tensor import TensorState, StatefulTensor - - -@pytest.mark.dist -def test_gemini_manager(): - # reset the manager, in case that there exists memory information left - manager = StatefulTensor.GST_MGR - manager.reset() - - # occupation 8 - st1 = StatefulTensor(torch.empty(2, 2, dtype=torch.float16, device='cuda')) - # occupation 60 - st2 = StatefulTensor(torch.empty(3, 5, dtype=torch.float32, device='cpu')) - - # occupation 28 - t1 = torch.empty(7, device='cuda') - # occupation 12 - t2 = torch.empty(3, device='cpu') - st3 = StatefulTensor(t1, TensorState.HOLD_AFTER_FWD) - st4 = StatefulTensor(None, TensorState.FREE) - - assert manager.total_number == 4 - assert manager.total_mem['cpu'] == 60 - assert manager.total_mem['cuda'] == 36 - assert manager.state_mem['cpu'][TensorState.HOLD] == 60 - assert manager.state_mem['cuda'][TensorState.HOLD] == 8 - assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_FWD] == 28 - - st4.payload_reset(t2) - st3.payload_reset(t2) - - assert manager.total_number == 4 - assert manager.total_mem['cpu'] == 84 - assert manager.total_mem['cuda'] == 8 - assert manager.state_mem['cpu'][TensorState.HOLD] == 72 - assert manager.state_mem['cuda'][TensorState.HOLD] == 8 - assert manager.state_mem['cpu'][TensorState.HOLD_AFTER_FWD] == 12 - assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_FWD] == 0 - - st1.move_to(torch.device('cpu')) - st2.move_to(torch.device('cpu')) - st3.move_to(torch.device('cuda', 0)) - - assert manager.total_number == 4 - assert manager.total_mem['cpu'] == 80 - assert manager.total_mem['cuda'] == 12 - assert manager.state_mem['cpu'][TensorState.HOLD] == 80 - assert manager.state_mem['cuda'][TensorState.HOLD] == 0 - assert manager.state_mem['cpu'][TensorState.HOLD_AFTER_FWD] == 0 - assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_FWD] == 12 - - st1.trans_state(TensorState.COMPUTE) - st2.trans_state(TensorState.COMPUTE) - st2.trans_state(TensorState.HOLD_AFTER_BWD) - - assert manager.total_number == 4 - assert manager.total_mem['cpu'] == 80 - assert manager.total_mem['cuda'] == 12 - assert manager.state_mem['cpu'][TensorState.HOLD] == 12 - assert manager.state_mem['cuda'][TensorState.HOLD] == 0 - assert manager.state_mem['cpu'][TensorState.HOLD_AFTER_FWD] == 0 - assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_FWD] == 12 - assert manager.state_mem['cpu'][TensorState.HOLD_AFTER_BWD] == 60 - assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_BWD] == 0 - assert manager.state_mem['cpu'][TensorState.COMPUTE] == 8 - assert manager.state_mem['cuda'][TensorState.COMPUTE] == 0 - - -if __name__ == '__main__': - test_gemini_manager() +import pytest +import torch + +from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor, TensorState + + +@pytest.mark.dist +def test_gemini_manager(): + # reset the manager, in case that there exists memory information left + manager = StatefulTensor.GST_MGR + manager.reset() + + # occupation 8 + st1 = StatefulTensor(torch.empty(2, 2, dtype=torch.float16, device='cuda')) + # occupation 60 + st2 = StatefulTensor(torch.empty(3, 5, dtype=torch.float32, device='cpu')) + + # occupation 28 + t1 = torch.empty(7, device='cuda') + # occupation 12 + t2 = torch.empty(3, device='cpu') + st3 = StatefulTensor(t1, TensorState.HOLD_AFTER_FWD) + st4 = StatefulTensor(None, TensorState.FREE) + + assert manager.total_number == 4 + assert manager.total_mem['cpu'] == 60 + assert manager.total_mem['cuda'] == 36 + assert manager.state_mem['cpu'][TensorState.HOLD] == 60 + assert manager.state_mem['cuda'][TensorState.HOLD] == 8 + assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_FWD] == 28 + + st4.payload_reset(t2) + st3.payload_reset(t2) + + assert manager.total_number == 4 + assert manager.total_mem['cpu'] == 84 + assert manager.total_mem['cuda'] == 8 + assert manager.state_mem['cpu'][TensorState.HOLD] == 72 + assert manager.state_mem['cuda'][TensorState.HOLD] == 8 + assert manager.state_mem['cpu'][TensorState.HOLD_AFTER_FWD] == 12 + assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_FWD] == 0 + + st1.move_to(torch.device('cpu')) + st2.move_to(torch.device('cpu')) + st3.move_to(torch.device('cuda', 0)) + + assert manager.total_number == 4 + assert manager.total_mem['cpu'] == 80 + assert manager.total_mem['cuda'] == 12 + assert manager.state_mem['cpu'][TensorState.HOLD] == 80 + assert manager.state_mem['cuda'][TensorState.HOLD] == 0 + assert manager.state_mem['cpu'][TensorState.HOLD_AFTER_FWD] == 0 + assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_FWD] == 12 + + st1.trans_state(TensorState.COMPUTE) + st2.trans_state(TensorState.COMPUTE) + st2.trans_state(TensorState.HOLD_AFTER_BWD) + + assert manager.total_number == 4 + assert manager.total_mem['cpu'] == 80 + assert manager.total_mem['cuda'] == 12 + assert manager.state_mem['cpu'][TensorState.HOLD] == 12 + assert manager.state_mem['cuda'][TensorState.HOLD] == 0 + assert manager.state_mem['cpu'][TensorState.HOLD_AFTER_FWD] == 0 + assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_FWD] == 12 + assert manager.state_mem['cpu'][TensorState.HOLD_AFTER_BWD] == 60 + assert manager.state_mem['cuda'][TensorState.HOLD_AFTER_BWD] == 0 + assert manager.state_mem['cpu'][TensorState.COMPUTE] == 8 + assert manager.state_mem['cuda'][TensorState.COMPUTE] == 0 + + +if __name__ == '__main__': + test_gemini_manager() diff --git a/tests/test_gemini/test_param_op.py b/tests/test_gemini/test_param_op.py index daf386d6d6af..9ebacdb70f1a 100644 --- a/tests/test_gemini/test_param_op.py +++ b/tests/test_gemini/test_param_op.py @@ -2,7 +2,7 @@ import torch -from colossalai.gemini.paramhooks import BaseParamHookMgr +from colossalai.zero.legacy.gemini.paramhooks import BaseParamHookMgr from tests.components_to_test.registry import non_distributed_component_funcs diff --git a/tests/test_gemini/test_runtime_mem_tracer.py b/tests/test_gemini/test_runtime_mem_tracer.py index 294868458c47..9a3e93493ec3 100644 --- a/tests/test_gemini/test_runtime_mem_tracer.py +++ b/tests/test_gemini/test_runtime_mem_tracer.py @@ -3,8 +3,8 @@ import numpy as np import torch -from colossalai.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext +from colossalai.zero.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer from tests.components_to_test import run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs diff --git a/tests/test_gemini/update/test_chunk_mgrv2.py b/tests/test_gemini/update/test_chunk_mgrv2.py index 7d192fc631a6..ba0945551b18 100644 --- a/tests/test_gemini/update/test_chunk_mgrv2.py +++ b/tests/test_gemini/update/test_chunk_mgrv2.py @@ -5,10 +5,10 @@ import torch.multiprocessing as mp import colossalai -from colossalai.gemini.chunk import ChunkManager from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port +from colossalai.zero.gemini.chunk import ChunkManager from tests.test_tensor.common_utils import debug_print CUDA_MEM_0 = {False: 512, True: 1024} diff --git a/tests/test_gemini/update/test_chunkv2.py b/tests/test_gemini/update/test_chunkv2.py index 96855410bea6..5f9ba5d3a827 100644 --- a/tests/test_gemini/update/test_chunkv2.py +++ b/tests/test_gemini/update/test_chunkv2.py @@ -6,12 +6,12 @@ import torch.multiprocessing as mp import colossalai -from colossalai.gemini import TensorState -from colossalai.gemini.chunk import Chunk from colossalai.tensor import ColoParameter from colossalai.tensor import ProcessGroup as ColoProcessGroup from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port, get_current_device +from colossalai.zero.gemini import TensorState +from colossalai.zero.gemini.chunk import Chunk def dist_sum(x): diff --git a/tests/test_gemini/update/test_fwd_bwd.py b/tests/test_gemini/update/test_fwd_bwd.py index 2821dc78d984..8cfacd018324 100644 --- a/tests/test_gemini/update/test_fwd_bwd.py +++ b/tests/test_gemini/update/test_fwd_bwd.py @@ -8,16 +8,14 @@ import colossalai from colossalai.amp import convert_to_apex_amp -from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration -from colossalai.gemini.gemini_mgr import GeminiManager from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer -from colossalai.nn.parallel import ZeroDDP from colossalai.tensor import ProcessGroup from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer +from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager from tests.components_to_test import run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import set_seed diff --git a/tests/test_gemini/update/test_gemini_use_rmt.py b/tests/test_gemini/update/test_gemini_use_rmt.py index 8cf17a0a726e..9d5419e9452d 100644 --- a/tests/test_gemini/update/test_gemini_use_rmt.py +++ b/tests/test_gemini/update/test_gemini_use_rmt.py @@ -5,15 +5,13 @@ import torch.multiprocessing as mp import colossalai -from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration -from colossalai.gemini.gemini_mgr import GeminiManager -from colossalai.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer -from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer -from colossalai.nn.parallel import GeminiDDP, ZeroDDP from colossalai.tensor import ProcessGroup from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, GeminiDDP, ZeroDDP +from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager +from colossalai.zero.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer from tests.components_to_test import run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import set_seed diff --git a/tests/test_gemini/update/test_get_torch_model.py b/tests/test_gemini/update/test_get_torch_model.py index e6d586b37041..c014ced975ce 100644 --- a/tests/test_gemini/update/test_get_torch_model.py +++ b/tests/test_gemini/update/test_get_torch_model.py @@ -6,13 +6,12 @@ import torch.multiprocessing as mp import colossalai -from colossalai.nn.parallel import GeminiDDP -from colossalai.nn.parallel.utils import get_static_torch_model from colossalai.tensor import ColoParameter from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, GeminiDDP +from colossalai.zero.gemini.utils import get_static_torch_model from tests.components_to_test.registry import non_distributed_component_funcs diff --git a/tests/test_gemini/update/test_grad_clip.py b/tests/test_gemini/update/test_grad_clip.py index d97ba94399c0..65f252c558d7 100644 --- a/tests/test_gemini/update/test_grad_clip.py +++ b/tests/test_gemini/update/test_grad_clip.py @@ -10,15 +10,13 @@ import colossalai from colossalai.amp import convert_to_apex_amp -from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration -from colossalai.gemini.gemini_mgr import GeminiManager from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer -from colossalai.nn.parallel import ZeroDDP from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer +from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager from tests.components_to_test import run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import debug_print, set_seed diff --git a/tests/test_gemini/update/test_inference.py b/tests/test_gemini/update/test_inference.py index b057448ad378..12392d6e57ad 100644 --- a/tests/test_gemini/update/test_inference.py +++ b/tests/test_gemini/update/test_inference.py @@ -10,15 +10,13 @@ import colossalai from colossalai.amp import convert_to_apex_amp -from colossalai.gemini.chunk import ChunkManager, init_chunk_manager, search_chunk_configuration -from colossalai.gemini.gemini_mgr import GeminiManager from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer -from colossalai.nn.parallel import ZeroDDP, zero_model_wrapper from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext, post_process_colo_init_ctx +from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer, post_process_colo_init_ctx, zero_model_wrapper +from colossalai.zero.gemini.chunk import ChunkManager, init_chunk_manager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager from tests.components_to_test import run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import debug_print, set_seed diff --git a/tests/test_gemini/update/test_optim.py b/tests/test_gemini/update/test_optim.py index cd3aa6051d78..7364e59d10b4 100644 --- a/tests/test_gemini/update/test_optim.py +++ b/tests/test_gemini/update/test_optim.py @@ -9,16 +9,14 @@ import colossalai from colossalai.amp import convert_to_apex_amp -from colossalai.gemini.chunk import ChunkManager, init_chunk_manager, search_chunk_configuration -from colossalai.gemini.gemini_mgr import GeminiManager from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer -from colossalai.nn.parallel import ZeroDDP from colossalai.tensor import ColoParameter, ColoTensor from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext, post_process_colo_init_ctx +from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer, post_process_colo_init_ctx +from colossalai.zero.gemini.chunk import ChunkManager, init_chunk_manager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager from tests.components_to_test import run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import debug_print, set_seed diff --git a/tests/test_gemini/update/test_search.py b/tests/test_gemini/update/test_search.py index 2fcdd5380906..71cdf9a18840 100644 --- a/tests/test_gemini/update/test_search.py +++ b/tests/test_gemini/update/test_search.py @@ -6,11 +6,11 @@ import torch.multiprocessing as mp import colossalai -from colossalai.gemini.chunk import init_chunk_manager, search_chunk_configuration from colossalai.tensor import ComputePattern, ComputeSpec, ProcessGroup, ShardSpec from colossalai.testing import rerun_if_address_is_in_use from colossalai.utils import free_port, get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext +from colossalai.zero.gemini.chunk import init_chunk_manager, search_chunk_configuration from tests.components_to_test.registry import non_distributed_component_funcs diff --git a/tests/test_gemini/update/test_zeroddp_state_dict.py b/tests/test_gemini/update/test_zeroddp_state_dict.py index 00d835842f79..7e759808d1cb 100644 --- a/tests/test_gemini/update/test_zeroddp_state_dict.py +++ b/tests/test_gemini/update/test_zeroddp_state_dict.py @@ -7,13 +7,12 @@ from torch.testing import assert_close import colossalai -from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration -from colossalai.gemini.gemini_mgr import GeminiManager -from colossalai.nn.parallel import ZeroDDP from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, ZeroDDP +from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import debug_print, set_seed diff --git a/tests/test_gemini/update/test_zerooptim_state_dict.py b/tests/test_gemini/update/test_zerooptim_state_dict.py index fd13af6b2b0a..996dc4eb8b56 100644 --- a/tests/test_gemini/update/test_zerooptim_state_dict.py +++ b/tests/test_gemini/update/test_zerooptim_state_dict.py @@ -6,15 +6,13 @@ import torch.multiprocessing as mp import colossalai -from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration -from colossalai.gemini.gemini_mgr import GeminiManager from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer -from colossalai.nn.parallel import ZeroDDP from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer +from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import debug_print, set_seed diff --git a/tests/test_moe/test_moe_checkpoint.py b/tests/test_moe/test_moe_checkpoint.py index f99e74ea55c1..5b6fe441112d 100644 --- a/tests/test_moe/test_moe_checkpoint.py +++ b/tests/test_moe/test_moe_checkpoint.py @@ -11,7 +11,7 @@ from colossalai.nn.layer.moe import load_moe_model, save_moe_model from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port, get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext from tests.test_moe.test_moe_zero_init import MoeModel from tests.test_tensor.common_utils import debug_print from tests.test_zero.common import CONFIG diff --git a/tests/test_moe/test_moe_colo_init.py b/tests/test_moe/test_moe_colo_init.py index ae0c1390c129..23ad1a3dc6a4 100644 --- a/tests/test_moe/test_moe_colo_init.py +++ b/tests/test_moe/test_moe_colo_init.py @@ -1,63 +1,60 @@ -from functools import partial - -import colossalai -import pytest -import torch -import torch.multiprocessing as mp -import torch.distributed as dist -from colossalai.testing import parameterize -from colossalai.utils import free_port -from colossalai.context import MOE_CONTEXT -from colossalai.tensor import ColoParameter -from colossalai.utils.model.colo_init_context import ColoInitContext - -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import get_current_device - -from tests.test_zero.common import CONFIG -from tests.test_moe.test_moe_zero_init import MoeModel -from tests.test_tensor.common_utils import debug_print - - -@parameterize("init_device_type", ['cpu', 'cuda']) -def exam_moe_colo_init(init_device_type): - world_size = dist.get_world_size() - - if init_device_type == 'cuda': - init_device = get_current_device() - elif init_device_type == 'cpu': - init_device = torch.device("cpu") - else: - raise NotImplementedError("Unknown device found.") - - with ColoInitContext(device=init_device): - model = MoeModel(checkpoint=True) - - for name, param in model.named_parameters(): - assert isinstance(param, ColoParameter), "parameter `{}` has an init problem".format(name) - - if hasattr(param, "moe_info"): - param.set_process_group(param.moe_info.pg) - - if hasattr(param, "moe_info"): - assert param.process_group.dp_world_size() == param.moe_info.dp_size - else: - assert param.process_group.dp_world_size() == world_size - - -def _run_dist(rank, world_size, port): - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - MOE_CONTEXT.setup(seed=42) - exam_moe_colo_init() - - -@pytest.mark.dist -@pytest.mark.parametrize("world_size", [4]) -@rerun_if_address_is_in_use() -def test_moe_colo_init(world_size): - run_func = partial(_run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_moe_colo_init(world_size=4) +from functools import partial + +import pytest +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +import colossalai +from colossalai.context import MOE_CONTEXT +from colossalai.tensor import ColoParameter +from colossalai.testing import parameterize, rerun_if_address_is_in_use +from colossalai.utils import free_port, get_current_device +from colossalai.zero import ColoInitContext +from tests.test_moe.test_moe_zero_init import MoeModel +from tests.test_tensor.common_utils import debug_print +from tests.test_zero.common import CONFIG + + +@parameterize("init_device_type", ['cpu', 'cuda']) +def exam_moe_colo_init(init_device_type): + world_size = dist.get_world_size() + + if init_device_type == 'cuda': + init_device = get_current_device() + elif init_device_type == 'cpu': + init_device = torch.device("cpu") + else: + raise NotImplementedError("Unknown device found.") + + with ColoInitContext(device=init_device): + model = MoeModel(checkpoint=True) + + for name, param in model.named_parameters(): + assert isinstance(param, ColoParameter), "parameter `{}` has an init problem".format(name) + + if hasattr(param, "moe_info"): + param.set_process_group(param.moe_info.pg) + + if hasattr(param, "moe_info"): + assert param.process_group.dp_world_size() == param.moe_info.dp_size + else: + assert param.process_group.dp_world_size() == world_size + + +def _run_dist(rank, world_size, port): + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + MOE_CONTEXT.setup(seed=42) + exam_moe_colo_init() + + +@pytest.mark.dist +@pytest.mark.parametrize("world_size", [4]) +@rerun_if_address_is_in_use() +def test_moe_colo_init(world_size): + run_func = partial(_run_dist, world_size=world_size, port=free_port()) + mp.spawn(run_func, nprocs=world_size) + + +if __name__ == '__main__': + test_moe_colo_init(world_size=4) diff --git a/tests/test_moe/test_moe_zero_init.py b/tests/test_moe/test_moe_zero_init.py index 04dc9c514dd0..5987e31f71c9 100644 --- a/tests/test_moe/test_moe_zero_init.py +++ b/tests/test_moe/test_moe_zero_init.py @@ -1,114 +1,112 @@ -from functools import partial - -import colossalai -import pytest -import torch -import torch.multiprocessing as mp -import torch.nn as nn -from colossalai.nn import CheckpointModule -from colossalai.logging import get_dist_logger -from colossalai.testing import parameterize -from colossalai.utils import free_port -from colossalai.context import MOE_CONTEXT -from colossalai.nn.layer import MoeModule -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy) - -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import get_current_device -from tests.test_zero.common import CONFIG - - -class MoeModel(nn.Module): - - def __init__(self, checkpoint: bool = False): - - class TestSubModule(CheckpointModule): - - def __init__(self): - super().__init__(checkpoint) - expert_cls = nn.Linear - expert_args_dict = dict(in_features=16, out_features=16) - self.moe = MoeModule(dim_model=16, - num_experts=8, - use_residual=True, - expert_cls=expert_cls, - **expert_args_dict) - self.proj = nn.Linear(16, 4) - - def _forward(self, x): - x, y = self.moe(x) - x = self.proj(x) - return x, y - - super().__init__() - self.test_embed = nn.Linear(4, 16) - self.test_transform = TestSubModule() - - def forward(self, x): - MOE_CONTEXT.reset_loss() - - x = self.test_embed(x) - x, y = self.test_transform(x) - - MOE_CONTEXT.add_loss(y) - return x - - -@parameterize("init_device_type", ['cpu', 'cuda']) -@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy]) -def run_moe_zero_init(init_device_type, shard_strategy_class): - logger = get_dist_logger("test_moe_zero_init") - - if init_device_type == 'cuda': - init_device = get_current_device() - elif init_device_type == 'cpu': - init_device = torch.device("cpu") - else: - raise NotImplementedError("Unknown device found.") - - model_numel_tensor = torch.zeros(1, dtype=torch.int) - with ZeroInitContext(target_device=init_device, - shard_strategy=shard_strategy_class(), - shard_param=True, - model_numel_tensor=model_numel_tensor): - model = MoeModel(checkpoint=True) - - for name, param in model.named_parameters(): - assert hasattr(param, 'colo_attr') - - # the parameters in moe experts and its gate should not be sharded - if ('experts' in name) or ('gate' in name) or ('residual_combine' in name): - assert not param.colo_attr.sharded_data_tensor.is_sharded, "`{}` parameter has problem".format(name) - else: - assert param.colo_attr.sharded_data_tensor.is_sharded - - # the parameters in moe experts is not replicated - if 'experts' in name: - assert not param.colo_attr.is_replicated - else: - assert param.colo_attr.is_replicated - - if param.colo_attr.param_is_sharded: - assert param.colo_attr.data_payload.device.type == init_device.type, \ - f'{param.colo_attr.data_payload.device.type} vs. {init_device.type}' - else: - assert param.colo_attr.data_payload.device.type == 'cuda' - - -def _run_dist(rank, world_size, port): - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - MOE_CONTEXT.setup(seed=42) - run_moe_zero_init() - - -@pytest.mark.dist -@pytest.mark.parametrize("world_size", [2, 4]) -@rerun_if_address_is_in_use() -def test_moe_zero_init(world_size): - run_func = partial(_run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_moe_zero_init(world_size=2) +from functools import partial + +import pytest +import torch +import torch.multiprocessing as mp +import torch.nn as nn + +import colossalai +from colossalai.context import MOE_CONTEXT +from colossalai.logging import get_dist_logger +from colossalai.nn import CheckpointModule +from colossalai.nn.layer import MoeModule +from colossalai.testing import parameterize, rerun_if_address_is_in_use +from colossalai.utils import free_port, get_current_device +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy +from tests.test_zero.common import CONFIG + + +class MoeModel(nn.Module): + + def __init__(self, checkpoint: bool = False): + + class TestSubModule(CheckpointModule): + + def __init__(self): + super().__init__(checkpoint) + expert_cls = nn.Linear + expert_args_dict = dict(in_features=16, out_features=16) + self.moe = MoeModule(dim_model=16, + num_experts=8, + use_residual=True, + expert_cls=expert_cls, + **expert_args_dict) + self.proj = nn.Linear(16, 4) + + def _forward(self, x): + x, y = self.moe(x) + x = self.proj(x) + return x, y + + super().__init__() + self.test_embed = nn.Linear(4, 16) + self.test_transform = TestSubModule() + + def forward(self, x): + MOE_CONTEXT.reset_loss() + + x = self.test_embed(x) + x, y = self.test_transform(x) + + MOE_CONTEXT.add_loss(y) + return x + + +@parameterize("init_device_type", ['cpu', 'cuda']) +@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy]) +def run_moe_zero_init(init_device_type, shard_strategy_class): + logger = get_dist_logger("test_moe_zero_init") + + if init_device_type == 'cuda': + init_device = get_current_device() + elif init_device_type == 'cpu': + init_device = torch.device("cpu") + else: + raise NotImplementedError("Unknown device found.") + + model_numel_tensor = torch.zeros(1, dtype=torch.int) + with ZeroInitContext(target_device=init_device, + shard_strategy=shard_strategy_class(), + shard_param=True, + model_numel_tensor=model_numel_tensor): + model = MoeModel(checkpoint=True) + + for name, param in model.named_parameters(): + assert hasattr(param, 'colo_attr') + + # the parameters in moe experts and its gate should not be sharded + if ('experts' in name) or ('gate' in name) or ('residual_combine' in name): + assert not param.colo_attr.sharded_data_tensor.is_sharded, "`{}` parameter has problem".format(name) + else: + assert param.colo_attr.sharded_data_tensor.is_sharded + + # the parameters in moe experts is not replicated + if 'experts' in name: + assert not param.colo_attr.is_replicated + else: + assert param.colo_attr.is_replicated + + if param.colo_attr.param_is_sharded: + assert param.colo_attr.data_payload.device.type == init_device.type, \ + f'{param.colo_attr.data_payload.device.type} vs. {init_device.type}' + else: + assert param.colo_attr.data_payload.device.type == 'cuda' + + +def _run_dist(rank, world_size, port): + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + MOE_CONTEXT.setup(seed=42) + run_moe_zero_init() + + +@pytest.mark.dist +@pytest.mark.parametrize("world_size", [2, 4]) +@rerun_if_address_is_in_use() +def test_moe_zero_init(world_size): + run_func = partial(_run_dist, world_size=world_size, port=free_port()) + mp.spawn(run_func, nprocs=world_size) + + +if __name__ == '__main__': + test_moe_zero_init(world_size=2) diff --git a/tests/test_moe/test_moe_zero_model.py b/tests/test_moe/test_moe_zero_model.py index d608ebf0718e..d38f66fef658 100644 --- a/tests/test_moe/test_moe_zero_model.py +++ b/tests/test_moe/test_moe_zero_model.py @@ -10,11 +10,11 @@ from colossalai.nn import MoeLoss from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import BucketTensorShardStrategy, TensorShardStrategy -from colossalai.zero.sharded_model import ShardedModelV2 -from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16 -from colossalai.zero.sharded_model.utils import col_model_deepcopy +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy +from colossalai.zero.legacy.sharded_model import ShardedModelV2 +from colossalai.zero.legacy.sharded_model._utils import cast_tensor_to_fp16 +from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_moe.test_moe_zero_init import MoeModel from tests.test_zero.common import CONFIG, check_grads_padding, run_fwd_bwd diff --git a/tests/test_moe/test_moe_zero_optim.py b/tests/test_moe/test_moe_zero_optim.py index 9d9a7bd17390..7e140bf862f2 100644 --- a/tests/test_moe/test_moe_zero_optim.py +++ b/tests/test_moe/test_moe_zero_optim.py @@ -12,12 +12,12 @@ from colossalai.nn.optimizer import CPUAdam from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port, get_current_device -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import BucketTensorShardStrategy, TensorShardStrategy -from colossalai.zero.sharded_model import ShardedModelV2 -from colossalai.zero.sharded_model.utils import col_model_deepcopy -from colossalai.zero.sharded_optim import ShardedOptimizerV2 -from colossalai.zero.sharded_optim._utils import has_inf_or_nan +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy +from colossalai.zero.legacy.sharded_model import ShardedModelV2 +from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy +from colossalai.zero.legacy.sharded_optim import ShardedOptimizerV2 +from colossalai.zero.low_level._utils import has_inf_or_nan from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_moe.test_moe_zero_init import MoeModel from tests.test_zero.common import CONFIG, check_sharded_model_params diff --git a/tests/test_optimizer/test_cpu_adam.py b/tests/test_optimizer/test_cpu_adam.py index d317dc2e34ad..ea1c044f5820 100644 --- a/tests/test_optimizer/test_cpu_adam.py +++ b/tests/test_optimizer/test_cpu_adam.py @@ -56,7 +56,7 @@ def test_cpu_adam(adamw, step, p_dtype, g_dtype): eps = 1e-8 weight_decay = 0 - for i in range(1024): + for i in range(3): p_data = torch.rand(64, dtype=p_dtype) p_data_copy = p_data.clone().float() p_grad = torch.rand(64, dtype=g_dtype) diff --git a/tests/test_optimizer/test_fused_adam_kernel.py b/tests/test_optimizer/test_fused_adam_kernel.py index 7b9b6e9c48ba..8ff6618aee2e 100644 --- a/tests/test_optimizer/test_fused_adam_kernel.py +++ b/tests/test_optimizer/test_fused_adam_kernel.py @@ -54,7 +54,7 @@ def test_adam(adamw, step, p_dtype, g_dtype): count = 0 - for i in range(1024): + for i in range(3): p = torch.rand(64, dtype=p_dtype).cuda() p_copy = p.clone().float() g = torch.rand(p.shape, dtype=g_dtype).cuda() diff --git a/tests/test_optimizer/test_hybrid_adam.py b/tests/test_optimizer/test_hybrid_adam.py index d19192add3fb..2576d8ffee43 100644 --- a/tests/test_optimizer/test_hybrid_adam.py +++ b/tests/test_optimizer/test_hybrid_adam.py @@ -1,12 +1,12 @@ import torch import torch.nn as nn -from torch.optim.adam import Adam from torch.optim import AdamW +from torch.optim.adam import Adam from colossalai.nn.optimizer.hybrid_adam import HybridAdam from colossalai.testing import parameterize -RE = 1024 +RE = 3 @parameterize('adamw', [False, True]) diff --git a/tests/test_tensor/model/test_gpt2.py b/tests/test_tensor/model/test_gpt2.py index ad8ac87b2e1e..0d6a3fe26c2d 100644 --- a/tests/test_tensor/model/test_gpt2.py +++ b/tests/test_tensor/model/test_gpt2.py @@ -11,7 +11,7 @@ from colossalai.testing import rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import ( debug_print, diff --git a/tests/test_tensor/model/test_model.py b/tests/test_tensor/model/test_model.py index 3f53b94e0642..83abc641cbd4 100644 --- a/tests/test_tensor/model/test_model.py +++ b/tests/test_tensor/model/test_model.py @@ -11,7 +11,7 @@ from colossalai.testing import rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import ( check_equal, diff --git a/tests/test_tensor/model/test_module_spec.py b/tests/test_tensor/model/test_module_spec.py index 997b416f12c3..739bf2b0a641 100644 --- a/tests/test_tensor/model/test_module_spec.py +++ b/tests/test_tensor/model/test_module_spec.py @@ -20,7 +20,7 @@ from colossalai.testing import rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import set_seed, tensor_equal, tensor_shard_equal diff --git a/tests/test_tensor/test_context.py b/tests/test_tensor/test_context.py index 2f7aebed5bc4..047371f45bda 100644 --- a/tests/test_tensor/test_context.py +++ b/tests/test_tensor/test_context.py @@ -17,7 +17,7 @@ from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import set_seed diff --git a/tests/test_tensor/test_tp_with_zero.py b/tests/test_tensor/test_tp_with_zero.py index 1a6d23f6a2eb..94e39e5d1546 100644 --- a/tests/test_tensor/test_tp_with_zero.py +++ b/tests/test_tensor/test_tp_with_zero.py @@ -7,14 +7,12 @@ import colossalai from colossalai.amp import convert_to_apex_amp -from colossalai.gemini.chunk import search_chunk_configuration -from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer -from colossalai.nn.parallel import GeminiDDP, ZeroDDP from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, GeminiDDP, ZeroDDP +from colossalai.zero.gemini import search_chunk_configuration from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import set_seed, tensor_shard_equal from tests.test_tensor.model.test_gpt2 import init_megatron_spec diff --git a/tests/test_utils/test_colo_checkpoint.py b/tests/test_utils/test_colo_checkpoint.py index a5ea75fffc36..7c2ad9078f98 100644 --- a/tests/test_utils/test_colo_checkpoint.py +++ b/tests/test_utils/test_colo_checkpoint.py @@ -1,25 +1,23 @@ -import os, shutil -import torch -import pytest +import os +import shutil from copy import deepcopy from functools import partial -import torch.multiprocessing as mp +import pytest +import torch import torch.distributed as dist - -from torch.optim.lr_scheduler import CosineAnnealingLR -from torch.optim.lr_scheduler import MultiplicativeLR -from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR +import torch.multiprocessing as mp +from torch.optim.lr_scheduler import CosineAnnealingLR, MultiplicativeLR import colossalai +from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR +from colossalai.nn.optimizer import ColossalaiOptimizer +from colossalai.tensor import ColoTensor, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils.cuda import get_current_device from colossalai.utils import free_port -from colossalai.utils.model.colo_init_context import ColoInitContext -from colossalai.tensor import ComputePattern, ComputeSpec, ColoTensor, ShardSpec, ProcessGroup -from colossalai.utils.checkpoint import save_checkpoint, load_checkpoint -from colossalai.nn.optimizer import ColossalaiOptimizer - +from colossalai.utils.checkpoint import load_checkpoint, save_checkpoint +from colossalai.utils.cuda import get_current_device +from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs diff --git a/tests/test_utils/test_commons.py b/tests/test_utils/test_commons.py index 0ecb7446c788..6bfa6f33c812 100644 --- a/tests/test_utils/test_commons.py +++ b/tests/test_utils/test_commons.py @@ -1,13 +1,12 @@ -from colossalai.utils import free_port -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.zero.sharded_param import ShardedTensor -from colossalai.gemini.tensor_utils import colo_model_data_tensor_move, colo_model_data_tensor_move_inline -import colossalai - import torch - import torch.multiprocessing as mp +import colossalai +from colossalai.testing import rerun_if_address_is_in_use +from colossalai.utils import free_port +from colossalai.zero.legacy.gemini.tensor_utils import colo_model_data_tensor_move, colo_model_data_tensor_move_inline +from colossalai.zero.legacy.sharded_param import ShardedTensor + def run_tensor_move(rank): colossalai.launch(config={}, rank=0, world_size=1, host='localhost', port=free_port(), backend='nccl') diff --git a/tests/test_utils/test_zero_gradient_clippling.py b/tests/test_utils/test_zero_gradient_clippling.py index 8bdae88464b1..920656726d63 100644 --- a/tests/test_utils/test_zero_gradient_clippling.py +++ b/tests/test_utils/test_zero_gradient_clippling.py @@ -2,21 +2,22 @@ # -*- encoding: utf-8 -*- import copy +from functools import partial -import colossalai -from colossalai.zero.sharded_model.sharded_model_v2 import ShardedModelV2 import pytest import torch import torch.distributed as dist import torch.multiprocessing as mp import torch.nn as nn -from colossalai.logging import disable_existing_loggers -from colossalai.utils import checkpoint, clip_grad_norm_fp32, free_port from torch.nn.parallel import DistributedDataParallel as DDP from torch.nn.utils import clip_grad_norm_ -from colossalai.zero.shard_utils.tensor_shard_strategy import TensorShardStrategy -from functools import partial + +import colossalai +from colossalai.logging import disable_existing_loggers from colossalai.testing import parameterize, rerun_if_address_is_in_use +from colossalai.utils import checkpoint, clip_grad_norm_fp32, free_port +from colossalai.zero.legacy.shard_utils.tensor_shard_strategy import TensorShardStrategy +from colossalai.zero.legacy.sharded_model.sharded_model_v2 import ShardedModelV2 def checkpoint_wrapper(module, enable=True): diff --git a/tests/test_zero/common.py b/tests/test_zero/common.py index bc6cd75a6a60..2c3d122c79af 100644 --- a/tests/test_zero/common.py +++ b/tests/test_zero/common.py @@ -2,10 +2,11 @@ import torch import torch.distributed as dist + from colossalai.logging import get_dist_logger from colossalai.utils import checkpoint -from colossalai.zero.shard_utils import TensorShardStrategy -from colossalai.zero.sharded_model import ShardedModelV2 +from colossalai.zero.legacy.shard_utils import TensorShardStrategy +from colossalai.zero.legacy.sharded_model import ShardedModelV2 LOGGER = get_dist_logger('zero_test') diff --git a/tests/test_zero/low_level_zero/test_zero_init.py b/tests/test_zero/low_level_zero/test_zero_init.py index 1305da5df9c5..803d0021df96 100644 --- a/tests/test_zero/low_level_zero/test_zero_init.py +++ b/tests/test_zero/low_level_zero/test_zero_init.py @@ -9,8 +9,7 @@ import colossalai from colossalai.tensor import ProcessGroup from colossalai.utils import free_port, get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext -from colossalai.zero import LowLevelZeroOptimizer +from colossalai.zero import ColoInitContext, LowLevelZeroOptimizer class MlpModel(nn.Module): diff --git a/tests/test_zero/low_level_zero/test_zero_tp.py b/tests/test_zero/low_level_zero/test_zero_tp.py index 15d3530ff90a..bb7495583dc9 100644 --- a/tests/test_zero/low_level_zero/test_zero_tp.py +++ b/tests/test_zero/low_level_zero/test_zero_tp.py @@ -11,8 +11,7 @@ from colossalai.tensor import ProcessGroup from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port, get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext -from colossalai.zero import LowLevelZeroOptimizer +from colossalai.zero import ColoInitContext, LowLevelZeroOptimizer from tests.test_tensor.common_utils import set_seed, split_param_col_tp1d, split_param_row_tp1d, tensor_shard_equal diff --git a/tests/test_zero/test_found_inf.py b/tests/test_zero/test_found_inf.py index 34283f5015e1..641136718161 100644 --- a/tests/test_zero/test_found_inf.py +++ b/tests/test_zero/test_found_inf.py @@ -1,72 +1,72 @@ -from functools import partial - -import colossalai -from colossalai.utils.cuda import get_current_device -import pytest -import torch -import torch.multiprocessing as mp -from colossalai.nn.optimizer import HybridAdam -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import BucketTensorShardStrategy -from colossalai.zero.sharded_model import ShardedModelV2 -from colossalai.zero.sharded_optim import ShardedOptimizerV2 -from colossalai.zero.sharded_optim._utils import has_inf_or_nan -from tests.components_to_test.registry import non_distributed_component_funcs -from tests.test_zero.test_sharded_optim_v2 import _run_step - -from common import CONFIG - - -@parameterize("cpu_offload", [True, False]) -@parameterize("shard_strategy_class", [BucketTensorShardStrategy]) -@parameterize("gpu_margin_mem_ratio", [0.0, 0.7]) -def _run_test_found_inf(cpu_offload, shard_strategy_class, gpu_margin_mem_ratio): - test_models = ['repeated_computed_layers'] - shard_strategy = shard_strategy_class() - - for model_name in test_models: - get_components_func = non_distributed_component_funcs.get_callable(model_name) - model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func() - - with ZeroInitContext(target_device=torch.device(f'cpu:0') if cpu_offload else get_current_device(), - shard_strategy=shard_strategy, - shard_param=True): - zero_model = model_builder(checkpoint=True) - zero_model = ShardedModelV2( - zero_model, - shard_strategy, - tensor_placement_policy='cpu' if cpu_offload else 'cuda', - reuse_fp16_shard=True, - ) - - sharded_optim = HybridAdam(zero_model.parameters(), lr=1e-3) - sharded_optim = ShardedOptimizerV2(zero_model, sharded_optim, gpu_margin_mem_ratio=gpu_margin_mem_ratio) - - for i, (data, label) in enumerate(train_dataloader): - if i > 1: - break - assert zero_model.overflow_counter == 0 - data, label = data.cuda(), label.cuda() - _run_step(zero_model, sharded_optim, data, label, criterion, False) - for param in zero_model.parameters(): - assert not has_inf_or_nan(param.colo_attr.data_payload) - - -def _run_dist(rank, world_size, port): - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - _run_test_found_inf() - - -# use_cpuadam = True can be used with cpu_offload = False -@pytest.mark.dist -@pytest.mark.parametrize("world_size", [1, 2]) -@rerun_if_address_is_in_use() -def test_found_inf(world_size): - run_func = partial(_run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_found_inf(world_size=2) +from functools import partial + +import pytest +import torch +import torch.multiprocessing as mp +from common import CONFIG + +import colossalai +from colossalai.nn.optimizer import HybridAdam +from colossalai.testing import parameterize, rerun_if_address_is_in_use +from colossalai.utils import free_port +from colossalai.utils.cuda import get_current_device +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy +from colossalai.zero.legacy.sharded_model import ShardedModelV2 +from colossalai.zero.legacy.sharded_optim import ShardedOptimizerV2 +from colossalai.zero.low_level._utils import has_inf_or_nan +from tests.components_to_test.registry import non_distributed_component_funcs +from tests.test_zero.test_sharded_optim_v2 import _run_step + + +@parameterize("cpu_offload", [True, False]) +@parameterize("shard_strategy_class", [BucketTensorShardStrategy]) +@parameterize("gpu_margin_mem_ratio", [0.0, 0.7]) +def _run_test_found_inf(cpu_offload, shard_strategy_class, gpu_margin_mem_ratio): + test_models = ['repeated_computed_layers'] + shard_strategy = shard_strategy_class() + + for model_name in test_models: + get_components_func = non_distributed_component_funcs.get_callable(model_name) + model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func() + + with ZeroInitContext(target_device=torch.device(f'cpu:0') if cpu_offload else get_current_device(), + shard_strategy=shard_strategy, + shard_param=True): + zero_model = model_builder(checkpoint=True) + zero_model = ShardedModelV2( + zero_model, + shard_strategy, + tensor_placement_policy='cpu' if cpu_offload else 'cuda', + reuse_fp16_shard=True, + ) + + sharded_optim = HybridAdam(zero_model.parameters(), lr=1e-3) + sharded_optim = ShardedOptimizerV2(zero_model, sharded_optim, gpu_margin_mem_ratio=gpu_margin_mem_ratio) + + for i, (data, label) in enumerate(train_dataloader): + if i > 1: + break + assert zero_model.overflow_counter == 0 + data, label = data.cuda(), label.cuda() + _run_step(zero_model, sharded_optim, data, label, criterion, False) + for param in zero_model.parameters(): + assert not has_inf_or_nan(param.colo_attr.data_payload) + + +def _run_dist(rank, world_size, port): + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + _run_test_found_inf() + + +# use_cpuadam = True can be used with cpu_offload = False +@pytest.mark.dist +@pytest.mark.parametrize("world_size", [1, 2]) +@rerun_if_address_is_in_use() +def test_found_inf(world_size): + run_func = partial(_run_dist, world_size=world_size, port=free_port()) + mp.spawn(run_func, nprocs=world_size) + + +if __name__ == '__main__': + test_found_inf(world_size=2) diff --git a/tests/test_zero/test_init_context.py b/tests/test_zero/test_init_context.py index 0cba7a492380..0eb8842de3e3 100644 --- a/tests/test_zero/test_init_context.py +++ b/tests/test_zero/test_init_context.py @@ -9,14 +9,14 @@ from common import CONFIG import colossalai -from colossalai.gemini.memory_tracer.utils import colo_model_mem_usage from colossalai.logging import get_dist_logger from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device from colossalai.utils.memory import colo_device_memory_used -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import BucketTensorShardStrategy, TensorShardStrategy +from colossalai.zero.gemini.memory_tracer.utils import colo_model_mem_usage +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy from tests.components_to_test.registry import non_distributed_component_funcs diff --git a/tests/test_zero/test_shard_model_v2.py b/tests/test_zero/test_shard_model_v2.py index 95a9dee38acf..884444adf167 100644 --- a/tests/test_zero/test_shard_model_v2.py +++ b/tests/test_zero/test_shard_model_v2.py @@ -12,11 +12,11 @@ import colossalai from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import BucketTensorShardStrategy -from colossalai.zero.sharded_model import ShardedModelV2 -from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16 -from colossalai.zero.sharded_model.utils import col_model_deepcopy +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy +from colossalai.zero.legacy.sharded_model import ShardedModelV2 +from colossalai.zero.legacy.sharded_model._utils import cast_tensor_to_fp16 +from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy from tests.components_to_test.registry import non_distributed_component_funcs diff --git a/tests/test_zero/test_shard_param.py b/tests/test_zero/test_shard_param.py index 8db2b7e79604..6085de3c8919 100644 --- a/tests/test_zero/test_shard_param.py +++ b/tests/test_zero/test_shard_param.py @@ -1,17 +1,18 @@ from copy import deepcopy from functools import partial -import colossalai import pytest import torch import torch.multiprocessing as mp + +import colossalai from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port -from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy) -from colossalai.zero.sharded_param import ShardedTensor -from colossalai.zero.sharded_param.sharded_param import ShardedParamV2 +from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor +from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy +from colossalai.zero.legacy.sharded_param import ShardedTensor +from colossalai.zero.legacy.sharded_param.sharded_param import ShardedParamV2 from tests.test_zero.common import CONFIG, allclose -from colossalai.gemini.stateful_tensor import StatefulTensor @parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy]) diff --git a/tests/test_zero/test_sharded_optim_state_dict.py b/tests/test_zero/test_sharded_optim_state_dict.py index f8c42930b281..d257a02854c6 100644 --- a/tests/test_zero/test_sharded_optim_state_dict.py +++ b/tests/test_zero/test_sharded_optim_state_dict.py @@ -1,20 +1,21 @@ +from functools import partial + import pytest -import colossalai import torch import torch.multiprocessing as mp -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils.cuda import get_current_device -from colossalai.utils import free_port -from functools import partial -from tests.test_tensor.common_utils import set_seed -from tests.components_to_test.registry import non_distributed_component_funcs -from colossalai.testing import parameterize + +import colossalai from colossalai.nn.optimizer import HybridAdam -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import TensorShardStrategy -from colossalai.zero.sharded_model import ShardedModelV2 -from colossalai.zero.sharded_optim import ShardedOptimizerV2 from colossalai.tensor import ProcessGroup +from colossalai.testing import parameterize, rerun_if_address_is_in_use +from colossalai.utils import free_port +from colossalai.utils.cuda import get_current_device +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import TensorShardStrategy +from colossalai.zero.legacy.sharded_model import ShardedModelV2 +from colossalai.zero.legacy.sharded_optim import ShardedOptimizerV2 +from tests.components_to_test.registry import non_distributed_component_funcs +from tests.test_tensor.common_utils import set_seed def init_zero(model_builder, placement_policy): diff --git a/tests/test_zero/test_sharded_optim_v2.py b/tests/test_zero/test_sharded_optim_v2.py index 8fe7eb639eab..3eea13d5d5c2 100644 --- a/tests/test_zero/test_sharded_optim_v2.py +++ b/tests/test_zero/test_sharded_optim_v2.py @@ -13,12 +13,12 @@ from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port from colossalai.utils.cuda import get_current_device -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import BucketTensorShardStrategy, TensorShardStrategy -from colossalai.zero.sharded_model import ShardedModelV2 -from colossalai.zero.sharded_model.utils import col_model_deepcopy -from colossalai.zero.sharded_optim import ShardedOptimizerV2 -from colossalai.zero.sharded_optim._utils import has_inf_or_nan +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy +from colossalai.zero.legacy.sharded_model import ShardedModelV2 +from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy +from colossalai.zero.legacy.sharded_optim import ShardedOptimizerV2 +from colossalai.zero.low_level._utils import has_inf_or_nan from tests.components_to_test.registry import non_distributed_component_funcs diff --git a/tests/test_zero/test_sharded_optim_with_sync_bn.py b/tests/test_zero/test_sharded_optim_with_sync_bn.py index ea5b315188a3..05512f59a36a 100644 --- a/tests/test_zero/test_sharded_optim_with_sync_bn.py +++ b/tests/test_zero/test_sharded_optim_with_sync_bn.py @@ -3,18 +3,19 @@ from functools import partial -import colossalai import pytest import torch import torch.distributed as dist import torch.multiprocessing as mp +from torchvision.models import resnet50 + +import colossalai from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.testing import rerun_if_address_is_in_use from colossalai.utils import free_port -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import TensorShardStrategy -from torchvision.models import resnet50 +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import TensorShardStrategy def run_dist(rank, world_size, port): diff --git a/tests/test_zero/test_state_dict.py b/tests/test_zero/test_state_dict.py index 7ac9b151e4d6..c435d9bb1ef7 100644 --- a/tests/test_zero/test_state_dict.py +++ b/tests/test_zero/test_state_dict.py @@ -4,20 +4,20 @@ from copy import deepcopy from functools import partial -import colossalai import pytest import torch import torch.multiprocessing as mp +from common import CONFIG + +import colossalai from colossalai.testing import parameterize, rerun_if_address_is_in_use from colossalai.utils import free_port -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy) -from colossalai.zero.sharded_model import ShardedModelV2 -from colossalai.zero.sharded_model.utils import col_model_deepcopy +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy +from colossalai.zero.legacy.sharded_model import ShardedModelV2 +from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy from tests.components_to_test.registry import non_distributed_component_funcs -from common import CONFIG - @parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy]) def run_zero_state_dict(shard_strategy_class): diff --git a/tests/test_zero/test_tensor_utils.py b/tests/test_zero/test_tensor_utils.py index 81855ff5e10a..3114481707c2 100644 --- a/tests/test_zero/test_tensor_utils.py +++ b/tests/test_zero/test_tensor_utils.py @@ -1,18 +1,21 @@ +from functools import partial + import pytest +import torch +import torch.multiprocessing as mp import colossalai -from colossalai.utils.cuda import get_current_device -from colossalai.gemini.tensor_utils import (colo_tensor_mem_usage, colo_model_data_tensor_move, - colo_model_data_tensor_move_inline, colo_model_data_move_to_cpu, - colo_model_tensor_clone) -from colossalai.gemini.stateful_tensor import StatefulTensor -from colossalai.utils import free_port from colossalai.testing import rerun_if_address_is_in_use - -import torch - -from functools import partial -import torch.multiprocessing as mp +from colossalai.utils import free_port +from colossalai.utils.cuda import get_current_device +from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor +from colossalai.zero.legacy.gemini.tensor_utils import ( + colo_model_data_move_to_cpu, + colo_model_data_tensor_move, + colo_model_data_tensor_move_inline, + colo_model_tensor_clone, + colo_tensor_mem_usage, +) def _run_colo_tensor_mem_usage(): diff --git a/tests/test_zero/test_zero_engine.py b/tests/test_zero/test_zero_engine.py index 80ded65d634c..1e7f53358526 100644 --- a/tests/test_zero/test_zero_engine.py +++ b/tests/test_zero/test_zero_engine.py @@ -3,21 +3,21 @@ from functools import partial -import colossalai import pytest import torch import torch.distributed as dist import torch.multiprocessing as mp +from common import MP_PARALLEL_CONFIG, ZERO_PARALLEL_CONFIG, check_params, check_sharded_model_params +from torch.nn.parallel import DistributedDataParallel as DDP + +import colossalai from colossalai.core import global_context as gpc from colossalai.testing import rerun_if_address_is_in_use from colossalai.utils import free_port -from colossalai.zero.init_ctx import ZeroInitContext -from colossalai.zero.sharded_model.utils import col_model_deepcopy -from colossalai.zero.sharded_optim._utils import has_inf_or_nan +from colossalai.zero.legacy.init_ctx import ZeroInitContext +from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy +from colossalai.zero.low_level._utils import has_inf_or_nan from tests.components_to_test.registry import non_distributed_component_funcs -from torch.nn.parallel import DistributedDataParallel as DDP - -from common import (MP_PARALLEL_CONFIG, ZERO_PARALLEL_CONFIG, check_params, check_sharded_model_params) def run_dist(rank, world_size, port, parallel_config): From 1beb85cc25f35d51083bde6fbaa99a5c4c7fd387 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 4 Apr 2023 15:23:01 +0800 Subject: [PATCH 095/413] [checkpoint] refactored the API and added safetensors support (#3427) * [checkpoint] refactored the API and added safetensors support * polish code --- colossalai/booster/plugin/torch_ddp_plugin.py | 4 +- colossalai/checkpoint_io/__init__.py | 5 +- .../checkpoint_io/checkpoint_io_base.py | 332 ++++-------------- .../checkpoint_io/general_checkpoint_io.py | 53 ++- colossalai/checkpoint_io/index_file.py | 150 ++++++++ colossalai/checkpoint_io/utils.py | 278 +++++++++++++++ requirements/requirements.txt | 1 + .../test_plugin/test_torch_ddp_plugin.py | 23 ++ .../test_general_checkpoint_io.py | 13 +- 9 files changed, 579 insertions(+), 280 deletions(-) create mode 100644 colossalai/checkpoint_io/index_file.py create mode 100644 colossalai/checkpoint_io/utils.py diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index d7f3d22d93cc..e2abe11ba143 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -33,7 +33,7 @@ def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool = # the model should be unwrapped in self.load_model via ModelWrapper.unwrap return super().load_unsharded_model(model, checkpoint, strict=strict) - def save_unsharded_model(self, model: nn.Module, checkpoint: str): + def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool): """ Save model to checkpoint but only on master process. """ @@ -41,7 +41,7 @@ def save_unsharded_model(self, model: nn.Module, checkpoint: str): if self.coordinator.is_master(): super().save_unsharded_model(model, checkpoint) - def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str): + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): """ Save optimizer to checkpoint but only on master process. """ diff --git a/colossalai/checkpoint_io/__init__.py b/colossalai/checkpoint_io/__init__.py index 3cec630b2f86..c25048e25754 100644 --- a/colossalai/checkpoint_io/__init__.py +++ b/colossalai/checkpoint_io/__init__.py @@ -1,4 +1,5 @@ -from .checkpoint_io_base import CheckpointIO, ShardCheckpointIndexFile +from .checkpoint_io_base import CheckpointIO from .general_checkpoint_io import GeneralCheckpointIO +from .index_file import CheckpointIndexFile -__all__ = ['CheckpointIO', 'ShardCheckpointIndexFile', 'GeneralCheckpointIO'] +__all__ = ['CheckpointIO', 'CheckpointIndexFile', 'GeneralCheckpointIO'] diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py index d6eef7a96cdc..b91b00831e52 100644 --- a/colossalai/checkpoint_io/checkpoint_io_base.py +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -1,7 +1,6 @@ -import json from abc import ABC, abstractmethod from pathlib import Path -from typing import Any, Union +from typing import Union import torch import torch.nn as nn @@ -10,7 +9,9 @@ from colossalai.interface import ModelWrapper -__all__ = ['CheckpointIO', 'ShardCheckpointIndexFile'] +from .utils import has_index_file + +__all__ = ['CheckpointIO'] class CheckpointIO(ABC): @@ -25,15 +26,31 @@ class CheckpointIO(ABC): >>> # load model from checkpoint >>> model = checkpoint_io.load_model(model, 'model.pt') >>> - >>> # save model to checkpoint + >>> # save model to checkpoint, any distributed tensor is gathered by default >>> checkpoint_io.save_model(model, 'model.pt') >>> + >>> # if the model contains distributed tensor, and you don't want to gather it + >>> # each rank will save its own shard of the distributed tensor + >>> checkpoint_io.save_model(model, 'model.pt', gather_dtensor=False) + >>> >>> # save model to sharded checkpoints >>> checkpoint_io.save_model(model, './checkpoints/', shard=True) >>> + >>> # save model to sharded and assume we don't want to gather distributed tensors + >>> checkpoint_io.save_model(model, './checkpoints/', shard=True, gather_dtensor=False) + >>> + >>> # Note: + >>> # 1. we don't support loading from distributed tensors, conversion from distributed tensors + >>> # checkpoints to full tensor checkpoint should be done offline via our CLI + >>> # 2. you don't have to specify whether the model is sharded or not when loading the model + >>> # as it will be automatically detected + >>> >>> # load model from sharded checkpoints >>> model = checkpoint_io.load_model(model, './checkpoints/') >>> + >>> # load model from unsharded checkpoints + >>> model = checkpoint_io.load_model(model, './checkpoints/') + >>> >>> # load optimizer from checkpoint >>> optimizer = checkpoint_io.load_optimizer(optimizer, 'optimizer.pt') >>> @@ -58,21 +75,27 @@ def load_model(self, 1. a file path, e.g. 'model.pt' 2. a path to a json file which defines the index to the sharded checkpoint 3. a path to a folder containing a unique .index.json file for sharded checkpoint + Distributed tensors cannot be loaded directly unless gathered offline via our CLI. strict (bool): whether to strictly enforce that the param name in the checkpoint match the keys returned by this module's. """ + # since we only support loaded sharded and unsharded weight format + # containing no distributed tensors, dtensor -> full tensor conversion + # should be done offline via our CLI + # the existence of index file means it is a sharded checkpoint ckpt_path = Path(checkpoint) - is_sharded = self.is_sharded_checkpoint(ckpt_path) + index_file_exists, index_file_path = has_index_file(checkpoint) + # return the origin model instead of the unwrapped model origin_model = model if isinstance(model, ModelWrapper): model = model.unwrap() - if is_sharded: - self.load_sharded_model(model, ckpt_path, strict) + if index_file_exists: + self.load_sharded_model(model, index_file_path, strict) else: - self.load_unsharded_model(model, ckpt_path, strict) + self.load_unsharded_model(model, checkpoint, strict) return origin_model @@ -80,8 +103,10 @@ def save_model(self, model: Union[nn.Module, ModelWrapper], checkpoint: str, shard: bool = False, + gather_dtensor: bool = True, prefix: str = None, - size_per_shard: int = 1024): + size_per_shard: int = 1024, + use_safetensors: bool = False): """ Save model to checkpoint. @@ -103,17 +128,19 @@ def save_model(self, shard (bool): whether to shard the checkpoint. Default: False. If set to True, the checkpoint will be sharded into multiple files. The model shards will be specificed by a `model.index.json` file. When shard = True, please ensure that the checkpoint path is a directory path instead of a file path. + gather_dtensor (bool): whether to gather the distributed tensor to the first device. Default: True. prefix (str): prefix for the model checkpoint file name when shard=True. Default: None. size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard = True. + use_safetensors (bool): whether to use safe tensors. Default: False. If set to True, the checkpoint will be saved """ if isinstance(model, ModelWrapper): model = model.unwrap() if shard: - self.save_sharded_model(model, checkpoint, prefix, size_per_shard) + self.save_sharded_model(model, checkpoint, gather_dtensor, prefix, size_per_shard, use_safetensors) else: - self.save_unsharded_model(model, checkpoint) + self.save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors) def load_optimizer(self, optimizer: Optimizer, checkpoint: str): """ @@ -123,22 +150,27 @@ def load_optimizer(self, optimizer: Optimizer, checkpoint: str): optimizer (Optimizer): optimizer to be loaded. checkpoint (str): checkpoint path. This value is made compatiblity with the model checkpoints in the """ - ckpt_path = Path(checkpoint) - is_sharded = self.is_sharded_checkpoint(ckpt_path) + index_file_exists, index_file_path = has_index_file(checkpoint) - if is_sharded: - self.load_sharded_optimizer(optimizer, ckpt_path) + if Path(checkpoint).is_dir() and not index_file_exists: + # if the checkpoint is a directory and there is no index file, raise error + raise ValueError(f'Cannot find index file in {checkpoint}') + + if index_file_exists: + # the existence of index file means it is a sharded checkpoint + self.load_sharded_optimizer(optimizer, index_file_path) else: - self.load_unsharded_optimizer(optimizer, ckpt_path) + self.load_unsharded_optimizer(optimizer, checkpoint) def save_optimizer(self, optimizer: Optimizer, checkpoint: str, shard: bool = False, + gather_dtensor=True, prefix: str = None, size_per_shard: int = 1024): """ - Save optimizer to checkpoint. + Save optimizer to checkpoint. Optimizer states saving is not compatible with safetensors. Args: optimizer (Optimizer): optimizer to be saved. @@ -148,30 +180,33 @@ def save_optimizer(self, 3. a path to a folder containing a unique .index.json file for sharded checkpoint shard (bool): whether to shard the checkpoint. Default: False. If set to True, the checkpoint will be sharded into multiple files. The optimizer shards will be specificed by a `optimizer.index.json` file. + gather_dtensor (bool): whether to gather the distributed tensor to the first device. Default: True. prefix (str): prefix for the optimizer checkpoint when shard = True. Default: None. size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard is set to True. """ if shard: - self.save_sharded_optimizer(optimizer, checkpoint, prefix, size_per_shard) + self.save_sharded_optimizer(optimizer, checkpoint, gather_dtensor, prefix, size_per_shard) else: - self.save_unsharded_optimizer(optimizer, checkpoint) + self.save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor) # ======================================================== # Abstract methods for model loading/saving implementation # ======================================================== @abstractmethod - def load_sharded_model(self, model: nn.Module, checkpoint: Path, strict: bool): + def load_sharded_model(self, model: nn.Module, index_file_path: str, strict: bool): """ Load model from sharded checkpoint. Args: model (nn.Module): model to be loaded. - checkpoint (str): checkpoint path. It should be path to the .index.json file or a path to a directory which contains a .index.json file. + index_file_path (str): checkpoint path. It should be path to the .index.json file or a path to a directory which contains a .index.json file. + strict (bool): whether to strictly enforce that the param name in + the checkpoint match the keys returned by this module's. """ pass @abstractmethod - def load_unsharded_model(self, model: nn.Module, checkpoint: Path, strict: bool): + def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool): """ Load model from unsharded checkpoint. @@ -184,26 +219,31 @@ def load_unsharded_model(self, model: nn.Module, checkpoint: Path, strict: bool) pass @abstractmethod - def save_sharded_model(self, model: nn.Module, checkpoint: Path, prefix: str, size_per_shard: int): + def save_sharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, prefix: str, + size_per_shard: int, use_safetensors: bool): """ Save model to sharded checkpoint. Args: model (nn.Module): model to be saved. - checkpoint (Path): checkpoint path. It should be a directory path. + checkpoint (str): checkpoint path. It should be a directory path. + gather_dtensor (bool): whether to gather the distributed tensor to the first device. prefix (str): prefix for the model checkpoint. size_per_shard (int): size per shard in MB. + use_safetensors (bool): whether to use safe tensors. """ pass @abstractmethod - def save_unsharded_model(self, model: nn.Module, checkpoint: Path): + def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, use_safetensors: bool): """ Save model to unsharded checkpoint. Args: model (nn.Module): model to be saved. - checkpoint (Path): checkpoint path. It should be a single file path pointing to a model weight binary. + checkpoint (str): checkpoint path. It should be a single file path pointing to a model weight binary. + gather_dtensor (bool): whether to gather the distributed tensor to the first device. + use_safetensors (bool): whether to use safe tensors. """ pass @@ -212,13 +252,13 @@ def save_unsharded_model(self, model: nn.Module, checkpoint: Path): # ======================================================== @abstractmethod - def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int): + def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str, size_per_shard: int): """ Load optimizer from sharded checkpoint. Args: optimizer (Optimizer): optimizer to be loaded. - checkpoint (str): checkpoint path. It should be path to the .index.json file or a path to a directory which contains a .index.json file. + index_file_path (str): checkpoint path. It should be path to the .index.json file or a path to a directory which contains a .index.json file. prefix (str): prefix for the optimizer checkpoint. size_per_shard (int): size per shard in MB. """ @@ -236,26 +276,29 @@ def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): pass @abstractmethod - def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int): + def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, gather_dtensor: bool, prefix: str, + size_per_shard: int): """ Save optimizer to sharded checkpoint. Args: optimizer (Optimizer): optimizer to be saved. checkpoint (Path): checkpoint path. It should be a directory path. + gather_dtensor (bool): whether to gather the distributed tensor to the first device. prefix (str): prefix for the optimizer checkpoint. size_per_shard (int): size per shard in MB. """ pass @abstractmethod - def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, gather_dtensor: bool): """ Save optimizer to unsharded checkpoint. Args: optimizer (Optimizer): optimizer to be saved. checkpoint (str): checkpoint path. It should be a single file path pointing to a model weight binary. + gather_dtensor (bool): whether to gather the distributed tensor to the first device. """ pass @@ -264,7 +307,6 @@ def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): # as this is quite standard, there is no need # to make them abstract # ============================================ - def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): """ Save lr scheduler to checkpoint. @@ -285,231 +327,3 @@ def load_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): """ state_dict = torch.load(checkpoint) lr_scheduler.load_state_dict(state_dict) - - # ======================================== - # Helper functions for loading state dict - # ======================================== - - def get_sharded_checkpoint_index_file(self, checkpoint_path: Path): - """ - Get the index file path for a sharded checkpoint. - - Args: - checkpoint_path (Path): path to the checkpoint. - - Returns: - Path: path to the index file. - """ - if checkpoint_path.is_file(): - # check if it is .index.json - if checkpoint_path.name.endswith('.index.json'): - return checkpoint_path - else: - raise ValueError(f'Invalid checkpoint path: {checkpoint_path}. ') - elif checkpoint_path.is_dir(): - # check if there is only one a file ending with .index.json in this directory - index_files = list(checkpoint_path.glob('*.index.json')) - if len(index_files) == 1: - return index_files[0] - else: - raise ValueError(f'Found {len(index_files)} index files in {checkpoint_path}. ') - - def is_sharded_checkpoint(self, checkpoint_path: Path): - """ - Check whether the checkpoint is sharded. - - Args: - checkpoint (str): checkpoint path. - - Returns: - bool: whether the checkpoint is sharded. - """ - if checkpoint_path.is_file(): - # check if it is .index.json - if checkpoint_path.name.endswith('.index.json'): - return True - else: - return False - elif checkpoint_path.is_dir(): - # check if there is only one a file ending with .index.json in this directory - index_files = list(checkpoint_path.glob('*.index.json')) - if len(index_files) == 1: - return True - else: - raise ValueError(f'Found {len(index_files)} index files in {checkpoint_path}. ') - - def get_checkpoint_shard_filenames(self, index_file_path: Path): - """ - Get checkpoint shard filenames from a json file. - - Args: - index_file_path (Path): path to the json file. - - Returns: - list: checkpoint shard filenames. - """ - with open(str(index_file_path), 'r') as f: - shard_filenames = json.load(f) - - if "weight_map" in index: - index = index["weight_map"] - - checkpoint_root_path = index_file_path.absolute().parent - - # read the checkpoint file list from the json file and get a list of unique file names - checkpoint_files = sorted(list(set(index.values()))) - - # get the absolute paths for all checkpoint files - checkpoint_files = [checkpoint_root_path.joinpath(f) for f in checkpoint_files] - return shard_filenames - - def load_safetensors_state_dict(self, *args, **kwargs): - """ - Load safetensors state dict from checkpoint. - """ - # TODO(FrankLeeeee): support huggingface safetensors - raise NotImplementedError("This method is not implemented to support safe tensors") - - def load_state_dict(self, checkpoint_file_path: Path): - """ - Load state dict from checkpoint. - - Args: - checkpoint_file_path (Path): path to the checkpoint file. - - Returns: - dict: state dict. - """ - return torch.load(str(checkpoint_file_path)) - - # ====================================== - # Helper functions for saving state dict - # ====================================== - - def save_safetensors_state_dict(self, *args, **kwargs): - """ - Save safetensors state dict to checkpoint. - """ - # TODO(FrankLeeeee): support huggingface safetensors - raise NotImplementedError("This method is not implemented to support safe tensors") - - def generate_checkpoint_shard_file_name(self, index: int, total_number: int, prefix: str = None): - """ - Generate checkpoint shard file name. - - Args: - index (int): index of the shard. - total_number (int): total number of shards. - prefix (str): prefix of the shard file name. Default: None. - """ - if prefix is None: - return f"{index}-of-{total_number}.bin" - else: - return f"{prefix}-{index}-of-{total_number}.bin" - - def save_checkpoint(self, state_dict: dict, checkpoint_file_path: Path): - """ - Save state dict to checkpoint. - - Args: - state_dict (dict): state dict. - checkpoint_file_path (Path): path to the checkpoint file. - """ - torch.save(state_dict, str(checkpoint_file_path)) - - def save_state_dict_as_shard(self, state_dict: dict, index: int, total_number: int, prefix: str, - checkpoint_path: Path): - """ - Save state dict as shard. - - Args: - state_dict (dict): state dict. - checkpoint_path (Path): path to the checkpoint file. - """ - # generate the shard name - shard_file_name = self.generate_checkpoint_shard_file_name(index, total_number, prefix) - shard_file_path = checkpoint_path.joinpath(shard_file_name) - - # save the shard - self.save_checkpoint(state_dict, shard_file_path) - - def calculate_param_size(self, param: torch.Tensor): - """ - Calculate the size of a parameter in MB. Used to compute whether a group of params exceed the shard size. - If so, a new shard should be created. - - ArgsL - param (torch.Tensor): parameter tensor. - """ - # TODO(FrankLeeeee): check if this tensor is a DTensor, compute its global size if so - return param.numel() * param.element_size() / 1024 / 1024 - - -class ShardCheckpointIndexFile: - """ - This class is a data structure to keep the content in the index.json file for sharded checkpoint. - - Example: - >>> index = ShardCheckpointIndexFile() - >>> index.load('index.json') - >>> index.append_metadata('model_type', 'bert') - >>> index.append_weight_map('bert.embeddings.word_embeddings.weight', 'bert.embeddings.word_embeddings.weight-0-of-2.bin') - >>> index.export('index.json') - """ - - def __init__(self) -> None: - self.metadata: dict = dict() - self.weight_map: dict = dict() - - def load(self, json_path: str): - """ - Load the index file from a json file. - - Args: - json_path (str): path to the json file. - """ - # load the json file - with open(json_path, 'r') as f: - index = json.load(f) - - # assign attributes if exists - if "metadata" in index: - self.metadata = index["metadata"] - if "weight_map" in index: - self.weight_map = index["weight_map"] - - def export(self, json_path: str): - """ - Export the index file to a json file. - - Args: - json_path (str): path to the json file. - """ - # create the index file - index = dict() - index["metadata"] = self.metadata - index["weight_map"] = self.weight_map - - # export the index file - with open(json_path, 'w') as f: - json.dump(index, f, indent=4) - - def append_weight_map(self, param_name: str, shard_file: str): - """ - Append a weight map entry to the index file. - - Args: - param_name (str): name of the parameter. - shard_file (str): name of the shard file. - """ - self.weight_map[param_name] = shard_file - - def append_meta_data(self, name: str, val: Any): - """ - Append a metadata entry to the index file. - - Args: - name (str): name of the metadata. - val (Any): value of the metadata. - """ - self.metadata[name] = val diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index cfabcfa5589f..c779f4c17355 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -4,42 +4,67 @@ from torch.optim import Optimizer from .checkpoint_io_base import CheckpointIO +from .index_file import CheckpointIndexFile +from .utils import has_index_file, load_state_dict, save_state_dict __all__ = ['GeneralCheckpointIO'] class GeneralCheckpointIO(CheckpointIO): - def load_sharded_model(self, model: nn.Module, checkpoint: Path, strict: bool): - index_file_path = self.get_sharded_checkpoint_index_file(checkpoint) + def load_sharded_model(self, model: nn.Module, index_file_path: Path, strict: bool): + # load the index file + index_file = CheckpointIndexFile.from_file(index_file_path) # iterate over the shard checkpoint files # and load each - shard_files = self.get_checkpoint_shard_filenames(index_file_path) - for shard_file in shard_files: - shard_checkpoint = self.load_state_dict(shard_file) + index_file.assert_no_dtensor_checkpoint() + checkpoint_file_list, _ = index_file.get_checkpoint_fileanames() + for shard_file in checkpoint_file_list: + shard_checkpoint = load_state_dict(shard_file) model.load_state_dict(shard_checkpoint, strict=strict) - def load_unsharded_model(self, model: nn.Module, checkpoint: Path, strict: bool): - checkpoint = self.load_state_dict(str(checkpoint)) + def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool): + checkpoint = load_state_dict(checkpoint) model.load_state_dict(checkpoint, strict=strict) - def save_sharded_model(self, model: nn.Module, checkpoint: Path, prefix: str, size_per_shard: int): + def save_sharded_model(self, model: nn.Module, checkpoint: Path, gather_dtensor: bool, prefix: str, + size_per_shard: int, use_safetensors: bool): # TODO(FrankLeeeee): implement this method as it can be supported by Huggingface model raise NotImplementedError("Sharded model checkpoint is not supported yet.") - def save_unsharded_model(self, model: nn.Module, checkpoint: Path): - self.save_checkpoint(model.state_dict(), checkpoint) + def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, use_safetensors: bool): + state_dict = model.state_dict() + + # TODO(FrankLeeeee): add support for gather_dtensor + if gather_dtensor: + pass + + # save the checkpoint + save_state_dict(state_dict, checkpoint, use_safetensors) def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int): raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.") def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): - checkpoint = self.load_state_dict(checkpoint) + checkpoint = load_state_dict(checkpoint) optimizer.load_state_dict(checkpoint) - def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int): + def save_sharded_optimizer( + self, + optimizer: Optimizer, + checkpoint: Path, + gather_dtensor: bool, + prefix: str, + size_per_shard: int, + ): raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.") - def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): - self.save_checkpoint(optimizer.state_dict(), checkpoint) + def save_unsharded_optimizer( + self, + optimizer: Optimizer, + checkpoint: Path, + gather_dtensor: bool, + ): + # TODO(FrankLeeeee): handle distributed tensors + save_state_dict(optimizer.state_dict(), checkpoint, use_safetensors=False) diff --git a/colossalai/checkpoint_io/index_file.py b/colossalai/checkpoint_io/index_file.py new file mode 100644 index 000000000000..32ff1b762e88 --- /dev/null +++ b/colossalai/checkpoint_io/index_file.py @@ -0,0 +1,150 @@ +import json +from pathlib import Path +from typing import Any, List, Union + +from .utils import is_dtensor_checkpoint + +__all__ = ['CheckpointIndexFile'] + + +class CheckpointIndexFile: + """ + This class is a data structure to keep the content in the index.json file for sharded checkpoint. + + Example: + >>> index = CheckpointIndexFile.from_file('model.index.json') + >>> index.append_metadata('model_type', 'bert') + >>> index.append_weight_map('bert.embeddings.word_embeddings.weight', 'model_0001-of-0002.bin') + >>> index.export('new_index.json') + """ + + def __init__(self) -> None: + self.root_path = None + self.metadata: dict = dict() + self.weight_map: dict = dict() + + @staticmethod + def from_file(index_path: Union[str, Path]): + """ + Create a CheckpointIndexFile object from a json file. + + Args: + index_path (str): path to the json file. + + Returns: + CheckpointIndexFile: CheckpointIndexFile object. + """ + index = CheckpointIndexFile() + index.load(index_path) + return index + + def load(self, json_path: str): + """ + Load the index file from a json file. + + Args: + json_path (str): path to the json file. + """ + # load the json file + with open(json_path, 'r') as f: + index = json.load(f) + + # assign attributes if exists + if "metadata" in index: + self.metadata = index["metadata"] + if "weight_map" in index: + self.weight_map = index["weight_map"] + + # assign the root directory for the index file + self.root_path = Path(json_path).absolute().parent + + def export(self, json_path: str): + """ + Export the index file to a json file. + + Args: + json_path (str): path to the json file. + """ + # create the index file + index = dict() + index["metadata"] = self.metadata + index["weight_map"] = self.weight_map + + # export the index file + with open(json_path, 'w') as f: + json.dump(index, f, indent=4) + + def append_weight_map(self, param_name: str, shard_file: str): + """ + Append a weight map entry to the index file. + + Args: + param_name (str): name of the parameter. + shard_file (str): name of the shard file. + """ + self.weight_map[param_name] = shard_file + + def append_meta_data(self, name: str, val: Any): + """ + Append a metadata entry to the index file. + + Args: + name (str): name of the metadata. + val (Any): value of the metadata. + """ + self.metadata[name] = val + + def contains_dtensor(self): + """ + Check if the index file contains any distributed tensor. The distributed tensors will be stored in + `dtensor/module.linear.weight.*.bin` or `dtensor/module.linear.weight.*.safetensors` in the weight map. + + Returns: + bool: True if the index file contains any distributed tensor, False otherwise. + """ + for value in self.weight_map.values(): + if value.endswith(".*.bin") or value.endswith(".*.safetensors"): + return True + return False + + def get_checkpoint_fileanames(self) -> List[str]: + """ + Get the set of checkpoint filenames in the weight map. + + Returns: + list: checkpoint shard filenames. + """ + # read the checkpoint file list from the json file and get a list of unique file names + checkpoint_files = sorted(list(set(self.weight_map.values()))) + + # get the absolute paths for all checkpoint files + checkpoint_files = [str(self.root_path.joinpath(f)) for f in checkpoint_files] + + dtensor_list = [] + checkpoint_list = [] + + for ckpt_file in checkpoint_files: + if is_dtensor_checkpoint(ckpt_file): + dtensor_list.append(ckpt_file) + else: + checkpoint_list.append(ckpt_file) + + return checkpoint_list, dtensor_list + + def assert_no_dtensor_checkpoint(self): + for val in self.weight_map.values(): + if is_dtensor_checkpoint(val): + raise ValueError(f"Checkpoint file {val} contains distributed tensor") + + def get_checkpoint_file(self, param_name: str) -> str: + """ + Get the checkpoint file name for a parameter. + + Args: + param_name (str): name of the parameter. + + Returns: + str: checkpoint file name. + """ + ckpt_path = self.weight_map[param_name] + return ckpt_path diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py new file mode 100644 index 000000000000..76c9db0afaff --- /dev/null +++ b/colossalai/checkpoint_io/utils.py @@ -0,0 +1,278 @@ +from pathlib import Path +from typing import List, Optional, Tuple + +import torch + +# ====================================== +# General helper functions +# ====================================== + + +def calculate_tensor_size(tensor: torch.Tensor) -> float: + """ + Calculate the size of a parameter in MB. Used to compute whether a group of params exceed the shard size. + If so, a new shard should be created. + + Args: + tenosr (torch.Tensor): the tensor to calculate size for. + + Returns: + float: size of the tensor in MB. + """ + return tensor.numel() * tensor.element_size() / 1024 / 1024 + + +def is_safetensors_available() -> bool: + """ + Check whether safetensors is available. + + Returns: + bool: whether safetensors is available. + """ + try: + import safetensors + return True + except ImportError: + return False + + +def is_dtensor_checkpoint(checkpoint_file_path: str) -> bool: + """ + Check whether the checkpoint file is a dtensor checkpoint. + + Args: + checkpoint_file_path (str): path to the checkpoint file. + + Returns: + bool: whether the checkpoint file is a dtensor checkpoint. + """ + if checkpoint_file_path.endswith('.*.safetensors') or checkpoint_file_path.endswith('.*.bin'): + return True + else: + return False + + +def is_safetensor_checkpoint(checkpoint_file_path: str) -> bool: + """ + Check whether the checkpoint file is a safetensor checkpoint. + + Args: + checkpoint_file_path (str): path to the checkpoint file. + + Returns: + bool: whether the checkpoint file is a safetensor checkpoint. + """ + if checkpoint_file_path.endswith('.safetensors'): + return True + else: + return False + + +# ====================================== +# Helper functions for saving state dict +# ====================================== + + +def save_state_dict(state_dict: dict, checkpoint_file_path: str, use_safetensors: bool) -> None: + """ + Save state dict to checkpoint. + + Args: + state_dict (dict): state dict. + checkpoint_file_path (str): path to the checkpoint file. + use_safetensors (bool): whether to use safetensors to save the checkpoint. + """ + if use_safetensors: + assert is_safetensors_available(), "safetensors is not available." + assert checkpoint_file_path.endswith('.safetensors'), \ + "safetensors only supports .safetensors suffix for checkpoint file." + from safetensors.torch import save_file + save_file(state_dict, checkpoint_file_path) + else: + torch.save(state_dict, checkpoint_file_path) + + +def save_dtensor(name: str, tensor: torch.Tensor, index_file: "CheckpointIndexFile", use_safetensors: bool) -> None: + """ + Save distributed tensor to checkpoint. This checkpoint will be a dictionary which contains + only one tensor. + + Args: + tensor (Tensor): tensor to be saved. + index_file (CheckpointIndexFile): path to the checkpoint file. + size_per_shard (int): size per shard in MB. + """ + root_path = index_file.root_path + output_root_path = root_path.joinpath('dtensor') + + # create directory + output_root_path.mkdir(exist_ok=True) + + # save tensor to this directory + # TODO(YuliangLiu): get index of the tensor shard + # e.g. index = + index = 0 + + # save tensor to file + ckpt_file_name = generate_dtensor_file_name(name, index, use_safetensors) + ckpt_file_path = output_root_path.joinpath(ckpt_file_name) + + # dtensor ckpt file always contains only one tensor + state_dict = {name: tensor} + save_state_dict(state_dict, str(ckpt_file_path), use_safetensors) + + # update the weight map + # * means all shards + ckpt_file_name_in_weight_map = 'dtensor/' + generate_dtensor_file_name(name, '*', use_safetensors) + index_file.append_weight_map(name, ckpt_file_name_in_weight_map) + + +def get_checkpoint_file_suffix(use_safetensors: bool) -> str: + """ + Get checkpoint file suffix. + + Args: + use_safetensors (bool): whether to use safetensors to save the checkpoint. + + Returns: + str: checkpoint file suffix. + """ + if use_safetensors: + return '.safetensors' + else: + return '.bin' + + +def generate_checkpoint_shard_file_name(index: int, + total_number: int, + use_safetensors: bool, + prefix: str = None) -> str: + """ + Generate checkpoint shard file name. + + Args: + index (int): index of the shard. + total_number (int): total number of shards. + use_safetensors (bool): whether to use safetensors to save the checkpoint. + prefix (str): prefix of the shard file name. Default: None. + + Returns: + str: checkpoint shard file name. + """ + suffix = get_checkpoint_file_suffix(use_safetensors) + + if prefix is None: + return f"{index:05d}-of-{total_number:05d}.{suffix}" + else: + return f"{prefix}-{index:05d}-of-{total_number:05d}.{suffix}" + + +def generate_dtensor_file_name(param_name: str, index: int, use_safetensors: bool) -> str: + """ + Generate dtensor file name. + + Args: + param_name (str): name of the distributed parameter. + index (int): index of the shard. + use_safetensors (bool): whether to use safetensors to save the checkpoint. + + Returns: + str: dtensor file name. + """ + suffix = get_checkpoint_file_suffix(use_safetensors) + return f'{param_name}.{index}.{suffix}' + + +def save_state_dict_as_shard( + state_dict: dict, + checkpoint_path: str, + index: int, + total_number: int, + use_safetensors: bool, + prefix: str = None, +) -> None: + """ + Save state dict as shard. + + Args: + state_dict (dict): state dict. + checkpoint_path (str): path to the checkpoint file. + index (int): index of the shard. + total_number (int): total number of shards. + prefix (str): prefix of the shard file name. + use_safetensors (bool): whether to use safetensors to save the checkpoint. + """ + # generate the shard name + shard_file_name = generate_checkpoint_shard_file_name(index, total_number, use_safetensors, prefix) + shard_file_path = Path(checkpoint_path).joinpath(shard_file_name).absolute() + + # save the shard + save_state_dict(state_dict, str(shard_file_path), use_safetensors) + + +# ======================================== +# Helper functions for loading state dict +# ======================================== + + +def has_index_file(checkpoint_path: str) -> Tuple[bool, Optional[Path]]: + """ + Check whether the checkpoint has an index file. + + Args: + checkpoint_path (str): path to the checkpoint. + + Returns: + Tuple[bool, Optional[Path]]: a tuple of (has_index_file, index_file_path) + """ + checkpoint_path = Path(checkpoint_path) + if checkpoint_path.is_file(): + # check if it is .index.json + if checkpoint_path.name.endswith('.index.json'): + return True, checkpoint_path + else: + return False, None + elif checkpoint_path.is_dir(): + # check if there is only one a file ending with .index.json in this directory + index_files = list(checkpoint_path.glob('*.index.json')) + + # if we found a .index.json file, make sure there is only one + if len(index_files) > 0: + assert len( + index_files + ) == 1, f'Expected to find one .index.json file in {checkpoint_path}, but found {len(index_files)}' + + if len(index_files) == 1: + return True, index_files[0] + else: + return False, None + + +def load_state_dict(checkpoint_file_path: Path): + """ + Load state dict from checkpoint. + + Args: + checkpoint_file_path (Path): path to the checkpoint file. + + Returns: + dict: state dict. + """ + + assert not is_dtensor_checkpoint(checkpoint_file_path), \ + f'Cannot load state dict from dtensor checkpoint {checkpoint_file_path}, you should convert the distributed tensors to gathered tensors with our CLI offline.' + + if is_safetensor_checkpoint(checkpoint_file_path): + assert is_safetensors_available(), \ + f'Cannot load state dict from safetensor checkpoint {checkpoint_file_path}, because safetensors is not available. Please install safetensors first with pip install safetensors.' + # load with safetensors + from safetensors import safe_open + state_dict = {} + with safe_open(checkpoint_file_path, framework="pt", device="cpu") as f: + for k in f.keys(): + state_dict[k] = f.get_tensor(k) + return state_dict + + else: + # load with torch + return torch.load(checkpoint_file_path) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 4e4f35edb2d9..b34dc2e223ae 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -9,3 +9,4 @@ fabric contexttimer ninja torch>=1.11 +safetensors diff --git a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py index 2dcc5a5bba27..71e8582cc364 100644 --- a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py @@ -71,6 +71,29 @@ def check_dataloader_sharding(): batch_to_compare), 'Same number was found across ranks but expected it to be different' +def check_checkpoint_save_and_load(): + model_fn, data_gen_fn, output_transform_fn, _ = model_zoo['timm_resnet'] + + plugin = TorchDDPPlugin() + booster = Booster(plugin=plugin) + + model = model_fn() + optimizer = SGD(model.parameters(), lr=1e-3) + criterion = lambda x: x.mean() + data = data_gen_fn() + + data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()} + + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + + booster.backward(loss, optimizer) + + def run_dist(rank, world_size, port): # init dist env colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') diff --git a/tests/test_checkpoint_io/test_general_checkpoint_io.py b/tests/test_checkpoint_io/test_general_checkpoint_io.py index f9f0e03c4fa1..dfbb16af4ec6 100644 --- a/tests/test_checkpoint_io/test_general_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_general_checkpoint_io.py @@ -1,5 +1,6 @@ import tempfile +import pytest import torch from torch.optim import Adam from torchvision.models import resnet18 @@ -14,7 +15,8 @@ # ======== -def test_unsharded_checkpoint(): +@pytest.mark.parametrize('use_safetensors', [True, False]) +def test_unsharded_checkpoint(use_safetensors: bool): # create a model and optimizer model = resnet18() optimizer = Adam(model.parameters(), lr=0.001) @@ -29,12 +31,16 @@ def test_unsharded_checkpoint(): optimizer.step() # create a temp file for checkpoint - model_ckpt_tempfile = tempfile.NamedTemporaryFile() + if use_safetensors: + suffix = ".safetensors" + else: + suffix = ".bin" + model_ckpt_tempfile = tempfile.NamedTemporaryFile(suffix=suffix) optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile() # save the model and optimizer ckpt_io = GeneralCheckpointIO() - ckpt_io.save_model(model, model_ckpt_tempfile.name) + ckpt_io.save_model(model, model_ckpt_tempfile.name, use_safetensors=use_safetensors) ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name) # create new model @@ -68,3 +74,4 @@ def recursive_check(d1, d2): # check for model and optimizer state dict recursively recursive_check(model.state_dict(), new_model.state_dict()) recursive_check(optimizer.state_dict(), new_optimizer.state_dict()) + recursive_check(optimizer.state_dict(), new_optimizer.state_dict()) From 773955abfaa3b5aef832ad4a33ce053183edee0e Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Tue, 4 Apr 2023 15:30:01 +0800 Subject: [PATCH 096/413] fix save_model inin naive and ddp strategy (#3436) Co-authored-by: Yuanchen Xu --- .../Chat/coati/trainer/strategies/ddp.py | 34 ++++++++++++++----- .../Chat/coati/trainer/strategies/naive.py | 27 ++++++++++++--- 2 files changed, 49 insertions(+), 12 deletions(-) diff --git a/applications/Chat/coati/trainer/strategies/ddp.py b/applications/Chat/coati/trainer/strategies/ddp.py index 83cbbe633de9..8a8c4b3c2f4e 100644 --- a/applications/Chat/coati/trainer/strategies/ddp.py +++ b/applications/Chat/coati/trainer/strategies/ddp.py @@ -1,3 +1,5 @@ +from typing import Optional + import os import random @@ -5,12 +7,13 @@ import torch import torch.distributed as dist import torch.nn as nn -from coati.models.base import Actor +from coati.models.base import LM, Actor, RewardModel from coati.models.lora import LoraLinear from coati.replay_buffer import ReplayBuffer from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import Optimizer from torch.utils.data import DataLoader +from transformers.tokenization_utils_base import PreTrainedTokenizerBase from .base import Strategy from .naive import NaiveStrategy @@ -72,17 +75,32 @@ def _unwrap_actor(actor: Actor) -> nn.Module: model: DDP = Strategy._unwrap_actor(actor) return model.module - def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None: + def save_model(self, model: nn.Module, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + if only_rank0 and dist.get_rank() != 0: + return None + for module in model.modules(): if isinstance(module, LoraLinear): module.merge_weights = True module.eval() - - if only_rank0 and dist.get_rank() != 0: - return - model = model.model.module - state_dict = model.state_dict() - torch.save(state_dict, path) + + if isinstance(model, RewardModel): + state_dict = model.state_dict() + if only_rank0 and dist.get_rank() != 0: + return + torch.save(state_dict, path) + else: + try: + if isinstance(model, LM): + model = model.model + model.save_pretrained(path) + if tokenizer is not None: + tokenizer.save_pretrained(path) + except AttributeError: + state_dict = model.state_dict() + if only_rank0 and dist.get_rank() != 0: + return + torch.save(state_dict, path) def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: if only_rank0 and dist.get_rank() != 0: diff --git a/applications/Chat/coati/trainer/strategies/naive.py b/applications/Chat/coati/trainer/strategies/naive.py index 80768d7e649c..bb47e5ab2688 100644 --- a/applications/Chat/coati/trainer/strategies/naive.py +++ b/applications/Chat/coati/trainer/strategies/naive.py @@ -1,11 +1,14 @@ -from typing import Any +from typing import Any, Optional import torch import torch.nn as nn import torch.optim as optim from coati.replay_buffer import ReplayBuffer +from coati.models.base import LM, RewardModel +from coati.models.lora import LoraLinear from torch.optim import Optimizer from torch.utils.data import DataLoader +from transformers.tokenization_utils_base import PreTrainedTokenizerBase from .base import Strategy @@ -38,9 +41,25 @@ def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False pin_memory=pin_memory, collate_fn=replay_buffer.collate_fn) - def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None: - unwrapped_model = self._unwrap_model(model) - torch.save(unwrapped_model.state_dict(), path) + def save_model(self, model: nn.Module, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + for module in model.modules(): + if isinstance(module, LoraLinear): + module.merge_weights = True + module.eval() + + if isinstance(model, RewardModel): + state_dict = model.state_dict() + torch.save(state_dict, path) + else: + try: + if isinstance(model, LM): + model = model.model + model.save_pretrained(path) + if tokenizer is not None: + tokenizer.save_pretrained(path) + except AttributeError: + state_dict = model.state_dict() + torch.save(state_dict, path) def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None: unwrapped_model = self._unwrap_model(model) From 573af8418406a319e91be07f58fca798a6e72dbd Mon Sep 17 00:00:00 2001 From: ver217 Date: Tue, 4 Apr 2023 17:32:51 +0800 Subject: [PATCH 097/413] [example] update examples related to zero/gemini (#3431) * [zero] update legacy import * [zero] update examples * [example] fix opt tutorial * [example] fix opt tutorial * [example] fix opt tutorial * [example] fix opt tutorial * [example] fix import --- colossalai/zero/legacy/__init__.py | 3 ++- .../roberta/configs/colossalai_ddp.py | 7 ++++++- .../roberta/configs/colossalai_zero.py | 9 ++++++-- examples/tutorial/opt/opt/colossalai_zero.py | 6 +++++- examples/tutorial/opt/opt/requirements.txt | 1 + examples/tutorial/opt/opt/run_clm.py | 6 +++++- examples/tutorial/opt/opt/test_ci.sh | 21 +++++++++++++++++++ examples/tutorial/opt/test_ci.sh | 3 +++ 8 files changed, 50 insertions(+), 6 deletions(-) create mode 100755 examples/tutorial/opt/opt/test_ci.sh create mode 100755 examples/tutorial/opt/test_ci.sh diff --git a/colossalai/zero/legacy/__init__.py b/colossalai/zero/legacy/__init__.py index 35570a1f539a..3783d38e61b2 100644 --- a/colossalai/zero/legacy/__init__.py +++ b/colossalai/zero/legacy/__init__.py @@ -6,6 +6,7 @@ from colossalai.logging import get_dist_logger from .init_ctx import ZeroInitContext, no_shard_zero_context, no_shard_zero_decrator +from .shard_utils import BucketTensorShardStrategy, TensorShardStrategy from .sharded_model import ShardedModelV2 from .sharded_optim import ShardedOptimizerV2 @@ -40,5 +41,5 @@ def convert_to_zero_v2(model: nn.Module, optimizer: torch.optim.Optimizer, model __all__ = [ 'convert_to_zero_v2', 'ShardedModelV2', 'ShardedOptimizerV2', 'ZeroInitContext', 'no_shard_zero_context', - 'no_shard_zero_decrator' + 'no_shard_zero_decrator', 'TensorShardStrategy', 'BucketTensorShardStrategy' ] diff --git a/examples/language/roberta/configs/colossalai_ddp.py b/examples/language/roberta/configs/colossalai_ddp.py index c3c59aa4079c..3146ffc45eef 100644 --- a/examples/language/roberta/configs/colossalai_ddp.py +++ b/examples/language/roberta/configs/colossalai_ddp.py @@ -1,4 +1,9 @@ -from colossalai.zero.shard_utils import TensorShardStrategy from colossalai.nn.optimizer import FusedAdam +try: + from colossalai.zero.shard_utils import TensorShardStrategy +except ImportError: + # colossalai > 0.2.8 + from colossalai.zero.legacy import TensorShardStrategy + clip_grad_norm = 1.0 diff --git a/examples/language/roberta/configs/colossalai_zero.py b/examples/language/roberta/configs/colossalai_zero.py index c5debdce0988..bae4c723ccc8 100644 --- a/examples/language/roberta/configs/colossalai_zero.py +++ b/examples/language/roberta/configs/colossalai_zero.py @@ -1,6 +1,11 @@ -from colossalai.zero.shard_utils import TensorShardStrategy from colossalai.nn.optimizer import FusedAdam +try: + from colossalai.zero.shard_utils import TensorShardStrategy +except ImportError: + # colossalai > 0.2.8 + from colossalai.zero.legacy import TensorShardStrategy + # fp16 = dict( # mode=AMP_TYPE.TORCH, # ) @@ -29,4 +34,4 @@ weight_decay=1e-2, ) -# 64433 \ No newline at end of file +# 64433 diff --git a/examples/tutorial/opt/opt/colossalai_zero.py b/examples/tutorial/opt/opt/colossalai_zero.py index 833745f3e8d8..7c2c152450c5 100644 --- a/examples/tutorial/opt/opt/colossalai_zero.py +++ b/examples/tutorial/opt/opt/colossalai_zero.py @@ -1,4 +1,8 @@ -from colossalai.zero.shard_utils import TensorShardStrategy +try: + from colossalai.zero.shard_utils import TensorShardStrategy +except ImportError: + # colossalai > 0.2.8 + from colossalai.zero.legacy import TensorShardStrategy zero = dict(model_config=dict(shard_strategy=TensorShardStrategy(), tensor_placement_policy="auto", diff --git a/examples/tutorial/opt/opt/requirements.txt b/examples/tutorial/opt/opt/requirements.txt index c34df7992d3f..d0ed2c717aee 100644 --- a/examples/tutorial/opt/opt/requirements.txt +++ b/examples/tutorial/opt/opt/requirements.txt @@ -4,3 +4,4 @@ datasets >= 1.8.0 sentencepiece != 0.1.92 protobuf accelerate == 0.13.2 +transformers diff --git a/examples/tutorial/opt/opt/run_clm.py b/examples/tutorial/opt/opt/run_clm.py index e618b4d66957..fdc86adab665 100755 --- a/examples/tutorial/opt/opt/run_clm.py +++ b/examples/tutorial/opt/opt/run_clm.py @@ -413,7 +413,11 @@ def main(): cai_version = colossalai.__version__ logger.info(f'using Colossal-AI version {cai_version}') if version.parse(cai_version) > version.parse("0.1.10"): - from colossalai.nn.parallel import GeminiDDP + try: + from colossalai.nn.parallel import GeminiDDP + except ImportError: + # this works for unreleased main branch, and this may be released on 0.2.9 + from colossalai.zero import GeminiDDP model = GeminiDDP(model, device=get_current_device(), placement_policy=PLACEMENT_POLICY, pin_memory=True) elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"): from colossalai.gemini import ChunkManager, GeminiManager diff --git a/examples/tutorial/opt/opt/test_ci.sh b/examples/tutorial/opt/opt/test_ci.sh new file mode 100755 index 000000000000..e505da1364de --- /dev/null +++ b/examples/tutorial/opt/opt/test_ci.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +set -xue + +pip install -r requirements.txt + +BS=8 +MEMCAP=0 +GPUNUM=2 +MODLE="facebook/opt-125m" + +torchrun \ + --nproc_per_node ${GPUNUM} \ + --master_port 19198 \ + run_clm.py \ + -s \ + --output_dir $PWD \ + --mem_cap ${MEMCAP} \ + --model_name_or_path ${MODLE} \ + --per_device_train_batch_size ${BS} \ + --num_train_epochs 1 diff --git a/examples/tutorial/opt/test_ci.sh b/examples/tutorial/opt/test_ci.sh new file mode 100755 index 000000000000..8341bb10510f --- /dev/null +++ b/examples/tutorial/opt/test_ci.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +cd opt && bash test_ci.sh From ffcdbf0f6519366f322f2809e19f741492779c6c Mon Sep 17 00:00:00 2001 From: YuliangLiu0306 <72588413+YuliangLiu0306@users.noreply.github.com> Date: Tue, 4 Apr 2023 17:40:45 +0800 Subject: [PATCH 098/413] [autoparallel]integrate auto parallel feature with new tracer (#3408) * [autoparallel] integrate new analyzer in module level * unify the profiling method * polish * fix no codegen bug * fix pass bug * fix liveness test * polish --- .../_analyzer/_subclasses/flop_tensor.py | 23 +++- colossalai/_analyzer/fx/codegen.py | 19 +-- colossalai/_analyzer/fx/node_util.py | 2 +- colossalai/_analyzer/fx/passes/shape_prop.py | 9 +- .../_analyzer/fx/tracer/bias_addition.py | 114 +++++++++++----- colossalai/_analyzer/fx/tracer/tracer.py | 2 +- .../auto_parallel/meta_profiler/__init__.py | 2 +- .../meta_profiler/meta_registry/activation.py | 4 +- .../meta_registry/binary_elementwise_ops.py | 6 +- .../meta_profiler/meta_registry/conv.py | 28 ++-- .../meta_profiler/meta_registry/embedding.py | 8 +- .../meta_profiler/meta_registry/linear.py | 79 +++++------ .../meta_profiler/meta_registry/non_spmd.py | 2 - .../meta_profiler/meta_registry/norm.py | 34 ++--- .../meta_profiler/meta_registry/pooling.py | 14 +- .../meta_profiler/meta_registry/tensor.py | 10 +- .../meta_profiler/meta_registry/where.py | 4 +- .../{metainfo.py => shard_metainfo.py} | 16 +-- .../passes/comm_metainfo_pass.py | 28 ++-- .../auto_parallel/passes/meta_info_prop.py | 10 +- .../passes/runtime_apply_pass.py | 16 ++- .../passes/runtime_preparation_pass.py | 14 +- .../auto_parallel/tensor_shard/initialize.py | 17 ++- .../node_handler/batch_norm_handler.py | 2 - .../tensor_shard/node_handler/node_handler.py | 22 +-- .../solver/strategies_constructor.py | 18 +-- tests/test_analyzer/__init__.py | 0 .../test_size_value_converting_pass.py | 11 +- .../test_bias_addition_forward.py | 8 +- .../test_tensor_shard/test_checkpoint.py | 14 +- .../test_compatibility_with_ddp.py | 8 +- .../test_compatibility_with_gemini.py | 8 +- .../test_find_repeat_block.py | 8 +- .../test_gpt/test_runtime_with_gpt_modules.py | 34 +++-- .../test_gpt/test_solver_with_gpt_module.py | 8 +- .../test_liveness_analysis.py | 16 ++- .../test_metainfo/test_embedding_metainfo.py | 2 +- .../test_metainfo/test_linear_metainfo.py | 2 +- .../test_metainfo/test_matmul_metainfo.py | 2 +- .../test_metainfo/test_norm_metainfo.py | 2 +- .../test_metainfo/test_tensor_metainfo.py | 2 +- .../test_metainfo/test_where_metainfo.py | 2 +- .../test_tensor_shard/test_metainfo/utils.py | 19 ++- .../test_param_resharding_cost.py | 126 ------------------ .../test_shape_consistency_pass.py | 86 ------------ .../test_solver_with_resnet_v2.py | 7 +- 46 files changed, 397 insertions(+), 471 deletions(-) rename colossalai/auto_parallel/meta_profiler/{metainfo.py => shard_metainfo.py} (94%) create mode 100644 tests/test_analyzer/__init__.py delete mode 100644 tests/test_auto_parallel/test_tensor_shard/test_param_resharding_cost.py delete mode 100644 tests/test_auto_parallel/test_tensor_shard/test_shape_consistency_pass.py diff --git a/colossalai/_analyzer/_subclasses/flop_tensor.py b/colossalai/_analyzer/_subclasses/flop_tensor.py index dd35b00b3fab..59991dc50912 100644 --- a/colossalai/_analyzer/_subclasses/flop_tensor.py +++ b/colossalai/_analyzer/_subclasses/flop_tensor.py @@ -235,7 +235,28 @@ def matmul_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number: # Inputs contains the shapes of two matrices. input_shapes = [v.shape for v in inputs] assert len(input_shapes) == 2, input_shapes - assert input_shapes[0][-1] == input_shapes[1][-2], input_shapes + + # There are three cases: 1) gemm, 2) gemv, 3) dot + if all(len(shape) == 2 for shape in input_shapes): + # gemm + assert input_shapes[0][-1] == input_shapes[1][-2], input_shapes + elif all(len(shape) == 1 for shape in input_shapes): + # dot + assert input_shapes[0][0] == input_shapes[1][0], input_shapes + + # expand shape + input_shapes[0] = torch.Size([1, input_shapes[0][0]]) + input_shapes[1] = torch.Size([input_shapes[1][0], 1]) + else: + # gemv + if len(input_shapes[0]) == 1: + assert input_shapes[0][0] == input_shapes[1][-2], input_shapes + input_shapes.reverse() + else: + assert input_shapes[1][0] == input_shapes[0][-1], input_shapes + + # expand the shape of the vector to [batch size, 1] + input_shapes[-1] = torch.Size([input_shapes[-1][-1], 1]) flops = reduce(operator.mul, input_shapes[0]) * input_shapes[-1][-1] return flops diff --git a/colossalai/_analyzer/fx/codegen.py b/colossalai/_analyzer/fx/codegen.py index 1117c0103166..b768e59004b1 100644 --- a/colossalai/_analyzer/fx/codegen.py +++ b/colossalai/_analyzer/fx/codegen.py @@ -1,8 +1,12 @@ from typing import Any, Callable, Dict, Iterable, List, Tuple import torch + +try: + from torch.fx.graph import CodeGen +except: + pass from torch.fx.graph import ( - CodeGen, PythonCode, _custom_builtins, _format_target, @@ -48,8 +52,8 @@ def _end_of_ckpt(node: Node, ckpt_level: int) -> bool: """ Check if the node could end the ckpt region at `ckpt_level` """ - if len(node.meta['info'].to_recompute) > ckpt_level: - return node.meta['info'].to_recompute[ckpt_level] is not None + if len(node.meta['info'].activation_checkpoint) > ckpt_level: + return node.meta['info'].activation_checkpoint[ckpt_level] is not None return True @@ -90,8 +94,8 @@ def _find_nested_ckpt_regions(node_list: List[Node], ckpt_level: int = 0): current_region = None for idx, node in enumerate(node_list): - if len(node.meta['info'].to_recompute) > ckpt_level: - act_ckpt_label = node.meta['info'].to_recompute[ckpt_level] + if len(node.meta['info'].activation_checkpoint) > ckpt_level: + act_ckpt_label = node.meta['info'].activation_checkpoint[ckpt_level] # this activation checkpoint label is not set yet # meaning this is the first node of the activation ckpt region @@ -152,12 +156,12 @@ def emit_ckpt_func(body, # label given by each layer, e.g. if you are currently at level (0, 1, 1) # the label will be '0_1_1' - label = "_".join([str(idx) for idx in node_list[0].meta['info'].to_recompute[:ckpt_level + 1]]) + label = "_".join([str(idx) for idx in node_list[0].meta['info'].activation_checkpoint[:ckpt_level + 1]]) ckpt_fn_def = _gen_ckpt_fn_def(label, inputs) ckpt_func.append(f'{ckpt_fn_def}\n') # if there is more level to fetch - if ckpt_level + 1 < max(map(lambda node: len(node.meta['info'].to_recompute), node_list)): + if ckpt_level + 1 < max(map(lambda node: len(node.meta['info'].activation_checkpoint), node_list)): ckpt_regions = _find_nested_ckpt_regions(node_list, ckpt_level + 1) start_idx = [item[0] for item in ckpt_regions] end_idx = [item[1] for item in ckpt_regions] @@ -215,7 +219,6 @@ def emit_code_with_activation_checkpoint(body, ckpt_func, nodes, emit_node_func, ckpt_regions = _find_nested_ckpt_regions(nodes, 0) start_idx = [item[0] for item in ckpt_regions] end_idx = [item[1] for item in ckpt_regions] - node_list = list(nodes) node_idx = 0 diff --git a/colossalai/_analyzer/fx/node_util.py b/colossalai/_analyzer/fx/node_util.py index 8c8956d8ea7c..fbe8400a437e 100644 --- a/colossalai/_analyzer/fx/node_util.py +++ b/colossalai/_analyzer/fx/node_util.py @@ -112,7 +112,7 @@ class MetaInfo: # should keep the same whenever manipulated # ============================= Invariant ================================== - to_recompute: Tuple[torch.Tensor] = () # (region_0, region_1, ...) support nested codegen + activation_checkpoint: Tuple[torch.Tensor] = () # (region_0, region_1, ...) support nested codegen to_offload: Optional[bool] = False sharding_spec: str = 'RR' diff --git a/colossalai/_analyzer/fx/passes/shape_prop.py b/colossalai/_analyzer/fx/passes/shape_prop.py index b3859e250ac8..23e83013e02f 100644 --- a/colossalai/_analyzer/fx/passes/shape_prop.py +++ b/colossalai/_analyzer/fx/passes/shape_prop.py @@ -237,7 +237,14 @@ def propagate(self, *args, device=None): Returns: Any: The value returned from executing the Module """ - wrap_fn = lambda elem: MetaTensor(elem, device=device) + + # wrap_fn = lambda elem: MetaTensor(elem, device=device) + def wrap_fn(elem, device=device): + if isinstance(elem, torch.Tensor): + return MetaTensor(elem, device=device) + else: + return elem + with self._mode: return super().run(*tree_map(wrap_fn, args)) diff --git a/colossalai/_analyzer/fx/tracer/bias_addition.py b/colossalai/_analyzer/fx/tracer/bias_addition.py index 495678501664..1e75b47ca5b0 100644 --- a/colossalai/_analyzer/fx/tracer/bias_addition.py +++ b/colossalai/_analyzer/fx/tracer/bias_addition.py @@ -21,69 +21,111 @@ def linear_impl(input, weight, bias=None): @register_tracer_impl(F.conv1d, name='_bias_addition_impl') -def conv1d_impl(input, weight, **kwargs): - bias = getattr(kwargs, 'bias', None) +def conv1d_impl(input, weight, bias=None, stride=_single(1), padding=_single(0), dilation=_single(1), groups=1): if bias is None: - return F.conv1d(input, weight, **kwargs) + return F.conv1d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) else: - new_kwargs = kwargs - new_kwargs['bias'] = None - return F.conv1d(input, weight, **kwargs) + bias.reshape((-1, 1)) + return F.conv1d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape( + (-1, 1)) @register_tracer_impl(F.conv2d, name='_bias_addition_impl') -def conv2d_impl(input, weight, **kwargs): - bias = getattr(kwargs, 'bias', None) +def conv2d_impl(input, weight, bias=None, stride=_pair(1), padding=_pair(0), dilation=_pair(1), groups=1): if bias is None: - return F.conv2d(input, weight, **kwargs) + return F.conv2d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) else: - new_kwargs = kwargs - new_kwargs['bias'] = None - return F.conv2d(input, weight, **kwargs) + bias.reshape((-1, 1, 1)) + return F.conv2d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape( + (-1, 1, 1)) @register_tracer_impl(F.conv3d, name='_bias_addition_impl') -def conv3d_impl(input, weight, **kwargs): - bias = getattr(kwargs, 'bias', None) +def conv3d_impl(input, weight, bias=None, stride=_triple(1), padding=_triple(0), dilation=_triple(1), groups=1): if bias is None: - return F.conv3d(input, weight, **kwargs) + return F.conv3d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) else: - new_kwargs = kwargs - new_kwargs['bias'] = None - return F.conv3d(input, weight, **new_kwargs) + bias.reshape((-1, 1, 1, 1)) + return F.conv3d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape( + (-1, 1, 1, 1)) @register_tracer_impl(F.conv_transpose1d, name='_bias_addition_impl') -def conv_transpose1d_impl(input, weight, **kwargs): - bias = getattr(kwargs, 'bias', None) +def conv_transpose1d_impl(input, + weight, + bias=None, + stride=_single(1), + padding=_single(0), + output_padding=_single(0), + groups=1, + dilation=_single(1)): if bias is None: - return F.conv_transpose1d(input, weight, **kwargs) + return F.conv_transpose1d(input, + weight, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + dilation=dilation) else: - new_kwargs = kwargs - new_kwargs['bias'] = None - return F.conv_transpose1d(input, weight, **new_kwargs) + bias.reshape((-1, 1)) + return F.conv_transpose1d(input, + weight, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + dilation=dilation) + bias.reshape((-1, 1)) @register_tracer_impl(F.conv_transpose2d, name='_bias_addition_impl') -def conv_transpose2d_impl(input, weight, **kwargs): - bias = getattr(kwargs, 'bias', None) +def conv_transpose2d_impl(input, + weight, + bias=None, + stride=_pair(1), + padding=_pair(0), + output_padding=_pair(0), + groups=1, + dilation=_pair(1)): if bias is None: - return F.conv_transpose2d(input, weight, **kwargs) + return F.conv_transpose2d(input, + weight, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + dilation=dilation) else: - new_kwargs = kwargs - new_kwargs['bias'] = None - return F.conv_transpose2d(input, weight, **new_kwargs) + bias.reshape((-1, 1, 1)) + return F.conv_transpose2d(input, + weight, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + dilation=dilation) + bias.reshape((-1, 1, 1)) @register_tracer_impl(F.conv_transpose3d, name='_bias_addition_impl') -def conv_transpose3d_impl(input, weight, **kwargs): - bias = getattr(kwargs, 'bias', None) +def conv_transpose3d_impl(input, + weight, + bias=None, + stride=_triple(1), + padding=_triple(0), + output_padding=_triple(0), + groups=1, + dilation=_triple(1)): if bias is None: - return F.conv_transpose3d(input, weight, **kwargs) + return F.conv_transpose3d(input, + weight, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + dilation=dilation) else: - new_kwargs = kwargs - new_kwargs['bias'] = None - return F.conv_transpose3d(input, weight, **new_kwargs) + bias.reshape((-1, 1, 1, 1)) + return F.conv_transpose3d(input, + weight, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + dilation=dilation) + bias.reshape((-1, 1, 1, 1)) @register_tracer_impl(torch.addmm, name='_bias_addition_impl') diff --git a/colossalai/_analyzer/fx/tracer/tracer.py b/colossalai/_analyzer/fx/tracer/tracer.py index 1a247449f3d8..6958a00a6a72 100644 --- a/colossalai/_analyzer/fx/tracer/tracer.py +++ b/colossalai/_analyzer/fx/tracer/tracer.py @@ -155,7 +155,7 @@ def create_proxy(self, def create_node(self, *args, **kwargs) -> Node: node = super().create_node(*args, **kwargs) - n_info = MetaInfo(node, mod_dir=self.mod_dir, to_recompute=tuple(self.ckpt_regions)) + n_info = MetaInfo(node, mod_dir=self.mod_dir, activation_checkpoint=tuple(self.ckpt_regions)) return node def trace(self, diff --git a/colossalai/auto_parallel/meta_profiler/__init__.py b/colossalai/auto_parallel/meta_profiler/__init__.py index bfd36195149b..3741d8e5a8ad 100644 --- a/colossalai/auto_parallel/meta_profiler/__init__.py +++ b/colossalai/auto_parallel/meta_profiler/__init__.py @@ -1,3 +1,3 @@ from .meta_registry import * -from .metainfo import * from .registry import meta_register +from .shard_metainfo import * diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/activation.py b/colossalai/auto_parallel/meta_profiler/meta_registry/activation.py index faeed9f29e61..0f2e9e44f91c 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/activation.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/activation.py @@ -2,9 +2,9 @@ import torch +from colossalai._analyzer._subclasses.flop_tensor import ewise_flop_counter as elementwise_flop_counter +from colossalai._analyzer.fx.node_util import compute_size_in_bytes as activation_size from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import elementwise_flop_counter from ..registry import meta_register diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py b/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py index 281a92c0d4f1..e451748512b9 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py @@ -2,9 +2,9 @@ import torch +from colossalai._analyzer._subclasses.flop_tensor import flop_mapping +from colossalai._analyzer.fx.node_util import compute_size_in_bytes as activation_size from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import flop_mapping from ..constants import BCAST_FUNC_OP, NO_SAVE_ACTIVATION from ..registry import meta_register @@ -17,7 +17,7 @@ def binary_elementwise_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, Train """Meta information generator for binary elementwise operations NOTE: Some of the binary elementwise operations will discard the input activation after computation, as they don't need those tensors for back propagation, for example, if there are two tensors being sent for `torch.add`, - they will be discarded right after add operation is done. We create a simple API in `MetaInfo` class to identify + they will be discarded right after add operation is done. We create a simple API in `ShardMetaInfo` class to identify this behavior, it is critical for better memory estimation. Returns: diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/conv.py b/colossalai/auto_parallel/meta_profiler/meta_registry/conv.py index d1bb6e7fa798..4336bf68363c 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/conv.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/conv.py @@ -2,6 +2,8 @@ import torch +from colossalai._analyzer._subclasses.flop_tensor import flop_mapping +from colossalai._analyzer.fx.node_util import compute_size_in_bytes from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( MemoryCost, OperationData, @@ -10,8 +12,6 @@ StrategiesVector, TrainCycleItem, ) -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import flop_mapping from colossalai.tensor.sharding_spec import ShardingSpec from ..registry import meta_register @@ -110,18 +110,18 @@ def convnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L # calculate memory cost # TODO: use profiler to check conv temp memory # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward - fwd_memory_cost = MemoryCost( - activation=activation_size([input_tensor, output_tensor]), - parameter=activation_size([weight_tensor, bias_tensor]) if has_bias else activation_size(weight_tensor), - temp=0, - buffer=0) - - bwd_memory_cost = MemoryCost( - activation=activation_size([input_tensor, weight_tensor, bias_tensor]) - if has_bias else activation_size([input_tensor, weight_tensor]), - parameter=activation_size([weight_tensor, bias_tensor]) if has_bias else activation_size(weight_tensor), - temp=0, - buffer=0) + fwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, output_tensor]), + parameter=compute_size_in_bytes([weight_tensor, bias_tensor]) + if has_bias else compute_size_in_bytes(weight_tensor), + temp=0, + buffer=0) + + bwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor]) + if has_bias else compute_size_in_bytes([input_tensor, weight_tensor]), + parameter=compute_size_in_bytes([weight_tensor, bias_tensor]) + if has_bias else compute_size_in_bytes(weight_tensor), + temp=0, + buffer=0) # total cost is the sum of forward and backward cost total_cost = MemoryCost(activation=fwd_memory_cost.activation + bwd_memory_cost.activation, diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/embedding.py b/colossalai/auto_parallel/meta_profiler/meta_registry/embedding.py index 2997f31adff8..d5d80f5b3700 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/embedding.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/embedding.py @@ -2,9 +2,9 @@ import torch +from colossalai._analyzer._subclasses.flop_tensor import flop_mapping +from colossalai._analyzer.fx.node_util import compute_size_in_bytes from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import flop_mapping from ..registry import meta_register @@ -34,11 +34,11 @@ def embedding_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem # NOTE: during the backward phase of torch.nn.Embedding, it seems when the input is large enough, it will # have a temp memory which is kind of weird and we don't know the reason yet, so currently we just assume # that there will be no temp memory, as the temp memory is significantly smaller than the gradient memory - fwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor]), + fwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, output_tensor]), parameter=0, temp=0, buffer=0) - bwd_memory_cost = MemoryCost(activation=activation_size([weight_tensor]), parameter=0, temp=0, buffer=0) + bwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([weight_tensor]), parameter=0, temp=0, buffer=0) total_memory_cost = MemoryCost(activation=fwd_memory_cost.activation + bwd_memory_cost.activation) diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/linear.py b/colossalai/auto_parallel/meta_profiler/meta_registry/linear.py index 617375721222..7697fc6c383d 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/linear.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/linear.py @@ -3,6 +3,8 @@ import torch +from colossalai._analyzer._subclasses.flop_tensor import flop_mapping +from colossalai._analyzer.fx.node_util import compute_size_in_bytes from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( MemoryCost, OperationData, @@ -11,8 +13,6 @@ StrategiesVector, TrainCycleItem, ) -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import flop_mapping from colossalai.tensor.sharding_spec import ShardingSpec from ..registry import meta_register @@ -112,14 +112,14 @@ def linear_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L # NOTE: Linear don't have buffer and temp in forward and backward phase # the forward activation cost is the size of output_tensor, parameter cost is the size of weight_tensor and bias_tensor # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward - fwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor]), - parameter=activation_size([weight_tensor, bias_tensor]), + fwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, output_tensor]), + parameter=compute_size_in_bytes([weight_tensor, bias_tensor]), temp=0, buffer=0) # the backward activation cost is the size of input_tensor, weight_tensor and bias_tensor, parameter cost is 0 - bwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, weight_tensor, bias_tensor]), - parameter=activation_size([weight_tensor, bias_tensor]), + bwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor]), + parameter=compute_size_in_bytes([weight_tensor, bias_tensor]), temp=0, buffer=0) @@ -148,14 +148,14 @@ def linear_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L # NOTE: Linear don't have buffer and temp in forward and backward phase # the forward activation cost is the size of output_tensor, parameter cost is the size of weight_tensor # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward - fwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor]), - parameter=activation_size(weight_tensor), + fwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, output_tensor]), + parameter=compute_size_in_bytes(weight_tensor), temp=0, buffer=0) # the backward activation cost is the size of input_tensor and weight_tensor, parameter cost is 0 - bwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, weight_tensor]), - parameter=activation_size(weight_tensor), + bwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, weight_tensor]), + parameter=compute_size_in_bytes(weight_tensor), temp=0, buffer=0) @@ -210,48 +210,48 @@ def matmul_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L # Check dimension if all(len(tensor.shape) == 1 for tensor in input_tensors): # Dot - fwd_compute_cost = flop_mapping[torch.ops.aten.dot.default](input_tensors, output_tensors) + fwd_compute_cost = flop_mapping[torch.ops.aten.matmul.default](input_tensors, output_tensors) bwd_compute_cost = flop_mapping[torch.ops.aten.mul.Tensor](input_tensors[0], output_tensors) * 2 - fwd_mem_cost = MemoryCost(activation=activation_size(output_tensors), parameter=0, temp=0, buffer=0) - bwd_mem_cost = MemoryCost(activation=activation_size(input_tensors), parameter=0, temp=0, buffer=0) + fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(output_tensors), parameter=0, temp=0, buffer=0) + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensors), parameter=0, temp=0, buffer=0) elif len(input_tensors[0].shape) >= 2 and len(input_tensors[1].shape) == 1: # gemv case 1: matrix-vector multiplication # & # batched gemv case 1: batched matrix-vector multiplication - fwd_compute_cost = flop_mapping[torch.ops.aten.mv.default]( + fwd_compute_cost = flop_mapping[torch.ops.aten.matmul.default]( [input_tensors[0].reshape(-1, input_tensors[0].shape[-1]), input_tensors[1]], output_tensors) # combine the dimensions of output bwd_compute_cost = flop_mapping[torch.ops.aten.mul.Tensor]( [output_tensors[0].reshape(-1), input_tensors[1]], output_tensors) + \ - flop_mapping[torch.ops.aten.mv.default]( + flop_mapping[torch.ops.aten.matmul.default]( [input_tensors[0].reshape(-1, input_tensors[0].shape[-1]).transpose(0, 1), output_tensors[0].reshape(-1)], output_tensors) - fwd_mem_cost = MemoryCost(activation=activation_size(output_tensors), parameter=0, temp=0, buffer=0) - bwd_mem_cost = MemoryCost(activation=activation_size(input_tensors), parameter=0, temp=0, buffer=0) + fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(output_tensors), parameter=0, temp=0, buffer=0) + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensors), parameter=0, temp=0, buffer=0) elif len(input_tensors[0].shape) == 1 and len(input_tensors[1].shape) == 2: # gemv case 2: vector-matrix multiplication - fwd_compute_cost = flop_mapping[torch.ops.aten.mv.default](input_tensors, output_tensors) + fwd_compute_cost = flop_mapping[torch.ops.aten.matmul.default](input_tensors, output_tensors) bwd_compute_cost = flop_mapping[torch.ops.aten.mul.Tensor]([output_tensors[0], input_tensors[0]], output_tensors) + \ - flop_mapping[torch.ops.aten.mv.default]([input_tensors[1], output_tensors[0]], output_tensors) + flop_mapping[torch.ops.aten.matmul.default]([input_tensors[1], output_tensors[0]], output_tensors) - fwd_mem_cost = MemoryCost(activation=activation_size(output_tensors), parameter=0, temp=0, buffer=0) - bwd_mem_cost = MemoryCost(activation=activation_size(input_tensors), + fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(output_tensors), parameter=0, temp=0, buffer=0) + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensors), parameter=0, - temp=activation_size(input_tensors[1]), + temp=compute_size_in_bytes(input_tensors[1]), buffer=0) elif len(input_tensors[0].shape) == 1 and len(input_tensors[1].shape) >= 3: # batched gemv case 2: vector-batched matrix multiplication - fwd_compute_cost = flop_mapping[torch.ops.aten.mv.default]( + fwd_compute_cost = flop_mapping[torch.ops.aten.matmul.default]( [input_tensors[1].transpose(-2, -1).reshape(-1, input_tensors[1].shape[-2]), input_tensors[0]], [output_tensors[0].reshape(-1)]) @@ -260,15 +260,15 @@ def matmul_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L [output_tensors[0].reshape(-1), input_tensors[0]], output_tensors ) + \ - flop_mapping[torch.ops.aten.mv.default]( + flop_mapping[torch.ops.aten.matmul.default]( [input_tensors[1].transpose(-2, -1).reshape(-1, input_tensors[1].shape[-2]).transpose(0, 1), output_tensors[0].reshape(-1)], output_tensors ) - fwd_mem_cost = MemoryCost(activation=activation_size(output_tensors + [input_tensors[1]])) - bwd_mem_cost = MemoryCost(activation=activation_size(input_tensors[0]), + fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(output_tensors + [input_tensors[1]])) + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensors[0]), parameter=0, - temp=activation_size(input_tensors[1]), + temp=compute_size_in_bytes(input_tensors[1]), buffer=0) elif len(input_tensors[0].shape) >= 2 and len(input_tensors[1].shape) == 2: @@ -287,8 +287,8 @@ def matmul_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L [input_tensors[0].reshape(-1, input_tensors[0].shape[-1])] ) - fwd_mem_cost = MemoryCost(activation=activation_size(output_tensors), parameter=0, temp=0, buffer=0) - bwd_mem_cost = MemoryCost(activation=activation_size(input_tensors), parameter=0, temp=0, buffer=0) + fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(output_tensors), parameter=0, temp=0, buffer=0) + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensors), parameter=0, temp=0, buffer=0) elif len(input_tensors[0].shape) == 2 and len(input_tensors[1].shape) >= 3: # batched gemm case 2: matrix-batched matrix multiplication @@ -306,11 +306,12 @@ def matmul_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L [input_tensors[1].transpose(-2, -1).reshape(-1, input_tensors[1].shape[-2])] ) - fwd_mem_cost = MemoryCost(activation=activation_size(output_tensors) + activation_size(input_tensors[1]), - temp=activation_size(output_tensors)) - bwd_mem_cost = MemoryCost(activation=activation_size(input_tensors[0]), + fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(output_tensors) + + compute_size_in_bytes(input_tensors[1]), + temp=compute_size_in_bytes(output_tensors)) + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensors[0]), parameter=0, - temp=activation_size(input_tensors[1]) + activation_size(output_tensors)) + temp=compute_size_in_bytes(input_tensors[1]) + compute_size_in_bytes(output_tensors)) elif all(len(tensor.shape) >= 3 for tensor in input_tensors): # Batched matrix-batched matrix multiplication @@ -351,8 +352,8 @@ def matmul_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L [input_tensors[0].reshape(-1, input_dim_00, input_dim_01)] ) - fwd_mem_cost = MemoryCost(activation=activation_size(output_tensors)) - bwd_mem_cost = MemoryCost(activation=activation_size(input_tensors)) + fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(output_tensors)) + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensors)) else: # Case 2: batch dimensions are different @@ -381,10 +382,10 @@ def matmul_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L ) fwd_mem_cost = MemoryCost( - activation=activation_size([output_tensors[0], extended_input_0, extended_input_1])) - bwd_mem_cost = MemoryCost(activation=activation_size(input_tensors) - - activation_size([extended_input_0, extended_input_1]), - temp=activation_size([extended_input_0, extended_input_1])) + activation=compute_size_in_bytes([output_tensors[0], extended_input_0, extended_input_1])) + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensors) - + compute_size_in_bytes([extended_input_0, extended_input_1]), + temp=compute_size_in_bytes([extended_input_0, extended_input_1])) # compute cost compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost) diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/non_spmd.py b/colossalai/auto_parallel/meta_profiler/meta_registry/non_spmd.py index 4634d3ccdcfd..12874810b13e 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/non_spmd.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/non_spmd.py @@ -4,8 +4,6 @@ import torch from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import flop_mapping from ..registry import meta_register diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/norm.py b/colossalai/auto_parallel/meta_profiler/meta_registry/norm.py index 3a1db396e188..b872fdc8bdcd 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/norm.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/norm.py @@ -2,6 +2,8 @@ import torch +from colossalai._analyzer._subclasses.flop_tensor import flop_mapping +from colossalai._analyzer.fx.node_util import compute_size_in_bytes from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( MemoryCost, OperationData, @@ -10,8 +12,6 @@ StrategiesVector, TrainCycleItem, ) -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import flop_mapping from colossalai.tensor.sharding_spec import ShardingSpec from ..registry import meta_register @@ -77,17 +77,18 @@ def batchnormnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleIt # calculate memory cost # the fwd activation cost is output plus saved mean and saved inv std # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward - fwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor, mean_tensor, var_tensor]), - parameter=activation_size([weight_tensor, bias_tensor]), + fwd_memory_cost = MemoryCost(activation=compute_size_in_bytes( + [input_tensor, output_tensor, mean_tensor, var_tensor]), + parameter=compute_size_in_bytes([weight_tensor, bias_tensor]), temp=0, - buffer=activation_size([mean_tensor, var_tensor])) + buffer=compute_size_in_bytes([mean_tensor, var_tensor])) # the bwd memory cost is quite tricky here, BatchNorm will remove saved mean # and saved inv std during backward phase - bwd_memory_cost = MemoryCost(activation=activation_size([input_tensor]), - parameter=activation_size([weight_tensor, bias_tensor]), - temp=activation_size([mean_tensor, var_tensor]), - buffer=activation_size([mean_tensor, var_tensor])) + bwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor]), + parameter=compute_size_in_bytes([weight_tensor, bias_tensor]), + temp=compute_size_in_bytes([mean_tensor, var_tensor]), + buffer=compute_size_in_bytes([mean_tensor, var_tensor])) # total cost is the sum of forward and backward cost total_cost = MemoryCost(activation=fwd_memory_cost.activation + bwd_memory_cost.activation, @@ -131,15 +132,16 @@ def layernorm_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem # memory cost # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward - fwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor, weight_tensor, bias_tensor]), - parameter=activation_size([weight_tensor, bias_tensor]), + fwd_memory_cost = MemoryCost(activation=compute_size_in_bytes( + [input_tensor, output_tensor, weight_tensor, bias_tensor]), + parameter=compute_size_in_bytes([weight_tensor, bias_tensor]), temp=0, - buffer=activation_size([running_mean, running_var])) + buffer=compute_size_in_bytes([running_mean, running_var])) - bwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, weight_tensor, bias_tensor]), - parameter=activation_size([weight_tensor, bias_tensor]), - temp=activation_size([running_mean, running_var]), - buffer=activation_size([running_mean, running_var])) + bwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor]), + parameter=compute_size_in_bytes([weight_tensor, bias_tensor]), + temp=compute_size_in_bytes([running_mean, running_var]), + buffer=compute_size_in_bytes([running_mean, running_var])) total_cost = MemoryCost(activation=fwd_memory_cost.activation + bwd_memory_cost.activation, parameter=fwd_memory_cost.parameter + bwd_memory_cost.parameter, diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py b/colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py index 21272ea09ac1..d785dfcca9ba 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py @@ -2,9 +2,9 @@ import torch +from colossalai._analyzer._subclasses.flop_tensor import flop_mapping +from colossalai._analyzer.fx.node_util import compute_size_in_bytes from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import flop_mapping from ..registry import meta_register @@ -52,8 +52,8 @@ def avgpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost) # calculate memory cost - fwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=activation_size(output_tensor)) - bwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=activation_size(input_tensor)) + fwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=compute_size_in_bytes(output_tensor)) + bwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=compute_size_in_bytes(input_tensor)) # total cost total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation) @@ -114,11 +114,11 @@ def maxpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, # calculate memory cost # NOTE: the index matrix will be discarded in backward phase # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward - fwd_mem_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor, index_matrix])) + fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, output_tensor, index_matrix])) # temp memory for backward is the index matrix to be discarded - bwd_mem_cost = MemoryCost(activation=activation_size(input_tensor) - activation_size(index_matrix), - temp=activation_size(index_matrix)) + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensor) - compute_size_in_bytes(index_matrix), + temp=compute_size_in_bytes(index_matrix)) # total cost total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation, temp=bwd_mem_cost.temp) diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/tensor.py b/colossalai/auto_parallel/meta_profiler/meta_registry/tensor.py index 332e649d2d7e..97fe3c6196f5 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/tensor.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/tensor.py @@ -2,9 +2,9 @@ import torch +from colossalai._analyzer._subclasses.flop_tensor import flop_mapping +from colossalai._analyzer.fx.node_util import compute_size_in_bytes from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import flop_mapping from ..registry import meta_register @@ -35,11 +35,11 @@ def meta_func(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[tor # memory costs # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward - fwd_mem_cost = MemoryCost(activation=activation_size(outputs) * 2, parameter=0, temp=0, buffer=0) + fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(outputs) * 2, parameter=0, temp=0, buffer=0) - bwd_mem_cost = MemoryCost(activation=activation_size(outputs) * bwd_mem_out_factor, + bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(outputs) * bwd_mem_out_factor, parameter=0, - temp=activation_size(outputs) * bwd_mem_tmp_factor, + temp=compute_size_in_bytes(outputs) * bwd_mem_tmp_factor, buffer=0) total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation, diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/where.py b/colossalai/auto_parallel/meta_profiler/meta_registry/where.py index c67eb40bc80e..5cba1b5b6e2b 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/where.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/where.py @@ -2,9 +2,9 @@ import torch +from colossalai._analyzer._subclasses.flop_tensor import flop_mapping +from colossalai._analyzer.fx.node_util import compute_size_in_bytes as activation_size from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem -from colossalai.fx.profiler.memory_utils import activation_size -from colossalai.fx.profiler.opcount import flop_mapping from ..registry import meta_register diff --git a/colossalai/auto_parallel/meta_profiler/metainfo.py b/colossalai/auto_parallel/meta_profiler/shard_metainfo.py similarity index 94% rename from colossalai/auto_parallel/meta_profiler/metainfo.py rename to colossalai/auto_parallel/meta_profiler/shard_metainfo.py index 44b1882e06cc..0eee908b48b7 100644 --- a/colossalai/auto_parallel/meta_profiler/metainfo.py +++ b/colossalai/auto_parallel/meta_profiler/shard_metainfo.py @@ -15,11 +15,11 @@ from .constants import INPLACE_MODULE, INPLACE_OPS, NO_SAVE_ACTIVATION from .registry import meta_register -__all__ = ['MetaInfo'] +__all__ = ['ShardMetaInfo'] -class MetaInfo: - """MetaInfo class +class ShardMetaInfo: + """ShardMetaInfo class This class is used to store meta info based on sharding strategy and the given target function. """ @@ -46,9 +46,9 @@ def __init__(self, strategy: ShardingStrategy = None, target: Callable = None) - # target function self._target = target - # compute metainfo if possible + # compute shard_metainfo if possible if self._strategy is not None and self._target is not None: - self.compute_metainfo() + self.compute_shard_metainfo() @property def strategy(self) -> ShardingStrategy: @@ -62,13 +62,13 @@ def target(self) -> Callable: def strategy(self, strategy: ShardingStrategy) -> None: self._strategy = strategy if self._strategy is not None and self._target is not None: - self.compute_metainfo() + self.compute_shard_metainfo() @target.setter def target(self, target: Callable) -> None: self._target = target if self._strategy is not None and self._target is not None: - self.compute_metainfo() + self.compute_shard_metainfo() def compute_sharded_opdata(self, operation_data: OperationData, sharding_spec: ShardingSpec): """ @@ -93,7 +93,7 @@ def compute_sharded_opdata(self, operation_data: OperationData, sharding_spec: S return op_data - def compute_metainfo(self): + def compute_shard_metainfo(self): """ Compute meta info based on sharding strategy and the given target function. """ diff --git a/colossalai/auto_parallel/passes/comm_metainfo_pass.py b/colossalai/auto_parallel/passes/comm_metainfo_pass.py index ab3acb0563ff..ffda58e0689f 100644 --- a/colossalai/auto_parallel/passes/comm_metainfo_pass.py +++ b/colossalai/auto_parallel/passes/comm_metainfo_pass.py @@ -4,7 +4,7 @@ from torch.fx import GraphModule from torch.fx.node import Node -from colossalai.auto_parallel.meta_profiler import MetaInfo +from colossalai.auto_parallel.meta_profiler import ShardMetaInfo from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply, runtime_comm_spec_apply from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, TrainCycleItem from colossalai.tensor.comm_spec import CommSpec @@ -14,15 +14,15 @@ shape_consistency_manager = ShapeConsistencyManager() -def _construct_meta_info(node: Node, origin_sharding_spec: ShardingSpec, - target_sharding_spec: ShardingSpec) -> MetaInfo: +def _construct_shard_meta_info(node: Node, origin_sharding_spec: ShardingSpec, + target_sharding_spec: ShardingSpec) -> ShardMetaInfo: # get comm_action_sequence and total_cost from shape_consistency_manager _, comm_action_sequence, total_cost = shape_consistency_manager.shape_consistency( origin_sharding_spec, target_sharding_spec) - meta_info = MetaInfo() + meta_info = ShardMetaInfo() # NOTE: the cost in shape_consistency_manager.mem_cost is the count in number of numel - # get mem cost for MetaInfo + # get mem cost for ShardMetaInfo mem_cost = shape_consistency_manager.mem_cost(comm_action_sequence) # extract user that has _meta_data and extract element length input_node = next(n for n in node._input_nodes if hasattr(n, '_meta_data')) @@ -36,12 +36,12 @@ def _construct_meta_info(node: Node, origin_sharding_spec: ShardingSpec, meta_info.memory_cost = mem_cost - # get computation cost for MetaInfo + # get computation cost for ShardMetaInfo meta_info.compute_cost = TrainCycleItem(total_cost['forward'] * element_length, total_cost['backward'] * element_length, total_cost['total'] * element_length) - # get tensor shape for MetaInfo + # get tensor shape for ShardMetaInfo origin_sharding_spec: ShardingSpec target_sharding_spec: ShardingSpec input_shape = origin_sharding_spec.get_sharded_shape_per_device() @@ -54,7 +54,7 @@ def _construct_meta_info(node: Node, origin_sharding_spec: ShardingSpec, return meta_info -def _runtime_apply_meta_info(node: Node, origin_spec_dict, sharding_spec_dict) -> MetaInfo: +def _runtime_apply_meta_info(node: Node, origin_spec_dict, sharding_spec_dict) -> ShardMetaInfo: """ This method is used to construct `MetaInto` for shape consistency node """ @@ -65,17 +65,17 @@ def _runtime_apply_meta_info(node: Node, origin_spec_dict, sharding_spec_dict) - origin_sharding_spec, target_sharding_spec = origin_spec_dict[node_index], sharding_spec_dict[node_index][ user_node_index] - return _construct_meta_info(node, origin_sharding_spec, target_sharding_spec) + return _construct_shard_meta_info(node, origin_sharding_spec, target_sharding_spec) -def _runtime_comm_spec_apply_meta_info(node: Node, comm_actions_dict: Dict) -> MetaInfo: +def _runtime_comm_spec_apply_meta_info(node: Node, comm_actions_dict: Dict) -> ShardMetaInfo: # extract node_index and op_data_name node_index, op_data_name = node.args[2], node.args[3] comm_action = comm_actions_dict[node_index][op_data_name] if isinstance(comm_action.comm_spec, CommSpec): # this case is for all_reduce, there will be no memory cost - meta_info = MetaInfo() + meta_info = ShardMetaInfo() meta_info.memory_cost = TrainCycleItem(MemoryCost(), MemoryCost(), MemoryCost) output_node = next(n for n in node.users if hasattr(n, '_meta_data')) element_length = output_node._meta_data.element_size() @@ -93,7 +93,7 @@ def _runtime_comm_spec_apply_meta_info(node: Node, comm_actions_dict: Dict) -> M # this case will be handled by shape consistency manager origin_sharding_spec, target_sharding_spec = comm_action.comm_spec['src_spec'], comm_action.comm_spec[ 'tgt_spec'] - meta_info = _construct_meta_info(node, origin_sharding_spec, target_sharding_spec) + meta_info = _construct_shard_meta_info(node, origin_sharding_spec, target_sharding_spec) return meta_info @@ -105,9 +105,9 @@ def comm_metainfo_pass(gm: GraphModule, sharding_spec_dict: Dict, origin_spec_di """ for node in gm.graph.nodes: if node.target == runtime_apply: - setattr(node, 'best_metainfo', _runtime_apply_meta_info(node, origin_spec_dict, sharding_spec_dict)) + setattr(node, 'best_strategy_info', _runtime_apply_meta_info(node, origin_spec_dict, sharding_spec_dict)) elif node.target == runtime_comm_spec_apply: - setattr(node, 'best_metainfo', _runtime_comm_spec_apply_meta_info(node, comm_actions_dict)) + setattr(node, 'best_strategy_info', _runtime_comm_spec_apply_meta_info(node, comm_actions_dict)) else: pass return gm diff --git a/colossalai/auto_parallel/passes/meta_info_prop.py b/colossalai/auto_parallel/passes/meta_info_prop.py index f7e07ef1ec18..bc0960483980 100644 --- a/colossalai/auto_parallel/passes/meta_info_prop.py +++ b/colossalai/auto_parallel/passes/meta_info_prop.py @@ -7,7 +7,7 @@ from torch.fx import GraphModule from torch.fx.node import Node -from colossalai.auto_parallel.meta_profiler import MetaInfo +from colossalai.auto_parallel.meta_profiler import ShardMetaInfo from colossalai.auto_parallel.passes.constants import OUTPUT_SAVED_MOD, OUTPUT_SAVED_OPS from colossalai.fx._compatibility import compatibility from colossalai.fx.profiler import GraphInfo @@ -96,12 +96,12 @@ def node_handler(self, node: Node) -> None: """ Handle other kind of nodes """ - assert hasattr(node, 'best_metainfo'), f"Cannot find best_metainfo in node {node}, {node.op}" + assert hasattr(node, 'best_strategy_info'), f"Cannot find best_strategy_info in node {node}, {node.op}" graph_info = GraphInfo() - meta_info = node.best_metainfo - meta_info: MetaInfo + meta_info = node.best_strategy_info + meta_info: ShardMetaInfo - # set data_ptr for input_tensor in MetaInfo class + # set data_ptr for input_tensor in ShardMetaInfo class input_tensors: List[torch.Tensor] = meta_info.fwd_in buffer_tensors: List[torch.Tensor] = meta_info.fwd_buffer output_tensors: List[torch.Tensor] = meta_info.fwd_out diff --git a/colossalai/auto_parallel/passes/runtime_apply_pass.py b/colossalai/auto_parallel/passes/runtime_apply_pass.py index 9d83f105748b..a473bb6e973d 100644 --- a/colossalai/auto_parallel/passes/runtime_apply_pass.py +++ b/colossalai/auto_parallel/passes/runtime_apply_pass.py @@ -4,7 +4,7 @@ import torch from torch.fx.node import Node -from colossalai.auto_parallel.meta_profiler import MetaInfo +from colossalai._analyzer.fx.node_util import MetaInfo from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( CommAction, CommType, @@ -128,9 +128,10 @@ def _shape_consistency_apply(gm: torch.fx.GraphModule): runtime_apply, args=(node, origin_dict_node, input_dict_node, node_to_index_dict[node], user_node_index)) - if 'activation_checkpoint' in user_node.meta: - shape_consistency_node.meta['activation_checkpoint'] = user_node.meta['activation_checkpoint'] - + if hasattr(user_node.meta['info'], 'activation_checkpoint'): + MetaInfo(shape_consistency_node, + mod_dir=user_node.meta['info'].mod_dir, + activation_checkpoint=tuple(user_node.meta['info'].activation_checkpoint)) new_args = list(user_node.args) new_kwargs = dict(user_node.kwargs) # the origin node may be a positional argument or key word argument of user node @@ -210,9 +211,10 @@ def _comm_spec_apply(gm: torch.fx.GraphModule): # substitute the origin node with comm_spec_apply_node new_kwargs[str(node)] = comm_spec_apply_node user.kwargs = new_kwargs - - if 'activation_checkpoint' in node.meta: - comm_spec_apply_node.meta['activation_checkpoint'] = node.meta['activation_checkpoint'] + if hasattr(node.meta['info'], 'activation_checkpoint'): + MetaInfo(comm_spec_apply_node, + mod_dir=node.meta['info'].mod_dir, + activation_checkpoint=tuple(node.meta['info'].activation_checkpoint)) return gm diff --git a/colossalai/auto_parallel/passes/runtime_preparation_pass.py b/colossalai/auto_parallel/passes/runtime_preparation_pass.py index 3be3084222fe..e1d0c627274e 100644 --- a/colossalai/auto_parallel/passes/runtime_preparation_pass.py +++ b/colossalai/auto_parallel/passes/runtime_preparation_pass.py @@ -6,6 +6,7 @@ from torch.fx import symbolic_trace from torch.fx.node import Node +from colossalai._analyzer.fx.node_util import MetaInfo from colossalai.auto_parallel.tensor_shard.constants import RESHAPE_FUNC_OP from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( CommAction, @@ -74,9 +75,9 @@ def solution_annotatation_pass(gm: torch.fx.GraphModule, solution: List[int], origin_node_sharding_spec_dict[node_index] = strategies_vector[strategy_index].get_sharding_spec_by_name( str(node)) - # attach the corresponding metainfo if node has the attribute `metainfo_vector` - if hasattr(node, 'metainfo_vector'): - setattr(node, 'best_metainfo', node.metainfo_vector[strategy_index]) + # attach the corresponding metainfo if node has the attribute `strategies_info` + if hasattr(node, 'strategies_info'): + setattr(node, 'best_strategy_info', node.strategies_info[strategy_index]) # the dict to get input sharding specs of user node sharding_spec_convert_dict = {} @@ -172,8 +173,11 @@ def _post_processing(node, size_processing_node): # It will be used to replace the original node with processing node in slice object node_pairs[node] = size_processing_node size_processing_node._meta_data = node._meta_data - if 'activation_checkpoint' in node.meta: - size_processing_node.meta['activation_checkpoint'] = node.meta['activation_checkpoint'] + + if hasattr(node.meta['info'], 'activation_checkpoint'): + MetaInfo(size_processing_node, + mod_dir=node.meta['info'].mod_dir, + activation_checkpoint=tuple(node.meta['info'].activation_checkpoint)) user_list = list(node.users.keys()) for user in user_list: diff --git a/colossalai/auto_parallel/tensor_shard/initialize.py b/colossalai/auto_parallel/tensor_shard/initialize.py index 60472eee52ca..b406ca6fb7e0 100644 --- a/colossalai/auto_parallel/tensor_shard/initialize.py +++ b/colossalai/auto_parallel/tensor_shard/initialize.py @@ -6,6 +6,10 @@ from torch.fx import GraphModule from torch.fx.graph import Graph +from colossalai._analyzer.fx.codegen import ActivationCheckpointCodeGen +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass from colossalai.auto_parallel.tensor_shard.options import DataloaderOption, ShardOption, SolverOptions, SolverPerference @@ -13,8 +17,6 @@ from colossalai.auto_parallel.tensor_shard.solver import CostGraph, GraphAnalyser, Solver, StrategiesConstructor from colossalai.device.alpha_beta_profiler import AlphaBetaProfiler from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.graph_module import ColoGraphModule -from colossalai.fx.tracer import ColoTracer from colossalai.tensor.sharding_spec import ShardingSpec @@ -126,6 +128,7 @@ def solve_solution(gm: ColoGraphModule, strategy_constructor: StrategiesConstruc def transform_to_sharded_model(gm: ColoGraphModule, + meta_args: Dict, solution: List[int], device_mesh: DeviceMesh, strategies_constructor: StrategiesConstructor, @@ -142,6 +145,7 @@ def transform_to_sharded_model(gm: ColoGraphModule, strategies_constructor, overlap=overlap) gm = runtime_apply_pass(gm) + shape_prop_pass(gm, *meta_args.values(), sharding_spec_dict, origin_spec_dict, comm_actions_dict) gm.recompile() sharding_spec_dicts = (sharding_spec_dict, origin_spec_dict, comm_actions_dict) @@ -243,10 +247,13 @@ def initialize_model(model: nn.Module, solution will be used to debug or help to analyze the sharding result. Therefore, we will not just return a series of integers, but return the best strategies. ''' - tracer = ColoTracer(trace_act_ckpt=True) + tracer = ColoTracer(trace_act_ckpt=True, bias_addition_split=True) graph = tracer.trace(root=model, meta_args=meta_args) + graph.set_codegen(ActivationCheckpointCodeGen()) gm = ColoGraphModule(model, graph, model.__class__.__name__) + + shape_prop_pass(gm, *meta_args.values()) gm.recompile() strategies_constructor = build_strategy_constructor(graph, @@ -261,7 +268,9 @@ def initialize_model(model: nn.Module, if save_solver_solution: torch.save(solution, solution_path) - gm, sharding_spec_dicts = transform_to_sharded_model(gm, solution, device_mesh, strategies_constructor, overlap) + gm, sharding_spec_dicts = transform_to_sharded_model(gm, meta_args, solution, device_mesh, strategies_constructor, + overlap) + model_to_return = ModuleWrapper(gm, *sharding_spec_dicts) if return_solution: diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/batch_norm_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/batch_norm_handler.py index 57b623b0122c..cb1bb36b7879 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/batch_norm_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/batch_norm_handler.py @@ -2,8 +2,6 @@ import torch -from colossalai.auto_parallel.meta_profiler.metainfo import MetaInfo - from ..sharding_strategy import OperationData, OperationDataType, StrategiesVector from .node_handler import MetaInfoModuleHandler, ModuleHandler from .registry import operator_registry diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py index 136e57c5e0f5..ab391ebfaf80 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py @@ -4,7 +4,7 @@ import torch from torch.fx.node import Node -from colossalai.auto_parallel.meta_profiler.metainfo import MetaInfo, meta_register +from colossalai.auto_parallel.meta_profiler.shard_metainfo import ShardMetaInfo, meta_register from colossalai.auto_parallel.tensor_shard.options import ShardOption, SolverPerference from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( OperationData, @@ -258,7 +258,7 @@ class MetaInfoNodeHandler(NodeHandler): def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesVector: """ This method is inherited from NodeHandler. It will register the strategies first, - and rewrite the memory_cost and compute_cost of the strategy using the MetaInfo class. + and rewrite the memory_cost and compute_cost of the strategy using the ShardMetaInfo class. """ super().register_strategy(compute_resharding_cost=compute_resharding_cost) target = self.get_target_function() @@ -266,15 +266,15 @@ def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesV # is not patched, we will use the default cost model to compute the cost. # TODO: patch all torch functions and modules to make it clean if meta_register.has(target.__class__) or meta_register.has(target): - metainfo_vector = [] + strategies_info = [] for strategy in self.strategies_vector: - metainfo = MetaInfo(strategy, target) + metainfo = ShardMetaInfo(strategy, target) strategy.compute_cost = metainfo.compute_cost strategy.memory_cost = metainfo.memory_cost - metainfo_vector.append(metainfo) + strategies_info.append(metainfo) # attach metainfos to the handler - setattr(self, "metainfo_vector", metainfo_vector) + setattr(self, "strategies_info", strategies_info) else: logger = get_dist_logger() @@ -313,7 +313,7 @@ class MetaInfoModuleHandler(ModuleHandler): def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesVector: """ This method is inherited from NodeHandler. It will register the strategies first, - and rewrite the memory_cost and compute_cost of the strategy using the MetaInfo class. + and rewrite the memory_cost and compute_cost of the strategy using the ShardMetaInfo class. """ super().register_strategy(compute_resharding_cost=compute_resharding_cost) target = self.get_target_function() @@ -321,15 +321,15 @@ def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesV # is not patched, we will use the default cost model to compute the cost. # TODO: patch all torch functions and modules to make it clean if meta_register.has(target.__class__) or meta_register.has(target): - metainfo_vector = [] + strategies_info = [] for strategy in self.strategies_vector: - metainfo = MetaInfo(strategy, target) + metainfo = ShardMetaInfo(strategy, target) strategy.compute_cost = metainfo.compute_cost strategy.memory_cost = metainfo.memory_cost - metainfo_vector.append(metainfo) + strategies_info.append(metainfo) # attach metainfos to the handler - setattr(self, "metainfo_vector", metainfo_vector) + setattr(self, "strategies_info", strategies_info) else: logger = get_dist_logger() diff --git a/colossalai/auto_parallel/tensor_shard/solver/strategies_constructor.py b/colossalai/auto_parallel/tensor_shard/solver/strategies_constructor.py index 59ead1ca8fac..044a8ac847ea 100644 --- a/colossalai/auto_parallel/tensor_shard/solver/strategies_constructor.py +++ b/colossalai/auto_parallel/tensor_shard/solver/strategies_constructor.py @@ -137,9 +137,9 @@ def _check_no_strategy_for_data(data): shard_option=self.solver_options.shard_option, solver_perference=self.solver_options.solver_perference) handler.register_strategy() - # attach metainfo_vector to node - if hasattr(handler, 'metainfo_vector'): - setattr(node, 'metainfo_vector', handler.metainfo_vector) + # attach strategies_info to node + if hasattr(handler, 'strategies_info'): + setattr(node, 'strategies_info', handler.strategies_info) # call_function node elif node.op == 'call_function': @@ -150,9 +150,9 @@ def _check_no_strategy_for_data(data): shard_option=self.solver_options.shard_option, solver_perference=self.solver_options.solver_perference) handler.register_strategy() - # attach metainfo_vector to node - if hasattr(handler, 'metainfo_vector'): - setattr(node, 'metainfo_vector', handler.metainfo_vector) + # attach strategies_info to node + if hasattr(handler, 'strategies_info'): + setattr(node, 'strategies_info', handler.strategies_info) # call_method node elif node.op == 'call_method': @@ -163,9 +163,9 @@ def _check_no_strategy_for_data(data): shard_option=self.solver_options.shard_option, solver_perference=self.solver_options.solver_perference) handler.register_strategy() - # attach metainfo_vector to node - if hasattr(handler, 'metainfo_vector'): - setattr(node, 'metainfo_vector', handler.metainfo_vector) + # attach strategies_info to node + if hasattr(handler, 'strategies_info'): + setattr(node, 'strategies_info', handler.strategies_info) # output node elif node.op == 'output': diff --git a/tests/test_analyzer/__init__.py b/tests/test_analyzer/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/test_auto_parallel/test_pass/test_size_value_converting_pass.py b/tests/test_auto_parallel/test_pass/test_size_value_converting_pass.py index 3494830080ff..7d4fd844ab26 100644 --- a/tests/test_auto_parallel/test_pass/test_size_value_converting_pass.py +++ b/tests/test_auto_parallel/test_pass/test_size_value_converting_pass.py @@ -1,10 +1,12 @@ +import pytest import torch import torch.nn.functional as F +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.passes.runtime_preparation_pass import size_value_converting_pass from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.graph_module import ColoGraphModule -from colossalai.fx.tracer import ColoTracer from colossalai.tensor.sharding_spec import ShardingSpec @@ -33,6 +35,7 @@ def recover_narrow(gm, narrow_node): return gm +@pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') def test_size_value_converting_pass(): model = TestModule() physical_mesh_id = torch.arange(0, 4) @@ -40,14 +43,14 @@ def test_size_value_converting_pass(): device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) meta_args = {'x': torch.rand(4, 8).to('meta')} input = torch.rand(4, 8) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) graph = tracer.trace(root=model, meta_args=meta_args) - x_node = list(graph.nodes)[0] x_sharding_spec = ShardingSpec(device_mesh, entire_shape=(4, 8), dim_partition_dict={0: [0]}) setattr(x_node, 'sharding_spec', x_sharding_spec) gm = ColoGraphModule(model, graph) gm = insert_narrow(gm, x_node) + shape_prop_pass(gm, *meta_args.values()) gm.recompile() size = gm(input) assert size == torch.Size([2, 8]) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py b/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py index f43885a6ac44..6d1b28912c9b 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py @@ -4,7 +4,12 @@ import torch import torch.multiprocessing as mp -from colossalai.auto_parallel.tensor_shard.initialize import initialize_model +try: + from colossalai.auto_parallel.tensor_shard.initialize import initialize_model + NO_CODEGEN = False +except: + NO_CODEGEN = True + from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers @@ -77,6 +82,7 @@ def check_conv_module(rank, world_size, port): @run_on_environment_flag(name='AUTO_PARALLEL') +@pytest.mark.skipif(NO_CODEGEN, reason='No codegen found') @pytest.mark.dist @rerun_if_address_is_in_use() def test_bias_addition_module(): diff --git a/tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py b/tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py index 0b42722fec5f..7a4c8d32ed80 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py @@ -8,13 +8,15 @@ from torch.utils.checkpoint import checkpoint from transformers.pytorch_utils import Conv1D -from colossalai.auto_parallel.tensor_shard.initialize import initialize_model +try: + from colossalai.auto_parallel.tensor_shard.initialize import initialize_model + NO_CODEGEN = False +except: + NO_CODEGEN = True + from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.graph_module import ColoGraphModule -from colossalai.fx.tracer import ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.tensor.shape_consistency import ShapeConsistencyManager from colossalai.testing import rerun_if_address_is_in_use from colossalai.testing.pytest_wrapper import run_on_environment_flag from colossalai.utils import free_port @@ -43,6 +45,7 @@ def check_act_ckpt(rank, world_size, port): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = GPT2MLPWithCkpt(intermediate_size=4 * HIDDEN_SIZE, hidden_size=HIDDEN_SIZE) + input = torch.rand(1, 64, HIDDEN_SIZE) input_sample = { 'hidden_states': torch.rand(1, 64, HIDDEN_SIZE).to('meta'), } @@ -54,10 +57,11 @@ def check_act_ckpt(rank, world_size, port): gm = initialize_model(model, input_sample, device_mesh) code = gm.module.graph.python_code('self').src assert "runtime_comm_spec_apply_1 = colossalai_auto_parallel_passes_runtime_apply_pass_runtime_comm_spec_apply(linear_1, comm_actions_dict, 12, 'linear_1')" in code - assert "view_3 = colossalai.utils.activation_checkpoint.checkpoint(self.checkpoint_0, False, view_1, comm_actions_dict, use_reentrant=True)" in code + assert "view_3 = torch.utils.checkpoint.checkpoint(self.checkpoint_0, view_1, comm_actions_dict, use_reentrant=False)" in code @run_on_environment_flag(name='AUTO_PARALLEL') +@pytest.mark.skipif(NO_CODEGEN, reason='No codegen found') @pytest.mark.dist @rerun_if_address_is_in_use() def test_mlp_layer(): diff --git a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py index e4982a5d7f5a..7c3277c69970 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py @@ -6,7 +6,12 @@ import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP -from colossalai.auto_parallel.tensor_shard.initialize import initialize_model +try: + from colossalai.auto_parallel.tensor_shard.initialize import initialize_model + NO_CODEGEN = False +except: + NO_CODEGEN = True + from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers @@ -93,6 +98,7 @@ def check_compatibility_with_ddp(rank, world_size, port): @run_on_environment_flag(name='AUTO_PARALLEL') +@pytest.mark.skipif(NO_CODEGEN, reason='No codegen found') @pytest.mark.dist @rerun_if_address_is_in_use() def test_compatibility_with_ddp(): diff --git a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py index 9879ae461848..e4435a049f62 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py @@ -6,7 +6,12 @@ import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP -from colossalai.auto_parallel.tensor_shard.initialize import initialize_model +try: + from colossalai.auto_parallel.tensor_shard.initialize import initialize_model + NO_CODEGEN = False +except: + NO_CODEGEN = True + from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers @@ -101,6 +106,7 @@ def check_auto_parallel_with_gemini(rank, world_size, port): @run_on_environment_flag(name='AUTO_PARALLEL') +@pytest.mark.skipif(NO_CODEGEN, reason='No codegen found') @pytest.mark.dist @rerun_if_address_is_in_use() def test_auto_parallel_with_gemini(): diff --git a/tests/test_auto_parallel/test_tensor_shard/test_find_repeat_block.py b/tests/test_auto_parallel/test_tensor_shard/test_find_repeat_block.py index 90301521f207..e7fccad36caf 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_find_repeat_block.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_find_repeat_block.py @@ -5,8 +5,11 @@ from torch.fx import GraphModule from transformers.pytorch_utils import Conv1D +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes import shape_prop_pass +# from colossalai.fx.tracer.tracer import ColoTracer +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.utils.factory import find_repeat_blocks -from colossalai.fx.tracer.tracer import ColoTracer from colossalai.testing import parameterize from colossalai.testing.pytest_wrapper import run_on_environment_flag @@ -83,11 +86,12 @@ def test_repeat_blocks(model_cls): model = model_cls(4 * HIDDEN_DIM, HIDDEN_DIM, NUM_REPEAT_BLOCKS) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) input_sample = {'x': torch.rand(BATCH_SIZE, SEQ_LENGTH, HIDDEN_DIM).to('meta')} graph = tracer.trace(root=model, meta_args=input_sample) gm = GraphModule(model, graph, model.__class__.__name__) + shape_prop_pass(gm, *input_sample.values()) gm.recompile() node_list = list(graph.nodes) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py index ebeef9870fe9..8688890efc93 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py @@ -10,15 +10,23 @@ import transformers from torch.fx import GraphModule -from colossalai.auto_parallel.tensor_shard.initialize import ( - ModuleWrapper, - build_strategy_constructor, - solve_solution, - transform_to_sharded_model, -) +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +# from colossalai.fx.tracer.tracer import ColoTracer +from colossalai._analyzer.fx.tracer.tracer import ColoTracer + +try: + from colossalai.auto_parallel.tensor_shard.initialize import ( + ModuleWrapper, + build_strategy_constructor, + solve_solution, + transform_to_sharded_model, + ) + NO_CODEGEN = False +except: + NO_CODEGEN = True + from colossalai.auto_parallel.tensor_shard.sharding_strategy import ShardingSpec from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.tracer.tracer import ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.tensor.shape_consistency import to_global @@ -52,9 +60,8 @@ def _check_module_grad(module: torch.nn.Module, origin_param_dict: Dict[str, tor param_sharding_spec = best_sharding_spec_dict[new_name] grad_to_compare = copy.deepcopy(param_grad) param_grad_global = to_global(grad_to_compare, param_sharding_spec) - try: - assert_close_loose(param_grad_global, origin_param_grad, rtol=1e-03, atol=1e-03) + assert_close_loose(param_grad_global, origin_param_grad, rtol=1e-03, atol=1e-05) except: difference = param_grad_global - origin_param_grad avg_diff = difference.abs().sum() / difference.numel() @@ -66,7 +73,7 @@ def check_attention_layer(rank, model_cls, world_size, port): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - config = transformers.GPT2Config(n_position=64, n_layer=1, n_head=16, n_embd=HIDDEN_DIM) + config = transformers.GPT2Config(n_position=64, n_layer=2, n_head=16, n_embd=HIDDEN_DIM) if model_cls == GPT2MLP: model = model_cls(intermediate_size=4 * config.hidden_size, config=config).to('cuda') @@ -111,15 +118,17 @@ def check_attention_layer(rank, model_cls, world_size, port): # [[0, 1] # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) graph = tracer.trace(root=model, meta_args=meta_input_sample) gm = GraphModule(model, graph, model.__class__.__name__) + shape_prop_pass(gm, *meta_input_sample.values()) gm.recompile() strategies_constructor = build_strategy_constructor(graph, device_mesh, 'standard', 'replicated', 'standard') solution = solve_solution(gm, strategies_constructor, memory_budget=-1) - gm, sharding_spec_dicts = transform_to_sharded_model(gm, solution, device_mesh, strategies_constructor) + gm, sharding_spec_dicts = transform_to_sharded_model(gm, meta_input_sample, solution, device_mesh, + strategies_constructor) gm = ModuleWrapper(gm, *sharding_spec_dicts) nodes = [strategies_vector.node for strategies_vector in strategies_constructor.leaf_strategies] @@ -176,6 +185,7 @@ def check_attention_layer(rank, model_cls, world_size, port): @run_on_environment_flag(name='AUTO_PARALLEL') +@pytest.mark.skipif(NO_CODEGEN, reason="no codegen module") @pytest.mark.dist @parameterize('model_cls', [GPT2MLP, GPT2Block, GPT2Attention, GPT2Model]) @rerun_if_address_is_in_use() diff --git a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_solver_with_gpt_module.py b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_solver_with_gpt_module.py index 4adb4fbaf047..5f0688d5f5f2 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_solver_with_gpt_module.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_solver_with_gpt_module.py @@ -3,11 +3,12 @@ import transformers from torch.fx import GraphModule +from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.constants import BATCHNORM_MODULE_OP from colossalai.auto_parallel.tensor_shard.options import SolverOptions from colossalai.auto_parallel.tensor_shard.solver import CostGraph, GraphAnalyser, Solver, StrategiesConstructor from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.tracer.tracer import ColoTracer from colossalai.tensor.shape_consistency import ShapeConsistencyManager from colossalai.testing import parameterize from colossalai.testing.pytest_wrapper import run_on_environment_flag @@ -21,7 +22,7 @@ @run_on_environment_flag(name='AUTO_PARALLEL') @parameterize('model_cls', [GPT2Block, GPT2Attention, GPT2MLP, GPT2Model]) def test_self_attention_block(model_cls): - config = transformers.GPT2Config(n_position=64, n_layer=12, n_head=16, n_embd=HIDDEN_DIM) + config = transformers.GPT2Config(n_position=64, n_layer=2, n_head=16, n_embd=HIDDEN_DIM) if model_cls == GPT2MLP: model = model_cls(intermediate_size=4 * config.hidden_size, config=config) else: @@ -33,7 +34,7 @@ def test_self_attention_block(model_cls): device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) shape_consistency_manager = ShapeConsistencyManager() - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) if model_cls == GPT2MLP: input_sample = { 'hidden_states': torch.rand(BATCH_SIZE, SEQ_LENGTH, HIDDEN_DIM).to('meta'), @@ -52,6 +53,7 @@ def test_self_attention_block(model_cls): graph = tracer.trace(root=model, meta_args=input_sample) gm = GraphModule(model, graph, model.__class__.__name__) + shape_prop_pass(gm, *input_sample.values()) print(gm.graph) gm.recompile() solver_options = SolverOptions() diff --git a/tests/test_auto_parallel/test_tensor_shard/test_liveness_analysis.py b/tests/test_auto_parallel/test_tensor_shard/test_liveness_analysis.py index f5de7bf702ff..8d421243827e 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_liveness_analysis.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_liveness_analysis.py @@ -1,8 +1,11 @@ +import pytest import torch import torch.nn as nn +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes import shape_prop_pass +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.solver import GraphAnalyser -from colossalai.fx import ColoGraphModule, ColoTracer class LinearModel(nn.Module): @@ -22,15 +25,14 @@ def forward(self, x1, x2): return out +@pytest.mark.skip('meta tensor has some bugs in 1.11') def test_liveness_analysis(): model = LinearModel() - tracer = ColoTracer() - graph = tracer.trace(model, - meta_args={ - 'x1': torch.rand(4, 4, device='meta'), - 'x2': torch.rand(4, 4, device='meta') - }) + tracer = ColoTracer(bias_addition_split=True) + meta_args = {'x1': torch.rand(4, 4, device='meta'), 'x2': torch.rand(4, 4, device='meta')} + graph = tracer.trace(model, meta_args=meta_args) gm = ColoGraphModule(root=model, graph=graph, class_name=model.__class__.__name__) + shape_prop_pass(gm, *meta_args.values()) graph_analyser = GraphAnalyser(gm) liveness_list = graph_analyser.liveness_analysis() diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_embedding_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_embedding_metainfo.py index 2fb1306546ca..5f3d2df503f6 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_embedding_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_embedding_metainfo.py @@ -24,7 +24,7 @@ from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register + from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations") diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py index e9c0601eb1e4..ddc8e3c6ac02 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py @@ -17,7 +17,7 @@ from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register + from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register class MyModule(nn.Module): diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_matmul_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_matmul_metainfo.py index fd29c63fb522..1242b9db0750 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_matmul_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_matmul_metainfo.py @@ -24,7 +24,7 @@ from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register + from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations") diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py index 9d3ab9c82670..d3342d310157 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py @@ -23,7 +23,7 @@ from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy, print_results if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register + from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register def _batchnorm_module_mem_test(rank, world_size, port): diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py index a0ab66fdc060..a544e9a3cb2f 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py @@ -24,7 +24,7 @@ from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register + from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register class SplitModule(nn.Module): diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py index 20156f9ab4d5..2ae13ea2b1f8 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py @@ -22,7 +22,7 @@ from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register + from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations") diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py index 60ecd1dd9801..4ca85d34da30 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py @@ -5,16 +5,19 @@ import torch from torch.fx import GraphModule +from colossalai._analyzer.fx.graph_module import ColoGraphModule +from colossalai._analyzer.fx.passes import shape_prop_pass +# from colossalai.fx.tracer.tracer import ColoTracer +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass from colossalai.auto_parallel.tensor_shard.options import SolverOptions from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDataType, TrainCycleItem from colossalai.auto_parallel.tensor_shard.solver import StrategiesConstructor from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.tracer.tracer import ColoTracer if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import MetaInfo + from colossalai.auto_parallel.meta_profiler import ShardMetaInfo def mem_test_for_node_strategy(rank: int, @@ -30,14 +33,16 @@ def mem_test_for_node_strategy(rank: int, model_to_shard, args_to_shard, kwargs_to_shard = copy.deepcopy(model), copy.deepcopy(input_args), copy.deepcopy( input_kwargs) - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) input_sample = {} for input_arg, meta_arg_name in zip(input_args, meta_arg_names): input_sample[meta_arg_name] = torch.rand(input_arg.shape).to('meta') for meta_kwarg_name, input_kwarg in input_kwargs.items(): input_sample[meta_kwarg_name] = torch.rand(input_kwarg.shape).to('meta') graph = tracer.trace(root=model_to_shard, meta_args=input_sample) - gm = GraphModule(model_to_shard, graph, model_to_shard.__class__.__name__) + gm = ColoGraphModule(model_to_shard, graph, model_to_shard.__class__.__name__) + shape_prop_pass(gm, *input_sample.values()) + gm.recompile() solver_options = SolverOptions() strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options) strategies_constructor.build_strategies_and_cost() @@ -108,10 +113,10 @@ def mem_test_for_node_strategy(rank: int, # estimated memory if target_node.op == "call_module": - metainfo = MetaInfo(target_node.strategies_vector[strategy_index], - target_node.graph.owning_module.get_submodule(target_node.target)) + metainfo = ShardMetaInfo(target_node.strategies_vector[strategy_index], + target_node.graph.owning_module.get_submodule(target_node.target)) else: - metainfo = MetaInfo(target_node.strategies_vector[strategy_index], target_node.target) + metainfo = ShardMetaInfo(target_node.strategies_vector[strategy_index], target_node.target) print("estimated memory:") print( diff --git a/tests/test_auto_parallel/test_tensor_shard/test_param_resharding_cost.py b/tests/test_auto_parallel/test_tensor_shard/test_param_resharding_cost.py deleted file mode 100644 index 92f011ba30d2..000000000000 --- a/tests/test_auto_parallel/test_tensor_shard/test_param_resharding_cost.py +++ /dev/null @@ -1,126 +0,0 @@ -import torch - -from colossalai.auto_parallel.tensor_shard.options import SolverOptions -from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDataType -from colossalai.auto_parallel.tensor_shard.solver import CostGraph, GraphAnalyser, Solver, StrategiesConstructor -from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer -from colossalai.testing.pytest_wrapper import run_on_environment_flag - - -def _param_resharding_cost_assertion(node): - for strategy in node.strategies_vector: - for prev_node, resharding_cost in strategy.resharding_costs.items(): - if strategy.get_op_data_by_name(str(prev_node)).type == OperationDataType.PARAM: - for cost in resharding_cost: - assert cost.fwd == 0 - assert cost.bwd == 0 - assert cost.total == 0 - - -class LinearModel(torch.nn.Module): - - def __init__(self, in_features, out_features): - super().__init__() - self.linear = torch.nn.Linear(in_features, out_features) - - def forward(self, x): - x = self.linear(x) - x = x * 2 - - return x - - -class ConvModel(torch.nn.Module): - - def __init__(self, in_channels, out_channels, kernel_size, bias=True): - super().__init__() - self.conv = torch.nn.Conv2d(in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - bias=bias) - - def forward(self, x): - x = self.conv(x) - x = x * 2 - - return x - - -@run_on_environment_flag(name='AUTO_PARALLEL') -def test_linear_module(): - model = LinearModel(4, 8) - physical_mesh_id = torch.arange(0, 4) - mesh_shape = (2, 2) - # [[0, 1] - # [2, 3]] - device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - tracer = ColoTracer() - # graph(): - # %x : torch.Tensor [#users=1] = placeholder[target=x] - # %linear_weight : [#users=1] = get_attr[target=linear.weight] - # %linear_bias : [#users=1] = get_attr[target=linear.bias] - # %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%x, %linear_weight), kwargs = {}) - # %add : [#users=1] = call_function[target=operator.add](args = (%linear, %linear_bias), kwargs = {}) - # %mul : [#users=1] = call_function[target=operator.mul](args = (%add, 2), kwargs = {}) - # return mul - graph = tracer.trace(root=model, meta_args={'x': torch.rand(4, 4).to('meta')}) - # def forward(self, x : torch.Tensor): - # linear_weight = self.linear.weight - # linear_bias = self.linear.bias - # linear = torch._C._nn.linear(x, linear_weight); x = linear_weight = None - # add = linear + linear_bias; linear = linear_bias = None - # mul = add * 2; add = None - # return mul - gm = ColoGraphModule(model, graph) - gm.recompile() - node_list = list(graph.nodes) - - solver_options = SolverOptions() - strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options) - strategies_constructor.build_strategies_and_cost() - linear_node = node_list[3] - _param_resharding_cost_assertion(linear_node) - - -@run_on_environment_flag(name='AUTO_PARALLEL') -def test_conv_module(): - model = ConvModel(3, 6, 2) - physical_mesh_id = torch.arange(0, 4) - mesh_shape = (2, 2) - # [[0, 1] - # [2, 3]] - device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - tracer = ColoTracer() - # graph(): - # %x : torch.Tensor [#users=1] = placeholder[target=x] - # %conv_weight : [#users=1] = get_attr[target=conv.weight] - # %conv_bias : [#users=1] = get_attr[target=conv.bias] - # %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%x, %conv_weight), kwargs = {}) - # %view : [#users=1] = call_method[target=view](args = (%conv_bias, [1, -1, 1, 1]), kwargs = {}) - # %add : [#users=1] = call_function[target=operator.add](args = (%conv2d, %view), kwargs = {}) - # %mul : [#users=1] = call_function[target=operator.mul](args = (%add, 2), kwargs = {}) - # return mul - graph = tracer.trace(root=model, meta_args={'x': torch.rand(4, 3, 64, 64).to('meta')}) - # def forward(self, x : torch.Tensor): - # conv_weight = self.conv.weight - # conv_bias = self.conv.bias - # conv2d = torch.conv2d(x, conv_weight); x = conv_weight = None - # view = conv_bias.view([1, -1, 1, 1]); conv_bias = None - # add = conv2d + view; conv2d = view = None - # mul = add * 2; add = None - # return mul - gm = ColoGraphModule(model, graph) - - gm.recompile() - node_list = list(graph.nodes) - conv_node = node_list[3] - solver_options = SolverOptions() - strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options) - strategies_constructor.build_strategies_and_cost() - _param_resharding_cost_assertion(conv_node) - - -if __name__ == '__main__': - test_linear_module() - test_conv_module() diff --git a/tests/test_auto_parallel/test_tensor_shard/test_shape_consistency_pass.py b/tests/test_auto_parallel/test_tensor_shard/test_shape_consistency_pass.py deleted file mode 100644 index 24a3ae5b42c3..000000000000 --- a/tests/test_auto_parallel/test_tensor_shard/test_shape_consistency_pass.py +++ /dev/null @@ -1,86 +0,0 @@ -import copy -from functools import partial - -import pytest -import torch -import torch.multiprocessing as mp -import torch.nn as nn - -from colossalai.auto_parallel.tensor_shard.initialize import initialize_model -from colossalai.device.device_mesh import DeviceMesh -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port - - -class ConvModel(nn.Module): - - def __init__(self, c_in, c_out): - super().__init__() - self.conv = nn.Conv2d(c_in, c_out, kernel_size=3, padding=1, bias=False) - - def forward(self, x): - x = self.conv(x) - x = torch.flatten(x) - return x - - -def check_apply(rank, world_size, port): - disable_existing_loggers() - launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - input = torch.rand(4, 4, 4, 4).cuda() - test_input = copy.deepcopy(input) - # graph(): - # %x : torch.Tensor [#users=1] = placeholder[target=x] - # %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {}) - # return conv - model = ConvModel(4, 4).cuda() - test_model = copy.deepcopy(model) - physical_mesh_id = torch.arange(0, 4) - mesh_shape = (2, 2) - # [[0, 1] - # [2, 3]] - device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - meta_args = {'x': torch.rand(4, 4, 4, 4).to('meta')} - gm = initialize_model(model, meta_args, device_mesh) - - output = gm(input) - origin_output = test_model(test_input) - assert output.equal(origin_output) - origin_loss = origin_output.sum() - loss = output.sum() - - origin_loss.backward() - loss.backward() - - grad_0 = test_model.conv.weight.grad.narrow(0, 0, 1) - grad_1 = test_model.conv.weight.grad.narrow(0, 1, 1) - grad_2 = test_model.conv.weight.grad.narrow(0, 2, 1) - grad_3 = test_model.conv.weight.grad.narrow(0, 3, 1) - - if rank == 0: - assert_close(gm.module.conv.weight.grad.data, grad_0.data) - elif rank == 1: - assert_close(gm.module.conv.weight.grad.data, grad_1.data) - elif rank == 2: - assert_close(gm.module.conv.weight.grad.data, grad_2.data) - elif rank == 3: - assert_close(gm.module.conv.weight.grad.data, grad_3.data) - else: - raise ValueError(f'rank {rank} does not exist.') - - -# skip this test due to pulp not installed in CI environment -@run_on_environment_flag(name='AUTO_PARALLEL') -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_apply(): - world_size = 4 - run_func = partial(check_apply, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_apply() diff --git a/tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py b/tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py index bbfc3e1fcc14..fb47baab9476 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py @@ -2,11 +2,13 @@ from torch.fx import GraphModule from torchvision.models import resnet50 +from colossalai._analyzer.fx.passes import shape_prop_pass +# from colossalai.fx.tracer.tracer import ColoTracer +from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.constants import BATCHNORM_MODULE_OP from colossalai.auto_parallel.tensor_shard.options import SolverOptions from colossalai.auto_parallel.tensor_shard.solver import CostGraph, GraphAnalyser, Solver, StrategiesConstructor from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.tracer.tracer import ColoTracer from colossalai.tensor.shape_consistency import ShapeConsistencyManager from colossalai.testing.pytest_wrapper import run_on_environment_flag @@ -20,7 +22,7 @@ def test_cost_graph(): device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) shape_consistency_manager = ShapeConsistencyManager() - tracer = ColoTracer() + tracer = ColoTracer(bias_addition_split=True) model = resnet50(num_classes=100000) input_sample = {'x': torch.rand(128, 3, 224, 224).to('meta')} @@ -50,6 +52,7 @@ def test_cost_graph(): # %fc : [#users=1] = call_module[target=fc](args = (%flatten,), kwargs = {}) # return fc gm = GraphModule(model, graph, model.__class__.__name__) + shape_prop_pass(gm, *input_sample.values()) gm.recompile() solver_options = SolverOptions() From b92313903f36e863c91b3e9bac621dba31cf52c0 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Wed, 5 Apr 2023 09:45:42 +0800 Subject: [PATCH 099/413] fix save_model indent error in ppo trainer (#3450) Co-authored-by: Yuanchen Xu --- applications/Chat/coati/trainer/ppo.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index 84254d50d7e7..6b99855be20e 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -117,6 +117,9 @@ def training_step(self, experience: Experience) -> Dict[str, float]: return {'reward': experience.reward.mean().item()} + def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer) + def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None: origin_model = strategy._unwrap_actor(actor) @@ -129,7 +132,3 @@ def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, acto new_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn return new_kwargs - - -def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer) From 46c009dba462800b4f7bd54b4558a37e6326726d Mon Sep 17 00:00:00 2001 From: Hakjin Lee Date: Thu, 6 Apr 2023 00:24:43 +0900 Subject: [PATCH 100/413] [format] Run lint on colossalai.engine (#3367) --- .../engine/gradient_accumulation/__init__.py | 15 +++++++++++---- .../gradient_handler/_base_gradient_handler.py | 2 +- .../_data_parallel_gradient_handler.py | 7 ++++--- .../_pipeline_parallel_gradient_handler.py | 7 ++++--- .../_sequence_parallel_gradient_handler.py | 7 ++++--- .../gradient_handler/_zero_gradient_handler.py | 1 + colossalai/engine/schedule/__init__.py | 2 +- colossalai/engine/schedule/_base_schedule.py | 2 +- .../engine/schedule/_non_pipeline_schedule.py | 9 +++++---- 9 files changed, 32 insertions(+), 20 deletions(-) diff --git a/colossalai/engine/gradient_accumulation/__init__.py b/colossalai/engine/gradient_accumulation/__init__.py index 4585b9a2529c..4cb6f4ad7384 100644 --- a/colossalai/engine/gradient_accumulation/__init__.py +++ b/colossalai/engine/gradient_accumulation/__init__.py @@ -1,10 +1,17 @@ +from typing import Iterable, List + import torch.nn as nn -from typing import List -from colossalai.engine import BaseGradientHandler -from typing import Iterable from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler -from ._gradient_accumulation import GradAccumDataloader, GradAccumOptimizer, GradAccumLrSchedulerByStep, GradAccumGradientHandler + +from colossalai.engine import BaseGradientHandler + +from ._gradient_accumulation import ( + GradAccumDataloader, + GradAccumGradientHandler, + GradAccumLrSchedulerByStep, + GradAccumOptimizer, +) __all__ = [ 'accumulate_gradient', 'GradAccumDataloader', 'GradAccumOptimizer', 'GradAccumLrSchedulerByStep', diff --git a/colossalai/engine/gradient_handler/_base_gradient_handler.py b/colossalai/engine/gradient_handler/_base_gradient_handler.py index c212359867d1..7d96dd8a88a6 100644 --- a/colossalai/engine/gradient_handler/_base_gradient_handler.py +++ b/colossalai/engine/gradient_handler/_base_gradient_handler.py @@ -5,7 +5,7 @@ class BaseGradientHandler(ABC): - """A basic helper class to handle all-reduce operations of gradients across different parallel groups + """A basic helper class to handle all-reduce operations of gradients across different parallel groups before optimization. Args: diff --git a/colossalai/engine/gradient_handler/_data_parallel_gradient_handler.py b/colossalai/engine/gradient_handler/_data_parallel_gradient_handler.py index d113fc516459..5cc7169c5a9f 100644 --- a/colossalai/engine/gradient_handler/_data_parallel_gradient_handler.py +++ b/colossalai/engine/gradient_handler/_data_parallel_gradient_handler.py @@ -1,16 +1,17 @@ from colossalai.core import global_context as gpc from colossalai.registry import GRADIENT_HANDLER -from ._base_gradient_handler import BaseGradientHandler + from ...context.parallel_mode import ParallelMode +from ._base_gradient_handler import BaseGradientHandler from .utils import bucket_allreduce @GRADIENT_HANDLER.register_module class DataParallelGradientHandler(BaseGradientHandler): """A helper class to handle all-reduce operations in a data parallel group. - A all-reduce collective communication will be operated in + A all-reduce collective communication will be operated in :func:`handle_gradient` among a data parallel group. - For better performance, it bucketizes the gradients of all parameters that are + For better performance, it bucketizes the gradients of all parameters that are the same type to improve the efficiency of communication. Args: diff --git a/colossalai/engine/gradient_handler/_pipeline_parallel_gradient_handler.py b/colossalai/engine/gradient_handler/_pipeline_parallel_gradient_handler.py index 83f5c00cf2af..5b49a9c0360d 100644 --- a/colossalai/engine/gradient_handler/_pipeline_parallel_gradient_handler.py +++ b/colossalai/engine/gradient_handler/_pipeline_parallel_gradient_handler.py @@ -4,9 +4,10 @@ import torch import torch.distributed as dist +from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors + from colossalai.core import global_context as gpc from colossalai.registry import GRADIENT_HANDLER -from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors from ._base_gradient_handler import BaseGradientHandler @@ -14,9 +15,9 @@ @GRADIENT_HANDLER.register_module class PipelineSharedModuleGradientHandler(BaseGradientHandler): """A helper class to handle all-reduce operations in sub parallel groups. - A all-reduce collective communication will be operated in + A all-reduce collective communication will be operated in :func:`handle_gradient` among all sub pipeline parallel groups. - For better performance, it bucketizes the gradients of all parameters that are + For better performance, it bucketizes the gradients of all parameters that are the same type to improve the efficiency of communication. Args: diff --git a/colossalai/engine/gradient_handler/_sequence_parallel_gradient_handler.py b/colossalai/engine/gradient_handler/_sequence_parallel_gradient_handler.py index 53a8ea935a42..ea4f0fbb1c71 100644 --- a/colossalai/engine/gradient_handler/_sequence_parallel_gradient_handler.py +++ b/colossalai/engine/gradient_handler/_sequence_parallel_gradient_handler.py @@ -1,16 +1,17 @@ from colossalai.core import global_context as gpc from colossalai.registry import GRADIENT_HANDLER -from ._base_gradient_handler import BaseGradientHandler + from ...context.parallel_mode import ParallelMode +from ._base_gradient_handler import BaseGradientHandler from .utils import bucket_allreduce @GRADIENT_HANDLER.register_module class SequenceParallelGradientHandler(BaseGradientHandler): """A helper class to handle all-reduce operations in a data parallel group. - A all-reduce collective communication will be operated in + A all-reduce collective communication will be operated in :func:`handle_gradient` among a data parallel group. - For better performance, it bucketizes the gradients of all parameters that are + For better performance, it bucketizes the gradients of all parameters that are the same type to improve the efficiency of communication. Args: diff --git a/colossalai/engine/gradient_handler/_zero_gradient_handler.py b/colossalai/engine/gradient_handler/_zero_gradient_handler.py index f85303e75184..19fd1e97f86f 100644 --- a/colossalai/engine/gradient_handler/_zero_gradient_handler.py +++ b/colossalai/engine/gradient_handler/_zero_gradient_handler.py @@ -1,4 +1,5 @@ from colossalai.registry import GRADIENT_HANDLER + from ._base_gradient_handler import BaseGradientHandler diff --git a/colossalai/engine/schedule/__init__.py b/colossalai/engine/schedule/__init__.py index 54170286e99b..0f2c039d7057 100644 --- a/colossalai/engine/schedule/__init__.py +++ b/colossalai/engine/schedule/__init__.py @@ -1,5 +1,5 @@ from ._base_schedule import BaseSchedule -from ._pipeline_schedule import PipelineSchedule, InterleavedPipelineSchedule, get_tensor_shape from ._non_pipeline_schedule import NonPipelineSchedule +from ._pipeline_schedule import InterleavedPipelineSchedule, PipelineSchedule, get_tensor_shape __all__ = ['BaseSchedule', 'NonPipelineSchedule', 'PipelineSchedule', 'InterleavedPipelineSchedule', 'get_tensor_shape'] diff --git a/colossalai/engine/schedule/_base_schedule.py b/colossalai/engine/schedule/_base_schedule.py index ba797bad9778..a2d50041127a 100644 --- a/colossalai/engine/schedule/_base_schedule.py +++ b/colossalai/engine/schedule/_base_schedule.py @@ -2,10 +2,10 @@ # -*- encoding: utf-8 -*- from abc import ABC, abstractmethod +from typing import Callable, Iterable import torch -from typing import Iterable, Callable from colossalai.logging import get_dist_logger from colossalai.utils import get_current_device diff --git a/colossalai/engine/schedule/_non_pipeline_schedule.py b/colossalai/engine/schedule/_non_pipeline_schedule.py index c62bfb7d7375..b9239d928a7b 100644 --- a/colossalai/engine/schedule/_non_pipeline_schedule.py +++ b/colossalai/engine/schedule/_non_pipeline_schedule.py @@ -1,13 +1,14 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from typing import Iterable +import inspect +from typing import Callable, Iterable import torch -import inspect -from ._base_schedule import BaseSchedule + from colossalai.utils import conditional_context -from typing import Callable + +from ._base_schedule import BaseSchedule class NonPipelineSchedule(BaseSchedule): From 72cb4dd433aa58bab00e207840f50c18eab7d9b2 Mon Sep 17 00:00:00 2001 From: Camille Zhong <44392324+Camille7777@users.noreply.github.com> Date: Thu, 6 Apr 2023 09:30:28 +0800 Subject: [PATCH 101/413] [Chat] fix the tokenizer "int too big to convert" error in SFT training (#3453) * Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) * Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. * Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci * Update test_ci.sh * Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. * Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) * Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. * Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci * Update test_ci.sh * Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. * update roberta with coati * chat ci update * Revert "chat ci update" This reverts commit 17ae7ae01fa752bd3289fc39069868fde99cf846. * [Chat] fix the tokenizer "int too big to convert" error in SFT training fix the tokenizer error during SFT training using Bloom and OPT --- applications/Chat/coati/dataset/sft_dataset.py | 11 ++++++----- applications/Chat/examples/train_sft.py | 9 ++++++--- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/applications/Chat/coati/dataset/sft_dataset.py b/applications/Chat/coati/dataset/sft_dataset.py index 76ae6b158822..91e38f06daba 100644 --- a/applications/Chat/coati/dataset/sft_dataset.py +++ b/applications/Chat/coati/dataset/sft_dataset.py @@ -78,14 +78,14 @@ def __getitem__(self, idx): # return dict(self.prompts[idx], self.prompts[idx]) -def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: +def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, max_length: int) -> Dict: """Tokenize a list of strings.""" tokenized_list = [ tokenizer( text, return_tensors="pt", padding="longest", - max_length=tokenizer.model_max_length, + max_length=max_length, truncation=True, ) for text in strings ] @@ -105,10 +105,11 @@ def preprocess( sources: Sequence[str], targets: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, + max_length: int, ) -> Dict: """Preprocess the data by tokenizing.""" examples = [s + t for s, t in zip(sources, targets)] - examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)] + examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer, max_length) for strings in (examples, sources)] input_ids = examples_tokenized["input_ids"] labels = copy.deepcopy(input_ids) for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): @@ -119,7 +120,7 @@ def preprocess( class SupervisedDataset(Dataset): """Dataset for supervised fine-tuning.""" - def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, max_datasets_size: int = None): + def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, max_datasets_size: int = None, max_length: int = 512): super(SupervisedDataset, self).__init__() logger.info("Loading data...") list_data_dict = jload(data_path) @@ -138,7 +139,7 @@ def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, targets = [f"{example['output']}{tokenizer.eos_token}" for example in list_data_dict] logger.info("Tokenizing inputs... This may take some time...") - data_dict = preprocess(sources, targets, tokenizer) + data_dict = preprocess(sources, targets, tokenizer, max_length) self.input_ids = data_dict["input_ids"] self.labels = data_dict["labels"] diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index 035d5a1ded1d..c0ac7b177694 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -71,6 +71,7 @@ def train(args): else: raise ValueError(f'Unsupported model "{args.model}"') tokenizer.pad_token = tokenizer.eos_token + max_len = args.max_len if args.model == 'llama': tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model) @@ -99,13 +100,14 @@ def train(args): train_data = load_dataset(args.dataset, 'super_natural_instructions', split='train') eval_data = load_dataset(args.dataset, 'super_natural_instructions', split='test') - train_dataset = SFTDataset(train_data, tokenizer) - eval_dataset = SFTDataset(eval_data, tokenizer) + train_dataset = SFTDataset(train_data, tokenizer, max_len) + eval_dataset = SFTDataset(eval_data, tokenizer, max_len) else: train_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.dataset, - max_datasets_size=args.max_datasets_size) + max_datasets_size=args.max_datasets_size, + max_length=max_len) eval_dataset = None data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) @@ -176,6 +178,7 @@ def train(args): parser.add_argument('--need_optim_ckpt', type=bool, default=False) parser.add_argument('--max_epochs', type=int, default=3) parser.add_argument('--batch_size', type=int, default=4) + parser.add_argument('--max_len', type=int, default=512) parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log") parser.add_argument('--lr', type=float, default=5e-6) From 933048ad3e68f243d0cfeda1e7a8cf1b55a4cf88 Mon Sep 17 00:00:00 2001 From: ver217 Date: Thu, 6 Apr 2023 09:38:25 +0800 Subject: [PATCH 102/413] [test] reorganize zero/gemini tests (#3445) --- tests/test_moe/test_moe_checkpoint.py | 2 +- tests/test_moe/test_moe_colo_init.py | 2 +- tests/test_moe/test_moe_zero_init.py | 2 +- tests/test_moe/test_moe_zero_model.py | 2 +- tests/test_moe/test_moe_zero_optim.py | 2 +- .../update => test_zero/test_gemini}/test_chunk_mgrv2.py | 0 .../update => test_zero/test_gemini}/test_chunkv2.py | 0 .../update => test_zero/test_gemini}/test_fwd_bwd.py | 0 .../update => test_zero/test_gemini}/test_gemini_use_rmt.py | 0 .../update => test_zero/test_gemini}/test_get_torch_model.py | 0 .../update => test_zero/test_gemini}/test_grad_clip.py | 0 .../update => test_zero/test_gemini}/test_inference.py | 0 .../{test_gemini/update => test_zero/test_gemini}/test_optim.py | 0 tests/{ => test_zero}/test_gemini/test_runtime_mem_tracer.py | 0 .../update => test_zero/test_gemini}/test_search.py | 0 .../update => test_zero/test_gemini}/test_zeroddp_state_dict.py | 0 .../test_gemini}/test_zerooptim_state_dict.py | 0 tests/test_zero/{ => test_legacy}/common.py | 0 tests/test_zero/{ => test_legacy}/test_found_inf.py | 2 +- .../test_legacy}/test_gemini_manager.py | 0 tests/test_zero/{ => test_legacy}/test_init_context.py | 0 tests/{test_gemini => test_zero/test_legacy}/test_param_op.py | 0 tests/test_zero/{ => test_legacy}/test_shard_model_v2.py | 0 tests/test_zero/{ => test_legacy}/test_shard_param.py | 2 +- .../{ => test_legacy}/test_sharded_optim_state_dict.py | 0 tests/test_zero/{ => test_legacy}/test_sharded_optim_v2.py | 0 .../{ => test_legacy}/test_sharded_optim_with_sync_bn.py | 0 tests/test_zero/{ => test_legacy}/test_state_dict.py | 1 - tests/test_zero/{ => test_legacy}/test_tensor_utils.py | 0 tests/test_zero/{ => test_legacy}/test_zero_engine.py | 0 .../{low_level_zero => test_low_level}/test_grad_acc.py | 0 .../{low_level_zero => test_low_level}/test_zero1_2.py | 0 .../{low_level_zero => test_low_level}/test_zero_init.py | 0 .../{low_level_zero => test_low_level}/test_zero_tp.py | 0 34 files changed, 7 insertions(+), 8 deletions(-) rename tests/{test_gemini/update => test_zero/test_gemini}/test_chunk_mgrv2.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_chunkv2.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_fwd_bwd.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_gemini_use_rmt.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_get_torch_model.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_grad_clip.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_inference.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_optim.py (100%) rename tests/{ => test_zero}/test_gemini/test_runtime_mem_tracer.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_search.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_zeroddp_state_dict.py (100%) rename tests/{test_gemini/update => test_zero/test_gemini}/test_zerooptim_state_dict.py (100%) rename tests/test_zero/{ => test_legacy}/common.py (100%) rename tests/test_zero/{ => test_legacy}/test_found_inf.py (97%) rename tests/{test_gemini => test_zero/test_legacy}/test_gemini_manager.py (100%) rename tests/test_zero/{ => test_legacy}/test_init_context.py (100%) rename tests/{test_gemini => test_zero/test_legacy}/test_param_op.py (100%) rename tests/test_zero/{ => test_legacy}/test_shard_model_v2.py (100%) rename tests/test_zero/{ => test_legacy}/test_shard_param.py (98%) rename tests/test_zero/{ => test_legacy}/test_sharded_optim_state_dict.py (100%) rename tests/test_zero/{ => test_legacy}/test_sharded_optim_v2.py (100%) rename tests/test_zero/{ => test_legacy}/test_sharded_optim_with_sync_bn.py (100%) rename tests/test_zero/{ => test_legacy}/test_state_dict.py (98%) rename tests/test_zero/{ => test_legacy}/test_tensor_utils.py (100%) rename tests/test_zero/{ => test_legacy}/test_zero_engine.py (100%) rename tests/test_zero/{low_level_zero => test_low_level}/test_grad_acc.py (100%) rename tests/test_zero/{low_level_zero => test_low_level}/test_zero1_2.py (100%) rename tests/test_zero/{low_level_zero => test_low_level}/test_zero_init.py (100%) rename tests/test_zero/{low_level_zero => test_low_level}/test_zero_tp.py (100%) diff --git a/tests/test_moe/test_moe_checkpoint.py b/tests/test_moe/test_moe_checkpoint.py index 5b6fe441112d..d2cff44ad9bd 100644 --- a/tests/test_moe/test_moe_checkpoint.py +++ b/tests/test_moe/test_moe_checkpoint.py @@ -14,7 +14,7 @@ from colossalai.zero import ColoInitContext from tests.test_moe.test_moe_zero_init import MoeModel from tests.test_tensor.common_utils import debug_print -from tests.test_zero.common import CONFIG +from tests.test_zero.test_legacy.common import CONFIG def exam_moe_checkpoint(): diff --git a/tests/test_moe/test_moe_colo_init.py b/tests/test_moe/test_moe_colo_init.py index 23ad1a3dc6a4..4826d87ac044 100644 --- a/tests/test_moe/test_moe_colo_init.py +++ b/tests/test_moe/test_moe_colo_init.py @@ -13,7 +13,7 @@ from colossalai.zero import ColoInitContext from tests.test_moe.test_moe_zero_init import MoeModel from tests.test_tensor.common_utils import debug_print -from tests.test_zero.common import CONFIG +from tests.test_zero.test_legacy.common import CONFIG @parameterize("init_device_type", ['cpu', 'cuda']) diff --git a/tests/test_moe/test_moe_zero_init.py b/tests/test_moe/test_moe_zero_init.py index 5987e31f71c9..18b50eb5c482 100644 --- a/tests/test_moe/test_moe_zero_init.py +++ b/tests/test_moe/test_moe_zero_init.py @@ -14,7 +14,7 @@ from colossalai.utils import free_port, get_current_device from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy -from tests.test_zero.common import CONFIG +from tests.test_zero.test_legacy.common import CONFIG class MoeModel(nn.Module): diff --git a/tests/test_moe/test_moe_zero_model.py b/tests/test_moe/test_moe_zero_model.py index d38f66fef658..49c452938e25 100644 --- a/tests/test_moe/test_moe_zero_model.py +++ b/tests/test_moe/test_moe_zero_model.py @@ -17,7 +17,7 @@ from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_moe.test_moe_zero_init import MoeModel -from tests.test_zero.common import CONFIG, check_grads_padding, run_fwd_bwd +from tests.test_zero.test_legacy.common import CONFIG, check_grads_padding, run_fwd_bwd @parameterize("enable_autocast", [False]) diff --git a/tests/test_moe/test_moe_zero_optim.py b/tests/test_moe/test_moe_zero_optim.py index 7e140bf862f2..b43e52bb4c6a 100644 --- a/tests/test_moe/test_moe_zero_optim.py +++ b/tests/test_moe/test_moe_zero_optim.py @@ -20,7 +20,7 @@ from colossalai.zero.low_level._utils import has_inf_or_nan from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_moe.test_moe_zero_init import MoeModel -from tests.test_zero.common import CONFIG, check_sharded_model_params +from tests.test_zero.test_legacy.common import CONFIG, check_sharded_model_params def _run_step(model, optimizer, data, label, criterion, grad_handler): diff --git a/tests/test_gemini/update/test_chunk_mgrv2.py b/tests/test_zero/test_gemini/test_chunk_mgrv2.py similarity index 100% rename from tests/test_gemini/update/test_chunk_mgrv2.py rename to tests/test_zero/test_gemini/test_chunk_mgrv2.py diff --git a/tests/test_gemini/update/test_chunkv2.py b/tests/test_zero/test_gemini/test_chunkv2.py similarity index 100% rename from tests/test_gemini/update/test_chunkv2.py rename to tests/test_zero/test_gemini/test_chunkv2.py diff --git a/tests/test_gemini/update/test_fwd_bwd.py b/tests/test_zero/test_gemini/test_fwd_bwd.py similarity index 100% rename from tests/test_gemini/update/test_fwd_bwd.py rename to tests/test_zero/test_gemini/test_fwd_bwd.py diff --git a/tests/test_gemini/update/test_gemini_use_rmt.py b/tests/test_zero/test_gemini/test_gemini_use_rmt.py similarity index 100% rename from tests/test_gemini/update/test_gemini_use_rmt.py rename to tests/test_zero/test_gemini/test_gemini_use_rmt.py diff --git a/tests/test_gemini/update/test_get_torch_model.py b/tests/test_zero/test_gemini/test_get_torch_model.py similarity index 100% rename from tests/test_gemini/update/test_get_torch_model.py rename to tests/test_zero/test_gemini/test_get_torch_model.py diff --git a/tests/test_gemini/update/test_grad_clip.py b/tests/test_zero/test_gemini/test_grad_clip.py similarity index 100% rename from tests/test_gemini/update/test_grad_clip.py rename to tests/test_zero/test_gemini/test_grad_clip.py diff --git a/tests/test_gemini/update/test_inference.py b/tests/test_zero/test_gemini/test_inference.py similarity index 100% rename from tests/test_gemini/update/test_inference.py rename to tests/test_zero/test_gemini/test_inference.py diff --git a/tests/test_gemini/update/test_optim.py b/tests/test_zero/test_gemini/test_optim.py similarity index 100% rename from tests/test_gemini/update/test_optim.py rename to tests/test_zero/test_gemini/test_optim.py diff --git a/tests/test_gemini/test_runtime_mem_tracer.py b/tests/test_zero/test_gemini/test_runtime_mem_tracer.py similarity index 100% rename from tests/test_gemini/test_runtime_mem_tracer.py rename to tests/test_zero/test_gemini/test_runtime_mem_tracer.py diff --git a/tests/test_gemini/update/test_search.py b/tests/test_zero/test_gemini/test_search.py similarity index 100% rename from tests/test_gemini/update/test_search.py rename to tests/test_zero/test_gemini/test_search.py diff --git a/tests/test_gemini/update/test_zeroddp_state_dict.py b/tests/test_zero/test_gemini/test_zeroddp_state_dict.py similarity index 100% rename from tests/test_gemini/update/test_zeroddp_state_dict.py rename to tests/test_zero/test_gemini/test_zeroddp_state_dict.py diff --git a/tests/test_gemini/update/test_zerooptim_state_dict.py b/tests/test_zero/test_gemini/test_zerooptim_state_dict.py similarity index 100% rename from tests/test_gemini/update/test_zerooptim_state_dict.py rename to tests/test_zero/test_gemini/test_zerooptim_state_dict.py diff --git a/tests/test_zero/common.py b/tests/test_zero/test_legacy/common.py similarity index 100% rename from tests/test_zero/common.py rename to tests/test_zero/test_legacy/common.py diff --git a/tests/test_zero/test_found_inf.py b/tests/test_zero/test_legacy/test_found_inf.py similarity index 97% rename from tests/test_zero/test_found_inf.py rename to tests/test_zero/test_legacy/test_found_inf.py index 641136718161..03a1a609b672 100644 --- a/tests/test_zero/test_found_inf.py +++ b/tests/test_zero/test_legacy/test_found_inf.py @@ -4,6 +4,7 @@ import torch import torch.multiprocessing as mp from common import CONFIG +from test_sharded_optim_v2 import _run_step import colossalai from colossalai.nn.optimizer import HybridAdam @@ -16,7 +17,6 @@ from colossalai.zero.legacy.sharded_optim import ShardedOptimizerV2 from colossalai.zero.low_level._utils import has_inf_or_nan from tests.components_to_test.registry import non_distributed_component_funcs -from tests.test_zero.test_sharded_optim_v2 import _run_step @parameterize("cpu_offload", [True, False]) diff --git a/tests/test_gemini/test_gemini_manager.py b/tests/test_zero/test_legacy/test_gemini_manager.py similarity index 100% rename from tests/test_gemini/test_gemini_manager.py rename to tests/test_zero/test_legacy/test_gemini_manager.py diff --git a/tests/test_zero/test_init_context.py b/tests/test_zero/test_legacy/test_init_context.py similarity index 100% rename from tests/test_zero/test_init_context.py rename to tests/test_zero/test_legacy/test_init_context.py diff --git a/tests/test_gemini/test_param_op.py b/tests/test_zero/test_legacy/test_param_op.py similarity index 100% rename from tests/test_gemini/test_param_op.py rename to tests/test_zero/test_legacy/test_param_op.py diff --git a/tests/test_zero/test_shard_model_v2.py b/tests/test_zero/test_legacy/test_shard_model_v2.py similarity index 100% rename from tests/test_zero/test_shard_model_v2.py rename to tests/test_zero/test_legacy/test_shard_model_v2.py diff --git a/tests/test_zero/test_shard_param.py b/tests/test_zero/test_legacy/test_shard_param.py similarity index 98% rename from tests/test_zero/test_shard_param.py rename to tests/test_zero/test_legacy/test_shard_param.py index 6085de3c8919..b76648321451 100644 --- a/tests/test_zero/test_shard_param.py +++ b/tests/test_zero/test_legacy/test_shard_param.py @@ -4,6 +4,7 @@ import pytest import torch import torch.multiprocessing as mp +from common import CONFIG, allclose import colossalai from colossalai.testing import parameterize, rerun_if_address_is_in_use @@ -12,7 +13,6 @@ from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy from colossalai.zero.legacy.sharded_param import ShardedTensor from colossalai.zero.legacy.sharded_param.sharded_param import ShardedParamV2 -from tests.test_zero.common import CONFIG, allclose @parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy]) diff --git a/tests/test_zero/test_sharded_optim_state_dict.py b/tests/test_zero/test_legacy/test_sharded_optim_state_dict.py similarity index 100% rename from tests/test_zero/test_sharded_optim_state_dict.py rename to tests/test_zero/test_legacy/test_sharded_optim_state_dict.py diff --git a/tests/test_zero/test_sharded_optim_v2.py b/tests/test_zero/test_legacy/test_sharded_optim_v2.py similarity index 100% rename from tests/test_zero/test_sharded_optim_v2.py rename to tests/test_zero/test_legacy/test_sharded_optim_v2.py diff --git a/tests/test_zero/test_sharded_optim_with_sync_bn.py b/tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py similarity index 100% rename from tests/test_zero/test_sharded_optim_with_sync_bn.py rename to tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py diff --git a/tests/test_zero/test_state_dict.py b/tests/test_zero/test_legacy/test_state_dict.py similarity index 98% rename from tests/test_zero/test_state_dict.py rename to tests/test_zero/test_legacy/test_state_dict.py index c435d9bb1ef7..40d2820d800a 100644 --- a/tests/test_zero/test_state_dict.py +++ b/tests/test_zero/test_legacy/test_state_dict.py @@ -1,7 +1,6 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from copy import deepcopy from functools import partial import pytest diff --git a/tests/test_zero/test_tensor_utils.py b/tests/test_zero/test_legacy/test_tensor_utils.py similarity index 100% rename from tests/test_zero/test_tensor_utils.py rename to tests/test_zero/test_legacy/test_tensor_utils.py diff --git a/tests/test_zero/test_zero_engine.py b/tests/test_zero/test_legacy/test_zero_engine.py similarity index 100% rename from tests/test_zero/test_zero_engine.py rename to tests/test_zero/test_legacy/test_zero_engine.py diff --git a/tests/test_zero/low_level_zero/test_grad_acc.py b/tests/test_zero/test_low_level/test_grad_acc.py similarity index 100% rename from tests/test_zero/low_level_zero/test_grad_acc.py rename to tests/test_zero/test_low_level/test_grad_acc.py diff --git a/tests/test_zero/low_level_zero/test_zero1_2.py b/tests/test_zero/test_low_level/test_zero1_2.py similarity index 100% rename from tests/test_zero/low_level_zero/test_zero1_2.py rename to tests/test_zero/test_low_level/test_zero1_2.py diff --git a/tests/test_zero/low_level_zero/test_zero_init.py b/tests/test_zero/test_low_level/test_zero_init.py similarity index 100% rename from tests/test_zero/low_level_zero/test_zero_init.py rename to tests/test_zero/test_low_level/test_zero_init.py diff --git a/tests/test_zero/low_level_zero/test_zero_tp.py b/tests/test_zero/test_low_level/test_zero_tp.py similarity index 100% rename from tests/test_zero/low_level_zero/test_zero_tp.py rename to tests/test_zero/test_low_level/test_zero_tp.py From 8f740deb5323bf940724911541456a1ab0329179 Mon Sep 17 00:00:00 2001 From: YH <100389977+yhna940@users.noreply.github.com> Date: Thu, 6 Apr 2023 10:43:31 +0900 Subject: [PATCH 103/413] Fix typo (#3448) --- colossalai/tensor/param_op_hook.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/colossalai/tensor/param_op_hook.py b/colossalai/tensor/param_op_hook.py index ed705da0eb0d..9c2e0d4adbf1 100644 --- a/colossalai/tensor/param_op_hook.py +++ b/colossalai/tensor/param_op_hook.py @@ -168,12 +168,12 @@ def _get_grad_args(*args): # if there is no grad tensor, the backward of PreFwdPostBwd can't be triggered arg_zero = args[0] if not isinstance(arg_zero, tuple): - raise NotImplementedError("Some torch function is incompatible because of its complcated inputs.") + raise NotImplementedError("Some torch function is incompatible because of its complicated inputs.") check_grad_flag = False for obj in arg_zero: check_grad_flag |= _is_grad_tensor(obj) if not check_grad_flag: - raise NotImplementedError("Some torch function is incompatible because of its complcated inputs.") + raise NotImplementedError("Some torch function is incompatible because of its complicated inputs.") return arg_zero, args[1:] From 7d8d825681a41df62f9f546d8586c50a3757e3a8 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 6 Apr 2023 09:43:51 +0800 Subject: [PATCH 104/413] [booster] fixed the torch ddp plugin with the new checkpoint api (#3442) --- colossalai/booster/plugin/gemini_plugin.py | 9 +++++---- colossalai/booster/plugin/torch_ddp_plugin.py | 6 +++--- examples/tutorial/new_api/torch_ddp/README.md | 6 +++--- 3 files changed, 11 insertions(+), 10 deletions(-) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 3c6e539ba972..6693b1f44d62 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -13,6 +13,7 @@ from torch.utils.data.distributed import DistributedSampler from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO +from colossalai.checkpoint_io.utils import save_state_dict from colossalai.cluster import DistCoordinator from colossalai.interface import ModelWrapper, OptimizerWrapper from colossalai.tensor.colo_parameter import ColoParameter @@ -83,7 +84,7 @@ def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool = # the model should be unwrapped in self.load_model via ModelWrapper.unwrap return super().load_unsharded_model(model, checkpoint, strict=strict) - def save_unsharded_model(self, model: GeminiDDP, checkpoint: str): + def save_unsharded_model(self, model: GeminiDDP, checkpoint: str, gather_dtensor: bool, use_safetensors: bool): """ Save model to checkpoint but only on master process. """ @@ -91,14 +92,14 @@ def save_unsharded_model(self, model: GeminiDDP, checkpoint: str): # as there is communication when get state dict, this must be called on all processes state_dict = model.state_dict(only_rank_0=True) if self.coordinator.is_master(): - self.save_checkpoint(state_dict, checkpoint) + save_state_dict(state_dict, checkpoint, use_safetensors) - def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str): + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): """ Save optimizer to checkpoint but only on master process. """ # TODO(ver217): optimizer state dict is sharded - super().save_unsharded_optimizer(optimizer, checkpoint) + super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor) def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): """ diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index e2abe11ba143..c5e310c7e769 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -33,20 +33,20 @@ def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool = # the model should be unwrapped in self.load_model via ModelWrapper.unwrap return super().load_unsharded_model(model, checkpoint, strict=strict) - def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool): + def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, use_safetensors: bool): """ Save model to checkpoint but only on master process. """ # the model should be unwrapped in self.load_model via ModelWrapper.unwrap if self.coordinator.is_master(): - super().save_unsharded_model(model, checkpoint) + super().save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors) def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): """ Save optimizer to checkpoint but only on master process. """ if self.coordinator.is_master(): - super().save_unsharded_optimizer(optimizer, checkpoint) + super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor) def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): """ diff --git a/examples/tutorial/new_api/torch_ddp/README.md b/examples/tutorial/new_api/torch_ddp/README.md index 62d5a083d0a1..e120bacb0c84 100644 --- a/examples/tutorial/new_api/torch_ddp/README.md +++ b/examples/tutorial/new_api/torch_ddp/README.md @@ -2,10 +2,10 @@ ## 🚀 Quick Start -This example provides a training script and and evaluation script. The training script provides a an example of training ResNet on CIFAR10 dataset from scratch. +This example provides a training script and an evaluation script. The training script provides an example of training ResNet on CIFAR10 dataset from scratch. - Training Arguments - - `-r, `--resume`: resume from checkpoint file path + - `-r`, `--resume`: resume from checkpoint file path - `-c`, `--checkpoint`: the folder to save checkpoints - `-i`, `--interval`: epoch interval to save checkpoints - `-f`, `--fp16`: use fp16 @@ -41,4 +41,4 @@ Expected accuracy performance will be: | --------- | ------------------------ | --------------------- | --------------------- | | ResNet-18 | 85.85% | 85.03% | 85.12% | -**Note: the baseline is a adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`** +**Note: the baseline is adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`** From 57a3c4db6d5af3f3d46dfcbc52afb55cb5415298 Mon Sep 17 00:00:00 2001 From: kingkingofall <83848390+kingkingofall@users.noreply.github.com> Date: Thu, 6 Apr 2023 10:58:53 +0800 Subject: [PATCH 105/413] [chat]fix readme (#3429) * fix stage 2 fix stage 2 * add torch --- applications/Chat/examples/README.md | 2 +- applications/Chat/inference/README.md | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 49401ec30db5..6c02606eab93 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -57,7 +57,7 @@ You can run the `examples/train_rm.sh` to start a reward model training. You can also use the following cmd to start training a reward model. ``` -torchrun --standalone --nproc_per_node=4 train_reward_model.py +torchrun --standalone --nproc_per_node=4 train_reward_model.py \ --pretrain "/path/to/LLaMa-7B/" \ --model 'llama' \ --strategy colossalai_zero2 \ diff --git a/applications/Chat/inference/README.md b/applications/Chat/inference/README.md index 6c23bc73cd60..434677c98fa5 100644 --- a/applications/Chat/inference/README.md +++ b/applications/Chat/inference/README.md @@ -51,6 +51,7 @@ Please ensure you have downloaded HF-format model weights of LLaMA models. Usage: ```python +import torch from transformers import LlamaForCausalLM USE_8BIT = True # use 8-bit quantization; otherwise, use fp16 From 73afb6359489295eafa95378341b9da866fe6097 Mon Sep 17 00:00:00 2001 From: Dr-Corgi Date: Thu, 6 Apr 2023 11:19:14 +0800 Subject: [PATCH 106/413] [chat]fix save_model(#3377) The function save_model should be a part of PPOTrainer. --- applications/Chat/coati/trainer/ppo.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index 6b99855be20e..5c7c71d20b16 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -116,6 +116,9 @@ def training_step(self, experience: Experience) -> Dict[str, float]: self.critic_optim.zero_grad() return {'reward': experience.reward.mean().item()} + + def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer) def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer) From 62f4e2eb0760ac8bfe28834b061dbc2bda93ade9 Mon Sep 17 00:00:00 2001 From: YY Lin Date: Thu, 6 Apr 2023 11:54:52 +0800 Subject: [PATCH 107/413] [Chat]Add Peft support & fix the ptx bug (#3433) * Update ppo.py Fix the bug of fetching wrong batch data * Add peft model support in SFT and Prompts training In stage-1 and stage-3, the peft model supports are added. So the trained artifacts will be only a small lora additions instead of the whole bunch of files. * Delete test_prompts.txt * Delete test_pretrained.txt * Move the peft stuffs to a community folder. * Move the demo sft to community * delete dirty files * Add instructions to install peft using source * Remove Chinese comments * remove the Chinese comments --- applications/Chat/coati/trainer/ppo.py | 7 +- .../Chat/examples/community/EasyPeftModel.md | 24 ++ .../Chat/examples/community/easy_dataset.py | 242 ++++++++++++++++++ .../Chat/examples/community/easy_models.py | 97 +++++++ .../examples/community/train_peft_prompts.py | 227 ++++++++++++++++ .../Chat/examples/community/train_peft_sft.py | 187 ++++++++++++++ 6 files changed, 781 insertions(+), 3 deletions(-) create mode 100644 applications/Chat/examples/community/EasyPeftModel.md create mode 100644 applications/Chat/examples/community/easy_dataset.py create mode 100644 applications/Chat/examples/community/easy_models.py create mode 100644 applications/Chat/examples/community/train_peft_prompts.py create mode 100644 applications/Chat/examples/community/train_peft_sft.py diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index 5c7c71d20b16..2b0cfcc16f24 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -92,9 +92,10 @@ def training_step(self, experience: Experience) -> Dict[str, float]: # ptx loss if self.ptx_coef != 0: - ptx = next(iter(self.pretrain_dataloader))['input_ids'].to(torch.cuda.current_device()) - label = next(iter(self.pretrain_dataloader))['labels'].to(torch.cuda.current_device())[:, 1:] - attention_mask = next(iter(self.pretrain_dataloader))['attention_mask'].to(torch.cuda.current_device()) + batch = next(iter(self.pretrain_dataloader)) + ptx = batch['input_ids'].to(torch.cuda.current_device()) + label = batch['labels'].to(torch.cuda.current_device())[:, 1:] + attention_mask = batch['attention_mask'].to(torch.cuda.current_device()) ptx_log_probs = self.actor.get_base_model()(ptx, attention_mask=attention_mask)['logits'][..., :-1, :] ptx_loss = self.ptx_loss_fn(ptx_log_probs.view(-1, ptx_log_probs.size(-1)), label.view(-1)) actor_loss = ptx_loss * self.ptx_coef + actor_loss * (1 - self.ptx_coef) diff --git a/applications/Chat/examples/community/EasyPeftModel.md b/applications/Chat/examples/community/EasyPeftModel.md new file mode 100644 index 000000000000..16c4af76b91f --- /dev/null +++ b/applications/Chat/examples/community/EasyPeftModel.md @@ -0,0 +1,24 @@ +# Add Peft support for SFT and Prompts model training + +The orginal implementation just adopts the loralib and merges the layers into the final model. The huggingface peft is a better lora model implementation and can be easily training and distributed. + +Since reward model is relative small, I just keep it as original one. I suggest train full model to get the proper reward/critic model. + +# Prelimenary installation +Since the current pypi peft package(0.2) has some bugs, please install the peft package using source. +``` +git clone https://github.com/huggingface/peft +cd peft +pip install . +``` + +# Usage +For SFT training, just call train_peft_sft.py + +Its arguments are almost identical to train_sft.py instead adding a new eval_dataset if you have a eval_dataset file. The data file is just a plain datafile, please check the format in the easy_dataset.py. + +For stage-3 rlhf training, call train_peft_prompts.py. +Its arguments are almost idential to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported. + +# Dataformat +Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt. \ No newline at end of file diff --git a/applications/Chat/examples/community/easy_dataset.py b/applications/Chat/examples/community/easy_dataset.py new file mode 100644 index 000000000000..15dd9a3ccd1d --- /dev/null +++ b/applications/Chat/examples/community/easy_dataset.py @@ -0,0 +1,242 @@ +import copy +from typing import Dict, Sequence +from datasets import load_dataset +from torch.utils.data import Dataset +from transformers import AutoTokenizer +import torch +from tqdm import tqdm +import json + +from tqdm import tqdm +import json + +IGNORE_INDEX = -100 + + +def _tokenize_fn(strings: Sequence[str], tokenizer: AutoTokenizer,max_length :int = 512) -> Dict: + """Tokenize a list of strings.""" + tokenized_list = [ + tokenizer( + text, + return_tensors="pt", + padding="longest", + max_length=max_length, + truncation=True, + ) for text in strings + ] + input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] + input_ids_lens = labels_lens = [ + tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list + ] + return dict( + input_ids=input_ids, + labels=labels, + input_ids_lens=input_ids_lens, + labels_lens=labels_lens, + ) + + +def preprocess( + sources: Sequence[str], + targets: Sequence[str], + tokenizer: AutoTokenizer, + max_length :int = 512 +) -> Dict: + """Preprocess the data by tokenizing.""" + examples = [s + t for s, t in zip(sources, targets)] + examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer,max_length) for strings in (examples, sources)] + input_ids = examples_tokenized["input_ids"] + labels = copy.deepcopy(input_ids) + for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): + label[:source_len] = IGNORE_INDEX + return dict(input_ids=input_ids, labels=labels) + + +class EasySupervisedDataset(Dataset): + def __init__(self, data_file :str, tokenizer :AutoTokenizer,max_length :int = 512) -> None: + super(EasySupervisedDataset,self).__init__() + with open(data_file,"r",encoding="UTF-8") as f: + all_lines = f.readlines() + #split to source and target ,source the characters before "回答：" including "回答：", target the characters after "回答：" + sources,targets = [],[] + for line in all_lines: + if "回答：" in line: + sep_index = line.index("回答：") + sources.append(line[:sep_index+3]) + targets.append(line[sep_index+3:]+tokenizer.eos_token) + else: + sources.append(line) + targets.append(""+tokenizer.eos_token) + data_dict = preprocess(sources, targets, tokenizer,max_length) + + self.input_ids = data_dict["input_ids"] + self.labels = data_dict["labels"] + self.data_file = data_file + + def __len__(self): + return len(self.input_ids) + + def __getitem__(self, i) -> Dict[str, torch.Tensor]: + return dict(input_ids=self.input_ids[i], labels=self.labels[i]) + + def __repr__(self): + return f"LawSupervisedDataset(data_file={self.data_file}, input_ids_len={len(self.input_ids)}, labels_len={len(self.labels)})" + + def __str__(self): + return f"LawSupervisedDataset(data_file={self.data_file}, input_ids_len={len(self.input_ids)}, labels_len={len(self.labels)})" + +class EasyPromptsDataset(Dataset): + def __init__(self,data_file :str, tokenizer :AutoTokenizer, max_length :int = 96) -> None: + super(EasyPromptsDataset,self).__init__() + with open(data_file,"r",encoding="UTF-8") as f: + all_lines = f.readlines() + all_lines = [line if "回答：" not in line else line[:line.index("回答：")+3] for line in all_lines] + self.prompts = [ + tokenizer(line, + return_tensors='pt', + max_length=max_length, + padding='max_length', + truncation=True)['input_ids'].to(torch.cuda.current_device()).squeeze(0) + for line in tqdm(all_lines) + ] + self.data_file = data_file + def __len__(self): + return len(self.prompts) + + def __getitem__(self, idx): + return self.prompts[idx] + + def __repr__(self): + return f"LawPromptsDataset(data_file={self.data_file}, prompts_len={len(self.prompts)})" + + def __str__(self): + return f"LawPromptsDataset(data_file={self.data_file}, prompts_len={len(self.prompts)})" + + +class EasyRewardDataset(Dataset): + def __init__(self,train_file :str,tokenizer :AutoTokenizer, special_token = None,max_length = 512) -> None: + super(EasyRewardDataset,self).__init__() + self.chosen = [] + self.reject = [] + if special_token is None: + self.end_token = tokenizer.eos_token + else: + self.end_token = special_token + print(self.end_token) + #read all lines in the train_file to a list + with open(train_file,"r",encoding="UTF-8") as f: + all_lines = f.readlines() + for line in tqdm(all_lines): + data = json.loads(line) + prompt = "提问："+data['prompt']+" 回答：" + + chosen = prompt + data['chosen'] + self.end_token + chosen_token = tokenizer(chosen, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + self.chosen.append({ + "input_ids": chosen_token['input_ids'], + "attention_mask": chosen_token['attention_mask'] + }) + + reject = prompt + data['rejected'] + self.end_token + reject_token = tokenizer(reject, + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt") + self.reject.append({ + "input_ids": reject_token['input_ids'], + "attention_mask": reject_token['attention_mask'] + }) + + def __len__(self): + length = len(self.chosen) + return length + + def __getitem__(self, idx): + return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][ + "input_ids"], self.reject[idx]["attention_mask"] + + #python representation of the object and the string representation of the object + def __repr__(self): + return f"LawRewardDataset(chosen_len={len(self.chosen)}, reject_len={len(self.reject)})" + + def __str__(self): + return f"LawRewardDataset(chosen_len={len(self.chosen)}, reject_len={len(self.reject)})" + +''' +Easy SFT just accept a text file which can be read line by line. However the datasest will group texts together to max_length so LLM will learn the texts meaning better. +If individual lines are not related, just set is_group_texts to False. +''' +class EasySFTDataset(Dataset): + + def __init__(self,data_file :str,tokenizer :AutoTokenizer,max_length = 512,is_group_texts = True) -> None: + super().__init__() + #read the data_file line by line + with open(data_file,"r",encoding="UTF-8") as f: + #encode the text data line by line and put raw python list input_ids only to raw_input_ids list + raw_input_ids = [] + for line in f: + encoded_ids = tokenizer.encode(line) + #if the encoded_ids is longer than max_length, then split it into several parts + if len(encoded_ids) > max_length: + for i in range(0,len(encoded_ids),max_length): + raw_input_ids.append(encoded_ids[i:i+max_length]) + else: + raw_input_ids.append(encoded_ids) + + grouped_inpup_ids = [] + current_input_ids = [] + attention_mask = [] + if tokenizer.pad_token_id is None: + tokenizer.pad_token_id = tokenizer.eos_token_id + if is_group_texts: + for input_ids in raw_input_ids: + if len(current_input_ids) + len(input_ids) > max_length: + #pad the current_input_ids to max_length with tokenizer.pad_token_id + padded_length = max_length - len(current_input_ids) + current_input_ids.extend([tokenizer.pad_token_id] * padded_length) + grouped_inpup_ids.append(torch.tensor(current_input_ids,dtype=torch.long)) + attention_mask.append(torch.tensor([1] * (max_length - padded_length) + [0] * padded_length,dtype=torch.long)) + current_input_ids = [] + else: + current_input_ids.extend(input_ids) + if len(current_input_ids) > 0: + padded_length = max_length - len(current_input_ids) + current_input_ids.extend([tokenizer.pad_token_id] * padded_length) + grouped_inpup_ids.append(torch.tensor(current_input_ids,dtype=torch.long)) + attention_mask.append(torch.tensor([1] * (max_length - padded_length) + [0] * padded_length,dtype=torch.long)) + else: + #just append the raw_input_ids to max_length + for input_ids in raw_input_ids: + padded_length = max_length - len(input_ids) + input_ids.extend([tokenizer.pad_token_id] * padded_length) + attention_mask.append(torch.tensor([1] * (max_length - padded_length) + [0] * padded_length,dtype=torch.long)) + grouped_inpup_ids.append(torch.tensor(input_ids,dtype=torch.long)) + self.input_ids = grouped_inpup_ids + self.labels = copy.deepcopy(self.input_ids) + self.file_name = data_file + self.attention_mask = attention_mask + + def __len__(self): + return len(self.input_ids) + + #get item from dataset + def __getitem__(self,idx): + return dict(input_ids=self.input_ids[idx],labels=self.labels[idx],attention_mask=self.attention_mask[idx]) + + #generate the dataset description to be printed by print in python + def __repr__(self): + return f"EasySFTDataset(len={len(self)},\nfile_name is {self.file_name})" + + #generate the dataset description to be printed by print in python + def __str__(self): + return f"EasySFTDataset(len={len(self)},\nfile_name is {self.file_name})" + + + + + \ No newline at end of file diff --git a/applications/Chat/examples/community/easy_models.py b/applications/Chat/examples/community/easy_models.py new file mode 100644 index 000000000000..080fc1802a02 --- /dev/null +++ b/applications/Chat/examples/community/easy_models.py @@ -0,0 +1,97 @@ +from typing import Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules import Module + +from coati.models.generation import generate +from coati.models.utils import log_probs_from_logits,masked_mean +from transformers import BloomConfig,BloomForCausalLM +from peft import PeftModel + +class Actor(Module): + """ + Actor model base class. + + Args: + model (nn.Module): Actor Model. + """ + + def __init__(self, model: nn.Module) -> None: + super().__init__() + self.model = model + + @torch.no_grad() + def generate( + self, + input_ids: torch.Tensor, + return_action_mask: bool = True, + **kwargs + ) -> Union[Tuple[torch.LongTensor, torch.LongTensor], Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor]]: + sequences = generate(self.model, input_ids, **kwargs) + attention_mask = None + pad_token_id = kwargs.get('pad_token_id', None) + if pad_token_id is not None: + attention_mask = sequences.not_equal(pad_token_id).to(dtype=torch.long, device=sequences.device) + if not return_action_mask: + return sequences, attention_mask, None + input_len = input_ids.size(1) + eos_token_id = kwargs.get('eos_token_id', None) + if eos_token_id is None: + action_mask = torch.ones_like(sequences, dtype=torch.bool) + else: + # left padding may be applied, only mask action + action_mask = (sequences[:, input_len:] == eos_token_id).cumsum(dim=-1) == 0 + action_mask = F.pad(action_mask, (1 + input_len, -1), value=True) # include eos token and input + action_mask[:, :input_len] = False + action_mask = action_mask[:, 1:] + return sequences, attention_mask, action_mask[:, -(sequences.size(1) - input_len):] + + def forward(self, + sequences: torch.LongTensor, + num_actions: int, + attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: + """Returns action log probs + """ + output = self.model(sequences, attention_mask=attention_mask) + logits = output['logits'] + log_probs = log_probs_from_logits(logits[:, :-1, :], sequences[:, 1:]) + return log_probs[:, -num_actions:] + + def get_base_model(self): + return self.model + + +class BLOOMActor(Actor): + """ + BLOOM Actor model. + + Args: + pretrained (str): Pretrained model name or path. + config (BloomConfig): Model config. + checkpoint (bool): Enable gradient checkpointing. + lora_rank (int): LoRA rank. + lora_train_bias (str): LoRA bias training mode. + """ + + def __init__(self, + pretrained: str = None, + config: Optional[BloomConfig] = None, + checkpoint: bool = False, + lora_path: str = None) -> None: + if pretrained is not None: + model = BloomForCausalLM.from_pretrained(pretrained) + elif config is not None: + model = BloomForCausalLM(config) + else: + model = BloomForCausalLM(BloomConfig()) + if lora_path is not None: + model = PeftModel.from_pretrained(model,lora_path) + if checkpoint: + model.gradient_checkpointing_enable() + super().__init__(model) + + def print_trainable_parameters(self): + self.get_base_model().print_trainable_parameters() + diff --git a/applications/Chat/examples/community/train_peft_prompts.py b/applications/Chat/examples/community/train_peft_prompts.py new file mode 100644 index 000000000000..b9394c9e4190 --- /dev/null +++ b/applications/Chat/examples/community/train_peft_prompts.py @@ -0,0 +1,227 @@ +import argparse + +import pandas as pd +import torch +import torch.distributed as dist +from coati.dataset import DataCollatorForSupervisedDataset, PromptDataset, SupervisedDataset +from coati.models.bloom import BLOOMRM, BLOOMCritic +from easy_models import BLOOMActor +from coati.models.gpt import GPTRM, GPTActor, GPTCritic +from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM +from coati.models.opt import OPTRM, OPTActor, OPTCritic +from coati.trainer import PPOTrainer +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.utils import prepare_llama_tokenizer_and_embedding +from torch.optim import Adam +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer + +from colossalai.nn.optimizer import HybridAdam +from peft import PeftModel +from easy_dataset import EasyPromptsDataset,EasySupervisedDataset + +def main(args): + # configure strategy + if args.strategy == 'naive': + strategy = NaiveStrategy() + elif args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + if args.rm_path is not None: + state_dict = torch.load(args.rm_path, map_location='cpu') + + # configure model + if args.model == 'bloom': + # initial_model = BLOOMActor(pretrained=args.pretrain) + print('Using peft lora to load Bloom model as inital_model') + initial_model = BLOOMActor(pretrained=args.pretrain,lora_path=args.sft_lora_path) + print('Using peft lora to load Bloom model as initial_model (Done)') + else: + raise ValueError(f'Unsupported actor model "{args.model}"') + + if args.rm_model == None: + rm_model_name = args.model + else: + rm_model_name = args.rm_model + + if rm_model_name == 'gpt2': + reward_model = GPTRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'bloom': + print("load bloom reward model ",args.rm_pretrain) + reward_model = BLOOMRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'opt': + reward_model = OPTRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'llama': + reward_model = LlamaRM(pretrained=args.rm_pretrain) + else: + raise ValueError(f'Unsupported reward model "{rm_model_name}"') + + if args.rm_path is not None: + print('Loading reward model from', args.rm_path) + reward_model.load_state_dict(state_dict) + + if args.strategy != 'colossalai_gemini': + initial_model.to(torch.float16).to(torch.cuda.current_device()) + reward_model.to(torch.float16).to(torch.cuda.current_device()) + + with strategy.model_init_context(): + if args.model == 'bloom': + # actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank) + print('Using peft lora to load Bloom model as Actor') + actor = BLOOMActor(pretrained=args.pretrain,lora_path=args.sft_lora_path) + print('Using peft lora to load Bloom model as Actor (Done)') + else: + raise ValueError(f'Unsupported actor model "{args.model}"') + + if rm_model_name == 'gpt2': + critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + elif rm_model_name == 'bloom': + print("load bloom critic ",args.rm_pretrain," lora_rank ",args.lora_rank," use_action_mask ",True) + critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + print("load bloom critic (Done) ") + elif rm_model_name == 'opt': + critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + elif rm_model_name == 'llama': + critic = LlamaCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) + else: + raise ValueError(f'Unsupported reward model "{rm_model_name}"') + + if args.rm_path is not None: + print('Loading reward model from', args.rm_path) + critic.load_state_dict(state_dict) + del state_dict + + if args.strategy != 'colossalai_gemini': + critic.to(torch.float16).to(torch.cuda.current_device()) + actor.to(torch.float16).to(torch.cuda.current_device()) + + # configure optimizer + if args.strategy.startswith('colossalai'): + actor_optim = HybridAdam(actor.parameters(), lr=1e-7) + critic_optim = HybridAdam(critic.parameters(), lr=1e-7) + else: + actor_optim = Adam(actor.parameters(), lr=1e-7) + critic_optim = Adam(critic.parameters(), lr=1e-7) + + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained(args.rm_pretrain) + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained(args.rm_pretrain) + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained(args.rm_pretrain) + elif args.model == 'llama': + tokenizer = LlamaTokenizer.from_pretrained(args.pretrain) + tokenizer.eos_token = '<\s>' + else: + raise ValueError(f'Unsupported model "{args.model}"') + + if args.model == 'llama': + tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, actor) + else: + tokenizer.pad_token = tokenizer.eos_token + + data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) + + prompt_dataset = EasyPromptsDataset(args.prompt_path,tokenizer) + if dist.is_initialized() and dist.get_world_size() > 1: + prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True) + else: + prompt_sampler = None + prompt_dataloader = DataLoader(prompt_dataset, + shuffle=(prompt_sampler is None), + sampler=prompt_sampler, + batch_size=args.train_batch_size) + + pretrain_dataset = EasySupervisedDataset(args.pretrain_dataset, tokenizer) + if dist.is_initialized() and dist.get_world_size() > 1: + pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True) + else: + pretrain_sampler = None + pretrain_dataloader = DataLoader(pretrain_dataset, + shuffle=(pretrain_sampler is None), + sampler=pretrain_sampler, + batch_size=args.ptx_batch_size, + collate_fn=data_collator) + + def tokenize_fn(texts): + # MUST padding to max length to ensure inputs of all ranks have the same length + # Different length may lead to hang when using gemini, as different generation steps + batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) + return {k: v.to(torch.cuda.current_device()) for k, v in batch.items()} + + (actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim)) + + # configure trainer + trainer = PPOTrainer( + strategy, + actor, + critic, + reward_model, + initial_model, + actor_optim, + critic_optim, + kl_coef=args.kl_coef, + ptx_coef=args.ptx_coef, + max_epochs=args.max_epochs, + train_batch_size=args.train_batch_size, + experience_batch_size=args.experience_batch_size, + tokenizer=tokenize_fn, + max_length=512, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + ) + + trainer.fit(prompt_dataloader=prompt_dataloader, + pretrain_dataloader=pretrain_dataloader, + num_episodes=args.num_episodes, + max_timesteps=args.max_timesteps, + update_timesteps=args.update_timesteps) + + # save model checkpoint after fitting + trainer.save_model(args.save_path, only_rank0=True, tokenizer=tokenizer) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + strategy.save_optimizer(actor_optim, + 'actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--prompt_path', type=str, default=None, help='path to the prompt dataset') + parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset') + parser.add_argument('--strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive', + help='strategy to use') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--sft_lora_path', type=str, default=None) + parser.add_argument('--rm_model', default=None, choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--rm_path', type=str, default=None) + parser.add_argument('--rm_pretrain', type=str, default=None) + parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--num_episodes', type=int, default=10) + parser.add_argument('--max_timesteps', type=int, default=10) + parser.add_argument('--update_timesteps', type=int, default=10) + parser.add_argument('--max_epochs', type=int, default=5) + parser.add_argument('--train_batch_size', type=int, default=2) + parser.add_argument('--ptx_batch_size', type=int, default=1) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + parser.add_argument('--kl_coef', type=float, default=0.1) + parser.add_argument('--ptx_coef', type=float, default=0.9) + args = parser.parse_args() + main(args) diff --git a/applications/Chat/examples/community/train_peft_sft.py b/applications/Chat/examples/community/train_peft_sft.py new file mode 100644 index 000000000000..65d901261bfc --- /dev/null +++ b/applications/Chat/examples/community/train_peft_sft.py @@ -0,0 +1,187 @@ +import argparse +import os + +import loralib as lora +import torch +import torch.distributed as dist +from coati.dataset import DataCollatorForSupervisedDataset, SFTDataset, SupervisedDataset +from coati.models.base import RewardModel +from coati.models.bloom import BLOOMLM +from coati.models.gpt import GPTLM +from coati.models.llama import LlamaLM +from coati.models.opt import OPTLM +from coati.trainer import SFTTrainer +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.utils import prepare_llama_tokenizer_and_embedding +from datasets import load_dataset +from torch.optim import Adam +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from transformers import AutoTokenizer, BloomTokenizerFast,AutoModelForCausalLM +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.logging import get_dist_logger +from colossalai.nn.optimizer import HybridAdam +from colossalai.tensor import ColoParameter + +from torch.utils.data.dataloader import default_collate +from peft import LoraConfig, TaskType,get_peft_model,PeftModel +from easy_dataset import EasyDataset + +def train(args): + # configure strategy + if args.strategy == 'naive': + strategy = NaiveStrategy() + elif args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + # configure model + with strategy.model_init_context(): + print('Warning: currently only bloom is tested, gpt2,llama and opt are not tested') + model = AutoModelForCausalLM.from_pretrained(args.pretrain).to(torch.cuda.current_device()) + #if the args.save_path exists and args.save_path+'/adapter_config.json' exists, we'll load the adapter_config.json + if os.path.exists(args.save_path) and os.path.exists(args.save_path+'/adapter_config.json') \ + and os.path.exists(args.save_path+'/adapter_model.bin'): + print("loading from saved peft model ",args.save_path) + model = PeftModel.from_pretrained(model, args.save_path) + else: + #we'll use peft lora library to do the lora + lora_rank = args.lora_rank if args.lora_rank > 0 else 32 + #config lora with rank of lora_rank + lora_config = LoraConfig(task_type=TaskType.CAUSAL_LM, inference_mode=False, r=lora_rank, lora_alpha=32, lora_dropout=0.1) + model = get_peft_model(model, lora_config) + model.print_trainable_parameters() + + + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + elif args.model == 'llama': + tokenizer = AutoTokenizer.from_pretrained( + args.pretrain, + padding_side="right", + use_fast=False, + ) + tokenizer.eos_token = '<\s>' + else: + raise ValueError(f'Unsupported model "{args.model}"') + tokenizer.pad_token = tokenizer.eos_token + if args.model == 'llama': + tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model) + + if args.strategy == 'colossalai_gemini': + # this is a hack to deal with the resized embedding + # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatiblity + for name, param in model.named_parameters(): + if not isinstance(param, ColoParameter): + sub_module_name = '.'.join(name.split('.')[:-1]) + weight_name = name.split('.')[-1] + sub_module = model.get_submodule(sub_module_name) + setattr(sub_module, weight_name, ColoParameter(param)) + else: + tokenizer.pad_token = tokenizer.eos_token + + # configure optimizer + if args.strategy.startswith('colossalai'): + optim = HybridAdam(model.parameters(), lr=args.lr, clipping_norm=1.0) + else: + optim = Adam(model.parameters(), lr=args.lr) + + logger = get_dist_logger() + logger.set_level('WARNING') + + # configure dataset + law_dataset = EasyDataset(args.dataset,tokenizer=tokenizer,is_group_texts=not args.is_short_text) + train_dataset = law_dataset + print(train_dataset) + eval_dataset = None + if args.eval_dataset is not None: + eval_dataset = EasyDataset(args.eval_dataset,tokenizer=tokenizer,is_group_texts=not args.is_short_text) + data_collator = default_collate + if dist.is_initialized() and dist.get_world_size() > 1: + train_sampler = DistributedSampler(train_dataset, + shuffle=True, + seed=42, + drop_last=True, + rank=dist.get_rank(), + num_replicas=dist.get_world_size()) + if eval_dataset is not None: + eval_sampler = DistributedSampler(eval_dataset, + shuffle=False, + seed=42, + drop_last=False, + rank=dist.get_rank(), + num_replicas=dist.get_world_size()) + else: + train_sampler = None + eval_sampler = None + + train_dataloader = DataLoader(train_dataset, + shuffle=(train_sampler is None), + sampler=train_sampler, + batch_size=args.batch_size, + collate_fn=data_collator, + pin_memory=True) + if eval_dataset is not None: + eval_dataloader = DataLoader(eval_dataset, + shuffle=(eval_sampler is None), + sampler=eval_sampler, + batch_size=args.batch_size, + collate_fn=data_collator, + pin_memory=True) + else: + eval_dataloader = None + + trainer = SFTTrainer(model=model, + strategy=strategy, + optim=optim, + train_dataloader=train_dataloader, + eval_dataloader=eval_dataloader, + batch_size=args.batch_size, + max_epochs=args.max_epochs, + accimulation_steps=args.accimulation_steps) + + trainer.fit(logger=logger, log_interval=args.log_interval) + + # save model checkpoint after fitting on only rank0 + trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + strategy.save_optimizer(trainer.optimizer, + 'rm_optim_checkpoint_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom') + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--dataset', type=str, default=None) + parser.add_argument('--eval_dataset', type=str, default=None) + parser.add_argument('--save_path', type=str, default='output') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--max_epochs', type=int, default=3) + parser.add_argument('--batch_size', type=int, default=4) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log") + parser.add_argument('--lr', type=float, default=5e-6) + parser.add_argument('--accimulation_steps', type=int, default=8) + parser.add_argument('--enable_peft_lora',action='store_true', default=False) + parser.add_argument("--is_short_text",action='store_true', default=False) + args = parser.parse_args() + train(args) From 80eba05b0abc0ce24f02254cbe2c7b8f9ff5d688 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 6 Apr 2023 14:51:35 +0800 Subject: [PATCH 108/413] [test] refactor tests with spawn (#3452) * [test] added spawn decorator * polish code * polish code * polish code * polish code * polish code * polish code --- .github/workflows/build_on_pr.yml | 19 +- applications/Chat/tests/test_checkpoint.py | 8 +- applications/Chat/tests/test_data.py | 8 +- colossalai/cli/benchmark/benchmark.py | 3 +- colossalai/testing/__init__.py | 16 +- colossalai/testing/utils.py | 88 ++++++- colossalai/utils/__init__.py | 2 - colossalai/utils/common.py | 17 -- docs/requirements-doc-test.txt | 1 + docs/source/en/basics/colotensor_concept.md | 7 +- .../zh-Hans/basics/colotensor_concept.md | 7 +- examples/images/vit/test_vit.py | 8 +- .../auto_offload/train_gpt_offload.py | 39 ++-- .../auto_parallel/auto_parallel_with_gpt.py | 7 +- .../auto_parallel/auto_ckpt_batchsize_test.py | 11 +- .../auto_parallel/auto_ckpt_solver_test.py | 6 +- requirements/requirements-test.txt | 1 + tests/test_amp/test_naive_fp16.py | 10 +- tests/test_amp/test_torch_fp16.py | 10 +- .../test_fx/test_bias_addition.py | 3 +- tests/test_analyzer/test_fx/test_mod_dir.py | 11 +- .../test_analyzer/test_fx/test_nested_ckpt.py | 5 +- .../test_analyzer/test_fx/test_shape_prop.py | 4 +- .../test_fx/test_symbolic_profile.py | 4 +- .../test_subclasses/test_aten.py | 5 +- .../test_subclasses/test_flop_tensor.py | 4 +- .../test_subclasses/test_meta_mode.py | 5 +- .../test_C_solver_consistency.py | 10 +- .../test_ckpt_torchvision.py | 17 +- .../test_ckpt_solvers/test_linearize.py | 3 + .../test_offload/test_perf.py | 10 +- .../test_offload/test_solver.py | 23 +- .../test_pass/test_node_converting_pass.py | 2 + .../test_size_value_converting_pass.py | 2 + .../test_bias_addition_forward.py | 12 +- .../test_tensor_shard/test_checkpoint.py | 12 +- .../test_compatibility_with_ddp.py | 10 +- .../test_compatibility_with_gemini.py | 14 +- .../test_find_repeat_block.py | 4 +- .../test_gpt/test_runtime_with_gpt_modules.py | 9 +- .../test_gpt/test_solver_with_gpt_module.py | 6 +- .../test_liveness_analysis.py | 3 + .../test_metainfo/test_activation_metainfo.py | 15 +- .../test_binary_elementwise_metainfo.py | 10 +- .../test_metainfo/test_conv_metainfo.py | 17 +- .../test_metainfo/test_embedding_metainfo.py | 27 +-- .../test_metainfo/test_linear_metainfo.py | 20 +- .../test_metainfo/test_matmul_metainfo.py | 25 +- .../test_metainfo/test_norm_metainfo.py | 22 +- .../test_metainfo/test_pooling_metainfo.py | 15 +- .../test_metainfo/test_tensor_metainfo.py | 22 +- .../test_metainfo/test_where_metainfo.py | 23 +- .../test_node_handler/test_addbmm_handler.py | 39 ++-- .../test_node_handler/test_addmm_handler.py | 17 +- .../test_batch_norm_handler.py | 11 +- .../test_bias_linear_function_node.py | 12 +- .../test_bias_linear_module_node.py | 16 +- .../test_binary_elementwise_handler.py | 39 ++-- .../test_node_handler/test_bmm_handler.py | 14 +- .../test_node_handler/test_conv_handler.py | 19 +- .../test_default_reshape_handler.py | 3 +- .../test_embedding_handler.py | 14 +- .../test_node_handler/test_getattr_handler.py | 2 + .../test_node_handler/test_getitem_handler.py | 13 +- .../test_layer_norm_handler.py | 11 +- .../test_node_handler/test_linear_handler.py | 35 ++- .../test_node_handler/test_matmul_handler.py | 3 +- .../test_norm_pooling_handler.py | 5 +- .../test_node_handler/test_output_handler.py | 4 +- .../test_permute_and_transpose_handler.py | 21 +- .../test_placeholder_handler.py | 4 +- .../test_node_handler/test_shard_option.py | 4 +- .../test_node_handler/test_softmax_handler.py | 17 +- .../test_node_handler/test_split_handler.py | 18 +- .../test_node_handler/test_sum_handler.py | 13 +- .../test_tensor_constructor.py | 3 +- .../test_unary_element_wise_handler.py | 3 +- .../test_node_handler/test_view_handler.py | 14 +- .../test_node_handler/test_where_handler.py | 2 + .../test_solver_with_resnet_v2.py | 3 +- .../benchmark_autochunk_alphafold.py | 2 +- .../test_autochunk_alphafold_utils.py | 2 +- .../test_autochunk_evoformer_block.py | 12 +- .../test_autochunk_evoformer_stack.py | 12 +- .../test_autochunk_extramsa_block.py | 12 +- .../test_autochunk_diffuser_utils.py | 2 +- .../test_autochunk_unet.py | 14 +- .../test_autochunk_gpt.py | 14 +- .../test_autochunk_transformer_utils.py | 2 +- .../test_autochunk_vit/test_autochunk_vit.py | 12 +- .../test_autochunk_vit_utils.py | 2 +- tests/test_booster/test_accelerator.py | 19 +- .../test_mixed_precision/test_fp16_torch.py | 10 +- .../test_plugin/test_gemini_plugin.py | 11 +- .../test_plugin/test_torch_ddp_plugin.py | 10 +- .../test_general_checkpoint_io.py | 4 +- .../test_cluster/test_device_mesh_manager.py | 11 +- .../test_comm/test_boardcast_send_recv_v2.py | 15 +- tests/test_comm/test_comm.py | 12 +- tests/test_comm/test_object_list_p2p.py | 21 +- tests/test_comm/test_object_list_p2p_v2.py | 17 +- tests/test_context/test_hybrid_parallel.py | 22 +- tests/test_data/test_data_parallel_sampler.py | 14 +- .../test_deterministic_dataloader.py | 15 +- .../test_cifar_with_data_pipeline_tensor.py | 21 +- ...test_cifar_with_data_pipeline_tensor_v2.py | 215 +++++++++--------- tests/test_ddp/test_ddp_ignore_params.py | 8 +- tests/test_ddp/test_ddp_state_dict.py | 9 +- tests/test_ddp/test_reducer.py | 17 +- tests/test_device/test_alpha_beta.py | 12 +- tests/test_device/test_extract_alpha_beta.py | 15 +- tests/test_device/test_init_logical_pg.py | 13 +- .../test_search_logical_device_mesh.py | 12 +- tests/test_engine/test_engine.py | 11 +- .../test_engine/test_gradient_accumluation.py | 18 +- .../test_activation_checkpoint_codegen.py | 18 +- ...st_nested_activation_checkpoint_codegen.py | 19 +- .../test_codegen/test_offload_codegen.py | 18 +- tests/test_fx/test_coloproxy.py | 7 +- tests/test_fx/test_comm_size_compute.py | 11 +- tests/test_fx/test_complete_workflow.py | 87 ------- tests/test_fx/test_graph_manipulation.py | 9 +- tests/test_fx/test_meta/test_aten.py | 3 + tests/test_fx/test_meta/test_backward.py | 5 + tests/test_fx/test_meta/test_meta_trace.py | 5 + tests/test_fx/test_meta_info_prop.py | 5 +- tests/test_fx/test_parallel_1d.py | 18 +- tests/test_fx/test_pipeline_passes.py | 16 +- .../test_profiler_meta_info_prop.py | 4 +- .../test_activation_checkpoint_annotation.py | 2 + .../test_tracer/test_bias_addition_module.py | 3 + .../test_fx/test_tracer/test_control_flow.py | 3 + .../test_tracer/test_functional_conv.py | 3 + .../test_hf_model/test_hf_albert.py | 2 + .../test_tracer/test_hf_model/test_hf_bert.py | 2 + .../test_hf_model/test_hf_diffuser.py | 3 + .../test_tracer/test_hf_model/test_hf_gpt.py | 2 + .../test_tracer/test_hf_model/test_hf_opt.py | 2 + .../test_tracer/test_hf_model/test_hf_t5.py | 2 + .../test_tracer/test_patched_module.py | 16 ++ tests/test_fx/test_tracer/test_patched_op.py | 7 +- .../test_timm_model/test_timm_model.py | 2 + .../test_torchaudio_model.py | 2 + .../test_torchrec_model/test_deepfm_model.py | 2 + .../test_torchrec_model/test_dlrm_model.py | 2 + .../test_torchvision_model.py | 2 + tests/test_layers/test_1d/test_1d.py | 10 +- tests/test_layers/test_2d/test_2d.py | 31 +-- tests/test_layers/test_2p5d/test_2p5d.py | 15 +- tests/test_layers/test_3d/test_3d.py | 27 ++- tests/test_layers/test_cache_embedding.py | 38 ++-- .../test_sequence/test_sequence.py | 19 +- tests/test_moe/test_grad_handler.py | 17 +- tests/test_moe/test_kernel.py | 21 +- tests/test_moe/test_moe_checkpoint.py | 10 +- tests/test_moe/test_moe_colo_init.py | 10 +- tests/test_moe/test_moe_group.py | 17 +- tests/test_moe/test_moe_zero_init.py | 10 +- tests/test_moe/test_moe_zero_model.py | 9 +- tests/test_moe/test_moe_zero_optim.py | 10 +- tests/test_ops/test_addmm_tp.py | 18 +- tests/test_ops/test_embedding_bag_tp.py | 14 +- tests/test_ops/test_embedding_tp.py | 16 +- tests/test_ops/test_linear_tp.py | 16 +- tests/test_ops/test_loss_func.py | 100 ++++---- tests/test_ops/test_op.py | 20 +- tests/test_ops/test_view.py | 197 ++++++++-------- tests/test_optimizer/test_cpu_adam.py | 3 +- tests/test_optimizer/test_fused_adam.py | 5 +- .../test_optimizer/test_fused_adam_kernel.py | 3 +- tests/test_optimizer/test_hybrid_adam.py | 3 +- tests/test_optimizer/test_nvme.py | 11 +- tests/test_pipeline/rpc_test_utils.py | 14 +- tests/test_pipeline/test_middleware_1f1b.py | 71 +++--- tests/test_pipeline/test_pipelinable.py | 10 +- .../test_pipeline_process_group.py | 9 +- tests/test_tensor/core/test_dist_spec_mgr.py | 14 +- tests/test_tensor/core/test_tensor.py | 15 +- tests/test_tensor/model/test_gpt2.py | 9 +- tests/test_tensor/model/test_model.py | 12 +- tests/test_tensor/model/test_module_spec.py | 14 +- .../test_tensor/test_colo_checkpoint_tools.py | 88 ++++--- tests/test_tensor/test_comm_spec_apply.py | 11 +- tests/test_tensor/test_context.py | 9 +- .../test_dtensor/test_comm_spec.py | 9 +- .../test_tensor/test_dtensor/test_dtensor.py | 9 +- .../test_dtensor/test_layout_converter.py | 16 +- tests/test_tensor/test_mix_gather.py | 9 +- tests/test_tensor/test_parameter.py | 7 +- .../test_shape_consistency_apply.py | 9 +- tests/test_tensor/test_sharded_linear.py | 9 +- tests/test_tensor/test_tp_with_zero.py | 9 +- tests/test_trainer/test_pipeline/test_p2p.py | 26 ++- .../test_pipeline/test_pipeline_schedule.py | 29 +-- .../test_trainer_with_non_pipe_schedule.py | 13 +- .../test_trainer_with_pipe_schedule.py | 21 +- .../test_activation_checkpointing.py | 9 +- .../test_checkpoint/test_checkpoint_1d.py | 157 +++++++------ .../test_checkpoint/test_checkpoint_2d.py | 157 +++++++------ .../test_checkpoint/test_checkpoint_2p5d.py | 157 +++++++------ .../test_checkpoint/test_checkpoint_3d.py | 157 +++++++------ .../test_checkpoint_io/test_load.py | 18 +- .../test_checkpoint_io/test_merge.py | 31 ++- .../test_checkpoint_io/test_redist.py | 25 +- .../test_checkpoint_io/test_save.py | 26 ++- tests/test_utils/test_colo_checkpoint.py | 13 +- tests/test_utils/test_commons.py | 10 +- tests/test_utils/test_flash_attention.py | 13 +- .../test_lazy_init/test_distribute.py | 9 +- tests/test_utils/test_memory.py | 10 +- .../test_utils/test_norm_gradient_clipping.py | 20 +- .../test_zero_gradient_clippling.py | 9 +- .../test_zero/test_gemini/test_chunk_mgrv2.py | 9 +- tests/test_zero/test_gemini/test_chunkv2.py | 10 +- tests/test_zero/test_gemini/test_fwd_bwd.py | 9 +- .../test_gemini/test_gemini_use_rmt.py | 11 +- .../test_gemini/test_get_torch_model.py | 10 +- tests/test_zero/test_gemini/test_grad_clip.py | 12 +- tests/test_zero/test_gemini/test_inference.py | 8 +- tests/test_zero/test_gemini/test_optim.py | 10 +- .../test_gemini/test_runtime_mem_tracer.py | 2 + tests/test_zero/test_gemini/test_search.py | 11 +- .../test_gemini/test_zeroddp_state_dict.py | 10 +- .../test_gemini/test_zerooptim_state_dict.py | 9 +- tests/test_zero/test_legacy/test_found_inf.py | 9 +- .../test_legacy/test_gemini_manager.py | 2 + .../test_legacy/test_init_context.py | 9 +- tests/test_zero/test_legacy/test_param_op.py | 2 + .../test_legacy/test_shard_model_v2.py | 9 +- .../test_zero/test_legacy/test_shard_param.py | 11 +- .../test_sharded_optim_state_dict.py | 9 +- .../test_legacy/test_sharded_optim_v2.py | 9 +- .../test_sharded_optim_with_sync_bn.py | 10 +- .../test_zero/test_legacy/test_state_dict.py | 7 +- .../test_legacy/test_tensor_utils.py | 9 +- .../test_zero/test_legacy/test_zero_engine.py | 12 +- .../test_zero/test_low_level/test_grad_acc.py | 8 +- .../test_zero/test_low_level/test_zero1_2.py | 9 +- .../test_low_level/test_zero_init.py | 10 +- .../test_zero/test_low_level/test_zero_tp.py | 11 +- 240 files changed, 1721 insertions(+), 2340 deletions(-) delete mode 100644 tests/test_fx/test_complete_workflow.py diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index f595e677394a..e6febeeb4d87 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -8,10 +8,10 @@ jobs: detect: name: Detect file change if: | - github.event.pull_request.draft == false && - github.base_ref == 'main' && - github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && - contains( github.event.pull_request.labels.*.name, 'Run Build and Test') + github.event.pull_request.draft == false && + github.base_ref == 'main' && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && + contains( github.event.pull_request.labels.*.name, 'Run Build and Test') outputs: changedExtenisonFiles: ${{ steps.find-extension-change.outputs.all_changed_files }} anyExtensionFileChanged: ${{ steps.find-extension-change.outputs.any_changed }} @@ -27,10 +27,10 @@ jobs: - name: Locate base commit id: locate-base-sha run: | - curBranch=$(git rev-parse --abbrev-ref HEAD) - commonCommit=$(git merge-base origin/main $curBranch) - echo $commonCommit - echo "baseSHA=$commonCommit" >> $GITHUB_OUTPUT + curBranch=$(git rev-parse --abbrev-ref HEAD) + commonCommit=$(git merge-base origin/main $curBranch) + echo $commonCommit + echo "baseSHA=$commonCommit" >> $GITHUB_OUTPUT - name: Find the changed extension-related files id: find-extension-change @@ -63,7 +63,6 @@ jobs: echo "$file was changed" done - build: name: Build and Test Colossal-AI needs: detect @@ -124,7 +123,7 @@ jobs: - name: Execute Unit Testing if: needs.detect.outputs.anyLibraryFileChanged == 'true' run: | - PYTHONPATH=$PWD pytest --cov=. --cov-report xml tests/ + CURL_CA_BUNDLE="" PYTHONPATH=$PWD pytest --cov=. --cov-report xml tests/ env: DATA: /data/scratch/cifar-10 NCCL_SHM_DISABLE: 1 diff --git a/applications/Chat/tests/test_checkpoint.py b/applications/Chat/tests/test_checkpoint.py index 8c7848525201..4c05a3431699 100644 --- a/applications/Chat/tests/test_checkpoint.py +++ b/applications/Chat/tests/test_checkpoint.py @@ -1,19 +1,16 @@ import os import tempfile from contextlib import nullcontext -from functools import partial import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from coati.models.gpt import GPTActor from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy from transformers.models.gpt2.configuration_gpt2 import GPT2Config from colossalai.nn.optimizer import HybridAdam -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn GPT_CONFIG = GPT2Config(n_embd=128, n_layer=4, n_head=4) @@ -90,8 +87,7 @@ def run_dist(rank, world_size, port, strategy): @pytest.mark.parametrize('strategy', ['ddp', 'colossalai_zero2', 'colossalai_gemini']) @rerun_if_address_is_in_use() def test_checkpoint(world_size, strategy): - run_func = partial(run_dist, world_size=world_size, port=free_port(), strategy=strategy) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size, strategy=strategy) if __name__ == '__main__': diff --git a/applications/Chat/tests/test_data.py b/applications/Chat/tests/test_data.py index 577309a0fceb..2e4d4ceac05f 100644 --- a/applications/Chat/tests/test_data.py +++ b/applications/Chat/tests/test_data.py @@ -1,11 +1,9 @@ import os from copy import deepcopy -from functools import partial import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from coati.experience_maker import NaiveExperienceMaker from coati.models.base import RewardModel from coati.models.gpt import GPTActor, GPTCritic @@ -13,8 +11,7 @@ from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy from transformers.models.gpt2.configuration_gpt2 import GPT2Config -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn GPT_CONFIG = GPT2Config(n_embd=128, n_layer=4, n_head=4) @@ -114,8 +111,7 @@ def run_dist(rank, world_size, port, strategy): @pytest.mark.parametrize('strategy', ['ddp', 'colossalai']) @rerun_if_address_is_in_use() def test_data(world_size, strategy): - run_func = partial(run_dist, world_size=world_size, port=free_port(), strategy=strategy) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size, strategy=strategy) if __name__ == '__main__': diff --git a/colossalai/cli/benchmark/benchmark.py b/colossalai/cli/benchmark/benchmark.py index f40f8f2f995e..97a9f45722dd 100644 --- a/colossalai/cli/benchmark/benchmark.py +++ b/colossalai/cli/benchmark/benchmark.py @@ -10,7 +10,8 @@ from colossalai.context.random import reset_seeds from colossalai.core import global_context as gpc from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.utils import MultiTimer, free_port +from colossalai.testing import free_port +from colossalai.utils import MultiTimer from .models import MLP diff --git a/colossalai/testing/__init__.py b/colossalai/testing/__init__.py index e3dd500dea8e..c53e0f44c7e0 100644 --- a/colossalai/testing/__init__.py +++ b/colossalai/testing/__init__.py @@ -1,7 +1,17 @@ -from .comparison import assert_equal, assert_not_equal, assert_close, assert_close_loose, assert_equal_in_group -from .utils import parameterize, rerun_on_exception, rerun_if_address_is_in_use, skip_if_not_enough_gpus +from .comparison import assert_close, assert_close_loose, assert_equal, assert_equal_in_group, assert_not_equal +from .pytest_wrapper import run_on_environment_flag +from .utils import ( + clear_cache_before_run, + free_port, + parameterize, + rerun_if_address_is_in_use, + rerun_on_exception, + skip_if_not_enough_gpus, + spawn, +) __all__ = [ 'assert_equal', 'assert_not_equal', 'assert_close', 'assert_close_loose', 'assert_equal_in_group', 'parameterize', - 'rerun_on_exception', 'rerun_if_address_is_in_use', 'skip_if_not_enough_gpus' + 'rerun_on_exception', 'rerun_if_address_is_in_use', 'skip_if_not_enough_gpus', 'free_port', 'spawn', + 'clear_cache_before_run', 'run_on_environment_flag' ] diff --git a/colossalai/testing/utils.py b/colossalai/testing/utils.py index 64c1d6e7bcd0..eac83e6d7bd5 100644 --- a/colossalai/testing/utils.py +++ b/colossalai/testing/utils.py @@ -1,8 +1,13 @@ +import gc +import random import re -import torch -from typing import Callable, List, Any +import socket from functools import partial from inspect import signature +from typing import Any, Callable, List + +import torch +import torch.multiprocessing as mp from packaging import version @@ -43,7 +48,7 @@ def say_something(person, msg): # > davis: hello # > davis: bye # > davis: stop - + Args: argument (str): the name of the argument to parameterize values (List[Any]): a list of values to iterate for this argument @@ -85,13 +90,13 @@ def test_method(): def test_method(): print('hey') raise RuntimeError('Address already in use') - + # rerun for infinite times if Runtime error occurs @rerun_on_exception(exception_type=RuntimeError, max_try=None) def test_method(): print('hey') raise RuntimeError('Address already in use') - + # rerun only the exception message is matched with pattern # for infinite times if Runtime error occurs @rerun_on_exception(exception_type=RuntimeError, pattern="^Address.*$") @@ -101,10 +106,10 @@ def test_method(): Args: exception_type (Exception, Optional): The type of exception to detect for rerun - pattern (str, Optional): The pattern to match the exception message. + pattern (str, Optional): The pattern to match the exception message. If the pattern is not None and matches the exception message, the exception will be detected for rerun - max_try (int, Optional): Maximum reruns for this function. The default value is 5. + max_try (int, Optional): Maximum reruns for this function. The default value is 5. If max_try is None, it will rerun foreven if exception keeps occurings """ @@ -202,3 +207,72 @@ def _execute_by_gpu_num(*args, **kwargs): return _execute_by_gpu_num return _wrap_func + + +def free_port() -> int: + """Get a free port on localhost. + + Returns: + int: A free port on localhost. + """ + while True: + port = random.randint(20000, 65000) + try: + with socket.socket() as sock: + sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + sock.bind(("localhost", port)) + return port + except OSError: + continue + + +def spawn(func, nprocs=1, **kwargs): + """ + This function is used to spawn processes for testing. + + Usage: + # must contians arguments rank, world_size, port + def do_something(rank, world_size, port): + ... + + spawn(do_something, nprocs=8) + + # can also pass other arguments + def do_something(rank, world_size, port, arg1, arg2): + ... + + spawn(do_something, nprocs=8, arg1=1, arg2=2) + + Args: + func (Callable): The function to be spawned. + nprocs (int, optional): The number of processes to spawn. Defaults to 1. + """ + port = free_port() + wrapped_func = partial(func, world_size=nprocs, port=port, **kwargs) + mp.spawn(wrapped_func, nprocs=nprocs) + + +def clear_cache_before_run(): + """ + This function is a wrapper to clear CUDA and python cache before executing the function. + + Usage: + @clear_cache_before_run() + def test_something(): + ... + """ + + def _wrap_func(f): + + def _clear_cache(*args, **kwargs): + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + torch.cuda.reset_max_memory_allocated() + torch.cuda.reset_max_memory_cached() + torch.cuda.synchronize() + gc.collect() + f(*args, **kwargs) + + return _clear_cache + + return _wrap_func diff --git a/colossalai/utils/__init__.py b/colossalai/utils/__init__.py index 3f16bd91e5fe..7b2e8480c66c 100644 --- a/colossalai/utils/__init__.py +++ b/colossalai/utils/__init__.py @@ -7,7 +7,6 @@ count_zeros_fp32, disposable, ensure_path_exists, - free_port, is_ddp_ignored, is_dp_rank_0, is_model_parallel_parameter, @@ -37,7 +36,6 @@ __all__ = [ 'checkpoint', - 'free_port', 'print_rank_0', 'sync_model_param', 'is_ddp_ignored', diff --git a/colossalai/utils/common.py b/colossalai/utils/common.py index e15981140be1..95b3b8014af1 100644 --- a/colossalai/utils/common.py +++ b/colossalai/utils/common.py @@ -50,23 +50,6 @@ def ensure_path_exists(filename: str): Path(dirpath).mkdir(parents=True, exist_ok=True) -def free_port() -> int: - """Get a free port on localhost. - - Returns: - int: A free port on localhost. - """ - while True: - port = random.randint(20000, 65000) - try: - with socket.socket() as sock: - sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - sock.bind(("localhost", port)) - return port - except OSError: - continue - - def sync_model_param(model, parallel_mode): r"""Make sure data parameters are consistent during Data Parallel Mode. diff --git a/docs/requirements-doc-test.txt b/docs/requirements-doc-test.txt index 6a6bb3bee9b0..79e04bd5615d 100644 --- a/docs/requirements-doc-test.txt +++ b/docs/requirements-doc-test.txt @@ -4,3 +4,4 @@ packaging tensornvme psutil transformers +pytest diff --git a/docs/source/en/basics/colotensor_concept.md b/docs/source/en/basics/colotensor_concept.md index 2d8acd88dfd4..1b855c03b919 100644 --- a/docs/source/en/basics/colotensor_concept.md +++ b/docs/source/en/basics/colotensor_concept.md @@ -56,12 +56,12 @@ Let's see an example. A ColoTensor is initialized and sharded on 8 GPUs using tp ```python import torch import torch.multiprocessing as mp -from colossalai.utils import free_port, print_rank_0 +from colossalai.utils import print_rank_0 from functools import partial import colossalai from colossalai.tensor import ProcessGroup, ColoTensor, ColoTensorSpec, ShardSpec, ComputeSpec, ComputePattern -from colossalai.utils import free_port +from colossalai.testing import spawn import torch @@ -83,8 +83,7 @@ def run_dist_tests(rank, world_size, port): print_rank_0(f"shape {t1.shape}, {t1.data}") def test_dist_cases(world_size): - run_func = partial(run_dist_tests, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist_tests, world_size) if __name__ == '__main__': test_dist_cases(4) diff --git a/docs/source/zh-Hans/basics/colotensor_concept.md b/docs/source/zh-Hans/basics/colotensor_concept.md index cac5b9a4b40d..d6a332df2e9c 100644 --- a/docs/source/zh-Hans/basics/colotensor_concept.md +++ b/docs/source/zh-Hans/basics/colotensor_concept.md @@ -57,12 +57,12 @@ ColoTensor 包含额外的属性[ColoTensorSpec](https://colossalai.readthedocs. ```python import torch import torch.multiprocessing as mp -from colossalai.utils import free_port, print_rank_0 +from colossalai.utils import print_rank_0 from functools import partial import colossalai from colossalai.tensor import ProcessGroup, ColoTensor, ColoTensorSpec, ShardSpec, ComputeSpec, ComputePattern -from colossalai.utils import free_port +from colossalai.testing import spawn import torch @@ -84,8 +84,7 @@ def run_dist_tests(rank, world_size, port): print_rank_0(f"shape {t1.shape}, {t1.data}") def test_dist_cases(world_size): - run_func = partial(run_dist_tests, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist_tests, world_size) if __name__ == '__main__': test_dist_cases(4) diff --git a/examples/images/vit/test_vit.py b/examples/images/vit/test_vit.py index 6a587e1df96a..c0ae35bca871 100644 --- a/examples/images/vit/test_vit.py +++ b/examples/images/vit/test_vit.py @@ -1,11 +1,9 @@ import os import random -from functools import partial import numpy as np import pytest import torch -import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from vit import get_training_components @@ -15,8 +13,7 @@ from colossalai.core import global_context as gpc from colossalai.nn.parallel.data_parallel import ColoDDP from colossalai.tensor import ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup, ShardSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext @@ -156,8 +153,7 @@ def run_dist(rank, world_size, port, use_ddp): @pytest.mark.parametrize('use_ddp', [False, True]) @rerun_if_address_is_in_use() def test_vit(world_size, use_ddp): - run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size, use_ddp=use_ddp) if __name__ == '__main__': diff --git a/examples/language/gpt/experiments/auto_offload/train_gpt_offload.py b/examples/language/gpt/experiments/auto_offload/train_gpt_offload.py index 729d1ce4456b..89415c23f93c 100644 --- a/examples/language/gpt/experiments/auto_offload/train_gpt_offload.py +++ b/examples/language/gpt/experiments/auto_offload/train_gpt_offload.py @@ -1,20 +1,20 @@ -import time -import pytest import argparse -from functools import partial +import time +import pytest import torch +from model_zoo import GPTLMLoss, get_gpt2_components from torch.utils._pytree import tree_map -import torch.multiprocessing as mp import colossalai -from colossalai.nn.optimizer import HybridAdam -from colossalai.fx.profiler import parameter_size -from colossalai.utils import free_port, get_current_device from colossalai.auto_parallel.offload.amp_optimizer import AMPOptimizer from colossalai.auto_parallel.offload.mem_optimize import memory_optimize from colossalai.auto_parallel.offload.solver import NOT_NVML -from model_zoo import get_gpt2_components, GPTLMLoss +from colossalai.fx.profiler import parameter_size +from colossalai.nn.optimizer import HybridAdam +from colossalai.testing import spawn +from colossalai.utils import get_current_device + def parse_args(): parser = argparse.ArgumentParser() @@ -24,6 +24,7 @@ def parse_args(): parser.add_argument('--memory_budget', type=float, default=16) return parser.parse_args() + @pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed') def train_gpt(args): memory_budget = args.memory_budget * 1024 * 1024 * 1024 @@ -33,13 +34,16 @@ def train_gpt(args): # build model model_builder, data_gen = get_gpt2_components(model_type=model_type, batch_size=batch_size) - label = torch.randint(low=0, high=128, size=(64, 8,), device=get_current_device()) + label = torch.randint(low=0, high=128, size=( + 64, + 8, + ), device=get_current_device()) criterion = GPTLMLoss() start_time = time.time() model = model_builder() model.train() - param_size = parameter_size(model) / 1024 ** 2 / 2 + param_size = parameter_size(model) / 1024**2 / 2 init_time = time.time() - start_time print(f"init_param_size={param_size:.3f} MB | init_model_time={init_time:.3f} s") @@ -74,21 +78,20 @@ def train_gpt(args): torch.cuda.synchronize() exec_time = sum(sorted(time_list)[:5]) / 5 - runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024 ** 2 - runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024 ** 2 + runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024**2 + runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024**2 print(f'solver_type: {solver_type} | model_type: {model_type}') - print( - f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB ' - f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|' - ) + print(f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB ' + f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|') print(time_list) + def run(rank, world_size, port, args): config = {} colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') train_gpt(args) + if __name__ == '__main__': args = parse_args() - run_func = partial(run, world_size=1, port=free_port(), args=args) - mp.spawn(run_func, nprocs=1) + spawn(run, 1, args=args) diff --git a/examples/language/gpt/experiments/auto_parallel/auto_parallel_with_gpt.py b/examples/language/gpt/experiments/auto_parallel/auto_parallel_with_gpt.py index 6ceb7fd87c0a..e331fc8fcf10 100644 --- a/examples/language/gpt/experiments/auto_parallel/auto_parallel_with_gpt.py +++ b/examples/language/gpt/experiments/auto_parallel/auto_parallel_with_gpt.py @@ -1,18 +1,13 @@ from functools import partial from time import time -from typing import Dict, Optional, Tuple, Union import psutil import torch -import torch.multiprocessing as mp -import torch.nn as nn import transformers from gpt_modules import GPT2LMHeadModel, GPTLMLoss -from torch.fx import GraphModule -from colossalai.auto_parallel.tensor_shard.initialize import autoparallelize, initialize_model +from colossalai.auto_parallel.tensor_shard.initialize import autoparallelize from colossalai.core import global_context as gpc -from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch_from_torch from colossalai.logging import disable_existing_loggers, get_dist_logger diff --git a/examples/tutorial/auto_parallel/auto_ckpt_batchsize_test.py b/examples/tutorial/auto_parallel/auto_ckpt_batchsize_test.py index 5decfc695f6f..5a68aae18041 100644 --- a/examples/tutorial/auto_parallel/auto_ckpt_batchsize_test.py +++ b/examples/tutorial/auto_parallel/auto_ckpt_batchsize_test.py @@ -1,19 +1,14 @@ -import time -from argparse import ArgumentParser from copy import deepcopy from functools import partial -import matplotlib.pyplot as plt -import numpy as np import torch -import torch.multiprocessing as mp import torchvision.models as tm from bench_utils import bench, data_gen_resnet import colossalai from colossalai.auto_parallel.checkpoint import CheckpointSolverRotor from colossalai.fx import metainfo_trace, symbolic_trace -from colossalai.utils import free_port +from colossalai.testing import spawn def _benchmark(rank, world_size, port): @@ -50,9 +45,7 @@ def _benchmark(rank, world_size, port): def auto_activation_checkpoint_batchsize_benchmark(): - world_size = 1 - run_func_module = partial(_benchmark, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_benchmark, 1) if __name__ == "__main__": diff --git a/examples/tutorial/auto_parallel/auto_ckpt_solver_test.py b/examples/tutorial/auto_parallel/auto_ckpt_solver_test.py index ab0f2ef661df..aa5c47294a82 100644 --- a/examples/tutorial/auto_parallel/auto_ckpt_solver_test.py +++ b/examples/tutorial/auto_parallel/auto_ckpt_solver_test.py @@ -4,14 +4,13 @@ import matplotlib.pyplot as plt import torch -import torch.multiprocessing as mp import torchvision.models as tm from bench_utils import GPTLMLoss, bench_rotor, data_gen_gpt2, data_gen_resnet, gpt2_medium import colossalai from colossalai.auto_parallel.checkpoint import CheckpointSolverRotor from colossalai.fx import metainfo_trace, symbolic_trace -from colossalai.utils import free_port +from colossalai.testing import spawn def _benchmark(rank, world_size, port, args): @@ -77,8 +76,7 @@ def _benchmark(rank, world_size, port, args): def auto_activation_checkpoint_benchmark(args): world_size = 1 - run_func_module = partial(_benchmark, world_size=world_size, port=free_port(), args=args) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_benchmark, world_size, args=args) if __name__ == "__main__": diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index 05c0e6ac5e5c..82b6173b3517 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -12,3 +12,4 @@ contexttimer einops triton==2.0.0.dev20221202 git+https://github.com/HazyResearch/flash-attention.git@c422fee3776eb3ea24e011ef641fd5fbeb212623#egg=flash_attn +requests==2.27.1 # downgrade to avoid huggingface error https://github.com/huggingface/transformers/issues/17611 diff --git a/tests/test_amp/test_naive_fp16.py b/tests/test_amp/test_naive_fp16.py index c01de469b8f1..6ce4c7f49725 100644 --- a/tests/test_amp/test_naive_fp16.py +++ b/tests/test_amp/test_naive_fp16.py @@ -1,14 +1,11 @@ import copy -from functools import partial import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.amp import convert_to_apex_amp, convert_to_naive_amp -from colossalai.testing import assert_close_loose, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import assert_close_loose, clear_cache_before_run, rerun_if_address_is_in_use, spawn from tests.components_to_test.registry import non_distributed_component_funcs @@ -87,10 +84,9 @@ def run_dist(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() +@clear_cache_before_run() def test_naive_amp(): - world_size = 1 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 1) if __name__ == '__main__': diff --git a/tests/test_amp/test_torch_fp16.py b/tests/test_amp/test_torch_fp16.py index e65dd8cded26..6451aa6264a3 100644 --- a/tests/test_amp/test_torch_fp16.py +++ b/tests/test_amp/test_torch_fp16.py @@ -1,14 +1,11 @@ import copy -from functools import partial import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.amp import convert_to_apex_amp, convert_to_torch_amp -from colossalai.testing import assert_close_loose, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import assert_close_loose, clear_cache_before_run, rerun_if_address_is_in_use, spawn from tests.components_to_test.registry import non_distributed_component_funcs @@ -87,10 +84,9 @@ def run_dist(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() +@clear_cache_before_run() def test_torch_amp(): - world_size = 1 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 1) if __name__ == '__main__': diff --git a/tests/test_analyzer/test_fx/test_bias_addition.py b/tests/test_analyzer/test_fx/test_bias_addition.py index 61951e9a5da9..f7b5eb140f24 100644 --- a/tests/test_analyzer/test_fx/test_bias_addition.py +++ b/tests/test_analyzer/test_fx/test_bias_addition.py @@ -3,7 +3,7 @@ from packaging import version from torch.utils.checkpoint import checkpoint -from colossalai.testing.utils import parameterize +from colossalai.testing.utils import clear_cache_before_run, parameterize try: from colossalai._analyzer.fx import symbolic_trace @@ -81,6 +81,7 @@ def forward(self, x): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() @parameterize("bias", [True, False]) @parameterize("bias_addition_split", [True, False]) @parameterize("shape", [(3, 3, 3), (3, 3, 3, 3)]) diff --git a/tests/test_analyzer/test_fx/test_mod_dir.py b/tests/test_analyzer/test_fx/test_mod_dir.py index 15e0c2ec21c7..f62147b297a2 100644 --- a/tests/test_analyzer/test_fx/test_mod_dir.py +++ b/tests/test_analyzer/test_fx/test_mod_dir.py @@ -1,6 +1,8 @@ import pytest import torch +from colossalai.testing import clear_cache_before_run, parameterize + try: from colossalai._analyzer.fx import symbolic_trace except: @@ -62,9 +64,10 @@ def forward(self, x): @pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') -@pytest.mark.parametrize("bias", [True, False]) -@pytest.mark.parametrize("bias_addition_split", [True, False]) -@pytest.mark.parametrize("shape", [(3, 3, 3), (3, 3, 3, 3)]) +@clear_cache_before_run() +@parameterize("bias", [True, False]) +@parameterize("bias_addition_split", [True, False]) +@parameterize("shape", [(3, 3, 3), (3, 3, 3, 3)]) def test_mod_dir(bias, bias_addition_split, shape): model = AModel(bias=bias) x = torch.rand(shape) @@ -75,4 +78,4 @@ def test_mod_dir(bias, bias_addition_split, shape): if __name__ == '__main__': - test_mod_dir(True, True, (3, 3, 3)) + test_mod_dir(bias=True, bias_addition_split=True, shape=(3, 3, 3)) diff --git a/tests/test_analyzer/test_fx/test_nested_ckpt.py b/tests/test_analyzer/test_fx/test_nested_ckpt.py index c31aab6752f8..bd16f5a4f95d 100644 --- a/tests/test_analyzer/test_fx/test_nested_ckpt.py +++ b/tests/test_analyzer/test_fx/test_nested_ckpt.py @@ -1,7 +1,9 @@ +import pytest import torch import torch.nn as nn from torch.utils.checkpoint import checkpoint -import pytest + +from colossalai.testing import clear_cache_before_run try: from colossalai._analyzer.fx import symbolic_trace @@ -42,6 +44,7 @@ def forward(self, x): @pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') +@clear_cache_before_run() def test_nested_ckpt(): model = MyModule() x = torch.rand(10, 10) diff --git a/tests/test_analyzer/test_fx/test_shape_prop.py b/tests/test_analyzer/test_fx/test_shape_prop.py index 08f4ff2cbd1f..a849feb795e5 100644 --- a/tests/test_analyzer/test_fx/test_shape_prop.py +++ b/tests/test_analyzer/test_fx/test_shape_prop.py @@ -3,7 +3,7 @@ import torchvision.models as tm from packaging import version -from colossalai.testing.utils import parameterize +from colossalai.testing.utils import clear_cache_before_run, parameterize from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models try: @@ -32,6 +32,7 @@ def _check_gm_validity(gm: torch.fx.GraphModule): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() @parameterize('m', tm_models) def test_torchvision_shape_prop(m): with MetaTensorMode(): @@ -46,6 +47,7 @@ def test_torchvision_shape_prop(m): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() @parameterize('m', tmm_models) def test_timm_shape_prop(m): with MetaTensorMode(): diff --git a/tests/test_analyzer/test_fx/test_symbolic_profile.py b/tests/test_analyzer/test_fx/test_symbolic_profile.py index be781599f14b..17deee7a7118 100644 --- a/tests/test_analyzer/test_fx/test_symbolic_profile.py +++ b/tests/test_analyzer/test_fx/test_symbolic_profile.py @@ -3,7 +3,7 @@ import torchvision.models as tm from packaging import version -from colossalai.testing.utils import parameterize +from colossalai.testing.utils import clear_cache_before_run, parameterize from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models try: @@ -19,6 +19,7 @@ def _check_gm_validity(gm: torch.fx.GraphModule): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() @parameterize('m', tm_models) def test_torchvision_profile(m, verbose=False, bias_addition_split=False): with MetaTensorMode(): @@ -33,6 +34,7 @@ def test_torchvision_profile(m, verbose=False, bias_addition_split=False): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() @parameterize('m', tmm_models) def test_timm_profile(m, verbose=False, bias_addition_split=False): with MetaTensorMode(): diff --git a/tests/test_analyzer/test_subclasses/test_aten.py b/tests/test_analyzer/test_subclasses/test_aten.py index 591a8d617580..b7858110ac09 100644 --- a/tests/test_analyzer/test_subclasses/test_aten.py +++ b/tests/test_analyzer/test_subclasses/test_aten.py @@ -1,9 +1,11 @@ from typing import Any, Callable, Union -import pytest +import pytest import torch import torch.nn as nn +from colossalai.testing import clear_cache_before_run + try: from colossalai._analyzer._subclasses import MetaTensor except: @@ -72,6 +74,7 @@ def run_and_compare(f: Union[nn.Module, Callable], x: torch.Tensor, requires_bac @pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12') +@clear_cache_before_run() def test_meta_aten(): for (aten_op, requires_backward), v in registered_meta.items(): for f, x in v: diff --git a/tests/test_analyzer/test_subclasses/test_flop_tensor.py b/tests/test_analyzer/test_subclasses/test_flop_tensor.py index 752836141fe7..da3829e40146 100644 --- a/tests/test_analyzer/test_subclasses/test_flop_tensor.py +++ b/tests/test_analyzer/test_subclasses/test_flop_tensor.py @@ -4,6 +4,7 @@ import torchvision.models as tm from packaging import version +from colossalai.testing import clear_cache_before_run, parameterize from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models try: @@ -39,7 +40,8 @@ def test_flop_count_module(m): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') -@pytest.mark.parametrize('func, args, kwargs', odd_cases) +@clear_cache_before_run() +@parameterize('func, args, kwargs', odd_cases) def test_flop_count_function(func, args, kwargs): rs_fwd, rs_bwd = flop_count(func, *args, **kwargs, verbose=True) assert rs_fwd > 0, f'fwd flop count of {func.__name__} is {rs_fwd}' diff --git a/tests/test_analyzer/test_subclasses/test_meta_mode.py b/tests/test_analyzer/test_subclasses/test_meta_mode.py index 160d411f6c39..d2a0a1b9cfb5 100644 --- a/tests/test_analyzer/test_subclasses/test_meta_mode.py +++ b/tests/test_analyzer/test_subclasses/test_meta_mode.py @@ -3,6 +3,8 @@ import torchvision.models as tm from packaging import version +from colossalai.testing import clear_cache_before_run, parameterize + try: from colossalai._analyzer._subclasses import MetaTensor, MetaTensorMode except: @@ -30,7 +32,8 @@ def run_and_compare(model): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') -@pytest.mark.parametrize('m', tm_models + tmm_models) +@clear_cache_before_run() +@parameterize('m', tm_models + tmm_models) def test_meta_mode_shape(m): run_and_compare(m()) diff --git a/tests/test_auto_parallel/test_ckpt_solvers/test_C_solver_consistency.py b/tests/test_auto_parallel/test_ckpt_solvers/test_C_solver_consistency.py index f8dd0b16b7f6..f184f64b35d0 100644 --- a/tests/test_auto_parallel/test_ckpt_solvers/test_C_solver_consistency.py +++ b/tests/test_auto_parallel/test_ckpt_solvers/test_C_solver_consistency.py @@ -3,7 +3,6 @@ import pytest import torch import torch.fx -import torch.multiprocessing as mp import torchvision.models as tm import colossalai @@ -13,7 +12,7 @@ # from colossalai.fx.passes.algorithms import solver_rotor # from colossalai.fx.passes.algorithms.operation import Sequence from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn if is_compatible_with_meta(): from colossalai.fx.profiler.tensor import MetaTensor @@ -26,8 +25,8 @@ withcodegen = False -def _run_C_solver_consistency_test(rank=0): - colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl') +def _run_C_solver_consistency_test(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') for M, mem_budget in [(tm.resnet50, 4000), (tm.densenet121, 8080)]: model = M() @@ -70,8 +69,9 @@ def _run_C_solver_consistency_test(rank=0): @pytest.mark.skip("TODO(lyl): refactor all tests.") @pytest.mark.skipif(not withcodegen, reason="torch version is less than 1.12.0") +@rerun_if_address_is_in_use() def test_C_solver_consistency(): - mp.spawn(_run_C_solver_consistency_test, nprocs=1) + spawn(_run_C_solver_consistency_test, 1) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_ckpt_solvers/test_ckpt_torchvision.py b/tests/test_auto_parallel/test_ckpt_solvers/test_ckpt_torchvision.py index 89600ea098a9..db268b91d0a0 100644 --- a/tests/test_auto_parallel/test_ckpt_solvers/test_ckpt_torchvision.py +++ b/tests/test_auto_parallel/test_ckpt_solvers/test_ckpt_torchvision.py @@ -4,7 +4,6 @@ import pytest import torch -import torch.multiprocessing as mp import torchvision.models as tm from torch.fx import GraphModule @@ -15,7 +14,7 @@ from colossalai.fx.graph_module import ColoGraphModule # from colossalai.fx.passes.algorithms import chen_greedy, solver_rotor from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn if is_compatible_with_meta(): from colossalai.fx.profiler.tensor import MetaTensor @@ -68,8 +67,8 @@ def check_backward_consistency(m: torch.nn.Module, gm: GraphModule, solver: Call assert _is_all_gradient_close(m, gm), f'Solver {solver} did not work correctly in backward pass on {model_cls}' -def _run_ckpt_solver(rank): - colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl') +def _run_ckpt_solver(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') MODEL_LIST = [tm.densenet121] torch.backends.cudnn.deterministic = True @@ -98,12 +97,13 @@ def _run_ckpt_solver(rank): @pytest.mark.skip("TODO(super-dainiu): refactor all tests.") @pytest.mark.skipif(not with_codegen, reason='torch version is lower than 1.12.0') +@rerun_if_address_is_in_use() def test_ckpt_solver(): - mp.spawn(_run_ckpt_solver, nprocs=1) + spawn(_run_ckpt_solver, 1) -def _run_ckpt_solver_torch11(rank): - colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl') +def _run_ckpt_solver_torch11(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') MODEL_LIST = [tm.densenet121] torch.backends.cudnn.deterministic = True @@ -131,8 +131,9 @@ def _run_ckpt_solver_torch11(rank): @pytest.mark.skipif(with_codegen, reason='torch version is equal to or higher than 1.12.0') @pytest.mark.skip(reason="currently torch11 ColoGraphModule is not done") +@rerun_if_address_is_in_use() def test_ckpt_solver_torch11(): - mp.spawn(_run_ckpt_solver_torch11, nprocs=1) + spawn(_run_ckpt_solver_torch11, 1) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_ckpt_solvers/test_linearize.py b/tests/test_auto_parallel/test_ckpt_solvers/test_linearize.py index 0f90ba0b0989..59880815dc5e 100644 --- a/tests/test_auto_parallel/test_ckpt_solvers/test_linearize.py +++ b/tests/test_auto_parallel/test_ckpt_solvers/test_linearize.py @@ -8,6 +8,7 @@ # from colossalai.fx.passes.algorithms import linearize, solver_rotor # from colossalai.fx.passes.algorithms.operation import (ForwardCheck, ForwardEnable, ForwardNograd, Loss) from colossalai.fx.passes.meta_info_prop import MetaInfoProp +from colossalai.testing import clear_cache_before_run if is_compatible_with_meta(): from colossalai.fx.profiler.tensor import MetaTensor @@ -24,6 +25,7 @@ @pytest.mark.skip(reason='TODO: modify the logger') @pytest.mark.skip("TODO(lyl): refactor all tests.") @pytest.mark.skipif(not with_codegen, reason="torch version is lower than 1.12.0") +@clear_cache_before_run() def test_linearize(): MODEL_DICT = {tm.resnet18: [2100, 3000], tm.densenet121: [8100, 17000]} tracer = ColoTracer() @@ -84,6 +86,7 @@ def test_linearize(): @pytest.mark.skip("TODO(lyl): refactor all tests.") @pytest.mark.skip(reason="torch11 meta tensor not implemented") @pytest.mark.skipif(with_codegen, reason="torch version is equal to or higher than 1.12.0") +@clear_cache_before_run() def test_linearize_torch11(): MODEL_DICT = {tm.resnet18: [2100, 3000], tm.densenet121: [8100, 17000]} tracer = ColoTracer() diff --git a/tests/test_auto_parallel/test_offload/test_perf.py b/tests/test_auto_parallel/test_offload/test_perf.py index c925843fb2b6..80f134fd85d0 100644 --- a/tests/test_auto_parallel/test_offload/test_perf.py +++ b/tests/test_auto_parallel/test_offload/test_perf.py @@ -1,9 +1,7 @@ import time -from functools import partial import pytest import torch -import torch.multiprocessing as mp from torch.utils._pytree import tree_map import colossalai @@ -12,8 +10,8 @@ from colossalai.auto_parallel.offload.solver import NOT_NVML from colossalai.fx.profiler import parameter_size from colossalai.nn.optimizer import HybridAdam -from colossalai.testing import parameterize -from colossalai.utils import free_port, get_current_device +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext, zero_model_wrapper, zero_optim_wrapper from tests.test_auto_parallel.test_offload.model_utils import * from tests.test_tensor.common_utils import set_seed @@ -140,9 +138,9 @@ def run_dist(rank, world_size, port): @pytest.mark.skip("this test failed") @pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed') +@rerun_if_address_is_in_use() def test_perf(): - run_func = partial(run_dist, world_size=1, port=free_port()) - mp.spawn(run_func, nprocs=1) + spawn(run_dist, 1) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_offload/test_solver.py b/tests/test_auto_parallel/test_offload/test_solver.py index 2efbb750f80d..aa2c9a36849f 100644 --- a/tests/test_auto_parallel/test_offload/test_solver.py +++ b/tests/test_auto_parallel/test_offload/test_solver.py @@ -3,20 +3,20 @@ from torch.fx import GraphModule from torch.utils._pytree import tree_map +from colossalai.auto_parallel.offload.region_manager import RegionManager +from colossalai.auto_parallel.offload.solver import NOT_NVML, SolverFactory from colossalai.fx import ColoTracer, is_compatible_with_meta from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.auto_parallel.offload.region_manager import RegionManager -from colossalai.auto_parallel.offload.solver import SolverFactory, NOT_NVML -from colossalai.testing import parameterize +from colossalai.testing import clear_cache_before_run, parameterize from tests.test_auto_parallel.test_offload.model_utils import * + @pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed') +@clear_cache_before_run() @parameterize('model_name', ['gpt2_', 'bert_']) @parameterize('memory_budget', [4000]) @parameterize('solver_name', ['syn', 'asyn']) -def solver_test(model_name: str, - memory_budget: float, - solver_name: str): +def solver_test(model_name: str, memory_budget: float, solver_name: str): get_components_func = non_distributed_component_funcs.get_callable(model_name) model_builder, data_gen = get_components_func() @@ -52,11 +52,16 @@ def solver_test(model_name: str, for region in region_list: need_offload = region.need_offload to_prefetch = region.fwd_prefetch_region.r_id if region.fwd_prefetch_region is not None else None - print(f'| {model_name} forward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}') + print( + f'| {model_name} forward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}' + ) for region in region_list.__reversed__(): need_offload = region.need_offload to_prefetch = region.bwd_prefetch_region.r_id if region.bwd_prefetch_region is not None else None - print(f'| {model_name} backward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}') + print( + f'| {model_name} backward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}' + ) + if __name__ == '__main__': - solver_test() \ No newline at end of file + solver_test() diff --git a/tests/test_auto_parallel/test_pass/test_node_converting_pass.py b/tests/test_auto_parallel/test_pass/test_node_converting_pass.py index d0d107610f7a..429e89aae5d3 100644 --- a/tests/test_auto_parallel/test_pass/test_node_converting_pass.py +++ b/tests/test_auto_parallel/test_pass/test_node_converting_pass.py @@ -6,6 +6,7 @@ from colossalai.fx.graph_module import ColoGraphModule from colossalai.fx.tracer import ColoTracer from colossalai.tensor.sharding_spec import ShardingSpec +from colossalai.testing import clear_cache_before_run class TestModule(torch.nn.Module): @@ -26,6 +27,7 @@ def insert_narrow(gm, x_node): return gm +@clear_cache_before_run() def test_node_args_converting_pass(): model = TestModule() physical_mesh_id = torch.arange(0, 4) diff --git a/tests/test_auto_parallel/test_pass/test_size_value_converting_pass.py b/tests/test_auto_parallel/test_pass/test_size_value_converting_pass.py index 7d4fd844ab26..bca81201c6ef 100644 --- a/tests/test_auto_parallel/test_pass/test_size_value_converting_pass.py +++ b/tests/test_auto_parallel/test_pass/test_size_value_converting_pass.py @@ -8,6 +8,7 @@ from colossalai.auto_parallel.passes.runtime_preparation_pass import size_value_converting_pass from colossalai.device.device_mesh import DeviceMesh from colossalai.tensor.sharding_spec import ShardingSpec +from colossalai.testing import clear_cache_before_run class TestModule(torch.nn.Module): @@ -36,6 +37,7 @@ def recover_narrow(gm, narrow_node): @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') +@clear_cache_before_run() def test_size_value_converting_pass(): model = TestModule() physical_mesh_id = torch.arange(0, 4) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py b/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py index 6d1b28912c9b..9fbe674ef4f4 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py @@ -2,7 +2,6 @@ import pytest import torch -import torch.multiprocessing as mp try: from colossalai.auto_parallel.tensor_shard.initialize import initialize_model @@ -13,9 +12,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import assert_close, rerun_if_address_is_in_use, run_on_environment_flag, spawn class LinearModel(torch.nn.Module): @@ -86,11 +83,8 @@ def check_conv_module(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_bias_addition_module(): - world_size = 4 - run_func_linear = partial(check_linear_module, world_size=world_size, port=free_port()) - mp.spawn(run_func_linear, nprocs=world_size) - run_func_conv = partial(check_conv_module, world_size=world_size, port=free_port()) - mp.spawn(run_func_conv, nprocs=world_size) + spawn(check_linear_module, 4) + spawn(check_conv_module, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py b/tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py index 7a4c8d32ed80..398458306e3d 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py @@ -1,9 +1,7 @@ -from functools import partial -from typing import Optional, Tuple, Union +from typing import Optional, Tuple import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from torch.utils.checkpoint import checkpoint from transformers.pytorch_utils import Conv1D @@ -17,9 +15,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn HIDDEN_SIZE = 16 @@ -65,9 +61,7 @@ def check_act_ckpt(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_mlp_layer(): - world_size = 4 - run_func = partial(check_act_ckpt, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_act_ckpt, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py index 7c3277c69970..6908a1781869 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py @@ -1,9 +1,7 @@ import copy -from functools import partial import pytest import torch -import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP try: @@ -15,9 +13,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import assert_close, rerun_if_address_is_in_use, run_on_environment_flag, spawn class MLP(torch.nn.Module): @@ -102,9 +98,7 @@ def check_compatibility_with_ddp(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_compatibility_with_ddp(): - world_size = 4 - run_func = partial(check_compatibility_with_ddp, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_compatibility_with_ddp, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py index e4435a049f62..05704acbf7fd 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py @@ -1,10 +1,7 @@ import copy -from functools import partial import pytest import torch -import torch.multiprocessing as mp -from torch.nn.parallel import DistributedDataParallel as DDP try: from colossalai.auto_parallel.tensor_shard.initialize import initialize_model @@ -17,10 +14,9 @@ from colossalai.logging import disable_existing_loggers from colossalai.nn.optimizer import HybridAdam from colossalai.tensor.process_group import ProcessGroup -from colossalai.testing import assert_close, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port, get_current_device -from colossalai.zero import ColoInitContext, post_process_colo_init_ctx, zero_model_wrapper, zero_optim_wrapper +from colossalai.testing import assert_close, rerun_if_address_is_in_use, run_on_environment_flag, spawn +from colossalai.utils import get_current_device +from colossalai.zero import post_process_colo_init_ctx, zero_model_wrapper, zero_optim_wrapper class MLP(torch.nn.Module): @@ -110,9 +106,7 @@ def check_auto_parallel_with_gemini(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_auto_parallel_with_gemini(): - world_size = 4 - run_func = partial(check_auto_parallel_with_gemini, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_auto_parallel_with_gemini, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_find_repeat_block.py b/tests/test_auto_parallel/test_tensor_shard/test_find_repeat_block.py index e7fccad36caf..a0b407b240e1 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_find_repeat_block.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_find_repeat_block.py @@ -10,8 +10,7 @@ # from colossalai.fx.tracer.tracer import ColoTracer from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.utils.factory import find_repeat_blocks -from colossalai.testing import parameterize -from colossalai.testing.pytest_wrapper import run_on_environment_flag +from colossalai.testing import clear_cache_before_run, parameterize, run_on_environment_flag NUM_REPEAT_BLOCKS = 4 BATCH_SIZE = 1 @@ -81,6 +80,7 @@ def forward(self, x): @run_on_environment_flag(name='AUTO_PARALLEL') +@clear_cache_before_run() @parameterize('model_cls', [RepeatModel, NonRepeatModel]) def test_repeat_blocks(model_cls): diff --git a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py index 8688890efc93..48d2672c6571 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py @@ -1,12 +1,10 @@ import copy import random -from functools import partial from typing import Dict import numpy as np import pytest import torch -import torch.multiprocessing as mp import transformers from torch.fx import GraphModule @@ -30,9 +28,8 @@ from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.tensor.shape_consistency import to_global -from colossalai.testing import assert_close, assert_close_loose, parameterize, rerun_if_address_is_in_use +from colossalai.testing import assert_close, assert_close_loose, parameterize, rerun_if_address_is_in_use, spawn from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port from tests.test_auto_parallel.test_tensor_shard.test_gpt.gpt_modules import GPT2MLP, GPT2Attention, GPT2Block, GPT2Model BATCH_SIZE = 1 @@ -190,9 +187,7 @@ def check_attention_layer(rank, model_cls, world_size, port): @parameterize('model_cls', [GPT2MLP, GPT2Block, GPT2Attention, GPT2Model]) @rerun_if_address_is_in_use() def test_mlp_layer(model_cls): - world_size = 4 - run_func = partial(check_attention_layer, model_cls=model_cls, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_attention_layer, 4, model_cls=model_cls) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_solver_with_gpt_module.py b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_solver_with_gpt_module.py index 5f0688d5f5f2..5a8c3c4bf5a0 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_solver_with_gpt_module.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_solver_with_gpt_module.py @@ -1,5 +1,4 @@ import torch -import torch.nn as nn import transformers from torch.fx import GraphModule @@ -7,10 +6,10 @@ from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.constants import BATCHNORM_MODULE_OP from colossalai.auto_parallel.tensor_shard.options import SolverOptions -from colossalai.auto_parallel.tensor_shard.solver import CostGraph, GraphAnalyser, Solver, StrategiesConstructor +from colossalai.auto_parallel.tensor_shard.solver import CostGraph, Solver, StrategiesConstructor from colossalai.device.device_mesh import DeviceMesh from colossalai.tensor.shape_consistency import ShapeConsistencyManager -from colossalai.testing import parameterize +from colossalai.testing import clear_cache_before_run, parameterize from colossalai.testing.pytest_wrapper import run_on_environment_flag from tests.test_auto_parallel.test_tensor_shard.test_gpt.gpt_modules import GPT2MLP, GPT2Attention, GPT2Block, GPT2Model @@ -20,6 +19,7 @@ @run_on_environment_flag(name='AUTO_PARALLEL') +@clear_cache_before_run() @parameterize('model_cls', [GPT2Block, GPT2Attention, GPT2MLP, GPT2Model]) def test_self_attention_block(model_cls): config = transformers.GPT2Config(n_position=64, n_layer=2, n_head=16, n_embd=HIDDEN_DIM) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_liveness_analysis.py b/tests/test_auto_parallel/test_tensor_shard/test_liveness_analysis.py index 8d421243827e..d10b222c060d 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_liveness_analysis.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_liveness_analysis.py @@ -6,6 +6,8 @@ from colossalai._analyzer.fx.passes import shape_prop_pass from colossalai._analyzer.fx.tracer.tracer import ColoTracer from colossalai.auto_parallel.tensor_shard.solver import GraphAnalyser +from colossalai.fx import ColoGraphModule, ColoTracer +from colossalai.testing import clear_cache_before_run class LinearModel(nn.Module): @@ -26,6 +28,7 @@ def forward(self, x1, x2): @pytest.mark.skip('meta tensor has some bugs in 1.11') +@clear_cache_before_run() def test_liveness_analysis(): model = LinearModel() tracer = ColoTracer(bias_addition_split=True) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_activation_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_activation_metainfo.py index e41ac4fa690b..e0a2133e654e 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_activation_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_activation_metainfo.py @@ -1,23 +1,14 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp -import torch.nn as nn from colossalai.auto_parallel.meta_profiler import meta_register from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType -from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port -from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy, print_results +from colossalai.testing.utils import clear_cache_before_run, parameterize +from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations") +@clear_cache_before_run() @parameterize('func', [ torch.nn.functional.softmax, torch.nn.functional.relu, diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_binary_elementwise_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_binary_elementwise_metainfo.py index 1b745d8906b0..68ccc7835bc3 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_binary_elementwise_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_binary_elementwise_metainfo.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai.device.device_mesh import DeviceMesh @@ -10,8 +7,7 @@ from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing.utils import rerun_if_address_is_in_use, spawn from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy @@ -62,9 +58,7 @@ def _binary_elementwise_mem_test(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_binary_elementwise_meta_concrete_info_match(): - world_size = 4 - run_func_module = partial(_binary_elementwise_mem_test, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_binary_elementwise_mem_test, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_conv_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_conv_metainfo.py index a973a8182cf3..c6f7b88f44a5 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_conv_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_conv_metainfo.py @@ -1,17 +1,12 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing.utils import rerun_if_address_is_in_use, spawn from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy @@ -25,7 +20,7 @@ def forward(self, input): return nn.functional.conv2d(input, self.conv_weight) -def _conv_module_mem_test(rank, bias, world_size, port): +def _conv_module_mem_test(rank, world_size, port, bias): """This function is for conv memory test Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL @@ -62,9 +57,7 @@ def _conv_module_mem_test(rank, bias, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_conv_meta_concrete_info_match(bias=False): - world_size = 4 - run_func_module = partial(_conv_module_mem_test, bias=bias, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_conv_module_mem_test, 4, bias=bias) def _conv_function_mem_test(rank, world_size, port): @@ -103,9 +96,7 @@ def _conv_function_mem_test(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_conv_function_concrete_info_match(): - world_size = 4 - run_func_module = partial(_conv_function_mem_test, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_conv_function_mem_test, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_embedding_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_embedding_metainfo.py index 5f3d2df503f6..e3f76a95c4a5 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_embedding_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_embedding_metainfo.py @@ -1,33 +1,16 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp -import torch.nn as nn - -from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler -from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( - MemoryCost, - OperationData, - OperationDataType, - ShardingStrategy, - StrategiesVector, - TrainCycleItem, -) -from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port + +from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType +from colossalai.testing.utils import clear_cache_before_run from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register + from colossalai.auto_parallel.meta_profiler import meta_register @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations") +@clear_cache_before_run() def test_embedding_meta_info(): meta_func = meta_register.get(torch.nn.Embedding) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py index ddc8e3c6ac02..fb3ded339ddf 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py @@ -1,24 +1,14 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn -from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler -from colossalai.auto_parallel.tensor_shard.sharding_strategy import ShardingStrategy, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing.utils import rerun_if_address_is_in_use, spawn from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy -if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register - class MyModule(nn.Module): @@ -63,9 +53,7 @@ def _linear_module_mem_test(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_linear_module_meta_concrete_info_match(): - world_size = 4 - run_func_module = partial(_linear_module_mem_test, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_linear_module_mem_test, 4) def _linear_function_mem_test(rank, world_size, port): @@ -101,9 +89,7 @@ def _linear_function_mem_test(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_linear_function_meta_concrete_info_match(): - world_size = 4 - run_func_module = partial(_linear_function_mem_test, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_linear_function_mem_test, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_matmul_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_matmul_metainfo.py index 1242b9db0750..2d2d77f0c637 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_matmul_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_matmul_metainfo.py @@ -1,26 +1,8 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp -import torch.nn as nn - -from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler -from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( - MemoryCost, - OperationData, - OperationDataType, - ShardingStrategy, - StrategiesVector, - TrainCycleItem, -) -from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port + +from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, TrainCycleItem +from colossalai.testing.utils import clear_cache_before_run, parameterize from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results if torch.__version__ >= '1.12.0': @@ -28,6 +10,7 @@ @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations") +@clear_cache_before_run() @parameterize( 'tensor_shapes', [ diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py index d3342d310157..808172977b60 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py @@ -1,29 +1,17 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn -from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( - MemoryCost, - OperationData, - OperationDataType, - ShardingStrategy, - StrategiesVector, - TrainCycleItem, -) +from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, TrainCycleItem from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use, spawn from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy, print_results if torch.__version__ >= '1.12.0': - from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register + from colossalai.auto_parallel.meta_profiler import meta_register def _batchnorm_module_mem_test(rank, world_size, port): @@ -62,9 +50,7 @@ def _batchnorm_module_mem_test(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_batchnorm_meta_concrete_info_match(): - world_size = 4 - run_func_module = partial(_batchnorm_module_mem_test, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_batchnorm_module_mem_test, 4) @pytest.mark.skipif(torch.__version__ < '1.12.0', reason='need pytorch 1.12.0 or higher for aten level operations') diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_pooling_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_pooling_metainfo.py index 529686d27d19..4cddf4e19fca 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_pooling_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_pooling_metainfo.py @@ -1,17 +1,12 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing.utils import rerun_if_address_is_in_use, spawn from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy @@ -51,9 +46,7 @@ def _adaptiveavgpool_module_mem_test(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_adaptiveavgpool_meta_concrete_info_match(): - world_size = 4 - run_func_module = partial(_adaptiveavgpool_module_mem_test, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_adaptiveavgpool_module_mem_test, 4) def _maxpool_module_mem_test(rank, world_size, port): @@ -92,9 +85,7 @@ def _maxpool_module_mem_test(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_maxpool_meta_concrete_info_match(): - world_size = 4 - run_func_module = partial(_maxpool_module_mem_test, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(_maxpool_module_mem_test, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py index a544e9a3cb2f..6e8145885d67 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py @@ -1,26 +1,9 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn -from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler -from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( - MemoryCost, - OperationData, - OperationDataType, - ShardingStrategy, - StrategiesVector, - TrainCycleItem, -) -from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType +from colossalai.testing.utils import clear_cache_before_run from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results if torch.__version__ >= '1.12.0': @@ -37,6 +20,7 @@ def forward(self, x): @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations") +@clear_cache_before_run() def test_tensor_meta_info(): """test tensor related meta information We will just use torch.Tensor.split for the test diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py index 2ae13ea2b1f8..b4564312eeb4 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py @@ -1,24 +1,8 @@ import pytest import torch -import torch.multiprocessing as mp -import torch.nn as nn - -from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler -from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( - MemoryCost, - OperationData, - OperationDataType, - ShardingStrategy, - StrategiesVector, - TrainCycleItem, -) -from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx import ColoGraphModule, ColoTracer -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port + +from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, TrainCycleItem +from colossalai.testing.utils import clear_cache_before_run from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results if torch.__version__ >= '1.12.0': @@ -26,6 +10,7 @@ @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations") +@clear_cache_before_run() def test_where_meta_info(): meta_func = meta_register.get(torch.where) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addbmm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addbmm_handler.py index ffc15e403f35..80e6a6c1460c 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addbmm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addbmm_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai.auto_parallel.tensor_shard.node_handler import BMMFunctionHandler @@ -11,9 +8,7 @@ from colossalai.fx import ColoGraphModule, ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -45,7 +40,7 @@ def forward(self, bias, x1, x2): return output -def check_2d_device_mesh(rank, module, bias_shape, using_kwargs, world_size, port): +def check_2d_device_mesh(rank, world_size, port, module, bias_shape, using_kwargs): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = module(using_kwargs).cuda() @@ -249,14 +244,13 @@ def check_1d_device_mesh(rank, module, bias_shape, using_kwargs, world_size, por @parameterize('using_kwargs', [True, False]) @rerun_if_address_is_in_use() def test_2d_device_mesh(module, bias_shape, using_kwargs): - world_size = 4 - run_func = partial(check_2d_device_mesh, - module=module, - bias_shape=bias_shape, - world_size=world_size, - using_kwargs=using_kwargs, - port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn( + check_2d_device_mesh, + 4, + module=module, + bias_shape=bias_shape, + using_kwargs=using_kwargs, + ) @pytest.mark.skip("skip due to bias cases not ready") @@ -267,14 +261,13 @@ def test_2d_device_mesh(module, bias_shape, using_kwargs): @parameterize('using_kwargs', [True, False]) @rerun_if_address_is_in_use() def test_1d_device_mesh(module, bias_shape, using_kwargs): - world_size = 4 - run_func = partial(check_1d_device_mesh, - module=module, - bias_shape=bias_shape, - using_kwargs=using_kwargs, - world_size=world_size, - port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn( + check_1d_device_mesh, + 4, + module=module, + bias_shape=bias_shape, + using_kwargs=using_kwargs, + ) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py index 35f12ce83af2..fe6554cd81ee 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -17,9 +14,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -45,7 +40,7 @@ def forward(self, m1): return x -def check_addmm_function_handler(rank, input_shape, model_cls, world_size, port): +def check_addmm_function_handler(rank, world_size, port, input_shape, model_cls): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') if model_cls == AddmmModel: @@ -189,13 +184,7 @@ def check_addmm_function_handler(rank, input_shape, model_cls, world_size, port) @parameterize('model_cls', [AddmmModel, AddmmModel_with_param]) @rerun_if_address_is_in_use() def test_addmm_handler(input_shape, model_cls): - world_size = 4 - run_func_function = partial(check_addmm_function_handler, - input_shape=input_shape, - model_cls=model_cls, - world_size=world_size, - port=free_port()) - mp.spawn(run_func_function, nprocs=world_size) + spawn(check_addmm_function_handler, 4, input_shape=input_shape, model_cls=model_cls) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py index 2069b5e8a4de..b47b3508ad1b 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -13,9 +10,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -114,9 +109,7 @@ def check_bn_module_handler(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_bn_module_handler(): - world_size = 4 - run_func = partial(check_bn_module_handler, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_bn_module_handler, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py index dca5f6e227fa..800bc11a50e4 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py @@ -1,9 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp -import torch.nn as nn import torch.nn.functional as F from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -19,9 +15,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy WEIGHT_SHAPE = (32, 16) @@ -168,9 +162,7 @@ def check_linear_module_handler(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_linear_handler(): - world_size = 4 - run_func_module = partial(check_linear_module_handler, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(check_linear_module_handler) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py index 14d4a73fb4f8..c29a065d10ba 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py @@ -1,14 +1,10 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp -import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass from colossalai._analyzer.fx.tracer.tracer import ColoTracer -from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler, LinearModuleHandler +from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import ( OperationData, OperationDataType, @@ -18,9 +14,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -35,7 +29,7 @@ def forward(self, x): return x -def check_linear_module_handler(rank, bias, world_size, port): +def check_linear_module_handler(rank, world_size, port, bias): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = LinearModule(16, 32, bias=bias).cuda() @@ -157,9 +151,7 @@ def check_linear_module_handler(rank, bias, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_linear_handler(bias=True): - world_size = 4 - run_func_module = partial(check_linear_module_handler, bias=bias, world_size=world_size, port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) + spawn(check_linear_module_handler, bias=bias) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py index 2414749f60a4..83f3aafe220e 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -13,13 +10,11 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy -def check_binary_elementwise_handler_with_tensor(rank, op, other_dim, world_size, port): +def check_binary_elementwise_handler_with_tensor(rank, world_size, port, op, other_dim): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') @@ -149,7 +144,7 @@ def forward(self, x1): return out -def check_binary_elementwise_handler_with_int(rank, op, other_dim, model_cls, world_size, port): +def check_binary_elementwise_handler_with_int(rank, world_size, port, op, other_dim, model_cls): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') @@ -236,13 +231,12 @@ def check_binary_elementwise_handler_with_int(rank, op, other_dim, model_cls, wo @pytest.mark.dist @rerun_if_address_is_in_use() def test_binary_elementwise_handler_with_tensor(op, other_dim): - world_size = 4 - run_func_tensor = partial(check_binary_elementwise_handler_with_tensor, - op=op, - other_dim=other_dim, - world_size=world_size, - port=free_port()) - mp.spawn(run_func_tensor, nprocs=world_size) + spawn( + check_binary_elementwise_handler_with_tensor, + 4, + op=op, + other_dim=other_dim, + ) @run_on_environment_flag(name='AUTO_PARALLEL') @@ -252,14 +246,13 @@ def test_binary_elementwise_handler_with_tensor(op, other_dim): @pytest.mark.dist @rerun_if_address_is_in_use() def test_binary_elementwise_handler_with_int(op, model_cls, other_dim): - world_size = 4 - run_func_int = partial(check_binary_elementwise_handler_with_int, - op=op, - model_cls=model_cls, - other_dim=other_dim, - world_size=world_size, - port=free_port()) - mp.spawn(run_func_int, nprocs=world_size) + spawn( + check_binary_elementwise_handler_with_int, + 4, + op=op, + model_cls=model_cls, + other_dim=other_dim, + ) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py index 34c20c1ac0fe..f4fdc458f80e 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -13,9 +10,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -207,11 +202,8 @@ def check_1d_device_mesh(rank, module, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_bmm_handler(module): - world_size = 4 - run_func_2d = partial(check_2d_device_mesh, module=module, world_size=world_size, port=free_port()) - mp.spawn(run_func_2d, nprocs=world_size) - run_func_1d = partial(check_1d_device_mesh, module=module, world_size=world_size, port=free_port()) - mp.spawn(run_func_1d, nprocs=world_size) + spawn(check_2d_device_mesh, 4, module=module) + spawn(check_1d_device_mesh, 4, module=module) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py index fe1a0d726db0..f9632b1cd8f9 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -13,13 +10,11 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy -def check_conv_module_handler(rank, bias, world_size, port): +def check_conv_module_handler(rank, world_size, port, bias): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = nn.Sequential(nn.Conv2d(4, 16, 3, padding=1, bias=bias)).cuda() @@ -155,7 +150,7 @@ def forward(self, input, others, bias=None): return x -def check_conv_function_handler(rank, bias, world_size, port): +def check_conv_function_handler(rank, world_size, port, bias): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = ConvModel().cuda() @@ -302,9 +297,7 @@ def check_conv_function_handler(rank, bias, world_size, port): # @parameterize('bias', [True, False]) @rerun_if_address_is_in_use() def test_conv_module_handler(bias=False): - world_size = 4 - run_func = partial(check_conv_module_handler, bias=bias, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_conv_module_handler, 4, bias=bias) @run_on_environment_flag(name='AUTO_PARALLEL') @@ -314,9 +307,7 @@ def test_conv_module_handler(bias=False): # @parameterize('bias', [True, False]) @rerun_if_address_is_in_use() def test_conv_function_handler(bias=False): - world_size = 4 - run_func = partial(check_conv_function_handler, bias=bias, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_conv_function_handler, 4, bias=bias) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_default_reshape_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_default_reshape_handler.py index 8e5b7512ca0e..64f56ba98e2b 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_default_reshape_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_default_reshape_handler.py @@ -8,7 +8,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.testing.pytest_wrapper import run_on_environment_flag +from colossalai.testing import clear_cache_before_run, run_on_environment_flag class ReshapeModel(nn.Module): @@ -23,6 +23,7 @@ def forward(self, input, other): @run_on_environment_flag(name='AUTO_PARALLEL') +@clear_cache_before_run() def test_reshape_handler(): model = ReshapeModel() tracer = ColoTracer(bias_addition_split=True) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py index a61d2ed5c108..4fa0313b1cb5 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -16,9 +13,8 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy NUM_EMBEDDINGS = 16 @@ -272,18 +268,14 @@ def check_embedding_function_handler(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_embedding_module_handler(): - world_size = 4 - run_func = partial(check_embedding_module_handler, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_embedding_module_handler, 4) @run_on_environment_flag(name='AUTO_PARALLEL') @pytest.mark.dist @rerun_if_address_is_in_use() def test_embedding_function_handler(): - world_size = 4 - run_func = partial(check_embedding_function_handler, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_embedding_function_handler, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py index fb611330946a..a089df743ec0 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py @@ -8,6 +8,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler.getattr_handler import GetattrHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh +from colossalai.testing import clear_cache_before_run class GetattrModel(nn.Module): @@ -22,6 +23,7 @@ def forward(self, input): @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') +@clear_cache_before_run() def test_getattr_handler(): model = GetattrModel() tracer = ColoTracer(bias_addition_split=True) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py index 9a29808ebb31..a2e0968b18bb 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py @@ -2,7 +2,6 @@ import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -14,12 +13,10 @@ from colossalai.auto_parallel.tensor_shard.node_handler.placeholder_handler import PlaceholderHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.tracer.meta_patch.patched_module import linear from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -103,12 +100,7 @@ def check_getitem_from_tensor_handler(rank, getitem_index, world_size, port): # @parameterize('getitem_index', [slice(0, 2), (slice(None), slice(None))]) @parameterize('getitem_index', [1, (1, 4), slice(0, 2), (slice(None), slice(None))]) def test_getitem_from_tensor_handler(getitem_index): - world_size = 4 - run_func = partial(check_getitem_from_tensor_handler, - getitem_index=getitem_index, - world_size=world_size, - port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_getitem_from_tensor_handler, 4) class GetItemFromTupleModel(nn.Module): @@ -123,6 +115,7 @@ def forward(self, input): @run_on_environment_flag(name='AUTO_PARALLEL') +@clear_cache_before_run() def test_getitem_from_tuple_handler(): model = GetItemFromTupleModel() tracer = ColoTracer() diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_layer_norm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_layer_norm_handler.py index edd7bae6c979..ad72c2026b9a 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_layer_norm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_layer_norm_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -11,12 +8,10 @@ from colossalai.auto_parallel.tensor_shard.node_handler.layer_norm_handler import LayerNormModuleHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.tracer.meta_patch.patched_module import linear from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -104,9 +99,7 @@ def check_ln_module_handler(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_ln_module_handler(): - world_size = 4 - run_func = partial(check_ln_module_handler, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_ln_module_handler, 4) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py index bec5c3dc5e28..ec695cd8f7b9 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -18,14 +15,13 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.testing.pytest_wrapper import run_on_environment_flag from colossalai.testing.utils import parameterize -from colossalai.utils import free_port from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy -def check_linear_module_handler(rank, bias, input_shape, world_size, port): +def check_linear_module_handler(rank, world_size, port, bias, input_shape): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = nn.Sequential(nn.Linear(16, 32, bias=bias)).cuda() @@ -172,7 +168,7 @@ def forward(self, input, others, bias=None): return x -def check_linear_function_handler(rank, bias, input_shape, world_size, port): +def check_linear_function_handler(rank, world_size, port, bias, input_shape): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = LinearModel().cuda() @@ -313,19 +309,18 @@ def check_linear_function_handler(rank, bias, input_shape, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_linear_handler(input_shape, bias=False): - world_size = 4 - run_func_module = partial(check_linear_module_handler, - bias=bias, - input_shape=input_shape, - world_size=world_size, - port=free_port()) - mp.spawn(run_func_module, nprocs=world_size) - run_func_function = partial(check_linear_function_handler, - bias=bias, - input_shape=input_shape, - world_size=world_size, - port=free_port()) - mp.spawn(run_func_function, nprocs=world_size) + spawn( + check_linear_module_handler, + 4, + bias=bias, + input_shape=input_shape, + ) + spawn( + check_linear_function_handler, + 4, + bias=bias, + input_shape=input_shape, + ) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_matmul_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_matmul_handler.py index 46c3ff4434d7..938acd3d1eea 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_matmul_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_matmul_handler.py @@ -18,7 +18,7 @@ StrategiesVector, ) from colossalai.device.device_mesh import DeviceMesh -from colossalai.testing.utils import parameterize +from colossalai.testing.utils import clear_cache_before_run, parameterize class MatMulModule(nn.Module): @@ -28,6 +28,7 @@ def forward(self, x1, x2): @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations") +@clear_cache_before_run() @parameterize( 'tensor_shapes', [ diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py index aacc7d9aeb64..6bff9f9648e2 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py @@ -1,4 +1,3 @@ -import pytest import torch import torch.nn as nn @@ -8,11 +7,11 @@ from colossalai.auto_parallel.tensor_shard.node_handler.normal_pooling_handler import NormPoolingHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.tracer.meta_patch.patched_module import linear -from colossalai.testing.pytest_wrapper import run_on_environment_flag +from colossalai.testing import clear_cache_before_run, run_on_environment_flag @run_on_environment_flag(name='AUTO_PARALLEL') +@clear_cache_before_run() def test_norm_pool_handler(): model = nn.Sequential(nn.MaxPool2d(4, padding=1).to('meta')) tracer = ColoTracer(bias_addition_split=True) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py index 5efbb4f5f6a4..5259455d2179 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py @@ -8,7 +8,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler.output_handler import OutputHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use +from colossalai.testing import clear_cache_before_run, parameterize class OutputModel(nn.Module): @@ -23,7 +23,7 @@ def forward(self, x): @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') @parameterize('output_option', ['distributed', 'replicated']) -@rerun_if_address_is_in_use() +@clear_cache_before_run() def test_output_handler(output_option): model = OutputModel() tracer = ColoTracer(bias_addition_split=True) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_permute_and_transpose_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_permute_and_transpose_handler.py index 0a5ad3e3523d..f071cd120fb7 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_permute_and_transpose_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_permute_and_transpose_handler.py @@ -2,7 +2,6 @@ import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -15,9 +14,8 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -55,7 +53,7 @@ def forward(self, input, other): return permute_node -def check_view_handler(rank, call_function, reshape_dims, model_cls, world_size, port): +def check_view_handler(rank, world_size, port, call_function, reshape_dims, model_cls): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') if call_function == torch.permute: @@ -328,14 +326,13 @@ def check_view_handler(rank, call_function, reshape_dims, model_cls, world_size, @parameterize('reshape_dims', [((0, 2, 1, 3), (1, 2)), ((2, 0, 1, 3), (1, 3))]) @parameterize('model_cls', [ConvReshapeModel, LinearReshapeModel]) def test_view_handler(call_function, reshape_dims, model_cls): - world_size = 4 - run_func = partial(check_view_handler, - call_function=call_function, - reshape_dims=reshape_dims, - model_cls=model_cls, - world_size=world_size, - port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn( + check_view_handler, + 4, + call_function=call_function, + reshape_dims=reshape_dims, + model_cls=model_cls, + ) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_placeholder_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_placeholder_handler.py index 5e8fb51edbff..6d02b0e0ba74 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_placeholder_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_placeholder_handler.py @@ -8,7 +8,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler.placeholder_handler import PlaceholderHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use +from colossalai.testing import clear_cache_before_run, parameterize class PlaceholderModel(nn.Module): @@ -22,7 +22,7 @@ def forward(self, input): @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') @parameterize('placeholder_option', ['distributed', 'replicated']) -@rerun_if_address_is_in_use() +@clear_cache_before_run() def test_placeholder_handler(placeholder_option): model = PlaceholderModel() tracer = ColoTracer(bias_addition_split=True) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py index e589fff996c6..14c364c45fc4 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py @@ -1,5 +1,4 @@ import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -9,7 +8,7 @@ from colossalai.auto_parallel.tensor_shard.options import ShardOption from colossalai.auto_parallel.tensor_shard.sharding_strategy import StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.testing.pytest_wrapper import run_on_environment_flag +from colossalai.testing import clear_cache_before_run, run_on_environment_flag class LinearModel(nn.Module): @@ -108,6 +107,7 @@ def check_shard_option(shard_option): @run_on_environment_flag(name='AUTO_PARALLEL') +@clear_cache_before_run() def test_shard_option(): # for shard_option in [ShardOption.STANDARD, ShardOption.SHARD, ShardOption.FULL_SHARD, ShardOption.SHARD_LAST_AXIS]: for shard_option in [ShardOption.SHARD_LAST_AXIS]: diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py index db463a4e9d6a..75ae0416ef98 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn import torch.nn.functional as F @@ -15,9 +12,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -33,7 +28,7 @@ def forward(self, input, other): return softmax_node -def check_split_handler(rank, softmax_dim, model_cls, world_size, port): +def check_split_handler(rank, world_size, port, softmax_dim, model_cls): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = model_cls(softmax_dim=softmax_dim).cuda() @@ -176,13 +171,7 @@ def check_split_handler(rank, softmax_dim, model_cls, world_size, port): @parameterize('softmax_dim', [0, 1, 2, 3]) @parameterize('model_cls', [LinearSplitModel]) def test_split_handler(softmax_dim, model_cls): - world_size = 4 - run_func = partial(check_split_handler, - softmax_dim=softmax_dim, - model_cls=model_cls, - world_size=world_size, - port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_split_handler, 4, softmax_dim=softmax_dim, model_cls=model_cls) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py index db59ea60ef4b..f860c629b0a0 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -15,9 +12,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -47,7 +42,7 @@ def forward(self, input, other): return split_node -def check_split_handler(rank, split_size, split_dim, model_cls, world_size, port): +def check_split_handler(rank, world_size, port, split_size, split_dim, model_cls): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = model_cls(split_size=split_size, split_dim=split_dim).cuda() @@ -258,14 +253,7 @@ def check_split_handler(rank, split_size, split_dim, model_cls, world_size, port @parameterize('split_dim', [0, 1, 2]) @parameterize('model_cls', [ConvSplitModel, LinearSplitModel]) def test_split_handler(split_size, split_dim, model_cls): - world_size = 4 - run_func = partial(check_split_handler, - split_size=split_size, - split_dim=split_dim, - model_cls=model_cls, - world_size=world_size, - port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_split_handler, 4, split_size=split_size, split_dim=split_dim, model_cls=model_cls) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py index add51d73f2a4..c11291ecac96 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -14,9 +11,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use -from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -36,7 +31,7 @@ def forward(self, input, other): return sum_node -def check_sum_handler(rank, sum_dims, keepdim, world_size, port): +def check_sum_handler(rank, world_size, port, sum_dims, keepdim): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model = LinearSumModel(sum_dims=sum_dims, keepdim=keepdim).cuda() @@ -228,9 +223,7 @@ def check_sum_handler(rank, sum_dims, keepdim, world_size, port): @parameterize('sum_dims', [(0, 2), 1]) @parameterize('keepdim', [False, True]) def test_sum_handler(sum_dims, keepdim): - world_size = 4 - run_func = partial(check_sum_handler, sum_dims=sum_dims, keepdim=keepdim, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_sum_handler, 4, sum_dims=sum_dims, keepdim=keepdim) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py index f54b208c3380..5b6ac051a8ef 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py @@ -7,7 +7,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler.tensor_constructor_handler import TensorConstructorHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.testing.pytest_wrapper import run_on_environment_flag +from colossalai.testing import clear_cache_before_run, run_on_environment_flag class TensorConstructorModel(nn.Module): @@ -22,6 +22,7 @@ def forward(self, x): @run_on_environment_flag(name='AUTO_PARALLEL') +@clear_cache_before_run() def test_where_handler(): model = TensorConstructorModel() tracer = ColoTracer(bias_addition_split=True) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_unary_element_wise_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_unary_element_wise_handler.py index bd88089734a7..f4e6dafdfd69 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_unary_element_wise_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_unary_element_wise_handler.py @@ -8,7 +8,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler.unary_elementwise_handler import UnaryElementwiseHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh -from colossalai.testing.pytest_wrapper import run_on_environment_flag +from colossalai.testing import clear_cache_before_run, run_on_environment_flag class ReLuModel(nn.Module): @@ -24,6 +24,7 @@ def forward(self, input, other): @run_on_environment_flag(name='AUTO_PARALLEL') +@clear_cache_before_run() def test_elementwise_handler(): model = ReLuModel() tracer = ColoTracer(bias_addition_split=True) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py index 300e8f94e7fe..fbb194d8e0b8 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from colossalai._analyzer.fx.graph_module import ColoGraphModule @@ -15,9 +12,8 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.testing.pytest_wrapper import run_on_environment_flag -from colossalai.utils import free_port from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy @@ -255,13 +251,7 @@ def check_view_handler(rank, tgt_shape, model_cls, world_size, port): @parameterize('tgt_shape', [(32, 4, 64, 16, 4), (8, 4, 4, 64, 16, 4)]) @parameterize('model_cls', [ConvViewModel, LinearViewModel]) def test_view_handler(tgt_shape, model_cls): - world_size = 4 - run_func = partial(check_view_handler, - tgt_shape=tgt_shape, - model_cls=model_cls, - world_size=world_size, - port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_view_handler, 4, tgt_shape=tgt_shape, model_cls=model_cls) if __name__ == '__main__': diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py index c150ebd90053..bd7635ac1737 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py @@ -8,6 +8,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler.where_handler import WhereHandler from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector from colossalai.device.device_mesh import DeviceMesh +from colossalai.testing import clear_cache_before_run class ConvModel(nn.Module): @@ -21,6 +22,7 @@ def forward(self, condition, x, y): @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0') +@clear_cache_before_run() def test_where_handler(): model = ConvModel() tracer = ColoTracer(bias_addition_split=True) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py b/tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py index fb47baab9476..0d93e4e40527 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py @@ -10,10 +10,11 @@ from colossalai.auto_parallel.tensor_shard.solver import CostGraph, GraphAnalyser, Solver, StrategiesConstructor from colossalai.device.device_mesh import DeviceMesh from colossalai.tensor.shape_consistency import ShapeConsistencyManager -from colossalai.testing.pytest_wrapper import run_on_environment_flag +from colossalai.testing import clear_cache_before_run, run_on_environment_flag @run_on_environment_flag(name='AUTO_PARALLEL') +@clear_cache_before_run() def test_cost_graph(): physical_mesh_id = torch.arange(0, 8) mesh_shape = (2, 4) diff --git a/tests/test_autochunk/test_autochunk_alphafold/benchmark_autochunk_alphafold.py b/tests/test_autochunk/test_autochunk_alphafold/benchmark_autochunk_alphafold.py index 9a2240d62de4..d07145e48e1f 100644 --- a/tests/test_autochunk/test_autochunk_alphafold/benchmark_autochunk_alphafold.py +++ b/tests/test_autochunk/test_autochunk_alphafold/benchmark_autochunk_alphafold.py @@ -8,7 +8,7 @@ from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE from colossalai.fx.graph_module import ColoGraphModule from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.utils import free_port +from colossalai.testing import free_port if AUTOCHUNK_AVAILABLE: from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen diff --git a/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_alphafold_utils.py b/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_alphafold_utils.py index cb250d6402e2..15610e2b50dc 100644 --- a/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_alphafold_utils.py +++ b/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_alphafold_utils.py @@ -9,7 +9,7 @@ from colossalai.core import global_context as gpc from colossalai.fx.graph_module import ColoGraphModule from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.utils import free_port +from colossalai.testing import free_port if AUTOCHUNK_AVAILABLE: from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen diff --git a/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_block.py b/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_block.py index 17a5abf4cab8..9e4cb7ee9f95 100644 --- a/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_block.py +++ b/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_block.py @@ -1,10 +1,8 @@ -from functools import partial from typing import Dict, List, Tuple import pytest import torch import torch.fx -import torch.multiprocessing as mp try: from fastfold.model.nn.evoformer import EvoformerBlock @@ -15,6 +13,7 @@ from test_autochunk_alphafold_utils import run_test from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE +from colossalai.testing import clear_cache_before_run, parameterize, spawn def get_model(): @@ -66,18 +65,19 @@ def get_chunk_target() -> Dict: not (AUTOCHUNK_AVAILABLE and HAS_REPO), reason="torch version is lower than 1.12.0", ) -@pytest.mark.parametrize("max_memory", [None, 20, 24]) -@pytest.mark.parametrize("data_args", [(32, 64)]) # (msa_len, pair_len) +@clear_cache_before_run() +@parameterize("max_memory", [None, 20, 24]) +@parameterize("data_args", [(32, 64)]) def test_evoformer_block(data_args, max_memory): - run_func = partial( + spawn( run_test, + 1, data_args=data_args, max_memory=max_memory, get_model=get_model, get_data=get_data, get_chunk_target=get_chunk_target, ) - mp.spawn(run_func, nprocs=1) if __name__ == "__main__": diff --git a/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_stack.py b/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_stack.py index 5210c1c8d48e..6b47033e199f 100644 --- a/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_stack.py +++ b/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_stack.py @@ -1,10 +1,8 @@ -from functools import partial from typing import List, Tuple import pytest import torch import torch.fx -import torch.multiprocessing as mp try: from fastfold.model.nn.evoformer import EvoformerStack @@ -15,6 +13,7 @@ from test_autochunk_alphafold_utils import run_test from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE +from colossalai.testing import clear_cache_before_run, parameterize, spawn def get_model(): @@ -61,17 +60,18 @@ def get_data(msa_len: int, pair_len: int) -> Tuple[List, List]: not (AUTOCHUNK_AVAILABLE and HAS_REPO), reason="torch version is lower than 1.12.0", ) -@pytest.mark.parametrize("max_memory", [None, 20, 24]) -@pytest.mark.parametrize("data_args", [(32, 64)]) # (msa_len, pair_len) +@clear_cache_before_run() +@parameterize("max_memory", [None, 20, 24]) +@parameterize("data_args", [(32, 64)]) # (msa_len, pair_len) def test_evoformer_stack(data_args, max_memory): - run_func = partial( + spawn( run_test, + 1, data_args=data_args, max_memory=max_memory, get_model=get_model, get_data=get_data, ) - mp.spawn(run_func, nprocs=1) if __name__ == "__main__": diff --git a/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_extramsa_block.py b/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_extramsa_block.py index ad955479e617..b4c577c18ee6 100644 --- a/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_extramsa_block.py +++ b/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_extramsa_block.py @@ -1,10 +1,8 @@ -from functools import partial from typing import Dict, List, Tuple import pytest import torch import torch.fx -import torch.multiprocessing as mp try: from fastfold.model.nn.evoformer import ExtraMSABlock @@ -14,6 +12,7 @@ from test_autochunk_alphafold_utils import run_test from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE +from colossalai.testing import clear_cache_before_run, parameterize, spawn def get_model(): @@ -57,17 +56,18 @@ def get_data(msa_len: int, pair_len: int) -> Tuple[List, List]: not (AUTOCHUNK_AVAILABLE and HAS_REPO), reason="torch version is lower than 1.12.0", ) -@pytest.mark.parametrize("max_memory", [None, 20, 24]) -@pytest.mark.parametrize("data_args", [(32, 64)]) # (msa_len, pair_len) +@clear_cache_before_run() +@parameterize("max_memory", [None, 20, 24]) +@parameterize("data_args", [(32, 64)]) # (msa_len, pair_len) def test_extramsa_block(data_args, max_memory): - run_func = partial( + spawn( run_test, + 1, data_args=data_args, max_memory=max_memory, get_model=get_model, get_data=get_data, ) - mp.spawn(run_func, nprocs=1) if __name__ == "__main__": diff --git a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_diffuser_utils.py b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_diffuser_utils.py index 529250fe8f51..e245f10d4576 100644 --- a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_diffuser_utils.py +++ b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_diffuser_utils.py @@ -8,7 +8,7 @@ from colossalai.core import global_context as gpc from colossalai.fx.graph_module import ColoGraphModule from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.utils import free_port +from colossalai.testing import free_port if AUTOCHUNK_AVAILABLE: from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen diff --git a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py index 16c5b10ff4ae..ff0d4a1b53f5 100644 --- a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py +++ b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py @@ -1,9 +1,7 @@ -from functools import partial from typing import List, Tuple import pytest import torch -import torch.multiprocessing as mp try: from diffusers import UNet2DModel @@ -16,6 +14,7 @@ from test_autochunk_diffuser_utils import run_test from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE +from colossalai.testing import clear_cache_before_run, parameterize, spawn BATCH_SIZE = 1 HEIGHT = 448 @@ -37,17 +36,18 @@ def get_data(shape: tuple) -> Tuple[List, List]: not (AUTOCHUNK_AVAILABLE and HAS_REPO), reason="torch version is lower than 1.12.0", ) -@pytest.mark.parametrize("model", MODELS) -@pytest.mark.parametrize("shape", [LATENTS_SHAPE]) -@pytest.mark.parametrize("max_memory", [None, 150, 300]) +@clear_cache_before_run() +@parameterize("model", MODELS) +@parameterize("shape", [LATENTS_SHAPE]) +@parameterize("max_memory", [None, 150, 300]) def test_evoformer_block(model, shape, max_memory): - run_func = partial( + spawn( run_test, + 1, max_memory=max_memory, model=model, data=get_data(shape), ) - mp.spawn(run_func, nprocs=1) if __name__ == "__main__": diff --git a/tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py b/tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py index 018a2557a974..384706639e10 100644 --- a/tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py +++ b/tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py @@ -1,9 +1,7 @@ -from functools import partial from typing import List, Tuple import pytest import torch -import torch.multiprocessing as mp try: from transformers import GPT2Config, GPT2Model @@ -16,6 +14,7 @@ from test_autochunk_transformer_utils import run_test from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE +from colossalai.testing import clear_cache_before_run, parameterize, spawn BATCH_SIZE = 1 SEQ_LENGTH = 512 @@ -35,18 +34,19 @@ def get_data(shape: tuple) -> Tuple[List, List]: not (AUTOCHUNK_AVAILABLE and HAS_REPO), reason="torch version is lower than 1.12.0", ) -@pytest.mark.parametrize("model", MODELS) -@pytest.mark.parametrize("shape", [(BATCH_SIZE, SEQ_LENGTH)]) -@pytest.mark.parametrize("max_memory", [None, 6, 8]) +@clear_cache_before_run() +@parameterize("model", MODELS) +@parameterize("shape", [(BATCH_SIZE, SEQ_LENGTH)]) +@parameterize("max_memory", [None, 6, 8]) def test_autochunk_gpt(model, shape, max_memory): - run_func = partial( + spawn( run_test, + 1, data=get_data(shape), max_memory=max_memory, model=model, config=GPT2Config(n_embd=96, n_positions=shape[1], n_layer=2, n_head=4), ) - mp.spawn(run_func, nprocs=1) if __name__ == "__main__": diff --git a/tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py b/tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py index bc5eda7edf91..faba138cd42c 100644 --- a/tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py +++ b/tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py @@ -8,7 +8,7 @@ from colossalai.core import global_context as gpc from colossalai.fx.graph_module import ColoGraphModule from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.utils import free_port +from colossalai.testing import free_port if AUTOCHUNK_AVAILABLE: from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen diff --git a/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit.py b/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit.py index 2b7cbf1390d2..a98aa0e03954 100644 --- a/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit.py +++ b/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit.py @@ -1,9 +1,7 @@ -from functools import partial from typing import List, Tuple import pytest import torch -import torch.multiprocessing as mp try: from timm.models.vision_transformer import vit_large_patch16_384 as vit @@ -16,6 +14,7 @@ from test_autochunk_vit_utils import run_test from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE +from colossalai.testing import clear_cache_before_run, parameterize, spawn def get_data() -> Tuple[List, List]: @@ -28,16 +27,17 @@ def get_data() -> Tuple[List, List]: not (AUTOCHUNK_AVAILABLE and HAS_REPO), reason="torch version is lower than 1.12.0", ) -@pytest.mark.parametrize("model", MODELS) -@pytest.mark.parametrize("max_memory", [None, 32, 40]) +@clear_cache_before_run() +@parameterize("model", MODELS) +@parameterize("max_memory", [None, 32, 40]) def test_evoformer_block(model, max_memory): - run_func = partial( + spawn( run_test, + 1, max_memory=max_memory, model=model, data=get_data(), ) - mp.spawn(run_func, nprocs=1) if __name__ == "__main__": diff --git a/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit_utils.py b/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit_utils.py index 035dd59799b4..317606fc4781 100644 --- a/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit_utils.py +++ b/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit_utils.py @@ -8,7 +8,7 @@ from colossalai.core import global_context as gpc from colossalai.fx.graph_module import ColoGraphModule from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.utils import free_port +from colossalai.testing import free_port if AUTOCHUNK_AVAILABLE: from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen diff --git a/tests/test_booster/test_accelerator.py b/tests/test_booster/test_accelerator.py index 6958a87e2a08..895c494d0c17 100644 --- a/tests/test_booster/test_accelerator.py +++ b/tests/test_booster/test_accelerator.py @@ -1,27 +1,14 @@ -from functools import partial - -import torch.multiprocessing as mp import torch.nn as nn from colossalai.booster.accelerator import Accelerator -from colossalai.testing import parameterize, rerun_if_address_is_in_use +from colossalai.testing import clear_cache_before_run, parameterize +@clear_cache_before_run() @parameterize('device', ['cpu', 'cuda']) -def run_accelerator(device): +def test_accelerator(device): acceleartor = Accelerator(device) model = nn.Linear(8, 8) model = acceleartor.configure_model(model) assert next(model.parameters()).device.type == device del model, acceleartor - - -def run_dist(rank): - run_accelerator() - - -@rerun_if_address_is_in_use() -def test_accelerator(): - world_size = 1 - run_func = partial(run_dist) - mp.spawn(run_func, nprocs=world_size) diff --git a/tests/test_booster/test_mixed_precision/test_fp16_torch.py b/tests/test_booster/test_mixed_precision/test_fp16_torch.py index bacf29014193..963387da262b 100644 --- a/tests/test_booster/test_mixed_precision/test_fp16_torch.py +++ b/tests/test_booster/test_mixed_precision/test_fp16_torch.py @@ -1,13 +1,9 @@ -from functools import partial - import torch -import torch.multiprocessing as mp from torch.optim import Adam import colossalai from colossalai.booster.mixed_precision import FP16TorchMixedPrecision -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo @@ -41,6 +37,4 @@ def run_torch_amp(rank, world_size, port): @rerun_if_address_is_in_use() def test_torch_ddp_plugin(): - world_size = 1 - run_func = partial(run_torch_amp, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_torch_amp, 1) diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index 169983a76110..a3c63fd09d26 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -1,17 +1,12 @@ -from functools import partial - -import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import colossalai from colossalai.booster import Booster from colossalai.booster.plugin import GeminiPlugin from colossalai.nn.optimizer import HybridAdam from colossalai.tensor.colo_parameter import ColoParameter -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo @@ -119,9 +114,7 @@ def run_dist(rank, world_size, port, early_stop: bool = True): @rerun_if_address_is_in_use() def test_gemini_plugin(early_stop: bool = True): - world_size = 2 - run_func = partial(run_dist, world_size=world_size, port=free_port(), early_stop=early_stop) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 2, early_stop=early_stop) if __name__ == '__main__': diff --git a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py index 71e8582cc364..c225a1a069dd 100644 --- a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py @@ -1,8 +1,5 @@ -from functools import partial - import torch import torch.distributed as dist -import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import SGD @@ -10,8 +7,7 @@ from colossalai.booster import Booster from colossalai.booster.plugin import TorchDDPPlugin from colossalai.interface import OptimizerWrapper -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo @@ -103,6 +99,4 @@ def run_dist(rank, world_size, port): @rerun_if_address_is_in_use() def test_torch_ddp_plugin(): - world_size = 2 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 2) diff --git a/tests/test_checkpoint_io/test_general_checkpoint_io.py b/tests/test_checkpoint_io/test_general_checkpoint_io.py index dfbb16af4ec6..0f78184f70e1 100644 --- a/tests/test_checkpoint_io/test_general_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_general_checkpoint_io.py @@ -6,6 +6,7 @@ from torchvision.models import resnet18 from colossalai.checkpoint_io import GeneralCheckpointIO +from colossalai.testing import clear_cache_before_run, parameterize # ======== # Note: @@ -15,7 +16,8 @@ # ======== -@pytest.mark.parametrize('use_safetensors', [True, False]) +@clear_cache_before_run() +@parameterize('use_safetensors', [True, False]) def test_unsharded_checkpoint(use_safetensors: bool): # create a model and optimizer model = resnet18() diff --git a/tests/test_cluster/test_device_mesh_manager.py b/tests/test_cluster/test_device_mesh_manager.py index b79814735325..b42ef1fe0062 100644 --- a/tests/test_cluster/test_device_mesh_manager.py +++ b/tests/test_cluster/test_device_mesh_manager.py @@ -1,14 +1,9 @@ -from functools import partial - import torch -import torch.multiprocessing as mp from colossalai.cluster.device_mesh_manager import DeviceMeshInfo, DeviceMeshManager -from colossalai.device.device_mesh import DeviceMesh -from colossalai.fx.tracer import ColoTracer from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.utils import free_port +from colossalai.testing import spawn def check_device_mesh_manager(rank, world_size, port): @@ -31,9 +26,7 @@ def check_device_mesh_manager(rank, world_size, port): def test_device_mesh_manager(): - world_size = 4 - run_func = partial(check_device_mesh_manager, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_device_mesh_manager, 4) if __name__ == '__main__': diff --git a/tests/test_comm/test_boardcast_send_recv_v2.py b/tests/test_comm/test_boardcast_send_recv_v2.py index 1520d6054043..253f6f21cd80 100644 --- a/tests/test_comm/test_boardcast_send_recv_v2.py +++ b/tests/test_comm/test_boardcast_send_recv_v2.py @@ -1,17 +1,12 @@ -from functools import partial -from typing import List - import pytest import torch -import torch.distributed as dist -import torch.multiprocessing as mp -from colossalai.communication.p2p_v2 import _send_object, _recv_object, init_process_group + +from colossalai.communication.p2p_v2 import _recv_object, _send_object from colossalai.context import ParallelMode from colossalai.core import global_context as gpc from colossalai.initialize import launch -from colossalai.utils import free_port, get_current_device -from colossalai.testing import rerun_if_address_is_in_use from colossalai.logging import disable_existing_loggers +from colossalai.testing import rerun_if_address_is_in_use, spawn disable_existing_loggers() world_size = 4 @@ -45,9 +40,7 @@ def check_layer(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_object_list_p2p(): - disable_existing_loggers() - run_func = partial(check_layer, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer, world_size) if __name__ == '__main__': diff --git a/tests/test_comm/test_comm.py b/tests/test_comm/test_comm.py index 07cb67730d24..747596bd2ded 100644 --- a/tests/test_comm/test_comm.py +++ b/tests/test_comm/test_comm.py @@ -1,15 +1,13 @@ -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp + from colossalai.communication import all_gather, all_reduce, reduce_scatter from colossalai.context import ParallelMode from colossalai.core import global_context as gpc from colossalai.initialize import launch -from colossalai.utils import free_port, get_current_device -from colossalai.testing import rerun_if_address_is_in_use +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device CONFIG = dict(parallel=dict(data=8, pipeline=1, tensor=dict(mode=None, size=1))) @@ -66,9 +64,7 @@ def check_layer(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_comm(): - world_size = 4 - run_func = partial(check_layer, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer, 4) if __name__ == '__main__': diff --git a/tests/test_comm/test_object_list_p2p.py b/tests/test_comm/test_object_list_p2p.py index 701e3e8ade79..e9d7630c1543 100644 --- a/tests/test_comm/test_object_list_p2p.py +++ b/tests/test_comm/test_object_list_p2p.py @@ -1,15 +1,18 @@ -from functools import partial - import pytest import torch -import torch.distributed as dist -import torch.multiprocessing as mp -from colossalai.communication.p2p import send_forward, recv_forward, send_backward, recv_backward, send_forward_recv_backward, send_backward_recv_forward + +from colossalai.communication.p2p import ( + recv_backward, + recv_forward, + send_backward, + send_backward_recv_forward, + send_forward, + send_forward_recv_backward, +) from colossalai.context import ParallelMode from colossalai.core import global_context as gpc from colossalai.initialize import launch -from colossalai.utils import free_port, get_current_device -from colossalai.testing import rerun_if_address_is_in_use +from colossalai.testing import rerun_if_address_is_in_use, spawn CONFIG = dict(parallel=dict(pipeline=2)) torch.manual_seed(123) @@ -96,9 +99,7 @@ def check_layer(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_object_list_p2p(): - world_size = 2 - run_func = partial(check_layer, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer, 2) if __name__ == '__main__': diff --git a/tests/test_comm/test_object_list_p2p_v2.py b/tests/test_comm/test_object_list_p2p_v2.py index c639ac9f8ef3..cae38385b6e1 100644 --- a/tests/test_comm/test_object_list_p2p_v2.py +++ b/tests/test_comm/test_object_list_p2p_v2.py @@ -1,16 +1,12 @@ -from functools import partial - import pytest import torch -import torch.distributed as dist -import torch.multiprocessing as mp -from colossalai.communication.p2p_v2 import send_forward, recv_forward, send_backward, recv_backward, init_process_group -from colossalai.context import ParallelMode, Initializer_Pipeline + +from colossalai.communication.p2p_v2 import recv_backward, recv_forward, send_backward, send_forward +from colossalai.context import ParallelMode from colossalai.core import global_context as gpc from colossalai.initialize import launch -from colossalai.utils import free_port, get_current_device -from colossalai.testing import rerun_if_address_is_in_use from colossalai.logging import disable_existing_loggers +from colossalai.testing import rerun_if_address_is_in_use, spawn disable_existing_loggers() @@ -121,10 +117,7 @@ def check_layer(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_object_list_p2p(): - disable_existing_loggers() - run_func = partial(check_layer, world_size=world_size, port=free_port()) - disable_existing_loggers() - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer, world_size) if __name__ == '__main__': diff --git a/tests/test_context/test_hybrid_parallel.py b/tests/test_context/test_hybrid_parallel.py index f311b1d2e736..9f26a5af53ce 100644 --- a/tests/test_context/test_hybrid_parallel.py +++ b/tests/test_context/test_hybrid_parallel.py @@ -1,19 +1,17 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial from pathlib import Path + import pytest import torch -import torch.multiprocessing as mp from colossalai import launch +from colossalai.context import reset_seeds from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc -from colossalai.utils import free_port -from colossalai.context import reset_seeds from colossalai.global_variables import tensor_parallel_env as tp_env -from colossalai.testing import rerun_if_address_is_in_use +from colossalai.testing import free_port, rerun_if_address_is_in_use, spawn CONFIG_PATH_LIST = list(Path(__file__).parent.glob('configs/*.py')) @@ -134,9 +132,14 @@ def init_context(config_path, rank, world_size, backend, port, host): torch.cuda.empty_cache() -def run_dist(rank, world_size, backend, port_list, host): - for config_path, port in zip(CONFIG_PATH_LIST, port_list): - init_context(config_path=config_path, rank=rank, world_size=world_size, backend=backend, port=port, host=host) +def run_dist(rank, world_size, port, backend, port_list, host): + for config_path, current_port in zip(CONFIG_PATH_LIST, port_list): + init_context(config_path=config_path, + rank=rank, + world_size=world_size, + backend=backend, + port=current_port, + host=host) reset_seeds() @@ -156,8 +159,7 @@ def test_context(): port_list.append(port) break - test_fn = partial(run_dist, world_size=world_size, backend='gloo', port_list=port_list, host='localhost') - mp.spawn(test_fn, nprocs=world_size) + spawn(run_dist, world_size, backend='gloo', port_list=port_list, host='localhost') if __name__ == '__main__': diff --git a/tests/test_data/test_data_parallel_sampler.py b/tests/test_data/test_data_parallel_sampler.py index 54fa44bdc0c2..2ad3fd696c39 100644 --- a/tests/test_data/test_data_parallel_sampler.py +++ b/tests/test_data/test_data_parallel_sampler.py @@ -2,20 +2,18 @@ # -*- encoding: utf-8 -*- import os -from functools import partial from pathlib import Path import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp +from torchvision import datasets, transforms import colossalai -from torchvision import transforms, datasets -from colossalai.context import ParallelMode, Config +from colossalai.context import Config, ParallelMode from colossalai.core import global_context as gpc -from colossalai.utils import get_dataloader, free_port -from colossalai.testing import rerun_if_address_is_in_use +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_dataloader CONFIG = Config(dict( parallel=dict( @@ -58,9 +56,7 @@ def run_data_sampler(rank, world_size, port): @pytest.mark.cpu @rerun_if_address_is_in_use() def test_data_sampler(): - world_size = 4 - test_func = partial(run_data_sampler, world_size=world_size, port=free_port()) - mp.spawn(test_func, nprocs=world_size) + spawn(run_data_sampler, 4) if __name__ == '__main__': diff --git a/tests/test_data/test_deterministic_dataloader.py b/tests/test_data/test_deterministic_dataloader.py index 4d76e7f137f1..239e79dff7d8 100644 --- a/tests/test_data/test_deterministic_dataloader.py +++ b/tests/test_data/test_deterministic_dataloader.py @@ -2,21 +2,18 @@ # -*- encoding: utf-8 -*- import os -from functools import partial from pathlib import Path import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp -from torchvision import transforms, datasets +from torchvision import datasets, transforms import colossalai -from colossalai.context import ParallelMode, Config +from colossalai.context import Config, ParallelMode from colossalai.core import global_context as gpc -from colossalai.utils import get_dataloader, free_port -from colossalai.testing import rerun_if_address_is_in_use -from torchvision import transforms +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_dataloader CONFIG = Config( dict( @@ -70,9 +67,7 @@ def run_data_sampler(rank, world_size, port): @pytest.mark.cpu @rerun_if_address_is_in_use() def test_data_sampler(): - world_size = 4 - test_func = partial(run_data_sampler, world_size=world_size, port=free_port()) - mp.spawn(test_func, nprocs=world_size) + spawn(run_data_sampler, 4) if __name__ == '__main__': diff --git a/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py b/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py index 3c2390c92837..4d63592f12b0 100644 --- a/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py +++ b/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py @@ -1,25 +1,22 @@ import os - -from functools import partial from pathlib import Path -import colossalai import pytest import torch -import torch.multiprocessing as mp +from torchvision import transforms +from torchvision.datasets import CIFAR10 + +import colossalai from colossalai.amp import AMP_TYPE -from colossalai.trainer import Trainer, hooks from colossalai.context import ParallelMode -from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus -from colossalai.utils import free_port from colossalai.core import global_context as gpc from colossalai.logging import get_dist_logger from colossalai.nn import CrossEntropyLoss from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR -from colossalai.utils import get_dataloader from colossalai.pipeline.pipelinable import PipelinableContext -from torchvision.datasets import CIFAR10 -from torchvision import transforms +from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus, spawn +from colossalai.trainer import Trainer, hooks +from colossalai.utils import get_dataloader BATCH_SIZE = 4 NUM_EPOCHS = 60 @@ -96,9 +93,7 @@ def run_trainer(rank, world_size, port): @skip_if_not_enough_gpus(min_gpus=8) @rerun_if_address_is_in_use() def test_hybrid_parallel(): - world_size = 8 - run_func = partial(run_trainer, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_trainer, 8) if __name__ == '__main__': diff --git a/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor_v2.py b/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor_v2.py index 2bafe0f7e374..67d2ba5f5d98 100644 --- a/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor_v2.py +++ b/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor_v2.py @@ -1,111 +1,104 @@ -import os - -from functools import partial -from pathlib import Path - -import colossalai -import pytest -import torch -import torch.multiprocessing as mp -from colossalai.amp import AMP_TYPE -from colossalai.trainer import Trainer, hooks -from colossalai.context import ParallelMode -from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus -from colossalai.utils import free_port -from colossalai.core import global_context as gpc -from colossalai.logging import get_dist_logger -from colossalai.nn import CrossEntropyLoss -from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR -from colossalai.utils import get_dataloader -from colossalai.pipeline.pipelinable import PipelinableContext -from colossalai.logging import disable_existing_loggers -from torchvision.datasets import CIFAR10 -from torchvision import transforms - -from colossalai.engine.schedule._pipeline_schedule_v2 import PipelineScheduleV2 - -disable_existing_loggers() -BATCH_SIZE = 4 -NUM_EPOCHS = 10 -WARMUP_EPOCHS = 5 -CONFIG = dict(NUM_MICRO_BATCHES=2, - parallel=dict(pipeline=2, tensor=dict(size=1, mode='1d')), - fp16=dict(mode=AMP_TYPE.NAIVE), - gradient_accumulation=2) - - -def run_trainer(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - - disable_existing_loggers() - # get logger - logger = get_dist_logger() - - pipelinable = PipelinableContext() - try: - from titans.model.vit import vit_tiny_patch4_32 - except ImportError: - logger.warning('skip the test_cifar_with_data_pipeline_tensor test because titan is not installed') - logger.warning('please install titan from https://github.com/hpcaitech/Titans') - return - with pipelinable: - model = vit_tiny_patch4_32() - pipelinable.to_layer_list() - pipelinable.policy = "uniform" - model = pipelinable.partition(1, gpc.pipeline_parallel_size, gpc.get_local_rank(ParallelMode.PIPELINE)) - - # craete dataloaders - root = Path(os.environ['DATA']) - transform_train = transforms.Compose([ - transforms.RandomCrop(32, padding=4, pad_if_needed=True), - transforms.AutoAugment(policy=transforms.AutoAugmentPolicy.CIFAR10), - transforms.ToTensor(), - transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), - ]) - train_dataset = CIFAR10(root=root, train=True, download=True, transform=transform_train) - train_dataloader = get_dataloader(dataset=train_dataset, shuffle=True, batch_size=BATCH_SIZE, pin_memory=True) - - # create loss function - criterion = CrossEntropyLoss(label_smoothing=0.1) - - # create optimizer - optimizer = torch.optim.AdamW(model.parameters(), lr=0.001, weight_decay=0) - - # create lr scheduler - lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer, total_steps=NUM_EPOCHS, warmup_steps=WARMUP_EPOCHS) - - # intiailize - engine, train_dataloader, *_ = colossalai.initialize(model=model, - optimizer=optimizer, - criterion=criterion, - train_dataloader=train_dataloader) - - engine._schedule = PipelineScheduleV2(num_microbatches=gpc.config.NUM_MICRO_BATCHES) - - logger = get_dist_logger() - - trainer = Trainer(engine=engine, logger=logger) - - hook_list = [ - hooks.LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=False), - ] - - trainer.fit(train_dataloader=train_dataloader, - max_steps=2, - epochs=NUM_EPOCHS, - hooks=hook_list, - display_progress=True) - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_hybrid_parallel(): - world_size = 2 - run_func = partial(run_trainer, world_size=world_size, port=free_port()) - disable_existing_loggers() - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_hybrid_parallel() +import os +from pathlib import Path + +import pytest +import torch +from torchvision import transforms +from torchvision.datasets import CIFAR10 + +import colossalai +from colossalai.amp import AMP_TYPE +from colossalai.context import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.engine.schedule._pipeline_schedule_v2 import PipelineScheduleV2 +from colossalai.logging import disable_existing_loggers, get_dist_logger +from colossalai.nn import CrossEntropyLoss +from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR +from colossalai.pipeline.pipelinable import PipelinableContext +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.trainer import Trainer, hooks +from colossalai.utils import get_dataloader + +disable_existing_loggers() +BATCH_SIZE = 4 +NUM_EPOCHS = 10 +WARMUP_EPOCHS = 5 +CONFIG = dict(NUM_MICRO_BATCHES=2, + parallel=dict(pipeline=2, tensor=dict(size=1, mode='1d')), + fp16=dict(mode=AMP_TYPE.NAIVE), + gradient_accumulation=2) + + +def run_trainer(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + disable_existing_loggers() + # get logger + logger = get_dist_logger() + + pipelinable = PipelinableContext() + try: + from titans.model.vit import vit_tiny_patch4_32 + except ImportError: + logger.warning('skip the test_cifar_with_data_pipeline_tensor test because titan is not installed') + logger.warning('please install titan from https://github.com/hpcaitech/Titans') + return + with pipelinable: + model = vit_tiny_patch4_32() + pipelinable.to_layer_list() + pipelinable.policy = "uniform" + model = pipelinable.partition(1, gpc.pipeline_parallel_size, gpc.get_local_rank(ParallelMode.PIPELINE)) + + # craete dataloaders + root = Path(os.environ['DATA']) + transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4, pad_if_needed=True), + transforms.AutoAugment(policy=transforms.AutoAugmentPolicy.CIFAR10), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), + ]) + train_dataset = CIFAR10(root=root, train=True, download=True, transform=transform_train) + train_dataloader = get_dataloader(dataset=train_dataset, shuffle=True, batch_size=BATCH_SIZE, pin_memory=True) + + # create loss function + criterion = CrossEntropyLoss(label_smoothing=0.1) + + # create optimizer + optimizer = torch.optim.AdamW(model.parameters(), lr=0.001, weight_decay=0) + + # create lr scheduler + lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer, total_steps=NUM_EPOCHS, warmup_steps=WARMUP_EPOCHS) + + # intiailize + engine, train_dataloader, *_ = colossalai.initialize(model=model, + optimizer=optimizer, + criterion=criterion, + train_dataloader=train_dataloader) + + engine._schedule = PipelineScheduleV2(num_microbatches=gpc.config.NUM_MICRO_BATCHES) + + logger = get_dist_logger() + + trainer = Trainer(engine=engine, logger=logger) + + hook_list = [ + hooks.LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=False), + ] + + trainer.fit(train_dataloader=train_dataloader, + max_steps=2, + epochs=NUM_EPOCHS, + hooks=hook_list, + display_progress=True) + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_hybrid_parallel(): + spawn(run_trainer, 2) + disable_existing_loggers() + + +if __name__ == '__main__': + test_hybrid_parallel() diff --git a/tests/test_ddp/test_ddp_ignore_params.py b/tests/test_ddp/test_ddp_ignore_params.py index 2ad20f6bec72..39efcd41a1d4 100644 --- a/tests/test_ddp/test_ddp_ignore_params.py +++ b/tests/test_ddp/test_ddp_ignore_params.py @@ -1,19 +1,16 @@ import os import random -from functools import partial from typing import Callable, Type import numpy as np import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import colossalai from colossalai.nn.parallel import ColoDDP from colossalai.tensor import ProcessGroup -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration @@ -88,8 +85,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [2]) @rerun_if_address_is_in_use() def test_ddp_ignore_params(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_ddp/test_ddp_state_dict.py b/tests/test_ddp/test_ddp_state_dict.py index bd4742ff2cc9..54f89f972765 100644 --- a/tests/test_ddp/test_ddp_state_dict.py +++ b/tests/test_ddp/test_ddp_state_dict.py @@ -1,16 +1,12 @@ -import copy from collections import OrderedDict -from functools import partial import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.nn.parallel import ColoDDP from colossalai.tensor import ColoParameter, ProcessGroup -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs @@ -64,8 +60,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 2]) @rerun_if_address_is_in_use() def test_state_dict(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_ddp/test_reducer.py b/tests/test_ddp/test_reducer.py index 5b302d99ffb1..e8d3a112c938 100644 --- a/tests/test_ddp/test_reducer.py +++ b/tests/test_ddp/test_reducer.py @@ -1,15 +1,15 @@ +from functools import partial + import pytest -import colossalai import torch -import torch.multiprocessing as mp -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils.cuda import get_current_device -from colossalai.utils import free_port -from functools import partial -from colossalai.nn.parallel.reducer import Reducer import torch.distributed as dist from torch.distributed.distributed_c10d import _get_default_group +import colossalai +from colossalai.nn.parallel.reducer import Reducer +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils.cuda import get_current_device + REDUCE_CNT = 0 @@ -40,8 +40,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 2]) @rerun_if_address_is_in_use() def test_reducer(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_device/test_alpha_beta.py b/tests/test_device/test_alpha_beta.py index 99abacd1342b..ab933ed57d0d 100644 --- a/tests/test_device/test_alpha_beta.py +++ b/tests/test_device/test_alpha_beta.py @@ -1,16 +1,12 @@ -from functools import partial - import pytest -import torch.multiprocessing as mp from colossalai.device import AlphaBetaProfiler from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn -def check_alpha_beta(rank, physical_devices, world_size, port): +def check_alpha_beta(rank, world_size, port, physical_devices): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') profiler = AlphaBetaProfiler(physical_devices) @@ -24,9 +20,7 @@ def check_alpha_beta(rank, physical_devices, world_size, port): @parameterize('physical_devices', [[0, 1, 2, 3], [0, 3]]) @rerun_if_address_is_in_use() def test_profile_alpha_beta(physical_devices): - world_size = 4 - run_func = partial(check_alpha_beta, physical_devices=physical_devices, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_alpha_beta, 4, physical_devices=physical_devices) if __name__ == '__main__': diff --git a/tests/test_device/test_extract_alpha_beta.py b/tests/test_device/test_extract_alpha_beta.py index e32bebdd908e..52604b9c6a49 100644 --- a/tests/test_device/test_extract_alpha_beta.py +++ b/tests/test_device/test_extract_alpha_beta.py @@ -1,16 +1,12 @@ -from functools import partial - import pytest -import torch.multiprocessing as mp from colossalai.device import AlphaBetaProfiler from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn -def check_extract_alpha_beta(rank, physical_devices, world_size, port): +def check_extract_alpha_beta(rank, world_size, port, physical_devices): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') profiler = AlphaBetaProfiler(physical_devices) @@ -27,12 +23,7 @@ def check_extract_alpha_beta(rank, physical_devices, world_size, port): @parameterize('physical_devices', [[0, 1, 2, 3], [0, 3]]) @rerun_if_address_is_in_use() def test_profile_alpha_beta(physical_devices): - world_size = 4 - run_func = partial(check_extract_alpha_beta, - physical_devices=physical_devices, - world_size=world_size, - port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_extract_alpha_beta, 4, physical_devices=physical_devices) if __name__ == '__main__': diff --git a/tests/test_device/test_init_logical_pg.py b/tests/test_device/test_init_logical_pg.py index 3172897fb5cd..2b7060c4846a 100644 --- a/tests/test_device/test_init_logical_pg.py +++ b/tests/test_device/test_init_logical_pg.py @@ -1,15 +1,12 @@ -import torch -from functools import partial import pytest +import torch import torch.distributed as dist -import torch.multiprocessing as mp from torch.distributed import ReduceOp from colossalai.core import global_context as gpc -from colossalai.initialize import launch -from colossalai.utils import free_port -from colossalai.testing import rerun_if_address_is_in_use from colossalai.device.device_mesh import DeviceMesh +from colossalai.initialize import launch +from colossalai.testing import rerun_if_address_is_in_use, spawn def check_layer(rank, world_size, port): @@ -40,9 +37,7 @@ def check_layer(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_logical_pg(): - world_size = 4 - run_func = partial(check_layer, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer, 4) if __name__ == '__main__': diff --git a/tests/test_device/test_search_logical_device_mesh.py b/tests/test_device/test_search_logical_device_mesh.py index 591eafb2a50d..b22a76eabc2f 100644 --- a/tests/test_device/test_search_logical_device_mesh.py +++ b/tests/test_device/test_search_logical_device_mesh.py @@ -1,16 +1,12 @@ -from functools import partial - import pytest -import torch.multiprocessing as mp from colossalai.device import AlphaBetaProfiler from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn -def check_alpha_beta(rank, physical_devices, world_size, port): +def check_alpha_beta(rank, world_size, port, physical_devices): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') profiler = AlphaBetaProfiler(physical_devices) @@ -27,9 +23,7 @@ def check_alpha_beta(rank, physical_devices, world_size, port): @parameterize('physical_devices', [[0, 1, 2, 3], [0, 3]]) @rerun_if_address_is_in_use() def test_profile_alpha_beta(physical_devices): - world_size = 4 - run_func = partial(check_alpha_beta, physical_devices=physical_devices, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_alpha_beta, 4, physical_devices=physical_devices) if __name__ == '__main__': diff --git a/tests/test_engine/test_engine.py b/tests/test_engine/test_engine.py index fb5bd1e1602e..62493cf3712d 100644 --- a/tests/test_engine/test_engine.py +++ b/tests/test_engine/test_engine.py @@ -1,13 +1,10 @@ -from functools import partial +import pytest import colossalai -import pytest -import torch.multiprocessing as mp from colossalai.amp import AMP_TYPE from colossalai.core import global_context as gpc -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from tests.components_to_test.registry import non_distributed_component_funcs -from colossalai.testing import parameterize, rerun_if_address_is_in_use CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)), fp16=dict(mode=None), @@ -58,9 +55,7 @@ def run_engine(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_engine(): - world_size = 2 - run_func = partial(run_engine, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_engine, 2) if __name__ == '__main__': diff --git a/tests/test_engine/test_gradient_accumluation.py b/tests/test_engine/test_gradient_accumluation.py index 7f5ee47be8e6..7783827c7c44 100644 --- a/tests/test_engine/test_gradient_accumluation.py +++ b/tests/test_engine/test_gradient_accumluation.py @@ -1,22 +1,20 @@ import os -from functools import partial from pathlib import Path -import colossalai -from colossalai.testing.utils import rerun_if_address_is_in_use import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn -from colossalai.core import global_context as gpc -from colossalai.logging import get_dist_logger -from colossalai.utils import free_port, get_dataloader -from colossalai.testing import rerun_if_address_is_in_use from torch.optim import Adam from torchvision import transforms from torchvision.datasets import CIFAR10 from torchvision.models import resnet18 +import colossalai +from colossalai.core import global_context as gpc +from colossalai.logging import get_dist_logger +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_dataloader + # Config BATCH_SIZE = 2 NUM_CLASSES = 10 @@ -90,9 +88,7 @@ def run_no_pipeline(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_engine(): - world_size = 4 - func = partial(run_no_pipeline, world_size=world_size, port=free_port()) - mp.spawn(func, nprocs=world_size) + spawn(run_no_pipeline, 4) if __name__ == '__main__': diff --git a/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py b/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py index 83df1bb5e69c..ab483f7e47a3 100644 --- a/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py +++ b/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py @@ -1,15 +1,13 @@ import pytest import torch -import torch.multiprocessing as mp import torch.nn.functional as F -from torch.fx import GraphModule from torch.utils.checkpoint import checkpoint import colossalai from colossalai.core import global_context as gpc from colossalai.fx import ColoTracer from colossalai.fx.graph_module import ColoGraphModule -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn try: from colossalai.fx.codegen import ActivationCheckpointCodeGen @@ -65,9 +63,9 @@ def forward(self, x, y): return y1 + y2 + y3 + y4 + y5 + y6 -def _run_act_ckpt_codegen(rank): +def _run_act_ckpt_codegen(rank, world_size, port): # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly - colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl') + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and run forward model = MyModule() @@ -118,13 +116,14 @@ def _run_act_ckpt_codegen(rank): @pytest.mark.skipif(not with_codegen, reason='torch version is lower than 1.12.0') +@rerun_if_address_is_in_use() def test_act_ckpt_codegen(): - mp.spawn(_run_act_ckpt_codegen, nprocs=1) + spawn(_run_act_ckpt_codegen, 1) -def _run_act_ckpt_python_code_torch11(rank): +def _run_act_ckpt_python_code_torch11(rank, world_size, port): # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly - colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl') + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and run forward model = MyModule() @@ -174,8 +173,9 @@ def _run_act_ckpt_python_code_torch11(rank): @pytest.mark.skipif(with_codegen, reason='torch version is equal to or higher than 1.12.0') @pytest.mark.skip(reason="currently torch11 ColoGraphModule is not done") +@rerun_if_address_is_in_use() def test_act_ckpt_python_code_torch11(): - mp.spawn(_run_act_ckpt_python_code_torch11, nprocs=1) + spawn(_run_act_ckpt_python_code_torch11, 1) if __name__ == '__main__': diff --git a/tests/test_fx/test_codegen/test_nested_activation_checkpoint_codegen.py b/tests/test_fx/test_codegen/test_nested_activation_checkpoint_codegen.py index 6b3a49d181e1..9064023d4f68 100644 --- a/tests/test_fx/test_codegen/test_nested_activation_checkpoint_codegen.py +++ b/tests/test_fx/test_codegen/test_nested_activation_checkpoint_codegen.py @@ -1,15 +1,11 @@ import pytest import torch -import torch.multiprocessing as mp -import torch.nn.functional as F -from torch.fx import GraphModule -from torch.utils.checkpoint import checkpoint import colossalai from colossalai.core import global_context as gpc from colossalai.fx import ColoTracer from colossalai.fx.graph_module import ColoGraphModule -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn try: from colossalai.fx.codegen import ActivationCheckpointCodeGen @@ -35,9 +31,9 @@ def forward(self, x): return self.linear6(self.linear5(self.linear4(self.linear3(self.linear2(self.linear1(x)))))) -def _run_act_ckpt_codegen(rank): +def _run_act_ckpt_codegen(rank, world_size, port): # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly - colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl') + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and run forward model = MyModule() @@ -89,12 +85,12 @@ def _run_act_ckpt_codegen(rank): @pytest.mark.skipif(not with_codegen, reason='torch version is lower than 1.12.0') def test_act_ckpt_codegen(): - mp.spawn(_run_act_ckpt_codegen, nprocs=1) + spawn(_run_act_ckpt_codegen, 1) -def _run_act_ckpt_python_code_torch11(rank): +def _run_act_ckpt_python_code_torch11(rank, world_size, port): # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly - colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl') + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and run forward model = MyModule() @@ -146,8 +142,9 @@ def _run_act_ckpt_python_code_torch11(rank): @pytest.mark.skipif(with_codegen, reason='torch version is equal to or higher than 1.12.0') @pytest.mark.skip(reason="currently torch11 ColoGraphModule is not done") +@rerun_if_address_is_in_use() def test_act_ckpt_python_code_torch11(): - mp.spawn(_run_act_ckpt_python_code_torch11, nprocs=1) + spawn(_run_act_ckpt_python_code_torch11, 1) if __name__ == '__main__': diff --git a/tests/test_fx/test_codegen/test_offload_codegen.py b/tests/test_fx/test_codegen/test_offload_codegen.py index 5d090066c763..96e88eb92b33 100644 --- a/tests/test_fx/test_codegen/test_offload_codegen.py +++ b/tests/test_fx/test_codegen/test_offload_codegen.py @@ -2,15 +2,13 @@ import pytest import torch -import torch.multiprocessing as mp -import torch.nn.functional as F from torch.fx import GraphModule import colossalai from colossalai.core import global_context as gpc from colossalai.fx import ColoTracer from colossalai.fx.graph_module import ColoGraphModule -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn try: from colossalai.fx.codegen import ActivationCheckpointCodeGen @@ -66,9 +64,9 @@ def _test_fwd_and_bwd(model: torch.nn.Module, gm: ColoGraphModule, data: torch.T assert _is_all_gradient_close(model, gm), "gm doesn't have the same gradient as original one" -def _run_offload_codegen(rank): +def _run_offload_codegen(rank, world_size, port): # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly - colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl') + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and input model = MyNet().cuda() @@ -116,13 +114,14 @@ def _run_offload_codegen(rank): @pytest.mark.skipif(not with_codegen, reason='torch version is lower than 1.12.0') +@rerun_if_address_is_in_use() def test_act_ckpt_codegen(): - mp.spawn(_run_offload_codegen, nprocs=1) + spawn(_run_offload_codegen, 1) -def _run_offload_codegen_torch11(rank): +def _run_offload_codegen_torch11(rank, world_size, port): # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly - colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl') + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and input model = MyNet().cuda() @@ -171,8 +170,9 @@ def _run_offload_codegen_torch11(rank): @pytest.mark.skip(reason="currently torch11 ColoGraphModule is not implemented") +@rerun_if_address_is_in_use() def test_act_ckpt_python_code_torch11(): - mp.spawn(_run_offload_codegen_torch11, nprocs=1) + spawn(_run_offload_codegen_torch11, 1) if __name__ == "__main__": diff --git a/tests/test_fx/test_coloproxy.py b/tests/test_fx/test_coloproxy.py index 2bb6cf86466c..96cf5198da10 100644 --- a/tests/test_fx/test_coloproxy.py +++ b/tests/test_fx/test_coloproxy.py @@ -1,9 +1,11 @@ +import pytest import torch import torch.nn as nn +from torch.fx import GraphModule + from colossalai.fx.proxy import ColoProxy from colossalai.fx.tracer.tracer import ColoTracer -from torch.fx import GraphModule -import pytest +from colossalai.testing import clear_cache_before_run class Conv1D(nn.Module): @@ -23,6 +25,7 @@ def forward(self, x): return x +@clear_cache_before_run() def test_coloproxy(): tracer = ColoTracer() diff --git a/tests/test_fx/test_comm_size_compute.py b/tests/test_fx/test_comm_size_compute.py index 8825bbb461d6..d3daadd71406 100644 --- a/tests/test_fx/test_comm_size_compute.py +++ b/tests/test_fx/test_comm_size_compute.py @@ -1,13 +1,11 @@ -import colossalai -import colossalai.nn as col_nn -import pytest import torch -import torch.nn as nn +from torch.fx import symbolic_trace + from colossalai.fx._compatibility import is_compatible_with_meta -from colossalai.fx.passes.adding_split_node_pass import (split_with_split_nodes_pass, uniform_split_pass) +from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, uniform_split_pass from colossalai.fx.passes.meta_info_prop import MetaInfoProp from colossalai.fx.passes.utils import get_comm_size -from torch.fx import symbolic_trace +from colossalai.testing import clear_cache_before_run is_compatible = is_compatible_with_meta() if is_compatible: @@ -35,6 +33,7 @@ def forward(self, x): return x +@clear_cache_before_run() def test_comm_size_compute(): model = MLP(MODEL_DIM) input_sample = torch.rand(BATCH_SIZE, MODEL_DIM, device='meta') diff --git a/tests/test_fx/test_complete_workflow.py b/tests/test_fx/test_complete_workflow.py deleted file mode 100644 index a21a351f8d77..000000000000 --- a/tests/test_fx/test_complete_workflow.py +++ /dev/null @@ -1,87 +0,0 @@ -from functools import partial - -import pytest -import torch -import torch.distributed as dist -import torch.multiprocessing as mp -import torch.nn as nn - -import colossalai -from colossalai.fx import ColoTracer -from colossalai.fx.passes.shard_1d_pass import transformer_mlp_pass -from colossalai.tensor import ProcessGroup -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.utils.model.lazy_init_context import LazyInitContext - - -class MLP(torch.nn.Module): - - def __init__(self, dim: int): - super().__init__() - self.linear1 = torch.nn.Linear(dim, dim) - self.linear2 = torch.nn.Linear(dim, dim) - self.dropout = torch.nn.Dropout(0) - self.relu = torch.nn.ReLU() - - def forward(self, x): - x = self.linear1(x) - x = self.dropout(x) - x = self.relu(x) - x = self.linear2(x) - return x - - -def run_workflow(world_size, dev): - # initailization - with LazyInitContext() as ctx: - model = MLP(16) - - for param in model.parameters(): - assert param.is_meta - - # tracing - tracer = ColoTracer() - graph = tracer.trace(model) - gm = torch.fx.GraphModule(model, graph, model.__class__.__name__) - - # annotate - annotated_gm = transformer_mlp_pass(gm, process_group=ProcessGroup(tp_degree=world_size)) - annotated_gm.recompile() - - # materialization and sharding - ctx.lazy_init_parameters(annotated_gm, device=dev) - for param in model.parameters(): - assert not param.is_meta - - # # check sharding - assert list(model.linear1.weight.shape) == [16 // world_size, 16] - assert list(model.linear1.bias.shape) == [16 // world_size] - assert list(model.linear2.weight.shape) == [16, 16 // world_size] - - # test forward to make sure that IR transform will produce the same results - # like how ColoTensor would do it normally - data = torch.rand(4, 16, device=dev) - non_fx_out = model(data) - fx_out = annotated_gm(data) - assert torch.equal(non_fx_out, fx_out), f'{non_fx_out} vs {fx_out}' - - -def run_dist(rank, world_size, dev, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_workflow(world_size, dev) - - -@pytest.mark.dist -@pytest.mark.parametrize('world_size', [1, 2]) -@pytest.mark.parametrize('dev', ['cuda', 'cpu']) -@rerun_if_address_is_in_use() -def test_complete_workflow(world_size, dev): - if dev == 'cpu' and world_size > 1: - return - run_func = partial(run_dist, world_size=world_size, dev=dev, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_complete_workflow(1, 'cuda') diff --git a/tests/test_fx/test_graph_manipulation.py b/tests/test_fx/test_graph_manipulation.py index fb33e58a778c..175b69dd96fe 100644 --- a/tests/test_fx/test_graph_manipulation.py +++ b/tests/test_fx/test_graph_manipulation.py @@ -1,9 +1,11 @@ -import colossalai import torch -from colossalai.fx.passes.utils import get_leaf, get_top, assign_bfs_level_to_nodes -from colossalai.fx import ColoTracer from torch.fx import GraphModule + +import colossalai +from colossalai.fx import ColoTracer from colossalai.fx.passes.meta_info_prop import MetaInfoProp, TensorMetadata +from colossalai.fx.passes.utils import assign_bfs_level_to_nodes, get_leaf, get_top +from colossalai.testing import clear_cache_before_run class MLP(torch.nn.Module): @@ -25,6 +27,7 @@ def forward(self, x): return l4, l5 +@clear_cache_before_run() def test_graph_manipulation(): model = MLP(4) tracer = ColoTracer() diff --git a/tests/test_fx/test_meta/test_aten.py b/tests/test_fx/test_meta/test_aten.py index 209ded89cfb9..e490522dbf15 100644 --- a/tests/test_fx/test_meta/test_aten.py +++ b/tests/test_fx/test_meta/test_aten.py @@ -3,7 +3,9 @@ import pytest import torch import torch.nn as nn + from colossalai.fx._compatibility import is_compatible_with_meta +from colossalai.testing import clear_cache_before_run if is_compatible_with_meta(): from colossalai.fx.profiler import MetaTensor @@ -71,6 +73,7 @@ def run_and_compare(f: Union[nn.Module, Callable], x: torch.Tensor, requires_bac @pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0') +@clear_cache_before_run() def test_meta_aten(): for (aten_op, requires_backward), v in registered_meta.items(): for f, x in v: diff --git a/tests/test_fx/test_meta/test_backward.py b/tests/test_fx/test_meta/test_backward.py index 351c02c5744a..7aed6fd4597b 100644 --- a/tests/test_fx/test_meta/test_backward.py +++ b/tests/test_fx/test_meta/test_backward.py @@ -2,11 +2,14 @@ import timm.models as tmm import torch import torchvision.models as tm + from colossalai.fx._compatibility import is_compatible_with_meta if is_compatible_with_meta(): from colossalai.fx.profiler import MetaTensor +from colossalai.testing import clear_cache_before_run + tm_models = [ tm.vgg11, tm.resnet18, @@ -28,6 +31,7 @@ @pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0') +@clear_cache_before_run() def test_torchvision_models(): for m in tm_models: model = m() @@ -36,6 +40,7 @@ def test_torchvision_models(): @pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0') +@clear_cache_before_run() def test_timm_models(): for m in tmm_models: model = m() diff --git a/tests/test_fx/test_meta/test_meta_trace.py b/tests/test_fx/test_meta/test_meta_trace.py index 404b6d27d2d4..61614f8a6623 100644 --- a/tests/test_fx/test_meta/test_meta_trace.py +++ b/tests/test_fx/test_meta/test_meta_trace.py @@ -2,11 +2,14 @@ import timm.models as tmm import torch import torchvision.models as tm + from colossalai.fx._compatibility import is_compatible_with_meta if is_compatible_with_meta(): from colossalai.fx import meta_trace +from colossalai.testing import clear_cache_before_run + tm_models = [ tm.vgg11, tm.resnet18, @@ -28,6 +31,7 @@ @pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0') +@clear_cache_before_run() def test_torchvision_models_trace(): for m in tm_models: model = m() @@ -36,6 +40,7 @@ def test_torchvision_models_trace(): @pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0') +@clear_cache_before_run() def test_timm_models_trace(): for m in tmm_models: model = m() diff --git a/tests/test_fx/test_meta_info_prop.py b/tests/test_fx/test_meta_info_prop.py index 6fac180d8ba2..a12512696a73 100644 --- a/tests/test_fx/test_meta_info_prop.py +++ b/tests/test_fx/test_meta_info_prop.py @@ -1,7 +1,9 @@ import torch +from torch.fx import symbolic_trace + from colossalai.fx._compatibility import is_compatible_with_meta from colossalai.fx.passes.meta_info_prop import MetaInfoProp, TensorMetadata -from torch.fx import symbolic_trace +from colossalai.testing import clear_cache_before_run if is_compatible_with_meta(): from colossalai.fx.profiler import MetaTensor @@ -18,6 +20,7 @@ def meta_check(meta_info_spec: TensorMetadata, orig_tensor: torch.Tensor): assert meta_info_spec.numel == orig_tensor.numel() +@clear_cache_before_run() def test_meta_info_prop(): model = torch.nn.Linear(DIM_IN, DIM_OUT) input_sample = torch.rand(BATCH_SIZE, DIM_IN, device='meta') diff --git a/tests/test_fx/test_parallel_1d.py b/tests/test_fx/test_parallel_1d.py index 8963ba29cb03..1044be7db1f4 100644 --- a/tests/test_fx/test_parallel_1d.py +++ b/tests/test_fx/test_parallel_1d.py @@ -1,18 +1,15 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial - import pytest import torch -import torch.multiprocessing as mp -from colossalai.core import global_context as gpc -from colossalai.logging import disable_existing_loggers -from colossalai.initialize import launch -from colossalai.utils import free_port -from colossalai.testing import rerun_if_address_is_in_use from torch.fx import symbolic_trace + +from colossalai.core import global_context as gpc from colossalai.fx.passes import column_shard_linear_pass +from colossalai.initialize import launch +from colossalai.logging import disable_existing_loggers +from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn class MLP(torch.nn.Module): @@ -52,11 +49,10 @@ def check_layer(rank, world_size, port): @pytest.mark.dist +@clear_cache_before_run() @rerun_if_address_is_in_use() def test_1d(): - world_size = 2 - run_func = partial(check_layer, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer, 2) if __name__ == '__main__': diff --git a/tests/test_fx/test_pipeline_passes.py b/tests/test_fx/test_pipeline_passes.py index de8a9402ba56..1078dac9db7c 100644 --- a/tests/test_fx/test_pipeline_passes.py +++ b/tests/test_fx/test_pipeline_passes.py @@ -1,12 +1,17 @@ +import pytest import torch import torch.nn as nn -import colossalai -import colossalai.nn as col_nn from torch.fx import symbolic_trace -from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, balanced_split_pass, \ - uniform_split_pass, balanced_split_pass_v2 -import pytest +import colossalai +import colossalai.nn as col_nn +from colossalai.fx.passes.adding_split_node_pass import ( + balanced_split_pass, + balanced_split_pass_v2, + split_with_split_nodes_pass, + uniform_split_pass, +) +from colossalai.testing import clear_cache_before_run MODEL_DIM = 16 BATCH_SIZE = 8 @@ -39,6 +44,7 @@ def pipeline_pass_test_helper(model, data, pass_func): assert output.equal(origin_output) +@clear_cache_before_run() def test_pipeline_passes(): model = MLP(MODEL_DIM) data = torch.rand(BATCH_SIZE, MODEL_DIM) diff --git a/tests/test_fx/test_profiler/test_profiler_meta_info_prop.py b/tests/test_fx/test_profiler/test_profiler_meta_info_prop.py index c717960181ad..b5a6bbe8bf18 100644 --- a/tests/test_fx/test_profiler/test_profiler_meta_info_prop.py +++ b/tests/test_fx/test_profiler/test_profiler_meta_info_prop.py @@ -9,7 +9,7 @@ from colossalai.fx.passes.meta_info_prop import MetaInfoProp from colossalai.fx.profiler import calculate_fwd_out, calculate_fwd_tmp, is_compatible_with_meta, parameter_size from colossalai.fx.tracer.tracer import ColoTracer -from colossalai.testing.pytest_wrapper import run_on_environment_flag +from colossalai.testing import clear_cache_before_run, run_on_environment_flag if is_compatible_with_meta(): from colossalai.fx.profiler import MetaTensor @@ -126,6 +126,7 @@ def run_gpt_forward(gm: torch.fx.GraphModule): @run_on_environment_flag(name='FX_PROFILER') +@clear_cache_before_run() def test_meta_info_prop(): for m in [ tm.alexnet, tm.resnet18, tm.resnet34, tm.resnet50, tm.resnet101, tm.resnet152, tm.densenet121, @@ -155,6 +156,7 @@ def test_meta_info_prop(): @run_on_environment_flag(name='FX_PROFILER') +@clear_cache_before_run() def test_gpt_meta_info_prop(): for m in [gpt2_medium]: model = m().cuda() diff --git a/tests/test_fx/test_tracer/test_activation_checkpoint_annotation.py b/tests/test_fx/test_tracer/test_activation_checkpoint_annotation.py index a834951bb695..632ab8c09750 100644 --- a/tests/test_fx/test_tracer/test_activation_checkpoint_annotation.py +++ b/tests/test_fx/test_tracer/test_activation_checkpoint_annotation.py @@ -4,6 +4,7 @@ from torch.utils.checkpoint import checkpoint from colossalai.fx import ColoTracer +from colossalai.testing import clear_cache_before_run class MLP(torch.nn.Module): @@ -35,6 +36,7 @@ def forward(self, x): return x +@clear_cache_before_run() def test_activation_checkpoint_annotation(): module = MyModule() diff --git a/tests/test_fx/test_tracer/test_bias_addition_module.py b/tests/test_fx/test_tracer/test_bias_addition_module.py index afa30a217604..2f88d8c784e8 100644 --- a/tests/test_fx/test_tracer/test_bias_addition_module.py +++ b/tests/test_fx/test_tracer/test_bias_addition_module.py @@ -1,6 +1,7 @@ import torch from colossalai.fx import ColoGraphModule, ColoTracer +from colossalai.testing import clear_cache_before_run class LinearModel(torch.nn.Module): @@ -32,6 +33,7 @@ def forward(self, x): return x +@clear_cache_before_run() def test_linear_module(): model = LinearModel(3, 6) tracer = ColoTracer() @@ -68,6 +70,7 @@ def test_linear_module(): assert add_node._meta_data.shape == (3, 6) +@clear_cache_before_run() def test_conv_module(): model = ConvModel(3, 6, 2) tracer = ColoTracer() diff --git a/tests/test_fx/test_tracer/test_control_flow.py b/tests/test_fx/test_tracer/test_control_flow.py index ed842cff2776..820729dadb3e 100644 --- a/tests/test_fx/test_tracer/test_control_flow.py +++ b/tests/test_fx/test_tracer/test_control_flow.py @@ -1,7 +1,9 @@ import torch import torch.nn as nn from torch.fx import GraphModule + from colossalai.fx import ColoTracer as Tracer +from colossalai.testing import clear_cache_before_run class ControlFlowModel(nn.Module): @@ -21,6 +23,7 @@ def forward(self, x, y): return x1 - y1 +@clear_cache_before_run() def test_control_flow(): model = ControlFlowModel() tracer = Tracer() diff --git a/tests/test_fx/test_tracer/test_functional_conv.py b/tests/test_fx/test_tracer/test_functional_conv.py index 95670b85f335..a552e905223d 100644 --- a/tests/test_fx/test_tracer/test_functional_conv.py +++ b/tests/test_fx/test_tracer/test_functional_conv.py @@ -1,8 +1,11 @@ import torch from torch.nn import functional as F + from colossalai.fx.tracer.meta_patch import patched_function +from colossalai.testing import clear_cache_before_run +@clear_cache_before_run() def test_conv(): # test F.conv_1d data_1d = torch.rand(3, 16, 10) diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py index 31ba2290ed99..f4d681221191 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py @@ -3,6 +3,7 @@ from hf_tracer_utils import trace_model_and_compare_output from packaging import version +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo BATCH_SIZE = 2 @@ -10,6 +11,7 @@ @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() def test_albert(): sub_registry = model_zoo.get_sub_registry('transformers_albert') diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py index 8db6817c66dc..a833bb30c056 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py @@ -3,10 +3,12 @@ from hf_tracer_utils import trace_model_and_compare_output from packaging import version +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() def test_bert(): sub_registry = model_zoo.get_sub_registry('transformers_bert') diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py index 92ece357bfed..0cbea82e083a 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py @@ -2,6 +2,7 @@ import torch from colossalai.fx import symbolic_trace +from colossalai.testing import clear_cache_before_run from colossalai.testing.random import seed_all from tests.kit.model_zoo import model_zoo @@ -40,6 +41,7 @@ def assert_fn(ta, tb): @pytest.mark.skip(reason='cannot pass this test yet') +@clear_cache_before_run() def test_diffusers(): seed_all(9091, cuda_deterministic=True) @@ -52,6 +54,7 @@ def test_diffusers(): print(f"{name:40s} √") +@clear_cache_before_run() def test_torch_diffusers(): seed_all(65535, cuda_deterministic=True) diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py index 796c17e398d5..67107469d8bb 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py @@ -3,10 +3,12 @@ from hf_tracer_utils import trace_model_and_compare_output from packaging import version +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() def test_gpt(): sub_registry = model_zoo.get_sub_registry('transformers_gpt') diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py index e7bfa607082e..369545b03de1 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py @@ -3,10 +3,12 @@ from hf_tracer_utils import trace_model_and_compare_output from packaging import version +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() def test_opt(): sub_registry = model_zoo.get_sub_registry('transformers_opt') diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py index 5f7e4f81c44e..811cf3b21430 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py @@ -3,10 +3,12 @@ from hf_tracer_utils import trace_model_and_compare_output from packaging import version +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() def test_t5(): sub_registry = model_zoo.get_sub_registry('transformers_t5') diff --git a/tests/test_fx/test_tracer/test_patched_module.py b/tests/test_fx/test_tracer/test_patched_module.py index 94a93e16f3c7..ef778e21801a 100644 --- a/tests/test_fx/test_tracer/test_patched_module.py +++ b/tests/test_fx/test_tracer/test_patched_module.py @@ -1,5 +1,7 @@ import torch + from colossalai.fx.tracer.meta_patch import patched_module +from colossalai.testing import clear_cache_before_run def _run(data, module, patch_fn): @@ -31,6 +33,7 @@ def _assert_output_shape(data, module, patch_fn, expect_exception, output_shape) assert output.shape == output_shape +@clear_cache_before_run() def test_linear(): # test linear patch can produce the meta output with correct shape data = torch.rand(2, 4, device='meta') @@ -42,6 +45,7 @@ def test_linear(): _assert_output_shape(data, module, patched_module.torch_nn_linear, True, None) +@clear_cache_before_run() def test_rnn(): # test rnn patch can produce the meta output with correct shape data = (torch.randn(5, 3, 10), torch.randn(2, 3, 20)) @@ -58,6 +62,7 @@ def test_rnn(): _assert_output_shape(meta_data, module, patched_module.torch_nn_rnn, True, None) +@clear_cache_before_run() def test_embedding(): data = torch.rand(2, 4, device='meta') @@ -134,6 +139,7 @@ def test_embedding(): output_shape=None) +@clear_cache_before_run() def test_conv1d(): # test conv 1d data = torch.rand(2, 3, 4) @@ -212,6 +218,7 @@ def test_conv2d(): output_shape=materialized_output.shape) +@clear_cache_before_run() def test_conv3d(): # test conv 3d data = torch.rand(2, 3, 4, 4, 4) @@ -253,6 +260,7 @@ def test_conv3d(): output_shape=materialized_output.shape) +@clear_cache_before_run() def test_conv_transpose1d(): # test conv transpose1d data = torch.rand(2, 3, 4) @@ -276,6 +284,7 @@ def test_conv_transpose1d(): output_shape=materialized_output.shape) +@clear_cache_before_run() def test_conv_transpose2d(): # test conv transpose2d data = torch.rand(2, 3, 4, 4) @@ -299,6 +308,7 @@ def test_conv_transpose2d(): output_shape=materialized_output.shape) +@clear_cache_before_run() def test_conv_transpose3d(): # test conv transpose2d data = torch.rand(2, 3, 4, 4, 4) @@ -322,6 +332,7 @@ def test_conv_transpose3d(): output_shape=materialized_output.shape) +@clear_cache_before_run() def test_pool1d(): combinations = [[torch.nn.MaxPool1d, patched_module.torch_nn_maxpool1d], [torch.nn.AvgPool1d, patched_module.torch_nn_avgpool1d]] @@ -349,6 +360,7 @@ def test_pool1d(): _assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None) +@clear_cache_before_run() def test_pool2d(): combinations = [[torch.nn.MaxPool2d, patched_module.torch_nn_maxpool2d], [torch.nn.AvgPool2d, patched_module.torch_nn_avgpool2d]] @@ -379,6 +391,7 @@ def test_pool2d(): _assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None) +@clear_cache_before_run() def test_pool3d(): combinations = [[torch.nn.MaxPool3d, patched_module.torch_nn_maxpool3d], [torch.nn.AvgPool3d, patched_module.torch_nn_avgpool3d]] @@ -410,6 +423,7 @@ def test_pool3d(): # adapative pooling is different from other pooling, so test it individually +@clear_cache_before_run() def test_adaptive_pooling_1d(): pooler = torch.nn.AdaptiveAvgPool1d(output_size=3) patch_func = patched_module.torch_nn_adapative_pooling_1d @@ -434,6 +448,7 @@ def test_adaptive_pooling_1d(): _assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None) +@clear_cache_before_run() def test_adaptive_pooling_2d(): pooler = torch.nn.AdaptiveAvgPool2d(output_size=3) patch_func = patched_module.torch_nn_adapative_pooling_2d @@ -458,6 +473,7 @@ def test_adaptive_pooling_2d(): output_shape=output.shape) +@clear_cache_before_run() def test_adaptive_pooling_3d(): pooler = torch.nn.AdaptiveAvgPool3d(output_size=3) patch_func = patched_module.torch_nn_adapative_pooling_3d diff --git a/tests/test_fx/test_tracer/test_patched_op.py b/tests/test_fx/test_tracer/test_patched_op.py index 4406f02db24b..e0c5f560c49e 100644 --- a/tests/test_fx/test_tracer/test_patched_op.py +++ b/tests/test_fx/test_tracer/test_patched_op.py @@ -1,6 +1,9 @@ +from functools import partial + import torch + from colossalai.fx.tracer.meta_patch import patched_function -from functools import partial +from colossalai.testing import clear_cache_before_run def _run(data, patch_fn): @@ -22,6 +25,7 @@ def _assert_output_shape(data, patch_fn, expect_exception, output_shape): assert output.shape == output_shape +@clear_cache_before_run() def test_repeat_interleave(): patch_fn = patched_function.torch_repeat_interleave @@ -63,6 +67,7 @@ def test_repeat_interleave(): output_shape=materialized_output.shape) +@clear_cache_before_run() def test_torch_max(): data = torch.rand(4, 3) out = torch.max(data) diff --git a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py index b175d8b10c67..aa14f514c7d6 100644 --- a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py +++ b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py @@ -3,6 +3,7 @@ from packaging import version from colossalai._analyzer.fx import symbolic_trace +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo @@ -43,6 +44,7 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') +@clear_cache_before_run() def test_timm_models(): torch.backends.cudnn.deterministic = True diff --git a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py index 66f4be5a6f7f..eafcaca10b1d 100644 --- a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py +++ b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py @@ -3,12 +3,14 @@ from packaging import version from torchaudio_utils import trace_and_compare +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo # We cannot handle the tensors constructed with constant during forward, such as ``torch.empty(0).to(device=Proxy.device)`` # TODO: We could handle this case by hijacking torch.Tensor.to function. @pytest.mark.skip +@clear_cache_before_run() def test_torchaudio_models(): torch.backends.cudnn.deterministic = True diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py index 40f83d47a7cc..df02568c0049 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py @@ -2,6 +2,7 @@ import torch from colossalai._analyzer.fx import symbolic_trace +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo BATCH = 2 @@ -47,6 +48,7 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): ), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}' +@clear_cache_before_run() def test_torchrec_deepfm_models(): deepfm_models = model_zoo.get_sub_registry('deepfm') torch.backends.cudnn.deterministic = True diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py index 6d4b6ab81b12..9776452be9c8 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py @@ -2,6 +2,7 @@ import torch from colossalai._analyzer.fx import symbolic_trace +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo BATCH = 2 @@ -47,6 +48,7 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): ), f'{model.__class__.__name__} has inconsistent outputs, {fx_out} vs {non_fx_out}' +@clear_cache_before_run() def test_torchrec_dlrm_models(): torch.backends.cudnn.deterministic = True dlrm_models = model_zoo.get_sub_registry('dlrm') diff --git a/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py b/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py index 8dbbf9f5aab7..bd259475ae5a 100644 --- a/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py +++ b/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py @@ -1,9 +1,11 @@ import torch from colossalai._analyzer.fx import symbolic_trace +from colossalai.testing import clear_cache_before_run from tests.kit.model_zoo import model_zoo +@clear_cache_before_run() def test_torchvision_models(): torch.backends.cudnn.deterministic = True tv_sub_registry = model_zoo.get_sub_registry('torchvision') diff --git a/tests/test_layers/test_1d/test_1d.py b/tests/test_layers/test_1d/test_1d.py index 897590f0d9c8..891512542475 100644 --- a/tests/test_layers/test_1d/test_1d.py +++ b/tests/test_layers/test_1d/test_1d.py @@ -1,18 +1,14 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial - import pytest import torch -import torch.multiprocessing as mp from checks_1d.check_layer_1d import * from colossalai.core import global_context as gpc from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=4, mode='1d')),) @@ -40,9 +36,7 @@ def check_layer(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_1d(): - world_size = 4 - run_func = partial(check_layer, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer, 4) if __name__ == '__main__': diff --git a/tests/test_layers/test_2d/test_2d.py b/tests/test_layers/test_2d/test_2d.py index da235d0cf168..bcea5ce7b25d 100644 --- a/tests/test_layers/test_2d/test_2d.py +++ b/tests/test_layers/test_2d/test_2d.py @@ -1,22 +1,27 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial - import pytest import torch -import torch.multiprocessing as mp +from checks_2d.check_layer_2d import ( + check_classifier_given_embed_weight, + check_classifier_no_given_weight, + check_embed, + check_layernorm, + check_linear, + check_loss, + check_patch_embed, + check_vocab_parallel_classifier_given_embed_weight, + check_vocab_parallel_classifier_no_given_weight, + check_vocab_parallel_embed, + check_vocab_parallel_loss, +) +from checks_2d.check_operation_2d import check_AB, check_ABT, check_ATB + from colossalai.core import global_context as gpc from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.utils import free_port -from colossalai.testing import rerun_if_address_is_in_use -from checks_2d.check_layer_2d import (check_classifier_given_embed_weight, check_classifier_no_given_weight, - check_embed, check_layernorm, check_linear, check_loss, check_patch_embed, - check_vocab_parallel_classifier_given_embed_weight, - check_vocab_parallel_classifier_no_given_weight, check_vocab_parallel_embed, - check_vocab_parallel_loss) -from checks_2d.check_operation_2d import check_AB, check_ABT, check_ATB +from colossalai.testing import rerun_if_address_is_in_use, spawn CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=4, mode='2d')),) @@ -57,9 +62,7 @@ def check_layer_and_operation(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_2d(): - world_size = 4 - run_func = partial(check_layer_and_operation, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer_and_operation, 4) if __name__ == '__main__': diff --git a/tests/test_layers/test_2p5d/test_2p5d.py b/tests/test_layers/test_2p5d/test_2p5d.py index 365e2d934df8..373d834d0032 100644 --- a/tests/test_layers/test_2p5d/test_2p5d.py +++ b/tests/test_layers/test_2p5d/test_2p5d.py @@ -1,15 +1,12 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp +from checks_2p5d.check_layer_2p5d import * +from checks_2p5d.check_operation_2p5d import check_AB, check_ABT, check_ATB + from colossalai.core import global_context as gpc from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.utils import free_port -from colossalai.testing import rerun_if_address_is_in_use -from checks_2p5d.check_layer_2p5d import * -from checks_2p5d.check_operation_2p5d import check_AB, check_ABT, check_ATB +from colossalai.testing import rerun_if_address_is_in_use, spawn CONFIG = dict(parallel=dict( pipeline=dict(size=1), @@ -53,9 +50,7 @@ def check_layer_and_operation(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_2p5d(): - world_size = 4 - run_func = partial(check_layer_and_operation, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer_and_operation, 4) if __name__ == '__main__': diff --git a/tests/test_layers/test_3d/test_3d.py b/tests/test_layers/test_3d/test_3d.py index 29a8b3aea239..fde71a4a0d26 100644 --- a/tests/test_layers/test_3d/test_3d.py +++ b/tests/test_layers/test_3d/test_3d.py @@ -1,19 +1,24 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial - import pytest import torch -import torch.multiprocessing as mp +from checks_3d.check_layer_3d import ( + check_classifier_no_given_weight, + check_embed, + check_layernorm, + check_linear, + check_loss, + check_patch_embed, + check_vocab_parallel_classifier_given_embed_weight, + check_vocab_parallel_classifier_no_given_weight, + check_vocab_parallel_embed, + check_vocab_parallel_loss, +) + from colossalai.core import global_context as gpc from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.utils import free_port -from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus -from checks_3d.check_layer_3d import (check_classifier_no_given_weight, check_embed, check_layernorm, check_linear, - check_loss, check_patch_embed, check_vocab_parallel_classifier_given_embed_weight, - check_vocab_parallel_classifier_no_given_weight, check_vocab_parallel_embed, - check_vocab_parallel_loss) +from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus, spawn CONFIG = dict( parallel=dict( @@ -52,9 +57,7 @@ def check_layer_and_operation(rank, world_size, port): @skip_if_not_enough_gpus(min_gpus=8) @rerun_if_address_is_in_use() def test_3d(): - world_size = 8 - run_func = partial(check_layer_and_operation, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_layer_and_operation, 8) if __name__ == '__main__': diff --git a/tests/test_layers/test_cache_embedding.py b/tests/test_layers/test_cache_embedding.py index cff9072c7a06..22d4f02a48d7 100644 --- a/tests/test_layers/test_cache_embedding.py +++ b/tests/test_layers/test_cache_embedding.py @@ -1,20 +1,21 @@ -import pytest -from functools import partial - -import numpy as np import random +from typing import List +import numpy as np +import pytest import torch -import torch.multiprocessing as mp import colossalai -from colossalai.utils import free_port -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.tensor import ColoParameter, ProcessGroup, ShardSpec, ComputePattern, ComputeSpec, \ - ColoTensor, ColoTensorSpec -from colossalai.nn.parallel.layers import CachedParamMgr, CachedEmbeddingBag, ParallelCachedEmbeddingBag, EvictionStrategy, \ - ParallelCachedEmbeddingBagTablewise, TablewiseEmbeddingBagConfig -from typing import List +from colossalai.nn.parallel.layers import ( + CachedEmbeddingBag, + CachedParamMgr, + EvictionStrategy, + ParallelCachedEmbeddingBag, + ParallelCachedEmbeddingBagTablewise, + TablewiseEmbeddingBagConfig, +) +from colossalai.tensor import ColoTensor, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn NUM_EMBED, EMBED_DIM = 10, 8 BATCH_SIZE = 8 @@ -44,6 +45,7 @@ def synthesize_1d_sparse_feature( @pytest.mark.skip +@clear_cache_before_run() def test_cachemgr(): model = torch.nn.EmbeddingBag(10000, 128) # 10 chunks, 5 in cuda @@ -72,6 +74,7 @@ def test_cachemgr(): assert mgr.cuda_available_chunk_num == 5 +@clear_cache_before_run() def test_reorder_with_freq(): num_embed = 100 chunk_size = 1 @@ -102,7 +105,8 @@ def test_reorder_with_freq(): f"offset in chunk: {offset_in_chunk}, mgr: {mgr_offsets}" -@pytest.mark.parametrize('use_LFU', [True, False]) +@clear_cache_before_run() +@parameterize('use_LFU', [True, False]) def test_freq_aware_embed(use_LFU: bool): device = torch.device('cuda', 0) evict_strategy = EvictionStrategy.LFU if use_LFU else EvictionStrategy.DATASET @@ -148,7 +152,8 @@ def test_freq_aware_embed(use_LFU: bool): f"model weight: {model_weight[10:18, :8]}, reference: {ref_weight[10:18, :8]}" -@pytest.mark.parametrize('init_freq', [True, False]) +@clear_cache_before_run() +@parameterize('init_freq', [True, False]) def test_lfu_strategy(init_freq: bool): # minimal test to check behavior Bag = CachedEmbeddingBag(5, @@ -248,7 +253,7 @@ def run_parallel_freq_aware_embed_tablewise(rank, world_size): input0 [1,2,3] [6,7] [] input1 [] [9] [13,15] input2 [1,5] [6,8] [11] - ↑ ↑ ↑ + ↑ ↑ ↑ rank 0 rank 0 rank 1 in KJT format ''' @@ -363,8 +368,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_parallel_freq_aware_embed(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_layers/test_sequence/test_sequence.py b/tests/test_layers/test_sequence/test_sequence.py index 3862c4ccd439..aac192d7eff0 100644 --- a/tests/test_layers/test_sequence/test_sequence.py +++ b/tests/test_layers/test_sequence/test_sequence.py @@ -1,14 +1,11 @@ -import colossalai -import colossalai.nn as col_nn +import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp -import pytest -from colossalai.core import global_context as gpc +import colossalai from colossalai.context import ParallelMode -from colossalai.testing import rerun_if_address_is_in_use -from functools import partial +from colossalai.core import global_context as gpc +from colossalai.testing import rerun_if_address_is_in_use, spawn CONFIG = dict(parallel=dict(tensor=dict(size=4, mode='sequence'))) @@ -121,8 +118,8 @@ def check_ring_av(rank, world_size): 'attention output cannot match' -def run_test(rank, world_size): - colossalai.launch(rank=rank, world_size=world_size, config=CONFIG, host='localhost', port=29500) +def run_test(rank, world_size, port): + colossalai.launch(rank=rank, world_size=world_size, config=CONFIG, host='localhost', port=port) # check_ring_qk(rank, world_size) check_ring_av(rank, world_size) @@ -134,9 +131,7 @@ def run_test(rank, world_size): @pytest.mark.dist @rerun_if_address_is_in_use() def test_sequence(): - world_size = 4 - run_func = partial(run_test, world_size=world_size) - mp.spawn(run_func, nprocs=world_size) + spawn(run_test, 4) if __name__ == '__main__': diff --git a/tests/test_moe/test_grad_handler.py b/tests/test_moe/test_grad_handler.py index e7b9a55277c6..e7002a75f3f7 100644 --- a/tests/test_moe/test_grad_handler.py +++ b/tests/test_moe/test_grad_handler.py @@ -1,16 +1,15 @@ -from functools import partial import pytest import torch -import torch.nn as nn -import torch.multiprocessing as mp import torch.distributed as dist +import torch.nn as nn + import colossalai -from colossalai.utils import free_port, get_current_device -from colossalai.nn.layer.moe import Top1Router, UniformNoiseGenerator, MoeLayer, Experts from colossalai.context.moe_context import MOE_CONTEXT -from colossalai.utils.moe import sync_moe_model_param from colossalai.engine.gradient_handler import MoeGradientHandler -from colossalai.testing import assert_equal_in_group, rerun_if_address_is_in_use +from colossalai.nn.layer.moe import Experts, MoeLayer, Top1Router, UniformNoiseGenerator +from colossalai.testing import assert_equal_in_group, rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device +from colossalai.utils.moe import sync_moe_model_param BATCH_SIZE = 4 DIM = 16 @@ -65,9 +64,7 @@ def run_test(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_grad_handler(): - world_size = 4 - run_func = partial(run_test, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_test, 4) if __name__ == '__main__': diff --git a/tests/test_moe/test_kernel.py b/tests/test_moe/test_kernel.py index 62f9241642b9..ad9a172b72aa 100644 --- a/tests/test_moe/test_kernel.py +++ b/tests/test_moe/test_kernel.py @@ -1,15 +1,14 @@ -from functools import partial import pytest import torch import torch.nn as nn -import torch.multiprocessing as mp + import colossalai from colossalai.context import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.utils import free_port, get_current_device -from colossalai.nn.layer.moe import Top1Router, Top2Router, MoeLayer, Experts from colossalai.context.moe_context import MOE_CONTEXT -from colossalai.testing import rerun_if_address_is_in_use +from colossalai.core import global_context as gpc +from colossalai.nn.layer.moe import Experts, MoeLayer, Top1Router, Top2Router +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device BATCH_SIZE = 16 NUM_EXPERTS = 4 @@ -90,15 +89,7 @@ def run_routing(rank, world_size, port, rs=2, hidden_size=128, data_type=torch.f @pytest.mark.parametrize("router", [Top1Router, Top2Router]) @rerun_if_address_is_in_use() def test_moe_kernel(rs, hidden_size, data_type, router): - world_size = 4 - run_func = partial(run_routing, - world_size=world_size, - port=free_port(), - rs=rs, - hidden_size=hidden_size, - data_type=data_type, - router=router) - mp.spawn(run_func, nprocs=world_size) + spawn(run_routing, 4, rs=rs, hidden_size=hidden_size, data_type=data_type, router=router) if __name__ == '__main__': diff --git a/tests/test_moe/test_moe_checkpoint.py b/tests/test_moe/test_moe_checkpoint.py index d2cff44ad9bd..8a0283ba71fc 100644 --- a/tests/test_moe/test_moe_checkpoint.py +++ b/tests/test_moe/test_moe_checkpoint.py @@ -1,19 +1,16 @@ import os -from functools import partial import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import colossalai from colossalai.context import MOE_CONTEXT from colossalai.nn.layer.moe import load_moe_model, save_moe_model -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port, get_current_device +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext from tests.test_moe.test_moe_zero_init import MoeModel -from tests.test_tensor.common_utils import debug_print from tests.test_zero.test_legacy.common import CONFIG @@ -46,8 +43,7 @@ def _run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [2, 4]) @rerun_if_address_is_in_use() def test_moe_checkpoint(world_size): - run_func = partial(_run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(_run_dist) if __name__ == '__main__': diff --git a/tests/test_moe/test_moe_colo_init.py b/tests/test_moe/test_moe_colo_init.py index 4826d87ac044..555338fcf9fc 100644 --- a/tests/test_moe/test_moe_colo_init.py +++ b/tests/test_moe/test_moe_colo_init.py @@ -1,15 +1,12 @@ -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import colossalai from colossalai.context import MOE_CONTEXT from colossalai.tensor import ColoParameter -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port, get_current_device +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext from tests.test_moe.test_moe_zero_init import MoeModel from tests.test_tensor.common_utils import debug_print @@ -52,8 +49,7 @@ def _run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [4]) @rerun_if_address_is_in_use() def test_moe_colo_init(world_size): - run_func = partial(_run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(_run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_moe/test_moe_group.py b/tests/test_moe/test_moe_group.py index 3126f59e246e..6dc3f5f18b6d 100644 --- a/tests/test_moe/test_moe_group.py +++ b/tests/test_moe/test_moe_group.py @@ -1,21 +1,20 @@ -from functools import partial import pytest -import torch.nn as nn -import torch.multiprocessing as mp import torch.distributed as dist +import torch.nn as nn + import colossalai -from colossalai.utils import free_port, get_current_device -from colossalai.nn.layer.moe import Experts from colossalai.context.moe_context import MOE_CONTEXT +from colossalai.nn.layer.moe import Experts +from colossalai.testing import assert_equal_in_group, rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.utils.moe import sync_moe_model_param -from colossalai.testing import assert_equal_in_group, rerun_if_address_is_in_use D_MODEL = 4 D_FF = 8 CONFIG = dict() -def run_test(rank, port): +def run_test(rank, world_size, port): world_size = 4 colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') expert_module = nn.Linear @@ -62,9 +61,7 @@ def run_test(rank, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_moe_initialization(): - world_size = 4 - run_func = partial(run_test, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_test, 4) if __name__ == '__main__': diff --git a/tests/test_moe/test_moe_zero_init.py b/tests/test_moe/test_moe_zero_init.py index 18b50eb5c482..79722f9f4056 100644 --- a/tests/test_moe/test_moe_zero_init.py +++ b/tests/test_moe/test_moe_zero_init.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn import colossalai @@ -10,8 +7,8 @@ from colossalai.logging import get_dist_logger from colossalai.nn import CheckpointModule from colossalai.nn.layer import MoeModule -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port, get_current_device +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy from tests.test_zero.test_legacy.common import CONFIG @@ -104,8 +101,7 @@ def _run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [2, 4]) @rerun_if_address_is_in_use() def test_moe_zero_init(world_size): - run_func = partial(_run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(_run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_moe/test_moe_zero_model.py b/tests/test_moe/test_moe_zero_model.py index 49c452938e25..ec37967f18c5 100644 --- a/tests/test_moe/test_moe_zero_model.py +++ b/tests/test_moe/test_moe_zero_model.py @@ -1,15 +1,11 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.context import MOE_CONTEXT from colossalai.engine.gradient_handler import MoeGradientHandler from colossalai.nn import MoeLoss -from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use, spawn from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy from colossalai.zero.legacy.sharded_model import ShardedModelV2 @@ -67,8 +63,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [2]) @rerun_if_address_is_in_use() def test_moe_zero_model(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_moe/test_moe_zero_optim.py b/tests/test_moe/test_moe_zero_optim.py index b43e52bb4c6a..efc6e9ddae27 100644 --- a/tests/test_moe/test_moe_zero_optim.py +++ b/tests/test_moe/test_moe_zero_optim.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.amp import convert_to_apex_amp @@ -10,8 +7,8 @@ from colossalai.engine.gradient_handler import MoeGradientHandler from colossalai.nn import MoeLoss from colossalai.nn.optimizer import CPUAdam -from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port, get_current_device +from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy from colossalai.zero.legacy.sharded_model import ShardedModelV2 @@ -116,8 +113,7 @@ def _run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [2]) @rerun_if_address_is_in_use() def test_moe_zero_optim(world_size): - run_func = partial(_run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(_run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_ops/test_addmm_tp.py b/tests/test_ops/test_addmm_tp.py index 5182868b5bbd..ecd3721b902e 100644 --- a/tests/test_ops/test_addmm_tp.py +++ b/tests/test_ops/test_addmm_tp.py @@ -1,14 +1,11 @@ -import colossalai -import torch import pytest +import torch import torch.nn as nn -import torch.multiprocessing as mp -from colossalai.tensor import ColoTensor, ProcessGroup -from colossalai.tensor import ColoTensorSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from functools import partial -from tests.test_tensor.common_utils import tensor_shard_equal, tensor_equal, split_param_row_tp1d, split_param_col_tp1d + +import colossalai +from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup +from colossalai.testing import rerun_if_address_is_in_use, spawn +from tests.test_tensor.common_utils import split_param_col_tp1d, split_param_row_tp1d, tensor_equal, tensor_shard_equal class Conv1D(nn.Module): @@ -69,8 +66,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_addmm_1d(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_ops/test_embedding_bag_tp.py b/tests/test_ops/test_embedding_bag_tp.py index c7a1604e5455..d3d3dcf7e2c9 100644 --- a/tests/test_ops/test_embedding_bag_tp.py +++ b/tests/test_ops/test_embedding_bag_tp.py @@ -1,14 +1,11 @@ +import pytest +import torch from torch.nn import functional as F -from functools import partial import colossalai -import pytest -import torch -import torch.multiprocessing as mp -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port from colossalai.tensor import ColoParameter, ColoTensorSpec, ProcessGroup -from tests.test_tensor.common_utils import tensor_equal, tensor_shard_equal, split_param_col_tp1d +from colossalai.testing import rerun_if_address_is_in_use, spawn +from tests.test_tensor.common_utils import split_param_col_tp1d, tensor_equal, tensor_shard_equal def run_with_spec(spec_init_func): @@ -39,8 +36,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_embedding_bag_1d(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_ops/test_embedding_tp.py b/tests/test_ops/test_embedding_tp.py index 541dc5c09324..c0b376e2c92a 100644 --- a/tests/test_ops/test_embedding_tp.py +++ b/tests/test_ops/test_embedding_tp.py @@ -1,14 +1,11 @@ +import pytest +import torch from torch.nn import functional as F -from functools import partial import colossalai -import pytest -import torch -import torch.multiprocessing as mp -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.tensor import ColoTensorSpec, ProcessGroup, ColoTensor -from tests.test_tensor.common_utils import tensor_equal, tensor_shard_equal, split_param_col_tp1d, split_param_row_tp1d +from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup +from colossalai.testing import rerun_if_address_is_in_use, spawn +from tests.test_tensor.common_utils import split_param_col_tp1d, split_param_row_tp1d, tensor_equal, tensor_shard_equal def run_with_spec(spec_init_func, pg: ProcessGroup): @@ -40,8 +37,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_embedding_1d(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_ops/test_linear_tp.py b/tests/test_ops/test_linear_tp.py index 603e98564de8..c88adfdd9a77 100644 --- a/tests/test_ops/test_linear_tp.py +++ b/tests/test_ops/test_linear_tp.py @@ -1,14 +1,11 @@ -from functools import partial - -import colossalai import pytest import torch -import torch.multiprocessing as mp import torch.nn.functional as F -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.tensor import ColoTensorSpec, ProcessGroup, ColoTensor -from tests.test_tensor.common_utils import tensor_equal, tensor_shard_equal, split_param_col_tp1d, split_param_row_tp1d + +import colossalai +from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup +from colossalai.testing import rerun_if_address_is_in_use, spawn +from tests.test_tensor.common_utils import split_param_col_tp1d, split_param_row_tp1d, tensor_equal, tensor_shard_equal def run_with_spec(spec_init_func, split_bias): @@ -44,8 +41,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_linear_1d(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_ops/test_loss_func.py b/tests/test_ops/test_loss_func.py index 9210242a0a9f..fc55c7f77254 100644 --- a/tests/test_ops/test_loss_func.py +++ b/tests/test_ops/test_loss_func.py @@ -1,52 +1,48 @@ -import torch -import pytest -import colossalai -import torch.nn.functional as F -import torch.multiprocessing as mp -from functools import partial -from colossalai.tensor import ColoTensor, ProcessGroup, ColoTensorSpec -from colossalai.utils import get_current_device -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.tensor import ShardSpec, ComputeSpec, ComputePattern - - -def check_cross_entropy(): - input_t = torch.randn(4, 4, device=get_current_device(), requires_grad=True) - input_ct = torch.randn(4, 4, device=get_current_device(), requires_grad=True) - with torch.no_grad(): - input_ct.copy_(input_t) - - target = torch.randint(4, (4,), dtype=torch.int64, device=get_current_device()) - - world_size = torch.distributed.get_world_size() - pg = ProcessGroup(tp_degree=world_size) - input_t_colo = ColoTensor.from_torch_tensor(tensor=input_ct, spec=ColoTensorSpec(pg)) - input_shard = input_t_colo.redistribute(ShardSpec([-1], [pg.tp_world_size()])) - input_shard.set_tensor_spec(dist_spec=None, compute_spec=ComputeSpec(ComputePattern.TP1D)) - - output = F.cross_entropy(input_t, target) - output_colo = F.cross_entropy(input_shard, target) - assert torch.allclose(output_colo, output) - - output.backward() - output_colo.backward() - - assert torch.allclose(input_t.grad, input_ct.grad) - - -def run_dist(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - check_cross_entropy() - - -@pytest.mark.dist -@pytest.mark.parametrize('world_size', [1, 2]) -@rerun_if_address_is_in_use() -def test_loss_func(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_loss_func(1) +import pytest +import torch +import torch.nn.functional as F + +import colossalai +from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device + + +def check_cross_entropy(): + input_t = torch.randn(4, 4, device=get_current_device(), requires_grad=True) + input_ct = torch.randn(4, 4, device=get_current_device(), requires_grad=True) + with torch.no_grad(): + input_ct.copy_(input_t) + + target = torch.randint(4, (4,), dtype=torch.int64, device=get_current_device()) + + world_size = torch.distributed.get_world_size() + pg = ProcessGroup(tp_degree=world_size) + input_t_colo = ColoTensor.from_torch_tensor(tensor=input_ct, spec=ColoTensorSpec(pg)) + input_shard = input_t_colo.redistribute(ShardSpec([-1], [pg.tp_world_size()])) + input_shard.set_tensor_spec(dist_spec=None, compute_spec=ComputeSpec(ComputePattern.TP1D)) + + output = F.cross_entropy(input_t, target) + output_colo = F.cross_entropy(input_shard, target) + assert torch.allclose(output_colo, output) + + output.backward() + output_colo.backward() + + assert torch.allclose(input_t.grad, input_ct.grad) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + check_cross_entropy() + + +@pytest.mark.dist +@pytest.mark.parametrize('world_size', [1, 2]) +@rerun_if_address_is_in_use() +def test_loss_func(world_size): + spawn(run_dist, world_size) + + +if __name__ == '__main__': + test_loss_func(1) diff --git a/tests/test_ops/test_op.py b/tests/test_ops/test_op.py index 8d3cf50ff2aa..4176d3b64d90 100644 --- a/tests/test_ops/test_op.py +++ b/tests/test_ops/test_op.py @@ -1,14 +1,12 @@ -import torch import pytest -import colossalai +import torch import torch.nn.functional as F -import torch.multiprocessing as mp -from functools import partial -from colossalai.tensor import ColoTensor, ProcessGroup, ColoTensorSpec, ShardSpec -from colossalai.utils import get_current_device from torch.nn import Parameter -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port + +import colossalai +from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup, ShardSpec +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device def _run_layer_norm(): @@ -66,8 +64,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [2]) @rerun_if_address_is_in_use() def test_element_wise_ops(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) def run_dist2(rank, world_size, port): @@ -79,8 +76,7 @@ def run_dist2(rank, world_size, port): @pytest.mark.parametrize('world_size', [1]) @rerun_if_address_is_in_use() def test_ln(world_size): - run_func = partial(run_dist2, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist2, world_size) def check_all(): diff --git a/tests/test_ops/test_view.py b/tests/test_ops/test_view.py index fc6fc2d3c291..a9f2033201c7 100644 --- a/tests/test_ops/test_view.py +++ b/tests/test_ops/test_view.py @@ -1,100 +1,97 @@ -from functools import partial - -import colossalai -import pytest -import torch -import torch.multiprocessing as mp -import torch.distributed as dist -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port, get_current_device -from colossalai.tensor import ColoTensorSpec, ProcessGroup, ColoTensor, ShardSpec -from colossalai.tensor.distspec import DistPlacementPattern -from tests.test_tensor.common_utils import split_param_row_tp1d, split_param_col_tp1d, debug_print - - -def exam_view_core(pg): - # the case of replicated ColoTensors - x = torch.randn(4, 4).cuda() - x_colo = ColoTensor(x, ColoTensorSpec(pg)) - - y = x.view(2, -1, 2) - y_colo = x_colo.view(2, -1, 2) - - assert torch.all(y == y_colo) - assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE - # the perfect case of col-sliced ColoTensors - split_param_col_tp1d(x_colo, pg) - - z = x.view(torch.Size((2, 1, 2, -1))) - z_colo = x_colo.view(torch.Size((2, 1, 2, -1))) - if dist.get_rank() == 0: - z = z[:, :, :, 0:2] - else: - z = z[:, :, :, 2:] - assert torch.all(z == z_colo) - assert z_colo.dist_spec == x_colo.dist_spec - # the perfect case of row-sliced ColoTensors - split_param_row_tp1d(x_colo, pg) - - z = x.view(torch.Size((-1, 2, 2))) - z_colo = x_colo.view(torch.Size((-1, 2, 2))) - if dist.get_rank() == 0: - z = z[0:2, :, :] - else: - z = z[2:, :, :] - assert torch.all(z == z_colo) - assert z_colo.dist_spec == x_colo.dist_spec - # the normal case of row-sliced ColoTensors - z = x.view(-1, 2, 2, 2) - z_colo = x_colo.view(-1, 2, 2, 2) - assert torch.all(z == z_colo) - assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE - - -def exam_view_autograd(pg): - x = torch.randn(8, 2, device=get_current_device(), requires_grad=True) - y = torch.randn(8, 2, device=get_current_device(), requires_grad=True) - with torch.no_grad(): - y.copy_(x) - y = ColoTensor(y, ColoTensorSpec(pg)) - y_slice = y.redistribute(ShardSpec([-1], [pg.tp_world_size()])) - - xx = x.view(2, 2, -1) - yy_slice = y_slice.view(2, 2, -1) - yy = yy_slice.to_replicate() - grad = torch.randn(2, 2, 4, device=get_current_device()) - - xx.backward(grad) - yy.backward(grad) - assert torch.all(x.grad == y.grad) - - -def exam_view_errors(pg): - x = torch.randn(8, 2, device=get_current_device()) - x = ColoTensor(x, ColoTensorSpec(pg)) - split_param_row_tp1d(x, pg) - - x.view('a', 'b', 'c') - x.view(8, -1) - x.view([-2, -2, -2]) - x.view((-1, -1, -1)) - - -def run_dist(rank, world_size, port): - colossalai.launch(config=dict(), rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - pg = ProcessGroup(tp_degree=torch.distributed.get_world_size()) - exam_view_core(pg) - exam_view_autograd(pg) - # exam_view_errors(pg) - - -@pytest.mark.dist -@pytest.mark.parametrize('world_size', [2]) -@rerun_if_address_is_in_use() -def test_view(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_view(2) +import pytest +import torch +import torch.distributed as dist + +import colossalai +from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup, ShardSpec +from colossalai.tensor.distspec import DistPlacementPattern +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device +from tests.test_tensor.common_utils import debug_print, split_param_col_tp1d, split_param_row_tp1d + + +def exam_view_core(pg): + # the case of replicated ColoTensors + x = torch.randn(4, 4).cuda() + x_colo = ColoTensor(x, ColoTensorSpec(pg)) + + y = x.view(2, -1, 2) + y_colo = x_colo.view(2, -1, 2) + + assert torch.all(y == y_colo) + assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE + # the perfect case of col-sliced ColoTensors + split_param_col_tp1d(x_colo, pg) + + z = x.view(torch.Size((2, 1, 2, -1))) + z_colo = x_colo.view(torch.Size((2, 1, 2, -1))) + if dist.get_rank() == 0: + z = z[:, :, :, 0:2] + else: + z = z[:, :, :, 2:] + assert torch.all(z == z_colo) + assert z_colo.dist_spec == x_colo.dist_spec + # the perfect case of row-sliced ColoTensors + split_param_row_tp1d(x_colo, pg) + + z = x.view(torch.Size((-1, 2, 2))) + z_colo = x_colo.view(torch.Size((-1, 2, 2))) + if dist.get_rank() == 0: + z = z[0:2, :, :] + else: + z = z[2:, :, :] + assert torch.all(z == z_colo) + assert z_colo.dist_spec == x_colo.dist_spec + # the normal case of row-sliced ColoTensors + z = x.view(-1, 2, 2, 2) + z_colo = x_colo.view(-1, 2, 2, 2) + assert torch.all(z == z_colo) + assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE + + +def exam_view_autograd(pg): + x = torch.randn(8, 2, device=get_current_device(), requires_grad=True) + y = torch.randn(8, 2, device=get_current_device(), requires_grad=True) + with torch.no_grad(): + y.copy_(x) + y = ColoTensor(y, ColoTensorSpec(pg)) + y_slice = y.redistribute(ShardSpec([-1], [pg.tp_world_size()])) + + xx = x.view(2, 2, -1) + yy_slice = y_slice.view(2, 2, -1) + yy = yy_slice.to_replicate() + grad = torch.randn(2, 2, 4, device=get_current_device()) + + xx.backward(grad) + yy.backward(grad) + assert torch.all(x.grad == y.grad) + + +def exam_view_errors(pg): + x = torch.randn(8, 2, device=get_current_device()) + x = ColoTensor(x, ColoTensorSpec(pg)) + split_param_row_tp1d(x, pg) + + x.view('a', 'b', 'c') + x.view(8, -1) + x.view([-2, -2, -2]) + x.view((-1, -1, -1)) + + +def run_dist(rank, world_size, port): + colossalai.launch(config=dict(), rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + pg = ProcessGroup(tp_degree=torch.distributed.get_world_size()) + exam_view_core(pg) + exam_view_autograd(pg) + # exam_view_errors(pg) + + +@pytest.mark.dist +@pytest.mark.parametrize('world_size', [2]) +@rerun_if_address_is_in_use() +def test_view(world_size): + spawn(run_dist, world_size) + + +if __name__ == '__main__': + test_view(2) diff --git a/tests/test_optimizer/test_cpu_adam.py b/tests/test_optimizer/test_cpu_adam.py index ea1c044f5820..8b3ecf8517f7 100644 --- a/tests/test_optimizer/test_cpu_adam.py +++ b/tests/test_optimizer/test_cpu_adam.py @@ -2,7 +2,7 @@ import torch -from colossalai.testing import parameterize +from colossalai.testing import clear_cache_before_run, parameterize def torch_adam_update( @@ -46,6 +46,7 @@ def assertTrue(condition, msg): assert condition, msg +@clear_cache_before_run() @parameterize('adamw', [True, False]) @parameterize('step', [1, 2]) @parameterize('p_dtype', [torch.float, torch.half]) diff --git a/tests/test_optimizer/test_fused_adam.py b/tests/test_optimizer/test_fused_adam.py index f7227c2d57c0..114d5293dad9 100644 --- a/tests/test_optimizer/test_fused_adam.py +++ b/tests/test_optimizer/test_fused_adam.py @@ -1,10 +1,10 @@ import torch import torch.nn as nn -from torch.optim.adam import Adam from torch.optim import AdamW +from torch.optim.adam import Adam from colossalai.nn.optimizer.fused_adam import FusedAdam -from colossalai.testing import parameterize +from colossalai.testing import clear_cache_before_run, parameterize class FC(nn.Module): @@ -17,6 +17,7 @@ def forward(self, x): return self.fc(x) +@clear_cache_before_run() @parameterize('adamw', [False, True]) @parameterize('p_dtype', [torch.float, torch.half]) @parameterize('g_dtype', [torch.float, torch.half]) diff --git a/tests/test_optimizer/test_fused_adam_kernel.py b/tests/test_optimizer/test_fused_adam_kernel.py index 8ff6618aee2e..4afa13349c1b 100644 --- a/tests/test_optimizer/test_fused_adam_kernel.py +++ b/tests/test_optimizer/test_fused_adam_kernel.py @@ -4,7 +4,7 @@ import torch.nn as nn from numpy import dtype -from colossalai.testing import parameterize +from colossalai.testing import clear_cache_before_run, parameterize from colossalai.utils import multi_tensor_applier @@ -41,6 +41,7 @@ def torch_adam_update( param.addcdiv_(exp_avg, denom, value=-step_size) +@clear_cache_before_run() @parameterize('adamw', [False, True]) @parameterize('step', [1, 2]) @parameterize('p_dtype', [torch.float, torch.half]) diff --git a/tests/test_optimizer/test_hybrid_adam.py b/tests/test_optimizer/test_hybrid_adam.py index 2576d8ffee43..d075149dfcb1 100644 --- a/tests/test_optimizer/test_hybrid_adam.py +++ b/tests/test_optimizer/test_hybrid_adam.py @@ -4,11 +4,12 @@ from torch.optim.adam import Adam from colossalai.nn.optimizer.hybrid_adam import HybridAdam -from colossalai.testing import parameterize +from colossalai.testing import clear_cache_before_run, parameterize RE = 3 +@clear_cache_before_run() @parameterize('adamw', [False, True]) @parameterize('device', ['cpu', 'cuda:0']) @parameterize('p_dtype', [torch.float]) diff --git a/tests/test_optimizer/test_nvme.py b/tests/test_optimizer/test_nvme.py index 243f785adaf9..5d794ac2dd1a 100644 --- a/tests/test_optimizer/test_nvme.py +++ b/tests/test_optimizer/test_nvme.py @@ -1,7 +1,9 @@ import pytest import torch -from tests.components_to_test.registry import non_distributed_component_funcs + from colossalai.nn.optimizer import CPUAdam, HybridAdam +from colossalai.testing import clear_cache_before_run, parameterize +from tests.components_to_test.registry import non_distributed_component_funcs def move_some_params_to_cuda(model, torch_model): @@ -16,9 +18,10 @@ def check_params_equal(model, torch_model): assert torch.allclose(p, torch_p, atol=1e-3), f'diff: {torch.abs(p - torch_p)}' -@pytest.mark.parametrize('nvme_offload_fraction', [0.0, 0.5, 1.0]) -@pytest.mark.parametrize('nvme_offload_dir', ['./offload', None]) -@pytest.mark.parametrize('adam_cls', [CPUAdam, HybridAdam]) +@clear_cache_before_run() +@parameterize('nvme_offload_fraction', [0.0, 0.5, 1.0]) +@parameterize('nvme_offload_dir', ['./offload', None]) +@parameterize('adam_cls', [CPUAdam, HybridAdam]) def test_nvme_adam(nvme_offload_fraction, nvme_offload_dir, adam_cls): get_components_func = non_distributed_component_funcs.get_callable('simple_net') model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() diff --git a/tests/test_pipeline/rpc_test_utils.py b/tests/test_pipeline/rpc_test_utils.py index 7ce2cd433b12..dab474a4ee21 100644 --- a/tests/test_pipeline/rpc_test_utils.py +++ b/tests/test_pipeline/rpc_test_utils.py @@ -6,13 +6,14 @@ import torch.distributed as dist import torch.distributed.rpc as rpc import torch.multiprocessing as mp -from colossalai import launch -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.pipeline_process_group import ppg from torch import nn from torch._C._distributed_rpc import _is_current_rpc_agent_set from torch.optim import SGD, Adam, Optimizer, RMSprop +from colossalai import launch +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.pipeline_process_group import ppg + rpc_is_initialized = _is_current_rpc_agent_set @@ -20,7 +21,9 @@ def color_debug(text, prefix=' ', color='blue'): color = color.upper() print(getattr(Back, color), prefix, Style.RESET_ALL, text) + class MLP(nn.Module): + def __init__(self, dim: int, layers: int): super().__init__() self.layers = torch.nn.ModuleList() @@ -32,8 +35,10 @@ def forward(self, x): for layer in self.layers: x = layer(x) return x.sum() - + + class DAG_MLP(nn.Module): + def __init__(self, dim: int, layers: int): super().__init__() self.layers = torch.nn.ModuleList() @@ -48,6 +53,7 @@ def forward(self, x, y): y = self.dag_layer(y) return x.sum(), y.sum() + class RpcTestModel(nn.Module): def __init__(self, stage_id, actual_stage_num, feat_num, h) -> None: diff --git a/tests/test_pipeline/test_middleware_1f1b.py b/tests/test_pipeline/test_middleware_1f1b.py index c4dc617b1683..5b3aad703275 100644 --- a/tests/test_pipeline/test_middleware_1f1b.py +++ b/tests/test_pipeline/test_middleware_1f1b.py @@ -1,27 +1,27 @@ -import torch -import pytest import os -import torch.multiprocessing as mp -import torch.distributed.rpc as rpc +from functools import partial -from torch import nn +import pytest +import torch +import torch.distributed.rpc as rpc +from rpc_test_utils import DAG_MLP, MLP from torch._C._distributed_rpc import _is_current_rpc_agent_set + from colossalai import launch +from colossalai.fx import ColoTracer +from colossalai.fx.passes.adding_split_node_pass import balanced_split_pass, split_with_split_nodes_pass from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.middleware.adaptor import get_fx_topology from colossalai.pipeline.pipeline_process_group import ppg from colossalai.pipeline.rpc._pipeline_schedule import OneFOneBPipelineEngine -from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, balanced_split_pass -from colossalai.fx import ColoTracer -from colossalai.pipeline.middleware.adaptor import get_fx_topology -from rpc_test_utils import MLP, DAG_MLP -from functools import partial -from colossalai.testing import parameterize, rerun_if_address_is_in_use +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn # global variable for model created batch_size = 16 dim = 10 rpc_is_initialized = _is_current_rpc_agent_set + def create_partition_module(pp_rank: int, stage_num: int, model, data_kwargs): model.eval() tracer = ColoTracer() @@ -34,13 +34,15 @@ def create_partition_module(pp_rank: int, stage_num: int, model, data_kwargs): for submodule in split_submodules: if isinstance(submodule, torch.fx.GraphModule): setattr(submodule, '_topo', topo) - return split_submodules[pp_rank+1] + return split_submodules[pp_rank + 1] + def partition(model, data_kwargs: dict, pp_rank: int, chunk: int, stage_num: int): torch.manual_seed(1024) partition = create_partition_module(pp_rank, stage_num, model, data_kwargs) return partition + def run_master(model_cls, world_size, forward_only): torch.manual_seed(100) @@ -50,23 +52,27 @@ def run_master(model_cls, world_size, forward_only): chunk = 1 num_microbatches = 8 use_checkpoint = 'store_true' - + if model_cls == MLP: + def data_gen(): x = torch.zeros((batch_size, dim)) kwargs = dict(x=x) return kwargs + model = model_cls(dim, stage_num * 3) if forward_only: labels = None else: labels = 1 elif model_cls == DAG_MLP: + def data_gen(): x = torch.zeros((batch_size, dim)) y = torch.zeros((batch_size, dim)) kwargs = dict(x=x, y=y) return kwargs + model = model_cls(dim, stage_num * 3) if forward_only: labels = None @@ -74,15 +80,17 @@ def data_gen(): labels = 1 else: pass - + data_kwargs = data_gen() - - engine = OneFOneBPipelineEngine(partition_fn=partial(partition, model, data_kwargs), - stage_num=stage_num, - num_microbatches=num_microbatches, - device=device, - chunk=chunk, - checkpoint=use_checkpoint,) + + engine = OneFOneBPipelineEngine( + partition_fn=partial(partition, model, data_kwargs), + stage_num=stage_num, + num_microbatches=num_microbatches, + device=device, + chunk=chunk, + checkpoint=use_checkpoint, + ) if not forward_only: engine.initialize_optimizer(getattr(torch.optim, 'SGD'), lr=1e-3) @@ -90,13 +98,14 @@ def data_gen(): input_x = torch.randn((batch_size, dim), device=device) input_y = torch.randn((batch_size, dim), device=device) logits = engine.forward_backward({'x': input_x, 'y': input_y}, labels=labels, forward_only=forward_only) - -def run_worker(rank, model_cls, world_size, forward_only, master_func): + + +def run_worker(rank, world_size, port, model_cls, forward_only, master_func): master_addr = 'localhost' master_port = 29020 os.environ['MASTER_ADDR'] = master_addr os.environ['MASTER_PORT'] = str(master_port) - + disable_existing_loggers() launch(dict(), rank, world_size, master_addr, master_port, 'nccl', verbose=False) @@ -113,7 +122,8 @@ def run_worker(rank, model_cls, world_size, forward_only, master_func): # barrier here if rpc_is_initialized(): rpc.shutdown() - + + @pytest.mark.skip("skip due to CI torch version 1.11") @parameterize('model_cls', [MLP, DAG_MLP]) @parameterize('forward_only', [True, False]) @@ -122,7 +132,14 @@ def run_worker(rank, model_cls, world_size, forward_only, master_func): def test_pp_middleware_fwd(model_cls, forward_only): world_size = 4 master_func = run_master - mp.spawn(run_worker, args=(model_cls, world_size, forward_only, master_func), nprocs=world_size) + spawn( + run_worker, + world_size, + model_cls=model_cls, + forward_only=forward_only, + master_func=master_func, + ) + if __name__ == "__main__": - test_pp_middleware_fwd() \ No newline at end of file + test_pp_middleware_fwd() diff --git a/tests/test_pipeline/test_pipelinable.py b/tests/test_pipeline/test_pipelinable.py index c99a88550b71..627cb5ac6f51 100644 --- a/tests/test_pipeline/test_pipelinable.py +++ b/tests/test_pipeline/test_pipelinable.py @@ -1,9 +1,7 @@ import torch -import torch.multiprocessing as mp from colossalai.pipeline.pipelinable import PipelinableContext - -from colossalai.testing import rerun_on_exception +from colossalai.testing import rerun_if_address_is_in_use, rerun_on_exception, spawn NUM_CHUNKS = 1 PIPELINE_SIZE = 2 @@ -27,7 +25,7 @@ def forward(self, x): return x -def run_pipelinable(rank): +def run_pipelinable(rank, world_size, port): pipelinable = PipelinableContext() with pipelinable: model = MLP() @@ -50,9 +48,9 @@ def run_pipelinable(rank): assert layers_count_in_part_0 + layers_count_in_part_1 == pipelinable.layers_count -@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*") +@rerun_if_address_is_in_use() def test_pipelinable(): - mp.spawn(run_pipelinable, nprocs=1) + spawn(run_pipelinable, 1) if __name__ == '__main__': diff --git a/tests/test_pipeline/test_pipeline_process_group.py b/tests/test_pipeline/test_pipeline_process_group.py index c67e4175df92..2a00e3ac55b1 100644 --- a/tests/test_pipeline/test_pipeline_process_group.py +++ b/tests/test_pipeline/test_pipeline_process_group.py @@ -1,13 +1,12 @@ import os import torch.distributed.rpc as rpc -import torch.multiprocessing as mp -import pytest +from rpc_test_utils import pg_parse_args, rpc_is_initialized -from colossalai.pipeline.pipeline_process_group import ppg from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from rpc_test_utils import pg_parse_args, rpc_is_initialized +from colossalai.pipeline.pipeline_process_group import ppg +from colossalai.testing import spawn def run_worker(rank, args): @@ -40,4 +39,4 @@ def run_worker(rank, args): if __name__ == "__main__": args = pg_parse_args() world_size = args.world_size - mp.spawn(run_worker, args=(args,), nprocs=world_size) \ No newline at end of file + spawn(run_worker, world_size, args=args) diff --git a/tests/test_tensor/core/test_dist_spec_mgr.py b/tests/test_tensor/core/test_dist_spec_mgr.py index e02f4e7977f6..89476a35b63a 100644 --- a/tests/test_tensor/core/test_dist_spec_mgr.py +++ b/tests/test_tensor/core/test_dist_spec_mgr.py @@ -1,13 +1,12 @@ import math + +import pytest import torch import torch.distributed as dist -import pytest + import colossalai -import torch.multiprocessing as mp -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.tensor import DistSpecManager, ProcessGroup, ShardSpec, ReplicaSpec -from functools import partial +from colossalai.tensor import DistSpecManager, ProcessGroup, ReplicaSpec, ShardSpec +from colossalai.testing import rerun_if_address_is_in_use, spawn def run(): @@ -58,8 +57,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_dist_spec_mgr(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/core/test_tensor.py b/tests/test_tensor/core/test_tensor.py index b48d9e9a2dfa..64d198b350a8 100644 --- a/tests/test_tensor/core/test_tensor.py +++ b/tests/test_tensor/core/test_tensor.py @@ -1,17 +1,11 @@ -import torch import pytest -from colossalai.tensor import ColoTensor +import torch from numpy import allclose import colossalai -from colossalai.utils import free_port -from colossalai.tensor import ColoTensorSpec from colossalai.core import global_context as gpc -import torch.multiprocessing as mp -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.tensor import distspec, ColoTensor, ProcessGroup, ShardSpec, ReplicaSpec -from functools import partial +from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup, ReplicaSpec, ShardSpec, distspec +from colossalai.testing import rerun_if_address_is_in_use, spawn def _run_tensor_indexing(): @@ -152,8 +146,7 @@ def run_dist_tests(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 2]) @rerun_if_address_is_in_use() def test_dist_cases(world_size): - run_func = partial(run_dist_tests, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist_tests, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/model/test_gpt2.py b/tests/test_tensor/model/test_gpt2.py index 0d6a3fe26c2d..337bfa840d5d 100644 --- a/tests/test_tensor/model/test_gpt2.py +++ b/tests/test_tensor/model/test_gpt2.py @@ -1,15 +1,11 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP import colossalai from colossalai.nn.parallel.data_parallel import ColoDDP from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs @@ -145,8 +141,7 @@ def run_dist(rank, world_size, port, use_ddp): @pytest.mark.parametrize('use_ddp', [False, True]) @rerun_if_address_is_in_use() def test_gpt(world_size, use_ddp): - run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size, use_ddp=use_ddp) if __name__ == '__main__': diff --git a/tests/test_tensor/model/test_model.py b/tests/test_tensor/model/test_model.py index 83abc641cbd4..79d70e53c5cb 100644 --- a/tests/test_tensor/model/test_model.py +++ b/tests/test_tensor/model/test_model.py @@ -1,15 +1,11 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.nn.optimizer import ColossalaiOptimizer from colossalai.tensor import ColoTensor, ProcessGroup from colossalai.tensor.colo_parameter import ColoParameter -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import free_port, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs @@ -313,8 +309,7 @@ def run_model_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_model(world_size): - run_func = partial(run_model_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_model_dist, world_size) def run_pretrain_load_dist(rank, world_size, port): @@ -329,8 +324,7 @@ def run_pretrain_load_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_pretrain_load(world_size): - run_func = partial(run_pretrain_load_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_pretrain_load_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/model/test_module_spec.py b/tests/test_tensor/model/test_module_spec.py index 739bf2b0a641..b50851e5eaf2 100644 --- a/tests/test_tensor/model/test_module_spec.py +++ b/tests/test_tensor/model/test_module_spec.py @@ -1,9 +1,7 @@ from copy import deepcopy -from functools import partial import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.nn.parallel.layers import check_colo_module, init_colo_module @@ -17,8 +15,7 @@ ShardSpec, distspec, ) -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs @@ -207,8 +204,7 @@ def run_dist_check(rank, world_size, port): @pytest.mark.skip("for higher testing speed") @rerun_if_address_is_in_use() def test_module_linear_1d(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) @pytest.mark.dist @@ -216,8 +212,7 @@ def test_module_linear_1d(world_size): @pytest.mark.skip("for higher testing speed") @rerun_if_address_is_in_use() def test_module_model(world_size): - run_func = partial(run_dist_model, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist_model, world_size) @pytest.mark.dist @@ -225,8 +220,7 @@ def test_module_model(world_size): @pytest.mark.skip("for higher testing speed") @rerun_if_address_is_in_use() def test_module_check(world_size): - run_func = partial(run_dist_check, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist_check, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/test_colo_checkpoint_tools.py b/tests/test_tensor/test_colo_checkpoint_tools.py index aa333d55276c..a53a3f37a664 100644 --- a/tests/test_tensor/test_colo_checkpoint_tools.py +++ b/tests/test_tensor/test_colo_checkpoint_tools.py @@ -1,47 +1,41 @@ -import torch -import pytest -from functools import partial - -import torch.multiprocessing as mp -import torch.distributed as dist - -import colossalai -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils.cuda import get_current_device -from colossalai.utils import free_port -from colossalai.tensor import ComputePattern, ComputeSpec, ColoTensor, ShardSpec, ProcessGroup, ColoTensorSpec -from colossalai.utils.checkpoint.utils import gather_tensor, scatter_tensor -from tests.test_tensor.common_utils import tensor_shard_equal - - -def run_dist(rank, world_size, port, dp_degree, tp_degree): - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - pg = ProcessGroup(dp_degree=dp_degree, tp_degree=tp_degree) - x = torch.randn(4, 4) - param = ColoTensor(torch.nn.Parameter(x), spec=ColoTensorSpec(pg)) - spec = ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D) - param.set_tensor_spec(*spec) - - gather_tensor(param) - if dist.get_rank() == 0: - assert torch.all(x == param) - else: - assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size()) - dist.barrier() - - scatter_tensor(param, spec[0]) - assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size()) - assert param.requires_grad is True - dist.barrier() - - -@pytest.mark.dist -@pytest.mark.parametrize('world_size', [4]) -@rerun_if_address_is_in_use() -def test_checkpoint(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port(), dp_degree=2, tp_degree=world_size // 2) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == '__main__': - test_checkpoint(world_size=4) +import pytest +import torch +import torch.distributed as dist + +import colossalai +from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils.checkpoint.utils import gather_tensor, scatter_tensor +from tests.test_tensor.common_utils import tensor_shard_equal + + +def run_dist(rank, world_size, port, dp_degree, tp_degree): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + pg = ProcessGroup(dp_degree=dp_degree, tp_degree=tp_degree) + x = torch.randn(4, 4) + param = ColoTensor(torch.nn.Parameter(x), spec=ColoTensorSpec(pg)) + spec = ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D) + param.set_tensor_spec(*spec) + + gather_tensor(param) + if dist.get_rank() == 0: + assert torch.all(x == param) + else: + assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size()) + dist.barrier() + + scatter_tensor(param, spec[0]) + assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size()) + assert param.requires_grad is True + dist.barrier() + + +@pytest.mark.dist +@pytest.mark.parametrize('world_size', [4]) +@rerun_if_address_is_in_use() +def test_checkpoint(world_size): + spawn(run_dist, world_size, dp_degree=2, tp_degree=world_size // 2) + + +if __name__ == '__main__': + test_checkpoint(world_size=4) diff --git a/tests/test_tensor/test_comm_spec_apply.py b/tests/test_tensor/test_comm_spec_apply.py index 46eee61f1ecf..2c68633aabc8 100644 --- a/tests/test_tensor/test_comm_spec_apply.py +++ b/tests/test_tensor/test_comm_spec_apply.py @@ -1,10 +1,5 @@ -from functools import partial - import pytest import torch -import torch.distributed as dist -import torch.multiprocessing as mp -from torch.distributed import ReduceOp from colossalai.core import global_context as gpc from colossalai.device.device_mesh import DeviceMesh @@ -12,8 +7,7 @@ from colossalai.logging import disable_existing_loggers from colossalai.tensor.shape_consistency import CollectiveCommPattern, CommSpec from colossalai.tensor.sharding_spec import ShardingSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn def check_all_gather(device_mesh, rank): @@ -218,8 +212,7 @@ def check_comm(rank, world_size, port): @rerun_if_address_is_in_use() def test_comm_spec(): world_size = 4 - run_func = partial(check_comm, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_comm, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/test_context.py b/tests/test_tensor/test_context.py index 047371f45bda..45def034ba8e 100644 --- a/tests/test_tensor/test_context.py +++ b/tests/test_tensor/test_context.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.tensor import ( @@ -14,8 +11,7 @@ ReplicaSpec, ShardSpec, ) -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext from tests.components_to_test.registry import non_distributed_component_funcs @@ -61,8 +57,7 @@ def run_colo_init_context(rank: int, world_size: int, port: int): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_colo_init_context(world_size): - run_func = partial(run_colo_init_context, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_colo_init_context, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/test_dtensor/test_comm_spec.py b/tests/test_tensor/test_dtensor/test_comm_spec.py index 547a96b264dc..d1f5b9299397 100644 --- a/tests/test_tensor/test_dtensor/test_comm_spec.py +++ b/tests/test_tensor/test_dtensor/test_comm_spec.py @@ -1,9 +1,6 @@ -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from torch.distributed import ReduceOp from colossalai.core import global_context as gpc @@ -12,8 +9,7 @@ from colossalai.logging import disable_existing_loggers from colossalai.tensor.d_tensor.comm_spec import CollectiveCommPattern, CommSpec from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn def check_all_gather(process_groups_dict, rank): @@ -182,8 +178,7 @@ def check_comm(rank, world_size, port): @rerun_if_address_is_in_use() def test_comm_spec(): world_size = 4 - run_func = partial(check_comm, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_comm, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/test_dtensor/test_dtensor.py b/tests/test_tensor/test_dtensor/test_dtensor.py index a99ac6e41c5e..3ca369acbf87 100644 --- a/tests/test_tensor/test_dtensor/test_dtensor.py +++ b/tests/test_tensor/test_dtensor/test_dtensor.py @@ -1,7 +1,4 @@ -from functools import partial - import torch -import torch.multiprocessing as mp from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch @@ -9,7 +6,7 @@ from colossalai.tensor.d_tensor.d_tensor import DTensor, distribute_tensor from colossalai.tensor.d_tensor.layout import Layout from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn class TestModel(torch.nn.Module): @@ -92,10 +89,10 @@ def check_dtensor(rank, world_size, port): raise ValueError(f'rank {rank} is not in the device mesh') +@rerun_if_address_is_in_use() def test_dtensor(): world_size = 4 - run_func = partial(check_dtensor, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_dtensor, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/test_dtensor/test_layout_converter.py b/tests/test_tensor/test_dtensor/test_layout_converter.py index 70cf8726dbd0..5f56decb5e5d 100644 --- a/tests/test_tensor/test_dtensor/test_layout_converter.py +++ b/tests/test_tensor/test_dtensor/test_layout_converter.py @@ -1,9 +1,7 @@ import math -from functools import partial import pytest import torch -import torch.multiprocessing as mp from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch @@ -12,8 +10,7 @@ from colossalai.tensor.d_tensor.layout import Layout from colossalai.tensor.d_tensor.layout_converter import LayoutConverter from colossalai.tensor.d_tensor.sharding_spec import DimSpec, ShardingSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn entire_shape = torch.Size((64, 32, 16)) layout_converter = LayoutConverter() @@ -192,14 +189,9 @@ def check_layout_converting_apply(rank, world_size, port): @rerun_if_address_is_in_use() def test_layout_converter(): world_size = 4 - run_func = partial(check_one_step_transform, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - run_func = partial(check_layout_converting, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - run_func = partial(check_layout_converting_apply, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_one_step_transform, world_size) + spawn(check_layout_converting, world_size) + spawn(check_layout_converting_apply, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/test_mix_gather.py b/tests/test_tensor/test_mix_gather.py index c1ab30601501..9122808eb5a3 100644 --- a/tests/test_tensor/test_mix_gather.py +++ b/tests/test_tensor/test_mix_gather.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp from colossalai.core import global_context as gpc from colossalai.device.device_mesh import DeviceMesh @@ -11,7 +8,7 @@ from colossalai.tensor.shape_consistency import CollectiveCommPattern, CommSpec from colossalai.tensor.sharding_spec import ShardingSpec from colossalai.tensor.utils import mix_gather_simulator -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn def check_mix_gather_S0S1(device_mesh, rank): @@ -323,10 +320,10 @@ def check_comm(rank, world_size, port): @pytest.mark.skip(reason="Skip because the check functions assume 8 GPUS but CI only have 4 GPUs") +@rerun_if_address_is_in_use() def test_mix_gather(): world_size = 8 - run_func = partial(check_comm, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_comm, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/test_parameter.py b/tests/test_tensor/test_parameter.py index 7c3c4b2132e4..9c3f05da1ffa 100644 --- a/tests/test_tensor/test_parameter.py +++ b/tests/test_tensor/test_parameter.py @@ -1,9 +1,10 @@ -from colossalai.tensor import ColoParameter, ColoTensor, ColoTensorSpec, ProcessGroup -import torch import pytest +import torch from common_utils import tensor_equal + import colossalai -from colossalai.utils import free_port +from colossalai.tensor import ColoParameter, ColoTensor, ColoTensorSpec, ProcessGroup +from colossalai.testing import free_port @pytest.mark.skip diff --git a/tests/test_tensor/test_shape_consistency_apply.py b/tests/test_tensor/test_shape_consistency_apply.py index 4c838bc83fad..b57952df401f 100644 --- a/tests/test_tensor/test_shape_consistency_apply.py +++ b/tests/test_tensor/test_shape_consistency_apply.py @@ -1,16 +1,12 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.tensor.shape_consistency import CollectiveCommPattern, ShapeConsistencyManager from colossalai.tensor.sharding_spec import ShardingSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn def check_apply(rank, world_size, port): @@ -73,8 +69,7 @@ def check_apply(rank, world_size, port): @rerun_if_address_is_in_use() def test_apply(): world_size = 4 - run_func = partial(check_apply, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(check_apply, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/test_sharded_linear.py b/tests/test_tensor/test_sharded_linear.py index 85008c67a9c2..d66d4fec14d1 100644 --- a/tests/test_tensor/test_sharded_linear.py +++ b/tests/test_tensor/test_sharded_linear.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn.functional as F import colossalai @@ -10,8 +7,7 @@ from colossalai.nn._ops._utils import gather_forward_split_backward from colossalai.tensor import ColoParameter, ColoTensor, ProcessGroup from colossalai.tensor.sharding_spec import ShardingSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn def run_dist(rank, world_size, port): @@ -229,8 +225,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [4]) @rerun_if_address_is_in_use() def test_sharded_mlp(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_tensor/test_tp_with_zero.py b/tests/test_tensor/test_tp_with_zero.py index 94e39e5d1546..c636d9442902 100644 --- a/tests/test_tensor/test_tp_with_zero.py +++ b/tests/test_tensor/test_tp_with_zero.py @@ -1,15 +1,11 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP import colossalai from colossalai.amp import convert_to_apex_amp from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, GeminiDDP, ZeroDDP from colossalai.zero.gemini import search_chunk_configuration @@ -140,8 +136,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_gpt(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_trainer/test_pipeline/test_p2p.py b/tests/test_trainer/test_pipeline/test_p2p.py index 72820c6a1f0d..cb7a193d2bfa 100644 --- a/tests/test_trainer/test_pipeline/test_p2p.py +++ b/tests/test_trainer/test_pipeline/test_p2p.py @@ -1,21 +1,26 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp -from colossalai.communication import (recv_backward, recv_forward, recv_obj_meta, send_backward, - send_backward_recv_forward, send_forward, send_forward_recv_backward, - send_obj_meta) + +from colossalai.communication import ( + recv_backward, + recv_forward, + recv_obj_meta, + send_backward, + send_backward_recv_forward, + send_forward, + send_forward_recv_backward, + send_obj_meta, +) from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.initialize import launch from colossalai.logging import get_dist_logger -from colossalai.utils import free_port, get_current_device -from colossalai.testing import rerun_on_exception +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device BATCH_SIZE = 4 SEQ_LENGTH = 2 @@ -93,11 +98,10 @@ def run_check(rank, world_size, port): @pytest.mark.dist -@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*") +@rerun_if_address_is_in_use() def test_p2p(): world_size = 4 - run_func = partial(run_check, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_check, world_size) if __name__ == '__main__': diff --git a/tests/test_trainer/test_pipeline/test_pipeline_schedule.py b/tests/test_trainer/test_pipeline/test_pipeline_schedule.py index 48f729658134..6d7bf6b3d89f 100644 --- a/tests/test_trainer/test_pipeline/test_pipeline_schedule.py +++ b/tests/test_trainer/test_pipeline/test_pipeline_schedule.py @@ -1,34 +1,26 @@ # referenced from Megatron and used to testify communication import os -import os.path as osp -from functools import partial from pathlib import Path -import colossalai import pytest import torch import torch.nn as nn -import torch.multiprocessing as mp -from colossalai.core import global_context as gpc -from colossalai.context import ParallelMode -from colossalai.initialize import launch -from colossalai.utils import free_port, get_dataloader, print_rank_0 -from colossalai.testing import rerun_on_exception from torchvision import transforms from torchvision.datasets import CIFAR10 from torchvision.models import resnet18 +import colossalai +from colossalai.context import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.initialize import launch +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_dataloader, print_rank_0 BATCH_SIZE = 8 -CONFIG=dict( - NUM_MICRO_BATCHES=2, - parallel = dict( - pipeline=dict(size=2), - tensor=dict(size=1, mode=None) - ) -) +CONFIG = dict(NUM_MICRO_BATCHES=2, parallel=dict(pipeline=dict(size=2), tensor=dict(size=1, mode=None))) + def run_schedule(rank, world_size, port): launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') @@ -85,11 +77,10 @@ def forward(self, x): @pytest.mark.dist -@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*") +@rerun_if_address_is_in_use() def test_pipeline_schedule(): world_size = 2 - run_func = partial(run_schedule, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_schedule, world_size) if __name__ == '__main__': diff --git a/tests/test_trainer/test_trainer_with_non_pipe_schedule.py b/tests/test_trainer/test_trainer_with_non_pipe_schedule.py index b013433293cd..753f82222f9d 100644 --- a/tests/test_trainer/test_trainer_with_non_pipe_schedule.py +++ b/tests/test_trainer/test_trainer_with_non_pipe_schedule.py @@ -1,15 +1,13 @@ -from functools import partial - -import colossalai import pytest import torch -import torch.multiprocessing as mp + +import colossalai from colossalai.amp.amp_type import AMP_TYPE from colossalai.logging import get_dist_logger +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.trainer import Trainer -from colossalai.utils import MultiTimer, free_port +from colossalai.utils import MultiTimer from tests.components_to_test.registry import non_distributed_component_funcs -from colossalai.testing import parameterize, rerun_if_address_is_in_use BATCH_SIZE = 4 IMG_SIZE = 32 @@ -54,8 +52,7 @@ def run_dist(rank, world_size, port): @rerun_if_address_is_in_use() def test_trainer_no_pipeline(): world_size = 4 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_trainer/test_trainer_with_pipe_schedule.py b/tests/test_trainer/test_trainer_with_pipe_schedule.py index 3698526a8e6c..bb63d51a0b65 100644 --- a/tests/test_trainer/test_trainer_with_pipe_schedule.py +++ b/tests/test_trainer/test_trainer_with_pipe_schedule.py @@ -1,23 +1,21 @@ import os -from functools import partial from pathlib import Path -import colossalai import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn -from colossalai.context.parallel_mode import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.engine.schedule import PipelineSchedule -from colossalai.logging import get_dist_logger -from colossalai.trainer import Trainer -from colossalai.utils import MultiTimer, free_port, get_dataloader from torch.optim import Adam from torchvision import transforms from torchvision.datasets import CIFAR10 from torchvision.models import resnet18 -from colossalai.testing import rerun_if_address_is_in_use + +import colossalai +from colossalai.context.parallel_mode import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.logging import get_dist_logger +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.trainer import Trainer +from colossalai.utils import MultiTimer, get_dataloader BATCH_SIZE = 4 IMG_SIZE = 32 @@ -91,8 +89,7 @@ def forward(self, x): @rerun_if_address_is_in_use() def test_trainer_with_pipeline(): world_size = 4 - run_func = partial(run_trainer_with_pipeline, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_trainer_with_pipeline, world_size) if __name__ == '__main__': diff --git a/tests/test_utils/test_activation_checkpointing.py b/tests/test_utils/test_activation_checkpointing.py index 3ac75fb00c86..59a8acd4b210 100644 --- a/tests/test_utils/test_activation_checkpointing.py +++ b/tests/test_utils/test_activation_checkpointing.py @@ -4,8 +4,10 @@ import pytest import torch import torch.nn.functional as F + from colossalai.context.parallel_mode import ParallelMode -from colossalai.context.random import add_seed, seed, set_mode, reset_seeds +from colossalai.context.random import add_seed, reset_seeds, seed, set_mode +from colossalai.testing import clear_cache_before_run, parameterize from colossalai.utils.activation_checkpoint import checkpoint @@ -39,8 +41,9 @@ def forward_inplace(x, weight): @pytest.mark.gpu -@pytest.mark.parametrize("use_reentrant", [True, False]) -@pytest.mark.parametrize("cpu_offload", [True, False]) +@clear_cache_before_run() +@parameterize("use_reentrant", [True, False]) +@parameterize("cpu_offload", [True, False]) def test_activation_checkpointing(cpu_offload, use_reentrant): # as seed manager is singleton diff --git a/tests/test_utils/test_checkpoint/test_checkpoint_1d.py b/tests/test_utils/test_checkpoint/test_checkpoint_1d.py index 8a0fea9ae47a..335be61359ed 100644 --- a/tests/test_utils/test_checkpoint/test_checkpoint_1d.py +++ b/tests/test_utils/test_checkpoint/test_checkpoint_1d.py @@ -1,80 +1,77 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -import pprint -from functools import partial - -import colossalai.nn as col_nn -import pytest -import torch -import torch.multiprocessing as mp -import torch.nn as nn -from colossalai.context.parallel_mode import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.utils import free_port, is_using_pp -from colossalai.utils.checkpointing import gather_pipeline_parallel_state_dict, load_checkpoint, save_checkpoint -from colossalai.testing import rerun_on_exception, skip_if_not_enough_gpus - - -def build_pipeline(model): - from colossalai.pipeline.utils import partition_uniform - - pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE) - pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) - depth = len(model) - start, end = partition_uniform(depth, pipeline_size, 1)[pipeline_rank][0] - layers = [] - for i in range(depth): - if start <= i < end: - layers.append(model[i]) - else: - layers.append(nn.Identity()) - return nn.Sequential(*tuple(layers)) - - -def check_equal(A, B): - assert torch.allclose(A, B, rtol=1e-3, atol=1e-2) - - -def check_checkpoint_1d(rank, world_size, port): - config = dict(parallel=dict(pipeline=dict(size=2), tensor=dict(size=4, mode="1d")),) - - disable_existing_loggers() - launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") - - m1 = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 4)) - sd1 = m1.state_dict() - if gpc.get_global_rank() == 0: - print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd1)}\n") - save_checkpoint("test.pt", 0, m1) - - m2 = nn.Sequential(col_nn.Linear(4, 8), col_nn.Linear(8, 4)) - if is_using_pp(): - m2 = build_pipeline(m2) - - load_checkpoint("test.pt", m2) - sd2 = m2.state_dict() - if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0: - sd2 = gather_pipeline_parallel_state_dict(sd2) - print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd2)}\n") - - if gpc.get_global_rank() == 0: - for k, v in sd1.items(): - assert k in sd2 - check_equal(v, sd2[k].to(torch.device("cpu"))) - - -@pytest.mark.dist -@pytest.mark.skip("takes too long") -@skip_if_not_enough_gpus(min_gpus=8) -@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*") -def test_checkpoint_1d(): - world_size = 8 - run_func = partial(check_checkpoint_1d, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == "__main__": - test_checkpoint_1d() +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +import pprint + +import pytest +import torch +import torch.nn as nn + +import colossalai.nn as col_nn +from colossalai.context.parallel_mode import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.initialize import launch +from colossalai.logging import disable_existing_loggers +from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus, spawn +from colossalai.utils import is_using_pp +from colossalai.utils.checkpointing import gather_pipeline_parallel_state_dict, load_checkpoint, save_checkpoint + + +def build_pipeline(model): + from colossalai.pipeline.utils import partition_uniform + + pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE) + pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) + depth = len(model) + start, end = partition_uniform(depth, pipeline_size, 1)[pipeline_rank][0] + layers = [] + for i in range(depth): + if start <= i < end: + layers.append(model[i]) + else: + layers.append(nn.Identity()) + return nn.Sequential(*tuple(layers)) + + +def check_equal(A, B): + assert torch.allclose(A, B, rtol=1e-3, atol=1e-2) + + +def check_checkpoint_1d(rank, world_size, port): + config = dict(parallel=dict(pipeline=dict(size=2), tensor=dict(size=4, mode="1d")),) + + disable_existing_loggers() + launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") + + m1 = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 4)) + sd1 = m1.state_dict() + if gpc.get_global_rank() == 0: + print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd1)}\n") + save_checkpoint("test.pt", 0, m1) + + m2 = nn.Sequential(col_nn.Linear(4, 8), col_nn.Linear(8, 4)) + if is_using_pp(): + m2 = build_pipeline(m2) + + load_checkpoint("test.pt", m2) + sd2 = m2.state_dict() + if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0: + sd2 = gather_pipeline_parallel_state_dict(sd2) + print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd2)}\n") + + if gpc.get_global_rank() == 0: + for k, v in sd1.items(): + assert k in sd2 + check_equal(v, sd2[k].to(torch.device("cpu"))) + + +@pytest.mark.dist +@pytest.mark.skip("takes too long") +@skip_if_not_enough_gpus(min_gpus=8) +@rerun_if_address_is_in_use() +def test_checkpoint_1d(): + spawn(check_checkpoint_1d, 8) + + +if __name__ == "__main__": + test_checkpoint_1d() diff --git a/tests/test_utils/test_checkpoint/test_checkpoint_2d.py b/tests/test_utils/test_checkpoint/test_checkpoint_2d.py index 26314290d4de..175d9ef6ceb9 100644 --- a/tests/test_utils/test_checkpoint/test_checkpoint_2d.py +++ b/tests/test_utils/test_checkpoint/test_checkpoint_2d.py @@ -1,80 +1,77 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -import pprint -from functools import partial - -import colossalai.nn as col_nn -import pytest -import torch -import torch.multiprocessing as mp -import torch.nn as nn -from colossalai.context.parallel_mode import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.utils import free_port, get_current_device, is_using_pp -from colossalai.utils.checkpointing import gather_pipeline_parallel_state_dict, load_checkpoint, save_checkpoint -from colossalai.testing import rerun_on_exception, skip_if_not_enough_gpus - - -def build_pipeline(model): - from colossalai.pipeline.utils import partition_uniform - - pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE) - pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) - depth = len(model) - start, end = partition_uniform(depth, pipeline_size, 1)[pipeline_rank][0] - layers = [] - for i in range(depth): - if start <= i < end: - layers.append(model[i]) - else: - layers.append(nn.Identity()) - return nn.Sequential(*tuple(layers)) - - -def check_equal(A, B): - assert torch.allclose(A, B, rtol=1e-3, atol=1e-2) - - -def check_checkpoint_2d(rank, world_size, port): - config = dict(parallel=dict(pipeline=dict(size=2), tensor=dict(size=4, mode="2d")),) - - disable_existing_loggers() - launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") - - m1 = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 4)) - sd1 = m1.state_dict() - if gpc.get_global_rank() == 0: - print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd1)}\n") - save_checkpoint("test.pt", 0, m1) - - m2 = nn.Sequential(col_nn.Linear(4, 8), col_nn.Linear(8, 4)) - if is_using_pp(): - m2 = build_pipeline(m2) - - load_checkpoint("test.pt", m2) - sd2 = m2.state_dict() - if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0: - sd2 = gather_pipeline_parallel_state_dict(sd2) - print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd2)}\n") - - if gpc.get_global_rank() == 0: - for k, v in sd1.items(): - assert k in sd2 - check_equal(v, sd2[k].to(torch.device("cpu"))) - - -@pytest.mark.dist -@pytest.mark.skip("takes too long") -@skip_if_not_enough_gpus(min_gpus=8) -@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*") -def test_checkpoint_2d(): - world_size = 8 - run_func = partial(check_checkpoint_2d, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == "__main__": - test_checkpoint_2d() +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +import pprint + +import pytest +import torch +import torch.nn as nn + +import colossalai.nn as col_nn +from colossalai.context.parallel_mode import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.initialize import launch +from colossalai.logging import disable_existing_loggers +from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus, spawn +from colossalai.utils import is_using_pp +from colossalai.utils.checkpointing import gather_pipeline_parallel_state_dict, load_checkpoint, save_checkpoint + + +def build_pipeline(model): + from colossalai.pipeline.utils import partition_uniform + + pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE) + pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) + depth = len(model) + start, end = partition_uniform(depth, pipeline_size, 1)[pipeline_rank][0] + layers = [] + for i in range(depth): + if start <= i < end: + layers.append(model[i]) + else: + layers.append(nn.Identity()) + return nn.Sequential(*tuple(layers)) + + +def check_equal(A, B): + assert torch.allclose(A, B, rtol=1e-3, atol=1e-2) + + +def check_checkpoint_2d(rank, world_size, port): + config = dict(parallel=dict(pipeline=dict(size=2), tensor=dict(size=4, mode="2d")),) + + disable_existing_loggers() + launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") + + m1 = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 4)) + sd1 = m1.state_dict() + if gpc.get_global_rank() == 0: + print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd1)}\n") + save_checkpoint("test.pt", 0, m1) + + m2 = nn.Sequential(col_nn.Linear(4, 8), col_nn.Linear(8, 4)) + if is_using_pp(): + m2 = build_pipeline(m2) + + load_checkpoint("test.pt", m2) + sd2 = m2.state_dict() + if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0: + sd2 = gather_pipeline_parallel_state_dict(sd2) + print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd2)}\n") + + if gpc.get_global_rank() == 0: + for k, v in sd1.items(): + assert k in sd2 + check_equal(v, sd2[k].to(torch.device("cpu"))) + + +@pytest.mark.dist +@pytest.mark.skip("takes too long") +@skip_if_not_enough_gpus(min_gpus=8) +@rerun_if_address_is_in_use() +def test_checkpoint_2d(): + spawn(check_checkpoint_2d, 8) + + +if __name__ == "__main__": + test_checkpoint_2d() diff --git a/tests/test_utils/test_checkpoint/test_checkpoint_2p5d.py b/tests/test_utils/test_checkpoint/test_checkpoint_2p5d.py index 3dbd340fd42d..33cb3a65d184 100644 --- a/tests/test_utils/test_checkpoint/test_checkpoint_2p5d.py +++ b/tests/test_utils/test_checkpoint/test_checkpoint_2p5d.py @@ -1,80 +1,77 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -import pprint -from functools import partial - -import colossalai.nn as col_nn -import pytest -import torch -import torch.multiprocessing as mp -import torch.nn as nn -from colossalai.context.parallel_mode import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.utils import free_port, get_current_device, is_using_pp -from colossalai.utils.checkpointing import gather_pipeline_parallel_state_dict, load_checkpoint, save_checkpoint -from colossalai.testing import rerun_on_exception, skip_if_not_enough_gpus - - -def build_pipeline(model): - from colossalai.pipeline.utils import partition_uniform - - pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE) - pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) - depth = len(model) - start, end = partition_uniform(depth, pipeline_size, 1)[pipeline_rank][0] - layers = [] - for i in range(depth): - if start <= i < end: - layers.append(model[i]) - else: - layers.append(nn.Identity()) - return nn.Sequential(*tuple(layers)) - - -def check_equal(A, B): - assert torch.allclose(A, B, rtol=1e-3, atol=1e-2) - - -def check_checkpoint_2p5d(rank, world_size, port): - config = dict(parallel=dict(pipeline=dict(size=2), tensor=dict(size=4, depth=1, mode="2.5d")),) - - disable_existing_loggers() - launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") - - m1 = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 4)) - sd1 = m1.state_dict() - if gpc.get_global_rank() == 0: - print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd1)}\n") - save_checkpoint("test.pt", 0, m1) - - m2 = nn.Sequential(col_nn.Linear(4, 8), col_nn.Linear(8, 4)) - if is_using_pp(): - m2 = build_pipeline(m2) - - load_checkpoint("test.pt", m2) - sd2 = m2.state_dict() - if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0: - sd2 = gather_pipeline_parallel_state_dict(sd2) - print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd2)}\n") - - if gpc.get_global_rank() == 0: - for k, v in sd1.items(): - assert k in sd2 - check_equal(v, sd2[k].to(torch.device("cpu"))) - - -@pytest.mark.dist -@pytest.mark.skip("takes too long") -@skip_if_not_enough_gpus(min_gpus=8) -@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*") -def test_checkpoint_2p5d(): - world_size = 8 - run_func = partial(check_checkpoint_2p5d, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == "__main__": - test_checkpoint_2p5d() +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +import pprint + +import pytest +import torch +import torch.nn as nn + +import colossalai.nn as col_nn +from colossalai.context.parallel_mode import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.initialize import launch +from colossalai.logging import disable_existing_loggers +from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus, spawn +from colossalai.utils import is_using_pp +from colossalai.utils.checkpointing import gather_pipeline_parallel_state_dict, load_checkpoint, save_checkpoint + + +def build_pipeline(model): + from colossalai.pipeline.utils import partition_uniform + + pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE) + pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) + depth = len(model) + start, end = partition_uniform(depth, pipeline_size, 1)[pipeline_rank][0] + layers = [] + for i in range(depth): + if start <= i < end: + layers.append(model[i]) + else: + layers.append(nn.Identity()) + return nn.Sequential(*tuple(layers)) + + +def check_equal(A, B): + assert torch.allclose(A, B, rtol=1e-3, atol=1e-2) + + +def check_checkpoint_2p5d(rank, world_size, port): + config = dict(parallel=dict(pipeline=dict(size=2), tensor=dict(size=4, depth=1, mode="2.5d")),) + + disable_existing_loggers() + launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") + + m1 = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 4)) + sd1 = m1.state_dict() + if gpc.get_global_rank() == 0: + print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd1)}\n") + save_checkpoint("test.pt", 0, m1) + + m2 = nn.Sequential(col_nn.Linear(4, 8), col_nn.Linear(8, 4)) + if is_using_pp(): + m2 = build_pipeline(m2) + + load_checkpoint("test.pt", m2) + sd2 = m2.state_dict() + if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0: + sd2 = gather_pipeline_parallel_state_dict(sd2) + print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd2)}\n") + + if gpc.get_global_rank() == 0: + for k, v in sd1.items(): + assert k in sd2 + check_equal(v, sd2[k].to(torch.device("cpu"))) + + +@pytest.mark.dist +@pytest.mark.skip("takes too long") +@skip_if_not_enough_gpus(min_gpus=8) +@rerun_if_address_is_in_use() +def test_checkpoint_2p5d(): + spawn(check_checkpoint_2p5d, 8) + + +if __name__ == "__main__": + test_checkpoint_2p5d() diff --git a/tests/test_utils/test_checkpoint/test_checkpoint_3d.py b/tests/test_utils/test_checkpoint/test_checkpoint_3d.py index 38f650547585..73ac2dd5fe18 100644 --- a/tests/test_utils/test_checkpoint/test_checkpoint_3d.py +++ b/tests/test_utils/test_checkpoint/test_checkpoint_3d.py @@ -1,80 +1,77 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -import pprint -from functools import partial - -import colossalai.nn as col_nn -import pytest -import torch -import torch.multiprocessing as mp -import torch.nn as nn -from colossalai.context.parallel_mode import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.initialize import launch -from colossalai.logging import disable_existing_loggers -from colossalai.utils import free_port, get_current_device, is_using_pp -from colossalai.utils.checkpointing import gather_pipeline_parallel_state_dict, load_checkpoint, save_checkpoint -from colossalai.testing import rerun_on_exception, skip_if_not_enough_gpus - - -def build_pipeline(model): - from colossalai.pipeline.utils import partition_uniform - - pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE) - pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) - depth = len(model) - start, end = partition_uniform(depth, pipeline_size, 1)[pipeline_rank][0] - layers = [] - for i in range(depth): - if start <= i < end: - layers.append(model[i]) - else: - layers.append(nn.Identity()) - return nn.Sequential(*tuple(layers)) - - -def check_equal(A, B): - assert torch.allclose(A, B, rtol=1e-3, atol=1e-2) - - -def check_checkpoint_3d(rank, world_size, port): - config = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=8, mode="3d")),) - - disable_existing_loggers() - launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") - - m1 = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 4)) - sd1 = m1.state_dict() - if gpc.get_global_rank() == 0: - print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd1)}\n") - save_checkpoint("test.pt", 0, m1) - - m2 = nn.Sequential(col_nn.Linear(4, 8), col_nn.Linear(8, 4)) - if is_using_pp(): - m2 = build_pipeline(m2) - - load_checkpoint("test.pt", m2) - sd2 = m2.state_dict() - if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0: - sd2 = gather_pipeline_parallel_state_dict(sd2) - print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd2)}\n") - - if gpc.get_global_rank() == 0: - for k, v in sd1.items(): - assert k in sd2 - check_equal(v, sd2[k].to(torch.device("cpu"))) - - -@pytest.mark.dist -@pytest.mark.skip("takes too long") -@skip_if_not_enough_gpus(min_gpus=8) -@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*") -def test_checkpoint_3d(): - world_size = 8 - run_func = partial(check_checkpoint_3d, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) - - -if __name__ == "__main__": - test_checkpoint_3d() +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +import pprint + +import pytest +import torch +import torch.nn as nn + +import colossalai.nn as col_nn +from colossalai.context.parallel_mode import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.initialize import launch +from colossalai.logging import disable_existing_loggers +from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus, spawn +from colossalai.utils import is_using_pp +from colossalai.utils.checkpointing import gather_pipeline_parallel_state_dict, load_checkpoint, save_checkpoint + + +def build_pipeline(model): + from colossalai.pipeline.utils import partition_uniform + + pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE) + pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) + depth = len(model) + start, end = partition_uniform(depth, pipeline_size, 1)[pipeline_rank][0] + layers = [] + for i in range(depth): + if start <= i < end: + layers.append(model[i]) + else: + layers.append(nn.Identity()) + return nn.Sequential(*tuple(layers)) + + +def check_equal(A, B): + assert torch.allclose(A, B, rtol=1e-3, atol=1e-2) + + +def check_checkpoint_3d(rank, world_size, port): + config = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=8, mode="3d")),) + + disable_existing_loggers() + launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") + + m1 = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 4)) + sd1 = m1.state_dict() + if gpc.get_global_rank() == 0: + print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd1)}\n") + save_checkpoint("test.pt", 0, m1) + + m2 = nn.Sequential(col_nn.Linear(4, 8), col_nn.Linear(8, 4)) + if is_using_pp(): + m2 = build_pipeline(m2) + + load_checkpoint("test.pt", m2) + sd2 = m2.state_dict() + if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0: + sd2 = gather_pipeline_parallel_state_dict(sd2) + print(f"Rank {gpc.get_global_rank()}:\n{pprint.pformat(sd2)}\n") + + if gpc.get_global_rank() == 0: + for k, v in sd1.items(): + assert k in sd2 + check_equal(v, sd2[k].to(torch.device("cpu"))) + + +@pytest.mark.dist +@pytest.mark.skip("takes too long") +@skip_if_not_enough_gpus(min_gpus=8) +@rerun_if_address_is_in_use() +def test_checkpoint_3d(): + spawn(check_checkpoint_3d, 8) + + +if __name__ == "__main__": + test_checkpoint_3d() diff --git a/tests/test_utils/test_checkpoint_io/test_load.py b/tests/test_utils/test_checkpoint_io/test_load.py index 780c13dc534a..b1a741515728 100644 --- a/tests/test_utils/test_checkpoint_io/test_load.py +++ b/tests/test_utils/test_checkpoint_io/test_load.py @@ -3,20 +3,19 @@ from tempfile import TemporaryDirectory from typing import Dict -import colossalai import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import torch.nn as nn -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.utils.checkpoint_io.io import load, save -from colossalai.utils.checkpoint_io.meta import (ParamDistMeta, ParamRedistMeta, RankRedistMeta, RedistMeta) from torch import Tensor from torch.nn import Module from torch.optim import Adam, Optimizer +import colossalai +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils.checkpoint_io.io import load, save +from colossalai.utils.checkpoint_io.meta import ParamDistMeta, ParamRedistMeta, RankRedistMeta, RedistMeta + def check_model_state_dict(a: Dict[str, Tensor], b: Dict[str, Tensor]) -> None: assert set(a.keys()) == set(b.keys()) @@ -120,14 +119,13 @@ def test_save_global_load_global(max_shard_size_gb: float): check_optim_state_dict(optimizer.state_dict(), new_optimizer.state_dict()) -def run_dist(rank, world_size, port, func): +def run_dist(rank, world_size, port, test_fn): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - func() + test_fn() def launch_dist(fn, world_size: int): - proc_fn = partial(run_dist, world_size=world_size, port=free_port(), func=fn) - mp.spawn(proc_fn, nprocs=world_size) + spawn(run_dist, world_size, test_fn=fn) def save_dist(dir_name: str, zero: bool): diff --git a/tests/test_utils/test_checkpoint_io/test_merge.py b/tests/test_utils/test_checkpoint_io/test_merge.py index 04e454dcb713..255c74adf0a2 100644 --- a/tests/test_utils/test_checkpoint_io/test_merge.py +++ b/tests/test_utils/test_checkpoint_io/test_merge.py @@ -1,18 +1,18 @@ -from colossalai.utils.checkpoint_io.meta import ParamDistMeta -from colossalai.utils.checkpoint_io.constant import GLOBAL_META_FILE_NAME -from colossalai.utils.checkpoint_io.io import save, merge -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from tempfile import TemporaryDirectory -from torch.optim import Adam -from functools import partial -import torch import os +from functools import partial +from tempfile import TemporaryDirectory + import pytest -import colossalai -import torch.nn as nn +import torch import torch.distributed as dist -import torch.multiprocessing as mp +import torch.nn as nn +from torch.optim import Adam + +import colossalai +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils.checkpoint_io.constant import GLOBAL_META_FILE_NAME +from colossalai.utils.checkpoint_io.io import merge, save +from colossalai.utils.checkpoint_io.meta import ParamDistMeta class DummyModel(nn.Module): @@ -52,7 +52,7 @@ def test_merge_global(): assert len(os.listdir(output_dir)) == 0 -def run_dist(rank, world_size, port, func): +def run_dist(rank, world_size, port, test_fn): colossalai.launch(config={'parallel': { 'tensor': { 'mode': '1d', @@ -64,7 +64,7 @@ def run_dist(rank, world_size, port, func): host='localhost', port=port, backend='nccl') - func() + test_fn() def run_save_dist(dir_name: str, zero: bool): @@ -100,8 +100,7 @@ def test_merge_tp_dp(zero: bool): with TemporaryDirectory() as dir_name: fn = partial(run_save_dist, dir_name, zero) world_size = 4 - proc_fn = partial(run_dist, world_size=world_size, port=free_port(), func=fn) - mp.spawn(proc_fn, nprocs=world_size) + spawn(run_dist, world_size, test_fn=fn) with TemporaryDirectory() as output_dir: merge(dir_name, output_dir) assert len(os.listdir(output_dir)) == 5 diff --git a/tests/test_utils/test_checkpoint_io/test_redist.py b/tests/test_utils/test_checkpoint_io/test_redist.py index 6e76f3167e31..144715bdfcca 100644 --- a/tests/test_utils/test_checkpoint_io/test_redist.py +++ b/tests/test_utils/test_checkpoint_io/test_redist.py @@ -2,19 +2,23 @@ from functools import partial from tempfile import TemporaryDirectory -import colossalai import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import torch.nn as nn -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from torch.optim import Adam + +import colossalai +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.utils.checkpoint_io.constant import GLOBAL_META_FILE_NAME from colossalai.utils.checkpoint_io.io import redist, save -from colossalai.utils.checkpoint_io.meta import (ParamDistMeta, ParamRedistMeta, PipelineRedistMeta, RankRedistMeta, - RedistMeta) -from torch.optim import Adam +from colossalai.utils.checkpoint_io.meta import ( + ParamDistMeta, + ParamRedistMeta, + PipelineRedistMeta, + RankRedistMeta, + RedistMeta, +) class DummyModel(nn.Module): @@ -105,7 +109,7 @@ def test_global_to_dist(): check_checkpoint_shape(output_dir) -def run_dist(rank, world_size, port, func): +def run_dist(rank, world_size, port, test_fn): colossalai.launch(config={'parallel': { 'tensor': { 'mode': '1d', @@ -117,7 +121,7 @@ def run_dist(rank, world_size, port, func): host='localhost', port=port, backend='nccl') - func() + test_fn() def run_save_dist(dir_name: str, zero: bool): @@ -133,8 +137,7 @@ def test_dist_to_dist(zero: bool): with TemporaryDirectory() as dir_name: fn = partial(run_save_dist, dir_name, zero) world_size = 4 - proc_fn = partial(run_dist, world_size=world_size, port=free_port(), func=fn) - mp.spawn(proc_fn, nprocs=world_size) + spawn(run_dist, world_size, test_fn=fn) with TemporaryDirectory() as output_dir: redist(dir_name, output_dir, get_redist_meta(4), get_dist_metas(4)) if not zero: diff --git a/tests/test_utils/test_checkpoint_io/test_save.py b/tests/test_utils/test_checkpoint_io/test_save.py index 5ff9d0aa2217..e35e566f6ff8 100644 --- a/tests/test_utils/test_checkpoint_io/test_save.py +++ b/tests/test_utils/test_checkpoint_io/test_save.py @@ -3,21 +3,24 @@ from tempfile import TemporaryDirectory from typing import Dict -import colossalai import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import torch.nn as nn -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.utils.checkpoint_io.constant import (GLOBAL_META_FILE_NAME, META_CKPT_FILE_NAME, MODEL_CKPT_FILE_NAME, - OTHER_CKPT_FILE_NAME) -from colossalai.utils.checkpoint_io.io import save -from colossalai.utils.checkpoint_io.meta import ParamDistMeta from torch import Tensor from torch.optim import Adam +import colossalai +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils.checkpoint_io.constant import ( + GLOBAL_META_FILE_NAME, + META_CKPT_FILE_NAME, + MODEL_CKPT_FILE_NAME, + OTHER_CKPT_FILE_NAME, +) +from colossalai.utils.checkpoint_io.io import save +from colossalai.utils.checkpoint_io.meta import ParamDistMeta + def check_model_state_dict(a: Dict[str, Tensor], b: Dict[str, Tensor]) -> None: assert set(a.keys()) == set(b.keys()) @@ -104,9 +107,9 @@ def test_save_global_shard(): }) -def run_dist(rank, world_size, port, func): +def run_dist(rank, world_size, port, test_fn): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - func() + test_fn() def run_save_dist(dir_name): @@ -124,8 +127,7 @@ def test_save_dist(): with TemporaryDirectory() as dir_name: fn = partial(run_save_dist, dir_name) world_size = 2 - proc_fn = partial(run_dist, world_size=world_size, port=free_port(), func=fn) - mp.spawn(proc_fn, nprocs=world_size) + spawn(run_dist, world_size, test_fn=fn) assert len(os.listdir(dir_name)) == 8 global_meta = torch.load(os.path.join(dir_name, GLOBAL_META_FILE_NAME)) assert len(global_meta['meta']) == 2 diff --git a/tests/test_utils/test_colo_checkpoint.py b/tests/test_utils/test_colo_checkpoint.py index 7c2ad9078f98..89760a5456e7 100644 --- a/tests/test_utils/test_colo_checkpoint.py +++ b/tests/test_utils/test_colo_checkpoint.py @@ -1,20 +1,17 @@ import os import shutil from copy import deepcopy -from functools import partial import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from torch.optim.lr_scheduler import CosineAnnealingLR, MultiplicativeLR import colossalai from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR from colossalai.nn.optimizer import ColossalaiOptimizer from colossalai.tensor import ColoTensor, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.utils.checkpoint import load_checkpoint, save_checkpoint from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext @@ -202,13 +199,7 @@ def run_dist(rank, world_size, port, use_ddp, use_mp_reload, test_scheduler): # @pytest.mark.parametrize('test_scheduler', ['colossalai_cosine_warmup', 'torch_cosine', 'torch_lambda']) @rerun_if_address_is_in_use() def test_checkpoint(world_size, use_ddp, use_mp_reload, test_scheduler=None): - run_func = partial(run_dist, - world_size=world_size, - port=free_port(), - use_ddp=use_ddp, - use_mp_reload=use_mp_reload, - test_scheduler=test_scheduler) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size, use_ddp=use_ddp, use_mp_reload=use_mp_reload, test_scheduler=test_scheduler) if __name__ == '__main__': diff --git a/tests/test_utils/test_commons.py b/tests/test_utils/test_commons.py index 6bfa6f33c812..2633d7da21aa 100644 --- a/tests/test_utils/test_commons.py +++ b/tests/test_utils/test_commons.py @@ -1,15 +1,13 @@ import torch -import torch.multiprocessing as mp import colossalai -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.zero.legacy.gemini.tensor_utils import colo_model_data_tensor_move, colo_model_data_tensor_move_inline from colossalai.zero.legacy.sharded_param import ShardedTensor -def run_tensor_move(rank): - colossalai.launch(config={}, rank=0, world_size=1, host='localhost', port=free_port(), backend='nccl') +def run_tensor_move(rank, world_size, port): + colossalai.launch(config={}, rank=0, world_size=world_size, host='localhost', port=port, backend='nccl') src_t = torch.ones(2, 3).cuda() tgt_t = torch.zeros(2, 3) @@ -36,7 +34,7 @@ def run_tensor_move(rank): @rerun_if_address_is_in_use() def test_tensor_move(): - mp.spawn(run_tensor_move, nprocs=1) + spawn(run_tensor_move, 1) if __name__ == '__main__': diff --git a/tests/test_utils/test_flash_attention.py b/tests/test_utils/test_flash_attention.py index 441cbbb22ce7..7a28b0157384 100644 --- a/tests/test_utils/test_flash_attention.py +++ b/tests/test_utils/test_flash_attention.py @@ -5,6 +5,7 @@ from einops import rearrange from colossalai.kernel.cuda_native.flash_attention import HAS_MEM_EFF_ATTN +from colossalai.testing import clear_cache_before_run, parameterize if HAS_MEM_EFF_ATTN: from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention @@ -22,7 +23,8 @@ def baseline_attention(Z, N_CTX, H, q, k, v, sm_scale): @pytest.mark.skipif(HAS_MEM_EFF_ATTN == False, reason="xformers is not available") -@pytest.mark.parametrize('B, S, H, D_HEAD', [(6, 8, 4, 16)]) +@clear_cache_before_run() +@parameterize('B, S, H, D_HEAD', [(6, 8, 4, 16)]) def test_attention_gpt(B, S, H, D_HEAD, dtype=torch.float16): D = H * D_HEAD @@ -42,7 +44,8 @@ def test_attention_gpt(B, S, H, D_HEAD, dtype=torch.float16): @pytest.mark.skipif(HAS_MEM_EFF_ATTN == False, reason="xformers is not available") -@pytest.mark.parametrize('B, S, H, D_HEAD', [(6, 8, 4, 16)]) +@clear_cache_before_run() +@parameterize('B, S, H, D_HEAD', [(6, 8, 4, 16)]) def test_attention_bert(B, S, H, D_HEAD, dtype=torch.float16): D = H * D_HEAD @@ -65,7 +68,8 @@ def test_attention_bert(B, S, H, D_HEAD, dtype=torch.float16): @pytest.mark.skipif(HAS_MEM_EFF_ATTN == False, reason="xformers is not available") -@pytest.mark.parametrize('B, S, H, D_HEAD', [(6, 8, 4, 16)]) +@clear_cache_before_run() +@parameterize('B, S, H, D_HEAD', [(6, 8, 4, 16)]) def test_attention_no_mask(B, S, H, D_HEAD, dtype=torch.float16): D = H * D_HEAD @@ -84,7 +88,8 @@ def test_attention_no_mask(B, S, H, D_HEAD, dtype=torch.float16): @pytest.mark.skipif(HAS_MEM_EFF_ATTN == False, reason="xformers is not available") -@pytest.mark.parametrize('B, S, T, H, D_HEAD', [(6, 24, 8, 4, 16)]) +@clear_cache_before_run() +@parameterize('B, S, T, H, D_HEAD', [(6, 24, 8, 4, 16)]) def test_cross_attention(B, S, T, H, D_HEAD, dtype=torch.float16): D = H * D_HEAD diff --git a/tests/test_utils/test_lazy_init/test_distribute.py b/tests/test_utils/test_lazy_init/test_distribute.py index 1e32814ab147..2c15ca84efaa 100644 --- a/tests/test_utils/test_lazy_init/test_distribute.py +++ b/tests/test_utils/test_lazy_init/test_distribute.py @@ -1,17 +1,14 @@ -from functools import partial from typing import Optional import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn import colossalai from colossalai.device.device_mesh import DeviceMesh from colossalai.tensor.d_tensor.layout import Layout from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.common import print_rank_0 try: @@ -105,9 +102,7 @@ def run_dist(rank, world_size, port) -> None: @pytest.mark.dist @rerun_if_address_is_in_use() def test_dist_lazy_init(): - world_size = 4 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 4) if __name__ == '__main__': diff --git a/tests/test_utils/test_memory.py b/tests/test_utils/test_memory.py index 46a5aeba505b..c88c2f8ec3c5 100644 --- a/tests/test_utils/test_memory.py +++ b/tests/test_utils/test_memory.py @@ -1,12 +1,9 @@ import pytest import colossalai +from colossalai.testing import spawn from colossalai.utils.cuda import get_current_device -from colossalai.utils.memory import colo_set_process_memory_fraction, colo_device_memory_capacity -from colossalai.utils import free_port - -from functools import partial -import torch.multiprocessing as mp +from colossalai.utils.memory import colo_device_memory_capacity, colo_set_process_memory_fraction def _run_colo_set_process_memory_fraction_and_colo_device_memory_capacity(): @@ -24,8 +21,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist @pytest.mark.parametrize("world_size", [3, 4]) def test_memory_utils(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_utils/test_norm_gradient_clipping.py b/tests/test_utils/test_norm_gradient_clipping.py index 259286663033..c0d678026c5f 100644 --- a/tests/test_utils/test_norm_gradient_clipping.py +++ b/tests/test_utils/test_norm_gradient_clipping.py @@ -1,16 +1,15 @@ -from colossalai.tensor import distspec, ColoTensorSpec, ProcessGroup -from colossalai.tensor.colo_parameter import ColoParameter -import colossalai import pytest import torch -import torch.multiprocessing as mp -from colossalai.logging import disable_existing_loggers -from colossalai.utils import free_port, get_current_device +from torch.nn.parameter import Parameter from torch.nn.utils import clip_grad_norm_ -from functools import partial -from colossalai.testing import parameterize, rerun_if_address_is_in_use + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.tensor import ColoTensorSpec, ProcessGroup, distspec +from colossalai.tensor.colo_parameter import ColoParameter +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.utils.common import clip_grad_norm -from torch.nn.parameter import Parameter def close(num: float, other: float, rtol: float = 1e-5, atol: float = 1e-8): @@ -71,8 +70,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 2]) @rerun_if_address_is_in_use() def test_zero_clip_grad(world_size: int): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_utils/test_zero_gradient_clippling.py b/tests/test_utils/test_zero_gradient_clippling.py index 920656726d63..e99cf388e929 100644 --- a/tests/test_utils/test_zero_gradient_clippling.py +++ b/tests/test_utils/test_zero_gradient_clippling.py @@ -1,21 +1,19 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -import copy from functools import partial import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import torch.nn as nn from torch.nn.parallel import DistributedDataParallel as DDP from torch.nn.utils import clip_grad_norm_ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import checkpoint, clip_grad_norm_fp32, free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import checkpoint, clip_grad_norm_fp32 from colossalai.zero.legacy.shard_utils.tensor_shard_strategy import TensorShardStrategy from colossalai.zero.legacy.sharded_model.sharded_model_v2 import ShardedModelV2 @@ -106,8 +104,7 @@ def run_dist(rank, world_size, port): @rerun_if_address_is_in_use() def test_zero_clip_grad(): world_size = 4 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_chunk_mgrv2.py b/tests/test_zero/test_gemini/test_chunk_mgrv2.py index ba0945551b18..7ea063877b5c 100644 --- a/tests/test_zero/test_gemini/test_chunk_mgrv2.py +++ b/tests/test_zero/test_gemini/test_chunk_mgrv2.py @@ -1,13 +1,9 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.zero.gemini.chunk import ChunkManager from tests.test_tensor.common_utils import debug_print @@ -64,8 +60,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [2]) @rerun_if_address_is_in_use() def test_chunk_manager(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_chunkv2.py b/tests/test_zero/test_gemini/test_chunkv2.py index 5f9ba5d3a827..16764aa6b0b1 100644 --- a/tests/test_zero/test_gemini/test_chunkv2.py +++ b/tests/test_zero/test_gemini/test_chunkv2.py @@ -1,15 +1,12 @@ -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import colossalai from colossalai.tensor import ColoParameter from colossalai.tensor import ProcessGroup as ColoProcessGroup -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port, get_current_device +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.zero.gemini import TensorState from colossalai.zero.gemini.chunk import Chunk @@ -117,8 +114,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 2, 4]) @rerun_if_address_is_in_use() def test_chunk_function(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_fwd_bwd.py b/tests/test_zero/test_gemini/test_fwd_bwd.py index 8cfacd018324..697595bc3352 100644 --- a/tests/test_zero/test_gemini/test_fwd_bwd.py +++ b/tests/test_zero/test_gemini/test_fwd_bwd.py @@ -1,8 +1,5 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.testing import assert_close @@ -10,8 +7,7 @@ from colossalai.amp import convert_to_apex_amp from colossalai.nn.optimizer import HybridAdam from colossalai.tensor import ProcessGroup -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration @@ -103,8 +99,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_gpt(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_gemini_use_rmt.py b/tests/test_zero/test_gemini/test_gemini_use_rmt.py index 9d5419e9452d..dd580976d8ea 100644 --- a/tests/test_zero/test_gemini/test_gemini_use_rmt.py +++ b/tests/test_zero/test_gemini/test_gemini_use_rmt.py @@ -1,14 +1,10 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.tensor import ProcessGroup -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port -from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, GeminiDDP, ZeroDDP +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.zero import ColoInitContext, ZeroDDP from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration from colossalai.zero.gemini.gemini_mgr import GeminiManager from colossalai.zero.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer @@ -98,8 +94,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_gemini_use_rmt(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_get_torch_model.py b/tests/test_zero/test_gemini/test_get_torch_model.py index c014ced975ce..b3e3b2b22fc3 100644 --- a/tests/test_zero/test_gemini/test_get_torch_model.py +++ b/tests/test_zero/test_gemini/test_get_torch_model.py @@ -1,14 +1,9 @@ -import os -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.tensor import ColoParameter -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, GeminiDDP from colossalai.zero.gemini.utils import get_static_torch_model @@ -50,8 +45,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_convert_torch_module(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_grad_clip.py b/tests/test_zero/test_gemini/test_grad_clip.py index 65f252c558d7..38b6e474ea98 100644 --- a/tests/test_zero/test_gemini/test_grad_clip.py +++ b/tests/test_zero/test_gemini/test_grad_clip.py @@ -1,25 +1,20 @@ -from functools import partial -from time import time - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.testing import assert_close import colossalai from colossalai.amp import convert_to_apex_amp from colossalai.nn.optimizer import HybridAdam -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration from colossalai.zero.gemini.gemini_mgr import GeminiManager from tests.components_to_test import run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs -from tests.test_tensor.common_utils import debug_print, set_seed +from tests.test_tensor.common_utils import set_seed def check_param(model: ZeroDDP, torch_model: torch.nn.Module): @@ -105,8 +100,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 2]) @rerun_if_address_is_in_use() def test_grad_clip(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_inference.py b/tests/test_zero/test_gemini/test_inference.py index 12392d6e57ad..790a0611c9dd 100644 --- a/tests/test_zero/test_gemini/test_inference.py +++ b/tests/test_zero/test_gemini/test_inference.py @@ -1,18 +1,15 @@ -from functools import partial from typing import Callable import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.testing import assert_close import colossalai from colossalai.amp import convert_to_apex_amp from colossalai.nn.optimizer import HybridAdam -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer, post_process_colo_init_ctx, zero_model_wrapper from colossalai.zero.gemini.chunk import ChunkManager, init_chunk_manager, search_chunk_configuration @@ -128,8 +125,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_inference(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_optim.py b/tests/test_zero/test_gemini/test_optim.py index 7364e59d10b4..8ce20c16e8f9 100644 --- a/tests/test_zero/test_gemini/test_optim.py +++ b/tests/test_zero/test_gemini/test_optim.py @@ -1,18 +1,13 @@ -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP from torch.testing import assert_close import colossalai from colossalai.amp import convert_to_apex_amp from colossalai.nn.optimizer import HybridAdam -from colossalai.tensor import ColoParameter, ColoTensor -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer, post_process_colo_init_ctx from colossalai.zero.gemini.chunk import ChunkManager, init_chunk_manager, search_chunk_configuration @@ -157,8 +152,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_optim(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_runtime_mem_tracer.py b/tests/test_zero/test_gemini/test_runtime_mem_tracer.py index 9a3e93493ec3..0e6f283aa5d2 100644 --- a/tests/test_zero/test_gemini/test_runtime_mem_tracer.py +++ b/tests/test_zero/test_gemini/test_runtime_mem_tracer.py @@ -3,12 +3,14 @@ import numpy as np import torch +from colossalai.testing import clear_cache_before_run from colossalai.zero import ColoInitContext from colossalai.zero.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer from tests.components_to_test import run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs +@clear_cache_before_run() def test_runtime_mem_tracer(): test_models = ['gpt2', 'bert', 'simple_net', 'repeated_computed_layers', 'nested_model', 'albert'] diff --git a/tests/test_zero/test_gemini/test_search.py b/tests/test_zero/test_gemini/test_search.py index 71cdf9a18840..35b3b93ade0c 100644 --- a/tests/test_zero/test_gemini/test_search.py +++ b/tests/test_zero/test_gemini/test_search.py @@ -1,14 +1,10 @@ -from functools import partial - import pytest import torch -import torch.distributed as dist -import torch.multiprocessing as mp import colossalai from colossalai.tensor import ComputePattern, ComputeSpec, ProcessGroup, ShardSpec -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port, get_current_device +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext from colossalai.zero.gemini.chunk import init_chunk_manager, search_chunk_configuration from tests.components_to_test.registry import non_distributed_component_funcs @@ -115,8 +111,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_search(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_zeroddp_state_dict.py b/tests/test_zero/test_gemini/test_zeroddp_state_dict.py index 7e759808d1cb..66e05f3ed1ec 100644 --- a/tests/test_zero/test_gemini/test_zeroddp_state_dict.py +++ b/tests/test_zero/test_gemini/test_zeroddp_state_dict.py @@ -1,14 +1,9 @@ -from functools import partial - import pytest import torch -import torch.distributed as dist -import torch.multiprocessing as mp from torch.testing import assert_close import colossalai -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration @@ -105,8 +100,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_zero_ddp(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_gemini/test_zerooptim_state_dict.py b/tests/test_zero/test_gemini/test_zerooptim_state_dict.py index 996dc4eb8b56..a8af176c5b3d 100644 --- a/tests/test_zero/test_gemini/test_zerooptim_state_dict.py +++ b/tests/test_zero/test_gemini/test_zerooptim_state_dict.py @@ -1,14 +1,10 @@ -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import colossalai from colossalai.nn.optimizer import HybridAdam -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration @@ -83,8 +79,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 4]) @rerun_if_address_is_in_use() def test_zero_optim(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_found_inf.py b/tests/test_zero/test_legacy/test_found_inf.py index 03a1a609b672..e90158e0a43b 100644 --- a/tests/test_zero/test_legacy/test_found_inf.py +++ b/tests/test_zero/test_legacy/test_found_inf.py @@ -1,15 +1,11 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp from common import CONFIG from test_sharded_optim_v2 import _run_step import colossalai from colossalai.nn.optimizer import HybridAdam -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy @@ -64,8 +60,7 @@ def _run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [1, 2]) @rerun_if_address_is_in_use() def test_found_inf(world_size): - run_func = partial(_run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(_run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_gemini_manager.py b/tests/test_zero/test_legacy/test_gemini_manager.py index aee9432532c6..0e956f7cc617 100644 --- a/tests/test_zero/test_legacy/test_gemini_manager.py +++ b/tests/test_zero/test_legacy/test_gemini_manager.py @@ -1,10 +1,12 @@ import pytest import torch +from colossalai.testing import clear_cache_before_run from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor, TensorState @pytest.mark.dist +@clear_cache_before_run() def test_gemini_manager(): # reset the manager, in case that there exists memory information left manager = StatefulTensor.GST_MGR diff --git a/tests/test_zero/test_legacy/test_init_context.py b/tests/test_zero/test_legacy/test_init_context.py index 0eb8842de3e3..84493827193e 100644 --- a/tests/test_zero/test_legacy/test_init_context.py +++ b/tests/test_zero/test_legacy/test_init_context.py @@ -1,17 +1,13 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial - import pytest import torch -import torch.multiprocessing as mp from common import CONFIG import colossalai from colossalai.logging import get_dist_logger -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.utils.memory import colo_device_memory_used from colossalai.zero.gemini.memory_tracer.utils import colo_model_mem_usage @@ -70,8 +66,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [1, 4]) @rerun_if_address_is_in_use() def test_zero_init_context(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_param_op.py b/tests/test_zero/test_legacy/test_param_op.py index 9ebacdb70f1a..b91371b98922 100644 --- a/tests/test_zero/test_legacy/test_param_op.py +++ b/tests/test_zero/test_legacy/test_param_op.py @@ -2,6 +2,7 @@ import torch +from colossalai.testing import clear_cache_before_run from colossalai.zero.legacy.gemini.paramhooks import BaseParamHookMgr from tests.components_to_test.registry import non_distributed_component_funcs @@ -49,6 +50,7 @@ def hook(param, grad) -> torch.Tensor or None: return hookwrapper.hook_triggered_times +@clear_cache_before_run() def test_base_param_hook(): test_models = ['repeated_computed_layers', 'resnet18', 'hanging_param_model', 'inline_op_model'] # test_models = ['bert'] diff --git a/tests/test_zero/test_legacy/test_shard_model_v2.py b/tests/test_zero/test_legacy/test_shard_model_v2.py index 884444adf167..93d624aa2bbd 100644 --- a/tests/test_zero/test_legacy/test_shard_model_v2.py +++ b/tests/test_zero/test_legacy/test_shard_model_v2.py @@ -1,17 +1,13 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial - import pytest import torch -import torch.multiprocessing as mp from common import CONFIG, check_grads_padding, run_fwd_bwd from torch.nn.parallel import DistributedDataParallel as DDP import colossalai -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy from colossalai.zero.legacy.sharded_model import ShardedModelV2 @@ -61,8 +57,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [1, 2]) @rerun_if_address_is_in_use() def test_shard_model_v2(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_shard_param.py b/tests/test_zero/test_legacy/test_shard_param.py index b76648321451..4ba43edceb5d 100644 --- a/tests/test_zero/test_legacy/test_shard_param.py +++ b/tests/test_zero/test_legacy/test_shard_param.py @@ -1,14 +1,11 @@ from copy import deepcopy -from functools import partial import pytest import torch -import torch.multiprocessing as mp from common import CONFIG, allclose import colossalai -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy from colossalai.zero.legacy.sharded_param import ShardedTensor @@ -39,8 +36,7 @@ def _run_shard_tensor(rank, world_size, port): @pytest.mark.parametrize("world_size", [1, 2]) @rerun_if_address_is_in_use() def test_shard_tensor(world_size): - run_func = partial(_run_shard_tensor, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(_run_shard_tensor, world_size) def _run_shard_param_v2(rank, world_size, port): @@ -87,8 +83,7 @@ def _run_shard_param_v2(rank, world_size, port): @pytest.mark.parametrize("world_size", [1, 2]) @rerun_if_address_is_in_use() def test_shard_param_v2(world_size): - run_func = partial(_run_shard_param_v2, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(_run_shard_param_v2, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_sharded_optim_state_dict.py b/tests/test_zero/test_legacy/test_sharded_optim_state_dict.py index d257a02854c6..1ca144662722 100644 --- a/tests/test_zero/test_legacy/test_sharded_optim_state_dict.py +++ b/tests/test_zero/test_legacy/test_sharded_optim_state_dict.py @@ -1,14 +1,10 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import colossalai from colossalai.nn.optimizer import HybridAdam from colossalai.tensor import ProcessGroup -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import TensorShardStrategy @@ -86,8 +82,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize('world_size', [1, 2]) @rerun_if_address_is_in_use() def test_sharded_optim_state_dist(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_sharded_optim_v2.py b/tests/test_zero/test_legacy/test_sharded_optim_v2.py index 3eea13d5d5c2..c6f77995ebcd 100644 --- a/tests/test_zero/test_legacy/test_sharded_optim_v2.py +++ b/tests/test_zero/test_legacy/test_sharded_optim_v2.py @@ -1,17 +1,13 @@ -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from common import CONFIG, check_sharded_model_params from torch.nn.parallel import DistributedDataParallel as DDP import colossalai from colossalai.amp import convert_to_apex_amp from colossalai.nn.optimizer import CPUAdam -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy @@ -107,8 +103,7 @@ def _run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [1, 2]) @rerun_if_address_is_in_use() def test_sharded_optim_v2(world_size): - run_func = partial(_run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(_run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py b/tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py index 05512f59a36a..61d850d06080 100644 --- a/tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py +++ b/tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py @@ -1,19 +1,15 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from torchvision.models import resnet50 import colossalai from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import TensorShardStrategy @@ -84,9 +80,7 @@ def test_sharded_optim_with_sync_bn(): wanted if we are doing predictions. """ - world_size = 2 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 2) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_state_dict.py b/tests/test_zero/test_legacy/test_state_dict.py index 40d2820d800a..5f76fff3e5c3 100644 --- a/tests/test_zero/test_legacy/test_state_dict.py +++ b/tests/test_zero/test_legacy/test_state_dict.py @@ -5,12 +5,10 @@ import pytest import torch -import torch.multiprocessing as mp from common import CONFIG import colossalai -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy from colossalai.zero.legacy.sharded_model import ShardedModelV2 @@ -50,8 +48,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [1, 2]) @rerun_if_address_is_in_use() def test_zero_state_dict(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_tensor_utils.py b/tests/test_zero/test_legacy/test_tensor_utils.py index 3114481707c2..238bc3fe1a98 100644 --- a/tests/test_zero/test_legacy/test_tensor_utils.py +++ b/tests/test_zero/test_legacy/test_tensor_utils.py @@ -1,12 +1,8 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import colossalai -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.utils.cuda import get_current_device from colossalai.zero.legacy.gemini.stateful_tensor import StatefulTensor from colossalai.zero.legacy.gemini.tensor_utils import ( @@ -91,8 +87,7 @@ def run_dist(rank, world_size, port): @pytest.mark.parametrize("world_size", [2, 4]) @rerun_if_address_is_in_use() def test_zero_tensor_utils(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size) if __name__ == '__main__': diff --git a/tests/test_zero/test_legacy/test_zero_engine.py b/tests/test_zero/test_legacy/test_zero_engine.py index 1e7f53358526..dc8847ce56ab 100644 --- a/tests/test_zero/test_legacy/test_zero_engine.py +++ b/tests/test_zero/test_legacy/test_zero_engine.py @@ -1,19 +1,15 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp from common import MP_PARALLEL_CONFIG, ZERO_PARALLEL_CONFIG, check_params, check_sharded_model_params from torch.nn.parallel import DistributedDataParallel as DDP import colossalai from colossalai.core import global_context as gpc -from colossalai.testing import rerun_if_address_is_in_use -from colossalai.utils import free_port +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy from colossalai.zero.low_level._utils import has_inf_or_nan @@ -96,16 +92,14 @@ def run_dist(rank, world_size, port, parallel_config): @pytest.mark.parametrize("world_size", [2, 4]) @rerun_if_address_is_in_use() def test_mp_engine(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port(), parallel_config=MP_PARALLEL_CONFIG) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size, parallel_config=MP_PARALLEL_CONFIG) @pytest.mark.dist @pytest.mark.parametrize("world_size", [1, 2]) @rerun_if_address_is_in_use() def test_zero_engine(world_size): - run_func = partial(run_dist, world_size=world_size, port=free_port(), parallel_config=ZERO_PARALLEL_CONFIG) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, world_size, parallel_config=ZERO_PARALLEL_CONFIG) if __name__ == '__main__': diff --git a/tests/test_zero/test_low_level/test_grad_acc.py b/tests/test_zero/test_low_level/test_grad_acc.py index 504df202e168..2ae1f3a99d79 100644 --- a/tests/test_zero/test_low_level/test_grad_acc.py +++ b/tests/test_zero/test_low_level/test_grad_acc.py @@ -1,16 +1,14 @@ import copy -from functools import partial import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from torch.nn.parallel import DistributedDataParallel as DDP from torch.testing import assert_close import colossalai +from colossalai.testing import spawn from colossalai.testing.random import seed_all -from colossalai.utils import free_port from colossalai.zero import LowLevelZeroOptimizer @@ -158,9 +156,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist def test_grad_accumulation(): - world_size = 2 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 2) if __name__ == '__main__': diff --git a/tests/test_zero/test_low_level/test_zero1_2.py b/tests/test_zero/test_low_level/test_zero1_2.py index ed76e0171fb4..4086af9d896e 100644 --- a/tests/test_zero/test_low_level/test_zero1_2.py +++ b/tests/test_zero/test_low_level/test_zero1_2.py @@ -1,17 +1,14 @@ import copy -from functools import partial import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from torch.nn.parallel import DistributedDataParallel as DDP from torch.testing import assert_close import colossalai -from colossalai.testing import rerun_if_address_is_in_use +from colossalai.testing import rerun_if_address_is_in_use, spawn from colossalai.testing.random import seed_all -from colossalai.utils import free_port from colossalai.zero import LowLevelZeroOptimizer @@ -179,9 +176,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_zero_1_2(): - world_size = 2 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 2) if __name__ == '__main__': diff --git a/tests/test_zero/test_low_level/test_zero_init.py b/tests/test_zero/test_low_level/test_zero_init.py index 803d0021df96..aeeaff5b5cb9 100644 --- a/tests/test_zero/test_low_level/test_zero_init.py +++ b/tests/test_zero/test_low_level/test_zero_init.py @@ -1,14 +1,12 @@ -from functools import partial - import pytest import torch import torch.distributed as dist -import torch.multiprocessing as mp import torch.nn as nn import colossalai from colossalai.tensor import ProcessGroup -from colossalai.utils import free_port, get_current_device +from colossalai.testing import spawn +from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext, LowLevelZeroOptimizer @@ -51,9 +49,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist def test_zero_init(): - world_size = 4 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 4) if __name__ == '__main__': diff --git a/tests/test_zero/test_low_level/test_zero_tp.py b/tests/test_zero/test_low_level/test_zero_tp.py index bb7495583dc9..f0804f4bb5ba 100644 --- a/tests/test_zero/test_low_level/test_zero_tp.py +++ b/tests/test_zero/test_low_level/test_zero_tp.py @@ -1,16 +1,13 @@ -from functools import partial - import pytest import torch -import torch.multiprocessing as mp import torch.nn as nn from torch.nn.parallel import DistributedDataParallel as DDP from torch.testing import assert_close import colossalai from colossalai.tensor import ProcessGroup -from colossalai.testing import parameterize, rerun_if_address_is_in_use -from colossalai.utils import free_port, get_current_device +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext, LowLevelZeroOptimizer from tests.test_tensor.common_utils import set_seed, split_param_col_tp1d, split_param_row_tp1d, tensor_shard_equal @@ -89,9 +86,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() def test_zero_with_tp(): - world_size = 4 - run_func = partial(run_dist, world_size=world_size, port=free_port()) - mp.spawn(run_func, nprocs=world_size) + spawn(run_dist, 4) if __name__ == '__main__': From 6afeb1202aeedc68d9ef1f77b41f3e5f55e0e121 Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Thu, 6 Apr 2023 15:04:48 +0800 Subject: [PATCH 109/413] add community example dictionary (#3465) --- .../Chat/examples/community/README.md | 1 + .../{EasyPeftModel.md => peft/README.md} | 4 +- .../community/{ => peft}/easy_dataset.py | 126 +++++++++--------- .../community/{ => peft}/easy_models.py | 13 +- .../{ => peft}/train_peft_prompts.py | 17 +-- .../community/{ => peft}/train_peft_sft.py | 25 ++-- 6 files changed, 94 insertions(+), 92 deletions(-) create mode 100644 applications/Chat/examples/community/README.md rename applications/Chat/examples/community/{EasyPeftModel.md => peft/README.md} (98%) rename applications/Chat/examples/community/{ => peft}/easy_dataset.py (74%) rename applications/Chat/examples/community/{ => peft}/easy_models.py (94%) rename applications/Chat/examples/community/{ => peft}/train_peft_prompts.py (95%) rename applications/Chat/examples/community/{ => peft}/train_peft_sft.py (90%) diff --git a/applications/Chat/examples/community/README.md b/applications/Chat/examples/community/README.md new file mode 100644 index 000000000000..5e3f47db37b3 --- /dev/null +++ b/applications/Chat/examples/community/README.md @@ -0,0 +1 @@ +# Community Examples diff --git a/applications/Chat/examples/community/EasyPeftModel.md b/applications/Chat/examples/community/peft/README.md similarity index 98% rename from applications/Chat/examples/community/EasyPeftModel.md rename to applications/Chat/examples/community/peft/README.md index 16c4af76b91f..a82f02a87317 100644 --- a/applications/Chat/examples/community/EasyPeftModel.md +++ b/applications/Chat/examples/community/peft/README.md @@ -10,7 +10,7 @@ Since the current pypi peft package(0.2) has some bugs, please install the peft git clone https://github.com/huggingface/peft cd peft pip install . -``` +``` # Usage For SFT training, just call train_peft_sft.py @@ -21,4 +21,4 @@ For stage-3 rlhf training, call train_peft_prompts.py. Its arguments are almost idential to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported. # Dataformat -Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt. \ No newline at end of file +Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt. diff --git a/applications/Chat/examples/community/easy_dataset.py b/applications/Chat/examples/community/peft/easy_dataset.py similarity index 74% rename from applications/Chat/examples/community/easy_dataset.py rename to applications/Chat/examples/community/peft/easy_dataset.py index 15dd9a3ccd1d..13dceef79145 100644 --- a/applications/Chat/examples/community/easy_dataset.py +++ b/applications/Chat/examples/community/peft/easy_dataset.py @@ -1,19 +1,17 @@ import copy +import json from typing import Dict, Sequence + +import torch from datasets import load_dataset from torch.utils.data import Dataset -from transformers import AutoTokenizer -import torch from tqdm import tqdm -import json - -from tqdm import tqdm -import json +from transformers import AutoTokenizer IGNORE_INDEX = -100 -def _tokenize_fn(strings: Sequence[str], tokenizer: AutoTokenizer,max_length :int = 512) -> Dict: +def _tokenize_fn(strings: Sequence[str], tokenizer: AutoTokenizer, max_length: int = 512) -> Dict: """Tokenize a list of strings.""" tokenized_list = [ tokenizer( @@ -36,15 +34,12 @@ def _tokenize_fn(strings: Sequence[str], tokenizer: AutoTokenizer,max_length :in ) -def preprocess( - sources: Sequence[str], - targets: Sequence[str], - tokenizer: AutoTokenizer, - max_length :int = 512 -) -> Dict: +def preprocess(sources: Sequence[str], targets: Sequence[str], tokenizer: AutoTokenizer, max_length: int = 512) -> Dict: """Preprocess the data by tokenizing.""" examples = [s + t for s, t in zip(sources, targets)] - examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer,max_length) for strings in (examples, sources)] + examples_tokenized, sources_tokenized = [ + _tokenize_fn(strings, tokenizer, max_length) for strings in (examples, sources) + ] input_ids = examples_tokenized["input_ids"] labels = copy.deepcopy(input_ids) for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): @@ -53,59 +48,60 @@ def preprocess( class EasySupervisedDataset(Dataset): - def __init__(self, data_file :str, tokenizer :AutoTokenizer,max_length :int = 512) -> None: - super(EasySupervisedDataset,self).__init__() - with open(data_file,"r",encoding="UTF-8") as f: + + def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length: int = 512) -> None: + super(EasySupervisedDataset, self).__init__() + with open(data_file, "r", encoding="UTF-8") as f: all_lines = f.readlines() #split to source and target ,source the characters before "回答：" including "回答：", target the characters after "回答：" - sources,targets = [],[] + sources, targets = [], [] for line in all_lines: if "回答：" in line: sep_index = line.index("回答：") - sources.append(line[:sep_index+3]) - targets.append(line[sep_index+3:]+tokenizer.eos_token) + sources.append(line[:sep_index + 3]) + targets.append(line[sep_index + 3:] + tokenizer.eos_token) else: sources.append(line) - targets.append(""+tokenizer.eos_token) - data_dict = preprocess(sources, targets, tokenizer,max_length) + targets.append("" + tokenizer.eos_token) + data_dict = preprocess(sources, targets, tokenizer, max_length) self.input_ids = data_dict["input_ids"] self.labels = data_dict["labels"] self.data_file = data_file - + def __len__(self): return len(self.input_ids) def __getitem__(self, i) -> Dict[str, torch.Tensor]: return dict(input_ids=self.input_ids[i], labels=self.labels[i]) - + def __repr__(self): return f"LawSupervisedDataset(data_file={self.data_file}, input_ids_len={len(self.input_ids)}, labels_len={len(self.labels)})" - + def __str__(self): return f"LawSupervisedDataset(data_file={self.data_file}, input_ids_len={len(self.input_ids)}, labels_len={len(self.labels)})" + class EasyPromptsDataset(Dataset): - def __init__(self,data_file :str, tokenizer :AutoTokenizer, max_length :int = 96) -> None: - super(EasyPromptsDataset,self).__init__() - with open(data_file,"r",encoding="UTF-8") as f: + + def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length: int = 96) -> None: + super(EasyPromptsDataset, self).__init__() + with open(data_file, "r", encoding="UTF-8") as f: all_lines = f.readlines() - all_lines = [line if "回答：" not in line else line[:line.index("回答：")+3] for line in all_lines] + all_lines = [line if "回答：" not in line else line[:line.index("回答：") + 3] for line in all_lines] self.prompts = [ - tokenizer(line, - return_tensors='pt', - max_length=max_length, - padding='max_length', - truncation=True)['input_ids'].to(torch.cuda.current_device()).squeeze(0) + tokenizer(line, return_tensors='pt', max_length=max_length, padding='max_length', + truncation=True)['input_ids'].to(torch.cuda.current_device()).squeeze(0) for line in tqdm(all_lines) ] self.data_file = data_file + def __len__(self): return len(self.prompts) - + def __getitem__(self, idx): return self.prompts[idx] - + def __repr__(self): return f"LawPromptsDataset(data_file={self.data_file}, prompts_len={len(self.prompts)})" @@ -114,8 +110,9 @@ def __str__(self): class EasyRewardDataset(Dataset): - def __init__(self,train_file :str,tokenizer :AutoTokenizer, special_token = None,max_length = 512) -> None: - super(EasyRewardDataset,self).__init__() + + def __init__(self, train_file: str, tokenizer: AutoTokenizer, special_token=None, max_length=512) -> None: + super(EasyRewardDataset, self).__init__() self.chosen = [] self.reject = [] if special_token is None: @@ -124,11 +121,11 @@ def __init__(self,train_file :str,tokenizer :AutoTokenizer, special_token = None self.end_token = special_token print(self.end_token) #read all lines in the train_file to a list - with open(train_file,"r",encoding="UTF-8") as f: + with open(train_file, "r", encoding="UTF-8") as f: all_lines = f.readlines() for line in tqdm(all_lines): data = json.loads(line) - prompt = "提问："+data['prompt']+" 回答：" + prompt = "提问：" + data['prompt'] + " 回答：" chosen = prompt + data['chosen'] + self.end_token chosen_token = tokenizer(chosen, @@ -159,7 +156,7 @@ def __len__(self): def __getitem__(self, idx): return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][ "input_ids"], self.reject[idx]["attention_mask"] - + #python representation of the object and the string representation of the object def __repr__(self): return f"LawRewardDataset(chosen_len={len(self.chosen)}, reject_len={len(self.reject)})" @@ -167,27 +164,30 @@ def __repr__(self): def __str__(self): return f"LawRewardDataset(chosen_len={len(self.chosen)}, reject_len={len(self.reject)})" + ''' Easy SFT just accept a text file which can be read line by line. However the datasest will group texts together to max_length so LLM will learn the texts meaning better. If individual lines are not related, just set is_group_texts to False. ''' + + class EasySFTDataset(Dataset): - - def __init__(self,data_file :str,tokenizer :AutoTokenizer,max_length = 512,is_group_texts = True) -> None: + + def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length=512, is_group_texts=True) -> None: super().__init__() #read the data_file line by line - with open(data_file,"r",encoding="UTF-8") as f: + with open(data_file, "r", encoding="UTF-8") as f: #encode the text data line by line and put raw python list input_ids only to raw_input_ids list raw_input_ids = [] for line in f: encoded_ids = tokenizer.encode(line) #if the encoded_ids is longer than max_length, then split it into several parts if len(encoded_ids) > max_length: - for i in range(0,len(encoded_ids),max_length): - raw_input_ids.append(encoded_ids[i:i+max_length]) + for i in range(0, len(encoded_ids), max_length): + raw_input_ids.append(encoded_ids[i:i + max_length]) else: raw_input_ids.append(encoded_ids) - + grouped_inpup_ids = [] current_input_ids = [] attention_mask = [] @@ -199,44 +199,42 @@ def __init__(self,data_file :str,tokenizer :AutoTokenizer,max_length = 512,is_gr #pad the current_input_ids to max_length with tokenizer.pad_token_id padded_length = max_length - len(current_input_ids) current_input_ids.extend([tokenizer.pad_token_id] * padded_length) - grouped_inpup_ids.append(torch.tensor(current_input_ids,dtype=torch.long)) - attention_mask.append(torch.tensor([1] * (max_length - padded_length) + [0] * padded_length,dtype=torch.long)) + grouped_inpup_ids.append(torch.tensor(current_input_ids, dtype=torch.long)) + attention_mask.append( + torch.tensor([1] * (max_length - padded_length) + [0] * padded_length, dtype=torch.long)) current_input_ids = [] else: current_input_ids.extend(input_ids) if len(current_input_ids) > 0: padded_length = max_length - len(current_input_ids) current_input_ids.extend([tokenizer.pad_token_id] * padded_length) - grouped_inpup_ids.append(torch.tensor(current_input_ids,dtype=torch.long)) - attention_mask.append(torch.tensor([1] * (max_length - padded_length) + [0] * padded_length,dtype=torch.long)) + grouped_inpup_ids.append(torch.tensor(current_input_ids, dtype=torch.long)) + attention_mask.append( + torch.tensor([1] * (max_length - padded_length) + [0] * padded_length, dtype=torch.long)) else: #just append the raw_input_ids to max_length for input_ids in raw_input_ids: padded_length = max_length - len(input_ids) input_ids.extend([tokenizer.pad_token_id] * padded_length) - attention_mask.append(torch.tensor([1] * (max_length - padded_length) + [0] * padded_length,dtype=torch.long)) - grouped_inpup_ids.append(torch.tensor(input_ids,dtype=torch.long)) + attention_mask.append( + torch.tensor([1] * (max_length - padded_length) + [0] * padded_length, dtype=torch.long)) + grouped_inpup_ids.append(torch.tensor(input_ids, dtype=torch.long)) self.input_ids = grouped_inpup_ids self.labels = copy.deepcopy(self.input_ids) self.file_name = data_file self.attention_mask = attention_mask - + def __len__(self): return len(self.input_ids) - + #get item from dataset - def __getitem__(self,idx): - return dict(input_ids=self.input_ids[idx],labels=self.labels[idx],attention_mask=self.attention_mask[idx]) - + def __getitem__(self, idx): + return dict(input_ids=self.input_ids[idx], labels=self.labels[idx], attention_mask=self.attention_mask[idx]) + #generate the dataset description to be printed by print in python def __repr__(self): return f"EasySFTDataset(len={len(self)},\nfile_name is {self.file_name})" - + #generate the dataset description to be printed by print in python def __str__(self): return f"EasySFTDataset(len={len(self)},\nfile_name is {self.file_name})" - - - - - \ No newline at end of file diff --git a/applications/Chat/examples/community/easy_models.py b/applications/Chat/examples/community/peft/easy_models.py similarity index 94% rename from applications/Chat/examples/community/easy_models.py rename to applications/Chat/examples/community/peft/easy_models.py index 080fc1802a02..fe294868159d 100644 --- a/applications/Chat/examples/community/easy_models.py +++ b/applications/Chat/examples/community/peft/easy_models.py @@ -3,12 +3,12 @@ import torch import torch.nn as nn import torch.nn.functional as F -from torch.nn.modules import Module - from coati.models.generation import generate -from coati.models.utils import log_probs_from_logits,masked_mean -from transformers import BloomConfig,BloomForCausalLM +from coati.models.utils import log_probs_from_logits, masked_mean from peft import PeftModel +from torch.nn.modules import Module +from transformers import BloomConfig, BloomForCausalLM + class Actor(Module): """ @@ -87,11 +87,10 @@ def __init__(self, else: model = BloomForCausalLM(BloomConfig()) if lora_path is not None: - model = PeftModel.from_pretrained(model,lora_path) + model = PeftModel.from_pretrained(model, lora_path) if checkpoint: model.gradient_checkpointing_enable() super().__init__(model) - + def print_trainable_parameters(self): self.get_base_model().print_trainable_parameters() - diff --git a/applications/Chat/examples/community/train_peft_prompts.py b/applications/Chat/examples/community/peft/train_peft_prompts.py similarity index 95% rename from applications/Chat/examples/community/train_peft_prompts.py rename to applications/Chat/examples/community/peft/train_peft_prompts.py index b9394c9e4190..0e277021e917 100644 --- a/applications/Chat/examples/community/train_peft_prompts.py +++ b/applications/Chat/examples/community/peft/train_peft_prompts.py @@ -5,21 +5,22 @@ import torch.distributed as dist from coati.dataset import DataCollatorForSupervisedDataset, PromptDataset, SupervisedDataset from coati.models.bloom import BLOOMRM, BLOOMCritic -from easy_models import BLOOMActor from coati.models.gpt import GPTRM, GPTActor, GPTCritic from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM from coati.models.opt import OPTRM, OPTActor, OPTCritic from coati.trainer import PPOTrainer from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy from coati.utils import prepare_llama_tokenizer_and_embedding +from easy_dataset import EasyPromptsDataset, EasySupervisedDataset +from easy_models import BLOOMActor +from peft import PeftModel from torch.optim import Adam from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer from colossalai.nn.optimizer import HybridAdam -from peft import PeftModel -from easy_dataset import EasyPromptsDataset,EasySupervisedDataset + def main(args): # configure strategy @@ -41,7 +42,7 @@ def main(args): if args.model == 'bloom': # initial_model = BLOOMActor(pretrained=args.pretrain) print('Using peft lora to load Bloom model as inital_model') - initial_model = BLOOMActor(pretrained=args.pretrain,lora_path=args.sft_lora_path) + initial_model = BLOOMActor(pretrained=args.pretrain, lora_path=args.sft_lora_path) print('Using peft lora to load Bloom model as initial_model (Done)') else: raise ValueError(f'Unsupported actor model "{args.model}"') @@ -54,7 +55,7 @@ def main(args): if rm_model_name == 'gpt2': reward_model = GPTRM(pretrained=args.rm_pretrain) elif rm_model_name == 'bloom': - print("load bloom reward model ",args.rm_pretrain) + print("load bloom reward model ", args.rm_pretrain) reward_model = BLOOMRM(pretrained=args.rm_pretrain) elif rm_model_name == 'opt': reward_model = OPTRM(pretrained=args.rm_pretrain) @@ -75,7 +76,7 @@ def main(args): if args.model == 'bloom': # actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank) print('Using peft lora to load Bloom model as Actor') - actor = BLOOMActor(pretrained=args.pretrain,lora_path=args.sft_lora_path) + actor = BLOOMActor(pretrained=args.pretrain, lora_path=args.sft_lora_path) print('Using peft lora to load Bloom model as Actor (Done)') else: raise ValueError(f'Unsupported actor model "{args.model}"') @@ -83,7 +84,7 @@ def main(args): if rm_model_name == 'gpt2': critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) elif rm_model_name == 'bloom': - print("load bloom critic ",args.rm_pretrain," lora_rank ",args.lora_rank," use_action_mask ",True) + print("load bloom critic ", args.rm_pretrain, " lora_rank ", args.lora_rank, " use_action_mask ", True) critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) print("load bloom critic (Done) ") elif rm_model_name == 'opt': @@ -130,7 +131,7 @@ def main(args): data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) - prompt_dataset = EasyPromptsDataset(args.prompt_path,tokenizer) + prompt_dataset = EasyPromptsDataset(args.prompt_path, tokenizer) if dist.is_initialized() and dist.get_world_size() > 1: prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True) else: diff --git a/applications/Chat/examples/community/train_peft_sft.py b/applications/Chat/examples/community/peft/train_peft_sft.py similarity index 90% rename from applications/Chat/examples/community/train_peft_sft.py rename to applications/Chat/examples/community/peft/train_peft_sft.py index 65d901261bfc..fcc65e24478a 100644 --- a/applications/Chat/examples/community/train_peft_sft.py +++ b/applications/Chat/examples/community/peft/train_peft_sft.py @@ -14,19 +14,19 @@ from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset +from easy_dataset import EasyDataset +from peft import LoraConfig, PeftModel, TaskType, get_peft_model from torch.optim import Adam from torch.utils.data import DataLoader +from torch.utils.data.dataloader import default_collate from torch.utils.data.distributed import DistributedSampler -from transformers import AutoTokenizer, BloomTokenizerFast,AutoModelForCausalLM +from transformers import AutoModelForCausalLM, AutoTokenizer, BloomTokenizerFast from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import HybridAdam from colossalai.tensor import ColoParameter -from torch.utils.data.dataloader import default_collate -from peft import LoraConfig, TaskType,get_peft_model,PeftModel -from easy_dataset import EasyDataset def train(args): # configure strategy @@ -48,17 +48,20 @@ def train(args): #if the args.save_path exists and args.save_path+'/adapter_config.json' exists, we'll load the adapter_config.json if os.path.exists(args.save_path) and os.path.exists(args.save_path+'/adapter_config.json') \ and os.path.exists(args.save_path+'/adapter_model.bin'): - print("loading from saved peft model ",args.save_path) + print("loading from saved peft model ", args.save_path) model = PeftModel.from_pretrained(model, args.save_path) else: #we'll use peft lora library to do the lora lora_rank = args.lora_rank if args.lora_rank > 0 else 32 #config lora with rank of lora_rank - lora_config = LoraConfig(task_type=TaskType.CAUSAL_LM, inference_mode=False, r=lora_rank, lora_alpha=32, lora_dropout=0.1) + lora_config = LoraConfig(task_type=TaskType.CAUSAL_LM, + inference_mode=False, + r=lora_rank, + lora_alpha=32, + lora_dropout=0.1) model = get_peft_model(model, lora_config) model.print_trainable_parameters() - # configure tokenizer if args.model == 'gpt2': tokenizer = GPT2Tokenizer.from_pretrained('gpt2') @@ -103,12 +106,12 @@ def train(args): logger.set_level('WARNING') # configure dataset - law_dataset = EasyDataset(args.dataset,tokenizer=tokenizer,is_group_texts=not args.is_short_text) + law_dataset = EasyDataset(args.dataset, tokenizer=tokenizer, is_group_texts=not args.is_short_text) train_dataset = law_dataset print(train_dataset) eval_dataset = None if args.eval_dataset is not None: - eval_dataset = EasyDataset(args.eval_dataset,tokenizer=tokenizer,is_group_texts=not args.is_short_text) + eval_dataset = EasyDataset(args.eval_dataset, tokenizer=tokenizer, is_group_texts=not args.is_short_text) data_collator = default_collate if dist.is_initialized() and dist.get_world_size() > 1: train_sampler = DistributedSampler(train_dataset, @@ -181,7 +184,7 @@ def train(args): parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log") parser.add_argument('--lr', type=float, default=5e-6) parser.add_argument('--accimulation_steps', type=int, default=8) - parser.add_argument('--enable_peft_lora',action='store_true', default=False) - parser.add_argument("--is_short_text",action='store_true', default=False) + parser.add_argument('--enable_peft_lora', action='store_true', default=False) + parser.add_argument("--is_short_text", action='store_true', default=False) args = parser.parse_args() train(args) From 52a933e17509c71811e919b165de38cb3d5d6d41 Mon Sep 17 00:00:00 2001 From: jiangmingyan <37931082+jiangmingyan@users.noreply.github.com> Date: Thu, 6 Apr 2023 16:23:39 +0800 Subject: [PATCH 110/413] [checkpoint] support huggingface style sharded checkpoint (#3461) * [checkpoint] support huggingface style sharded checkpoint * [checkpoint] support huggingface style sharded checkpoint * [checkpoint] support huggingface style sharded checkpoint * [checkpoint] support huggingface style sharded checkpoint * [checkpoint] support huggingface style sharded checkpoint --------- Co-authored-by: luchen --- .../checkpoint_io/general_checkpoint_io.py | 101 ++++++++++--- colossalai/checkpoint_io/index_file.py | 6 + colossalai/checkpoint_io/utils.py | 140 +++++++++++++++++- .../test_general_checkpoint_io.py | 89 ++++++++--- 4 files changed, 291 insertions(+), 45 deletions(-) diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index c779f4c17355..2a76f1718469 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -2,37 +2,35 @@ import torch.nn as nn from torch.optim import Optimizer +import logging +import os +import json +import gc from .checkpoint_io_base import CheckpointIO from .index_file import CheckpointIndexFile -from .utils import has_index_file, load_state_dict, save_state_dict +from .utils import ( + has_index_file, + load_state_dict, + save_state_dict, + is_safetensors_available, + shard_checkpoint, + load_shard_state_dict, + load_state_dict_into_model + ) +from .utils import SAFE_WEIGHTS_NAME, WEIGHTS_NAME, SAFE_WEIGHTS_INDEX_NAME, WEIGHTS_INDEX_NAME __all__ = ['GeneralCheckpointIO'] class GeneralCheckpointIO(CheckpointIO): - - def load_sharded_model(self, model: nn.Module, index_file_path: Path, strict: bool): - # load the index file - index_file = CheckpointIndexFile.from_file(index_file_path) - - # iterate over the shard checkpoint files - # and load each - index_file.assert_no_dtensor_checkpoint() - checkpoint_file_list, _ = index_file.get_checkpoint_fileanames() - for shard_file in checkpoint_file_list: - shard_checkpoint = load_state_dict(shard_file) - model.load_state_dict(shard_checkpoint, strict=strict) - + """ + Checkpoint IO + """ def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool): checkpoint = load_state_dict(checkpoint) model.load_state_dict(checkpoint, strict=strict) - def save_sharded_model(self, model: nn.Module, checkpoint: Path, gather_dtensor: bool, prefix: str, - size_per_shard: int, use_safetensors: bool): - # TODO(FrankLeeeee): implement this method as it can be supported by Huggingface model - raise NotImplementedError("Sharded model checkpoint is not supported yet.") - def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, use_safetensors: bool): state_dict = model.state_dict() @@ -68,3 +66,68 @@ def save_unsharded_optimizer( ): # TODO(FrankLeeeee): handle distributed tensors save_state_dict(optimizer.state_dict(), checkpoint, use_safetensors=False) + + + def save_sharded_model(self, model: nn.Module, checkpoint_path: str, gather_dtensor:bool = False, + prefix: str = "", max_shard_size: int = 1024, use_safetensors: bool = False): + """ + implement this method as it can be supported by Huggingface model, + save shard model, save model to multiple files + """ + if os.path.isfile(checkpoint_path): + logging.error(f"Provided path ({checkpoint_path}) should be a directory, not a file") + return + + Path(checkpoint_path).mkdir(parents=True, exist_ok=True) + + # shard checkpoint + state_dict = model.state_dict() + weights_name = SAFE_WEIGHTS_NAME if use_safetensors else WEIGHTS_NAME + shards, index = shard_checkpoint(state_dict, max_shard_size=max_shard_size, weights_name=weights_name) + + # Save the model + for shard_file, shard in shards.items(): + checkpoint_file_path = os.path.join(checkpoint_path, shard_file) + save_state_dict(shard, checkpoint_file_path, use_safetensors) + + # save index file + save_index_file = SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME + save_index_file = os.path.join(checkpoint_path, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + logging.info( + f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be " + f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the " + f"index located at {save_index_file}." + ) + + + def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, strict: bool = False, use_safetensors: bool = False): + """ + load shard model, load model from multiple files + """ + use_safetensors = False + if "safetensors" in checkpoint_index_file.name: + use_safetensors = True + + if use_safetensors and not is_safetensors_available(): + raise ImportError("`safe_serialization` requires the `safetensors` library: `pip install safetensors`.") + + # read checkpoint index file + ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file) + checkpoint_files, _ = ckpt_index_file.get_checkpoint_fileanames() + missing_keys = ckpt_index_file.get_all_param_names() + + for shard_file in checkpoint_files: + state_dict = load_shard_state_dict(Path(shard_file), use_safetensors) + load_state_dict_into_model(model, state_dict, missing_keys, strict) + del state_dict + gc.collect() + + if strict and len(missing_keys) > 0: + error_msgs = 'Missing key(s) in state_dict: {}. '.format( + ', '.join('"{}"'.format(k) for k in missing_keys)) + raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( + self.__class__.__name__, "\n\t".join(error_msgs))) + diff --git a/colossalai/checkpoint_io/index_file.py b/colossalai/checkpoint_io/index_file.py index 32ff1b762e88..89224787a91b 100644 --- a/colossalai/checkpoint_io/index_file.py +++ b/colossalai/checkpoint_io/index_file.py @@ -148,3 +148,9 @@ def get_checkpoint_file(self, param_name: str) -> str: """ ckpt_path = self.weight_map[param_name] return ckpt_path + + def get_all_param_names(self): + """ + Get all the weight keys. + """ + return list(self.weight_map.keys()) diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 76c9db0afaff..81b666da5c78 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -1,13 +1,19 @@ +# coding=utf-8 from pathlib import Path -from typing import List, Optional, Tuple - import torch +import torch.nn as nn +from typing import List, Dict, Mapping, OrderedDict, Optional, Tuple +from colossalai.tensor.d_tensor.d_tensor import DTensor + +SAFE_WEIGHTS_NAME = "model.safetensors" +WEIGHTS_NAME = "model.bin" +SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json" +WEIGHTS_INDEX_NAME = "model.bin.index.json" # ====================================== # General helper functions # ====================================== - def calculate_tensor_size(tensor: torch.Tensor) -> float: """ Calculate the size of a parameter in MB. Used to compute whether a group of params exceed the shard size. @@ -68,6 +74,130 @@ def is_safetensor_checkpoint(checkpoint_file_path: str) -> bool: return False +# ====================================== +# Helper functions for saving shard file +# ====================================== +def shard_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024, weights_name: str = WEIGHTS_NAME): + + """ + Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a + given size. + """ + sharded_state_dicts = [] + current_block = {} + current_block_size = 0 + total_size = 0 + + for key, weight in state_dict.items(): + if type(weight) != DTensor: + weight_size = calculate_tensor_size(weight) + + # If this weight is going to tip up over the maximal size, we split. + if current_block_size + weight_size > max_shard_size: + sharded_state_dicts.append(current_block) + current_block = {} + current_block_size = 0 + + current_block[key] = weight + current_block_size += weight_size + total_size += weight_size + + # Add the last block + sharded_state_dicts.append(current_block) + + # If we only have one shard, we return it + if len(sharded_state_dicts) == 1: + return {weights_name: sharded_state_dicts[0]}, None + + # Otherwise, let's build the index + weight_map = {} + shards = {} + + for idx, shard in enumerate(sharded_state_dicts): + shard_file = weights_name.replace(".bin", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.bin") + shard_file = shard_file.replace( + ".safetensors", f"-{idx + 1:05d}-of-{len(sharded_state_dicts):05d}.safetensors" + ) + shards[shard_file] = shard + for key in shard.keys(): + weight_map[key] = shard_file + + # Add the metadata + metadata = {"total_size": total_size} + index = {"metadata": metadata, "weight_map": weight_map} + return shards, index + +def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool =False): + """ + load shard state dict into model + """ + if use_safetensors and not checkpoint_file.suffix == ".safetensors": + raise Exception("load the model using `safetensors`, but no file endwith .safetensors") + if use_safetensors: + from safetensors.torch import safe_open + from safetensors.torch import load_file as safe_load_file + with safe_open(checkpoint_file, framework="pt") as f: + metadata = f.metadata() + if metadata["format"] != "pt": + raise NotImplementedError( + f"Conversion from a {metadata['format']} safetensors archive to PyTorch is not implemented yet." + ) + return safe_load_file(checkpoint_file) + else: + return torch.load(checkpoint_file) + +def load_state_dict_into_model(model: nn.Module, state_dict: torch.Tensor, missing_keys: List, strict: bool = False): + r"""Copies parameters and buffers from :attr:`state_dict` into + this module and its descendants. + + Args: + state_dict (dict): a dict containing parameters and + persistent buffers. + """ + if not isinstance(state_dict, Mapping): + raise TypeError("Expected state_dict to be dict-like, got {}.".format(type(state_dict))) + + unexpected_keys: List[str] = [] + sub_missing_keys: List[str] = [] + error_msgs: List[str] = [] + + # copy state_dict so _load_from_state_dict can modify it + metadata = getattr(state_dict, '_metadata', None) + state_dict = OrderedDict(state_dict) + if metadata is not None: + state_dict._metadata = metadata + + def load(module: nn.Module, state_dict, prefix=""): + local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) + args = (state_dict, prefix, local_metadata, True, [], [], error_msgs) + # Parameters of module and children will start with prefix. We can exit early if there are none in this + # state_dict + if len([key for key in state_dict if key.startswith(prefix)]) > 0: + module._load_from_state_dict(*args) + + for name, child in module._modules.items(): + if child is not None: + load(child, state_dict, prefix + name + ".") + + load(model, state_dict, "") + del load + + # deal with missing key + if len(missing_keys) > 0: + deleted_keys = [] + for key in missing_keys: + if key not in sub_missing_keys: + deleted_keys.append(key) + for key in deleted_keys: + missing_keys.remove(key) + + if strict: + if len(unexpected_keys) > 0: + error_msgs = 'Unexpected key(s) in state_dict: {}. '.format( + ', '.join('"{}"'.format(k) for k in unexpected_keys)) + raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( + model.__class__.__name__, "\n\t".join(error_msgs))) + # ====================================== # Helper functions for saving state dict # ====================================== @@ -86,8 +216,8 @@ def save_state_dict(state_dict: dict, checkpoint_file_path: str, use_safetensors assert is_safetensors_available(), "safetensors is not available." assert checkpoint_file_path.endswith('.safetensors'), \ "safetensors only supports .safetensors suffix for checkpoint file." - from safetensors.torch import save_file - save_file(state_dict, checkpoint_file_path) + from safetensors.torch import save_file as safe_save_file + safe_save_file(state_dict, checkpoint_file_path, metadata={"format": "pt"}) else: torch.save(state_dict, checkpoint_file_path) diff --git a/tests/test_checkpoint_io/test_general_checkpoint_io.py b/tests/test_checkpoint_io/test_general_checkpoint_io.py index 0f78184f70e1..ca5ce10054f7 100644 --- a/tests/test_checkpoint_io/test_general_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_general_checkpoint_io.py @@ -1,9 +1,12 @@ import tempfile - import pytest import torch +import logging from torch.optim import Adam from torchvision.models import resnet18 +from pathlib import Path +import os +import subprocess from colossalai.checkpoint_io import GeneralCheckpointIO from colossalai.testing import clear_cache_before_run, parameterize @@ -12,7 +15,7 @@ # Note: # 1. due to checkpoint IO can be quite slow if tested with all models, we will only test on resnet for now # 2. we will test on both sharded and unsharded checkpoints -# 3. TODO(FrankLeeeee): implement sharded checkpoint and test it +# 3. implement sharded checkpoint and test it # ======== @@ -53,27 +56,71 @@ def test_unsharded_checkpoint(use_safetensors: bool): ckpt_io.load_model(new_model, model_ckpt_tempfile.name) ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) - # do recursive check for the optimizer state dict - # if the value is a dict, compare its values - # if the value is a list, comapre all elements one-by-one - # if the value is a torch.Tensor, use torch.equal - # otherwise use assertEqual - def recursive_check(d1, d2): - for k, v in d1.items(): - if isinstance(v, dict): - recursive_check(v, d2[k]) - elif isinstance(v, list): - for i in range(len(v)): - if isinstance(v[i], torch.Tensor): - assert torch.equal(v[i], d2[k][i]) - else: - assert v[i] == d2[k][i] - elif isinstance(v, torch.Tensor): - assert torch.equal(v, d2[k]) - else: - assert v == d2[k] # check for model and optimizer state dict recursively recursive_check(model.state_dict(), new_model.state_dict()) recursive_check(optimizer.state_dict(), new_optimizer.state_dict()) + +@pytest.mark.parametrize('use_safetensors', [True, False]) +def test_sharded_checkpoint(use_safetensors: bool): + # create a model and optimizer + model = resnet18() + optimizer = Adam(model.parameters(), lr=0.001) + # create test data sample + x = torch.randn(1, 3, 224, 224) + + # run fwd and bwd + y = model(x) + loss = y.sum() + loss.backward() + optimizer.step() + + # create a temp file for checkpoint + if use_safetensors: + suffix = ".safetensors" + SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json" + else: + suffix = ".bin" + WEIGHTS_INDEX_NAME = "model.bin.index.json" + + # model_ckpt_dir = tempfile.TemporaryDirectory(suffix=suffix) + model_ckpt_dir = tempfile.TemporaryDirectory() + optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile() + + # save the model and optimizer + ckpt_io = GeneralCheckpointIO() + + ckpt_io.save_model(model, model_ckpt_dir.name, True, True, "", 10, use_safetensors=use_safetensors) + ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name, shard=False) + + # create new model + new_model = resnet18() + new_optimizer = Adam(new_model.parameters(), lr=0.001) + + ckpt_io.load_model(new_model, str(model_ckpt_dir.name), strict=True) + ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) + + # check for model and optimizer state dict recursively + recursive_check(model.state_dict(), new_model.state_dict()) recursive_check(optimizer.state_dict(), new_optimizer.state_dict()) + + +# do recursive check for the optimizer state dict +# if the value is a dict, compare its values +# if the value is a list, comapre all elements one-by-one +# if the value is a torch.Tensor, use torch.equal +# otherwise use assertEqual +def recursive_check(d1, d2): + for k, v in d1.items(): + if isinstance(v, dict): + recursive_check(v, d2[k]) + elif isinstance(v, list): + for i in range(len(v)): + if isinstance(v[i], torch.Tensor): + assert torch.equal(v[i], d2[k][i]) + else: + assert v[i] == d2[k][i] + elif isinstance(v, torch.Tensor): + assert torch.equal(v, d2[k]) + else: + assert v == d2[k] From 4e9989344d20e3f8af44767f0eadeaab5fff8c00 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 6 Apr 2023 17:47:59 +0800 Subject: [PATCH 111/413] [doc] updated contributor list (#3474) --- README.md | 5 +++-- docs/README-zh-Hans.md | 6 +++--- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 65c8ae166608..8342a9fa0c9e 100644 --- a/README.md +++ b/README.md @@ -396,9 +396,10 @@ You may contact us or participate in the following ways: Thanks so much to all of our amazing contributors! - + + + -*The order of contributor avatars is randomly shuffled.*

    (back to top)

    diff --git a/docs/README-zh-Hans.md b/docs/README-zh-Hans.md index 3630e8539a8b..f43a5953022d 100644 --- a/docs/README-zh-Hans.md +++ b/docs/README-zh-Hans.md @@ -396,9 +396,9 @@ docker run -ti --gpus all --rm --ipc=host colossalai bash 真诚感谢所有贡献者！ - - -*贡献者头像的展示顺序是随机的。* + + +

    (返回顶端)

    From c701b77b1131a9095f3dca454da4ec667bcbf182 Mon Sep 17 00:00:00 2001 From: NatalieC323 <127177614+NatalieC323@users.noreply.github.com> Date: Thu, 6 Apr 2023 17:50:52 +0800 Subject: [PATCH 112/413] [dreambooth] fixing the incompatibity in requirements.txt (#3190) (#3378) * Update requirements.txt * Update environment.yaml * Update README.md * Update environment.yaml * Update README.md * Update README.md * Delete requirements_colossalai.txt * Update requirements.txt * Update README.md --- .../Teyvat/train_colossalai_teyvat.yaml | 16 ++-- .../diffusion/configs/train_colossalai.yaml | 22 ++--- .../configs/train_colossalai_cifar10.yaml | 16 ++-- .../images/diffusion/configs/train_ddp.yaml | 14 +-- .../diffusion/ldm/models/autoencoder.py | 5 +- .../ldm/models/diffusion/classifier.py | 9 +- .../diffusion/ldm/models/diffusion/ddpm.py | 22 +++-- examples/images/diffusion/main.py | 94 ++++++++++++------- examples/images/diffusion/scripts/img2img.py | 4 +- examples/images/diffusion/scripts/inpaint.py | 4 +- examples/images/diffusion/scripts/knn2img.py | 5 +- .../diffusion/scripts/sample_diffusion.py | 3 +- .../scripts/tests/test_checkpoint.py | 4 +- examples/images/diffusion/scripts/txt2img.py | 4 +- 14 files changed, 124 insertions(+), 98 deletions(-) diff --git a/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml b/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml index ff0f4c5a0463..fe883cdfd7f8 100644 --- a/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml +++ b/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml @@ -1,6 +1,6 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion + #target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -20,7 +20,7 @@ model: use_ema: False scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler + #target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases @@ -30,7 +30,7 @@ model: unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel + #target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True @@ -49,7 +49,7 @@ model: legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL + #target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -73,13 +73,13 @@ model: target: torch.nn.Identity cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + #target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: - target: main.DataModuleFromConfig + #target: main.DataModuleFromConfig params: batch_size: 16 num_workers: 4 @@ -105,7 +105,7 @@ lightning: precision: 16 auto_select_gpus: False strategy: - target: strategies.ColossalAIStrategy + #target: strategies.ColossalAIStrategy params: use_chunk: True enable_distributed_storage: True @@ -120,7 +120,7 @@ lightning: logger_config: wandb: - target: loggers.WandbLogger + #target: loggers.WandbLogger params: name: nowname save_dir: "/tmp/diff_log/" diff --git a/examples/images/diffusion/configs/train_colossalai.yaml b/examples/images/diffusion/configs/train_colossalai.yaml index 88432e978a0f..388ab2e8ff94 100644 --- a/examples/images/diffusion/configs/train_colossalai.yaml +++ b/examples/images/diffusion/configs/train_colossalai.yaml @@ -1,6 +1,6 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion + #target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -19,7 +19,7 @@ model: use_ema: False # we set this to false because this is an inference only config scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler + #target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases @@ -29,7 +29,7 @@ model: unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel + #target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True @@ -48,7 +48,7 @@ model: legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL + #target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -69,16 +69,16 @@ model: attn_resolutions: [] dropout: 0.0 lossconfig: - target: torch.nn.Identity + #target: torch.nn.Identity cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + #target: #ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: - target: main.DataModuleFromConfig + #target: #main.DataModuleFromConfig params: batch_size: 128 wrap: False @@ -88,20 +88,20 @@ data: train: target: ldm.data.base.Txt2ImgIterableBaseDataset params: - file_path: # YOUR DATASET_PATH + file_path: /data/scratch/diffuser/laion_part0/ world_size: 1 rank: 0 lightning: trainer: accelerator: 'gpu' - devices: 8 + devices: 2 log_gpu_memory: all max_epochs: 2 precision: 16 auto_select_gpus: False strategy: - target: strategies.ColossalAIStrategy + #target: #strategies.ColossalAIStrategy params: use_chunk: True enable_distributed_storage: True @@ -116,7 +116,7 @@ lightning: logger_config: wandb: - target: loggers.WandbLogger + #target: #loggers.WandbLogger params: name: nowname save_dir: "/tmp/diff_log/" diff --git a/examples/images/diffusion/configs/train_colossalai_cifar10.yaml b/examples/images/diffusion/configs/train_colossalai_cifar10.yaml index 0ba06f832178..1331f96e34d6 100644 --- a/examples/images/diffusion/configs/train_colossalai_cifar10.yaml +++ b/examples/images/diffusion/configs/train_colossalai_cifar10.yaml @@ -1,6 +1,6 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion + #target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -19,7 +19,7 @@ model: use_ema: False # we set this to false because this is an inference only config scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler + #target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases @@ -29,7 +29,7 @@ model: unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel + #target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True @@ -48,7 +48,7 @@ model: legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL + #target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -69,16 +69,16 @@ model: attn_resolutions: [] dropout: 0.0 lossconfig: - target: torch.nn.Identity + #target: torch.nn.Identity cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + #target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: - target: main.DataModuleFromConfig + #target: main.DataModuleFromConfig params: batch_size: 4 num_workers: 4 @@ -105,7 +105,7 @@ lightning: precision: 16 auto_select_gpus: False strategy: - target: strategies.ColossalAIStrategy + #target: strategies.ColossalAIStrategy params: use_chunk: True enable_distributed_storage: True diff --git a/examples/images/diffusion/configs/train_ddp.yaml b/examples/images/diffusion/configs/train_ddp.yaml index a63df887e719..df591f33d5fd 100644 --- a/examples/images/diffusion/configs/train_ddp.yaml +++ b/examples/images/diffusion/configs/train_ddp.yaml @@ -1,6 +1,6 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion + #target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -29,7 +29,7 @@ model: unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel + #target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True @@ -48,7 +48,7 @@ model: legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL + #target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -72,13 +72,13 @@ model: target: torch.nn.Identity cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + #target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: - target: main.DataModuleFromConfig + #target: main.DataModuleFromConfig params: batch_size: 128 # num_workwers should be 2 * batch_size, and the total num less than 1024 @@ -100,7 +100,7 @@ lightning: precision: 16 auto_select_gpus: False strategy: - target: strategies.DDPStrategy + #target: strategies.DDPStrategy params: find_unused_parameters: False log_every_n_steps: 2 @@ -111,7 +111,7 @@ lightning: logger_config: wandb: - target: loggers.WandbLogger + #target: loggers.WandbLogger params: name: nowname save_dir: "/data2/tmp/diff_log/" diff --git a/examples/images/diffusion/ldm/models/autoencoder.py b/examples/images/diffusion/ldm/models/autoencoder.py index b1bd8377835b..145ccf6fb271 100644 --- a/examples/images/diffusion/ldm/models/autoencoder.py +++ b/examples/images/diffusion/ldm/models/autoencoder.py @@ -6,11 +6,10 @@ import torch.nn.functional as F from contextlib import contextmanager +from torch.nn import Identity from ldm.modules.diffusionmodules.model import Encoder, Decoder from ldm.modules.distributions.distributions import DiagonalGaussianDistribution - -from ldm.util import instantiate_from_config from ldm.modules.ema import LitEma @@ -32,7 +31,7 @@ def __init__(self, self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) - self.loss = instantiate_from_config(lossconfig) + self.loss = Identity(**lossconfig.get("params", dict())) assert ddconfig["double_z"] self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) diff --git a/examples/images/diffusion/ldm/models/diffusion/classifier.py b/examples/images/diffusion/ldm/models/diffusion/classifier.py index 612a8371bf20..3cf12f093bea 100644 --- a/examples/images/diffusion/ldm/models/diffusion/classifier.py +++ b/examples/images/diffusion/ldm/models/diffusion/classifier.py @@ -9,9 +9,10 @@ from einops import rearrange from glob import glob from natsort import natsorted - +from ldm.models.diffusion.ddpm import LatentDiffusion +from ldm.lr_scheduler import LambdaLinearScheduler from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel -from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config +from ldm.util import log_txt_as_img, default, ismap __models__ = { 'class_label': EncoderUNetModel, @@ -86,7 +87,7 @@ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): print(f"Unexpected Keys: {unexpected}") def load_diffusion(self): - model = instantiate_from_config(self.diffusion_config) + model = LatentDiffusion(**self.diffusion_config.get('params',dict())) self.diffusion_model = model.eval() self.diffusion_model.train = disabled_train for param in self.diffusion_model.parameters(): @@ -221,7 +222,7 @@ def configure_optimizers(self): optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) if self.use_scheduler: - scheduler = instantiate_from_config(self.scheduler_config) + scheduler = LambdaLinearScheduler(**self.scheduler_config.get('params',dict())) print("Setting up LambdaLR scheduler...") scheduler = [ diff --git a/examples/images/diffusion/ldm/models/diffusion/ddpm.py b/examples/images/diffusion/ldm/models/diffusion/ddpm.py index b7315b048c66..11de828732ea 100644 --- a/examples/images/diffusion/ldm/models/diffusion/ddpm.py +++ b/examples/images/diffusion/ldm/models/diffusion/ddpm.py @@ -22,6 +22,7 @@ from functools import partial from einops import rearrange, repeat +from ldm.lr_scheduler import LambdaLinearScheduler from ldm.models.autoencoder import * from ldm.models.autoencoder import AutoencoderKL, IdentityFirstStage from ldm.models.diffusion.ddim import * @@ -29,9 +30,10 @@ from ldm.modules.diffusionmodules.model import * from ldm.modules.diffusionmodules.model import Decoder, Encoder, Model from ldm.modules.diffusionmodules.openaimodel import * -from ldm.modules.diffusionmodules.openaimodel import AttentionPool2d +from ldm.modules.diffusionmodules.openaimodel import AttentionPool2d, UNetModel from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule, noise_like from ldm.modules.distributions.distributions import DiagonalGaussianDistribution, normal_kl +from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation from ldm.modules.ema import LitEma from ldm.modules.encoders.modules import * from ldm.util import count_params, default, exists, instantiate_from_config, isimage, ismap, log_txt_as_img, mean_flat @@ -39,6 +41,7 @@ from torch.optim.lr_scheduler import LambdaLR from torchvision.utils import make_grid from tqdm import tqdm +from ldm.modules.midas.api import MiDaSInference __conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} @@ -690,7 +693,7 @@ def register_schedule(self, self.make_cond_schedule() def instantiate_first_stage(self, config): - model = instantiate_from_config(config) + model = AutoencoderKL(**config.get("params", dict())) self.first_stage_model = model.eval() self.first_stage_model.train = disabled_train for param in self.first_stage_model.parameters(): @@ -706,7 +709,7 @@ def instantiate_cond_stage(self, config): self.cond_stage_model = None # self.be_unconditional = True else: - model = instantiate_from_config(config) + model = FrozenOpenCLIPEmbedder(**config.get("params", dict())) self.cond_stage_model = model.eval() self.cond_stage_model.train = disabled_train for param in self.cond_stage_model.parameters(): @@ -714,7 +717,7 @@ def instantiate_cond_stage(self, config): else: assert config != '__is_first_stage__' assert config != '__is_unconditional__' - model = instantiate_from_config(config) + model = FrozenOpenCLIPEmbedder(**config.get("params", dict())) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): @@ -1479,8 +1482,7 @@ def configure_optimizers(self): # opt = torch.optim.AdamW(params, lr=lr) if self.use_scheduler: - assert 'target' in self.scheduler_config - scheduler = instantiate_from_config(self.scheduler_config) + scheduler = LambdaLinearScheduler(**self.scheduler_config.get("params", dict())) rank_zero_info("Setting up LambdaLR scheduler...") scheduler = [{'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1}] @@ -1502,7 +1504,7 @@ class DiffusionWrapper(pl.LightningModule): def __init__(self, diff_model_config, conditioning_key): super().__init__() self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False) - self.diffusion_model = instantiate_from_config(diff_model_config) + self.diffusion_model = UNetModel(**diff_model_config.get("params", dict())) self.conditioning_key = conditioning_key assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm'] @@ -1551,7 +1553,7 @@ def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key= self.noise_level_key = noise_level_key def instantiate_low_stage(self, config): - model = instantiate_from_config(config) + model = ImageConcatWithNoiseAugmentation(**config.get("params", dict())) self.low_scale_model = model.eval() self.low_scale_model.train = disabled_train for param in self.low_scale_model.parameters(): @@ -1933,7 +1935,7 @@ class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion): def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs): super().__init__(concat_keys=concat_keys, *args, **kwargs) - self.depth_model = instantiate_from_config(depth_stage_config) + self.depth_model = MiDaSInference(**depth_stage_config.get("params", dict())) self.depth_stage_key = concat_keys[0] @torch.no_grad() @@ -2006,7 +2008,7 @@ def __init__(self, self.low_scale_key = low_scale_key def instantiate_low_stage(self, config): - model = instantiate_from_config(config) + model = ImageConcatWithNoiseAugmentation(**config.get("params", dict())) self.low_scale_model = model.eval() self.low_scale_model.train = disabled_train for param in self.low_scale_model.parameters(): diff --git a/examples/images/diffusion/main.py b/examples/images/diffusion/main.py index 91b809d5a65c..aeed6d5566f5 100644 --- a/examples/images/diffusion/main.py +++ b/examples/images/diffusion/main.py @@ -23,19 +23,21 @@ from PIL import Image from prefetch_generator import BackgroundGenerator from torch.utils.data import DataLoader, Dataset, Subset, random_split - -try: - from lightning.pytorch import seed_everything - from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint - from lightning.pytorch.trainer import Trainer - from lightning.pytorch.utilities import rank_zero_info, rank_zero_only - LIGHTNING_PACK_NAME = "lightning.pytorch." -except: - from pytorch_lightning import seed_everything - from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint - from pytorch_lightning.trainer import Trainer - from pytorch_lightning.utilities import rank_zero_info, rank_zero_only - LIGHTNING_PACK_NAME = "pytorch_lightning." +from ldm.models.diffusion.ddpm import LatentDiffusion +#try: +from lightning.pytorch import seed_everything +from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint +from lightning.pytorch.trainer import Trainer +from lightning.pytorch.utilities import rank_zero_info, rank_zero_only +from lightning.pytorch.loggers import WandbLogger, TensorBoardLogger +from lightning.pytorch.strategies import ColossalAIStrategy,DDPStrategy +LIGHTNING_PACK_NAME = "lightning.pytorch." +# #except: +# from pytorch_lightning import seed_everything +# from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint +# from pytorch_lightning.trainer import Trainer +# from pytorch_lightning.utilities import rank_zero_info, rank_zero_only +# LIGHTNING_PACK_NAME = "pytorch_lightning." from ldm.data.base import Txt2ImgIterableBaseDataset from ldm.util import instantiate_from_config @@ -575,7 +577,7 @@ def on_train_epoch_end(self, trainer, pl_module): # target: path to test dataset # params: # key: value - # lightning: (optional, has sane defaults and can be specified on cmdline) + # lightning: (optional, has same defaults and can be specified on cmdline) # trainer: # additional arguments to trainer # logger: @@ -653,7 +655,7 @@ def on_train_epoch_end(self, trainer, pl_module): # Sets the seed for the random number generator to ensure reproducibility seed_everything(opt.seed) - # Intinalize and save configuratioon using teh OmegaConf library. + # Intinalize and save configuration using the OmegaConf library. try: # init and save configs configs = [OmegaConf.load(cfg) for cfg in opt.base] @@ -687,7 +689,7 @@ def on_train_epoch_end(self, trainer, pl_module): config.model["params"].update({"ckpt": ckpt}) rank_zero_info("Using ckpt_path = {}".format(config.model["params"]["ckpt"])) - model = instantiate_from_config(config.model) + model = LatentDiffusion(**config.model.get("params", dict())) # trainer and callbacks trainer_kwargs = dict() @@ -696,7 +698,7 @@ def on_train_epoch_end(self, trainer, pl_module): # These loggers are specified as targets in the dictionary, along with the configuration settings specific to each logger. default_logger_cfgs = { "wandb": { - "target": LIGHTNING_PACK_NAME + "loggers.WandbLogger", + #"target": LIGHTNING_PACK_NAME + "loggers.WandbLogger", "params": { "name": nowname, "save_dir": logdir, @@ -705,7 +707,7 @@ def on_train_epoch_end(self, trainer, pl_module): } }, "tensorboard": { - "target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger", + #"target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger", "params": { "save_dir": logdir, "name": "diff_tb", @@ -718,30 +720,32 @@ def on_train_epoch_end(self, trainer, pl_module): default_logger_cfg = default_logger_cfgs["tensorboard"] if "logger" in lightning_config: logger_cfg = lightning_config.logger + logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) + trainer_kwargs["logger"] = WandbLogger(**logger_cfg.get("params", dict())) else: logger_cfg = default_logger_cfg - logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) - trainer_kwargs["logger"] = instantiate_from_config(logger_cfg) + logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) + trainer_kwargs["logger"] = TensorBoardLogger(**logger_cfg.get("params", dict())) + # config the strategy, defualt is ddp if "strategy" in trainer_config: strategy_cfg = trainer_config["strategy"] - strategy_cfg["target"] = LIGHTNING_PACK_NAME + strategy_cfg["target"] + trainer_kwargs["strategy"] = ColossalAIStrategy(**strategy_cfg.get("params", dict())) else: strategy_cfg = { - "target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy", + #"target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy", "params": { "find_unused_parameters": False } } - - trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg) + trainer_kwargs["strategy"] = DDPStrategy(**strategy_cfg.get("params", dict())) # Set up ModelCheckpoint callback to save best models # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to # specify which metric is used to determine best models default_modelckpt_cfg = { - "target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint", + #"target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint", "params": { "dirpath": ckptdir, "filename": "{epoch:06}", @@ -759,13 +763,13 @@ def on_train_epoch_end(self, trainer, pl_module): modelckpt_cfg = OmegaConf.create() modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg) if version.parse(pl.__version__) < version.parse('1.4.0'): - trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg) + trainer_kwargs["checkpoint_callback"] = ModelCheckpoint(**modelckpt_cfg.get("params", dict())) # Set up various callbacks, including logging, learning rate monitoring, and CUDA management # add callback which sets up log directory default_callbacks_cfg = { "setup_callback": { # callback to set up the training - "target": "main.SetupCallback", + #"target": "main.SetupCallback", "params": { "resume": opt.resume, # resume training if applicable "now": now, @@ -777,7 +781,7 @@ def on_train_epoch_end(self, trainer, pl_module): } }, "image_logger": { # callback to log image data - "target": "main.ImageLogger", + #"target": "main.ImageLogger", "params": { "batch_frequency": 750, # how frequently to log images "max_images": 4, # maximum number of images to log @@ -785,14 +789,14 @@ def on_train_epoch_end(self, trainer, pl_module): } }, "learning_rate_logger": { # callback to log learning rate - "target": "main.LearningRateMonitor", + #"target": "main.LearningRateMonitor", "params": { "logging_interval": "step", # logging frequency (either 'step' or 'epoch') # "log_momentum": True # whether to log momentum (currently commented out) } }, "cuda_callback": { # callback to handle CUDA-related operations - "target": "main.CUDACallback" + #"target": "main.CUDACallback" }, } @@ -810,7 +814,7 @@ def on_train_epoch_end(self, trainer, pl_module): 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.') default_metrics_over_trainsteps_ckpt_dict = { 'metrics_over_trainsteps_checkpoint': { - "target": LIGHTNING_PACK_NAME + 'callbacks.ModelCheckpoint', + #"target": LIGHTNING_PACK_NAME + 'callbacks.ModelCheckpoint', 'params': { "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'), "filename": "{epoch:06}-{step:09}", @@ -825,15 +829,35 @@ def on_train_epoch_end(self, trainer, pl_module): # Merge the default callbacks configuration with the specified callbacks configuration, and instantiate the callbacks callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) + + #Instantiate items according to the configs + trainer_kwargs.setdefault("callbacks", []) - trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] + if "setup_callback" in callbacks_cfg: + setup_callback_config = callbacks_cfg["setup_callback"] + trainer_kwargs["callbacks"].append(SetupCallback(**setup_callback_config.get("params", dict()))) - # Create a Trainer object with the specified command-line arguments and keyword arguments, and set the log directory + if "image_logger" in callbacks_cfg: + image_logger_config = callbacks_cfg["image_logger"] + trainer_kwargs["callbacks"].append(ImageLogger(**image_logger_config.get("params", dict()))) + + if "learning_rate_logger" in callbacks_cfg: + learning_rate_logger_config = callbacks_cfg["learning_rate_logger"] + trainer_kwargs["callbacks"].append(LearningRateMonitor(**learning_rate_logger_config.get("params", dict()))) + + if "cuda_callback" in callbacks_cfg: + cuda_callback_config = callbacks_cfg["cuda_callback"] + trainer_kwargs["callbacks"].append(CUDACallback(**cuda_callback_config.get("params", dict()))) + + if "metrics_over_trainsteps_checkpoint" in callbacks_cfg: + metrics_over_config = callbacks_cfg['metrics_over_trainsteps_checkpoint'] + trainer_kwargs["callbacks"].append(ModelCheckpoint(**metrics_over_config.get("params", dict()))) + #trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs) trainer.logdir = logdir - + # Create a data module based on the configuration file - data = instantiate_from_config(config.data) + data = DataModuleFromConfig(**config.data.get("params", dict())) # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html # calling these ourselves should not be necessary but it is. # lightning still takes care of proper multiprocessing though diff --git a/examples/images/diffusion/scripts/img2img.py b/examples/images/diffusion/scripts/img2img.py index 877538d4733d..a3011005c16a 100644 --- a/examples/images/diffusion/scripts/img2img.py +++ b/examples/images/diffusion/scripts/img2img.py @@ -20,8 +20,8 @@ from scripts.txt2img import put_watermark -from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler +from ldm.models.diffusion.ddpm import LatentDiffusion from utils import replace_module, getModelSize @@ -36,7 +36,7 @@ def load_model_from_config(config, ckpt, verbose=False): if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] - model = instantiate_from_config(config.model) + model = LatentDiffusion(**config.model.get("params", dict())) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") diff --git a/examples/images/diffusion/scripts/inpaint.py b/examples/images/diffusion/scripts/inpaint.py index d6e6387a9a3b..993c67b0e6f7 100644 --- a/examples/images/diffusion/scripts/inpaint.py +++ b/examples/images/diffusion/scripts/inpaint.py @@ -4,7 +4,7 @@ from tqdm import tqdm import numpy as np import torch -from main import instantiate_from_config +from ldm.models.diffusion.ddpm import LatentgDiffusion from ldm.models.diffusion.ddim import DDIMSampler @@ -57,7 +57,7 @@ def make_batch(image, mask, device): print(f"Found {len(masks)} inputs.") config = OmegaConf.load("models/ldm/inpainting_big/config.yaml") - model = instantiate_from_config(config.model) + model = LatentDiffusion(**config.model.get("params", dict())) model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"], strict=False) diff --git a/examples/images/diffusion/scripts/knn2img.py b/examples/images/diffusion/scripts/knn2img.py index e6eaaecab53e..66d9aa57de66 100644 --- a/examples/images/diffusion/scripts/knn2img.py +++ b/examples/images/diffusion/scripts/knn2img.py @@ -13,9 +13,10 @@ import time from multiprocessing import cpu_count -from ldm.util import instantiate_from_config, parallel_data_prefetch +from ldm.util import parallel_data_prefetch from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler +from ldm.models.diffusion.ddpm import LatentDiffusion from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder DATABASES = [ @@ -44,7 +45,7 @@ def load_model_from_config(config, ckpt, verbose=False): if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] - model = instantiate_from_config(config.model) + model = LatentDiffusion(**config.model.get("params", dict())) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") diff --git a/examples/images/diffusion/scripts/sample_diffusion.py b/examples/images/diffusion/scripts/sample_diffusion.py index 876fe3c3642f..a25965ef74de 100644 --- a/examples/images/diffusion/scripts/sample_diffusion.py +++ b/examples/images/diffusion/scripts/sample_diffusion.py @@ -8,7 +8,6 @@ from PIL import Image from ldm.models.diffusion.ddim import DDIMSampler -from ldm.util import instantiate_from_config rescale = lambda x: (x + 1.) / 2. @@ -218,7 +217,7 @@ def get_parser(): def load_model_from_config(config, sd): - model = instantiate_from_config(config) + model = LatentDiffusion(**config.get("params", dict())) model.load_state_dict(sd,strict=False) model.cuda() model.eval() diff --git a/examples/images/diffusion/scripts/tests/test_checkpoint.py b/examples/images/diffusion/scripts/tests/test_checkpoint.py index a32e66d44cf2..a157d186d6e7 100644 --- a/examples/images/diffusion/scripts/tests/test_checkpoint.py +++ b/examples/images/diffusion/scripts/tests/test_checkpoint.py @@ -9,7 +9,7 @@ import torch from ldm.util import instantiate_from_config from main import get_parser - +from ldm.modules.diffusionmodules.openaimodel import UNetModel if __name__ == "__main__": with torch.no_grad(): yaml_path = "../../train_colossalai.yaml" @@ -17,7 +17,7 @@ config = f.read() base_config = yaml.load(config, Loader=yaml.FullLoader) unet_config = base_config['model']['params']['unet_config'] - diffusion_model = instantiate_from_config(unet_config).to("cuda:0") + diffusion_model = UNetModel(**unet_config.get("params", dict())).to("cuda:0") pipe = StableDiffusionPipeline.from_pretrained( "/data/scratch/diffuser/stable-diffusion-v1-4" diff --git a/examples/images/diffusion/scripts/txt2img.py b/examples/images/diffusion/scripts/txt2img.py index 364ebac6c67b..b198430f6d1c 100644 --- a/examples/images/diffusion/scripts/txt2img.py +++ b/examples/images/diffusion/scripts/txt2img.py @@ -16,9 +16,9 @@ from contextlib import nullcontext from imwatermark import WatermarkEncoder -from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler +from ldm.models.diffusion.ddpm import LatentDiffusion from ldm.models.diffusion.dpm_solver import DPMSolverSampler from utils import replace_module, getModelSize @@ -35,7 +35,7 @@ def load_model_from_config(config, ckpt, verbose=False): if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] - model = instantiate_from_config(config.model) + model = LatentDiffusion(**config.model.get("params", dict())) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") From 891b8e7fac993a7fee2dccb5a54ec18f6110d5e0 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Thu, 6 Apr 2023 18:08:16 +0800 Subject: [PATCH 113/413] [chat] fix stage3 PPO sample sh command (#3477) --- applications/Chat/examples/train_prompts.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/applications/Chat/examples/train_prompts.sh b/applications/Chat/examples/train_prompts.sh index db73ac8e8e85..b750cf3581a6 100755 --- a/applications/Chat/examples/train_prompts.sh +++ b/applications/Chat/examples/train_prompts.sh @@ -15,4 +15,4 @@ set_n_least_used_CUDA_VISIBLE_DEVICES() { set_n_least_used_CUDA_VISIBLE_DEVICES 2 -torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai_zero2 +torchrun --standalone --nproc_per_node=2 train_prompts.py --prompt_path /path/to/data.json --strategy colossalai_zero2 From fb8fae6f2905173b0136031751d0c520c11e7b10 Mon Sep 17 00:00:00 2001 From: NatalieC323 <127177614+NatalieC323@users.noreply.github.com> Date: Thu, 6 Apr 2023 20:22:52 +0800 Subject: [PATCH 114/413] Revert "[dreambooth] fixing the incompatibity in requirements.txt (#3190) (#3378)" (#3481) --- .../Teyvat/train_colossalai_teyvat.yaml | 16 ++-- .../diffusion/configs/train_colossalai.yaml | 22 ++--- .../configs/train_colossalai_cifar10.yaml | 16 ++-- .../images/diffusion/configs/train_ddp.yaml | 14 +-- .../diffusion/ldm/models/autoencoder.py | 5 +- .../ldm/models/diffusion/classifier.py | 9 +- .../diffusion/ldm/models/diffusion/ddpm.py | 22 ++--- examples/images/diffusion/main.py | 94 +++++++------------ examples/images/diffusion/scripts/img2img.py | 4 +- examples/images/diffusion/scripts/inpaint.py | 4 +- examples/images/diffusion/scripts/knn2img.py | 5 +- .../diffusion/scripts/sample_diffusion.py | 3 +- .../scripts/tests/test_checkpoint.py | 4 +- examples/images/diffusion/scripts/txt2img.py | 4 +- 14 files changed, 98 insertions(+), 124 deletions(-) diff --git a/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml b/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml index fe883cdfd7f8..ff0f4c5a0463 100644 --- a/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml +++ b/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml @@ -1,6 +1,6 @@ model: base_learning_rate: 1.0e-4 - #target: ldm.models.diffusion.ddpm.LatentDiffusion + target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -20,7 +20,7 @@ model: use_ema: False scheduler_config: # 10000 warmup steps - #target: ldm.lr_scheduler.LambdaLinearScheduler + target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases @@ -30,7 +30,7 @@ model: unet_config: - #target: ldm.modules.diffusionmodules.openaimodel.UNetModel + target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True @@ -49,7 +49,7 @@ model: legacy: False first_stage_config: - #target: ldm.models.autoencoder.AutoencoderKL + target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -73,13 +73,13 @@ model: target: torch.nn.Identity cond_stage_config: - #target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: - #target: main.DataModuleFromConfig + target: main.DataModuleFromConfig params: batch_size: 16 num_workers: 4 @@ -105,7 +105,7 @@ lightning: precision: 16 auto_select_gpus: False strategy: - #target: strategies.ColossalAIStrategy + target: strategies.ColossalAIStrategy params: use_chunk: True enable_distributed_storage: True @@ -120,7 +120,7 @@ lightning: logger_config: wandb: - #target: loggers.WandbLogger + target: loggers.WandbLogger params: name: nowname save_dir: "/tmp/diff_log/" diff --git a/examples/images/diffusion/configs/train_colossalai.yaml b/examples/images/diffusion/configs/train_colossalai.yaml index 388ab2e8ff94..88432e978a0f 100644 --- a/examples/images/diffusion/configs/train_colossalai.yaml +++ b/examples/images/diffusion/configs/train_colossalai.yaml @@ -1,6 +1,6 @@ model: base_learning_rate: 1.0e-4 - #target: ldm.models.diffusion.ddpm.LatentDiffusion + target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -19,7 +19,7 @@ model: use_ema: False # we set this to false because this is an inference only config scheduler_config: # 10000 warmup steps - #target: ldm.lr_scheduler.LambdaLinearScheduler + target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases @@ -29,7 +29,7 @@ model: unet_config: - #target: ldm.modules.diffusionmodules.openaimodel.UNetModel + target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True @@ -48,7 +48,7 @@ model: legacy: False first_stage_config: - #target: ldm.models.autoencoder.AutoencoderKL + target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -69,16 +69,16 @@ model: attn_resolutions: [] dropout: 0.0 lossconfig: - #target: torch.nn.Identity + target: torch.nn.Identity cond_stage_config: - #target: #ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: - #target: #main.DataModuleFromConfig + target: main.DataModuleFromConfig params: batch_size: 128 wrap: False @@ -88,20 +88,20 @@ data: train: target: ldm.data.base.Txt2ImgIterableBaseDataset params: - file_path: /data/scratch/diffuser/laion_part0/ + file_path: # YOUR DATASET_PATH world_size: 1 rank: 0 lightning: trainer: accelerator: 'gpu' - devices: 2 + devices: 8 log_gpu_memory: all max_epochs: 2 precision: 16 auto_select_gpus: False strategy: - #target: #strategies.ColossalAIStrategy + target: strategies.ColossalAIStrategy params: use_chunk: True enable_distributed_storage: True @@ -116,7 +116,7 @@ lightning: logger_config: wandb: - #target: #loggers.WandbLogger + target: loggers.WandbLogger params: name: nowname save_dir: "/tmp/diff_log/" diff --git a/examples/images/diffusion/configs/train_colossalai_cifar10.yaml b/examples/images/diffusion/configs/train_colossalai_cifar10.yaml index 1331f96e34d6..0ba06f832178 100644 --- a/examples/images/diffusion/configs/train_colossalai_cifar10.yaml +++ b/examples/images/diffusion/configs/train_colossalai_cifar10.yaml @@ -1,6 +1,6 @@ model: base_learning_rate: 1.0e-4 - #target: ldm.models.diffusion.ddpm.LatentDiffusion + target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -19,7 +19,7 @@ model: use_ema: False # we set this to false because this is an inference only config scheduler_config: # 10000 warmup steps - #target: ldm.lr_scheduler.LambdaLinearScheduler + target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases @@ -29,7 +29,7 @@ model: unet_config: - #target: ldm.modules.diffusionmodules.openaimodel.UNetModel + target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True @@ -48,7 +48,7 @@ model: legacy: False first_stage_config: - #target: ldm.models.autoencoder.AutoencoderKL + target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -69,16 +69,16 @@ model: attn_resolutions: [] dropout: 0.0 lossconfig: - #target: torch.nn.Identity + target: torch.nn.Identity cond_stage_config: - #target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: - #target: main.DataModuleFromConfig + target: main.DataModuleFromConfig params: batch_size: 4 num_workers: 4 @@ -105,7 +105,7 @@ lightning: precision: 16 auto_select_gpus: False strategy: - #target: strategies.ColossalAIStrategy + target: strategies.ColossalAIStrategy params: use_chunk: True enable_distributed_storage: True diff --git a/examples/images/diffusion/configs/train_ddp.yaml b/examples/images/diffusion/configs/train_ddp.yaml index df591f33d5fd..a63df887e719 100644 --- a/examples/images/diffusion/configs/train_ddp.yaml +++ b/examples/images/diffusion/configs/train_ddp.yaml @@ -1,6 +1,6 @@ model: base_learning_rate: 1.0e-4 - #target: ldm.models.diffusion.ddpm.LatentDiffusion + target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -29,7 +29,7 @@ model: unet_config: - #target: ldm.modules.diffusionmodules.openaimodel.UNetModel + target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True @@ -48,7 +48,7 @@ model: legacy: False first_stage_config: - #target: ldm.models.autoencoder.AutoencoderKL + target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -72,13 +72,13 @@ model: target: torch.nn.Identity cond_stage_config: - #target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: - #target: main.DataModuleFromConfig + target: main.DataModuleFromConfig params: batch_size: 128 # num_workwers should be 2 * batch_size, and the total num less than 1024 @@ -100,7 +100,7 @@ lightning: precision: 16 auto_select_gpus: False strategy: - #target: strategies.DDPStrategy + target: strategies.DDPStrategy params: find_unused_parameters: False log_every_n_steps: 2 @@ -111,7 +111,7 @@ lightning: logger_config: wandb: - #target: loggers.WandbLogger + target: loggers.WandbLogger params: name: nowname save_dir: "/data2/tmp/diff_log/" diff --git a/examples/images/diffusion/ldm/models/autoencoder.py b/examples/images/diffusion/ldm/models/autoencoder.py index 145ccf6fb271..b1bd8377835b 100644 --- a/examples/images/diffusion/ldm/models/autoencoder.py +++ b/examples/images/diffusion/ldm/models/autoencoder.py @@ -6,10 +6,11 @@ import torch.nn.functional as F from contextlib import contextmanager -from torch.nn import Identity from ldm.modules.diffusionmodules.model import Encoder, Decoder from ldm.modules.distributions.distributions import DiagonalGaussianDistribution + +from ldm.util import instantiate_from_config from ldm.modules.ema import LitEma @@ -31,7 +32,7 @@ def __init__(self, self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) - self.loss = Identity(**lossconfig.get("params", dict())) + self.loss = instantiate_from_config(lossconfig) assert ddconfig["double_z"] self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) diff --git a/examples/images/diffusion/ldm/models/diffusion/classifier.py b/examples/images/diffusion/ldm/models/diffusion/classifier.py index 3cf12f093bea..612a8371bf20 100644 --- a/examples/images/diffusion/ldm/models/diffusion/classifier.py +++ b/examples/images/diffusion/ldm/models/diffusion/classifier.py @@ -9,10 +9,9 @@ from einops import rearrange from glob import glob from natsort import natsorted -from ldm.models.diffusion.ddpm import LatentDiffusion -from ldm.lr_scheduler import LambdaLinearScheduler + from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel -from ldm.util import log_txt_as_img, default, ismap +from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config __models__ = { 'class_label': EncoderUNetModel, @@ -87,7 +86,7 @@ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): print(f"Unexpected Keys: {unexpected}") def load_diffusion(self): - model = LatentDiffusion(**self.diffusion_config.get('params',dict())) + model = instantiate_from_config(self.diffusion_config) self.diffusion_model = model.eval() self.diffusion_model.train = disabled_train for param in self.diffusion_model.parameters(): @@ -222,7 +221,7 @@ def configure_optimizers(self): optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) if self.use_scheduler: - scheduler = LambdaLinearScheduler(**self.scheduler_config.get('params',dict())) + scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ diff --git a/examples/images/diffusion/ldm/models/diffusion/ddpm.py b/examples/images/diffusion/ldm/models/diffusion/ddpm.py index 11de828732ea..b7315b048c66 100644 --- a/examples/images/diffusion/ldm/models/diffusion/ddpm.py +++ b/examples/images/diffusion/ldm/models/diffusion/ddpm.py @@ -22,7 +22,6 @@ from functools import partial from einops import rearrange, repeat -from ldm.lr_scheduler import LambdaLinearScheduler from ldm.models.autoencoder import * from ldm.models.autoencoder import AutoencoderKL, IdentityFirstStage from ldm.models.diffusion.ddim import * @@ -30,10 +29,9 @@ from ldm.modules.diffusionmodules.model import * from ldm.modules.diffusionmodules.model import Decoder, Encoder, Model from ldm.modules.diffusionmodules.openaimodel import * -from ldm.modules.diffusionmodules.openaimodel import AttentionPool2d, UNetModel +from ldm.modules.diffusionmodules.openaimodel import AttentionPool2d from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule, noise_like from ldm.modules.distributions.distributions import DiagonalGaussianDistribution, normal_kl -from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation from ldm.modules.ema import LitEma from ldm.modules.encoders.modules import * from ldm.util import count_params, default, exists, instantiate_from_config, isimage, ismap, log_txt_as_img, mean_flat @@ -41,7 +39,6 @@ from torch.optim.lr_scheduler import LambdaLR from torchvision.utils import make_grid from tqdm import tqdm -from ldm.modules.midas.api import MiDaSInference __conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} @@ -693,7 +690,7 @@ def register_schedule(self, self.make_cond_schedule() def instantiate_first_stage(self, config): - model = AutoencoderKL(**config.get("params", dict())) + model = instantiate_from_config(config) self.first_stage_model = model.eval() self.first_stage_model.train = disabled_train for param in self.first_stage_model.parameters(): @@ -709,7 +706,7 @@ def instantiate_cond_stage(self, config): self.cond_stage_model = None # self.be_unconditional = True else: - model = FrozenOpenCLIPEmbedder(**config.get("params", dict())) + model = instantiate_from_config(config) self.cond_stage_model = model.eval() self.cond_stage_model.train = disabled_train for param in self.cond_stage_model.parameters(): @@ -717,7 +714,7 @@ def instantiate_cond_stage(self, config): else: assert config != '__is_first_stage__' assert config != '__is_unconditional__' - model = FrozenOpenCLIPEmbedder(**config.get("params", dict())) + model = instantiate_from_config(config) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): @@ -1482,7 +1479,8 @@ def configure_optimizers(self): # opt = torch.optim.AdamW(params, lr=lr) if self.use_scheduler: - scheduler = LambdaLinearScheduler(**self.scheduler_config.get("params", dict())) + assert 'target' in self.scheduler_config + scheduler = instantiate_from_config(self.scheduler_config) rank_zero_info("Setting up LambdaLR scheduler...") scheduler = [{'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1}] @@ -1504,7 +1502,7 @@ class DiffusionWrapper(pl.LightningModule): def __init__(self, diff_model_config, conditioning_key): super().__init__() self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False) - self.diffusion_model = UNetModel(**diff_model_config.get("params", dict())) + self.diffusion_model = instantiate_from_config(diff_model_config) self.conditioning_key = conditioning_key assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm'] @@ -1553,7 +1551,7 @@ def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key= self.noise_level_key = noise_level_key def instantiate_low_stage(self, config): - model = ImageConcatWithNoiseAugmentation(**config.get("params", dict())) + model = instantiate_from_config(config) self.low_scale_model = model.eval() self.low_scale_model.train = disabled_train for param in self.low_scale_model.parameters(): @@ -1935,7 +1933,7 @@ class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion): def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs): super().__init__(concat_keys=concat_keys, *args, **kwargs) - self.depth_model = MiDaSInference(**depth_stage_config.get("params", dict())) + self.depth_model = instantiate_from_config(depth_stage_config) self.depth_stage_key = concat_keys[0] @torch.no_grad() @@ -2008,7 +2006,7 @@ def __init__(self, self.low_scale_key = low_scale_key def instantiate_low_stage(self, config): - model = ImageConcatWithNoiseAugmentation(**config.get("params", dict())) + model = instantiate_from_config(config) self.low_scale_model = model.eval() self.low_scale_model.train = disabled_train for param in self.low_scale_model.parameters(): diff --git a/examples/images/diffusion/main.py b/examples/images/diffusion/main.py index aeed6d5566f5..91b809d5a65c 100644 --- a/examples/images/diffusion/main.py +++ b/examples/images/diffusion/main.py @@ -23,21 +23,19 @@ from PIL import Image from prefetch_generator import BackgroundGenerator from torch.utils.data import DataLoader, Dataset, Subset, random_split -from ldm.models.diffusion.ddpm import LatentDiffusion -#try: -from lightning.pytorch import seed_everything -from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint -from lightning.pytorch.trainer import Trainer -from lightning.pytorch.utilities import rank_zero_info, rank_zero_only -from lightning.pytorch.loggers import WandbLogger, TensorBoardLogger -from lightning.pytorch.strategies import ColossalAIStrategy,DDPStrategy -LIGHTNING_PACK_NAME = "lightning.pytorch." -# #except: -# from pytorch_lightning import seed_everything -# from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint -# from pytorch_lightning.trainer import Trainer -# from pytorch_lightning.utilities import rank_zero_info, rank_zero_only -# LIGHTNING_PACK_NAME = "pytorch_lightning." + +try: + from lightning.pytorch import seed_everything + from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint + from lightning.pytorch.trainer import Trainer + from lightning.pytorch.utilities import rank_zero_info, rank_zero_only + LIGHTNING_PACK_NAME = "lightning.pytorch." +except: + from pytorch_lightning import seed_everything + from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint + from pytorch_lightning.trainer import Trainer + from pytorch_lightning.utilities import rank_zero_info, rank_zero_only + LIGHTNING_PACK_NAME = "pytorch_lightning." from ldm.data.base import Txt2ImgIterableBaseDataset from ldm.util import instantiate_from_config @@ -577,7 +575,7 @@ def on_train_epoch_end(self, trainer, pl_module): # target: path to test dataset # params: # key: value - # lightning: (optional, has same defaults and can be specified on cmdline) + # lightning: (optional, has sane defaults and can be specified on cmdline) # trainer: # additional arguments to trainer # logger: @@ -655,7 +653,7 @@ def on_train_epoch_end(self, trainer, pl_module): # Sets the seed for the random number generator to ensure reproducibility seed_everything(opt.seed) - # Intinalize and save configuration using the OmegaConf library. + # Intinalize and save configuratioon using teh OmegaConf library. try: # init and save configs configs = [OmegaConf.load(cfg) for cfg in opt.base] @@ -689,7 +687,7 @@ def on_train_epoch_end(self, trainer, pl_module): config.model["params"].update({"ckpt": ckpt}) rank_zero_info("Using ckpt_path = {}".format(config.model["params"]["ckpt"])) - model = LatentDiffusion(**config.model.get("params", dict())) + model = instantiate_from_config(config.model) # trainer and callbacks trainer_kwargs = dict() @@ -698,7 +696,7 @@ def on_train_epoch_end(self, trainer, pl_module): # These loggers are specified as targets in the dictionary, along with the configuration settings specific to each logger. default_logger_cfgs = { "wandb": { - #"target": LIGHTNING_PACK_NAME + "loggers.WandbLogger", + "target": LIGHTNING_PACK_NAME + "loggers.WandbLogger", "params": { "name": nowname, "save_dir": logdir, @@ -707,7 +705,7 @@ def on_train_epoch_end(self, trainer, pl_module): } }, "tensorboard": { - #"target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger", + "target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger", "params": { "save_dir": logdir, "name": "diff_tb", @@ -720,32 +718,30 @@ def on_train_epoch_end(self, trainer, pl_module): default_logger_cfg = default_logger_cfgs["tensorboard"] if "logger" in lightning_config: logger_cfg = lightning_config.logger - logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) - trainer_kwargs["logger"] = WandbLogger(**logger_cfg.get("params", dict())) else: logger_cfg = default_logger_cfg - logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) - trainer_kwargs["logger"] = TensorBoardLogger(**logger_cfg.get("params", dict())) - + logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) + trainer_kwargs["logger"] = instantiate_from_config(logger_cfg) # config the strategy, defualt is ddp if "strategy" in trainer_config: strategy_cfg = trainer_config["strategy"] - trainer_kwargs["strategy"] = ColossalAIStrategy(**strategy_cfg.get("params", dict())) + strategy_cfg["target"] = LIGHTNING_PACK_NAME + strategy_cfg["target"] else: strategy_cfg = { - #"target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy", + "target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy", "params": { "find_unused_parameters": False } } - trainer_kwargs["strategy"] = DDPStrategy(**strategy_cfg.get("params", dict())) + + trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg) # Set up ModelCheckpoint callback to save best models # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to # specify which metric is used to determine best models default_modelckpt_cfg = { - #"target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint", + "target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint", "params": { "dirpath": ckptdir, "filename": "{epoch:06}", @@ -763,13 +759,13 @@ def on_train_epoch_end(self, trainer, pl_module): modelckpt_cfg = OmegaConf.create() modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg) if version.parse(pl.__version__) < version.parse('1.4.0'): - trainer_kwargs["checkpoint_callback"] = ModelCheckpoint(**modelckpt_cfg.get("params", dict())) + trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg) # Set up various callbacks, including logging, learning rate monitoring, and CUDA management # add callback which sets up log directory default_callbacks_cfg = { "setup_callback": { # callback to set up the training - #"target": "main.SetupCallback", + "target": "main.SetupCallback", "params": { "resume": opt.resume, # resume training if applicable "now": now, @@ -781,7 +777,7 @@ def on_train_epoch_end(self, trainer, pl_module): } }, "image_logger": { # callback to log image data - #"target": "main.ImageLogger", + "target": "main.ImageLogger", "params": { "batch_frequency": 750, # how frequently to log images "max_images": 4, # maximum number of images to log @@ -789,14 +785,14 @@ def on_train_epoch_end(self, trainer, pl_module): } }, "learning_rate_logger": { # callback to log learning rate - #"target": "main.LearningRateMonitor", + "target": "main.LearningRateMonitor", "params": { "logging_interval": "step", # logging frequency (either 'step' or 'epoch') # "log_momentum": True # whether to log momentum (currently commented out) } }, "cuda_callback": { # callback to handle CUDA-related operations - #"target": "main.CUDACallback" + "target": "main.CUDACallback" }, } @@ -814,7 +810,7 @@ def on_train_epoch_end(self, trainer, pl_module): 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.') default_metrics_over_trainsteps_ckpt_dict = { 'metrics_over_trainsteps_checkpoint': { - #"target": LIGHTNING_PACK_NAME + 'callbacks.ModelCheckpoint', + "target": LIGHTNING_PACK_NAME + 'callbacks.ModelCheckpoint', 'params': { "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'), "filename": "{epoch:06}-{step:09}", @@ -829,35 +825,15 @@ def on_train_epoch_end(self, trainer, pl_module): # Merge the default callbacks configuration with the specified callbacks configuration, and instantiate the callbacks callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) - - #Instantiate items according to the configs - trainer_kwargs.setdefault("callbacks", []) - if "setup_callback" in callbacks_cfg: - setup_callback_config = callbacks_cfg["setup_callback"] - trainer_kwargs["callbacks"].append(SetupCallback(**setup_callback_config.get("params", dict()))) + trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] - if "image_logger" in callbacks_cfg: - image_logger_config = callbacks_cfg["image_logger"] - trainer_kwargs["callbacks"].append(ImageLogger(**image_logger_config.get("params", dict()))) - - if "learning_rate_logger" in callbacks_cfg: - learning_rate_logger_config = callbacks_cfg["learning_rate_logger"] - trainer_kwargs["callbacks"].append(LearningRateMonitor(**learning_rate_logger_config.get("params", dict()))) - - if "cuda_callback" in callbacks_cfg: - cuda_callback_config = callbacks_cfg["cuda_callback"] - trainer_kwargs["callbacks"].append(CUDACallback(**cuda_callback_config.get("params", dict()))) - - if "metrics_over_trainsteps_checkpoint" in callbacks_cfg: - metrics_over_config = callbacks_cfg['metrics_over_trainsteps_checkpoint'] - trainer_kwargs["callbacks"].append(ModelCheckpoint(**metrics_over_config.get("params", dict()))) - #trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] + # Create a Trainer object with the specified command-line arguments and keyword arguments, and set the log directory trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs) trainer.logdir = logdir - + # Create a data module based on the configuration file - data = DataModuleFromConfig(**config.data.get("params", dict())) + data = instantiate_from_config(config.data) # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html # calling these ourselves should not be necessary but it is. # lightning still takes care of proper multiprocessing though diff --git a/examples/images/diffusion/scripts/img2img.py b/examples/images/diffusion/scripts/img2img.py index a3011005c16a..877538d4733d 100644 --- a/examples/images/diffusion/scripts/img2img.py +++ b/examples/images/diffusion/scripts/img2img.py @@ -20,8 +20,8 @@ from scripts.txt2img import put_watermark +from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler -from ldm.models.diffusion.ddpm import LatentDiffusion from utils import replace_module, getModelSize @@ -36,7 +36,7 @@ def load_model_from_config(config, ckpt, verbose=False): if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] - model = LatentDiffusion(**config.model.get("params", dict())) + model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") diff --git a/examples/images/diffusion/scripts/inpaint.py b/examples/images/diffusion/scripts/inpaint.py index 993c67b0e6f7..d6e6387a9a3b 100644 --- a/examples/images/diffusion/scripts/inpaint.py +++ b/examples/images/diffusion/scripts/inpaint.py @@ -4,7 +4,7 @@ from tqdm import tqdm import numpy as np import torch -from ldm.models.diffusion.ddpm import LatentgDiffusion +from main import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler @@ -57,7 +57,7 @@ def make_batch(image, mask, device): print(f"Found {len(masks)} inputs.") config = OmegaConf.load("models/ldm/inpainting_big/config.yaml") - model = LatentDiffusion(**config.model.get("params", dict())) + model = instantiate_from_config(config.model) model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"], strict=False) diff --git a/examples/images/diffusion/scripts/knn2img.py b/examples/images/diffusion/scripts/knn2img.py index 66d9aa57de66..e6eaaecab53e 100644 --- a/examples/images/diffusion/scripts/knn2img.py +++ b/examples/images/diffusion/scripts/knn2img.py @@ -13,10 +13,9 @@ import time from multiprocessing import cpu_count -from ldm.util import parallel_data_prefetch +from ldm.util import instantiate_from_config, parallel_data_prefetch from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler -from ldm.models.diffusion.ddpm import LatentDiffusion from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder DATABASES = [ @@ -45,7 +44,7 @@ def load_model_from_config(config, ckpt, verbose=False): if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] - model = LatentDiffusion(**config.model.get("params", dict())) + model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") diff --git a/examples/images/diffusion/scripts/sample_diffusion.py b/examples/images/diffusion/scripts/sample_diffusion.py index a25965ef74de..876fe3c3642f 100644 --- a/examples/images/diffusion/scripts/sample_diffusion.py +++ b/examples/images/diffusion/scripts/sample_diffusion.py @@ -8,6 +8,7 @@ from PIL import Image from ldm.models.diffusion.ddim import DDIMSampler +from ldm.util import instantiate_from_config rescale = lambda x: (x + 1.) / 2. @@ -217,7 +218,7 @@ def get_parser(): def load_model_from_config(config, sd): - model = LatentDiffusion(**config.get("params", dict())) + model = instantiate_from_config(config) model.load_state_dict(sd,strict=False) model.cuda() model.eval() diff --git a/examples/images/diffusion/scripts/tests/test_checkpoint.py b/examples/images/diffusion/scripts/tests/test_checkpoint.py index a157d186d6e7..a32e66d44cf2 100644 --- a/examples/images/diffusion/scripts/tests/test_checkpoint.py +++ b/examples/images/diffusion/scripts/tests/test_checkpoint.py @@ -9,7 +9,7 @@ import torch from ldm.util import instantiate_from_config from main import get_parser -from ldm.modules.diffusionmodules.openaimodel import UNetModel + if __name__ == "__main__": with torch.no_grad(): yaml_path = "../../train_colossalai.yaml" @@ -17,7 +17,7 @@ config = f.read() base_config = yaml.load(config, Loader=yaml.FullLoader) unet_config = base_config['model']['params']['unet_config'] - diffusion_model = UNetModel(**unet_config.get("params", dict())).to("cuda:0") + diffusion_model = instantiate_from_config(unet_config).to("cuda:0") pipe = StableDiffusionPipeline.from_pretrained( "/data/scratch/diffuser/stable-diffusion-v1-4" diff --git a/examples/images/diffusion/scripts/txt2img.py b/examples/images/diffusion/scripts/txt2img.py index b198430f6d1c..364ebac6c67b 100644 --- a/examples/images/diffusion/scripts/txt2img.py +++ b/examples/images/diffusion/scripts/txt2img.py @@ -16,9 +16,9 @@ from contextlib import nullcontext from imwatermark import WatermarkEncoder +from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler -from ldm.models.diffusion.ddpm import LatentDiffusion from ldm.models.diffusion.dpm_solver import DPMSolverSampler from utils import replace_module, getModelSize @@ -35,7 +35,7 @@ def load_model_from_config(config, ckpt, verbose=False): if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] - model = LatentDiffusion(**config.model.get("params", dict())) + model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") From ab5fd127e393f7298c6497fd638d0f89c53a9452 Mon Sep 17 00:00:00 2001 From: mandoxzhang <111039218+mandoxzhang@users.noreply.github.com> Date: Fri, 7 Apr 2023 10:34:51 +0800 Subject: [PATCH 115/413] [example] update roberta with newer ColossalAI (#3472) * update roberta example * update roberta example --- examples/language/roberta/README.md | 16 +- .../roberta/configs/colossalai_ddp.py | 9 -- .../roberta/configs/colossalai_zero.py | 37 ----- .../roberta/preprocessing/get_mask.py | 7 +- .../roberta/preprocessing/sentence_split.py | 19 +-- .../roberta/preprocessing/tokenize_mask.py | 12 +- .../language/roberta/pretraining/arguments.py | 24 +++ .../roberta/pretraining/evaluation.py | 12 +- .../roberta/pretraining/pretrain_utils.py | 4 +- .../roberta/pretraining/run_pretrain.sh | 2 - .../pretraining/run_pretrain_resume.sh | 2 - .../roberta/pretraining/run_pretraining.py | 142 ++++++++++++++++-- examples/language/roberta/requirements.txt | 5 + 13 files changed, 188 insertions(+), 103 deletions(-) delete mode 100644 examples/language/roberta/configs/colossalai_ddp.py delete mode 100644 examples/language/roberta/configs/colossalai_zero.py diff --git a/examples/language/roberta/README.md b/examples/language/roberta/README.md index a42b1935dd85..0e080d00981a 100644 --- a/examples/language/roberta/README.md +++ b/examples/language/roberta/README.md @@ -1,9 +1,9 @@ # Introduction -This repo introduce how to pretrain a chinese roberta-large from scratch, including preprocessing, pretraining, finetune. The repo can help you quickly train a high-quality bert. +This example introduce how to pretrain roberta from scratch, including preprocessing, pretraining, finetune. The example can help you quickly train a high-quality roberta. ## 0. Prerequisite - Install Colossal-AI -- Editing the port from /etc/ssh/sshd_config and /etc/ssh/ssh_config, every host expose the same ssh port of server and client. If you are a root user, you also set the **PermitRootLogin** from /etc/ssh/sshd_config to "yes" +- Editing the port from `/etc/ssh/sshd_config` and `/etc/ssh/ssh_config`, every host expose the same ssh port of server and client. If you are a root user, you also set the **PermitRootLogin** from `/etc/ssh/sshd_config` to "yes" - Ensure that each host can log in to each other without password. If you have n hosts, need to execute n2 times ``` @@ -33,7 +33,7 @@ service ssh restart ```bash cd preprocessing ``` -following the `README.md`, preprocess original corpus to h5py+numpy +following the `README.md`, preprocess original corpus to h5py plus numpy ## 2. Pretrain @@ -47,12 +47,4 @@ following the `README.md`, load the h5py generated by preprocess of step 1 to pr The checkpoint produced by this repo can replace `pytorch_model.bin` from [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/tree/main) directly. Then use transfomers from Hugging Face to finetune downstream application. ## Contributors -The repo is contributed by AI team from [Moore Threads](https://www.mthreads.com/). If you find any problems for pretraining, please file an issue or send an email to yehua.zhang@mthreads.com. At last, welcome any form of contribution! - -``` -@misc{ - title={A simple Chinese RoBERTa Example for Whole Word Masked}, - author={Yehua Zhang, Chen Zhang}, - year={2022} -} -``` +The example is contributed by AI team from [Moore Threads](https://www.mthreads.com/). If you find any problems for pretraining, please file an issue or send an email to yehua.zhang@mthreads.com. At last, welcome any form of contribution! diff --git a/examples/language/roberta/configs/colossalai_ddp.py b/examples/language/roberta/configs/colossalai_ddp.py deleted file mode 100644 index 3146ffc45eef..000000000000 --- a/examples/language/roberta/configs/colossalai_ddp.py +++ /dev/null @@ -1,9 +0,0 @@ -from colossalai.nn.optimizer import FusedAdam - -try: - from colossalai.zero.shard_utils import TensorShardStrategy -except ImportError: - # colossalai > 0.2.8 - from colossalai.zero.legacy import TensorShardStrategy - -clip_grad_norm = 1.0 diff --git a/examples/language/roberta/configs/colossalai_zero.py b/examples/language/roberta/configs/colossalai_zero.py deleted file mode 100644 index bae4c723ccc8..000000000000 --- a/examples/language/roberta/configs/colossalai_zero.py +++ /dev/null @@ -1,37 +0,0 @@ -from colossalai.nn.optimizer import FusedAdam - -try: - from colossalai.zero.shard_utils import TensorShardStrategy -except ImportError: - # colossalai > 0.2.8 - from colossalai.zero.legacy import TensorShardStrategy - -# fp16 = dict( -# mode=AMP_TYPE.TORCH, -# ) - -# seed = 2 -zero = dict(model_config=dict(shard_strategy=TensorShardStrategy(), - reduce_scatter_bucket_size_mb=25, - fp32_reduce_scatter=False, - tensor_placement_policy="cuda", - gradient_predivide_factor=1.0, - reuse_fp16_shard=False), - optimizer_config=dict(gpu_margin_mem_ratio=0.8, - initial_scale=2**5, - min_scale=1, - growth_factor=2, - backoff_factor=0.5, - growth_interval=1000, - hysteresis=2, - max_scale=2**32)) - -# gradient_accumulation = 4 -clip_grad_norm = 1.0 -optimizer = dict( - type=FusedAdam, - lr=0.00015, - weight_decay=1e-2, -) - -# 64433 diff --git a/examples/language/roberta/preprocessing/get_mask.py b/examples/language/roberta/preprocessing/get_mask.py index da297f98e6c9..869ef2cb377c 100644 --- a/examples/language/roberta/preprocessing/get_mask.py +++ b/examples/language/roberta/preprocessing/get_mask.py @@ -163,16 +163,15 @@ def create_masked_lm_predictions(self, tokens): def get_new_segment(self, segment): """ - 输入一句话，返回一句经过处理的话: 为了支持中文全称mask，将被分开的词，将上特殊标记("#")，使得后续处理模块，能够知道哪些字是属于同一个词的。 - :param segment: 一句话 - :return: 一句处理过的话 + Input a sentence, return a processed sentence: In order to support the Chinese whole word mask, the words that are separated will be marked with a special mark ("#"), so that the subsequent processing module can know which words belong to the same word. + :param segment: a sentence """ seq_cws = jieba.lcut(''.join(segment)) seq_cws_dict = {x: 1 for x in seq_cws} new_segment = [] i = 0 while i < len(segment): - if len(self.rec.findall(segment[i])) == 0: # 不是中文的，原文加进去。 + if len(self.rec.findall(segment[i])) == 0: new_segment.append(segment[i]) i += 1 continue diff --git a/examples/language/roberta/preprocessing/sentence_split.py b/examples/language/roberta/preprocessing/sentence_split.py index 231be152b067..f0ed83f90114 100644 --- a/examples/language/roberta/preprocessing/sentence_split.py +++ b/examples/language/roberta/preprocessing/sentence_split.py @@ -10,26 +10,19 @@ import functools def split_sentence(document: str, flag: str = "all", limit: int = 510) -> List[str]: - """ - Args: - document: - flag: Type:str, "all" 中英文标点分句，"zh" 中文标点分句，"en" 英文标点分句 - limit: 默认单句最大长度为510个字符 - Returns: Type:list - """ sent_list = [] try: if flag == "zh": - document = re.sub('(?P([。？！…](?![”’"\'])))', r'\g\n', document) # 单字符断句符 - document = re.sub('(?P([。？！]|…{1,2})[”’"\'])', r'\g\n', document) # 特殊引号 + document = re.sub('(?P([。？！…](?![”’"\'])))', r'\g\n', document) + document = re.sub('(?P([。？！]|…{1,2})[”’"\'])', r'\g\n', document) elif flag == "en": - document = re.sub('(?P([.?!](?![”’"\'])))', r'\g\n', document) # 英文单字符断句符 - document = re.sub('(?P([?!.]["\']))', r'\g\n', document) # 特殊引号 + document = re.sub('(?P([.?!](?![”’"\'])))', r'\g\n', document) + document = re.sub('(?P([?!.]["\']))', r'\g\n', document) # Special quotation marks else: - document = re.sub('(?P([。？！….?!](?![”’"\'])))', r'\g\n', document) # 单字符断句符 + document = re.sub('(?P([。？！….?!](?![”’"\'])))', r'\g\n', document) document = re.sub('(?P(([。？！.!?]|…{1,2})[”’"\']))', r'\g\n', - document) # 特殊引号 + document) # Special quotation marks sent_list_ori = document.splitlines() for sent in sent_list_ori: diff --git a/examples/language/roberta/preprocessing/tokenize_mask.py b/examples/language/roberta/preprocessing/tokenize_mask.py index b33871d5d037..76c74868e1fc 100644 --- a/examples/language/roberta/preprocessing/tokenize_mask.py +++ b/examples/language/roberta/preprocessing/tokenize_mask.py @@ -15,8 +15,8 @@ def get_raw_instance(document, max_sequence_length=512): """ - 获取初步的训练实例，将整段按照max_sequence_length切分成多个部分,并以多个处理好的实例的形式返回。 - :param document: 一整段 + Get the initial training instances, split the whole segment into multiple parts according to the max_sequence_length, and return as multiple processed instances. + :param document: document :param max_sequence_length: :return: a list. each element is a sequence of text """ @@ -26,10 +26,9 @@ def get_raw_instance(document, max_sequence_length=512): sizes = [len(seq) for seq in document] result_list = [] - curr_seq = [] # 当前处理的序列 + curr_seq = [] sz_idx = 0 while sz_idx < len(sizes): - # 当前句子加上新的句子，如果长度小于最大限制，则合并当前句子和新句子；否则即超过了最大限制，那么做为一个新的序列加到目标列表中 if len(curr_seq) + sizes[sz_idx] <= max_sequence_length_allowed: # or len(curr_seq)==0: curr_seq += document[sz_idx] @@ -43,14 +42,13 @@ def get_raw_instance(document, max_sequence_length=512): else: result_list.append(curr_seq) curr_seq = [] - # 对最后一个序列进行处理，如果太短的话，丢弃掉。 + if len(curr_seq) > max_sequence_length_allowed / 2: # /2 result_list.append(curr_seq) - # # 计算总共可以得到多少份 # num_instance=int(len(big_list)/max_sequence_length_allowed)+1 # print("num_instance:",num_instance) - # # 切分成多份，添加到列表中 + # result_list=[] # for j in range(num_instance): # index=j*max_sequence_length_allowed diff --git a/examples/language/roberta/pretraining/arguments.py b/examples/language/roberta/pretraining/arguments.py index 3a9370e00b0c..87fa8dd8a8ae 100644 --- a/examples/language/roberta/pretraining/arguments.py +++ b/examples/language/roberta/pretraining/arguments.py @@ -6,6 +6,30 @@ def parse_args(): parser = colossalai.get_default_parser() + + parser.add_argument( + "--distplan", + type=str, + default='CAI_Gemini', + help="The distributed plan [colossalai, zero1, zero2, torch_ddp, torch_zero].", + ) + parser.add_argument( + "--tp_degree", + type=int, + default=1, + help="Tensor Parallelism Degree. Valid when using colossalai as dist plan.", + ) + parser.add_argument( + "--placement", + type=str, + default='cpu', + help="Placement Policy for Gemini. Valid when using colossalai as dist plan.", + ) + parser.add_argument( + "--shardinit", + action='store_true', + help="Shard the tensors when init the model to shrink peak memory size on the assigned device. Valid when using colossalai as dist plan.", + ) parser.add_argument( '--lr', diff --git a/examples/language/roberta/pretraining/evaluation.py b/examples/language/roberta/pretraining/evaluation.py index 83f94082f6c0..8fc019c121ac 100644 --- a/examples/language/roberta/pretraining/evaluation.py +++ b/examples/language/roberta/pretraining/evaluation.py @@ -5,11 +5,11 @@ from utils.global_vars import get_timers, get_tensorboard_writer from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider -def evaluate(engine, args, logger, global_step): +def evaluate(model, args, logger, global_step, criterion): evaluate_dataset_provider = NvidiaBertDatasetProvider(args, evaluate=True) start_shard = 0 - engine.eval() + model.eval() timers = get_timers() eval_step = 0 eval_loss = 0 @@ -39,9 +39,9 @@ def evaluate(engine, args, logger, global_step): mlm_label = batch_data[3].cuda() # nsp_label = batch_data[5].cuda() - output = engine(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) + output = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) - loss = engine.criterion(output.logits, mlm_label)#prediction_scores + loss = criterion(output.logits, mlm_label)#prediction_scores evaluate_dataset_provider.prefetch_batch() eval_loss += loss.float().item() @@ -67,5 +67,5 @@ def evaluate(engine, args, logger, global_step): logger.info('') evaluate_dataset_provider.release_shard() - engine.train() - return cur_loss + model.train() + return cur_loss \ No newline at end of file diff --git a/examples/language/roberta/pretraining/pretrain_utils.py b/examples/language/roberta/pretraining/pretrain_utils.py index ba17b0f5ee09..54fc2affe632 100644 --- a/examples/language/roberta/pretraining/pretrain_utils.py +++ b/examples/language/roberta/pretraining/pretrain_utils.py @@ -5,7 +5,7 @@ from transformers import BertForPreTraining, RobertaForMaskedLM, RobertaConfig from transformers import GPT2Config, GPT2LMHeadModel from transformers import AutoTokenizer, AutoModelForMaskedLM -from colossalai.nn.optimizer import FusedAdam +from colossalai.nn.optimizer import FusedAdam, HybridAdam from torch.optim import AdamW from colossalai.core import global_context as gpc import torch @@ -83,7 +83,7 @@ def get_optimizer(model, lr): 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] - optimizer = FusedAdam(optimizer_grouped_parameters, lr=lr, betas=[0.9, 0.95]) + optimizer = HybridAdam(optimizer_grouped_parameters, lr=lr, betas=[0.9, 0.95]) return optimizer diff --git a/examples/language/roberta/pretraining/run_pretrain.sh b/examples/language/roberta/pretraining/run_pretrain.sh index 144cd0ab96fd..38fdefe0af8a 100644 --- a/examples/language/roberta/pretraining/run_pretrain.sh +++ b/examples/language/roberta/pretraining/run_pretrain.sh @@ -7,7 +7,6 @@ tensorboard_path="$root_path/tensorboard" log_path="$root_path/exp_log" ckpt_path="$root_path/ckpt" -colossal_config="$root_path/../configs/colossalai_ddp.py" mkdir -p $tensorboard_path mkdir -p $log_path @@ -32,7 +31,6 @@ env OMP_NUM_THREADS=40 colossalai run --hostfile ./hostfile \ --tensorboard_path $tensorboard_path \ --log_path $log_path \ --ckpt_path $ckpt_path \ - --colossal_config $colossal_config \ --log_interval 50 \ --mlm bert \ --wandb \ diff --git a/examples/language/roberta/pretraining/run_pretrain_resume.sh b/examples/language/roberta/pretraining/run_pretrain_resume.sh index a0704cf7c517..351c98d3e9cb 100644 --- a/examples/language/roberta/pretraining/run_pretrain_resume.sh +++ b/examples/language/roberta/pretraining/run_pretrain_resume.sh @@ -7,7 +7,6 @@ tensorboard_path="$root_path/tensorboard" log_path="$root_path/exp_log" ckpt_path="$root_path/ckpt" -colossal_config="$root_path/../configs/colossalai_ddp.py" mkdir -p $tensorboard_path mkdir -p $log_path @@ -32,7 +31,6 @@ env OMP_NUM_THREADS=40 colossalai run --hostfile ./hostfile \ --tensorboard_path $tensorboard_path \ --log_path $log_path \ --ckpt_path $ckpt_path \ - --colossal_config $colossal_config \ --log_interval 50 \ --mlm bert \ --wandb \ diff --git a/examples/language/roberta/pretraining/run_pretraining.py b/examples/language/roberta/pretraining/run_pretraining.py index eef7bb6ad5cd..6e3cae6ec042 100644 --- a/examples/language/roberta/pretraining/run_pretraining.py +++ b/examples/language/roberta/pretraining/run_pretraining.py @@ -4,9 +4,31 @@ from functools import partial import torch +<<<<<<< HEAD +from tqdm import tqdm +import os +import time +from functools import partial +from transformers import AutoTokenizer + +import colossalai +from colossalai.context import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.nn.parallel import GeminiDDP, zero_model_wrapper, zero_optim_wrapper +from colossalai.utils import get_current_device +from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ZeroOptimizer +from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec + +======= +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 from arguments import parse_args from evaluation import evaluate from loss import LossForPretraining +<<<<<<< HEAD + +from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider +======= from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider from pretrain_utils import get_lr_scheduler, get_model, get_optimizer, save_ckpt from tqdm import tqdm @@ -27,6 +49,7 @@ from colossalai.zero.gemini import ChunkManager, ColoInitContext, GeminiManager from colossalai.zero.legacy import ShardedModelV2, ShardedOptimizerV2, ZeroInitContext from colossalai.zero.legacy.shard_utils import TensorShardStrategy +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 def main(): @@ -36,8 +59,13 @@ def main(): tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path) +<<<<<<< HEAD + # os.environ['CUDA_LAUNCH_BLOCKING'] = '1' + +======= os.environ['CUDA_LAUNCH_BLOCKING'] = '1' +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 logger = Logger(os.path.join(args.log_path, launch_time), cuda=torch.cuda.is_available(), debug=args.vscode_debug) if args.vscode_debug: @@ -50,7 +78,11 @@ def main(): args.local_rank = -1 args.log_interval = 1 else: +<<<<<<< HEAD + colossalai.launch_from_torch(config={}) #args.colossal_config +======= colossalai.launch_from_torch(args.colossal_config) # args.colossal_config +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 args.local_rank = int(os.environ["LOCAL_RANK"]) logger.info( f'launch_from_torch, world size: {torch.distributed.get_world_size()} | ' + @@ -61,32 +93,94 @@ def main(): args.tokenizer = tokenizer args.logger = logger set_global_variables(launch_time, args.tensorboard_path) +<<<<<<< HEAD + +======= use_zero = hasattr(gpc.config, 'zero') +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 world_size = torch.distributed.get_world_size() + init_dev = get_current_device() # build model, optimizer and criterion +<<<<<<< HEAD + if args.distplan.startswith("CAI"): + # all param must use the same process group. + world_size = torch.distributed.get_world_size() + shard_pg = ProcessGroup(tp_degree=world_size) if args.shardinit else None + default_dist_spec = ShardSpec([-1], [world_size]) if args.shardinit else None + + if args.shardinit and args.distplan != "CAI_Gemini": + raise RuntimeError("You can only use shardinit with CAI_Gemini") + + # build GPT model + with ColoInitContext(device=get_current_device(), + dtype=torch.half, + default_dist_spec=default_dist_spec, + default_pg=shard_pg): +======= if use_zero: shard_strategy = TensorShardStrategy() with ZeroInitContext(target_device=torch.cuda.current_device(), shard_strategy=shard_strategy, shard_param=True): +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 config, model, numel = get_model(args, logger) - # model = ShardedModelV2(model, shard_strategy, tensor_placement_policy='cpu', reuse_fp16_shard=True) + + # asign running configurations + gemini_config = None + if args.distplan.startswith("CAI_ZeRO"): + optim_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True, verbose=True) + elif args.distplan == "CAI_Gemini": + gemini_config = dict(strict_ddp_mode=args.tp_degree == 1, + device=get_current_device(), + placement_policy=args.placement, + pin_memory=True, + hidden_dim=model.config.hidden_size, + search_range_mb=128) + optim_config = dict(gpu_margin_mem_ratio=0.) + else: + raise RuntimeError + + # build a highly optimized gpu/cpu optimizer + optimizer = get_optimizer(model, lr=args.lr) + + if args.distplan == "CAI_ZeRO1": + zero_stage = 1 + elif args.distplan == "CAI_ZeRO2": + zero_stage = 2 + elif args.distplan == "CAI_Gemini": + zero_stage = 3 + else: + raise RuntimeError + + # wrap your model and optimizer + model = zero_model_wrapper(model, zero_stage, gemini_config) + optimizer = zero_optim_wrapper(model, optimizer, optim_config=optim_config) + + logger.info(get_mem_info(prefix='After init optim, ')) + else: config, model, numel = get_model(args, logger) logger.info("no_zero") + if torch.distributed.get_rank() == 0: os.mkdir(os.path.join(args.ckpt_path, launch_time)) logger.info(f'Model numel: {numel}') get_tflops_func = partial(get_tflops, numel, args.train_micro_batch_size_per_gpu, args.max_seq_length) +<<<<<<< HEAD + + # 144003367 is is the length of the entire dataset + steps_per_epoch = 144003367 // world_size // args.train_micro_batch_size_per_gpu // args.gradient_accumulation_steps // args.refresh_bucket_size #len(dataloader) +======= # len(dataloader) steps_per_epoch = 144003367 // world_size // args.train_micro_batch_size_per_gpu // args.gradient_accumulation_steps // args.refresh_bucket_size +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 total_steps = steps_per_epoch * args.epoch - # build optimizer and lr_scheduler + lr_scheduler = get_lr_scheduler(optimizer, total_steps=total_steps, last_epoch=-1) start_epoch = 0 start_shard = 0 @@ -95,7 +189,6 @@ def main(): assert os.path.exists(args.load_optimizer_lr) o_l_state_dict = torch.load(args.load_optimizer_lr, map_location='cpu') o_l_state_dict['lr_scheduler']['last_epoch'] = o_l_state_dict['lr_scheduler']['last_epoch'] - 1 - optimizer = get_optimizer(model, lr=args.lr) optimizer.load_state_dict(o_l_state_dict['optimizer']) # o_l_state_dict['lr_scheduler']['last_epoch'] lr_scheduler = get_lr_scheduler(optimizer, @@ -105,33 +198,38 @@ def main(): for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.cuda(f"cuda:{torch.cuda.current_device()}") - # if you want delete the above three code, have to move the model to gpu, because in optimizer.step() + # if you want delete the above three code, must move the model to gpu. Because in optimizer.step() lr_scheduler.load_state_dict(o_l_state_dict['lr_scheduler']) start_epoch = o_l_state_dict['epoch'] start_shard = o_l_state_dict['shard'] + 1 # global_step = o_l_state_dict['global_step'] + 1 +<<<<<<< HEAD + logger.info(f'resume from epoch {start_epoch} shard {start_shard} step {lr_scheduler.last_epoch} lr {lr_scheduler.get_last_lr()[0]}') +======= logger.info( f'resume from epoch {start_epoch} shard {start_shard} step {lr_scheduler.last_epoch} lr {lr_scheduler.get_last_lr()[0]}' ) else: optimizer = get_optimizer(model, lr=args.lr) lr_scheduler = get_lr_scheduler(optimizer, total_steps=total_steps, last_epoch=-1) +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 - # optimizer = gpc.config.optimizer.pop('type')( - # model.parameters(), **gpc.config.optimizer) - # optimizer = ShardedOptimizerV2(model, optimizer, initial_scale=2**5) criterion = LossForPretraining(config.vocab_size) # build dataloader pretrain_dataset_provider = NvidiaBertDatasetProvider(args) +<<<<<<< HEAD + +======= # initialize with colossalai engine, _, _, lr_scheduelr = colossalai.initialize(model=model, optimizer=optimizer, criterion=criterion, lr_scheduler=lr_scheduler) +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 logger.info(get_mem_info(prefix='After init model, ')) best_loss = None @@ -156,9 +254,15 @@ def main(): else: iterator_data = enumerate(dataset_iterator) +<<<<<<< HEAD + model.train() + + for step, batch_data in iterator_data: +======= engine.train() for step, batch_data in iterator_data: +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 # batch_data = pretrain_dataset_provider.get_batch(batch_index) input_ids = batch_data[0].cuda(f"cuda:{torch.cuda.current_device()}") @@ -167,18 +271,31 @@ def main(): mlm_label = batch_data[3].cuda(f"cuda:{torch.cuda.current_device()}") # nsp_label = batch_data[5].cuda() +<<<<<<< HEAD + output = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) + + loss = criterion(output.logits, mlm_label) +======= output = engine(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) loss = engine.criterion(output.logits, mlm_label) +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 pretrain_dataset_provider.prefetch_batch() - engine.backward(loss) + optimizer.backward(loss) train_loss += loss.float().item() # if (step + 1) % args.accumulation_step == 0: +<<<<<<< HEAD + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + +======= engine.step() lr_scheduelr.step() engine.zero_grad() +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 global_step += 1 if global_step % args.log_interval == 0 and global_step != 0 \ @@ -189,7 +306,7 @@ def main(): numel, args, config, elapsed_time, global_step, world_size) cur_loss = train_loss / args.log_interval - current_lr = lr_scheduelr.get_last_lr()[0] + current_lr = lr_scheduler.get_last_lr()[0] log_str = f'| epoch: {epoch} | shard: {shard} | step: {global_step} | lr {current_lr:.7f} | elapsed_time: {elapsed_time / 60 :.3f} minutes ' + \ f'| mins/batch: {elapsed_time_per_iteration :.3f} seconds | loss: {cur_loss:.7f} | ppl: {math.exp(cur_loss):.3f} | TFLOPS: {get_tflops_func(elapsed_time_per_iteration):.3f} or {tflops:.3f}' logger.info(log_str, print_=False) @@ -209,11 +326,18 @@ def main(): logger.info(f'epoch {epoch} shard {shard} has cost {timers("shard_time").elapsed() / 60 :.3f} mins') logger.info('*' * 100) +<<<<<<< HEAD + eval_loss += evaluate(model, args, logger, global_step, criterion) + save_ckpt(model, optimizer, lr_scheduler, os.path.join(args.ckpt_path, launch_time, f'epoch-{epoch}_shard-{shard}_' + launch_time), epoch, shard, global_step) + + +======= eval_loss += evaluate(engine, args, logger, global_step) save_ckpt(engine.model, optimizer, lr_scheduelr, os.path.join(args.ckpt_path, launch_time, f'epoch-{epoch}_shard-{shard}_' + launch_time), epoch, shard, global_step) +>>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 eval_loss /= len(os.listdir(args.data_path_prefix)) logger.info( f'epoch {epoch} | shard_length {len(os.listdir(args.data_path_prefix))} | elapsed_time: {timers("epoch_time").elapsed() / 60 :.3f} mins' diff --git a/examples/language/roberta/requirements.txt b/examples/language/roberta/requirements.txt index 137a69e80498..d351f362f3f7 100644 --- a/examples/language/roberta/requirements.txt +++ b/examples/language/roberta/requirements.txt @@ -1,2 +1,7 @@ colossalai >= 0.1.12 torch >= 1.8.1 +tqdm +tensorboard +numpy +h5py +wandb \ No newline at end of file From 8f2c55f9c99012e4cbfefa422ab2e91dfece447e Mon Sep 17 00:00:00 2001 From: mandoxzhang <111039218+mandoxzhang@users.noreply.github.com> Date: Fri, 7 Apr 2023 11:33:32 +0800 Subject: [PATCH 116/413] [example] remove redundant texts & update roberta (#3493) * update roberta example * update roberta example * modify conflict & update roberta --- .../roberta/pretraining/run_pretraining.py | 93 ------------------- 1 file changed, 93 deletions(-) diff --git a/examples/language/roberta/pretraining/run_pretraining.py b/examples/language/roberta/pretraining/run_pretraining.py index 6e3cae6ec042..a283c44cadbf 100644 --- a/examples/language/roberta/pretraining/run_pretraining.py +++ b/examples/language/roberta/pretraining/run_pretraining.py @@ -4,7 +4,6 @@ from functools import partial import torch -<<<<<<< HEAD from tqdm import tqdm import os import time @@ -20,15 +19,9 @@ from colossalai.zero import ZeroOptimizer from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec -======= ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 from arguments import parse_args from evaluation import evaluate from loss import LossForPretraining -<<<<<<< HEAD - -from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider -======= from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider from pretrain_utils import get_lr_scheduler, get_model, get_optimizer, save_ckpt from tqdm import tqdm @@ -37,20 +30,6 @@ from utils.global_vars import get_tensorboard_writer, get_timers, set_global_variables from utils.logger import Logger -import colossalai -import colossalai.nn as col_nn -from colossalai.context import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.parallel import ZeroDDP -from colossalai.tensor import ProcessGroup -from colossalai.utils import get_current_device -from colossalai.zero import ZeroOptimizer -from colossalai.zero.gemini import ChunkManager, ColoInitContext, GeminiManager -from colossalai.zero.legacy import ShardedModelV2, ShardedOptimizerV2, ZeroInitContext -from colossalai.zero.legacy.shard_utils import TensorShardStrategy ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 - def main(): @@ -59,13 +38,8 @@ def main(): tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path) -<<<<<<< HEAD # os.environ['CUDA_LAUNCH_BLOCKING'] = '1' -======= - os.environ['CUDA_LAUNCH_BLOCKING'] = '1' - ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 logger = Logger(os.path.join(args.log_path, launch_time), cuda=torch.cuda.is_available(), debug=args.vscode_debug) if args.vscode_debug: @@ -78,11 +52,7 @@ def main(): args.local_rank = -1 args.log_interval = 1 else: -<<<<<<< HEAD colossalai.launch_from_torch(config={}) #args.colossal_config -======= - colossalai.launch_from_torch(args.colossal_config) # args.colossal_config ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 args.local_rank = int(os.environ["LOCAL_RANK"]) logger.info( f'launch_from_torch, world size: {torch.distributed.get_world_size()} | ' + @@ -93,17 +63,11 @@ def main(): args.tokenizer = tokenizer args.logger = logger set_global_variables(launch_time, args.tensorboard_path) -<<<<<<< HEAD -======= - - use_zero = hasattr(gpc.config, 'zero') ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 world_size = torch.distributed.get_world_size() init_dev = get_current_device() # build model, optimizer and criterion -<<<<<<< HEAD if args.distplan.startswith("CAI"): # all param must use the same process group. world_size = torch.distributed.get_world_size() @@ -118,13 +82,6 @@ def main(): dtype=torch.half, default_dist_spec=default_dist_spec, default_pg=shard_pg): -======= - if use_zero: - shard_strategy = TensorShardStrategy() - with ZeroInitContext(target_device=torch.cuda.current_device(), shard_strategy=shard_strategy, - shard_param=True): - ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 config, model, numel = get_model(args, logger) # asign running configurations @@ -170,14 +127,9 @@ def main(): logger.info(f'Model numel: {numel}') get_tflops_func = partial(get_tflops, numel, args.train_micro_batch_size_per_gpu, args.max_seq_length) -<<<<<<< HEAD # 144003367 is is the length of the entire dataset steps_per_epoch = 144003367 // world_size // args.train_micro_batch_size_per_gpu // args.gradient_accumulation_steps // args.refresh_bucket_size #len(dataloader) -======= - # len(dataloader) - steps_per_epoch = 144003367 // world_size // args.train_micro_batch_size_per_gpu // args.gradient_accumulation_steps // args.refresh_bucket_size ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 total_steps = steps_per_epoch * args.epoch lr_scheduler = get_lr_scheduler(optimizer, total_steps=total_steps, last_epoch=-1) @@ -204,32 +156,14 @@ def main(): start_epoch = o_l_state_dict['epoch'] start_shard = o_l_state_dict['shard'] + 1 # global_step = o_l_state_dict['global_step'] + 1 -<<<<<<< HEAD logger.info(f'resume from epoch {start_epoch} shard {start_shard} step {lr_scheduler.last_epoch} lr {lr_scheduler.get_last_lr()[0]}') -======= - logger.info( - f'resume from epoch {start_epoch} shard {start_shard} step {lr_scheduler.last_epoch} lr {lr_scheduler.get_last_lr()[0]}' - ) - else: - optimizer = get_optimizer(model, lr=args.lr) - lr_scheduler = get_lr_scheduler(optimizer, total_steps=total_steps, last_epoch=-1) ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 criterion = LossForPretraining(config.vocab_size) # build dataloader pretrain_dataset_provider = NvidiaBertDatasetProvider(args) -<<<<<<< HEAD -======= - # initialize with colossalai - engine, _, _, lr_scheduelr = colossalai.initialize(model=model, - optimizer=optimizer, - criterion=criterion, - lr_scheduler=lr_scheduler) - ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 logger.info(get_mem_info(prefix='After init model, ')) best_loss = None @@ -254,15 +188,9 @@ def main(): else: iterator_data = enumerate(dataset_iterator) -<<<<<<< HEAD model.train() for step, batch_data in iterator_data: -======= - engine.train() - - for step, batch_data in iterator_data: ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 # batch_data = pretrain_dataset_provider.get_batch(batch_index) input_ids = batch_data[0].cuda(f"cuda:{torch.cuda.current_device()}") @@ -271,31 +199,18 @@ def main(): mlm_label = batch_data[3].cuda(f"cuda:{torch.cuda.current_device()}") # nsp_label = batch_data[5].cuda() -<<<<<<< HEAD output = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) loss = criterion(output.logits, mlm_label) -======= - output = engine(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) - - loss = engine.criterion(output.logits, mlm_label) ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 pretrain_dataset_provider.prefetch_batch() optimizer.backward(loss) train_loss += loss.float().item() # if (step + 1) % args.accumulation_step == 0: -<<<<<<< HEAD optimizer.step() lr_scheduler.step() optimizer.zero_grad() -======= - engine.step() - lr_scheduelr.step() - engine.zero_grad() - ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 global_step += 1 if global_step % args.log_interval == 0 and global_step != 0 \ @@ -326,18 +241,10 @@ def main(): logger.info(f'epoch {epoch} shard {shard} has cost {timers("shard_time").elapsed() / 60 :.3f} mins') logger.info('*' * 100) -<<<<<<< HEAD eval_loss += evaluate(model, args, logger, global_step, criterion) save_ckpt(model, optimizer, lr_scheduler, os.path.join(args.ckpt_path, launch_time, f'epoch-{epoch}_shard-{shard}_' + launch_time), epoch, shard, global_step) -======= - eval_loss += evaluate(engine, args, logger, global_step) - save_ckpt(engine.model, optimizer, lr_scheduelr, - os.path.join(args.ckpt_path, launch_time, f'epoch-{epoch}_shard-{shard}_' + launch_time), epoch, - shard, global_step) - ->>>>>>> 52a933e17509c71811e919b165de38cb3d5d6d41 eval_loss /= len(os.listdir(args.data_path_prefix)) logger.info( f'epoch {epoch} | shard_length {len(os.listdir(args.data_path_prefix))} | elapsed_time: {timers("epoch_time").elapsed() / 60 :.3f} mins' From a7ca2972810ac784754f0f31e21324687c03b324 Mon Sep 17 00:00:00 2001 From: gongenlei Date: Fri, 7 Apr 2023 11:39:09 +0800 Subject: [PATCH 117/413] [coati] Fix LlamaCritic (#3475) * mv LlamaForCausalLM to LlamaModel * rm unused imports --------- Co-authored-by: gongenlei --- applications/Chat/coati/models/llama/llama_critic.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/applications/Chat/coati/models/llama/llama_critic.py b/applications/Chat/coati/models/llama/llama_critic.py index cd565031e112..dd9e5e7bfa1a 100644 --- a/applications/Chat/coati/models/llama/llama_critic.py +++ b/applications/Chat/coati/models/llama/llama_critic.py @@ -1,8 +1,7 @@ from typing import Optional -import torch import torch.nn as nn -from transformers import AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM +from transformers import LlamaConfig, LlamaModel from ..base import Critic @@ -28,11 +27,11 @@ def __init__(self, **kwargs) -> None: if pretrained is not None: - model = LlamaForCausalLM.from_pretrained(pretrained) + model = LlamaModel.from_pretrained(pretrained) elif config is not None: - model = LlamaForCausalLM(config) + model = LlamaModel(config) else: - model = LlamaForCausalLM(LlamaConfig()) + model = LlamaModel(LlamaConfig()) if checkpoint: model.gradient_checkpointing_enable() From bcf0cbcbe78daeb7b4e732505e66b45a774a1460 Mon Sep 17 00:00:00 2001 From: YH <100389977+yhna940@users.noreply.github.com> Date: Mon, 10 Apr 2023 12:11:28 +0900 Subject: [PATCH 118/413] [doc] Add docs for clip args in zero optim (#3504) --- colossalai/zero/gemini/gemini_optimizer.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/colossalai/zero/gemini/gemini_optimizer.py b/colossalai/zero/gemini/gemini_optimizer.py index 8e0237ddc7bc..8940ab9a3251 100644 --- a/colossalai/zero/gemini/gemini_optimizer.py +++ b/colossalai/zero/gemini/gemini_optimizer.py @@ -46,12 +46,15 @@ class ZeroOptimizer(ColossalaiOptimizer): Defaults to 0.0. initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32. min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1. - growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2. - backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5. - growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000. - hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2. - max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32. - """ + growth_factor (float, optional): Growth_factor used by DynamicGradScaler. Defaults to 2. + backoff_factor (float, optional): Backoff_factor used by DynamicGradScaler. Defaults to 0.5. + growth_interval (float, optional): Growth_interval used by DynamicGradScaler. Defaults to 1000. + hysteresis (float, optional): Hysteresis used by DynamicGradScaler. Defaults to 2. + max_scale (int, optional): Max_scale used by DynamicGradScaler. Defaults to 2**32. + clipping_norm (float, optional): The norm value used to clip gradient. Defaults to 0.0. + norm_type (float, optional): The type of norm used for gradient clipping. Currently, only L2-norm (norm_type=2.0) + is supported in ZeroOptimizer. Defaults to 2.0. + """ def __init__(self, optim: Optimizer, From 635d0a1baff3e8a59dd5a6ff00f992ad00b5a660 Mon Sep 17 00:00:00 2001 From: NatalieC323 <127177614+NatalieC323@users.noreply.github.com> Date: Mon, 10 Apr 2023 14:36:39 +0800 Subject: [PATCH 119/413] [Chat Community] Update README.md (fixed#3487) (#3506) * Update README.md * Update README.md * Update README.md * Update README.md --------- Co-authored-by: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> --- .../Chat/examples/community/README.md | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/applications/Chat/examples/community/README.md b/applications/Chat/examples/community/README.md index 5e3f47db37b3..905418892611 100644 --- a/applications/Chat/examples/community/README.md +++ b/applications/Chat/examples/community/README.md @@ -1 +1,23 @@ # Community Examples +--- +We are thrilled to announce the latest updates to ColossalChat, an open-source solution for cloning ChatGPT with a complete RLHF (Reinforcement Learning with Human Feedback) pipeline. + +As Colossal-AI undergoes major updates, we are actively maintaining ColossalChat to stay aligned with the project's progress. With the introduction of Community-driven example, we aim to create a collaborative platform for developers to contribute exotic features built on top of ColossalChat. + +## Community Example + +Community-driven Examples is an initiative that allows users to contribute their own examples to the ColossalChat package, fostering a sense of community and making it easy for others to access and benefit from shared work. The primary goal with community-driven examples is to have a community-maintained collection of diverse and exotic functionalities built on top of the ColossalChat package, which is powered by the Colossal-AI project and its Coati module (ColossalAI Talking Intelligence). + +For more information about community pipelines, please have a look at this [issue](https://github.com/hpcaitech/ColossalAI/issues/3487). + +## Community Examples + +Community examples consist of both inference and training examples that have been added by the community. Please have a look at the following table to get an overview of all community examples. Click on the Code Example to get a copy-and-paste ready code example that you can try out. If a community doesn't work as expected, please open an issue and ping the author on it. + +| Example | Description | Code Example | Colab | Author | +|:---------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------:| +| Peft | Adding Peft support for SFT and Prompts model training | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/peft) | - | [YY Lin](https://github.com/yynil) | +|...|...|...|...|...| + +### How to get involved +To join our community-driven initiative, please visit the [ColossalChat GitHub repository](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples), review the provided information, and explore the codebase. To contribute, create a new issue outlining your proposed feature or enhancement, and our team will review and provide feedback. We look forward to collaborating with you on this exciting project! From 0c0455700fbea15a1ad663b890278dbdb0d581cd Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Mon, 10 Apr 2023 17:37:16 +0800 Subject: [PATCH 120/413] [doc] add requirement and highlight application (#3516) * [doc] add requirement and highlight application * [doc] link example and application --- README.md | 182 +++++++++--------- applications/README.md | 4 +- docs/README-zh-Hans.md | 181 ++++++++--------- docs/source/en/get_started/installation.md | 2 + .../zh-Hans/get_started/installation.md | 2 + examples/README.md | 2 + 6 files changed, 192 insertions(+), 181 deletions(-) diff --git a/README.md b/README.md index 8342a9fa0c9e..79f733122cb3 100644 --- a/README.md +++ b/README.md @@ -38,6 +38,14 @@
    • Why Colossal-AI
    • Features
      • +
    • + Colossal-AI for Real World Applications + +
    • Parallel Training Demo -
      • -
    • - Colossal-AI for Real World Applications -
    • Installation @@ -120,6 +120,88 @@ distributed training and inference in a few lines. - Inference - [Energon-AI](https://github.com/hpcaitech/EnergonAI) +

      (back to top)

      + +## Colossal-AI in the Real World + +### ColossalChat + + + +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[demo]](https://chat.colossalai.org) + +

      + +

      + +- Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference + +

      + +

      + +- Up to 10.3x growth in model capacity on one GPU +- A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU) + +

      + +

      + +- Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU +- Keep at a sufficiently high running speed + +

      (back to top)

      + + +### AIGC +Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) and [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion). +

      + +

      + +- [Training](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060). + +

      + +

      + +- [DreamBooth Fine-tuning](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): Personalize your model using just 3-5 images of the desired subject. + +

      + +

      + +- [Inference](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce inference GPU memory consumption by 2.5x. + + +

      (back to top)

      + +### Biomedicine +Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/) + +

      + +

      + +- [FastFold](https://github.com/hpcaitech/FastFold): Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues. + +

      + +

      + +- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3x inference acceleration and 39% cost reduce. + +

      + +

      + +- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): accelerating structure prediction of protein monomers and multimer by 11x. + +

      (back to top)

      ## Parallel Training Demo @@ -213,88 +295,6 @@ Please visit our [documentation](https://www.colossalai.org/) and [examples](htt - [BLOOM](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): Reduce hardware deployment costs of 176-billion-parameter BLOOM by more than 10 times. -

      (back to top)

      - -## Colossal-AI in the Real World - -### ColossalChat - - - -[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[demo]](https://chat.colossalai.org) - -

      - -

      - -- Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference - -

      - -

      - -- Up to 10.3x growth in model capacity on one GPU -- A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU) - -

      - -

      - -- Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU -- Keep at a sufficiently high running speed - -

      (back to top)

      - - -### AIGC -Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) and [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion). -

      - -

      - -- [Training](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060). - -

      - -

      - -- [DreamBooth Fine-tuning](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): Personalize your model using just 3-5 images of the desired subject. - -

      - -

      - -- [Inference](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce inference GPU memory consumption by 2.5x. - - -

      (back to top)

      - -### Biomedicine -Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/) - -

      - -

      - -- [FastFold](https://github.com/hpcaitech/FastFold): Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues. - -

      - -

      - -- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3x inference acceleration and 39% cost reduce. - -

      - -

      - -- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): accelerating structure prediction of protein monomers and multimer by 11x. - -

      (back to top)

      ## Installation @@ -303,6 +303,8 @@ Requirements: - PyTorch >= 1.11 (PyTorch 2.x in progress) - Python >= 3.7 - CUDA >= 11.0 +- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher) +- Linux OS If you encounter any problem with installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository. diff --git a/applications/README.md b/applications/README.md index b6bde313e54d..a9ea517e9a63 100644 --- a/applications/README.md +++ b/applications/README.md @@ -7,6 +7,8 @@ The list of applications include: - [X] [Chatbot](./Chat/README.md) - [ ] Stable Diffusion - [ ] Dreambooth - +- [X] [FastFold](https://github.com/hpcaitech/FastFold): Optimizing AlphaFold (Biomedicine) Training and Inference on GPU Clusters > Please note that the `Chatbot` application is migrated from the original `ChatGPT` folder. + +You can find more example code for the base model and functions in the [Examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) directory. \ No newline at end of file diff --git a/docs/README-zh-Hans.md b/docs/README-zh-Hans.md index f43a5953022d..daa42412cc3a 100644 --- a/docs/README-zh-Hans.md +++ b/docs/README-zh-Hans.md @@ -38,6 +38,14 @@
      • 为何选择 Colossal-AI
      • 特点
        • +
      • + Colossal-AI 成功案例 + +
      • 并行训练样例展示
        • -
      • - Colossal-AI 成功案例 - -
      • 安装
          @@ -117,8 +117,88 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的

          (返回顶端)

          -## 并行训练样例展示 +## Colossal-AI 成功案例 +### ColossalChat + + + +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[博客]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[在线样例]](https://chat.colossalai.org) + +

          + +

          + +- 最高可提升单机训练速度7.73倍，单卡推理速度1.42倍 + +

          + +

          + +- 单卡模型容量最多提升10.3倍 +- 最小demo训练流程最低仅需1.62GB显存 (任意消费级GPU) + +

          + +

          + +- 提升单卡的微调模型容量3.7倍 +- 同时保持高速运行 + +

          (back to top)

          + +### AIGC +加速AIGC(AI内容生成)模型，如[Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) 和 [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion) + +

          + +

          +- [训练](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 减少5.6倍显存消耗，硬件成本最高降低46倍(从A100到RTX3060) + +

          + +

          + +- [DreamBooth微调](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): 仅需3-5张目标主题图像个性化微调 + +

          + +

          + +- [推理](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): GPU推理显存消耗降低2.5倍 + + +

          (返回顶端)

          + +### 生物医药 + +加速 [AlphaFold](https://alphafold.ebi.ac.uk/) 蛋白质结构预测 + +

          + +

          + +- [FastFold](https://github.com/hpcaitech/FastFold): 加速AlphaFold训练与推理、数据前处理、推理序列长度超过10000残基 + +

          + +

          + +- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3倍推理加速和39%成本节省 + +

          + +

          + +- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): 11倍加速蛋白质单体与复合物结构预测 + +

          (返回顶端)

          + +## 并行训练样例展示 ### GPT-3

          @@ -213,87 +293,6 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的

          (返回顶端)

          -## Colossal-AI 成功案例 -### ColossalChat - - - -[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[博客]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[在线样例]](https://chat.colossalai.org) - -

          - -

          - -- 最高可提升单机训练速度7.73倍，单卡推理速度1.42倍 - -

          - -

          - -- 单卡模型容量最多提升10.3倍 -- 最小demo训练流程最低仅需1.62GB显存 (任意消费级GPU) - -

          - -

          - -- 提升单卡的微调模型容量3.7倍 -- 同时保持高速运行 - -

          (back to top)

          - -### AIGC -加速AIGC(AI内容生成)模型，如[Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) 和 [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion) - -

          - -

          - -- [训练](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 减少5.6倍显存消耗，硬件成本最高降低46倍(从A100到RTX3060) - -

          - -

          - -- [DreamBooth微调](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): 仅需3-5张目标主题图像个性化微调 - -

          - -

          - -- [推理](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): GPU推理显存消耗降低2.5倍 - - -

          (返回顶端)

          - -### 生物医药 - -加速 [AlphaFold](https://alphafold.ebi.ac.uk/) 蛋白质结构预测 - -

          - -

          - -- [FastFold](https://github.com/hpcaitech/FastFold): 加速AlphaFold训练与推理、数据前处理、推理序列长度超过10000残基 - -

          - -

          - -- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3倍推理加速和39%成本节省 - -

          - -

          - -- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): 11倍加速蛋白质单体与复合物结构预测 - -

          (返回顶端)

          - ## 安装 环境要求: @@ -301,6 +300,8 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的 - PyTorch >= 1.11 (PyTorch 2.x 正在适配中) - Python >= 3.7 - CUDA >= 11.0 +- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher) +- Linux OS 如果你遇到安装问题，可以向本项目 [反馈](https://github.com/hpcaitech/ColossalAI/issues/new/choose)。 diff --git a/docs/source/en/get_started/installation.md b/docs/source/en/get_started/installation.md index 672fd8ae03a4..290879219074 100644 --- a/docs/source/en/get_started/installation.md +++ b/docs/source/en/get_started/installation.md @@ -4,6 +4,8 @@ Requirements: - PyTorch >= 1.11 (PyTorch 2.x in progress) - Python >= 3.7 - CUDA >= 11.0 +- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher) +- Linux OS If you encounter any problem about installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository. diff --git a/docs/source/zh-Hans/get_started/installation.md b/docs/source/zh-Hans/get_started/installation.md index 7a9b20255e77..72f85393814f 100755 --- a/docs/source/zh-Hans/get_started/installation.md +++ b/docs/source/zh-Hans/get_started/installation.md @@ -5,6 +5,8 @@ - PyTorch >= 1.11 (PyTorch 2.x 正在适配中) - Python >= 3.7 - CUDA >= 11.0 +- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher) +- Linux OS 如果你遇到安装问题，可以向本项目 [反馈](https://github.com/hpcaitech/ColossalAI/issues/new/choose)。 diff --git a/examples/README.md b/examples/README.md index 710ced101768..dd5e7b10ae66 100644 --- a/examples/README.md +++ b/examples/README.md @@ -12,6 +12,8 @@ This folder provides several examples accelerated by Colossal-AI. The `tutorial` folder is for everyone to quickly try out the different features in Colossal-AI. Other folders such as `images` and `language` include a wide range of deep learning tasks and applications. +You can find applications such as Chatbot, Stable Diffusion and Biomedicine in the [Applications](https://github.com/hpcaitech/ColossalAI/tree/main/applications) directory. + ## Folder Structure ```text From 990d4c3e4e433fe08c3fd7f3a86f678bd0381f86 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Mon, 10 Apr 2023 17:52:24 +0800 Subject: [PATCH 121/413] [doc] hide diffusion in application path (#3519) - [ ] Stable Diffusion - [ ] Dreambooth It's easy for users to think that we don't support them yet. Add them after migrating them from example to application https://github.com/hpcaitech/ColossalAI/tree/main/examples/images --- applications/README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/applications/README.md b/applications/README.md index a9ea517e9a63..cd0435aae199 100644 --- a/applications/README.md +++ b/applications/README.md @@ -5,10 +5,8 @@ This directory contains the applications that are powered by Colossal-AI. The list of applications include: - [X] [Chatbot](./Chat/README.md) -- [ ] Stable Diffusion -- [ ] Dreambooth - [X] [FastFold](https://github.com/hpcaitech/FastFold): Optimizing AlphaFold (Biomedicine) Training and Inference on GPU Clusters > Please note that the `Chatbot` application is migrated from the original `ChatGPT` folder. -You can find more example code for the base model and functions in the [Examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) directory. \ No newline at end of file +You can find more example code for base models and functions in the [Examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) directory. From 89fd10a1c9189c7961c7750309a94e1ae6b623d4 Mon Sep 17 00:00:00 2001 From: ver217 Date: Mon, 10 Apr 2023 19:00:13 +0800 Subject: [PATCH 122/413] [chat] add zero2 cpu strategy for sft training (#3520) --- applications/Chat/examples/train_sft.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index c0ac7b177694..22f70e485843 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -35,6 +35,8 @@ def train(args): strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') elif args.strategy == 'colossalai_zero2': strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + elif args.strategy == 'colossalai_zero2_cpu': + strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') else: raise ValueError(f'Unsupported strategy "{args.strategy}"') @@ -168,7 +170,7 @@ def train(args): if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_zero2_cpu'], default='naive') parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) From e6a132a449d10489e65544e77f49190e2817b587 Mon Sep 17 00:00:00 2001 From: zhang-yi-chi <673865549@qq.com> Date: Tue, 11 Apr 2023 09:54:59 +0800 Subject: [PATCH 123/413] [chat]: add vf_coef argument for PPOTrainer (#3318) --- applications/Chat/coati/models/loss.py | 2 +- applications/Chat/coati/trainer/ppo.py | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/applications/Chat/coati/models/loss.py b/applications/Chat/coati/models/loss.py index 7fc437d90fdb..926c6e2a4e41 100644 --- a/applications/Chat/coati/models/loss.py +++ b/applications/Chat/coati/models/loss.py @@ -65,7 +65,7 @@ def forward(self, surr2 = (values - reward)**2 loss = torch.max(surr1, surr2) loss = loss.mean() - return loss + return 0.5 * loss class PPOPtxActorLoss(nn.Module): diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index 2b0cfcc16f24..d58e437e6e61 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -32,6 +32,7 @@ class PPOTrainer(Trainer): buffer_limit (int, defaults to 0): the max_size limitaiton of replay buffer buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu eps_clip (float, defaults to 0.2): the clip coefficient of policy loss + vf_coef (float, defaults to 1.0): the coefficient of value loss value_clip (float, defaults to 0.4): the clip coefficient of value loss experience_batch_size (int, defaults to 8): the batch size to use for experience generation max_epochs (int, defaults to 1): the number of epochs of training process @@ -56,6 +57,7 @@ def __init__(self, buffer_limit: int = 0, buffer_cpu_offload: bool = True, eps_clip: float = 0.2, + vf_coef: float = 1.0, value_clip: float = 0.4, experience_batch_size: int = 8, max_epochs: int = 1, @@ -74,6 +76,7 @@ def __init__(self, self.actor_loss_fn = PolicyLoss(eps_clip) self.critic_loss_fn = ValueLoss(value_clip) + self.vf_coef = vf_coef self.ptx_loss_fn = nn.CrossEntropyLoss(ignore_index=-100) self.ptx_coef = ptx_coef self.actor_optim = actor_optim @@ -112,6 +115,7 @@ def training_step(self, experience: Experience) -> Dict[str, float]: experience.values, experience.reward, action_mask=experience.action_mask) + critic_loss = critic_loss * self.vf_coef self.strategy.backward(critic_loss, self.critic, self.critic_optim) self.strategy.optimizer_step(self.critic_optim) self.critic_optim.zero_grad() From 7182ac2a04f6ddcade369f67bb78fe961982cde5 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Wed, 12 Apr 2023 15:47:09 +0800 Subject: [PATCH 124/413] [chat]add examples of training with limited resources in chat readme (#3536) Co-authored-by: Yuanchen Xu --- applications/Chat/README.md | 54 +++++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 8f22084953ba..e3b605d9b796 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -28,6 +28,7 @@ - [Limitation of dataset](#limitation-of-dataset) - [FAQ](#faq) - [How to save/load checkpoint](#how-to-saveload-checkpoint) + - [How to train with limited resources](#how-to-train-with-limited-resources) - [The Plan](#the-plan) - [Real-time progress](#real-time-progress) - [Invitation to open-source contribution](#invitation-to-open-source-contribution) @@ -324,6 +325,59 @@ trainer.fit() trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) ``` +### How to train with limited resources + +Here are some examples that can allow you to train a 7B model on a single or multiple consumer-grade GPUs. + +If you only have a single 24G GPU, you can use the following script. `batch_size` and `lora_rank` are the most important parameters to successfully train the model. +``` +torchrun --standalone --nproc_per_node=1 train_sft.py \ + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy naive \ + --log_interval 10 \ + --save_path /path/to/Coati-7B \ + --dataset /path/to/data.json \ + --batch_size 1 \ + --accimulation_steps 8 \ + --lr 2e-5 \ + --max_datasets_size 512 \ + --max_epochs 1 \ + --lora_rank 16 \ +``` + +`colossalai_gemini` strategy can enable a single 24G GPU to train the whole model without using LoRA if you have sufficient CPU memory. You can use the following script. +``` +torchrun --standalone --nproc_per_node=1 train_sft.py \ + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_gemini \ + --log_interval 10 \ + --save_path /path/to/Coati-7B \ + --dataset /path/to/data.json \ + --batch_size 1 \ + --accimulation_steps 8 \ + --lr 2e-5 \ + --max_datasets_size 512 \ + --max_epochs 1 \ +``` + +If you have 4x32 GB GPUs, you can even train the whole 7B model using our `colossalai_zero2_cpu` strategy! The script is given as follows. +``` +torchrun --standalone --nproc_per_node=4 train_sft.py \ + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2_cpu \ + --log_interval 10 \ + --save_path /path/to/Coati-7B \ + --dataset /path/to/data.json \ + --batch_size 1 \ + --accimulation_steps 8 \ + --lr 2e-5 \ + --max_datasets_size 512 \ + --max_epochs 1 \ +``` + ## The Plan - [x] implement PPO fine-tuning From 366a035552ff62d5f3dd9750bc9d263c2aa60dbc Mon Sep 17 00:00:00 2001 From: jiangmingyan <37931082+jiangmingyan@users.noreply.github.com> Date: Wed, 12 Apr 2023 16:02:17 +0800 Subject: [PATCH 125/413] [checkpoint] Shard saved checkpoint need to be compatible with the naming format of hf checkpoint files (#3479) * [checkpoint] support huggingface style sharded checkpoint, to be compatible with hf file naming format * [checkpoint] support huggingface style sharded checkpoint, to be compatible with hf file naming format * [checkpoint] Shard saved checkpoint add 'variant' field to customize filename * [checkpoint] Shard saved checkpoint add 'variant' field to customize filename * [checkpoint] Shard saved checkpoint add 'variant' field to customize filename * [checkpoint] Shard saved checkpoint add 'variant' field to customize filename --------- Co-authored-by: luchen Co-authored-by: luchen --- .../checkpoint_io/checkpoint_io_base.py | 9 ++++---- .../checkpoint_io/general_checkpoint_io.py | 11 +++++++--- colossalai/checkpoint_io/utils.py | 21 ++++++++++++++----- 3 files changed, 29 insertions(+), 12 deletions(-) diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py index b91b00831e52..3f8b0b0a6b47 100644 --- a/colossalai/checkpoint_io/checkpoint_io_base.py +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -1,6 +1,7 @@ from abc import ABC, abstractmethod from pathlib import Path from typing import Union +from typing import Optional import torch import torch.nn as nn @@ -104,7 +105,7 @@ def save_model(self, checkpoint: str, shard: bool = False, gather_dtensor: bool = True, - prefix: str = None, + variant: str = None, size_per_shard: int = 1024, use_safetensors: bool = False): """ @@ -129,7 +130,7 @@ def save_model(self, multiple files. The model shards will be specificed by a `model.index.json` file. When shard = True, please ensure that the checkpoint path is a directory path instead of a file path. gather_dtensor (bool): whether to gather the distributed tensor to the first device. Default: True. - prefix (str): prefix for the model checkpoint file name when shard=True. Default: None. + variant (str): If specified, weights are saved in the format pytorch_model..bin. Default: None. size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard = True. use_safetensors (bool): whether to use safe tensors. Default: False. If set to True, the checkpoint will be saved """ @@ -138,7 +139,7 @@ def save_model(self, model = model.unwrap() if shard: - self.save_sharded_model(model, checkpoint, gather_dtensor, prefix, size_per_shard, use_safetensors) + self.save_sharded_model(model, checkpoint, gather_dtensor, variant, size_per_shard, use_safetensors) else: self.save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors) @@ -219,7 +220,7 @@ def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool): pass @abstractmethod - def save_sharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, prefix: str, + def save_sharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, variant: Optional[str], size_per_shard: int, use_safetensors: bool): """ Save model to sharded checkpoint. diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index 2a76f1718469..bf584f45d045 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -6,6 +6,7 @@ import os import json import gc +from typing import Optional from .checkpoint_io_base import CheckpointIO from .index_file import CheckpointIndexFile @@ -16,10 +17,12 @@ is_safetensors_available, shard_checkpoint, load_shard_state_dict, - load_state_dict_into_model + load_state_dict_into_model, + add_variant ) from .utils import SAFE_WEIGHTS_NAME, WEIGHTS_NAME, SAFE_WEIGHTS_INDEX_NAME, WEIGHTS_INDEX_NAME + __all__ = ['GeneralCheckpointIO'] @@ -69,7 +72,7 @@ def save_unsharded_optimizer( def save_sharded_model(self, model: nn.Module, checkpoint_path: str, gather_dtensor:bool = False, - prefix: str = "", max_shard_size: int = 1024, use_safetensors: bool = False): + variant: Optional[str] = None, max_shard_size: int = 1024, use_safetensors: bool = False): """ implement this method as it can be supported by Huggingface model, save shard model, save model to multiple files @@ -83,6 +86,7 @@ def save_sharded_model(self, model: nn.Module, checkpoint_path: str, gather_dten # shard checkpoint state_dict = model.state_dict() weights_name = SAFE_WEIGHTS_NAME if use_safetensors else WEIGHTS_NAME + weights_name = add_variant(weights_name, variant) shards, index = shard_checkpoint(state_dict, max_shard_size=max_shard_size, weights_name=weights_name) # Save the model @@ -92,7 +96,8 @@ def save_sharded_model(self, model: nn.Module, checkpoint_path: str, gather_dten # save index file save_index_file = SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME - save_index_file = os.path.join(checkpoint_path, save_index_file) + + save_index_file = os.path.join(checkpoint_path, add_variant(save_index_file, variant)) with open(save_index_file, "w", encoding="utf-8") as f: content = json.dumps(index, indent=2, sort_keys=True) + "\n" f.write(content) diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 81b666da5c78..37d22d08df40 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -4,11 +4,12 @@ import torch.nn as nn from typing import List, Dict, Mapping, OrderedDict, Optional, Tuple from colossalai.tensor.d_tensor.d_tensor import DTensor +import re SAFE_WEIGHTS_NAME = "model.safetensors" -WEIGHTS_NAME = "model.bin" +WEIGHTS_NAME = "pytorch_model.bin" SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json" -WEIGHTS_INDEX_NAME = "model.bin.index.json" +WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json" # ====================================== # General helper functions @@ -27,7 +28,6 @@ def calculate_tensor_size(tensor: torch.Tensor) -> float: """ return tensor.numel() * tensor.element_size() / 1024 / 1024 - def is_safetensors_available() -> bool: """ Check whether safetensors is available. @@ -358,13 +358,14 @@ def has_index_file(checkpoint_path: str) -> Tuple[bool, Optional[Path]]: checkpoint_path = Path(checkpoint_path) if checkpoint_path.is_file(): # check if it is .index.json - if checkpoint_path.name.endswith('.index.json'): + reg = re.compile("(.*?).index((\..*)?).json") + if reg.fullmatch(checkpoint_path.name) is not None: return True, checkpoint_path else: return False, None elif checkpoint_path.is_dir(): # check if there is only one a file ending with .index.json in this directory - index_files = list(checkpoint_path.glob('*.index.json')) + index_files = list(checkpoint_path.glob('*.index.*json')) # if we found a .index.json file, make sure there is only one if len(index_files) > 0: @@ -406,3 +407,13 @@ def load_state_dict(checkpoint_file_path: Path): else: # load with torch return torch.load(checkpoint_file_path) + + + +def add_variant(weights_name: str, variant: Optional[str] = None) -> str: + if variant is not None and len(variant) > 0: + splits = weights_name.split(".") + splits = splits[:-1] + [variant] + splits[-1:] + weights_name = ".".join(splits) + + return weights_name From 152239bbfa6d5eca22633c9d73463ed8dcb300d7 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 12 Apr 2023 16:03:25 +0800 Subject: [PATCH 126/413] [gemini] gemini supports lazy init (#3379) * [gemini] fix nvme optimizer init * [gemini] gemini supports lazy init * [gemini] add init example * [gemini] add fool model * [zero] update gemini ddp * [zero] update init example * add chunk method * add chunk method * [lazyinit] fix lazy tensor tolist * [gemini] fix buffer materialization * [misc] remove useless file * [booster] update gemini plugin * [test] update gemini plugin test * [test] fix gemini plugin test * [gemini] fix import * [gemini] fix import * [lazyinit] use new metatensor * [lazyinit] use new metatensor * [lazyinit] fix __set__ method --- .../_analyzer/_subclasses/_monkey_patch.py | 3 +- colossalai/booster/plugin/gemini_plugin.py | 50 ------------------- colossalai/nn/optimizer/nvme_optimizer.py | 15 ++++-- colossalai/utils/model/experimental.py | 16 ++++-- colossalai/zero/gemini/chunk/search_utils.py | 9 ++-- colossalai/zero/gemini/gemini_ddp.py | 27 ++++++++-- .../test_plugin/test_gemini_plugin.py | 32 ++++++++++-- 7 files changed, 80 insertions(+), 72 deletions(-) diff --git a/colossalai/_analyzer/_subclasses/_monkey_patch.py b/colossalai/_analyzer/_subclasses/_monkey_patch.py index 7c1c3d3d8cd4..b3ec98f0811f 100644 --- a/colossalai/_analyzer/_subclasses/_monkey_patch.py +++ b/colossalai/_analyzer/_subclasses/_monkey_patch.py @@ -2,8 +2,6 @@ import torch.distributed as dist from packaging import version -aten = torch.ops.aten - __all__ = [ "_TorchFactoryMethod", "_TorchOverrideableFactoryMethod", @@ -51,6 +49,7 @@ ] if version.parse(torch.__version__) >= version.parse('1.12.0'): + aten = torch.ops.aten # TODO: dive deep here # refer to https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorShape.cpp _AliasATen = [ diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 6693b1f44d62..659f36c210f4 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -16,10 +16,8 @@ from colossalai.checkpoint_io.utils import save_state_dict from colossalai.cluster import DistCoordinator from colossalai.interface import ModelWrapper, OptimizerWrapper -from colossalai.tensor.colo_parameter import ColoParameter from colossalai.utils import get_current_device from colossalai.zero import GeminiDDP, zero_model_wrapper, zero_optim_wrapper -from colossalai.zero.gemini.colo_init_context import _convert_to_coloparam from colossalai.zero.gemini.memory_tracer import MemStats from .plugin_base import Plugin @@ -27,50 +25,6 @@ __all__ = ['GeminiPlugin'] -def convert_to_colo_param(module: nn.Module) -> None: - """Convert module's paramters to ColoParameter. This is a workaround and will be deprecated when lazy init is compatible with Gemini. - - Args: - module (nn.Module): Module to be converted. - """ - converted_modules = set() # handle shared modules - converted_params = dict() # record mapping between (torch.Tensor, ColoTensor) to distinguish the same reference - - def convert_recursively(m: nn.Module): - for child in m.children(): - if child not in converted_modules: - converted_modules.add(child) - convert_recursively(child) - - for name, p in m.named_parameters(recurse=False): - assert not isinstance(p, ColoParameter) - if p in converted_params: - target = converted_params[p] - else: - target = _convert_to_coloparam(p, p.device, p.dtype) - converted_params[p] = target - setattr(m, name, target) - target.shared_param_modules.append(m) - - convert_recursively(module) - - # optimizer should replace params in group as well. This attr should be deleted after replacing to avoid memory leak - module._converted_params = converted_params - - -def replace_param_in_group(optimizer: Optimizer, converted_params: dict) -> None: - """Replace param in optimizer's group with converted ColoParameter. - - Args: - optimizer (Optimizer): Optimizer to be replaced. - converted_params (dict): Mapping between (torch.Tensor, ColoTensor). - """ - for group in optimizer.param_groups: - for i, p in enumerate(group['params']): - if p in converted_params: - group['params'][i] = converted_params[p] - - class GeminiCheckpointIO(GeneralCheckpointIO): def __init__(self) -> None: @@ -113,8 +67,6 @@ class GeminiModel(ModelWrapper): def __init__(self, module: nn.Module, gemini_config: dict) -> None: super().__init__(module) - # TODO(ver217): only support Gemini now - convert_to_colo_param(module) self.module = zero_model_wrapper(module, zero_stage=3, gemini_config=gemini_config) def unwrap(self): @@ -125,8 +77,6 @@ def unwrap(self): class GeminiOptimizer(OptimizerWrapper): def __init__(self, module: GeminiDDP, optimizer: Optimizer, zero_optim_config: dict, optim_kwargs: dict) -> None: - replace_param_in_group(optimizer, module.module._converted_params) - del module.module._converted_params optimizer = zero_optim_wrapper(module, optimizer, optim_config=zero_optim_config, **optim_kwargs) super().__init__(optimizer) diff --git a/colossalai/nn/optimizer/nvme_optimizer.py b/colossalai/nn/optimizer/nvme_optimizer.py index cbb435a90f61..53e4a46c9741 100644 --- a/colossalai/nn/optimizer/nvme_optimizer.py +++ b/colossalai/nn/optimizer/nvme_optimizer.py @@ -1,9 +1,10 @@ -import torch +import math import os import tempfile -import math +from typing import Callable, Dict, List, Optional + +import torch from torch.nn.parameter import Parameter -from typing import Optional, List, Dict, Callable class NVMeOptimizer(torch.optim.Optimizer): @@ -42,8 +43,9 @@ def __init__(self, self.offloader = None self.is_on_nvme: Dict[Parameter, bool] = {} self.offloaded_numel: int = 0 - self.total_numel: int = self._get_numel() - self.can_offload_numel = math.floor(self.total_numel * self.nvme_offload_fraction) + # As param may be not materialized here, these attributes are initalized when the first step + self.total_numel: Optional[int] = None + self.can_offload_numel: Optional[int] = None self.prefetch_params: List[Parameter] = [] self.param_to_prefetch_idx: Dict[Parameter, int] = {} @@ -77,6 +79,9 @@ def _setup_prefetch_params(self) -> List[Parameter]: self.prefetch_params.append(p) def _pre_step(self, *state_keys: str) -> None: + if self.total_numel is None: + self.total_numel = self._get_numel() + self.can_offload_numel = math.floor(self.total_numel * self.nvme_offload_fraction) self._setup_prefetch_params() if self.offloader is None or len(self.prefetch_params) == 0: return diff --git a/colossalai/utils/model/experimental.py b/colossalai/utils/model/experimental.py index 6427a147a5c0..c91751f1cb28 100644 --- a/colossalai/utils/model/experimental.py +++ b/colossalai/utils/model/experimental.py @@ -7,7 +7,7 @@ from torch import Tensor from torch.utils._pytree import tree_map -from colossalai.fx.profiler.tensor import MetaTensor +from colossalai._analyzer._subclasses import MetaTensor from colossalai.tensor.d_tensor.d_tensor import DTensor from colossalai.tensor.d_tensor.layout import Layout @@ -37,7 +37,7 @@ # If your intent is to change the metadata of a Tensor (such as sizes / strides / storage / storage_offset) # without autograd tracking the change, remove the .data / .detach() call and wrap the change in a `with torch.no_grad():` block. # These ops cannot be unwrapped using .data -_CHANGE_META_OPS = ['_cudnn_rnn_flatten_weight', 'requires_grad_', '__get__'] +_CHANGE_META_OPS = ['_cudnn_rnn_flatten_weight', 'requires_grad_', '__get__', '__set__'] _LEGACY_TENSOR_CONSTRUCTOR = { 'FloatTensor': torch.float, @@ -75,6 +75,12 @@ def __torch_function__(cls, func, types, args=(), kwargs=None): return super().__torch_function__(func, types, args, kwargs) +def _data_tolist(tensor: torch.Tensor) -> list: + """tolist() method is not allowed for a subclass of tensor. Tensor.data returns a Tensor. + """ + return tensor.data.tolist() + + def _convert_cls(tensor: 'LazyTensor', target: torch.Tensor) -> torch.Tensor: """Convert a lazy tensor's class to target's class, with target's data. @@ -94,7 +100,7 @@ def _convert_cls(tensor: 'LazyTensor', target: torch.Tensor) -> torch.Tensor: tensor.requires_grad = target.requires_grad # subclass of torch.Tensor does not have tolist() method # overwrite this method after materialization or distribution - tensor.tolist = MethodType(torch.Tensor.tolist, target) + tensor.tolist = MethodType(_data_tolist, tensor) return tensor @@ -144,7 +150,7 @@ def __new__(cls, func, *args, meta_data=None, concrete_data=None, **kwargs): if meta_data is None: device = kwargs.get('device', 'cpu') elem = func(*args, **{**kwargs, 'device': 'meta'}) - meta_data = MetaTensor(elem, fake_device=device) + meta_data = MetaTensor(elem, device=device) elem = meta_data._tensor # As a meta tensor cannot be modified __class__ to torch.Tensor, we should use an empty real tensor here r = torch.Tensor._make_subclass(cls, _EMPTY_DATA, require_grad=elem.requires_grad) @@ -255,7 +261,7 @@ def __torch_function__(cls, func, types, args=(), kwargs=None): tree_map(cls._replace_with_materialized, args) tree_map(cls._replace_with_materialized, kwargs) is_inplace: bool = (func.__name__.endswith('_') and not (func.__name__.endswith('__')) - or func.__name__ == "__setitem__") + or func.__name__ in ('__setitem__', '__set__')) is_change_meta_op: bool = func.__name__ in _CHANGE_META_OPS diff --git a/colossalai/zero/gemini/chunk/search_utils.py b/colossalai/zero/gemini/chunk/search_utils.py index a69b782ead2e..c4deec8fe4a0 100644 --- a/colossalai/zero/gemini/chunk/search_utils.py +++ b/colossalai/zero/gemini/chunk/search_utils.py @@ -46,9 +46,10 @@ def _get_unused_byte(size_list: List[int], chunk_size: int) -> int: def _tensor_numel(local_param: ColoParameter, strict_ddp_flag: bool): - if strict_ddp_flag: + if strict_ddp_flag and type(local_param) is ColoParameter: return local_param.numel_global() else: + # if local_param is not ColoParameter, we assume it's replicated return local_param.numel() @@ -67,11 +68,13 @@ def classify_params_by_dp_degree(param_order: OrderedParamGenerator, """ params_dict: Dict[int, List[ColoParameter]] = dict() for param in param_order.generate(): - assert isinstance(param, ColoParameter), "please init model in the ColoInitContext" + # assert isinstance(param, ColoParameter), "please init model in the ColoInitContext" if is_ddp_ignored(param): continue - if strict_ddp_flag: + if strict_ddp_flag or type(param) is not ColoParameter: + # if model is not initialized with ColoInitContext, we assume it's replicated + # TODO(ver217): integrate DTensor param_key = dist.get_world_size() else: param_key = param.process_group.dp_world_size() diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index 50f1b1ef1ccc..c06239dfac20 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -1,7 +1,7 @@ import itertools from collections import OrderedDict from functools import partial -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Union import torch import torch.distributed as dist @@ -14,6 +14,7 @@ from colossalai.tensor.colo_parameter import ColoParameter, ColoTensor, ColoTensorSpec from colossalai.tensor.param_op_hook import ColoParamOpHookManager from colossalai.utils import get_current_device, is_ddp_ignored +from colossalai.utils.model.experimental import LazyTensor from .chunk import Chunk, ChunkManager, TensorState, init_chunk_manager from .gemini_hook import GeminiZeROHook @@ -55,7 +56,6 @@ def __init__(self, pin_memory: bool = False, force_outputs_fp32: bool = False, strict_ddp_mode: bool = False) -> None: - super().__init__(module, process_group=ColoProcessGroup()) self.gemini_manager = gemini_manager self.chunk_manager: ChunkManager = gemini_manager.chunk_manager self.force_outputs_fp32 = force_outputs_fp32 @@ -67,7 +67,6 @@ def __init__(self, self.param2name: Dict[nn.Parameter, str] = dict() self.name2param: Dict[str, nn.Parameter] = dict() - self._cast_buffers() self._logger = get_dist_logger() if self.gemini_manager._premade_memstats_: @@ -91,6 +90,8 @@ def __init__(self, for p_name, p_var in m_var.named_parameters(recurse=False): param_name = m_name + '.' + p_name if m_name else p_name self.name2param[param_name] = p_var + super().__init__(module, process_group=ColoProcessGroup()) + self._cast_buffers() def _post_forward(self): """This function is only triggered for inference. @@ -478,7 +479,8 @@ def load_fp32_parameter(chunk_slice, data): def _init_chunks(self, param_order, strict_ddp_mode: bool, cpu_offload: bool, pin_memory: bool): ddp_pg = ColoProcessGroup() for p in param_order.generate(): - assert isinstance(p, ColoParameter) + self._preprocess_param(p) + assert type(p) is ColoParameter # gather sharded parameters in the strict ddp mode if strict_ddp_mode: @@ -531,10 +533,27 @@ def _init_chunks(self, param_order, strict_ddp_mode: bool, cpu_offload: bool, pi def _cast_buffers(self): for buffer in self.module.buffers(): + if isinstance(buffer, LazyTensor): + buffer.materialize() buffer.data = buffer.cuda() if torch.is_floating_point(buffer): buffer.data = buffer.half() + def _preprocess_param(self, p: Union[nn.Parameter, ColoParameter, 'LazyTensor']) -> None: + """Convert parameter to ColoParameter in-place. + Args: + p (Union[nn.Parameter, ColoParameter, LazyTensor]): parameter to be converted + """ + if type(p) is ColoParameter: + # model is initialized with ColoInitContext + return + requires_grad = p.requires_grad + if isinstance(p, LazyTensor): + # model is initialized with LazyInitContext + p.materialize() + p.__class__ = ColoParameter + p.__init__(p, requires_grad=requires_grad) + class GeminiDDP(ZeroDDP): diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index a3c63fd09d26..d804c727ad3e 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -1,21 +1,31 @@ +from contextlib import nullcontext + import torch import torch.distributed as dist import colossalai from colossalai.booster import Booster from colossalai.booster.plugin import GeminiPlugin +from colossalai.fx import is_compatible_with_meta from colossalai.nn.optimizer import HybridAdam from colossalai.tensor.colo_parameter import ColoParameter -from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.zero import ColoInitContext from tests.kit.model_zoo import model_zoo -def check_gemini_plugin(early_stop: bool = True): +@parameterize('init_method', ['lazy', 'none', 'colo']) +def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): """check gemini plugin over model zoo Args: early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True. """ + is_support_meta = is_compatible_with_meta() + if not is_support_meta and init_method == 'lazy': + return + + from colossalai.utils.model.experimental import LazyInitContext passed_models = [] failed_info = {} # (model_name, error) pair @@ -40,10 +50,25 @@ def check_gemini_plugin(early_stop: bool = True): ]: continue + if init_method == 'lazy' and name in [ + 'timm_convmixer', 'timm_vision_transformer', 'timm_deit', 'timm_deit3', 'timm_inception_v3', + 'timm_tnt_b_patch16_224', 'timm_rexnet', 'torchvision_densenet121', 'torchvision_efficientnet_b0', + 'torchvision_mobilenet_v2', 'torchvision_mnasnet0_5', 'torchvision_regnet_x_16gf', + 'torchvision_shufflenet_v2_x0_5', 'torchvision_efficientnet_v2_s' + ]: + continue + try: + if init_method == 'colo': + ctx = ColoInitContext() + elif init_method == 'lazy': + ctx = LazyInitContext() + else: + ctx = nullcontext() plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, max_norm=1.0, initial_scale=2**5) booster = Booster(plugin=plugin) - model = model_fn() + with ctx: + model = model_fn() optimizer = HybridAdam(model.parameters(), lr=1e-3) criterion = lambda x: x.mean() data = data_gen_fn() @@ -76,6 +101,7 @@ def check_gemini_plugin(early_stop: bool = True): torch.cuda.empty_cache() if dist.get_rank() == 0: + print(f'Init method: {init_method}') print(f'Passed models({len(passed_models)}): {passed_models}\n\n') print(f'Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n') assert len(failed_info) == 0, '\n'.join([f'{k}: {v}' for k, v in failed_info.items()]) From de84c0311abb5f21d431660162eb519f0e604c1d Mon Sep 17 00:00:00 2001 From: natalie_cao Date: Tue, 11 Apr 2023 14:10:45 +0800 Subject: [PATCH 127/413] Polish Code --- .../configs/Inference/v2-inference-v.yaml | 79 ++++---- .../configs/Inference/v2-inference.yaml | 79 ++++---- .../Inference/v2-inpainting-inference.yaml | 179 ++++++++---------- .../configs/Inference/v2-midas-inference.yaml | 81 ++++---- .../configs/Inference/x4-upscaling.yaml | 83 ++++---- .../Teyvat/train_colossalai_teyvat.yaml | 144 +++++++------- .../diffusion/configs/train_colossalai.yaml | 141 ++++++-------- .../configs/train_colossalai_cifar10.yaml | 146 +++++++------- .../images/diffusion/configs/train_ddp.yaml | 127 ++++++------- .../diffusion/ldm/models/autoencoder.py | 13 +- .../ldm/models/diffusion/classifier.py | 9 +- .../diffusion/ldm/models/diffusion/ddpm.py | 26 +-- examples/images/diffusion/main.py | 170 ++++++----------- .../scripts/tests/test_checkpoint.py | 5 +- examples/images/diffusion/train_colossalai.sh | 1 + 15 files changed, 563 insertions(+), 720 deletions(-) diff --git a/examples/images/diffusion/configs/Inference/v2-inference-v.yaml b/examples/images/diffusion/configs/Inference/v2-inference-v.yaml index 8ec8dfbfefe9..b05955d3faf7 100644 --- a/examples/images/diffusion/configs/Inference/v2-inference-v.yaml +++ b/examples/images/diffusion/configs/Inference/v2-inference-v.yaml @@ -1,6 +1,5 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -19,50 +18,42 @@ model: use_ema: False # we set this to false because this is an inference only config unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" + freeze: True + layer: "penultimate" diff --git a/examples/images/diffusion/configs/Inference/v2-inference.yaml b/examples/images/diffusion/configs/Inference/v2-inference.yaml index 152c4f3c2b36..5d8d583d06d1 100644 --- a/examples/images/diffusion/configs/Inference/v2-inference.yaml +++ b/examples/images/diffusion/configs/Inference/v2-inference.yaml @@ -1,6 +1,5 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 @@ -18,50 +17,42 @@ model: use_ema: False # we set this to false because this is an inference only config unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" + freeze: True + layer: "penultimate" diff --git a/examples/images/diffusion/configs/Inference/v2-inpainting-inference.yaml b/examples/images/diffusion/configs/Inference/v2-inpainting-inference.yaml index 32a9471d71b8..ffaa5e8da2ad 100644 --- a/examples/images/diffusion/configs/Inference/v2-inpainting-inference.yaml +++ b/examples/images/diffusion/configs/Inference/v2-inpainting-inference.yaml @@ -19,106 +19,97 @@ model: use_ema: False unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - image_size: 32 # unused - in_channels: 9 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False + use_checkpoint: True + image_size: 32 # unused + in_channels: 9 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [ ] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" + freeze: True + layer: "penultimate" data: - target: ldm.data.laion.WebDataModuleFromConfig - params: - tar_base: null # for concat as in LAION-A - p_unsafe_threshold: 0.1 - filter_word_list: "data/filters.yaml" - max_pwatermark: 0.45 - batch_size: 8 - num_workers: 6 - multinode: True - min_size: 512 - train: - shards: - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" - shuffle: 10000 - image_key: jpg - image_transforms: - - target: torchvision.transforms.Resize - params: - size: 512 - interpolation: 3 - - target: torchvision.transforms.RandomCrop - params: - size: 512 - postprocess: - target: ldm.data.laion.AddMask - params: - mode: "512train-large" - p_drop: 0.25 - # NOTE use enough shards to avoid empty validation loops in workers - validation: - shards: - - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " - shuffle: 0 - image_key: jpg - image_transforms: - - target: torchvision.transforms.Resize - params: - size: 512 - interpolation: 3 - - target: torchvision.transforms.CenterCrop - params: - size: 512 - postprocess: - target: ldm.data.laion.AddMask - params: - mode: "512train-large" - p_drop: 0.25 + tar_base: null # for concat as in LAION-A + p_unsafe_threshold: 0.1 + filter_word_list: "data/filters.yaml" + max_pwatermark: 0.45 + batch_size: 8 + num_workers: 6 + multinode: True + min_size: 512 + train: + shards: + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: + - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 lightning: find_unused_parameters: True @@ -132,8 +123,6 @@ lightning: every_n_train_steps: 10000 image_logger: - target: main.ImageLogger - params: enable_autocast: False disabled: False batch_frequency: 1000 diff --git a/examples/images/diffusion/configs/Inference/v2-midas-inference.yaml b/examples/images/diffusion/configs/Inference/v2-midas-inference.yaml index 531199de4878..01d3729f1590 100644 --- a/examples/images/diffusion/configs/Inference/v2-midas-inference.yaml +++ b/examples/images/diffusion/configs/Inference/v2-midas-inference.yaml @@ -19,54 +19,45 @@ model: use_ema: False depth_stage_config: - target: ldm.modules.midas.api.MiDaSInference - params: - model_type: "dpt_hybrid" + model_type: "dpt_hybrid" unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - image_size: 32 # unused - in_channels: 5 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False + use_checkpoint: True + image_size: 32 # unused + in_channels: 5 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [ ] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" + freeze: True + layer: "penultimate" diff --git a/examples/images/diffusion/configs/Inference/x4-upscaling.yaml b/examples/images/diffusion/configs/Inference/x4-upscaling.yaml index 45ecbf9ad863..426d387ca611 100644 --- a/examples/images/diffusion/configs/Inference/x4-upscaling.yaml +++ b/examples/images/diffusion/configs/Inference/x4-upscaling.yaml @@ -20,56 +20,47 @@ model: use_ema: False low_scale_config: - target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation - params: - noise_schedule_config: # image space - linear_start: 0.0001 - linear_end: 0.02 - max_noise_level: 350 + noise_schedule_config: # image space + linear_start: 0.0001 + linear_end: 0.02 + max_noise_level: 350 unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - num_classes: 1000 # timesteps for noise conditioning (here constant, just need one) - image_size: 128 - in_channels: 7 - out_channels: 4 - model_channels: 256 - attention_resolutions: [ 2,4,8] - num_res_blocks: 2 - channel_mult: [ 1, 2, 2, 4] - disable_self_attentions: [True, True, True, False] - disable_middle_self_attn: False - num_heads: 8 - use_spatial_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - use_linear_in_transformer: True + use_checkpoint: True + num_classes: 1000 # timesteps for noise conditioning (here constant, just need one) + image_size: 128 + in_channels: 7 + out_channels: 4 + model_channels: 256 + attention_resolutions: [ 2,4,8] + num_res_blocks: 2 + channel_mult: [ 1, 2, 2, 4] + disable_self_attentions: [True, True, True, False] + disable_middle_self_attn: False + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + use_linear_in_transformer: True first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - ddconfig: - # attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though) - double_z: True - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1 - num_res_blocks: 2 - attn_resolutions: [ ] - dropout: 0.0 + embed_dim: 4 + ddconfig: + # attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though) + double_z: True + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: - lossconfig: - target: torch.nn.Identity cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" + freeze: True + layer: "penultimate" diff --git a/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml b/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml index ff0f4c5a0463..9e760124c7a4 100644 --- a/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml +++ b/examples/images/diffusion/configs/Teyvat/train_colossalai_teyvat.yaml @@ -1,6 +1,5 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -20,81 +19,70 @@ model: use_ema: False scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1.e-4 ] - f_min: [ 1.e-10 ] + warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1.e-4 ] + f_min: [ 1.e-10 ] unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" + freeze: True + layer: "penultimate" data: - target: main.DataModuleFromConfig - params: - batch_size: 16 - num_workers: 4 - train: - target: ldm.data.teyvat.hf_dataset - params: - path: Fazzie/Teyvat - image_transforms: - - target: torchvision.transforms.Resize - params: - size: 512 - - target: torchvision.transforms.RandomCrop - params: - size: 512 - - target: torchvision.transforms.RandomHorizontalFlip + batch_size: 16 + num_workers: 4 + train: + target: ldm.data.teyvat.hf_dataset + params: + path: Fazzie/Teyvat + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + - target: torchvision.transforms.RandomHorizontalFlip lightning: trainer: @@ -105,13 +93,11 @@ lightning: precision: 16 auto_select_gpus: False strategy: - target: strategies.ColossalAIStrategy - params: - use_chunk: True - enable_distributed_storage: True - placement_policy: cuda - force_outputs_fp32: true - min_chunk_size: 64 + use_chunk: True + enable_distributed_storage: True + placement_policy: cuda + force_outputs_fp32: true + min_chunk_size: 64 log_every_n_steps: 2 logger: True @@ -120,9 +106,7 @@ lightning: logger_config: wandb: - target: loggers.WandbLogger - params: - name: nowname - save_dir: "/tmp/diff_log/" - offline: opt.debug - id: nowname + name: nowname + save_dir: "/tmp/diff_log/" + offline: opt.debug + id: nowname diff --git a/examples/images/diffusion/configs/train_colossalai.yaml b/examples/images/diffusion/configs/train_colossalai.yaml index 88432e978a0f..5f745286a719 100644 --- a/examples/images/diffusion/configs/train_colossalai.yaml +++ b/examples/images/diffusion/configs/train_colossalai.yaml @@ -1,6 +1,5 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -19,95 +18,83 @@ model: use_ema: False # we set this to false because this is an inference only config scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1.e-4 ] - f_min: [ 1.e-10 ] + warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1.e-4 ] + f_min: [ 1.e-10 ] unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" + freeze: True + layer: "penultimate" data: - target: main.DataModuleFromConfig - params: - batch_size: 128 - wrap: False - # num_workwers should be 2 * batch_size, and total num less than 1024 - # e.g. if use 8 devices, no more than 128 - num_workers: 128 - train: - target: ldm.data.base.Txt2ImgIterableBaseDataset - params: - file_path: # YOUR DATASET_PATH - world_size: 1 - rank: 0 + batch_size: 128 + wrap: False + # num_workwers should be 2 * batch_size, and total num less than 1024 + # e.g. if use 8 devices, no more than 128 + num_workers: 128 + train: + target: ldm.data.base.Txt2ImgIterableBaseDataset + params: + file_path: # YOUR DATASET_PATH + world_size: 1 + rank: 0 lightning: trainer: accelerator: 'gpu' - devices: 8 + devices: 2 log_gpu_memory: all max_epochs: 2 precision: 16 auto_select_gpus: False strategy: - target: strategies.ColossalAIStrategy - params: - use_chunk: True - enable_distributed_storage: True - placement_policy: cuda - force_outputs_fp32: true - min_chunk_size: 64 + use_chunk: True + enable_distributed_storage: True + placement_policy: cuda + force_outputs_fp32: true + min_chunk_size: 64 log_every_n_steps: 2 logger: True @@ -116,9 +103,7 @@ lightning: logger_config: wandb: - target: loggers.WandbLogger - params: - name: nowname - save_dir: "/tmp/diff_log/" - offline: opt.debug - id: nowname + name: nowname + save_dir: "/tmp/diff_log/" + offline: opt.debug + id: nowname diff --git a/examples/images/diffusion/configs/train_colossalai_cifar10.yaml b/examples/images/diffusion/configs/train_colossalai_cifar10.yaml index 0ba06f832178..0d0f185426c2 100644 --- a/examples/images/diffusion/configs/train_colossalai_cifar10.yaml +++ b/examples/images/diffusion/configs/train_colossalai_cifar10.yaml @@ -1,6 +1,5 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -19,82 +18,71 @@ model: use_ema: False # we set this to false because this is an inference only config scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1.e-4 ] - f_min: [ 1.e-10 ] + warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1.e-4 ] + f_min: [ 1.e-10 ] unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" + freeze: True + layer: "penultimate" data: - target: main.DataModuleFromConfig - params: - batch_size: 4 - num_workers: 4 - train: - target: ldm.data.cifar10.hf_dataset - params: - name: cifar10 - image_transforms: - - target: torchvision.transforms.Resize - params: - size: 512 - interpolation: 3 - - target: torchvision.transforms.RandomCrop - params: - size: 512 - - target: torchvision.transforms.RandomHorizontalFlip + batch_size: 4 + num_workers: 4 + train: + target: ldm.data.cifar10.hf_dataset + params: + name: cifar10 + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + - target: torchvision.transforms.RandomHorizontalFlip lightning: trainer: @@ -105,13 +93,11 @@ lightning: precision: 16 auto_select_gpus: False strategy: - target: strategies.ColossalAIStrategy - params: - use_chunk: True - enable_distributed_storage: True - placement_policy: cuda - force_outputs_fp32: true - min_chunk_size: 64 + use_chunk: True + enable_distributed_storage: True + placement_policy: cuda + force_outputs_fp32: true + min_chunk_size: 64 log_every_n_steps: 2 logger: True @@ -120,9 +106,7 @@ lightning: logger_config: wandb: - target: loggers.WandbLogger - params: - name: nowname - save_dir: "/tmp/diff_log/" - offline: opt.debug - id: nowname + name: nowname + save_dir: "/tmp/diff_log/" + offline: opt.debug + id: nowname diff --git a/examples/images/diffusion/configs/train_ddp.yaml b/examples/images/diffusion/configs/train_ddp.yaml index a63df887e719..f3ae3ddb5ff6 100644 --- a/examples/images/diffusion/configs/train_ddp.yaml +++ b/examples/images/diffusion/configs/train_ddp.yaml @@ -1,6 +1,5 @@ model: base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 @@ -19,77 +18,65 @@ model: use_ema: False # we set this to false because this is an inference only config scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1.e-4 ] - f_min: [ 1.e-10 ] + warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1.e-4 ] + f_min: [ 1.e-10 ] unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" + freeze: True + layer: "penultimate" data: - target: main.DataModuleFromConfig - params: - batch_size: 128 - # num_workwers should be 2 * batch_size, and the total num less than 1024 - # e.g. if use 8 devices, no more than 128 - num_workers: 128 - train: - target: ldm.data.base.Txt2ImgIterableBaseDataset - params: - file_path: # YOUR DATAPATH - world_size: 1 - rank: 0 + batch_size: 128 + # num_workwers should be 2 * batch_size, and the total num less than 1024 + # e.g. if use 8 devices, no more than 128 + num_workers: 128 + train: + target: ldm.data.base.Txt2ImgIterableBaseDataset + params: + file_path: # YOUR DATAPATH + world_size: 1 + rank: 0 lightning: trainer: @@ -100,9 +87,7 @@ lightning: precision: 16 auto_select_gpus: False strategy: - target: strategies.DDPStrategy - params: - find_unused_parameters: False + find_unused_parameters: False log_every_n_steps: 2 # max_steps: 6o logger: True @@ -111,9 +96,7 @@ lightning: logger_config: wandb: - target: loggers.WandbLogger - params: - name: nowname - save_dir: "/data2/tmp/diff_log/" - offline: opt.debug - id: nowname + name: nowname + save_dir: "/data2/tmp/diff_log/" + offline: opt.debug + id: nowname diff --git a/examples/images/diffusion/ldm/models/autoencoder.py b/examples/images/diffusion/ldm/models/autoencoder.py index b1bd8377835b..f0a69fe63a8c 100644 --- a/examples/images/diffusion/ldm/models/autoencoder.py +++ b/examples/images/diffusion/ldm/models/autoencoder.py @@ -1,16 +1,13 @@ import torch -try: - import lightning.pytorch as pl -except: - import pytorch_lightning as pl +import lightning.pytorch as pl -import torch.nn.functional as F +from torch import nn +from torch.nn import functional as F +from torch.nn import Identity from contextlib import contextmanager from ldm.modules.diffusionmodules.model import Encoder, Decoder from ldm.modules.distributions.distributions import DiagonalGaussianDistribution - -from ldm.util import instantiate_from_config from ldm.modules.ema import LitEma @@ -32,7 +29,7 @@ def __init__(self, self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) - self.loss = instantiate_from_config(lossconfig) + self.loss = Identity() assert ddconfig["double_z"] self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) diff --git a/examples/images/diffusion/ldm/models/diffusion/classifier.py b/examples/images/diffusion/ldm/models/diffusion/classifier.py index 612a8371bf20..3cf12f093bea 100644 --- a/examples/images/diffusion/ldm/models/diffusion/classifier.py +++ b/examples/images/diffusion/ldm/models/diffusion/classifier.py @@ -9,9 +9,10 @@ from einops import rearrange from glob import glob from natsort import natsorted - +from ldm.models.diffusion.ddpm import LatentDiffusion +from ldm.lr_scheduler import LambdaLinearScheduler from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel -from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config +from ldm.util import log_txt_as_img, default, ismap __models__ = { 'class_label': EncoderUNetModel, @@ -86,7 +87,7 @@ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): print(f"Unexpected Keys: {unexpected}") def load_diffusion(self): - model = instantiate_from_config(self.diffusion_config) + model = LatentDiffusion(**self.diffusion_config.get('params',dict())) self.diffusion_model = model.eval() self.diffusion_model.train = disabled_train for param in self.diffusion_model.parameters(): @@ -221,7 +222,7 @@ def configure_optimizers(self): optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) if self.use_scheduler: - scheduler = instantiate_from_config(self.scheduler_config) + scheduler = LambdaLinearScheduler(**self.scheduler_config.get('params',dict())) print("Setting up LambdaLR scheduler...") scheduler = [ diff --git a/examples/images/diffusion/ldm/models/diffusion/ddpm.py b/examples/images/diffusion/ldm/models/diffusion/ddpm.py index b7315b048c66..842ec1371ea0 100644 --- a/examples/images/diffusion/ldm/models/diffusion/ddpm.py +++ b/examples/images/diffusion/ldm/models/diffusion/ddpm.py @@ -22,19 +22,22 @@ from functools import partial from einops import rearrange, repeat +from ldm.lr_scheduler import LambdaLinearScheduler from ldm.models.autoencoder import * from ldm.models.autoencoder import AutoencoderKL, IdentityFirstStage from ldm.models.diffusion.ddim import * from ldm.models.diffusion.ddim import DDIMSampler +from ldm.modules.midas.api import MiDaSInference from ldm.modules.diffusionmodules.model import * from ldm.modules.diffusionmodules.model import Decoder, Encoder, Model from ldm.modules.diffusionmodules.openaimodel import * -from ldm.modules.diffusionmodules.openaimodel import AttentionPool2d +from ldm.modules.diffusionmodules.openaimodel import AttentionPool2d, UNetModel from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule, noise_like from ldm.modules.distributions.distributions import DiagonalGaussianDistribution, normal_kl +from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation from ldm.modules.ema import LitEma from ldm.modules.encoders.modules import * -from ldm.util import count_params, default, exists, instantiate_from_config, isimage, ismap, log_txt_as_img, mean_flat +from ldm.util import count_params, default, exists, isimage, ismap, log_txt_as_img, mean_flat from omegaconf import ListConfig from torch.optim.lr_scheduler import LambdaLR from torchvision.utils import make_grid @@ -690,7 +693,7 @@ def register_schedule(self, self.make_cond_schedule() def instantiate_first_stage(self, config): - model = instantiate_from_config(config) + model = AutoencoderKL(**config) self.first_stage_model = model.eval() self.first_stage_model.train = disabled_train for param in self.first_stage_model.parameters(): @@ -706,15 +709,13 @@ def instantiate_cond_stage(self, config): self.cond_stage_model = None # self.be_unconditional = True else: - model = instantiate_from_config(config) + model = FrozenOpenCLIPEmbedder(**config) self.cond_stage_model = model.eval() self.cond_stage_model.train = disabled_train for param in self.cond_stage_model.parameters(): param.requires_grad = False else: - assert config != '__is_first_stage__' - assert config != '__is_unconditional__' - model = instantiate_from_config(config) + model = FrozenOpenCLIPEmbedder(**config) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): @@ -1479,8 +1480,7 @@ def configure_optimizers(self): # opt = torch.optim.AdamW(params, lr=lr) if self.use_scheduler: - assert 'target' in self.scheduler_config - scheduler = instantiate_from_config(self.scheduler_config) + scheduler = LambdaLinearScheduler(**self.scheduler_config) rank_zero_info("Setting up LambdaLR scheduler...") scheduler = [{'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1}] @@ -1502,7 +1502,7 @@ class DiffusionWrapper(pl.LightningModule): def __init__(self, diff_model_config, conditioning_key): super().__init__() self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False) - self.diffusion_model = instantiate_from_config(diff_model_config) + self.diffusion_model = UNetModel(**diff_model_config) self.conditioning_key = conditioning_key assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm'] @@ -1551,7 +1551,7 @@ def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key= self.noise_level_key = noise_level_key def instantiate_low_stage(self, config): - model = instantiate_from_config(config) + model = ImageConcatWithNoiseAugmentation(**config) self.low_scale_model = model.eval() self.low_scale_model.train = disabled_train for param in self.low_scale_model.parameters(): @@ -1933,7 +1933,7 @@ class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion): def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs): super().__init__(concat_keys=concat_keys, *args, **kwargs) - self.depth_model = instantiate_from_config(depth_stage_config) + self.depth_model = MiDaSInference(**depth_stage_config) self.depth_stage_key = concat_keys[0] @torch.no_grad() @@ -2006,7 +2006,7 @@ def __init__(self, self.low_scale_key = low_scale_key def instantiate_low_stage(self, config): - model = instantiate_from_config(config) + model = ImageConcatWithNoiseAugmentation(**config) self.low_scale_model = model.eval() self.low_scale_model.train = disabled_train for param in self.low_scale_model.parameters(): diff --git a/examples/images/diffusion/main.py b/examples/images/diffusion/main.py index 91b809d5a65c..e31d75e0874d 100644 --- a/examples/images/diffusion/main.py +++ b/examples/images/diffusion/main.py @@ -10,11 +10,8 @@ import numpy as np import torch import torchvision +import lightning.pytorch as pl -try: - import lightning.pytorch as pl -except: - import pytorch_lightning as pl from functools import partial @@ -23,19 +20,15 @@ from PIL import Image from prefetch_generator import BackgroundGenerator from torch.utils.data import DataLoader, Dataset, Subset, random_split +from ldm.models.diffusion.ddpm import LatentDiffusion -try: - from lightning.pytorch import seed_everything - from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint - from lightning.pytorch.trainer import Trainer - from lightning.pytorch.utilities import rank_zero_info, rank_zero_only - LIGHTNING_PACK_NAME = "lightning.pytorch." -except: - from pytorch_lightning import seed_everything - from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint - from pytorch_lightning.trainer import Trainer - from pytorch_lightning.utilities import rank_zero_info, rank_zero_only - LIGHTNING_PACK_NAME = "pytorch_lightning." +from lightning.pytorch import seed_everything +from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint +from lightning.pytorch.trainer import Trainer +from lightning.pytorch.utilities import rank_zero_info, rank_zero_only +from lightning.pytorch.loggers import WandbLogger, TensorBoardLogger +from lightning.pytorch.strategies import ColossalAIStrategy,DDPStrategy +LIGHTNING_PACK_NAME = "lightning.pytorch." from ldm.data.base import Txt2ImgIterableBaseDataset from ldm.util import instantiate_from_config @@ -687,153 +680,114 @@ def on_train_epoch_end(self, trainer, pl_module): config.model["params"].update({"ckpt": ckpt}) rank_zero_info("Using ckpt_path = {}".format(config.model["params"]["ckpt"])) - model = instantiate_from_config(config.model) + model = LatentDiffusion(**config.model.get("params", dict())) # trainer and callbacks trainer_kwargs = dict() # config the logger # Default logger configs to log training metrics during the training process. - # These loggers are specified as targets in the dictionary, along with the configuration settings specific to each logger. default_logger_cfgs = { "wandb": { - "target": LIGHTNING_PACK_NAME + "loggers.WandbLogger", - "params": { "name": nowname, "save_dir": logdir, "offline": opt.debug, "id": nowname, } - }, + , "tensorboard": { - "target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger", - "params": { "save_dir": logdir, "name": "diff_tb", "log_graph": True } - } } # Set up the logger for TensorBoard default_logger_cfg = default_logger_cfgs["tensorboard"] if "logger" in lightning_config: logger_cfg = lightning_config.logger + trainer_kwargs["logger"] = WandbLogger(**logger_cfg) else: logger_cfg = default_logger_cfg - logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) - trainer_kwargs["logger"] = instantiate_from_config(logger_cfg) + trainer_kwargs["logger"] = TensorBoardLogger(**logger_cfg) # config the strategy, defualt is ddp if "strategy" in trainer_config: strategy_cfg = trainer_config["strategy"] - strategy_cfg["target"] = LIGHTNING_PACK_NAME + strategy_cfg["target"] + trainer_kwargs["strategy"] = ColossalAIStrategy(**strategy_cfg) else: - strategy_cfg = { - "target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy", - "params": { - "find_unused_parameters": False - } - } - - trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg) + strategy_cfg = {"find_unused_parameters": False} + trainer_kwargs["strategy"] = DDPStrategy(**strategy_cfg) # Set up ModelCheckpoint callback to save best models # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to # specify which metric is used to determine best models default_modelckpt_cfg = { - "target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint", - "params": { "dirpath": ckptdir, "filename": "{epoch:06}", "verbose": True, "save_last": True, } - } if hasattr(model, "monitor"): - default_modelckpt_cfg["params"]["monitor"] = model.monitor - default_modelckpt_cfg["params"]["save_top_k"] = 3 + default_modelckpt_cfg["monitor"] = model.monitor + default_modelckpt_cfg["save_top_k"] = 3 if "modelcheckpoint" in lightning_config: - modelckpt_cfg = lightning_config.modelcheckpoint + modelckpt_cfg = lightning_config.modelcheckpoint["params"] else: modelckpt_cfg = OmegaConf.create() modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg) if version.parse(pl.__version__) < version.parse('1.4.0'): - trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg) - - # Set up various callbacks, including logging, learning rate monitoring, and CUDA management - # add callback which sets up log directory - default_callbacks_cfg = { - "setup_callback": { # callback to set up the training - "target": "main.SetupCallback", - "params": { - "resume": opt.resume, # resume training if applicable - "now": now, - "logdir": logdir, # directory to save the log file - "ckptdir": ckptdir, # directory to save the checkpoint file - "cfgdir": cfgdir, # directory to save the configuration file - "config": config, # configuration dictionary - "lightning_config": lightning_config, # LightningModule configuration - } - }, - "image_logger": { # callback to log image data - "target": "main.ImageLogger", - "params": { - "batch_frequency": 750, # how frequently to log images - "max_images": 4, # maximum number of images to log - "clamp": True # whether to clamp pixel values to [0,1] - } - }, - "learning_rate_logger": { # callback to log learning rate - "target": "main.LearningRateMonitor", - "params": { - "logging_interval": "step", # logging frequency (either 'step' or 'epoch') - # "log_momentum": True # whether to log momentum (currently commented out) - } - }, - "cuda_callback": { # callback to handle CUDA-related operations - "target": "main.CUDACallback" - }, - } - - # If the LightningModule configuration has specified callbacks, use those - # Otherwise, create an empty OmegaConf configuration object - if "callbacks" in lightning_config: - callbacks_cfg = lightning_config.callbacks - else: - callbacks_cfg = OmegaConf.create() + trainer_kwargs["checkpoint_callback"] = ModelCheckpoint(**modelckpt_cfg) + + #Create an empty OmegaConf configuration object + + callbacks_cfg = OmegaConf.create() + + #Instantiate items according to the configs + trainer_kwargs.setdefault("callbacks", []) + setup_callback_config = { + "resume": opt.resume, # resume training if applicable + "now": now, + "logdir": logdir, # directory to save the log file + "ckptdir": ckptdir, # directory to save the checkpoint file + "cfgdir": cfgdir, # directory to save the configuration file + "config": config, # configuration dictionary + "lightning_config": lightning_config, # LightningModule configuration + } + trainer_kwargs["callbacks"].append(SetupCallback(**setup_callback_config)) - # If the 'metrics_over_trainsteps_checkpoint' callback is specified in the - # LightningModule configuration, update the default callbacks configuration - if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg: - print( - 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.') - default_metrics_over_trainsteps_ckpt_dict = { - 'metrics_over_trainsteps_checkpoint': { - "target": LIGHTNING_PACK_NAME + 'callbacks.ModelCheckpoint', - 'params': { - "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'), - "filename": "{epoch:06}-{step:09}", - "verbose": True, - 'save_top_k': -1, - 'every_n_train_steps': 10000, - 'save_weights_only': True - } - } + image_logger_config = { + + "batch_frequency": 750, # how frequently to log images + "max_images": 4, # maximum number of images to log + "clamp": True # whether to clamp pixel values to [0,1] } - default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict) + trainer_kwargs["callbacks"].append(ImageLogger(**image_logger_config)) - # Merge the default callbacks configuration with the specified callbacks configuration, and instantiate the callbacks - callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) - - trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] + learning_rate_logger_config = { + "logging_interval": "step", # logging frequency (either 'step' or 'epoch') + # "log_momentum": True # whether to log momentum (currently commented out) + } + trainer_kwargs["callbacks"].append(LearningRateMonitor(**learning_rate_logger_config)) + + metrics_over_trainsteps_checkpoint_config= { + "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'), + "filename": "{epoch:06}-{step:09}", + "verbose": True, + 'save_top_k': -1, + 'every_n_train_steps': 10000, + 'save_weights_only': True + } + trainer_kwargs["callbacks"].append(ModelCheckpoint(**metrics_over_trainsteps_checkpoint_config)) + trainer_kwargs["callbacks"].append(CUDACallback()) # Create a Trainer object with the specified command-line arguments and keyword arguments, and set the log directory trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs) trainer.logdir = logdir # Create a data module based on the configuration file - data = instantiate_from_config(config.data) + data = DataModuleFromConfig(**config.data) + # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html # calling these ourselves should not be necessary but it is. # lightning still takes care of proper multiprocessing though @@ -846,7 +800,7 @@ def on_train_epoch_end(self, trainer, pl_module): # Configure learning rate based on the batch size, base learning rate and number of GPUs # If scale_lr is true, calculate the learning rate based on additional factors - bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate + bs, base_lr = config.data.batch_size, config.model.base_learning_rate if not cpu: ngpu = trainer_config["devices"] else: diff --git a/examples/images/diffusion/scripts/tests/test_checkpoint.py b/examples/images/diffusion/scripts/tests/test_checkpoint.py index a32e66d44cf2..13622c4989fd 100644 --- a/examples/images/diffusion/scripts/tests/test_checkpoint.py +++ b/examples/images/diffusion/scripts/tests/test_checkpoint.py @@ -7,8 +7,9 @@ from diffusers import StableDiffusionPipeline import torch -from ldm.util import instantiate_from_config + from main import get_parser +from ldm.modules.diffusionmodules.openaimodel import UNetModel if __name__ == "__main__": with torch.no_grad(): @@ -17,7 +18,7 @@ config = f.read() base_config = yaml.load(config, Loader=yaml.FullLoader) unet_config = base_config['model']['params']['unet_config'] - diffusion_model = instantiate_from_config(unet_config).to("cuda:0") + diffusion_model = UNetModel(**unet_config).to("cuda:0") pipe = StableDiffusionPipeline.from_pretrained( "/data/scratch/diffuser/stable-diffusion-v1-4" diff --git a/examples/images/diffusion/train_colossalai.sh b/examples/images/diffusion/train_colossalai.sh index c56ed7876e5a..7f1a1bd14615 100755 --- a/examples/images/diffusion/train_colossalai.sh +++ b/examples/images/diffusion/train_colossalai.sh @@ -3,3 +3,4 @@ TRANSFORMERS_OFFLINE=1 DIFFUSERS_OFFLINE=1 python main.py --logdir /tmp --train --base configs/Teyvat/train_colossalai_teyvat.yaml --ckpt diffuser_root_dir/512-base-ema.ckpt + From a3ac48ef3d13e59e2860e0ede70e2ff38599101e Mon Sep 17 00:00:00 2001 From: digger-yu Date: Wed, 12 Apr 2023 23:09:30 +0800 Subject: [PATCH 128/413] [doc] Update README-zh-Hans.md (#3541) Fixing document link errors using absolute paths --- docs/README-zh-Hans.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README-zh-Hans.md b/docs/README-zh-Hans.md index daa42412cc3a..9d5bcfe3f974 100644 --- a/docs/README-zh-Hans.md +++ b/docs/README-zh-Hans.md @@ -406,7 +406,7 @@ docker run -ti --gpus all --rm --ipc=host colossalai bash ## CI/CD -我们使用[GitHub Actions](https://github.com/features/actions)来自动化大部分开发以及部署流程。如果想了解这些工作流是如何运行的，请查看这个[文档](.github/workflows/README.md). +我们使用[GitHub Actions](https://github.com/features/actions)来自动化大部分开发以及部署流程。如果想了解这些工作流是如何运行的，请查看这个[文档](https://github.com/hpcaitech/ColossalAI/blob/main/.github/workflows/README.md). ## 引用我们 From 3f760da9f0fffd5c75092ddfa5d578d04130bbf8 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Thu, 13 Apr 2023 16:49:57 +0800 Subject: [PATCH 129/413] Update README.md (#3548) Delete more ")" --- op_builder/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/op_builder/README.md b/op_builder/README.md index b7ac6107300c..1f88b4dfd26c 100644 --- a/op_builder/README.md +++ b/op_builder/README.md @@ -15,7 +15,7 @@ Method 2 is good because it allows the user to only build the kernel they actual ## PyTorch Extensions in Colossal-AI -The project DeepSpeed (https://github.com/microsoft/DeepSpeed) has proposed a [solution](https://github.com/microsoft/DeepSpeed/tree/master/op_builder)) to support kernel-build during either installation or runtime. +The project DeepSpeed (https://github.com/microsoft/DeepSpeed) has proposed a [solution](https://github.com/microsoft/DeepSpeed/tree/master/op_builder) to support kernel-build during either installation or runtime. We have adapted from DeepSpeed's solution to build extensions. The extension build requries two main functions from PyTorch: 1. `torch.utils.cpp_extension.CUDAExtension`: used to build extensions in `setup.py` during `pip install`. From 77efdfe1ddf9cd9d680137d1fb6910daf78dc4e8 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Thu, 13 Apr 2023 17:11:55 +0800 Subject: [PATCH 130/413] [doc] Update README.md (#3549) Format Optimization ,Add [] outside of DeepSpeed --- op_builder/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/op_builder/README.md b/op_builder/README.md index 1f88b4dfd26c..0f646a186248 100644 --- a/op_builder/README.md +++ b/op_builder/README.md @@ -15,7 +15,7 @@ Method 2 is good because it allows the user to only build the kernel they actual ## PyTorch Extensions in Colossal-AI -The project DeepSpeed (https://github.com/microsoft/DeepSpeed) has proposed a [solution](https://github.com/microsoft/DeepSpeed/tree/master/op_builder) to support kernel-build during either installation or runtime. +The project [DeepSpeed](https://github.com/microsoft/DeepSpeed) has proposed a [solution](https://github.com/microsoft/DeepSpeed/tree/master/op_builder) to support kernel-build during either installation or runtime. We have adapted from DeepSpeed's solution to build extensions. The extension build requries two main functions from PyTorch: 1. `torch.utils.cpp_extension.CUDAExtension`: used to build extensions in `setup.py` during `pip install`. From 535b896435933e609973815138ddcafa4f6f21fb Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Thu, 13 Apr 2023 18:11:48 +0800 Subject: [PATCH 131/413] [chat] polish tutorial doc (#3551) * [chat] clean up duplicate tutorial * [chat] clean up duplicate tutorial * [chat] clean up duplicate tutorial * [chat] clean up duplicate tutorial --- applications/Chat/README.md | 136 +++++++++++------------------------- 1 file changed, 39 insertions(+), 97 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index e3b605d9b796..0a5f7840d997 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -15,20 +15,18 @@ - [Install the Transformers](#install-the-transformers) - [How to use?](#how-to-use) - [Supervised datasets collection](#supervised-datasets-collection) - - [Stage1 - Supervised instructs tuning](#stage1---supervised-instructs-tuning) - - [Stage2 - Training reward model](#stage2---training-reward-model) - - [Stage3 - Training model with reinforcement learning by human feedback](#stage3---training-model-with-reinforcement-learning-by-human-feedback) - - [Inference - After Training](#inference---after-training) - - [8-bit setup](#8-bit-setup) - - [4-bit setup](#4-bit-setup) + - [RLHF Training Stage1 - Supervised instructs tuning](#RLHF-training-stage1---supervised-instructs-tuning) + - [RLHF Training Stage2 - Training reward model](#RLHF-training-stage2---training-reward-model) + - [RLHF Training Stage3 - Training model with reinforcement learning by human feedback](#RLHF-training-stage3---training-model-with-reinforcement-learning-by-human-feedback) + - [Inference Quantization and Serving - After Training](#inference-quantization-and-serving---after-training) - [Coati7B examples](#coati7b-examples) - [Generation](#generation) - [Open QA](#open-qa) - - [Limitation for LLaMA-finetuned models](#limitation-for-llama-finetuned-models) - - [Limitation of dataset](#limitation-of-dataset) + - [Limitation for LLaMA-finetuned models](#limitation) + - [Limitation of dataset](#limitation) - [FAQ](#faq) - - [How to save/load checkpoint](#how-to-saveload-checkpoint) - - [How to train with limited resources](#how-to-train-with-limited-resources) + - [How to save/load checkpoint](#faq) + - [How to train with limited resources](#faq) - [The Plan](#the-plan) - [Real-time progress](#real-time-progress) - [Invitation to open-source contribution](#invitation-to-open-source-contribution) @@ -107,43 +105,19 @@ Here is how we collected the data

          -### Stage1 - Supervised instructs tuning +### RLHF Training Stage1 - Supervised instructs tuning -Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned earlier to fine-tune the model +Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned earlier to fine-tune the model. -you can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning +You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning. -``` -torchrun --standalone --nproc_per_node=4 train_sft.py \ - --pretrain "/path/to/LLaMa-7B/" \ - --model 'llama' \ - --strategy colossalai_zero2 \ - --log_interval 10 \ - --save_path /path/to/Coati-7B \ - --dataset /path/to/data.json \ - --batch_size 4 \ - --accimulation_steps 8 \ - --lr 2e-5 \ - --max_datasets_size 512 \ - --max_epochs 1 \ -``` - -### Stage2 - Training reward model +### RLHF Training Stage2 - Training reward model Stage2 trains a reward model, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model -you can run the `examples/train_rm.sh` to start a reward model training - -``` -torchrun --standalone --nproc_per_node=4 train_reward_model.py - --pretrain "/path/to/LLaMa-7B/" \ - --model 'llama' \ - --strategy colossalai_zero2 \ - --loss_fn 'log_exp'\ - --save_path 'rmstatic.pt' \ -``` +You can run the `examples/train_rm.sh` to start a reward model training. -### Stage3 - Training model with reinforcement learning by human feedback +### RLHF Training Stage3 - Training model with reinforcement learning by human feedback Stage3 uses reinforcement learning algorithm, which is the most complex part of the training process: @@ -151,63 +125,16 @@ Stage3 uses reinforcement learning algorithm, which is the most complex part of

          -you can run the `examples/train_prompts.sh` to start training PPO with human feedback - -``` -torchrun --standalone --nproc_per_node=4 train_prompts.py \ - --pretrain "/path/to/LLaMa-7B/" \ - --model 'llama' \ - --strategy colossalai_zero2 \ - --prompt_path /path/to/your/prompt_dataset \ - --pretrain_dataset /path/to/your/pretrain_dataset \ - --rm_pretrain /your/pretrain/rm/defination \ - --rm_path /your/rm/model/path -``` +You can run the `examples/train_prompts.sh` to start training PPO with human feedback. For more details, see [`examples/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples). -### Inference - After Training -#### 8-bit setup - -8-bit quantization is originally supported by the latest [transformers](https://github.com/huggingface/transformers). Please install it from source. - -Please ensure you have downloaded HF-format model weights of LLaMA models. +### Inference Quantization and Serving - After Training -Usage: +We provide an online inference server and a benchmark. We aim to run inference on single GPU, so quantization is essential when using large models. -```python -from transformers import LlamaForCausalLM -USE_8BIT = True # use 8-bit quantization; otherwise, use fp16 -model = LlamaForCausalLM.from_pretrained( - "pretrained/path", - load_in_8bit=USE_8BIT, - torch_dtype=torch.float16, - device_map="auto", - ) -if not USE_8BIT: - model.half() # use fp16 -model.eval() -``` - -**Troubleshooting**: if you get errors indicating your CUDA-related libraries are not found when loading the 8-bit model, you can check whether your `LD_LIBRARY_PATH` is correct. - -E.g. you can set `export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`. - -#### 4-bit setup - -Please ensure you have downloaded the HF-format model weights of LLaMA models first. - -Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight conversion scripts. - -After installing this lib, we may convert the original HF-format LLaMA model weights to a 4-bit version. - -```shell -CUDA_VISIBLE_DEVICES=0 python llama.py /path/to/pretrained/llama-7b c4 --wbits 4 --groupsize 128 --save llama7b-4bit.pt -``` - -Run this command in your cloned `GPTQ-for-LLaMa` directory, then you will get a 4-bit weight file `llama7b-4bit-128g.pt`. - -**Troubleshooting**: if you get errors about `position_ids`, you can checkout to commit `50287c3b9ae4a3b66f6b5127c643ec39b769b155`(`GPTQ-for-LLaMa` repo). +We support 8-bit quantization (RTN), 4-bit quantization (GPTQ), and FP16 inference. You can +Online inference server scripts can help you deploy your own services. For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference). @@ -283,24 +210,27 @@ For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tre You can find more examples in this [repo](https://github.com/XueFuzhao/InstructionWild/blob/main/comparison.md). -### Limitation for LLaMA-finetuned models +### Limitation +
          Limitation for LLaMA-finetuned models - Both Alpaca and ColossalChat are based on LLaMA. It is hard to compensate for the missing knowledge in the pre-training stage. - Lack of counting ability: Cannot count the number of items in a list. - Lack of Logics (reasoning and calculation) - Tend to repeat the last sentence (fail to produce the end token). - Poor multilingual results: LLaMA is mainly trained on English datasets (Generation performs better than QA). +
          -### Limitation of dataset +
          Limitation of dataset - Lack of summarization ability: No such instructions in finetune datasets. - Lack of multi-turn chat: No such instructions in finetune datasets - Lack of self-recognition: No such instructions in finetune datasets - Lack of Safety: - When the input contains fake facts, the model makes up false facts and explanations. - Cannot abide by OpenAI's policy: When generating prompts from OpenAI API, it always abides by its policy. So no violation case is in the datasets. +
          ## FAQ -### How to save/load checkpoint +
          How to save/load checkpoint We have integrated the Transformers save and load pipeline, allowing users to freely call Hugging Face's language models and save them in the HF format. @@ -325,7 +255,9 @@ trainer.fit() trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) ``` -### How to train with limited resources +
          + +
          How to train with limited resources Here are some examples that can allow you to train a 7B model on a single or multiple consumer-grade GPUs. @@ -360,7 +292,7 @@ torchrun --standalone --nproc_per_node=1 train_sft.py \ --lr 2e-5 \ --max_datasets_size 512 \ --max_epochs 1 \ -``` +``` If you have 4x32 GB GPUs, you can even train the whole 7B model using our `colossalai_zero2_cpu` strategy! The script is given as follows. ``` @@ -377,6 +309,8 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --max_datasets_size 512 \ --max_epochs 1 \ ``` +
          + ## The Plan @@ -409,6 +343,14 @@ and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/m Thanks so much to all of our amazing contributors! ## Quick Preview + + +- An open-source low cost solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. [[demo]](https://chat.colossalai.org) +

          From 1a809eddaa20d617e41439c9f4721c05d16a777a Mon Sep 17 00:00:00 2001 From: MisterLin1995 <16671583+MisterLin1995@users.noreply.github.com> Date: Thu, 13 Apr 2023 18:18:36 +0800 Subject: [PATCH 132/413] [chat] ChatGPT train prompts on ray example (#3309) * [feat][chatgpt]train prompts on ray example * [fix]simplify code * [fix]remove depreciated parameter * [fix]add dependencies * [fix]method calling * [fix]experience maker * [fix]missing loss function * [fix]init optimizer * [feat]add usage comment * [fix]rename files * [fix]add readme * [fix]file path * [fix]move directory --------- Co-authored-by: jiangwen --- .../Chat/examples/community/README.md | 5 +- .../Chat/examples/community/ray/README.md | 17 + .../examples/community/ray/ray_job_script.py | 22 + .../community/ray/train_prompts_on_ray.py | 555 ++++++++++++++++++ 4 files changed, 597 insertions(+), 2 deletions(-) create mode 100644 applications/Chat/examples/community/ray/README.md create mode 100644 applications/Chat/examples/community/ray/ray_job_script.py create mode 100644 applications/Chat/examples/community/ray/train_prompts_on_ray.py diff --git a/applications/Chat/examples/community/README.md b/applications/Chat/examples/community/README.md index 905418892611..c9c645032288 100644 --- a/applications/Chat/examples/community/README.md +++ b/applications/Chat/examples/community/README.md @@ -1,6 +1,6 @@ # Community Examples --- -We are thrilled to announce the latest updates to ColossalChat, an open-source solution for cloning ChatGPT with a complete RLHF (Reinforcement Learning with Human Feedback) pipeline. +We are thrilled to announce the latest updates to ColossalChat, an open-source solution for cloning ChatGPT with a complete RLHF (Reinforcement Learning with Human Feedback) pipeline. As Colossal-AI undergoes major updates, we are actively maintaining ColossalChat to stay aligned with the project's progress. With the introduction of Community-driven example, we aim to create a collaborative platform for developers to contribute exotic features built on top of ColossalChat. @@ -16,7 +16,8 @@ Community examples consist of both inference and training examples that have bee | Example | Description | Code Example | Colab | Author | |:---------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------:| -| Peft | Adding Peft support for SFT and Prompts model training | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/peft) | - | [YY Lin](https://github.com/yynil) | +| Peft | Adding Peft support for SFT and Prompts model training | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/peft) | - | [YY Lin](https://github.com/yynil) | +| Train prompts on Ray | A Ray based implementation of Train prompts example | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/ray) | - | [MisterLin1995](https://github.com/MisterLin1995) | |...|...|...|...|...| ### How to get involved diff --git a/applications/Chat/examples/community/ray/README.md b/applications/Chat/examples/community/ray/README.md new file mode 100644 index 000000000000..64360bd73ddc --- /dev/null +++ b/applications/Chat/examples/community/ray/README.md @@ -0,0 +1,17 @@ +# ColossalAI on Ray +## Abstract +This is an experimental effort to run ColossalAI Chat training on Ray +## How to use? +### 1. Setup Ray clusters +Please follow the official [Ray cluster setup instructions](https://docs.ray.io/en/latest/cluster/getting-started.html) to setup an cluster with GPU support. Record the cluster's api server endpoint, it should be something similar to http://your.head.node.addrees:8265 +### 2. Clone repo +Clone this project: +```shell +git clone https://github.com/hpcaitech/ColossalAI.git +``` +### 3. Submit the ray job +```shell +python applications/Chat/examples/community/ray/ray_job_script.py http://your.head.node.addrees:8265 +``` +### 4. View your job on the Ray Dashboard +Open your ray cluster dashboard http://your.head.node.addrees:8265 to view your submitted training job. diff --git a/applications/Chat/examples/community/ray/ray_job_script.py b/applications/Chat/examples/community/ray/ray_job_script.py new file mode 100644 index 000000000000..53f304d379fe --- /dev/null +++ b/applications/Chat/examples/community/ray/ray_job_script.py @@ -0,0 +1,22 @@ +import sys + +from ray.job_submission import JobSubmissionClient + + +def main(api_server_endpoint="http://127.0.0.1:8265"): + client = JobSubmissionClient(api_server_endpoint) + client.submit_job( + entrypoint= + "python experimental/ray/train_prompts_on_ray.py --strategy colossalai_zero2 --prompt_csv_url https://huggingface.co/datasets/fka/awesome-chatgpt-prompts/resolve/main/prompts.csv", + runtime_env={ + "working_dir": + "applications/Chat", + "pip": [ + "torch==1.13.1", "transformers>=4.20.1", "datasets", "loralib", "colossalai>=0.2.4", "langchain", + "tokenizers", "fastapi", "sse_starlette", "wandb", "sentencepiece", "gpustat" + ] + }) + + +if __name__ == "__main__": + main(sys.argv[1]) diff --git a/applications/Chat/examples/community/ray/train_prompts_on_ray.py b/applications/Chat/examples/community/ray/train_prompts_on_ray.py new file mode 100644 index 000000000000..289330ad8415 --- /dev/null +++ b/applications/Chat/examples/community/ray/train_prompts_on_ray.py @@ -0,0 +1,555 @@ +import argparse +import logging +import os +import socket +from copy import deepcopy +from typing import Type + +import ray +import torch +from coati.experience_maker.base import Experience +from coati.models.base import RewardModel +from coati.models.bloom import BLOOMActor, BLOOMCritic +from coati.models.gpt import GPTActor, GPTCritic +from coati.models.lora import LoRAModule +from coati.models.loss import PolicyLoss, ValueLoss +from coati.models.opt import OPTActor, OPTCritic +from coati.models.utils import compute_reward +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from ray.util.placement_group import placement_group +from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy +from torch.optim import Adam +from transformers import AutoTokenizer, BloomTokenizerFast +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.nn.optimizer import HybridAdam + + +class ExperienceCompositionRefs: + + def __init__(self, sequences_attention_mask_action_mask_ref: ray.ObjectRef, action_log_probs_ref: ray.ObjectRef, + base_action_log_probs_ref: ray.ObjectRef, value_ref: ray.ObjectRef, r_ref: ray.ObjectRef) -> None: + self.sequences_attention_mask_action_mask_ref = sequences_attention_mask_action_mask_ref + self.action_log_probs_ref = action_log_probs_ref + self.base_action_log_probs_ref = base_action_log_probs_ref + self.value_ref = value_ref + self.r_ref = r_ref + + +class ExperienceMaker: + + def __init__(self, kl_coef) -> None: + self.kl_coef = kl_coef + + @torch.no_grad() + def make_experience(self, experiment_computation_refs: ExperienceCompositionRefs): + sequences, attention_mask, action_mask = ray.get( + experiment_computation_refs.sequences_attention_mask_action_mask_ref) + action_log_probs = ray.get(experiment_computation_refs.action_log_probs_ref) + base_action_log_probs = ray.get(experiment_computation_refs.base_action_log_probs_ref) + r = ray.get(experiment_computation_refs.r_ref) + reward = compute_reward(r, self.kl_coef, action_log_probs, base_action_log_probs, action_mask=action_mask) + value = ray.get(experiment_computation_refs.value_ref) + advantage = reward - value + if advantage.ndim == 1: + advantage = advantage.unsqueeze(-1) + experience = Experience(sequences, action_log_probs, value, reward, advantage, attention_mask, action_mask) + return experience + + +class DistributedTorchRayActor: + + def __init__(self, world_size, rank, local_rank, master_addr, master_port): + logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', + level=logging.INFO, + datefmt='%Y-%m-%d %H:%M:%S') + self._model = None + self._world_size = world_size + self._rank = rank + self._local_rank = local_rank + self._master_addr = master_addr if master_addr else self._get_current_node_ip() + self._master_port = master_port if master_port else self._get_free_port() + os.environ["MASTER_ADDR"] = self._master_addr + os.environ["MASTER_PORT"] = str(self._master_port) + os.environ["WORLD_SIZE"] = str(self._world_size) + os.environ["RANK"] = str(self._rank) + os.environ["LOCAL_RANK"] = str(self._local_rank) + + @staticmethod + def _get_current_node_ip(): + return ray._private.services.get_node_ip_address() + + @staticmethod + def _get_free_port(): + with socket.socket() as sock: + sock.bind(('', 0)) + return sock.getsockname()[1] + + def get_master_addr_port(self): + return self._master_addr, self._master_port + + +class BasePPORole(DistributedTorchRayActor): + + def add_experience_maker(self, kl_coef: float = 0.1): + self._experience_maker = ExperienceMaker(kl_coef) + + def make_experience(self, experience_computation_ref: ExperienceCompositionRefs): + return self._experience_maker.make_experience(experience_computation_ref) + + def _init_strategy(self, strategy: str): + # configure strategy + if strategy == 'naive': + self._strategy = NaiveStrategy() + elif strategy == 'ddp': + self._strategy = DDPStrategy() + elif strategy == 'colossalai_gemini': + self._strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + elif strategy == 'colossalai_zero2': + self._strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{strategy}"') + + def _init_optimizer(self): + if isinstance(self._strategy, ColossalAIStrategy): + self._optimizer = HybridAdam(self._model.parameters(), lr=5e-6) + else: + self._optimizer = Adam(self._model.parameters(), lr=5e-6) + + def _prepare_model_with_strategy(self, has_optimizer: bool): + if has_optimizer: + self._init_optimizer() + (self._model, self._optimizer) = self._strategy.prepare((self._model, self._optimizer)) + else: + self._model = self._strategy.prepare(self._model) + + def _load_model_from_pretrained(self, model_class: Type[LoRAModule], pretrain: str): + raise NotImplementedError() + + def init_model_from_pretrained(self, + strategy: str, + model_class: Type[LoRAModule], + pretrain: str, + has_optimizer=False): + self._init_strategy(strategy) + self._load_model_from_pretrained(model_class, pretrain) + self._prepare_model_with_strategy(has_optimizer) + + def eval(self): + self._model.eval() + + +class TrainablePPORole(BasePPORole): + + def _load_model_from_pretrained(self, model_class, pretrain): + with self._strategy.model_init_context(): + self._model = model_class(pretrain).to(torch.cuda.current_device()) + + def _train(self): + self._model.train() + + def _training_step(self, experience: Experience): + raise NotImplementedError() + + def learn_on_experiences(self, experience_refs): + experiences = ray.get(experience_refs) + device = torch.cuda.current_device() + self._train() + for exp in experiences: + exp.to_device(device) + self._training_step(exp) + self.eval() + + +@ray.remote(num_gpus=1) +class RayPPOActor(TrainablePPORole): + + def set_loss_function(self, eps_clip: float): + self._actor_loss_fn = PolicyLoss(eps_clip) + + def load_tokenizer_from_pretrained(self, model_type: str, pretrained): + if model_type == 'gpt2': + self._model_tokenizer = GPT2Tokenizer.from_pretrained(pretrained) + self._model_tokenizer.pad_token = self._model_tokenizer.eos_token + elif model_type == 'bloom': + self._model_tokenizer = BloomTokenizerFast.from_pretrained(pretrained) + self._model_tokenizer.pad_token = self._model_tokenizer.eos_token + elif model_type == 'opt': + self._model_tokenizer = AutoTokenizer.from_pretrained(pretrained) + else: + raise ValueError(f'Unsupported model "{model_type}"') + + # Set tokenize function for sequence generation + def _text_input_tokenize_fn(texts): + batch = self._model_tokenizer(texts, return_tensors='pt', max_length=96, padding=True, truncation=True) + return {k: v.cuda() for k, v in batch.items()} + + self._sample_tokenize_function = _text_input_tokenize_fn + + def setup_generate_kwargs(self, generate_kwargs: dict): + from coati.trainer.ppo import _set_default_generate_kwargs + self._generate_kwargs = _set_default_generate_kwargs(self._strategy, generate_kwargs, self._model) + self._generate_kwargs['pad_token_id'] = self._model_tokenizer.pad_token_id + self._generate_kwargs['eos_token_id'] = self._model_tokenizer.eos_token_id + + def load_csv_prompt_file_from_url_to_sampler(self, prompt_url): + import pandas as pd + prompts = pd.read_csv(prompt_url)['prompt'] + self._sampler = self._strategy.setup_sampler(prompts) + + def _generate(self, input_ids, **generate_kwargs): + return self._model.generate(input_ids, return_action_mask=True, **generate_kwargs) + + def sample_prompts_and_make_sequence(self, experience_batch_size): + sampled_prompts = self._sampler.sample(experience_batch_size) + input_ids = self._sample_tokenize_function(sampled_prompts) + if isinstance(input_ids, dict): + return self._generate(**input_ids, **self._generate_kwargs) + else: + return self._generate(input_ids, **self._generate_kwargs) + + @torch.no_grad() + def calculate_action_log_probs(self, sequence_attention_action_mask): + sequences, attention_mask, action_mask = sequence_attention_action_mask + return self._model.forward(sequences, action_mask.size(1), attention_mask) + + def _training_step(self, experience): + num_actions = experience.action_mask.size(1) + action_log_probs = self._model(experience.sequences, num_actions, attention_mask=experience.attention_mask) + actor_loss = self._actor_loss_fn(action_log_probs, + experience.action_log_probs, + experience.advantages, + action_mask=experience.action_mask) + self._strategy.backward(actor_loss, self._model, self._optimizer) + self._strategy.optimizer_step(self._optimizer) + self._optimizer.zero_grad() + logging.info("actor_loss: {}".format(actor_loss)) + + def save_checkpoint(self, save_path, should_save_optimizer: bool): + if self._rank == 0: + # save model checkpoint only on rank 0 + self._strategy.save_model(self._model, save_path, only_rank0=True) + # save optimizer checkpoint on all ranks + if should_save_optimizer: + self._strategy.save_optimizer(self._optimizer, + 'actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + + def generate_answer(self, prompt, max_length=30, num_return_sequences=5): + encoded_input = self._model_tokenizer(prompt, return_tensors='pt') + input_ids = {k: v.cuda() for k, v in encoded_input.items()} + sequence, _ = self._model.generate(**input_ids, + max_length=max_length, + return_action_mask=False, + num_return_sequences=num_return_sequences) + token_list = list(sequence.data[0]) + output = " ".join([self._model_tokenizer.decode(token) for token in token_list]) + return output + + +@ray.remote(num_gpus=1) +class RayPPOCritic(TrainablePPORole): + + def set_loss_function(self, value_clip: float): + self._critic_loss_fn = ValueLoss(value_clip) + + def _training_step(self, experience): + values = self._model(experience.sequences, + action_mask=experience.action_mask, + attention_mask=experience.attention_mask) + critic_loss = self._critic_loss_fn(values, + experience.values, + experience.reward, + action_mask=experience.action_mask) + self._strategy.backward(critic_loss, self._model, self._optimizer) + self._strategy.optimizer_step(self._optimizer) + self._optimizer.zero_grad() + logging.info("critic_loss: {}".format(critic_loss)) + + @torch.no_grad() + def calculate_value(self, sequence_attention_action_mask): + sequences, attention_mask, action_mask = sequence_attention_action_mask + return self._model(sequences, action_mask, attention_mask) + + +@ray.remote(num_gpus=1) +class RayPPORewardModel(BasePPORole): + + def _load_model_from_pretrained(self, model_class, pretrain): + with self._strategy.model_init_context(): + critic = model_class(pretrained=pretrain).to(torch.cuda.current_device()) + self._model = RewardModel(deepcopy(critic.model), + deepcopy(critic.value_head)).to(torch.cuda.current_device()) + + @torch.no_grad() + def calculate_r(self, sequence_attention_action_mask): + sequences, attention_mask, _ = sequence_attention_action_mask + return self._model(sequences, attention_mask) + + +@ray.remote(num_gpus=1) +class RayPPOInitialModel(BasePPORole): + + def _load_model_from_pretrained(self, model_class, pretrain): + with self._strategy.model_init_context(): + self._model = model_class(pretrain).to(torch.cuda.current_device()) + + @torch.no_grad() + def calculate_base_action_log_probs(self, sequence_attention_action_mask): + sequences, attention_mask, action_mask = sequence_attention_action_mask + return self._model(sequences, action_mask.size(1), attention_mask) + + +class PPORayActorGroup: + """ + A group of ray actors + Functions start with 'async' should return list of object refs + """ + + def __init__(self, num_nodes, num_gpus_per_node, ray_actor_type: Type[BasePPORole]) -> None: + self._num_nodes = num_nodes + self._num_gpus_per_node = num_gpus_per_node + self.ray_actor_type = ray_actor_type + self._initiate_actors() + + def _initiate_actors(self): + world_size = self._num_nodes * self._num_gpus_per_node + # Use placement group to lock resources for models of same type + pg = None + if self._num_gpus_per_node > 1: + bundles = [{"GPU": self._num_gpus_per_node, "CPU": self._num_gpus_per_node} for _ in range(self._num_nodes)] + pg = placement_group(bundles, strategy="STRICT_SPREAD") + ray.get(pg.ready()) + if pg: + master_actor = self.ray_actor_type.options(scheduling_strategy=PlacementGroupSchedulingStrategy( + placement_group=pg, placement_group_bundle_index=0)).remote(world_size, 0, 0, None, None) + else: + master_actor = self.ray_actor_type.options(num_gpus=1).remote(world_size, 0, 0, None, None) + self._actor_handlers = [master_actor] + + # Create worker actors + if world_size > 1: + master_addr, master_port = ray.get(master_actor.get_master_addr_port.remote()) + for rank in range(1, world_size): + local_rank = rank % self._num_gpus_per_node + if pg: + worker_actor = self.ray_actor_type.options(scheduling_strategy=PlacementGroupSchedulingStrategy( + placement_group=pg, placement_group_bundle_index=rank // self._num_gpus_per_node)).remote( + world_size, rank, local_rank, master_addr, master_port) + else: + worker_actor = self.ray_actor_type.options(num_gpus=1).remote(world_size, rank, local_rank, + master_addr, master_port) + self._actor_handlers.append(worker_actor) + + def async_init_model_from_pretrained(self, strategy: str, model_class: Type[LoRAModule], pretrain: str, + has_optimizer: bool): + return [ + actor.init_model_from_pretrained.remote(strategy, model_class, pretrain, has_optimizer) + for actor in self._actor_handlers + ] + + +class TrainableModelRayActorGroup(PPORayActorGroup): + + def async_learn_on_experiences(self, experience_refs): + num_actors = len(self._actor_handlers) + learn_result_refs = [] + for i in range(num_actors): + exp_refs_batch = experience_refs[i::num_actors] + learn_result_refs.append(self._actor_handlers[i].learn_on_experiences.remote(exp_refs_batch)) + return learn_result_refs + + +class PPOActorRayActorGroup(TrainableModelRayActorGroup): + + def __init__(self, num_nodes, num_gpus_per_node) -> None: + super().__init__(num_nodes, num_gpus_per_node, RayPPOActor) + + def async_prepare_for_sequence_generation(self, model: str, pretrain: str, generation_kwargs: dict): + refs = [] + for actor in self._actor_handlers: + refs.append(actor.load_tokenizer_from_pretrained.remote(model, pretrain)) + refs.append(actor.setup_generate_kwargs.remote(generation_kwargs)) + return refs + + def load_csv_prompt_file_from_url_to_sampler(self, csv_url): + ray.get([actor.load_csv_prompt_file_from_url_to_sampler.remote(csv_url) for actor in self._actor_handlers]) + + def async_sample_prompts_and_make_sequence(self, experience_batch_size): + return [actor.sample_prompts_and_make_sequence.remote(experience_batch_size) for actor in self._actor_handlers] + + def async_calculate_action_log_probs(self, sequences_attention_mask_action_mask_refs): + num_actors = len(self._actor_handlers) + action_log_probs_refs = [] + for i in range(len(sequences_attention_mask_action_mask_refs)): + action_log_probs_ref = self._actor_handlers[i % num_actors].calculate_action_log_probs.remote( + sequences_attention_mask_action_mask_refs[i]) + action_log_probs_refs.append(action_log_probs_ref) + return action_log_probs_refs + + def set_loss_function(self, eps_clip: float = 0.2): + ray.get([actor.set_loss_function.remote(eps_clip) for actor in self._actor_handlers]) + + def save_checkpoint(self, save_path, should_save_optimizer): + ray.get([actor.save_checkpoint.remote(save_path, should_save_optimizer) for actor in self._actor_handlers]) + + +class PPOCriticRayActorGroup(TrainableModelRayActorGroup): + + def __init__(self, num_nodes, num_gpus_per_node) -> None: + super().__init__(num_nodes, num_gpus_per_node, RayPPOCritic) + + def async_calculate_value(self, sequences_attention_mask_action_mask_refs): + num_actors = len(self._actor_handlers) + value_refs = [] + for i in range(len(sequences_attention_mask_action_mask_refs)): + value_ref = self._actor_handlers[i % num_actors].calculate_value.remote( + sequences_attention_mask_action_mask_refs[i]) + value_refs.append(value_ref) + return value_refs + + def set_loss_function(self, value_clip: float = 0.4): + ray.get([actor.set_loss_function.remote(value_clip) for actor in self._actor_handlers]) + + +class PPOInitialRayActorGroup(PPORayActorGroup): + + def __init__(self, num_nodes, num_gpus_per_node) -> None: + super().__init__(num_nodes, num_gpus_per_node, RayPPOInitialModel) + + def async_calculate_base_action_log_probs(self, sequences_attention_mask_action_mask_refs): + num_actors = len(self._actor_handlers) + base_action_log_probs_refs = [] + for i in range(len(sequences_attention_mask_action_mask_refs)): + base_action_log_probs_ref = self._actor_handlers[i % num_actors].calculate_base_action_log_probs.remote( + sequences_attention_mask_action_mask_refs[i]) + base_action_log_probs_refs.append(base_action_log_probs_ref) + return base_action_log_probs_refs + + +class PPORewardRayActorGroup(PPORayActorGroup): + + def __init__(self, num_nodes, num_gpus_per_node) -> None: + super().__init__(num_nodes, num_gpus_per_node, RayPPORewardModel) + + def async_calculate_r(self, sequences_attention_mask_action_mask_refs): + num_actors = len(self._actor_handlers) + r_refs = [] + for i in range(len(sequences_attention_mask_action_mask_refs)): + r_ref = self._actor_handlers[i % num_actors].calculate_r.remote( + sequences_attention_mask_action_mask_refs[i]) + r_refs.append(r_ref) + return r_refs + + +def main(args): + logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', + level=logging.INFO, + datefmt='%Y-%m-%d %H:%M:%S') + if args.model == 'gpt2': + actor_model_class, critic_model_class = GPTActor, GPTCritic + elif args.model == 'bloom': + actor_model_class, critic_model_class = BLOOMActor, BLOOMCritic + elif args.model == 'opt': + actor_model_class, critic_model_class = OPTActor, OPTCritic + else: + raise ValueError(f'Unsupported model "{args.model}"') + + logging.info("Start creating actors") + # Initialize 4 models (actor, critic, initial_model and reward_model) + actor_group = PPOActorRayActorGroup(num_nodes=args.num_actor_nodes, num_gpus_per_node=args.num_gpus_per_node) + critic_group = PPOCriticRayActorGroup(num_nodes=args.num_critic_nodes, num_gpus_per_node=args.num_gpus_per_node) + initial_group = PPOInitialRayActorGroup(num_nodes=args.num_initial_nodes, num_gpus_per_node=args.num_gpus_per_node) + reward_group = PPORewardRayActorGroup(num_nodes=args.num_reward_nodes, num_gpus_per_node=args.num_gpus_per_node) + logging.info("Actors created") + + # Prepare model for training + generate_kwargs = {'max_length': 128, 'do_sample': True, 'temperature': 1.0, 'top_k': 50} + ray.get( + actor_group.async_init_model_from_pretrained(args.strategy, actor_model_class, args.pretrain, True) + + critic_group.async_init_model_from_pretrained(args.strategy, critic_model_class, args.pretrain, True) + + initial_group.async_init_model_from_pretrained(args.strategy, actor_model_class, args.pretrain, False) + + reward_group.async_init_model_from_pretrained(args.strategy, critic_model_class, args.pretrain, False) + + actor_group.async_prepare_for_sequence_generation(args.model, args.pretrain, generate_kwargs)) + logging.info("Models prepared for training") + + # Prepare models for training + actor_group.load_csv_prompt_file_from_url_to_sampler(args.prompt_csv_url) + actor_group.set_loss_function() + critic_group.set_loss_function() + # Training parameter + num_episodes = args.num_episodes + max_timesteps = args.max_timesteps + update_timesteps = args.update_timesteps + experience_batch_size = args.experience_batch_size + # Start training + logging.info("Training start") + # Set all models to eval and add experience maker + all_ray_actors = actor_group._actor_handlers + critic_group._actor_handlers + \ + initial_group._actor_handlers + reward_group._actor_handlers + num_ray_actors = len(all_ray_actors) + ray.get([ray_actor.eval.remote() for ray_actor in all_ray_actors]) + ray.get([ray_actor.add_experience_maker.remote() for ray_actor in all_ray_actors]) + # Used as a queue to coordinate experience making + experience_composition_refs = [] + time = 0 + for episode in range(num_episodes): + logging.info("episode {} started".format(episode)) + for _ in range(max_timesteps): + time += 1 + # Experience queueing stage + sequences_attention_mask_action_mask_refs = actor_group.async_sample_prompts_and_make_sequence( + experience_batch_size) + base_action_log_probs_refs = initial_group.async_calculate_base_action_log_probs( + sequences_attention_mask_action_mask_refs) + values_refs = critic_group.async_calculate_value(sequences_attention_mask_action_mask_refs) + r_refs = reward_group.async_calculate_r(sequences_attention_mask_action_mask_refs) + action_log_probs_refs = actor_group.async_calculate_action_log_probs( + sequences_attention_mask_action_mask_refs) + experience_composition_refs.extend([ + ExperienceCompositionRefs(sequences_attention_mask_action_mask_refs[i], action_log_probs_refs[i], + base_action_log_probs_refs[i], values_refs[i], r_refs[i]) + for i in range(len(sequences_attention_mask_action_mask_refs)) + ]) + # Learning stage + if time % update_timesteps == 0: + experience_refs = [] + # calculate experiences + for i, experience_composition_ref in enumerate(experience_composition_refs): + exp_composition_ref = experience_composition_ref + selected_ray_actor = all_ray_actors[i % num_ray_actors] + experience_refs.append(selected_ray_actor.make_experience.remote(exp_composition_ref)) + # backward + ray.get( + actor_group.async_learn_on_experiences(experience_refs) + + critic_group.async_learn_on_experiences(experience_refs)) + # clear refs queue + experience_composition_refs.clear() + logging.info("Training finished") + # Save checkpoint + actor_group.save_checkpoint(args.save_path, args.need_optim_ckpt) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--prompt_csv_url', type=str) + parser.add_argument('--strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) + parser.add_argument('--pretrain', type=str, default='gpt2') + parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--num_episodes', type=int, default=10) + parser.add_argument('--max_timesteps', type=int, default=10) + parser.add_argument('--update_timesteps', type=int, default=10) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--num_actor_nodes', type=int, help='num of nodes to use to host actor model', default=1) + parser.add_argument('--num_critic_nodes', type=int, help='num of nodes to use to host critic model', default=1) + parser.add_argument('--num_initial_nodes', type=int, help='num of nodes to use to host initial model', default=1) + parser.add_argument('--num_reward_nodes', type=int, help='num of nodes to use to host reward model', default=1) + parser.add_argument('--num_gpus_per_node', type=int, help='num of gpus on a ray node', default=1) + args = parser.parse_args() + ray.init() + main(args) From f1b3d60caee43e5dabf8bc48f529e663b94ace71 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Fri, 14 Apr 2023 16:27:48 +0800 Subject: [PATCH 133/413] [example] reorganize for community examples (#3557) --- examples/README.md | 14 +- examples/community/README.md | 28 +++ .../fp8/mnist/README.md | 26 +-- .../{tutorial => community}/fp8/mnist/main.py | 56 ++---- .../{language => community}/roberta/README.md | 4 +- .../roberta/preprocessing/Makefile | 0 .../roberta/preprocessing/README.md | 10 +- .../roberta/preprocessing/get_mask.py | 71 +++---- .../community/roberta/preprocessing/mask.cpp | 190 ++++++++++++++++++ .../roberta/preprocessing/sentence_split.py | 58 +++--- .../roberta/preprocessing/tokenize_mask.py | 110 +++++----- .../roberta/pretraining/README.md | 5 +- .../roberta/pretraining/arguments.py | 87 ++++++++ .../pretraining/bert_dataset_provider.py | 1 + .../roberta/pretraining/evaluation.py | 33 +-- .../roberta/pretraining/hostfile | 0 .../roberta/pretraining/loss.py | 2 +- .../roberta/pretraining/model/bert.py | 142 ++++++------- .../roberta/pretraining/model/deberta_v2.py | 158 ++++++++------- .../nvidia_bert_dataset_provider.py | 74 +++---- .../roberta/pretraining/pretrain_utils.py | 62 +++--- .../roberta/pretraining/run_pretrain.sh | 1 - .../pretraining/run_pretrain_resume.sh | 1 - .../roberta/pretraining/run_pretraining.py | 54 +++-- .../roberta/pretraining/utils/WandbLog.py | 14 +- .../roberta/pretraining/utils/exp_util.py | 27 ++- .../roberta/pretraining/utils/global_vars.py | 18 +- .../roberta/pretraining/utils/logger.py | 13 +- .../roberta/requirements.txt | 2 +- .../language/roberta/preprocessing/mask.cpp | 184 ----------------- .../language/roberta/pretraining/arguments.py | 176 ---------------- 31 files changed, 781 insertions(+), 840 deletions(-) create mode 100644 examples/community/README.md rename examples/{tutorial => community}/fp8/mnist/README.md (89%) rename examples/{tutorial => community}/fp8/mnist/main.py (81%) rename examples/{language => community}/roberta/README.md (96%) rename examples/{language => community}/roberta/preprocessing/Makefile (100%) rename examples/{language => community}/roberta/preprocessing/README.md (96%) rename examples/{language => community}/roberta/preprocessing/get_mask.py (85%) create mode 100644 examples/community/roberta/preprocessing/mask.cpp rename examples/{language => community}/roberta/preprocessing/sentence_split.py (86%) rename examples/{language => community}/roberta/preprocessing/tokenize_mask.py (76%) rename examples/{language => community}/roberta/pretraining/README.md (97%) create mode 100644 examples/community/roberta/pretraining/arguments.py rename examples/{language => community}/roberta/pretraining/bert_dataset_provider.py (99%) rename examples/{language => community}/roberta/pretraining/evaluation.py (74%) rename examples/{language => community}/roberta/pretraining/hostfile (100%) rename examples/{language => community}/roberta/pretraining/loss.py (91%) rename examples/{language => community}/roberta/pretraining/model/bert.py (96%) rename examples/{language => community}/roberta/pretraining/model/deberta_v2.py (92%) rename examples/{language => community}/roberta/pretraining/nvidia_bert_dataset_provider.py (76%) rename examples/{language => community}/roberta/pretraining/pretrain_utils.py (77%) rename examples/{language => community}/roberta/pretraining/run_pretrain.sh (98%) rename examples/{language => community}/roberta/pretraining/run_pretrain_resume.sh (98%) rename examples/{language => community}/roberta/pretraining/run_pretraining.py (94%) rename examples/{language => community}/roberta/pretraining/utils/WandbLog.py (98%) rename examples/{language => community}/roberta/pretraining/utils/exp_util.py (86%) rename examples/{language => community}/roberta/pretraining/utils/global_vars.py (91%) rename examples/{language => community}/roberta/pretraining/utils/logger.py (81%) rename examples/{language => community}/roberta/requirements.txt (91%) delete mode 100644 examples/language/roberta/preprocessing/mask.cpp delete mode 100644 examples/language/roberta/pretraining/arguments.py diff --git a/examples/README.md b/examples/README.md index dd5e7b10ae66..142a735c6819 100644 --- a/examples/README.md +++ b/examples/README.md @@ -10,9 +10,12 @@ ## Overview -This folder provides several examples accelerated by Colossal-AI. The `tutorial` folder is for everyone to quickly try out the different features in Colossal-AI. Other folders such as `images` and `language` include a wide range of deep learning tasks and applications. +This folder provides several examples accelerated by Colossal-AI. +Folders such as `images` and `language` include a wide range of deep learning tasks and applications. +The `community` folder aim to create a collaborative platform for developers to contribute exotic features built on top of Colossal-AI. +The `tutorial` folder is for everyone to quickly try out the different features in Colossal-AI. -You can find applications such as Chatbot, Stable Diffusion and Biomedicine in the [Applications](https://github.com/hpcaitech/ColossalAI/tree/main/applications) directory. +You can find applications such as Chatbot, AIGC and Biomedicine in the [Applications](https://github.com/hpcaitech/ColossalAI/tree/main/applications) directory. ## Folder Structure @@ -52,3 +55,10 @@ Therefore, it is essential for the example contributors to know how to integrate 2. Configure your testing parameters such as number steps, batch size in `test_ci.sh`, e.t.c. Keep these parameters small such that each example only takes several minutes. 3. Export your dataset path with the prefix `/data` and make sure you have a copy of the dataset in the `/data/scratch/examples-data` directory on the CI machine. Community contributors can contact us via slack to request for downloading the dataset on the CI machine. 4. Implement the logic such as dependency setup and example execution + +## Community Dependency +We are happy to introduce the following nice community dependency repos that are powered by Colossal-AI: +- [lightning-ColossalAI](https://github.com/Lightning-AI/lightning) +- [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion) +- [KoChatGPT](https://github.com/airobotlab/KoChatGPT) +- [minichatgpt](https://github.com/juncongmoo/minichatgpt) diff --git a/examples/community/README.md b/examples/community/README.md new file mode 100644 index 000000000000..2abf5c7e8404 --- /dev/null +++ b/examples/community/README.md @@ -0,0 +1,28 @@ +#Community Examples + +Community-driven Examples is an initiative that allows users to share their own examples to the Colossal-AI community, fostering a sense of community and making it easy for others to access and benefit from shared work. The primary goal with community-driven examples is to have a community-maintained collection of diverse and exotic functionalities built on top of the Colossal-AI package. + +If a community example doesn't work as expected, you can [open an issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) and @ the author to report it. + + +| Example | Description | Code Example | Colab |Author | +|:------------------|:---------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:-----------------------------------------|-----------------------------------------------------:| +| RoBERTa | Adding RoBERTa for SFT and Prompts model training | [RoBERTa](./roberta) | - | [YY Lin](https://github.com/yynil) (Moore Threads) | +| TransformerEngine FP8 | Adding TransformerEngine with FP8 training | [TransformerEngine FP8](./fp8) | - | [Kirthi Shankar Sivamani](https://github.com/ksivaman) (NVIDIA) | +|...|...|...|...|...| + +## Looking for Examples +* [Swin-Transformer](https://github.com/microsoft/Swin-Transformer) +* [T-5](https://github.com/google-research/text-to-text-transfer-transformer) +* [Segment Anything (SAM)](https://github.com/facebookresearch/segment-anything) +* [ControlNet](https://github.com/lllyasviel/ControlNet) +* [Consistency Models](https://github.com/openai/consistency_models) +* [MAE](https://github.com/facebookresearch/mae) +* [CLIP](https://github.com/openai/CLIP) + +Welcome to [open an issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) to share your insights and needs. + +## How to get involved +To join our community-driven initiative, please visit the [Colossal-AI examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples), review the provided information, and explore the codebase. + +To contribute, create a new issue outlining your proposed feature or enhancement, and our team will review and provide feedback. If you are confident enough you can also submit a PR directly. We look forward to collaborating with you on this exciting project! diff --git a/examples/tutorial/fp8/mnist/README.md b/examples/community/fp8/mnist/README.md similarity index 89% rename from examples/tutorial/fp8/mnist/README.md rename to examples/community/fp8/mnist/README.md index 46711f9ebdd8..e1128c1054b7 100644 --- a/examples/tutorial/fp8/mnist/README.md +++ b/examples/community/fp8/mnist/README.md @@ -1,13 +1,13 @@ -# Basic MNIST Example with optional FP8 of TransformerEngine - -[TransformerEngine](https://github.com/NVIDIA/TransformerEngine) is a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference. - -Thanks for the contribution to this tutorial from NVIDIA. - -```bash -python main.py -python main.py --use-te # Linear layers from TransformerEngine -python main.py --use-fp8 # FP8 + TransformerEngine for Linear layers -``` - -> We are working to integrate it with Colossal-AI and will finish it soon. +# Basic MNIST Example with optional FP8 of TransformerEngine + +[TransformerEngine](https://github.com/NVIDIA/TransformerEngine) is a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference. + +Thanks for the contribution to this tutorial from NVIDIA. + +```bash +python main.py +python main.py --use-te # Linear layers from TransformerEngine +python main.py --use-fp8 # FP8 + TransformerEngine for Linear layers +``` + +> We are working to integrate it with Colossal-AI and will finish it soon. diff --git a/examples/tutorial/fp8/mnist/main.py b/examples/community/fp8/mnist/main.py similarity index 81% rename from examples/tutorial/fp8/mnist/main.py rename to examples/community/fp8/mnist/main.py index 000ded2f111f..a534663d380f 100644 --- a/examples/tutorial/fp8/mnist/main.py +++ b/examples/community/fp8/mnist/main.py @@ -3,12 +3,13 @@ # See LICENSE for license information. import argparse + import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim -from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR +from torchvision import datasets, transforms try: from transformer_engine import pytorch as te @@ -18,6 +19,7 @@ class Net(nn.Module): + def __init__(self, use_te=False): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) @@ -62,12 +64,10 @@ def train(args, model, device, train_loader, optimizer, epoch, use_fp8): loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: - print( - f"Train Epoch: {epoch} " - f"[{batch_idx * len(data)}/{len(train_loader.dataset)} " - f"({100. * batch_idx / len(train_loader):.0f}%)]\t" - f"Loss: {loss.item():.6f}" - ) + print(f"Train Epoch: {epoch} " + f"[{batch_idx * len(data)}/{len(train_loader.dataset)} " + f"({100. * batch_idx / len(train_loader):.0f}%)]\t" + f"Loss: {loss.item():.6f}") if args.dry_run: break @@ -83,6 +83,7 @@ def calibrate(model, device, test_loader): with te.fp8_autocast(enabled=False, calibrating=True): output = model(data) + def test(model, device, test_loader, use_fp8): """Testing function.""" model.eval() @@ -93,21 +94,15 @@ def test(model, device, test_loader, use_fp8): data, target = data.to(device), target.to(device) with te.fp8_autocast(enabled=use_fp8): output = model(data) - test_loss += F.nll_loss( - output, target, reduction="sum" - ).item() # sum up batch loss - pred = output.argmax( - dim=1, keepdim=True - ) # get the index of the max log-probability + test_loss += F.nll_loss(output, target, reduction="sum").item() # sum up batch loss + pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) - print( - f"\nTest set: Average loss: {test_loss:.4f}, " - f"Accuracy: {correct}/{len(test_loader.dataset)} " - f"({100. * correct / len(test_loader.dataset):.0f}%)\n" - ) + print(f"\nTest set: Average loss: {test_loss:.4f}, " + f"Accuracy: {correct}/{len(test_loader.dataset)} " + f"({100. * correct / len(test_loader.dataset):.0f}%)\n") def main(): @@ -154,9 +149,7 @@ def main(): default=False, help="quickly check a single pass", ) - parser.add_argument( - "--seed", type=int, default=1, metavar="S", help="random seed (default: 1)" - ) + parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)") parser.add_argument( "--log-interval", type=int, @@ -170,15 +163,12 @@ def main(): default=False, help="For Saving the current Model", ) - parser.add_argument( - "--use-fp8", action="store_true", default=False, help="Use FP8 for inference and training without recalibration" - ) - parser.add_argument( - "--use-fp8-infer", action="store_true", default=False, help="Use FP8 inference only" - ) - parser.add_argument( - "--use-te", action="store_true", default=False, help="Use Transformer Engine" - ) + parser.add_argument("--use-fp8", + action="store_true", + default=False, + help="Use FP8 for inference and training without recalibration") + parser.add_argument("--use-fp8-infer", action="store_true", default=False, help="Use FP8 inference only") + parser.add_argument("--use-te", action="store_true", default=False, help="Use Transformer Engine") args = parser.parse_args() use_cuda = torch.cuda.is_available() @@ -205,9 +195,7 @@ def main(): train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) - transform = transforms.Compose( - [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] - ) + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform) dataset2 = datasets.MNIST("../data", train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs) @@ -227,7 +215,7 @@ def main(): if args.save_model or args.use_fp8_infer: torch.save(model.state_dict(), "mnist_cnn.pt") - print('Eval with reloaded checkpoint : fp8='+str(args.use_fp8_infer)) + print('Eval with reloaded checkpoint : fp8=' + str(args.use_fp8_infer)) weights = torch.load("mnist_cnn.pt") model.load_state_dict(weights) test(model, device, test_loader, args.use_fp8_infer) diff --git a/examples/language/roberta/README.md b/examples/community/roberta/README.md similarity index 96% rename from examples/language/roberta/README.md rename to examples/community/roberta/README.md index 0e080d00981a..8aefa327a4b4 100644 --- a/examples/language/roberta/README.md +++ b/examples/community/roberta/README.md @@ -11,7 +11,7 @@ ssh-keygen ssh-copy-id -i ~/.ssh/id_rsa.pub ip_destination ``` -- In all hosts, edit /etc/hosts to record all hosts' name and ip.The example is shown below. +- In all hosts, edit /etc/hosts to record all hosts' name and ip.The example is shown below. ```bash 192.168.2.1 GPU001 @@ -29,7 +29,7 @@ ssh-copy-id -i ~/.ssh/id_rsa.pub ip_destination service ssh restart ``` -## 1. Corpus Preprocessing +## 1. Corpus Preprocessing ```bash cd preprocessing ``` diff --git a/examples/language/roberta/preprocessing/Makefile b/examples/community/roberta/preprocessing/Makefile similarity index 100% rename from examples/language/roberta/preprocessing/Makefile rename to examples/community/roberta/preprocessing/Makefile diff --git a/examples/language/roberta/preprocessing/README.md b/examples/community/roberta/preprocessing/README.md similarity index 96% rename from examples/language/roberta/preprocessing/README.md rename to examples/community/roberta/preprocessing/README.md index 1dbd745ab9bd..17cc2f4dc22c 100644 --- a/examples/language/roberta/preprocessing/README.md +++ b/examples/community/roberta/preprocessing/README.md @@ -21,7 +21,7 @@ This folder is used to preprocess chinese corpus with Whole Word Masked. You can ### 2.1. Split Sentence & Split data into multiple shard: -Firstly, each file has multiple documents, and each document contains multiple sentences. Split sentence through punctuation, such as `。！`. **Secondly, split data into multiple shard based on server hardware (cpu, cpu memory, hard disk) and corpus size.** Each shard contains a part of corpus, and the model needs to train all the shards as one epoch. +Firstly, each file has multiple documents, and each document contains multiple sentences. Split sentence through punctuation, such as `。！`. **Secondly, split data into multiple shard based on server hardware (cpu, cpu memory, hard disk) and corpus size.** Each shard contains a part of corpus, and the model needs to train all the shards as one epoch. In this example, split 200G Corpus into 100 shard, and each shard is about 2G. The size of the shard is memory-dependent, taking into account the number of servers, the memory used by the tokenizer, and the memory used by the multi-process training to read the shard (n data parallel requires n\*shard_size memory). **To sum up, data preprocessing and model pretraining requires fighting with hardware, not just GPU.** ```python @@ -49,7 +49,7 @@ python sentence_split.py --input_path /orginal_corpus --output_path /shard --sha ] ``` -Output txt: +Output txt: ``` 我今天去打篮球。 @@ -76,7 +76,7 @@ make * `--input_path`: location of all shard with split sentences, e.g., /shard/0.txt, /shard/1.txt ... * `--output_path`: location of all h5 with token_id, input_mask, segment_ids and masked_lm_positions, e.g., /h5/0.h5, /h5/1.h5 ... -* `--tokenizer_path`: tokenizer path contains huggingface tokenizer.json. Download config.json, special_tokens_map.json, vocab.txt and tokenzier.json from [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/tree/main) +* `--tokenizer_path`: tokenizer path contains huggingface tokenizer.json. Download config.json, special_tokens_map.json, vocab.txt and tokenzier.json from [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/tree/main) * `--backend`: python or c++, **specifies c++ can obtain faster preprocess speed** * `--dupe_factor`: specifies how many times the preprocessor repeats to create the input from the same article/document * `--worker`: number of process @@ -91,7 +91,7 @@ make 下周请假。 ``` -Output h5+numpy: +Output h5+numpy: ``` 'input_ids': [[id0,id1,id2,id3,id4,id5,id6,0,0..], @@ -102,4 +102,4 @@ make ...] 'masked_lm_positions': [[label1,-1,-1,label2,-1...], ...] -``` \ No newline at end of file +``` diff --git a/examples/language/roberta/preprocessing/get_mask.py b/examples/community/roberta/preprocessing/get_mask.py similarity index 85% rename from examples/language/roberta/preprocessing/get_mask.py rename to examples/community/roberta/preprocessing/get_mask.py index 869ef2cb377c..74c97a63a9f3 100644 --- a/examples/language/roberta/preprocessing/get_mask.py +++ b/examples/community/roberta/preprocessing/get_mask.py @@ -1,20 +1,22 @@ -import torch +import collections +import logging import os -from enum import IntEnum -from random import choice import random -import collections import time -import logging +from enum import IntEnum +from random import choice + import jieba +import torch + jieba.setLogLevel(logging.CRITICAL) import re -import numpy as np + import mask +import numpy as np PAD = 0 -MaskedLMInstance = collections.namedtuple("MaskedLMInstance", - ["index", "label"]) +MaskedLMInstance = collections.namedtuple("MaskedLMInstance", ["index", "label"]) def map_to_numpy(data): @@ -22,6 +24,7 @@ def map_to_numpy(data): class PreTrainingDataset(): + def __init__(self, tokenizer, max_seq_length, @@ -43,17 +46,15 @@ def __init__(self, self.mlm_tamper_p = 0.05 self.mlm_maintain_p = 0.1 - def tokenize(self, doc): temp = [] for d in doc: temp.append(self.tokenizer.tokenize(d)) return temp - def create_training_instance(self, instance): is_next = 1 - raw_text_list = self.get_new_segment(instance) + raw_text_list = self.get_new_segment(instance) tokens_a = raw_text_list assert len(tokens_a) == len(instance) # tokens_a, tokens_b, is_next = instance.get_values() @@ -83,8 +84,9 @@ def create_training_instance(self, instance): # Get Masked LM predictions if self.backend == 'c++': - output_tokens, masked_lm_output = mask.create_whole_masked_lm_predictions(tokens, original_tokens, self.vocab_words, - self.tokenizer.vocab, self.max_predictions_per_seq, self.masked_lm_prob) + output_tokens, masked_lm_output = mask.create_whole_masked_lm_predictions( + tokens, original_tokens, self.vocab_words, self.tokenizer.vocab, self.max_predictions_per_seq, + self.masked_lm_prob) elif self.backend == 'python': output_tokens, masked_lm_output = self.create_whole_masked_lm_predictions(tokens) @@ -102,29 +104,25 @@ def create_training_instance(self, instance): map_to_numpy(input_mask), map_to_numpy(segment_ids), map_to_numpy(masked_lm_output), - map_to_numpy([is_next]) + map_to_numpy([is_next]) ]) - def create_masked_lm_predictions(self, tokens): cand_indexes = [] for i, token in enumerate(tokens): if token == "[CLS]" or token == "[SEP]": continue - if (self.do_whole_word_mask and len(cand_indexes) >= 1 and - token.startswith("##")): + if (self.do_whole_word_mask and len(cand_indexes) >= 1 and token.startswith("##")): cand_indexes[-1].append(i) else: cand_indexes.append([i]) - + # cand_indexes.append(i) random.shuffle(cand_indexes) output_tokens = list(tokens) - num_to_predict = min( - self.max_predictions_per_seq, - max(1, int(round(len(tokens) * self.masked_lm_prob)))) + num_to_predict = min(self.max_predictions_per_seq, max(1, int(round(len(tokens) * self.masked_lm_prob)))) masked_lms = [] covered_indexes = set() @@ -145,13 +143,10 @@ def create_masked_lm_predictions(self, tokens): masked_token = tokens[index] # 10% replace w/ random word else: - masked_token = self.vocab_words[random.randint( - 0, - len(self.vocab_words) - 1)] + masked_token = self.vocab_words[random.randint(0, len(self.vocab_words) - 1)] output_tokens[index] = masked_token - masked_lms.append( - MaskedLMInstance(index=index, label=tokens[index])) + masked_lms.append(MaskedLMInstance(index=index, label=tokens[index])) masked_lms = sorted(masked_lms, key=lambda x: x.index) masked_lm_output = [-1] * len(output_tokens) @@ -160,7 +155,6 @@ def create_masked_lm_predictions(self, tokens): return (output_tokens, masked_lm_output) - def get_new_segment(self, segment): """ Input a sentence, return a processed sentence: In order to support the Chinese whole word mask, the words that are separated will be marked with a special mark ("#"), so that the subsequent processing module can know which words belong to the same word. @@ -171,7 +165,7 @@ def get_new_segment(self, segment): new_segment = [] i = 0 while i < len(segment): - if len(self.rec.findall(segment[i])) == 0: + if len(self.rec.findall(segment[i])) == 0: new_segment.append(segment[i]) i += 1 continue @@ -180,10 +174,10 @@ def get_new_segment(self, segment): for length in range(3, 0, -1): if i + length > len(segment): continue - if ''.join(segment[i: i+length]) in seq_cws_dict: + if ''.join(segment[i:i + length]) in seq_cws_dict: new_segment.append(segment[i]) for l in range(1, length): - new_segment.append('##' + segment[i+l]) + new_segment.append('##' + segment[i + l]) i += length has_add = True break @@ -192,7 +186,6 @@ def get_new_segment(self, segment): i += 1 return new_segment - def create_whole_masked_lm_predictions(self, tokens): """Creates the predictions for the masked LM objective.""" @@ -209,18 +202,16 @@ def create_whole_masked_lm_predictions(self, tokens): # Note that Whole Word Masking does *not* change the training code # at all -- we still predict each WordPiece independently, softmaxed # over the entire vocabulary. - if (self.do_whole_word_mask and len(cand_indexes) >= 1 and - token.startswith("##")): + if (self.do_whole_word_mask and len(cand_indexes) >= 1 and token.startswith("##")): cand_indexes[-1].append(i) else: cand_indexes.append([i]) random.shuffle(cand_indexes) - output_tokens = [t[2:] if len(self.whole_rec.findall(t))>0 else t for t in tokens] # 去掉"##" + output_tokens = [t[2:] if len(self.whole_rec.findall(t)) > 0 else t for t in tokens] # 去掉"##" - num_to_predict = min(self.max_predictions_per_seq, - max(1, int(round(len(tokens) * self.masked_lm_prob)))) + num_to_predict = min(self.max_predictions_per_seq, max(1, int(round(len(tokens) * self.masked_lm_prob)))) masked_lms = [] covered_indexes = set() @@ -248,14 +239,18 @@ def create_whole_masked_lm_predictions(self, tokens): else: # 10% of the time, keep original if random.random() < 0.5: - masked_token = tokens[index][2:] if len(self.whole_rec.findall(tokens[index]))>0 else tokens[index] # 去掉"##" + masked_token = tokens[index][2:] if len(self.whole_rec.findall( + tokens[index])) > 0 else tokens[index] # 去掉"##" # 10% of the time, replace with random word else: masked_token = self.vocab_words[random.randint(0, len(self.vocab_words) - 1)] output_tokens[index] = masked_token - masked_lms.append(MaskedLMInstance(index=index, label=tokens[index][2:] if len(self.whole_rec.findall(tokens[index]))>0 else tokens[index])) + masked_lms.append( + MaskedLMInstance( + index=index, + label=tokens[index][2:] if len(self.whole_rec.findall(tokens[index])) > 0 else tokens[index])) assert len(masked_lms) <= num_to_predict masked_lms = sorted(masked_lms, key=lambda x: x.index) masked_lm_output = [-1] * len(output_tokens) diff --git a/examples/community/roberta/preprocessing/mask.cpp b/examples/community/roberta/preprocessing/mask.cpp new file mode 100644 index 000000000000..d44f58eccfc2 --- /dev/null +++ b/examples/community/roberta/preprocessing/mask.cpp @@ -0,0 +1,190 @@ +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace py = pybind11; + +const int32_t LONG_SENTENCE_LEN = 512; + +struct MaskedLMInstance { + int index; + std::string label; + MaskedLMInstance(int index, std::string label) { + this->index = index; + this->label = label; + } +}; + +auto get_new_segment( + std::vector segment, std::vector segment_jieba, + const std::vector chinese_vocab) { // const + // std::unordered_set + // &chinese_vocab + std::unordered_set seq_cws_dict; + for (auto word : segment_jieba) { + seq_cws_dict.insert(word); + } + int i = 0; + std::vector new_segment; + int segment_size = segment.size(); + while (i < segment_size) { + if (!chinese_vocab[i]) { // chinese_vocab.find(segment[i]) == + // chinese_vocab.end() + new_segment.emplace_back(segment[i]); + i += 1; + continue; + } + bool has_add = false; + for (int length = 3; length >= 1; length--) { + if (i + length > segment_size) { + continue; + } + std::string chinese_word = ""; + for (int j = i; j < i + length; j++) { + chinese_word += segment[j]; + } + if (seq_cws_dict.find(chinese_word) != seq_cws_dict.end()) { + new_segment.emplace_back(segment[i]); + for (int j = i + 1; j < i + length; j++) { + new_segment.emplace_back("##" + segment[j]); + } + i += length; + has_add = true; + break; + } + } + if (!has_add) { + new_segment.emplace_back(segment[i]); + i += 1; + } + } + + return new_segment; +} + +bool startsWith(const std::string &s, const std::string &sub) { + return s.find(sub) == 0 ? true : false; +} + +auto create_whole_masked_lm_predictions( + std::vector &tokens, + const std::vector &original_tokens, + const std::vector &vocab_words, + std::map &vocab, const int max_predictions_per_seq, + const double masked_lm_prob) { + // for (auto item : vocab) { + // std::cout << "key=" << std::string(py::str(item.first)) << ", " + // << "value=" << std::string(py::str(item.second)) << + // std::endl; + // } + std::vector > cand_indexes; + std::vector cand_temp; + int tokens_size = tokens.size(); + std::string prefix = "##"; + bool do_whole_masked = true; + + for (int i = 0; i < tokens_size; i++) { + if (tokens[i] == "[CLS]" || tokens[i] == "[SEP]") { + continue; + } + if (do_whole_masked && (cand_indexes.size() > 0) && + (tokens[i].rfind(prefix, 0) == 0)) { + cand_temp.emplace_back(i); + } else { + if (cand_temp.size() > 0) { + cand_indexes.emplace_back(cand_temp); + } + cand_temp.clear(); + cand_temp.emplace_back(i); + } + } + auto seed = std::chrono::system_clock::now().time_since_epoch().count(); + std::shuffle(cand_indexes.begin(), cand_indexes.end(), + std::default_random_engine(seed)); + // for (auto i : cand_indexes) { + // for (auto j : i) { + // std::cout << tokens[j] << " "; + // } + // std::cout << std::endl; + // } + // for (auto i : output_tokens) { + // std::cout << i; + // } + // std::cout << std::endl; + + int num_to_predict = std::min(max_predictions_per_seq, + std::max(1, int(tokens_size * masked_lm_prob))); + // std::cout << num_to_predict << std::endl; + + std::set covered_indexes; + std::vector masked_lm_output(tokens_size, -1); + int vocab_words_len = vocab_words.size(); + std::default_random_engine e(seed); + std::uniform_real_distribution u1(0.0, 1.0); + std::uniform_int_distribution u2(0, vocab_words_len - 1); + int mask_cnt = 0; + std::vector output_tokens; + output_tokens = original_tokens; + + for (auto index_set : cand_indexes) { + if (mask_cnt > num_to_predict) { + break; + } + int index_set_size = index_set.size(); + if (mask_cnt + index_set_size > num_to_predict) { + continue; + } + bool is_any_index_covered = false; + for (auto index : index_set) { + if (covered_indexes.find(index) != covered_indexes.end()) { + is_any_index_covered = true; + break; + } + } + if (is_any_index_covered) { + continue; + } + for (auto index : index_set) { + covered_indexes.insert(index); + std::string masked_token; + if (u1(e) < 0.8) { + masked_token = "[MASK]"; + } else { + if (u1(e) < 0.5) { + masked_token = output_tokens[index]; + } else { + int random_index = u2(e); + masked_token = vocab_words[random_index]; + } + } + // masked_lms.emplace_back(MaskedLMInstance(index, output_tokens[index])); + masked_lm_output[index] = vocab[output_tokens[index]]; + output_tokens[index] = masked_token; + mask_cnt++; + } + } + + // for (auto p : masked_lms) { + // masked_lm_output[p.index] = vocab[p.label]; + // } + return std::make_tuple(output_tokens, masked_lm_output); +} + +PYBIND11_MODULE(mask, m) { + m.def("create_whole_masked_lm_predictions", + &create_whole_masked_lm_predictions); + m.def("get_new_segment", &get_new_segment); +} diff --git a/examples/language/roberta/preprocessing/sentence_split.py b/examples/community/roberta/preprocessing/sentence_split.py similarity index 86% rename from examples/language/roberta/preprocessing/sentence_split.py rename to examples/community/roberta/preprocessing/sentence_split.py index f0ed83f90114..76e8bd428723 100644 --- a/examples/language/roberta/preprocessing/sentence_split.py +++ b/examples/community/roberta/preprocessing/sentence_split.py @@ -1,28 +1,30 @@ - +import argparse +import functools +import json import multiprocessing import os import re -from tqdm import tqdm -from typing import List -import json import time -import argparse -import functools +from typing import List + +from tqdm import tqdm + def split_sentence(document: str, flag: str = "all", limit: int = 510) -> List[str]: sent_list = [] try: if flag == "zh": - document = re.sub('(?P([。？！…](?![”’"\'])))', r'\g\n', document) + document = re.sub('(?P([。？！…](?![”’"\'])))', r'\g\n', document) document = re.sub('(?P([。？！]|…{1,2})[”’"\'])', r'\g\n', document) elif flag == "en": - document = re.sub('(?P([.?!](?![”’"\'])))', r'\g\n', document) - document = re.sub('(?P([?!.]["\']))', r'\g\n', document) # Special quotation marks + document = re.sub('(?P([.?!](?![”’"\'])))', r'\g\n', document) + document = re.sub('(?P([?!.]["\']))', r'\g\n', + document) # Special quotation marks else: - document = re.sub('(?P([。？！….?!](?![”’"\'])))', r'\g\n', document) - + document = re.sub('(?P([。？！….?!](?![”’"\'])))', r'\g\n', document) + document = re.sub('(?P(([。？！.!?]|…{1,2})[”’"\']))', r'\g\n', - document) # Special quotation marks + document) # Special quotation marks sent_list_ori = document.splitlines() for sent in sent_list_ori: @@ -43,17 +45,15 @@ def split_sentence(document: str, flag: str = "all", limit: int = 510) -> List[s return sent_list -def get_sent(output_path, - input_path, - fin_list=[], host=-1, seq_len=512) -> None: +def get_sent(output_path, input_path, fin_list=[], host=-1, seq_len=512) -> None: workers = 32 if input_path[-1] == '/': input_path = input_path[:-1] - + cur_path = os.path.join(output_path, str(host) + '.txt') - new_split_sentence = functools.partial(split_sentence, limit=seq_len-2) + new_split_sentence = functools.partial(split_sentence, limit=seq_len - 2) with open(cur_path, 'w', encoding='utf-8') as f: for fi, fin_path in enumerate(fin_list): if not os.path.exists(os.path.join(input_path, fin_path[0])): @@ -62,7 +62,7 @@ def get_sent(output_path, continue print("Processing ", fin_path[0], " ", fi) - + with open(os.path.join(input_path, fin_path[0]), 'r') as fin: f_data = [l['content'] for l in json.load(fin)] @@ -99,17 +99,17 @@ def getFileSize(filepath, shard): real_shard.append(temp) accu_size = 0 temp = [] - + if len(temp) > 0: real_shard.append(temp) - + return real_shard def get_start_end(real_shard, base=0, server_num=10, server_name='GPU'): import socket host = int(socket.gethostname().split(server_name)[-1]) - + fin_list = real_shard[server_num * base + host - 1] print(fin_list) print(f'I am server {host}, process {server_num * base + host - 1}, len {len(fin_list)}') @@ -126,28 +126,24 @@ def get_start_end(real_shard, base=0, server_num=10, server_name='GPU'): parser.add_argument('--output_path', type=str, required=True, help='output path of shard which has split sentence') args = parser.parse_args() - server_num = args.server_num + server_num = args.server_num seq_len = args.seq_len - shard = args.shard + shard = args.shard input_path = args.input_path - output_path = args.output_path + output_path = args.output_path real_shard = getFileSize(input_path, shard) start = time.time() for index, shard in enumerate(real_shard): - get_sent(output_path, - input_path, - fin_list=shard, - host=index, - seq_len=seq_len) + get_sent(output_path, input_path, fin_list=shard, host=index, seq_len=seq_len) print(f'cost {str(time.time() - start)}') # if you have multiple server, you can use code below or modify code to openmpi - + # for i in range(len(real_shard) // server_num + 1): # fin_list, host = get_start_end(real_shard, i) - + # start = time.time() # get_sent(output_path, # input_path, diff --git a/examples/language/roberta/preprocessing/tokenize_mask.py b/examples/community/roberta/preprocessing/tokenize_mask.py similarity index 76% rename from examples/language/roberta/preprocessing/tokenize_mask.py rename to examples/community/roberta/preprocessing/tokenize_mask.py index 76c74868e1fc..f3d49c3d965f 100644 --- a/examples/language/roberta/preprocessing/tokenize_mask.py +++ b/examples/community/roberta/preprocessing/tokenize_mask.py @@ -1,19 +1,19 @@ -import time +import argparse +import multiprocessing import os -import psutil -import h5py import socket -import argparse +import time +from random import shuffle + +import h5py import numpy as np -import multiprocessing +import psutil +from get_mask import PreTrainingDataset from tqdm import tqdm -from random import shuffle from transformers import AutoTokenizer -from get_mask import PreTrainingDataset def get_raw_instance(document, max_sequence_length=512): - """ Get the initial training instances, split the whole segment into multiple parts according to the max_sequence_length, and return as multiple processed instances. :param document: document @@ -26,24 +26,24 @@ def get_raw_instance(document, max_sequence_length=512): sizes = [len(seq) for seq in document] result_list = [] - curr_seq = [] + curr_seq = [] sz_idx = 0 while sz_idx < len(sizes): - - if len(curr_seq) + sizes[sz_idx] <= max_sequence_length_allowed: # or len(curr_seq)==0: + + if len(curr_seq) + sizes[sz_idx] <= max_sequence_length_allowed: # or len(curr_seq)==0: curr_seq += document[sz_idx] sz_idx += 1 elif sizes[sz_idx] >= max_sequence_length_allowed: if len(curr_seq) > 0: result_list.append(curr_seq) curr_seq = [] - result_list.append(document[sz_idx][ : max_sequence_length_allowed]) + result_list.append(document[sz_idx][:max_sequence_length_allowed]) sz_idx += 1 else: result_list.append(curr_seq) curr_seq = [] - if len(curr_seq) > max_sequence_length_allowed / 2: # /2 + if len(curr_seq) > max_sequence_length_allowed / 2: # /2 result_list.append(curr_seq) # num_instance=int(len(big_list)/max_sequence_length_allowed)+1 @@ -70,8 +70,7 @@ def split_numpy_chunk(path, tokenizer, pretrain_data, host): # document = line # if len(document.split("")) <= 3: # continue - if len(line - ) > 0 and line[:2] == "]]": # This is end of document + if len(line) > 0 and line[:2] == "]]": # This is end of document documents.append(document) document = [] elif len(line) >= 2: @@ -84,8 +83,8 @@ def split_numpy_chunk(path, tokenizer, pretrain_data, host): # print(len(documents)) # print(len(documents[0])) # print(documents[0][0:10]) - from typing import List import multiprocessing + from typing import List ans = [] for docs in tqdm(documents): @@ -98,7 +97,7 @@ def split_numpy_chunk(path, tokenizer, pretrain_data, host): raw_ins = get_raw_instance(a) instances.extend(raw_ins) del ans - + print('len instance', len(instances)) sen_num = len(instances) @@ -116,21 +115,15 @@ def split_numpy_chunk(path, tokenizer, pretrain_data, host): masked_lm_output[index] = mask_dict[3] with h5py.File(f'/output/{host}.h5', 'w') as hf: - hf.create_dataset("input_ids", data=input_ids) - hf.create_dataset("input_mask", data=input_ids) - hf.create_dataset("segment_ids", data=segment_ids) - hf.create_dataset("masked_lm_positions", data=masked_lm_output) + hf.create_dataset("input_ids", data=input_ids) + hf.create_dataset("input_mask", data=input_ids) + hf.create_dataset("segment_ids", data=segment_ids) + hf.create_dataset("masked_lm_positions", data=masked_lm_output) del instances -def split_numpy_chunk_pool(input_path, - output_path, - pretrain_data, - worker, - dupe_factor, - seq_len, - file_name): +def split_numpy_chunk_pool(input_path, output_path, pretrain_data, worker, dupe_factor, seq_len, file_name): if os.path.exists(os.path.join(output_path, f'{file_name}.h5')): print(f'{file_name}.h5 exists') @@ -144,8 +137,7 @@ def split_numpy_chunk_pool(input_path, document = [] for i, line in enumerate(tqdm(fd)): line = line.strip() - if len(line - ) > 0 and line[:2] == "]]": # This is end of document + if len(line) > 0 and line[:2] == "]]": # This is end of document documents.append(document) document = [] elif len(line) >= 2: @@ -153,7 +145,7 @@ def split_numpy_chunk_pool(input_path, if len(document) > 0: documents.append(document) print(f'read_file cost {time.time() - s}, length is {len(documents)}') - + ans = [] s = time.time() pool = multiprocessing.Pool(worker) @@ -169,7 +161,7 @@ def split_numpy_chunk_pool(input_path, raw_ins = get_raw_instance(a, max_sequence_length=seq_len) instances.extend(raw_ins) del ans - + print('len instance', len(instances)) new_instances = [] @@ -199,10 +191,10 @@ def split_numpy_chunk_pool(input_path, print((time.time() - s) / 60) with h5py.File(os.path.join(output_path, f'{file_name}.h5'), 'w') as hf: - hf.create_dataset("input_ids", data=input_ids) - hf.create_dataset("input_mask", data=input_mask) - hf.create_dataset("segment_ids", data=segment_ids) - hf.create_dataset("masked_lm_positions", data=masked_lm_output) + hf.create_dataset("input_ids", data=input_ids) + hf.create_dataset("input_mask", data=input_mask) + hf.create_dataset("segment_ids", data=segment_ids) + hf.create_dataset("masked_lm_positions", data=masked_lm_output) del instances @@ -212,22 +204,31 @@ def split_numpy_chunk_pool(input_path, parser = argparse.ArgumentParser() parser.add_argument('--tokenizer_path', type=str, required=True, default=10, help='path of tokenizer') parser.add_argument('--seq_len', type=int, default=512, help='sequence length') - parser.add_argument('--max_predictions_per_seq', type=int, default=80, help='number of shards, e.g., 10, 50, or 100') + parser.add_argument('--max_predictions_per_seq', + type=int, + default=80, + help='number of shards, e.g., 10, 50, or 100') parser.add_argument('--input_path', type=str, required=True, help='input path of shard which has split sentence') parser.add_argument('--output_path', type=str, required=True, help='output path of h5 contains token id') - parser.add_argument('--backend', type=str, default='python', help='backend of mask token, python, c++, numpy respectively') - parser.add_argument('--dupe_factor', type=int, default=1, help='specifies how many times the preprocessor repeats to create the input from the same article/document') + parser.add_argument('--backend', + type=str, + default='python', + help='backend of mask token, python, c++, numpy respectively') + parser.add_argument( + '--dupe_factor', + type=int, + default=1, + help='specifies how many times the preprocessor repeats to create the input from the same article/document') parser.add_argument('--worker', type=int, default=32, help='number of process') parser.add_argument('--server_num', type=int, default=10, help='number of servers') args = parser.parse_args() tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path) - pretrain_data = PreTrainingDataset(tokenizer, - args.seq_len, - args.backend, - max_predictions_per_seq=args.max_predictions_per_seq) - - + pretrain_data = PreTrainingDataset(tokenizer, + args.seq_len, + args.backend, + max_predictions_per_seq=args.max_predictions_per_seq) + data_len = len(os.listdir(args.input_path)) for i in range(data_len): @@ -235,15 +236,10 @@ def split_numpy_chunk_pool(input_path, if os.path.exists(input_path): start = time.time() print(f'process {input_path}') - split_numpy_chunk_pool(input_path, - args.output_path, - pretrain_data, - args.worker, - args.dupe_factor, - args.seq_len, - i) + split_numpy_chunk_pool(input_path, args.output_path, pretrain_data, args.worker, args.dupe_factor, + args.seq_len, i) end_ = time.time() - print(u'memory：%.4f GB' % (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024) ) + print(u'memory：%.4f GB' % (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024)) print(f'has cost {(end_ - start) / 60}') print('-' * 100) print('') @@ -257,9 +253,9 @@ def split_numpy_chunk_pool(input_path, # if os.path.exists(input_path): # start = time.time() # print(f'I am server {host}, process {input_path}') - # split_numpy_chunk_pool(input_path, - # args.output_path, - # pretrain_data, + # split_numpy_chunk_pool(input_path, + # args.output_path, + # pretrain_data, # args.worker, # args.dupe_factor, # args.seq_len, @@ -269,5 +265,3 @@ def split_numpy_chunk_pool(input_path, # print(f'has cost {(end_ - start) / 60}') # print('-' * 100) # print('') - - diff --git a/examples/language/roberta/pretraining/README.md b/examples/community/roberta/pretraining/README.md similarity index 97% rename from examples/language/roberta/pretraining/README.md rename to examples/community/roberta/pretraining/README.md index 055d6969654d..c248fc1f5708 100644 --- a/examples/language/roberta/pretraining/README.md +++ b/examples/community/roberta/pretraining/README.md @@ -19,6 +19,5 @@ bash run_pretrain.sh bash run_pretrain_resume.sh ``` * `--resume_train`: whether to resume training -* `--load_pretrain_model`: absolute path which contains model checkpoint -* `--load_optimizer_lr`: absolute path which contains optimizer checkpoint - +* `--load_pretrain_model`: absolute path which contains model checkpoint +* `--load_optimizer_lr`: absolute path which contains optimizer checkpoint diff --git a/examples/community/roberta/pretraining/arguments.py b/examples/community/roberta/pretraining/arguments.py new file mode 100644 index 000000000000..40210c4b1be7 --- /dev/null +++ b/examples/community/roberta/pretraining/arguments.py @@ -0,0 +1,87 @@ +from numpy import require + +import colossalai + +__all__ = ['parse_args'] + + +def parse_args(): + parser = colossalai.get_default_parser() + + parser.add_argument( + "--distplan", + type=str, + default='CAI_Gemini', + help="The distributed plan [colossalai, zero1, zero2, torch_ddp, torch_zero].", + ) + parser.add_argument( + "--tp_degree", + type=int, + default=1, + help="Tensor Parallelism Degree. Valid when using colossalai as dist plan.", + ) + parser.add_argument( + "--placement", + type=str, + default='cpu', + help="Placement Policy for Gemini. Valid when using colossalai as dist plan.", + ) + parser.add_argument( + "--shardinit", + action='store_true', + help= + "Shard the tensors when init the model to shrink peak memory size on the assigned device. Valid when using colossalai as dist plan.", + ) + + parser.add_argument('--lr', type=float, required=True, help='initial learning rate') + parser.add_argument('--epoch', type=int, required=True, help='number of epoch') + parser.add_argument('--data_path_prefix', type=str, required=True, help="location of the train data corpus") + parser.add_argument('--eval_data_path_prefix', + type=str, + required=True, + help='location of the evaluation data corpus') + parser.add_argument('--tokenizer_path', type=str, required=True, help='location of the tokenizer') + parser.add_argument('--max_seq_length', type=int, default=512, help='sequence length') + parser.add_argument('--refresh_bucket_size', + type=int, + default=1, + help="This param makes sure that a certain task is repeated for this time steps to \ + optimise on the back propogation speed with APEX's DistributedDataParallel") + parser.add_argument("--max_predictions_per_seq", + "--max_pred", + default=80, + type=int, + help="The maximum number of masked tokens in a sequence to be predicted.") + parser.add_argument("--gradient_accumulation_steps", default=1, type=int, help="accumulation_steps") + parser.add_argument("--train_micro_batch_size_per_gpu", default=2, type=int, required=True, help="train batch size") + parser.add_argument("--eval_micro_batch_size_per_gpu", default=2, type=int, required=True, help="eval batch size") + parser.add_argument("--num_workers", default=8, type=int, help="") + parser.add_argument("--async_worker", action='store_true', help="") + parser.add_argument("--bert_config", required=True, type=str, help="location of config.json") + parser.add_argument("--wandb", action='store_true', help="use wandb to watch model") + parser.add_argument("--wandb_project_name", default='roberta', help="wandb project name") + parser.add_argument("--log_interval", default=100, type=int, help="report interval") + parser.add_argument("--log_path", type=str, required=True, help="log file which records train step") + parser.add_argument("--tensorboard_path", type=str, required=True, help="location of tensorboard file") + parser.add_argument("--colossal_config", + type=str, + required=True, + help="colossal config, which contains zero config and so on") + parser.add_argument("--ckpt_path", + type=str, + required=True, + help="location of saving checkpoint, which contains model and optimizer") + parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") + parser.add_argument('--vscode_debug', action='store_true', help="use vscode to debug") + parser.add_argument('--load_pretrain_model', default='', type=str, help="location of model's checkpoin") + parser.add_argument( + '--load_optimizer_lr', + default='', + type=str, + help="location of checkpoint, which contains optimerzier, learning rate, epoch, shard and global_step") + parser.add_argument('--resume_train', action='store_true', help="whether resume training from a early checkpoint") + parser.add_argument('--mlm', default='bert', type=str, help="model type, bert or deberta") + parser.add_argument('--checkpoint_activations', action='store_true', help="whether to use gradient checkpointing") + + args = parser.parse_args() + return args diff --git a/examples/language/roberta/pretraining/bert_dataset_provider.py b/examples/community/roberta/pretraining/bert_dataset_provider.py similarity index 99% rename from examples/language/roberta/pretraining/bert_dataset_provider.py rename to examples/community/roberta/pretraining/bert_dataset_provider.py index 1d8cf2a910e9..eaf165ed18f4 100644 --- a/examples/language/roberta/pretraining/bert_dataset_provider.py +++ b/examples/community/roberta/pretraining/bert_dataset_provider.py @@ -1,4 +1,5 @@ class BertDatasetProviderInterface: + def get_shard(self, index, shuffle=True): raise NotImplementedError diff --git a/examples/language/roberta/pretraining/evaluation.py b/examples/community/roberta/pretraining/evaluation.py similarity index 74% rename from examples/language/roberta/pretraining/evaluation.py rename to examples/community/roberta/pretraining/evaluation.py index 8fc019c121ac..009242cd1cf5 100644 --- a/examples/language/roberta/pretraining/evaluation.py +++ b/examples/community/roberta/pretraining/evaluation.py @@ -1,9 +1,11 @@ -import os import math +import os + import torch +from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider from tqdm import tqdm -from utils.global_vars import get_timers, get_tensorboard_writer -from nvidia_bert_dataset_provider import NvidiaBertDatasetProvider +from utils.global_vars import get_tensorboard_writer, get_timers + def evaluate(model, args, logger, global_step, criterion): evaluate_dataset_provider = NvidiaBertDatasetProvider(args, evaluate=True) @@ -20,16 +22,19 @@ def evaluate(model, args, logger, global_step, criterion): for shard in range(start_shard, len(os.listdir(args.eval_data_path_prefix))): - timers('eval_shard_time').start() + timers('eval_shard_time').start() dataset_iterator, total_length = evaluate_dataset_provider.get_shard(shard) # evaluate_dataset_provider.prefetch_shard(shard + 1) if torch.distributed.get_rank() == 0: - iterator_data = tqdm(enumerate(dataset_iterator), total=(total_length // args.eval_micro_batch_size_per_gpu // world_size), colour='MAGENTA', smoothing=1) + iterator_data = tqdm(enumerate(dataset_iterator), + total=(total_length // args.eval_micro_batch_size_per_gpu // world_size), + colour='MAGENTA', + smoothing=1) else: iterator_data = enumerate(dataset_iterator) - - for step, batch_data in iterator_data: #tqdm(enumerate(dataset_iterator), total=(total_length // args.train_micro_batch_size_per_gpu // world_size), colour='cyan', smoothing=1): + + for step, batch_data in iterator_data: #tqdm(enumerate(dataset_iterator), total=(total_length // args.train_micro_batch_size_per_gpu // world_size), colour='cyan', smoothing=1): # batch_data = pretrain_dataset_provider.get_batch(batch_index) eval_step += 1 @@ -40,8 +45,8 @@ def evaluate(model, args, logger, global_step, criterion): # nsp_label = batch_data[5].cuda() output = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) - - loss = criterion(output.logits, mlm_label)#prediction_scores + + loss = criterion(output.logits, mlm_label) #prediction_scores evaluate_dataset_provider.prefetch_batch() eval_loss += loss.float().item() @@ -54,10 +59,10 @@ def evaluate(model, args, logger, global_step, criterion): if args.wandb and torch.distributed.get_rank() == 0: tensorboard_log = get_tensorboard_writer() tensorboard_log.log_eval({ - 'loss': cur_loss, - 'ppl': ppl, - 'mins_batch': elapsed_time_per_iteration - }, global_step) + 'loss': cur_loss, + 'ppl': ppl, + 'mins_batch': elapsed_time_per_iteration + }, global_step) eval_log_str = f'evaluation shard: {shard} | step: {eval_step} | elapsed_time: {elapsed_time / 60 :.3f} minutes ' + \ f'| mins/batch: {elapsed_time_per_iteration :.3f} seconds | loss: {cur_loss:.7f} | ppl: {ppl:.7f}' @@ -68,4 +73,4 @@ def evaluate(model, args, logger, global_step, criterion): evaluate_dataset_provider.release_shard() model.train() - return cur_loss \ No newline at end of file + return cur_loss diff --git a/examples/language/roberta/pretraining/hostfile b/examples/community/roberta/pretraining/hostfile similarity index 100% rename from examples/language/roberta/pretraining/hostfile rename to examples/community/roberta/pretraining/hostfile diff --git a/examples/language/roberta/pretraining/loss.py b/examples/community/roberta/pretraining/loss.py similarity index 91% rename from examples/language/roberta/pretraining/loss.py rename to examples/community/roberta/pretraining/loss.py index dc4f872a755d..989c2bd5c450 100644 --- a/examples/language/roberta/pretraining/loss.py +++ b/examples/community/roberta/pretraining/loss.py @@ -13,5 +13,5 @@ def __init__(self, vocab_size): def forward(self, prediction_scores, masked_lm_labels, next_sentence_labels=None): masked_lm_loss = self.loss_fn(prediction_scores.view(-1, self.vocab_size), masked_lm_labels.view(-1)) # next_sentence_loss = self.loss_fn(seq_relationship_score.view(-1, 2), next_sentence_labels.view(-1)) - total_loss = masked_lm_loss #+ next_sentence_loss + total_loss = masked_lm_loss #+ next_sentence_loss return total_loss diff --git a/examples/language/roberta/pretraining/model/bert.py b/examples/community/roberta/pretraining/model/bert.py similarity index 96% rename from examples/language/roberta/pretraining/model/bert.py rename to examples/community/roberta/pretraining/model/bert.py index 67c85f760776..a5da1bea6f65 100644 --- a/examples/language/roberta/pretraining/model/bert.py +++ b/examples/community/roberta/pretraining/model/bert.py @@ -15,7 +15,6 @@ # limitations under the License. """PyTorch BERT model.""" - import math import os import warnings @@ -27,7 +26,6 @@ from packaging import version from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss - from transformers.activations import ACT2FN from transformers.modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, @@ -41,8 +39,9 @@ TokenClassifierOutput, ) from transformers.modeling_utils import PreTrainedModel +from transformers.models.bert.configuration_bert import BertConfig from transformers.pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer -from transformers.utils import ( +from transformers.utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, @@ -50,8 +49,6 @@ logging, replace_return_docstrings, ) -from transformers.models.bert.configuration_bert import BertConfig - logger = logging.get_logger(__name__) @@ -62,8 +59,7 @@ # TokenClassification docstring _CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "dbmdz/bert-large-cased-finetuned-conll03-english" _TOKEN_CLASS_EXPECTED_OUTPUT = ( - "['O', 'I-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC'] " -) + "['O', 'I-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC'] ") _TOKEN_CLASS_EXPECTED_LOSS = 0.01 # QuestionAnswering docstring @@ -78,7 +74,6 @@ _SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'" _SEQ_CLASS_EXPECTED_LOSS = 0.01 - BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "bert-base-uncased", "bert-large-uncased", @@ -114,10 +109,8 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path): import numpy as np import tensorflow as tf except ImportError: - logger.error( - "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " - "https://www.tensorflow.org/install/ for installation instructions." - ) + logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " + "https://www.tensorflow.org/install/ for installation instructions.") raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") @@ -135,10 +128,8 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model - if any( - n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] - for n in name - ): + if any(n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] + for n in name): logger.info(f"Skipping {'/'.join(name)}") continue pointer = model @@ -218,7 +209,7 @@ def forward( seq_length = input_shape[1] if position_ids is None: - position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + position_ids = self.position_ids[:, past_key_values_length:seq_length + past_key_values_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves @@ -245,13 +236,12 @@ def forward( class BertSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): - raise ValueError( - f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " - f"heads ({config.num_attention_heads})" - ) + raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})") self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) @@ -262,9 +252,7 @@ def __init__(self, config, position_embedding_type=None): self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) - self.position_embedding_type = position_embedding_type or getattr( - config, "position_embedding_type", "absolute" - ) + self.position_embedding_type = position_embedding_type or getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) @@ -332,7 +320,7 @@ def forward( position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) - positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) @@ -372,6 +360,7 @@ def forward( class BertSelfOutput(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) @@ -386,6 +375,7 @@ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> to class BertAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): super().__init__() self.self = BertSelfAttention(config, position_embedding_type=position_embedding_type) @@ -395,9 +385,8 @@ def __init__(self, config, position_embedding_type=None): def prune_heads(self, heads): if len(heads) == 0: return - heads, index = find_pruneable_heads_and_indices( - heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads - ) + heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads, + self.self.attention_head_size, self.pruned_heads) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) @@ -430,11 +419,12 @@ def forward( output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) - outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class BertIntermediate(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) @@ -450,6 +440,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: class BertOutput(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) @@ -464,6 +455,7 @@ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> to class BertLayer(nn.Module): + def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward @@ -504,15 +496,14 @@ def forward( outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: - outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" - " by setting `config.add_cross_attention=True`" - ) + " by setting `config.add_cross_attention=True`") # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None @@ -526,15 +517,14 @@ def forward( output_attentions, ) attention_output = cross_attention_outputs[0] - outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value - layer_output = apply_chunking_to_forward( - self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output - ) + layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, + self.seq_len_dim, attention_output) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output @@ -550,6 +540,7 @@ def feed_forward_chunk(self, attention_output): class BertEncoder(nn.Module): + def __init__(self, config): super().__init__() self.config = config @@ -585,11 +576,11 @@ def forward( if use_cache: logger.warning( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") use_cache = False def create_custom_forward(module): + def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) @@ -626,17 +617,13 @@ def custom_forward(*inputs): all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: - return tuple( - v - for v in [ - hidden_states, - next_decoder_cache, - all_hidden_states, - all_self_attentions, - all_cross_attentions, - ] - if v is not None - ) + return tuple(v for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] if v is not None) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, @@ -647,6 +634,7 @@ def custom_forward(*inputs): class BertPooler(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) @@ -662,6 +650,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) @@ -679,6 +668,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: class BertLMPredictionHead(nn.Module): + def __init__(self, config): super().__init__() self.transform = BertPredictionHeadTransform(config) @@ -699,6 +689,7 @@ def forward(self, hidden_states): class BertOnlyMLMHead(nn.Module): + def __init__(self, config): super().__init__() self.predictions = BertLMPredictionHead(config) @@ -709,6 +700,7 @@ def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: class BertOnlyNSPHead(nn.Module): + def __init__(self, config): super().__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) @@ -719,6 +711,7 @@ def forward(self, pooled_output): class BertPreTrainingHeads(nn.Module): + def __init__(self, config): super().__init__() self.predictions = BertLMPredictionHead(config) @@ -950,9 +943,8 @@ def forward( `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: @@ -1051,6 +1043,7 @@ def forward( BERT_START_DOCSTRING, ) class BertForPreTraining(BertPreTrainedModel): + def __init__(self, config): super().__init__(config) @@ -1151,9 +1144,8 @@ def forward( ) -@add_start_docstrings( - """Bert Model with a `language modeling` head on top for CLM fine-tuning.""", BERT_START_DOCSTRING -) +@add_start_docstrings("""Bert Model with a `language modeling` head on top for CLM fine-tuning.""", + BERT_START_DOCSTRING) class BertLMHeadModel(BertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] @@ -1298,10 +1290,8 @@ def __init__(self, config): super().__init__(config) if config.is_decoder: - logger.warning( - "If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for " - "bi-directional self-attention." - ) + logger.warning("If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention.") self.bert = BertModel(config, add_pooling_layer=False) self.cls = BertOnlyMLMHead(config) @@ -1367,7 +1357,7 @@ def forward( masked_lm_loss = None if labels is not None: - loss_fct = CrossEntropyLoss() # -100 index = padding token + loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: @@ -1390,9 +1380,10 @@ def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_ raise ValueError("The PAD token should be defined for generation") attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1) - dummy_token = torch.full( - (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device - ) + dummy_token = torch.full((effective_batch_size, 1), + self.config.pad_token_id, + dtype=torch.long, + device=input_ids.device) input_ids = torch.cat([input_ids, dummy_token], dim=1) return {"input_ids": input_ids, "attention_mask": attention_mask} @@ -1403,6 +1394,7 @@ def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_ BERT_START_DOCSTRING, ) class BertForNextSentencePrediction(BertPreTrainedModel): + def __init__(self, config): super().__init__(config) @@ -1508,15 +1500,15 @@ def forward( BERT_START_DOCSTRING, ) class BertForSequenceClassification(BertPreTrainedModel): + def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.bert = BertModel(config) - classifier_dropout = ( - config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob - ) + classifier_dropout = (config.classifier_dropout + if config.classifier_dropout is not None else config.hidden_dropout_prob) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) @@ -1612,13 +1604,13 @@ def forward( BERT_START_DOCSTRING, ) class BertForMultipleChoice(BertPreTrainedModel): + def __init__(self, config): super().__init__(config) self.bert = BertModel(config) - classifier_dropout = ( - config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob - ) + classifier_dropout = (config.classifier_dropout + if config.classifier_dropout is not None else config.hidden_dropout_prob) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, 1) @@ -1658,11 +1650,8 @@ def forward( attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None - inputs_embeds = ( - inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) - if inputs_embeds is not None - else None - ) + inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None else None) outputs = self.bert( input_ids, @@ -1715,9 +1704,8 @@ def __init__(self, config): self.num_labels = config.num_labels self.bert = BertModel(config, add_pooling_layer=False) - classifier_dropout = ( - config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob - ) + classifier_dropout = (config.classifier_dropout + if config.classifier_dropout is not None else config.hidden_dropout_prob) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) diff --git a/examples/language/roberta/pretraining/model/deberta_v2.py b/examples/community/roberta/pretraining/model/deberta_v2.py similarity index 92% rename from examples/language/roberta/pretraining/model/deberta_v2.py rename to examples/community/roberta/pretraining/model/deberta_v2.py index c6ce82847f75..5fc284911e38 100644 --- a/examples/language/roberta/pretraining/model/deberta_v2.py +++ b/examples/community/roberta/pretraining/model/deberta_v2.py @@ -23,7 +23,7 @@ import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss - +from transformers import FillMaskPipeline, T5ForConditionalGeneration, T5Tokenizer from transformers.activations import ACT2FN from transformers.modeling_outputs import ( BaseModelOutput, @@ -34,10 +34,14 @@ TokenClassifierOutput, ) from transformers.modeling_utils import PreTrainedModel -from transformers.pytorch_utils import softmax_backward_data -from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from transformers.models.deberta_v2.configuration_deberta_v2 import DebertaV2Config -from transformers import T5Tokenizer, T5ForConditionalGeneration, FillMaskPipeline +from transformers.pytorch_utils import softmax_backward_data +from transformers.utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, +) logger = logging.get_logger(__name__) @@ -55,6 +59,7 @@ # Copied from transformers.models.deberta.modeling_deberta.ContextPooler class ContextPooler(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size) @@ -133,15 +138,15 @@ def symbolic(g, self, mask, dim): g.op("Sub", g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64)), mask_cast_value), to_i=sym_help.cast_pytorch_to_onnx["Byte"], ) - output = masked_fill( - g, self, r_mask, g.op("Constant", value_t=torch.tensor(torch.finfo(self.type().dtype()).min)) - ) + output = masked_fill(g, self, r_mask, + g.op("Constant", value_t=torch.tensor(torch.finfo(self.type().dtype()).min))) output = softmax(g, output, dim) return masked_fill(g, output, r_mask, g.op("Constant", value_t=torch.tensor(0, dtype=torch.uint8))) # Copied from transformers.models.deberta.modeling_deberta.DropoutContext class DropoutContext(object): + def __init__(self): self.dropout = 0 self.mask = None @@ -244,6 +249,7 @@ def get_context(self): # Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm class DebertaV2SelfOutput(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) @@ -259,6 +265,7 @@ def forward(self, hidden_states, input_tensor): # Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2 class DebertaV2Attention(nn.Module): + def __init__(self, config): super().__init__() self.self = DisentangledSelfAttention(config) @@ -296,6 +303,7 @@ def forward( # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2 class DebertaV2Intermediate(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) @@ -312,6 +320,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm class DebertaV2Output(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) @@ -328,6 +337,7 @@ def forward(self, hidden_states, input_tensor): # Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2 class DebertaV2Layer(nn.Module): + def __init__(self, config): super().__init__() self.attention = DebertaV2Attention(config) @@ -362,14 +372,17 @@ def forward( class ConvLayer(nn.Module): + def __init__(self, config): super().__init__() kernel_size = getattr(config, "conv_kernel_size", 3) groups = getattr(config, "conv_groups", 1) self.conv_act = getattr(config, "conv_act", "tanh") - self.conv = nn.Conv1d( - config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups - ) + self.conv = nn.Conv1d(config.hidden_size, + config.hidden_size, + kernel_size, + padding=(kernel_size - 1) // 2, + groups=groups) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = StableDropout(config.hidden_dropout_prob) self.config = config @@ -452,9 +465,10 @@ def get_attention_mask(self, attention_mask): def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None): if self.relative_attention and relative_pos is None: q = query_states.size(-2) if query_states is not None else hidden_states.size(-2) - relative_pos = build_relative_position( - q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions - ) + relative_pos = build_relative_position(q, + hidden_states.size(-2), + bucket_size=self.position_buckets, + max_position=self.max_relative_positions) return relative_pos def forward( @@ -491,6 +505,7 @@ def forward( if self.gradient_checkpointing and self.training: def create_custom_forward(module): + def custom_forward(*inputs): return module(*inputs, output_attentions) @@ -535,9 +550,9 @@ def custom_forward(*inputs): if not return_dict: return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None) - return BaseModelOutput( - last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions - ) + return BaseModelOutput(last_hidden_state=output_states, + hidden_states=all_hidden_states, + attentions=all_attentions) def make_log_bucket_position(relative_pos, bucket_size, max_position): @@ -610,10 +625,8 @@ class DisentangledSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0: - raise ValueError( - f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " - f"heads ({config.num_attention_heads})" - ) + raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})") self.num_attention_heads = config.num_attention_heads _attention_head_size = config.hidden_size // config.num_attention_heads self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size) @@ -706,28 +719,22 @@ def forward( attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale if self.relative_attention: rel_embeddings = self.pos_dropout(rel_embeddings) - rel_att = self.disentangled_attention_bias( - query_layer, key_layer, relative_pos, rel_embeddings, scale_factor - ) + rel_att = self.disentangled_attention_bias(query_layer, key_layer, relative_pos, rel_embeddings, + scale_factor) if rel_att is not None: attention_scores = attention_scores + rel_att attention_scores = attention_scores - attention_scores = attention_scores.view( - -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1) - ) + attention_scores = attention_scores.view(-1, self.num_attention_heads, attention_scores.size(-2), + attention_scores.size(-1)) # bsz x height x length x dimension attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1) attention_probs = self.dropout(attention_probs) - context_layer = torch.bmm( - attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer - ) - context_layer = ( - context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1)) - .permute(0, 2, 1, 3) - .contiguous() - ) + context_layer = torch.bmm(attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), + value_layer) + context_layer = (context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), + context_layer.size(-1)).permute(0, 2, 1, 3).contiguous()) new_context_layer_shape = context_layer.size()[:-2] + (-1,) context_layer = context_layer.view(new_context_layer_shape) if output_attentions: @@ -738,9 +745,10 @@ def forward( def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor): if relative_pos is None: q = query_layer.size(-2) - relative_pos = build_relative_position( - q, key_layer.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions - ) + relative_pos = build_relative_position(q, + key_layer.size(-2), + bucket_size=self.position_buckets, + max_position=self.max_relative_positions) if relative_pos.dim() == 2: relative_pos = relative_pos.unsqueeze(0).unsqueeze(0) elif relative_pos.dim() == 3: @@ -758,25 +766,22 @@ def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_ # rel_embeddings = rel_embeddings.unsqueeze(0) # rel_embeddings = rel_embeddings[0 : att_span * 2, :].unsqueeze(0) if self.share_att_key: - pos_query_layer = self.transpose_for_scores( - self.query_proj(rel_embeddings), self.num_attention_heads - ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) + pos_query_layer = self.transpose_for_scores(self.query_proj(rel_embeddings), + self.num_attention_heads).repeat( + query_layer.size(0) // self.num_attention_heads, 1, 1) pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat( - query_layer.size(0) // self.num_attention_heads, 1, 1 - ) + query_layer.size(0) // self.num_attention_heads, 1, 1) else: if "c2p" in self.pos_att_type: - pos_key_layer = self.transpose_for_scores( - self.pos_key_proj(rel_embeddings), self.num_attention_heads - ).repeat( - query_layer.size(0) // self.num_attention_heads, 1, 1 - ) # .split(self.all_head_size, dim=-1) + pos_key_layer = self.transpose_for_scores(self.pos_key_proj(rel_embeddings), + self.num_attention_heads).repeat( + query_layer.size(0) // self.num_attention_heads, 1, + 1) # .split(self.all_head_size, dim=-1) if "p2c" in self.pos_att_type: - pos_query_layer = self.transpose_for_scores( - self.pos_query_proj(rel_embeddings), self.num_attention_heads - ).repeat( - query_layer.size(0) // self.num_attention_heads, 1, 1 - ) # .split(self.all_head_size, dim=-1) + pos_query_layer = self.transpose_for_scores(self.pos_query_proj(rel_embeddings), + self.num_attention_heads).repeat( + query_layer.size(0) // self.num_attention_heads, 1, + 1) # .split(self.all_head_size, dim=-1) score = 0 # content->position @@ -787,7 +792,9 @@ def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_ c2p_att = torch.gather( c2p_att, dim=-1, - index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]), + index=c2p_pos.squeeze(0).expand([query_layer.size(0), + query_layer.size(1), + relative_pos.size(-1)]), ) score += c2p_att / scale @@ -810,7 +817,9 @@ def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_ p2c_att = torch.gather( p2c_att, dim=-1, - index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]), + index=p2c_pos.squeeze(0).expand([query_layer.size(0), + key_layer.size(-2), + key_layer.size(-2)]), ).transpose(-1, -2) score += p2c_att / scale @@ -990,6 +999,7 @@ def _set_gradient_checkpointing(self, module, value=False): ) # Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2 class DebertaV2Model(DebertaV2PreTrainedModel): + def __init__(self, config): super().__init__(config) @@ -1032,9 +1042,8 @@ def forward( return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: @@ -1091,7 +1100,7 @@ def forward( sequence_output = encoded_layers[-1] if not return_dict: - return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :] + return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2):] return BaseModelOutput( last_hidden_state=sequence_output, @@ -1165,7 +1174,7 @@ def forward( masked_lm_loss = None if labels is not None: - loss_fct = CrossEntropyLoss() # -100 index = padding token + loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: @@ -1182,6 +1191,7 @@ def forward( # copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta class DebertaV2PredictionHeadTransform(nn.Module): + def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) @@ -1200,6 +1210,7 @@ def forward(self, hidden_states): # copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta class DebertaV2LMPredictionHead(nn.Module): + def __init__(self, config): super().__init__() self.transform = DebertaV2PredictionHeadTransform(config) @@ -1221,6 +1232,7 @@ def forward(self, hidden_states): # copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta class DebertaV2OnlyMLMHead(nn.Module): + def __init__(self, config): super().__init__() self.predictions = DebertaV2LMPredictionHead(config) @@ -1239,6 +1251,7 @@ def forward(self, sequence_output): ) # Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification with Deberta->DebertaV2 class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel): + def __init__(self, config): super().__init__(config) @@ -1318,9 +1331,8 @@ def forward( label_index = (labels >= 0).nonzero() labels = labels.long() if label_index.size(0) > 0: - labeled_logits = torch.gather( - logits, 0, label_index.expand(label_index.size(0), logits.size(1)) - ) + labeled_logits = torch.gather(logits, 0, label_index.expand(label_index.size(0), + logits.size(1))) labels = torch.gather(labels, 0, label_index.view(-1)) loss_fct = CrossEntropyLoss() loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1)) @@ -1345,9 +1357,10 @@ def forward( output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output - return SequenceClassifierOutput( - loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions - ) + return SequenceClassifierOutput(loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions) @add_start_docstrings( @@ -1422,9 +1435,10 @@ def forward( output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output - return TokenClassifierOutput( - loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions - ) + return TokenClassifierOutput(loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions) @add_start_docstrings( @@ -1536,6 +1550,7 @@ def forward( DEBERTA_START_DOCSTRING, ) class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel): + def __init__(self, config): super().__init__(config) @@ -1591,11 +1606,8 @@ def forward( flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None - flat_inputs_embeds = ( - inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) - if inputs_embeds is not None - else None - ) + flat_inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None else None) outputs = self.deberta( flat_input_ids, diff --git a/examples/language/roberta/pretraining/nvidia_bert_dataset_provider.py b/examples/community/roberta/pretraining/nvidia_bert_dataset_provider.py similarity index 76% rename from examples/language/roberta/pretraining/nvidia_bert_dataset_provider.py rename to examples/community/roberta/pretraining/nvidia_bert_dataset_provider.py index cce836913505..72c7bd852a40 100644 --- a/examples/language/roberta/pretraining/nvidia_bert_dataset_provider.py +++ b/examples/community/roberta/pretraining/nvidia_bert_dataset_provider.py @@ -1,24 +1,25 @@ +import json +import logging import os import random -import h5py -import logging -import json import time from concurrent.futures import ProcessPoolExecutor +import h5py import numpy as np - import torch import torch.distributed as dist +from bert_dataset_provider import BertDatasetProviderInterface from torch.utils.data import DataLoader, Dataset -from torch.utils.data.sampler import RandomSampler from torch.utils.data.distributed import DistributedSampler +from torch.utils.data.sampler import RandomSampler -from bert_dataset_provider import BertDatasetProviderInterface import colossalai.utils as utils + # Workaround because python functions are not picklable class WorkerInitObj(object): + def __init__(self, seed): self.seed = seed @@ -27,29 +28,25 @@ def __call__(self, id): random.seed(self.seed + id) -def create_pretraining_dataset(input_file, max_predictions_per_seq, - num_workers, train_batch_size, worker_init, +def create_pretraining_dataset(input_file, max_predictions_per_seq, num_workers, train_batch_size, worker_init, data_sampler): - train_data = pretraining_dataset( - input_file=input_file, max_predictions_per_seq=max_predictions_per_seq) + train_data = pretraining_dataset(input_file=input_file, max_predictions_per_seq=max_predictions_per_seq) train_dataloader = DataLoader(train_data, sampler=data_sampler(train_data), batch_size=train_batch_size, num_workers=num_workers, worker_init_fn=worker_init, - pin_memory=True - ) + pin_memory=True) return train_dataloader, len(train_data) class pretraining_dataset(Dataset): + def __init__(self, input_file, max_predictions_per_seq): self.input_file = input_file self.max_predictions_per_seq = max_predictions_per_seq f = h5py.File(input_file, "r") - keys = [ - 'input_ids', 'input_mask', 'segment_ids', 'masked_lm_positions' - ] + keys = ['input_ids', 'input_mask', 'segment_ids', 'masked_lm_positions'] self.inputs = [np.asarray(f[key][:]) for key in keys] f.close() @@ -59,21 +56,16 @@ def __len__(self): def __getitem__(self, index): - [ - input_ids, input_mask, segment_ids, masked_lm_labels - ] = [ - torch.from_numpy(input[index].astype(np.int64)) if indice < 5 else - torch.from_numpy(np.asarray(input[index].astype(np.int64))) - for indice, input in enumerate(self.inputs) + [input_ids, input_mask, segment_ids, masked_lm_labels] = [ + torch.from_numpy(input[index].astype(np.int64)) if indice < 5 else torch.from_numpy( + np.asarray(input[index].astype(np.int64))) for indice, input in enumerate(self.inputs) ] - return [ - input_ids, input_mask, - segment_ids, masked_lm_labels - ] + return [input_ids, input_mask, segment_ids, masked_lm_labels] class NvidiaBertDatasetProvider(BertDatasetProviderInterface): + def __init__(self, args, evaluate=False): self.num_workers = args.num_workers self.max_seq_length = args.max_seq_length @@ -85,22 +77,24 @@ def __init__(self, args, evaluate=False): else: self.train_micro_batch_size_per_gpu = args.eval_micro_batch_size_per_gpu self.logger = args.logger - + self.global_rank = dist.get_rank() self.world_size = dist.get_world_size() # Initialize dataset files if not evaluate: self.dataset_files = [ - os.path.join(args.data_path_prefix, f) for f in os.listdir(args.data_path_prefix) if - os.path.isfile(os.path.join(args.data_path_prefix, f)) and 'h5' in f + os.path.join(args.data_path_prefix, f) + for f in os.listdir(args.data_path_prefix) + if os.path.isfile(os.path.join(args.data_path_prefix, f)) and 'h5' in f ] else: self.dataset_files = [ - os.path.join(args.eval_data_path_prefix, f) for f in os.listdir(args.eval_data_path_prefix) if - os.path.isfile(os.path.join(args.eval_data_path_prefix, f)) and 'h5' in f + os.path.join(args.eval_data_path_prefix, f) + for f in os.listdir(args.eval_data_path_prefix) + if os.path.isfile(os.path.join(args.eval_data_path_prefix, f)) and 'h5' in f ] - + self.dataset_files.sort() # random.shuffle(self.dataset_files) self.num_files = len(self.dataset_files) @@ -114,9 +108,7 @@ def __init__(self, args, evaluate=False): self.shuffle = True if self.global_rank == 0: - self.logger.info( - f"NvidiaBertDatasetProvider - Initialization: num_files = {self.num_files}" - ) + self.logger.info(f"NvidiaBertDatasetProvider - Initialization: num_files = {self.num_files}") def get_shard(self, index): start = time.time() @@ -130,9 +122,8 @@ def get_shard(self, index): worker_init=self.worker_init, data_sampler=self.data_sampler) else: - self.train_dataloader, sample_count = self.dataset_future.result( - timeout=None) - + self.train_dataloader, sample_count = self.dataset_future.result(timeout=None) + self.logger.info( f"Data Loading Completed for Pretraining Data from {self.data_file} with {sample_count} samples took {time.time()-start:.2f}s." ) @@ -145,11 +136,9 @@ def release_shard(self): def prefetch_shard(self, index): self.data_file = self._get_shard_file(index) - self.dataset_future = self.pool.submit( - create_pretraining_dataset, self.data_file, - self.max_predictions_per_seq, self.num_workers, - self.train_micro_batch_size_per_gpu, self.worker_init, - self.data_sampler) + self.dataset_future = self.pool.submit(create_pretraining_dataset, self.data_file, self.max_predictions_per_seq, + self.num_workers, self.train_micro_batch_size_per_gpu, self.worker_init, + self.data_sampler) def get_batch(self, batch_iter): return batch_iter @@ -179,4 +168,3 @@ def shuffle_dataset(self, epoch): indices = torch.randperm(self.num_files, generator=g).tolist() new_dataset = [self.dataset_files[i] for i in indices] self.dataset_files = new_dataset - \ No newline at end of file diff --git a/examples/language/roberta/pretraining/pretrain_utils.py b/examples/community/roberta/pretraining/pretrain_utils.py similarity index 77% rename from examples/language/roberta/pretraining/pretrain_utils.py rename to examples/community/roberta/pretraining/pretrain_utils.py index 54fc2affe632..cea6ac2c36e5 100644 --- a/examples/language/roberta/pretraining/pretrain_utils.py +++ b/examples/community/roberta/pretraining/pretrain_utils.py @@ -1,35 +1,45 @@ -import transformers import logging -from colossalai.nn.lr_scheduler import LinearWarmupLR -from transformers import get_linear_schedule_with_warmup -from transformers import BertForPreTraining, RobertaForMaskedLM, RobertaConfig -from transformers import GPT2Config, GPT2LMHeadModel -from transformers import AutoTokenizer, AutoModelForMaskedLM -from colossalai.nn.optimizer import FusedAdam, HybridAdam -from torch.optim import AdamW -from colossalai.core import global_context as gpc -import torch import os import sys -sys.path.append(os.getcwd()) -from model.deberta_v2 import DebertaV2ForMaskedLM -from model.bert import BertForMaskedLM -import torch.nn as nn +import torch +import transformers +from torch.optim import AdamW +from transformers import ( + AutoModelForMaskedLM, + AutoTokenizer, + BertForPreTraining, + GPT2Config, + GPT2LMHeadModel, + RobertaConfig, + RobertaForMaskedLM, + get_linear_schedule_with_warmup, +) + +from colossalai.core import global_context as gpc +from colossalai.nn.lr_scheduler import LinearWarmupLR +from colossalai.nn.optimizer import FusedAdam, HybridAdam + +sys.path.append(os.getcwd()) from collections import OrderedDict +import torch.nn as nn +from model.bert import BertForMaskedLM +from model.deberta_v2 import DebertaV2ForMaskedLM + __all__ = ['get_model', 'get_optimizer', 'get_lr_scheduler', 'get_dataloader_for_pretraining'] def get_new_state_dict(state_dict, start_index=13): - new_state_dict = OrderedDict() + new_state_dict = OrderedDict() for k, v in state_dict.items(): name = k[start_index:] - new_state_dict[name] = v + new_state_dict[name] = v return new_state_dict class LMModel(nn.Module): + def __init__(self, model, config, args): super().__init__() @@ -58,16 +68,18 @@ def get_model(args, logger): if len(args.load_pretrain_model) > 0: assert os.path.exists(args.load_pretrain_model) # load_checkpoint(args.load_pretrain_model, model, strict=False) - m_state_dict = torch.load(args.load_pretrain_model, map_location=torch.device(f"cuda:{torch.cuda.current_device()}")) + m_state_dict = torch.load(args.load_pretrain_model, + map_location=torch.device(f"cuda:{torch.cuda.current_device()}")) # new_state_dict = get_new_state_dict(m_state_dict) - model.load_state_dict(m_state_dict, strict=True) # must insure that every process have identical parameters !!!!!!! + model.load_state_dict(m_state_dict, + strict=True) # must insure that every process have identical parameters !!!!!!! logger.info("load model success") - + numel = sum([p.numel() for p in model.parameters()]) if args.checkpoint_activations: model.gradient_checkpointing_enable() # model = LMModel(model, config, args) - + return config, model, numel @@ -89,7 +101,10 @@ def get_optimizer(model, lr): def get_lr_scheduler(optimizer, total_steps, warmup_steps=2000, last_epoch=-1): # warmup_steps = int(total_steps * warmup_ratio) - lr_scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps, last_epoch=last_epoch) + lr_scheduler = get_linear_schedule_with_warmup(optimizer, + num_warmup_steps=warmup_steps, + num_training_steps=total_steps, + last_epoch=last_epoch) # lr_scheduler = LinearWarmupLR(optimizer, total_steps=total_steps, warmup_steps=warmup_steps) return lr_scheduler @@ -103,10 +118,7 @@ def save_ckpt(model, optimizer, lr_scheduler, path, epoch, shard, global_step): checkpoint['epoch'] = epoch checkpoint['shard'] = shard checkpoint['global_step'] = global_step - model_state = model.state_dict() #each process must run model.state_dict() + model_state = model.state_dict() #each process must run model.state_dict() if gpc.get_global_rank() == 0: torch.save(checkpoint, optimizer_lr_path) torch.save(model_state, model_path) - - - diff --git a/examples/language/roberta/pretraining/run_pretrain.sh b/examples/community/roberta/pretraining/run_pretrain.sh similarity index 98% rename from examples/language/roberta/pretraining/run_pretrain.sh rename to examples/community/roberta/pretraining/run_pretrain.sh index 38fdefe0af8a..280dba714de5 100644 --- a/examples/language/roberta/pretraining/run_pretrain.sh +++ b/examples/community/roberta/pretraining/run_pretrain.sh @@ -35,4 +35,3 @@ env OMP_NUM_THREADS=40 colossalai run --hostfile ./hostfile \ --mlm bert \ --wandb \ --checkpoint_activations \ - \ No newline at end of file diff --git a/examples/language/roberta/pretraining/run_pretrain_resume.sh b/examples/community/roberta/pretraining/run_pretrain_resume.sh similarity index 98% rename from examples/language/roberta/pretraining/run_pretrain_resume.sh rename to examples/community/roberta/pretraining/run_pretrain_resume.sh index 351c98d3e9cb..8f443b454d7d 100644 --- a/examples/language/roberta/pretraining/run_pretrain_resume.sh +++ b/examples/community/roberta/pretraining/run_pretrain_resume.sh @@ -38,4 +38,3 @@ env OMP_NUM_THREADS=40 colossalai run --hostfile ./hostfile \ --resume_train \ --load_pretrain_model /ckpt/1.pt \ --load_optimizer_lr /ckpt/1.op_lrs \ - \ No newline at end of file diff --git a/examples/language/roberta/pretraining/run_pretraining.py b/examples/community/roberta/pretraining/run_pretraining.py similarity index 94% rename from examples/language/roberta/pretraining/run_pretraining.py rename to examples/community/roberta/pretraining/run_pretraining.py index a283c44cadbf..9a6ffc1c5661 100644 --- a/examples/language/roberta/pretraining/run_pretraining.py +++ b/examples/community/roberta/pretraining/run_pretraining.py @@ -4,21 +4,6 @@ from functools import partial import torch -from tqdm import tqdm -import os -import time -from functools import partial -from transformers import AutoTokenizer - -import colossalai -from colossalai.context import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.nn.parallel import GeminiDDP, zero_model_wrapper, zero_optim_wrapper -from colossalai.utils import get_current_device -from colossalai.utils.model.colo_init_context import ColoInitContext -from colossalai.zero import ZeroOptimizer -from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec - from arguments import parse_args from evaluation import evaluate from loss import LossForPretraining @@ -30,6 +15,15 @@ from utils.global_vars import get_tensorboard_writer, get_timers, set_global_variables from utils.logger import Logger +import colossalai +from colossalai.context import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.nn.parallel import GeminiDDP, zero_model_wrapper, zero_optim_wrapper +from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec +from colossalai.utils import get_current_device +from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.zero import ZeroOptimizer + def main(): @@ -39,7 +33,7 @@ def main(): tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path) # os.environ['CUDA_LAUNCH_BLOCKING'] = '1' - + logger = Logger(os.path.join(args.log_path, launch_time), cuda=torch.cuda.is_available(), debug=args.vscode_debug) if args.vscode_debug: @@ -52,7 +46,7 @@ def main(): args.local_rank = -1 args.log_interval = 1 else: - colossalai.launch_from_torch(config={}) #args.colossal_config + colossalai.launch_from_torch(config={}) #args.colossal_config args.local_rank = int(os.environ["LOCAL_RANK"]) logger.info( f'launch_from_torch, world size: {torch.distributed.get_world_size()} | ' + @@ -63,7 +57,7 @@ def main(): args.tokenizer = tokenizer args.logger = logger set_global_variables(launch_time, args.tensorboard_path) - + world_size = torch.distributed.get_world_size() init_dev = get_current_device() @@ -116,7 +110,7 @@ def main(): optimizer = zero_optim_wrapper(model, optimizer, optim_config=optim_config) logger.info(get_mem_info(prefix='After init optim, ')) - + else: config, model, numel = get_model(args, logger) logger.info("no_zero") @@ -129,7 +123,7 @@ def main(): get_tflops_func = partial(get_tflops, numel, args.train_micro_batch_size_per_gpu, args.max_seq_length) # 144003367 is is the length of the entire dataset - steps_per_epoch = 144003367 // world_size // args.train_micro_batch_size_per_gpu // args.gradient_accumulation_steps // args.refresh_bucket_size #len(dataloader) + steps_per_epoch = 144003367 // world_size // args.train_micro_batch_size_per_gpu // args.gradient_accumulation_steps // args.refresh_bucket_size #len(dataloader) total_steps = steps_per_epoch * args.epoch lr_scheduler = get_lr_scheduler(optimizer, total_steps=total_steps, last_epoch=-1) @@ -156,14 +150,15 @@ def main(): start_epoch = o_l_state_dict['epoch'] start_shard = o_l_state_dict['shard'] + 1 # global_step = o_l_state_dict['global_step'] + 1 - logger.info(f'resume from epoch {start_epoch} shard {start_shard} step {lr_scheduler.last_epoch} lr {lr_scheduler.get_last_lr()[0]}') + logger.info( + f'resume from epoch {start_epoch} shard {start_shard} step {lr_scheduler.last_epoch} lr {lr_scheduler.get_last_lr()[0]}' + ) criterion = LossForPretraining(config.vocab_size) # build dataloader pretrain_dataset_provider = NvidiaBertDatasetProvider(args) - logger.info(get_mem_info(prefix='After init model, ')) best_loss = None @@ -189,8 +184,8 @@ def main(): iterator_data = enumerate(dataset_iterator) model.train() - - for step, batch_data in iterator_data: + + for step, batch_data in iterator_data: # batch_data = pretrain_dataset_provider.get_batch(batch_index) input_ids = batch_data[0].cuda(f"cuda:{torch.cuda.current_device()}") @@ -200,7 +195,7 @@ def main(): # nsp_label = batch_data[5].cuda() output = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) - + loss = criterion(output.logits, mlm_label) pretrain_dataset_provider.prefetch_batch() @@ -210,7 +205,7 @@ def main(): optimizer.step() lr_scheduler.step() optimizer.zero_grad() - + global_step += 1 if global_step % args.log_interval == 0 and global_step != 0 \ @@ -242,9 +237,10 @@ def main(): logger.info('*' * 100) eval_loss += evaluate(model, args, logger, global_step, criterion) - save_ckpt(model, optimizer, lr_scheduler, os.path.join(args.ckpt_path, launch_time, f'epoch-{epoch}_shard-{shard}_' + launch_time), epoch, shard, global_step) - - + save_ckpt(model, optimizer, lr_scheduler, + os.path.join(args.ckpt_path, launch_time, f'epoch-{epoch}_shard-{shard}_' + launch_time), epoch, + shard, global_step) + eval_loss /= len(os.listdir(args.data_path_prefix)) logger.info( f'epoch {epoch} | shard_length {len(os.listdir(args.data_path_prefix))} | elapsed_time: {timers("epoch_time").elapsed() / 60 :.3f} mins' diff --git a/examples/language/roberta/pretraining/utils/WandbLog.py b/examples/community/roberta/pretraining/utils/WandbLog.py similarity index 98% rename from examples/language/roberta/pretraining/utils/WandbLog.py rename to examples/community/roberta/pretraining/utils/WandbLog.py index 9dd28a98186b..b68ba8387dcd 100644 --- a/examples/language/roberta/pretraining/utils/WandbLog.py +++ b/examples/community/roberta/pretraining/utils/WandbLog.py @@ -1,8 +1,10 @@ +import os import time + import wandb -import os from torch.utils.tensorboard import SummaryWriter + class WandbLog: @classmethod @@ -15,7 +17,7 @@ def log(cls, result, model=None, gradient=None): if model: wandb.watch(model) - + if gradient: wandb.watch(gradient) @@ -30,7 +32,7 @@ def __init__(self, location, name=time.strftime("%Y-%m-%d %H:%M:%S", time.localt def log_train(self, result, step): for k, v in result.items(): self.writer.add_scalar(f'{k}/train', v, step) - + def log_eval(self, result, step): for k, v in result.items(): self.writer.add_scalar(f'{k}/eval', v, step) @@ -38,9 +40,3 @@ def log_eval(self, result, step): def log_zeroshot(self, result, step): for k, v in result.items(): self.writer.add_scalar(f'{k}_acc/eval', v, step) - - - - - - diff --git a/examples/language/roberta/pretraining/utils/exp_util.py b/examples/community/roberta/pretraining/utils/exp_util.py similarity index 86% rename from examples/language/roberta/pretraining/utils/exp_util.py rename to examples/community/roberta/pretraining/utils/exp_util.py index a02b0872acbc..0cdb56bad031 100644 --- a/examples/language/roberta/pretraining/utils/exp_util.py +++ b/examples/community/roberta/pretraining/utils/exp_util.py @@ -1,9 +1,13 @@ import functools -import os, shutil -import torch +import os +import shutil + import psutil +import torch + from colossalai.core import global_context as gpc + def logging(s, log_path, print_=True, log_=True): if print_: print(s) @@ -11,9 +15,11 @@ def logging(s, log_path, print_=True, log_=True): with open(log_path, 'a+') as f_log: f_log.write(s + '\n') + def get_logger(log_path, **kwargs): return functools.partial(logging, log_path=log_path, **kwargs) + def create_exp_dir(dir_path, scripts_to_save=None, debug=False): if debug: print('Debug Mode : no experiment dir created') @@ -33,6 +39,7 @@ def create_exp_dir(dir_path, scripts_to_save=None, debug=False): return get_logger(log_path=os.path.join(dir_path, 'log.txt')) + def get_cpu_mem(): return psutil.Process().memory_info().rss / 1024**2 @@ -52,11 +59,15 @@ def get_tflops(model_numel, batch_size, seq_len, step_time): def get_parameters_in_billions(model, world_size=1): gpus_per_model = world_size - approx_parameters_in_billions = sum([sum([p.ds_numel if hasattr(p,'ds_id') else p.nelement() for p in model_module.parameters()]) - for model_module in model]) + approx_parameters_in_billions = sum([ + sum([p.ds_numel if hasattr(p, 'ds_id') else p.nelement() + for p in model_module.parameters()]) + for model_module in model + ]) return approx_parameters_in_billions * gpus_per_model / (1e9) + def throughput_calculator(numel, args, config, iteration_time, total_iterations, world_size=1): gpus_per_model = 1 batch_size = args.train_micro_batch_size_per_gpu @@ -76,10 +87,13 @@ def throughput_calculator(numel, args, config, iteration_time, total_iterations, # The factor of 4 is when used with activation check-pointing, # otherwise it will be 3. checkpoint_activations_factor = 4 if args.checkpoint_activations else 3 - flops_per_iteration = (24 * checkpoint_activations_factor * batch_size * args.max_seq_length * num_layers * (hidden_size**2)) * (1. + (args.max_seq_length / (6. * hidden_size)) + (vocab_size / (16. * num_layers * hidden_size))) + flops_per_iteration = (24 * checkpoint_activations_factor * batch_size * args.max_seq_length * num_layers * + (hidden_size**2)) * (1. + (args.max_seq_length / (6. * hidden_size)) + + (vocab_size / (16. * num_layers * hidden_size))) tflops = flops_per_iteration / (elapsed_time_per_iter * (10**12)) return samples_per_second, tflops, approx_parameters_in_billions + def synchronize(): if not torch.distributed.is_available(): return @@ -90,10 +104,11 @@ def synchronize(): return torch.distributed.barrier() + def log_args(logger, args): logger.info('--------args----------') message = '\n'.join([f'{k:<30}: {v}' for k, v in vars(args).items()]) message += '\n' message += '\n'.join([f'{k:<30}: {v}' for k, v in gpc.config.items()]) logger.info(message) - logger.info('--------args----------\n') \ No newline at end of file + logger.info('--------args----------\n') diff --git a/examples/language/roberta/pretraining/utils/global_vars.py b/examples/community/roberta/pretraining/utils/global_vars.py similarity index 91% rename from examples/language/roberta/pretraining/utils/global_vars.py rename to examples/community/roberta/pretraining/utils/global_vars.py index 363cbf91c065..7b0c5a2be73d 100644 --- a/examples/language/roberta/pretraining/utils/global_vars.py +++ b/examples/community/roberta/pretraining/utils/global_vars.py @@ -1,5 +1,7 @@ import time + import torch + from .WandbLog import TensorboardLog _GLOBAL_TIMERS = None @@ -10,30 +12,34 @@ def set_global_variables(launch_time, tensorboard_path): _set_timers() _set_tensorboard_writer(launch_time, tensorboard_path) + def _set_timers(): """Initialize timers.""" global _GLOBAL_TIMERS _ensure_var_is_not_initialized(_GLOBAL_TIMERS, 'timers') _GLOBAL_TIMERS = Timers() + def _set_tensorboard_writer(launch_time, tensorboard_path): """Set tensorboard writer.""" global _GLOBAL_TENSORBOARD_WRITER - _ensure_var_is_not_initialized(_GLOBAL_TENSORBOARD_WRITER, - 'tensorboard writer') + _ensure_var_is_not_initialized(_GLOBAL_TENSORBOARD_WRITER, 'tensorboard writer') if torch.distributed.get_rank() == 0: _GLOBAL_TENSORBOARD_WRITER = TensorboardLog(tensorboard_path + f'/{launch_time}', launch_time) - + + def get_timers(): """Return timers.""" _ensure_var_is_initialized(_GLOBAL_TIMERS, 'timers') return _GLOBAL_TIMERS + def get_tensorboard_writer(): """Return tensorboard writer. It can be None so no need to check if it is initialized.""" return _GLOBAL_TENSORBOARD_WRITER + def _ensure_var_is_initialized(var, name): """Make sure the input variable is not None.""" assert var is not None, '{} is not initialized.'.format(name) @@ -115,12 +121,10 @@ def log(self, names, normalizer=1.0, reset=True): assert normalizer > 0.0 string = 'time (ms)' for name in names: - elapsed_time = self.timers[name].elapsed( - reset=reset) * 1000.0 / normalizer + elapsed_time = self.timers[name].elapsed(reset=reset) * 1000.0 / normalizer string += ' | {}: {:.2f}'.format(name, elapsed_time) if torch.distributed.is_initialized(): - if torch.distributed.get_rank() == ( - torch.distributed.get_world_size() - 1): + if torch.distributed.get_rank() == (torch.distributed.get_world_size() - 1): print(string, flush=True) else: print(string, flush=True) diff --git a/examples/language/roberta/pretraining/utils/logger.py b/examples/community/roberta/pretraining/utils/logger.py similarity index 81% rename from examples/language/roberta/pretraining/utils/logger.py rename to examples/community/roberta/pretraining/utils/logger.py index 481c4c6ce61b..75c9bf4bef25 100644 --- a/examples/language/roberta/pretraining/utils/logger.py +++ b/examples/community/roberta/pretraining/utils/logger.py @@ -1,22 +1,22 @@ -import os import logging +import os + import torch.distributed as dist -logging.basicConfig( - format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt='%m/%d/%Y %H:%M:%S', - level=logging.INFO) +logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt='%m/%d/%Y %H:%M:%S', + level=logging.INFO) logger = logging.getLogger(__name__) class Logger(): + def __init__(self, log_path, cuda=False, debug=False): self.logger = logging.getLogger(__name__) self.cuda = cuda self.log_path = log_path self.debug = debug - def info(self, message, log_=True, print_=True, *args, **kwargs): if (self.cuda and dist.get_rank() == 0) or not self.cuda: if print_: @@ -26,6 +26,5 @@ def info(self, message, log_=True, print_=True, *args, **kwargs): with open(self.log_path, 'a+') as f_log: f_log.write(message + '\n') - def error(self, message, *args, **kwargs): self.logger.error(message, *args, **kwargs) diff --git a/examples/language/roberta/requirements.txt b/examples/community/roberta/requirements.txt similarity index 91% rename from examples/language/roberta/requirements.txt rename to examples/community/roberta/requirements.txt index d351f362f3f7..de082defb14a 100644 --- a/examples/language/roberta/requirements.txt +++ b/examples/community/roberta/requirements.txt @@ -4,4 +4,4 @@ tqdm tensorboard numpy h5py -wandb \ No newline at end of file +wandb diff --git a/examples/language/roberta/preprocessing/mask.cpp b/examples/language/roberta/preprocessing/mask.cpp deleted file mode 100644 index 8355c45cff0a..000000000000 --- a/examples/language/roberta/preprocessing/mask.cpp +++ /dev/null @@ -1,184 +0,0 @@ -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -namespace py = pybind11; - -const int32_t LONG_SENTENCE_LEN = 512; - -struct MaskedLMInstance { - int index; - std::string label; - MaskedLMInstance(int index, std::string label) { - this->index = index; - this->label = label; - } -}; - -auto get_new_segment(std::vector segment, std::vector segment_jieba, const std::vector chinese_vocab) { // const std::unordered_set &chinese_vocab - std::unordered_set seq_cws_dict; - for (auto word : segment_jieba) { - seq_cws_dict.insert(word); - } - int i = 0; - std::vector new_segment; - int segment_size = segment.size(); - while (i < segment_size) { - if (!chinese_vocab[i]) { //chinese_vocab.find(segment[i]) == chinese_vocab.end() - new_segment.emplace_back(segment[i]); - i += 1; - continue; - } - bool has_add = false; - for (int length = 3; length >= 1; length--) { - if (i + length > segment_size) { - continue; - } - std::string chinese_word = ""; - for (int j = i; j < i + length; j++) { - chinese_word += segment[j]; - } - if (seq_cws_dict.find(chinese_word) != seq_cws_dict.end()) { - new_segment.emplace_back(segment[i]); - for (int j = i + 1; j < i + length; j++) { - new_segment.emplace_back("##" + segment[j]); - } - i += length; - has_add = true; - break; - } - } - if (!has_add) { - new_segment.emplace_back(segment[i]); - i += 1; - } - } - - return new_segment; -} - -bool startsWith(const std::string& s, const std::string& sub) { - return s.find(sub) == 0 ? true : false; -} - -auto create_whole_masked_lm_predictions(std::vector &tokens, - const std::vector &original_tokens, - const std::vector &vocab_words, - std::map &vocab, - const int max_predictions_per_seq, - const double masked_lm_prob) { - // for (auto item : vocab) { - // std::cout << "key=" << std::string(py::str(item.first)) << ", " - // << "value=" << std::string(py::str(item.second)) << std::endl; - // } - std::vector > cand_indexes; - std::vector cand_temp; - int tokens_size = tokens.size(); - std::string prefix = "##"; - bool do_whole_masked = true; - - for (int i = 0; i < tokens_size; i++) { - if (tokens[i] == "[CLS]" || tokens[i] == "[SEP]") { - continue; - } - if (do_whole_masked && (cand_indexes.size() > 0) && (tokens[i].rfind(prefix, 0) == 0)) { - cand_temp.emplace_back(i); - } - else { - if (cand_temp.size() > 0) { - cand_indexes.emplace_back(cand_temp); - } - cand_temp.clear(); - cand_temp.emplace_back(i); - } - } - auto seed = std::chrono::system_clock::now().time_since_epoch().count(); - std::shuffle(cand_indexes.begin(), cand_indexes.end(), std::default_random_engine(seed)); - // for (auto i : cand_indexes) { - // for (auto j : i) { - // std::cout << tokens[j] << " "; - // } - // std::cout << std::endl; - // } - // for (auto i : output_tokens) { - // std::cout << i; - // } - // std::cout << std::endl; - - int num_to_predict = std::min(max_predictions_per_seq, - std::max(1, int(tokens_size * masked_lm_prob))); - // std::cout << num_to_predict << std::endl; - - std::set covered_indexes; - std::vector masked_lm_output(tokens_size, -1); - int vocab_words_len = vocab_words.size(); - std::default_random_engine e(seed); - std::uniform_real_distribution u1(0.0, 1.0); - std::uniform_int_distribution u2(0, vocab_words_len - 1); - int mask_cnt = 0; - std::vector output_tokens; - output_tokens = original_tokens; - - for (auto index_set : cand_indexes) { - if (mask_cnt > num_to_predict) { - break; - } - int index_set_size = index_set.size(); - if (mask_cnt + index_set_size > num_to_predict) { - continue; - } - bool is_any_index_covered = false; - for (auto index : index_set) { - if (covered_indexes.find(index) != covered_indexes.end()) { - is_any_index_covered = true; - break; - } - } - if (is_any_index_covered) { - continue; - } - for (auto index : index_set) { - - covered_indexes.insert(index); - std::string masked_token; - if (u1(e) < 0.8) { - masked_token = "[MASK]"; - } - else { - if (u1(e) < 0.5) { - masked_token = output_tokens[index]; - } - else { - int random_index = u2(e); - masked_token = vocab_words[random_index]; - } - } - // masked_lms.emplace_back(MaskedLMInstance(index, output_tokens[index])); - masked_lm_output[index] = vocab[output_tokens[index]]; - output_tokens[index] = masked_token; - mask_cnt++; - } - } - - // for (auto p : masked_lms) { - // masked_lm_output[p.index] = vocab[p.label]; - // } - return std::make_tuple(output_tokens, masked_lm_output); -} - -PYBIND11_MODULE(mask, m) { - m.def("create_whole_masked_lm_predictions", &create_whole_masked_lm_predictions); - m.def("get_new_segment", &get_new_segment); -} diff --git a/examples/language/roberta/pretraining/arguments.py b/examples/language/roberta/pretraining/arguments.py deleted file mode 100644 index 87fa8dd8a8ae..000000000000 --- a/examples/language/roberta/pretraining/arguments.py +++ /dev/null @@ -1,176 +0,0 @@ -import colossalai -from numpy import require - -__all__ = ['parse_args'] - - -def parse_args(): - parser = colossalai.get_default_parser() - - parser.add_argument( - "--distplan", - type=str, - default='CAI_Gemini', - help="The distributed plan [colossalai, zero1, zero2, torch_ddp, torch_zero].", - ) - parser.add_argument( - "--tp_degree", - type=int, - default=1, - help="Tensor Parallelism Degree. Valid when using colossalai as dist plan.", - ) - parser.add_argument( - "--placement", - type=str, - default='cpu', - help="Placement Policy for Gemini. Valid when using colossalai as dist plan.", - ) - parser.add_argument( - "--shardinit", - action='store_true', - help="Shard the tensors when init the model to shrink peak memory size on the assigned device. Valid when using colossalai as dist plan.", - ) - - parser.add_argument( - '--lr', - type=float, - required=True, - help='initial learning rate') - parser.add_argument( - '--epoch', - type=int, - required=True, - help='number of epoch') - parser.add_argument( - '--data_path_prefix', - type=str, - required=True, - help="location of the train data corpus") - parser.add_argument( - '--eval_data_path_prefix', - type=str, - required=True, - help='location of the evaluation data corpus') - parser.add_argument( - '--tokenizer_path', - type=str, - required=True, - help='location of the tokenizer') - parser.add_argument( - '--max_seq_length', - type=int, - default=512, - help='sequence length') - parser.add_argument( - '--refresh_bucket_size', - type=int, - default=1, - help= - "This param makes sure that a certain task is repeated for this time steps to \ - optimise on the back propogation speed with APEX's DistributedDataParallel") - parser.add_argument( - "--max_predictions_per_seq", - "--max_pred", - default=80, - type=int, - help= - "The maximum number of masked tokens in a sequence to be predicted.") - parser.add_argument( - "--gradient_accumulation_steps", - default=1, - type=int, - help="accumulation_steps") - parser.add_argument( - "--train_micro_batch_size_per_gpu", - default=2, - type=int, - required=True, - help="train batch size") - parser.add_argument( - "--eval_micro_batch_size_per_gpu", - default=2, - type=int, - required=True, - help="eval batch size") - parser.add_argument( - "--num_workers", - default=8, - type=int, - help="") - parser.add_argument( - "--async_worker", - action='store_true', - help="") - parser.add_argument( - "--bert_config", - required=True, - type=str, - help="location of config.json") - parser.add_argument( - "--wandb", - action='store_true', - help="use wandb to watch model") - parser.add_argument( - "--wandb_project_name", - default='roberta', - help="wandb project name") - parser.add_argument( - "--log_interval", - default=100, - type=int, - help="report interval") - parser.add_argument( - "--log_path", - type=str, - required=True, - help="log file which records train step") - parser.add_argument( - "--tensorboard_path", - type=str, - required=True, - help="location of tensorboard file") - parser.add_argument( - "--colossal_config", - type=str, - required=True, - help="colossal config, which contains zero config and so on") - parser.add_argument( - "--ckpt_path", - type=str, - required=True, - help="location of saving checkpoint, which contains model and optimizer") - parser.add_argument( - '--seed', - type=int, - default=42, - help="random seed for initialization") - parser.add_argument( - '--vscode_debug', - action='store_true', - help="use vscode to debug") - parser.add_argument( - '--load_pretrain_model', - default='', - type=str, - help="location of model's checkpoin") - parser.add_argument( - '--load_optimizer_lr', - default='', - type=str, - help="location of checkpoint, which contains optimerzier, learning rate, epoch, shard and global_step") - parser.add_argument( - '--resume_train', - action='store_true', - help="whether resume training from a early checkpoint") - parser.add_argument( - '--mlm', - default='bert', - type=str, - help="model type, bert or deberta") - parser.add_argument( - '--checkpoint_activations', - action='store_true', - help="whether to use gradient checkpointing") - - args = parser.parse_args() - return args From 1c7734bc94ac1a7215e08368adc4e7e25e3b8102 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Fri, 14 Apr 2023 22:12:32 +0800 Subject: [PATCH 134/413] [doc] Update 1D_tensor_parallel.md (#3563) Display format optimization, fix bug#3562 Specific changes 1. "This is called a column-parallel fashion" Translate to Chinese 2. use the ```math code block syntax to display a math expression as a block, No modification of formula content Please check that the math formula is displayed correctly If OK, I will change the format of the English version of the formula in parallel --- .../zh-Hans/features/1D_tensor_parallel.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/docs/source/zh-Hans/features/1D_tensor_parallel.md b/docs/source/zh-Hans/features/1D_tensor_parallel.md index 8f3a3c6209da..2ddc27c7b50f 100644 --- a/docs/source/zh-Hans/features/1D_tensor_parallel.md +++ b/docs/source/zh-Hans/features/1D_tensor_parallel.md @@ -17,11 +17,19 @@ 张量并行将模型参数划分到多个设备上，以减少内存负荷。 [Megatron-LM](https://deepakn94.github.io/assets/papers/megatron-sc21.pdf) 介绍了一种高效的一维张量并行化实现。 -让我们以一个线性层为例，它包括一个 GEMM $Y = XA$。 给定2个处理器，我们把列 $A$ 划分为 $[A_1 ~ A_2]$, 并在每个处理器上计算 $Y_i = XA_i$ , which then forms $[Y_1 ~ Y_2] = [XA_1 ~ XA_2]$. This is called a column-parallel fashion. +让我们以一个线性层为例，它包括一个 GEMM $Y = XA$。 给定2个处理器，我们把列 $A$ 划分为 $[A_1 ~ A_2]$, 并在每个处理器上计算 $Y_i = XA_i$ , 然后形成 $[Y_1 ~ Y_2] = [XA_1 ~ XA_2]$. 这被称为列并行方式。 -当第二个线性层 $Z=YB$ 跟随上述列并行层的时候, 我们把 $B$ 划分为 $\left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right]$, -这就是所谓的行并行方式. -为了计算 $Z = [Y_1 ~ Y_2] \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right]$, 我们首先在每个处理器上计算 $Y_iB_i$ 然后使用一个all-reduce操作将结果汇总为 $Z=Y_1B_1+Y_2B_2$。 +当第二个线性层 $Z=YB$ 跟随上述列并行层的时候, 我们把 $B$ 划分为 +```math +\left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right] +``` +这就是所谓的行并行方式.
          + +为了计算 +```math +Z = [Y_1 ~ Y_2] \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right] +``` +我们首先在每个处理器上计算 $Y_iB_i$ 然后使用一个all-reduce操作将结果汇总为 $Z=Y_1B_1+Y_2B_2$。 我们还需要注意，在后向计算中，列并行线性层需要聚合输入张量 $X$, 因为在每个处理器 $i$ 上，我们只有 $\dot{X_i}=\dot{Y_i}A_i^T$，因此，我们在各处理器之间进行all-reduce，得到 $\dot{X}=\dot{Y}A^T=\dot{Y_1}A_1^T+\dot{Y_2}A_2^T$。 From 4341f5e8e65b532d49a6a4dfc64367a417091fb3 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 17 Apr 2023 11:25:13 +0800 Subject: [PATCH 135/413] [lazyinit] fix clone and deepcopy (#3553) --- colossalai/utils/model/experimental.py | 26 ++++++++++++++++++++++++-- 1 file changed, 24 insertions(+), 2 deletions(-) diff --git a/colossalai/utils/model/experimental.py b/colossalai/utils/model/experimental.py index c91751f1cb28..bf3e3d05b99c 100644 --- a/colossalai/utils/model/experimental.py +++ b/colossalai/utils/model/experimental.py @@ -14,8 +14,8 @@ # reference: https://pytorch.org/cppdocs/notes/tensor_creation.html _NORMAL_FACTORY = [ "arange", - "empty", "full", + "empty", "linspace", "logspace", "ones", @@ -324,7 +324,9 @@ def __torch_dispatch__(cls, func, types, args=(), kwargs=None): def clone(self) -> "LazyTensor": def factory_fn(): - return self.materialize().clone() + # if self is materialized, return self + new_tensor = self.materialize() if type(self) is LazyTensor else self + return new_tensor.clone() target = LazyTensor(factory_fn, meta_data=self._meta_data) @@ -333,6 +335,26 @@ def factory_fn(): def detach(self) -> Tensor: return self + def __deepcopy__(self, memo): + if not self.is_leaf: + raise RuntimeError("Only Tensors created explicitly by the user " + "(graph leaves) support the deepcopy protocol at the moment") + if id(self) in memo: + return memo[id(self)] + + def factory_fn(): + # if self is materialized, return self + new_tensor = self.materialize() if type(self) is LazyTensor else self + copied = new_tensor.detach().clone() + if new_tensor.requires_grad: + copied.requires_grad_() + return copied + + target = LazyTensor(factory_fn, meta_data=self._meta_data) + + memo[id(self)] = target + return target + @property def data(self): return self From 173dad05628489f50bd131892b0b3cfbc40b4eb4 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 17 Apr 2023 11:25:35 +0800 Subject: [PATCH 136/413] [misc] add verbose arg for zero and op builder (#3552) * [misc] add print verbose * [gemini] add print verbose * [zero] add print verbose for low level * [misc] add print verbose for op builder --- colossalai/booster/plugin/gemini_plugin.py | 25 +++++++++++++++----- colossalai/zero/gemini/chunk/utils.py | 3 ++- colossalai/zero/gemini/gemini_ddp.py | 6 +++-- colossalai/zero/gemini/gemini_optimizer.py | 6 ++++- colossalai/zero/low_level/low_level_optim.py | 2 ++ colossalai/zero/wrapper.py | 15 ++++++++---- op_builder/builder.py | 6 +++-- op_builder/utils.py | 20 +++++++--------- 8 files changed, 55 insertions(+), 28 deletions(-) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 659f36c210f4..deda00d8a7b3 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -65,9 +65,9 @@ def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): class GeminiModel(ModelWrapper): - def __init__(self, module: nn.Module, gemini_config: dict) -> None: + def __init__(self, module: nn.Module, gemini_config: dict, verbose: bool = False) -> None: super().__init__(module) - self.module = zero_model_wrapper(module, zero_stage=3, gemini_config=gemini_config) + self.module = zero_model_wrapper(module, zero_stage=3, gemini_config=gemini_config, verbose=verbose) def unwrap(self): # as save/load state dict is coupled with the GeminiDDP, we only return GeminiDDP model @@ -76,8 +76,17 @@ def unwrap(self): class GeminiOptimizer(OptimizerWrapper): - def __init__(self, module: GeminiDDP, optimizer: Optimizer, zero_optim_config: dict, optim_kwargs: dict) -> None: - optimizer = zero_optim_wrapper(module, optimizer, optim_config=zero_optim_config, **optim_kwargs) + def __init__(self, + module: GeminiDDP, + optimizer: Optimizer, + zero_optim_config: dict, + optim_kwargs: dict, + verbose: bool = False) -> None: + optimizer = zero_optim_wrapper(module, + optimizer, + optim_config=zero_optim_config, + **optim_kwargs, + verbose=verbose) super().__init__(optimizer) def backward(self, loss: Tensor, *args, **kwargs): @@ -138,6 +147,7 @@ class GeminiPlugin(Plugin): max_norm (float, optional): max_norm used for `clip_grad_norm`. You should notice that you shall not do clip_grad_norm by yourself when using ZeRO DDP. The ZeRO optimizer will take care of clip_grad_norm. norm_type (float, optional): norm_type used for `clip_grad_norm`. + verbose (bool, optional): verbose mode. Debug info including chunk search result will be printed. Defaults to False. """ def __init__( @@ -161,6 +171,7 @@ def __init__( max_scale: float = 2**32, max_norm: float = 0.0, norm_type: float = 2.0, + verbose: bool = False, ) -> None: assert dist.is_initialized( @@ -188,6 +199,7 @@ def __init__( max_scale=max_scale, max_norm=max_norm, norm_type=norm_type) + self.verbose = verbose def support_no_sync(self) -> bool: return False @@ -275,10 +287,11 @@ def configure( # model = nn.SyncBatchNorm.convert_sync_batchnorm(model, None) # wrap the model with Gemini - model = GeminiModel(model, self.gemini_config) + model = GeminiModel(model, self.gemini_config, self.verbose) if not isinstance(optimizer, OptimizerWrapper): - optimizer = GeminiOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs) + optimizer = GeminiOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs, + self.verbose) return model, optimizer, criterion, dataloader, lr_scheduler diff --git a/colossalai/zero/gemini/chunk/utils.py b/colossalai/zero/gemini/chunk/utils.py index 283f74203592..71242dcd6d49 100644 --- a/colossalai/zero/gemini/chunk/utils.py +++ b/colossalai/zero/gemini/chunk/utils.py @@ -20,6 +20,7 @@ def safe_div(a, b): def init_chunk_manager(model: nn.Module, init_device: Optional[torch.device] = None, hidden_dim: Optional[int] = None, + verbose: bool = False, **kwargs) -> ChunkManager: if hidden_dim: search_interval_byte = hidden_dim @@ -39,7 +40,7 @@ def init_chunk_manager(model: nn.Module, total_size /= mb_size wasted_size /= mb_size - if dist.get_rank() == 0: + if verbose and dist.get_rank() == 0: print("searching chunk configuration is completed in {:.2f} s.\n".format(span_s), "used number: {:.2f} MB, wasted number: {:.2f} MB\n".format(total_size, wasted_size), "total wasted percentage is {:.2f}%".format(100 * safe_div(wasted_size, total_size + wasted_size)), diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index c06239dfac20..2e35be0661e9 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -567,7 +567,8 @@ def __init__(self, search_range_mb: int = 32, hidden_dim: Optional[int] = None, min_chunk_size_mb: float = 32, - memstats: Optional[MemStats] = None) -> None: + memstats: Optional[MemStats] = None, + verbose: bool = False) -> None: """ A torch.Module warpper using ZeRO-DP and Genimi. ZeRO is for parallel. Gemini is for memory management. @@ -604,6 +605,7 @@ def __init__(self, hidden_dim=hidden_dim, search_range_mb=search_range_mb, min_chunk_size_mb=min_chunk_size_mb, - strict_ddp_flag=strict_ddp_mode) + strict_ddp_flag=strict_ddp_mode, + verbose=verbose) gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats) super().__init__(module, gemini_manager, pin_memory, force_outputs_fp32, strict_ddp_mode) diff --git a/colossalai/zero/gemini/gemini_optimizer.py b/colossalai/zero/gemini/gemini_optimizer.py index 8940ab9a3251..71c4f65cb8d2 100644 --- a/colossalai/zero/gemini/gemini_optimizer.py +++ b/colossalai/zero/gemini/gemini_optimizer.py @@ -54,6 +54,7 @@ class ZeroOptimizer(ColossalaiOptimizer): clipping_norm (float, optional): The norm value used to clip gradient. Defaults to 0.0. norm_type (float, optional): The type of norm used for gradient clipping. Currently, only L2-norm (norm_type=2.0) is supported in ZeroOptimizer. Defaults to 2.0. + verbose (bool, optional): Whether to print verbose information, including grad overflow info. Defaults to False. """ def __init__(self, @@ -69,6 +70,7 @@ def __init__(self, max_scale: float = 2**32, clipping_norm: float = 0.0, norm_type: float = 2.0, + verbose: bool = False, **defaults: Any): super().__init__(optim) assert isinstance(module, ZeroDDP) @@ -83,6 +85,7 @@ def __init__(self, self.chunk16_set: Set[Chunk] = set() self.clipping_flag = clipping_norm > 0.0 self.max_norm = clipping_norm + self.verbose = verbose if self.clipping_flag: assert norm_type == 2.0, "ZeroOptimizer only supports L2 norm now" @@ -221,7 +224,8 @@ def step(self, *args, **kwargs): if found_inf: self.optim_state = OptimState.UNSCALED # no need to unscale grad self.grad_scaler.update(found_inf) # update gradient scaler - self._logger.info(f'Found overflow. Skip step') + if self.verbose: + self._logger.info(f'Found overflow. Skip step') self._clear_global_norm() # clear recorded norm self.zero_grad() # reset all gradients self._update_fp16_params() diff --git a/colossalai/zero/low_level/low_level_optim.py b/colossalai/zero/low_level/low_level_optim.py index 49fb8b54b7d2..39ade27b9d98 100644 --- a/colossalai/zero/low_level/low_level_optim.py +++ b/colossalai/zero/low_level/low_level_optim.py @@ -440,6 +440,8 @@ def step(self, closure=None): # update loss scale if overflow occurs if found_inf: self._grad_store.reset_all_average_gradients() + if self._verbose: + self._logger.info(f'Found overflow. Skip step') self.zero_grad() return diff --git a/colossalai/zero/wrapper.py b/colossalai/zero/wrapper.py index 4553249e271d..6cdb8fc59ba5 100644 --- a/colossalai/zero/wrapper.py +++ b/colossalai/zero/wrapper.py @@ -7,7 +7,10 @@ from .gemini import GeminiDDP -def zero_model_wrapper(model: nn.Module, zero_stage: int = 1, gemini_config: Optional[Dict] = None): +def zero_model_wrapper(model: nn.Module, + zero_stage: int = 1, + gemini_config: Optional[Dict] = None, + verbose: bool = False): """This wrapper function is used to wrap your training model for ZeRO DDP. Example: @@ -40,7 +43,7 @@ def zero_model_wrapper(model: nn.Module, zero_stage: int = 1, gemini_config: Opt if zero_stage in [1, 2]: wrapped_model = model else: - wrapped_model = GeminiDDP(model, **gemini_config) + wrapped_model = GeminiDDP(model, **gemini_config, verbose=verbose) setattr(wrapped_model, "_colo_zero_stage", zero_stage) @@ -58,7 +61,8 @@ def zero_optim_wrapper(model: nn.Module, max_scale: float = 2**32, max_norm: float = 0.0, norm_type: float = 2.0, - optim_config: Optional[Dict] = None): + optim_config: Optional[Dict] = None, + verbose: bool = False): """This wrapper function is used to wrap your training optimizer for ZeRO DDP. Args: @@ -79,6 +83,7 @@ def zero_optim_wrapper(model: nn.Module, >>> zero2_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True) >>> optim = zero_optim_wrapper(model, optim, optim_config=zero2_config) + verbose (bool, optional): Whether to print the verbose info. """ assert hasattr(model, "_colo_zero_stage"), "You should use `zero_ddp_wrapper` first" zero_stage = getattr(model, "_colo_zero_stage") @@ -102,8 +107,8 @@ def zero_optim_wrapper(model: nn.Module, from colossalai.zero.low_level import LowLevelZeroOptimizer config_dict['partition_grad'] = zero_stage == 2 config_dict['clip_grad_norm'] = max_norm - return LowLevelZeroOptimizer(optimizer, **config_dict) + return LowLevelZeroOptimizer(optimizer, **config_dict, verbose=verbose) else: from colossalai.zero.gemini.gemini_optimizer import ZeroOptimizer config_dict['clipping_norm'] = max_norm - return ZeroOptimizer(optimizer, model, **config_dict) + return ZeroOptimizer(optimizer, model, **config_dict, verbose=verbose) diff --git a/op_builder/builder.py b/op_builder/builder.py index b9f44decc119..16bf173ffd04 100644 --- a/op_builder/builder.py +++ b/op_builder/builder.py @@ -7,7 +7,7 @@ import time from abc import ABC, abstractmethod from pathlib import Path -from typing import List +from typing import List, Optional from .utils import check_cuda_availability, check_system_pytorch_cuda_match, print_rank_0 @@ -138,7 +138,7 @@ def check_runtime_build_environment(self): # make sure system CUDA and pytorch CUDA match, an error will raised inside the function if not check_system_pytorch_cuda_match(CUDA_HOME) - def load(self, verbose=True): + def load(self, verbose: Optional[bool] = None): """ load the kernel during runtime. If the kernel is not built during pip install, it will build the kernel. If the kernel is built during runtime, it will be stored in `~/.cache/colossalai/torch_extensions/`. If the @@ -149,6 +149,8 @@ def load(self, verbose=True): Args: verbose (bool, optional): show detailed info. Defaults to True. """ + if verbose is None: + verbose = os.environ.get('CAI_KERNEL_VERBOSE', '0') == '1' # if the kernel has be compiled and cached, we directly use it if self.cached_op_module is not None: return self.cached_op_module diff --git a/op_builder/utils.py b/op_builder/utils.py index 4029703e4829..1b1bd5f49970 100644 --- a/op_builder/utils.py +++ b/op_builder/utils.py @@ -90,7 +90,6 @@ def check_system_pytorch_cuda_match(cuda_dir): 'Please make sure you have set the CUDA_HOME correctly and installed the correct PyTorch in https://pytorch.org/get-started/locally/ .' ) - print(bare_metal_minor != torch_cuda_minor) if bare_metal_minor != torch_cuda_minor: warnings.warn( f"[extension] The CUDA version on the system ({bare_metal_major}.{bare_metal_minor}) does not match with the version ({torch_cuda_major}.{torch_cuda_minor}) torch was compiled with. " @@ -156,16 +155,15 @@ def set_cuda_arch_list(cuda_dir): # we only need to set this when CUDA is not available for cross-compilation if not cuda_available: - warnings.warn( - '\n[extension] PyTorch did not find available GPUs on this system.\n' - 'If your intention is to cross-compile, this is not an error.\n' - 'By default, Colossal-AI will cross-compile for \n' - '1. Pascal (compute capabilities 6.0, 6.1, 6.2),\n' - '2. Volta (compute capability 7.0)\n' - '3. Turing (compute capability 7.5),\n' - '4. Ampere (compute capability 8.0, 8.6)if the CUDA version is >= 11.0\n' - '\nIf you wish to cross-compile for a single specific architecture,\n' - 'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n') + warnings.warn('\n[extension] PyTorch did not find available GPUs on this system.\n' + 'If your intention is to cross-compile, this is not an error.\n' + 'By default, Colossal-AI will cross-compile for \n' + '1. Pascal (compute capabilities 6.0, 6.1, 6.2),\n' + '2. Volta (compute capability 7.0)\n' + '3. Turing (compute capability 7.5),\n' + '4. Ampere (compute capability 8.0, 8.6)if the CUDA version is >= 11.0\n' + '\nIf you wish to cross-compile for a single specific architecture,\n' + 'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n') if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None: bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir) From 9edeadfb24bd769b33d500e05784d63bf1a8e5fd Mon Sep 17 00:00:00 2001 From: digger-yu Date: Mon, 17 Apr 2023 12:19:53 +0800 Subject: [PATCH 137/413] [doc] Update 1D_tensor_parallel.md (#3573) Display format optimization , same as fix#3562 Simultaneous modification of en version --- docs/source/en/features/1D_tensor_parallel.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/docs/source/en/features/1D_tensor_parallel.md b/docs/source/en/features/1D_tensor_parallel.md index 530c2e7b64bc..695a8f31f8c5 100644 --- a/docs/source/en/features/1D_tensor_parallel.md +++ b/docs/source/en/features/1D_tensor_parallel.md @@ -19,9 +19,16 @@ An efficient 1D tensor parallelism implementation was introduced by [Megatron-LM Let's take a linear layer as an example, which consists of a GEMM $Y = XA$. Given 2 processors, we split the columns of $A$ into $[A_1 ~ A_2]$, and calculate $Y_i = XA_i$ on each processor, which then forms $[Y_1 ~ Y_2] = [XA_1 ~ XA_2]$. This is called a column-parallel fashion. -When a second linear layer $Z=YB$ follows the column-parallel one, we split $B$ into $\left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right]$, +When a second linear layer $Z=YB$ follows the column-parallel one, we split $B$ into +```math +\left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right] +``` which is called a row-parallel fashion. -To calculate $Z = [Y_1 ~ Y_2] \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right]$, we first calculate $Y_iB_i$ on each processor, then use an all-reduce to aggregate the results as $Z=Y_1B_1+Y_2B_2$. +To calculate +```math +Z = [Y_1 ~ Y_2] \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right] +``` +we first calculate $Y_iB_i$ on each processor, then use an all-reduce to aggregate the results as $Z=Y_1B_1+Y_2B_2$. We also need to note that in the backward pass, the column-parallel linear layer needs to aggregate the gradients of the input tensor $X$, because on each processor $i$ we only have $\dot{X_i}=\dot{Y_i}A_i^T$. Thus, we apply an all-reduce across the processors to get $\dot{X}=\dot{Y}A^T=\dot{Y_1}A_1^T+\dot{Y_2}A_2^T$. From d329c294ec3e0139b603a490d344e993aeb6bfb9 Mon Sep 17 00:00:00 2001 From: YH <100389977+yhna940@users.noreply.github.com> Date: Mon, 17 Apr 2023 13:44:17 +0900 Subject: [PATCH 138/413] Add docstr for zero3 chunk search utils (#3572) --- colossalai/zero/gemini/chunk/search_utils.py | 34 ++++++++++++++++++-- 1 file changed, 31 insertions(+), 3 deletions(-) diff --git a/colossalai/zero/gemini/chunk/search_utils.py b/colossalai/zero/gemini/chunk/search_utils.py index c4deec8fe4a0..da58e038c879 100644 --- a/colossalai/zero/gemini/chunk/search_utils.py +++ b/colossalai/zero/gemini/chunk/search_utils.py @@ -11,8 +11,13 @@ def _filter_exlarge_params(model: nn.Module, size_dict: Dict[int, List[int]]) -> None: - """ + """_filter_exlarge_params + Filter those parameters whose size is too large (more than 3x standard deviations) from others. + + Args: + model (nn.Module): the model. + size_dict (Dict[int, List[int]]): the size dict of parameters. """ agg_size_list = [] for key in size_dict: @@ -33,7 +38,16 @@ def _filter_exlarge_params(model: nn.Module, size_dict: Dict[int, List[int]]) -> def _get_unused_byte(size_list: List[int], chunk_size: int) -> int: - """Get unused byte for a certain chunk size. + """_get_unused_byte + + Get unused byte for a certain chunk size. + + Args: + size_list (List[int]): the size list of parameters. + chunk_size (int): the chunk size. + + Returns: + int: the unused byte. """ acc = 0 left = 0 @@ -45,7 +59,18 @@ def _get_unused_byte(size_list: List[int], chunk_size: int) -> int: return left + acc -def _tensor_numel(local_param: ColoParameter, strict_ddp_flag: bool): +def _tensor_numel(local_param: ColoParameter, strict_ddp_flag: bool) -> int: + """_tensor_numel + + Get the number of elements of a tensor. + + Args: + local_param (ColoParameter): The local parameter. + strict_ddp_flag (bool): whether to enable the strict ddp mode. + + Returns: + int: the number of elements. + """ if strict_ddp_flag and type(local_param) is ColoParameter: return local_param.numel_global() else: @@ -61,6 +86,7 @@ def classify_params_by_dp_degree(param_order: OrderedParamGenerator, Args: param_order (OrderedParamGenerator): the order of param be visied + strict_ddp_flag (bool, optional): whether to enable the strict ddp mode. Defaults to False. Returns: Dict[int, List[ColoParameter]]: a dict contains the classification results. @@ -96,6 +122,8 @@ def search_chunk_configuration( memstas: Optional[MemStats] = None) -> Tuple[Dict, int, int]: """search_chunk_configuration + Search the chunk configuration for a model. + Args: model (nn.Module): torch module search_range_mb (float): searching range in mega byte. From e3551443751e5ff1ced4215afe76fb8e22ded06b Mon Sep 17 00:00:00 2001 From: csric <59389055+CsRic@users.noreply.github.com> Date: Mon, 17 Apr 2023 14:46:50 +0800 Subject: [PATCH 139/413] [chatgpt] Detached PPO Training (#3195) * run the base * working on dist ppo * sync * detached trainer * update detached trainer. no maker update function * facing init problem * 1 maker 1 trainer detached run. but no model update * facing cuda problem * fix save functions * verified maker update * nothing * add ignore * analyize loss issue * remove some debug codes * facing 2m1t stuck issue * 2m1t verified * do not use torchrun * working on 2m2t * working on 2m2t * initialize strategy in ray actor env * facing actor's init order issue * facing ddp model update issue (need unwarp ddp) * unwrap ddp actor * checking 1m2t stuck problem * nothing * set timeout for trainer choosing. It solves the stuck problem! * delete some debug output * rename to sync with upstream * rename to sync with upstream * coati rename * nothing * I am going to detach the replaybuffer from trainer and make it a Ray Actor. Two benefits: 1. support TP trainer. 2. asynchronized buffer operations * experience_maker_holder performs target-revolving _send_experience() instead of length comparison. * move code to ray subfolder * working on pipeline inference * apply comments --------- Co-authored-by: csric --- applications/Chat/.gitignore | 2 + applications/Chat/coati/ray/__init__.py | 2 + applications/Chat/coati/ray/example/1m1t.py | 153 +++++++++++++ applications/Chat/coati/ray/example/1m1t.sh | 23 ++ applications/Chat/coati/ray/example/1m2t.py | 186 ++++++++++++++++ applications/Chat/coati/ray/example/1m2t.sh | 23 ++ applications/Chat/coati/ray/example/2m1t.py | 140 ++++++++++++ applications/Chat/coati/ray/example/2m1t.sh | 23 ++ applications/Chat/coati/ray/example/2m2t.py | 209 ++++++++++++++++++ applications/Chat/coati/ray/example/2m2t.sh | 23 ++ applications/Chat/coati/ray/src/__init__.py | 0 .../coati/ray/src/detached_replay_buffer.py | 88 ++++++++ .../coati/ray/src/detached_trainer_base.py | 121 ++++++++++ .../coati/ray/src/detached_trainer_ppo.py | 192 ++++++++++++++++ .../coati/ray/src/experience_maker_holder.py | 172 ++++++++++++++ .../Chat/coati/ray/src/pipeline_strategy.py | 105 +++++++++ applications/Chat/coati/ray/src/utils.py | 48 ++++ applications/Chat/coati/trainer/utils.py | 9 + applications/Chat/coati/utils/__init__.py | 2 +- applications/Chat/examples/train_prompts.sh | 2 + 20 files changed, 1522 insertions(+), 1 deletion(-) create mode 100644 applications/Chat/coati/ray/__init__.py create mode 100644 applications/Chat/coati/ray/example/1m1t.py create mode 100644 applications/Chat/coati/ray/example/1m1t.sh create mode 100644 applications/Chat/coati/ray/example/1m2t.py create mode 100644 applications/Chat/coati/ray/example/1m2t.sh create mode 100644 applications/Chat/coati/ray/example/2m1t.py create mode 100644 applications/Chat/coati/ray/example/2m1t.sh create mode 100644 applications/Chat/coati/ray/example/2m2t.py create mode 100644 applications/Chat/coati/ray/example/2m2t.sh create mode 100644 applications/Chat/coati/ray/src/__init__.py create mode 100644 applications/Chat/coati/ray/src/detached_replay_buffer.py create mode 100644 applications/Chat/coati/ray/src/detached_trainer_base.py create mode 100644 applications/Chat/coati/ray/src/detached_trainer_ppo.py create mode 100644 applications/Chat/coati/ray/src/experience_maker_holder.py create mode 100644 applications/Chat/coati/ray/src/pipeline_strategy.py create mode 100644 applications/Chat/coati/ray/src/utils.py diff --git a/applications/Chat/.gitignore b/applications/Chat/.gitignore index 1ec5f53a8b8d..2b9b4f345d0f 100644 --- a/applications/Chat/.gitignore +++ b/applications/Chat/.gitignore @@ -144,3 +144,5 @@ docs/.build # wandb log example/wandb/ + +examples/awesome-chatgpt-prompts/ \ No newline at end of file diff --git a/applications/Chat/coati/ray/__init__.py b/applications/Chat/coati/ray/__init__.py new file mode 100644 index 000000000000..5802c05bc03f --- /dev/null +++ b/applications/Chat/coati/ray/__init__.py @@ -0,0 +1,2 @@ +from .src.detached_replay_buffer import DetachedReplayBuffer +from .src.detached_trainer_ppo import DetachedPPOTrainer diff --git a/applications/Chat/coati/ray/example/1m1t.py b/applications/Chat/coati/ray/example/1m1t.py new file mode 100644 index 000000000000..a6527370505b --- /dev/null +++ b/applications/Chat/coati/ray/example/1m1t.py @@ -0,0 +1,153 @@ +import argparse +from copy import deepcopy + +import pandas as pd +import torch +from coati.trainer import PPOTrainer + + +from coati.ray.src.experience_maker_holder import ExperienceMakerHolder +from coati.ray.src.detached_trainer_ppo import DetachedPPOTrainer + +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.experience_maker import NaiveExperienceMaker +from torch.optim import Adam +from transformers import AutoTokenizer, BloomTokenizerFast +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.nn.optimizer import HybridAdam + +import ray +import os +import socket + +def get_free_port(): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(('', 0)) + return s.getsockname()[1] + + +def get_local_ip(): + with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: + s.connect(('8.8.8.8', 80)) + return s.getsockname()[0] + +def main(args): + master_addr = str(get_local_ip()) + # trainer_env_info + trainer_port = str(get_free_port()) + env_info_trainer = {'local_rank' : '0', + 'rank' : '0', + 'world_size' : '1', + 'master_port' : trainer_port, + 'master_addr' : master_addr} + + # maker_env_info + maker_port = str(get_free_port()) + env_info_maker = {'local_rank' : '0', + 'rank' : '0', + 'world_size' : '1', + 'master_port' : maker_port, + 'master_addr' : master_addr} + + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + else: + raise ValueError(f'Unsupported model "{args.model}"') + + # configure Trainer + trainer_ref = DetachedPPOTrainer.options(name="trainer1", num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=["maker1"], + strategy=args.trainer_strategy, + model=args.model, + env_info = env_info_trainer, + pretrained=args.pretrain, + lora_rank=args.lora_rank, + train_batch_size=args.train_batch_size, + buffer_limit=16, + experience_batch_size=args.experience_batch_size, + max_epochs=args.max_epochs, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + # configure Experience Maker + experience_holder_ref = ExperienceMakerHolder.options(name="maker1", num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=["trainer1"], + strategy=args.maker_strategy, + env_info = env_info_maker, + experience_batch_size=args.experience_batch_size, + kl_coef=0.1, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + # trainer send its actor and critic to experience holders. + ray.get(trainer_ref.initialize_remote_makers.remote()) + + # configure sampler + dataset = pd.read_csv(args.prompt_path)['prompt'] + + def tokenize_fn(texts): + # MUST padding to max length to ensure inputs of all ranks have the same length + # Different length may lead to hang when using gemini, as different generation steps + batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) + return {k: v.cuda() for k, v in batch.items()} + + trainer_done_ref = trainer_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) + num_exp_per_maker = args.num_episodes * args.max_timesteps // args.update_timesteps * args.max_epochs + 3 # +3 for fault tolerance + maker_done_ref = experience_holder_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) + + ray.get([trainer_done_ref, maker_done_ref]) + + # save model checkpoint after fitting + trainer_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + trainer_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('prompt_path') + parser.add_argument('--trainer_strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--maker_strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--num_episodes', type=int, default=10) + parser.add_argument('--max_timesteps', type=int, default=10) + parser.add_argument('--update_timesteps', type=int, default=10) + parser.add_argument('--max_epochs', type=int, default=5) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + + parser.add_argument('--debug', action='store_true') + args = parser.parse_args() + ray.init(namespace=os.environ["RAY_NAMESPACE"]) + main(args) diff --git a/applications/Chat/coati/ray/example/1m1t.sh b/applications/Chat/coati/ray/example/1m1t.sh new file mode 100644 index 000000000000..f7c5054c800e --- /dev/null +++ b/applications/Chat/coati/ray/example/1m1t.sh @@ -0,0 +1,23 @@ +set_n_least_used_CUDA_VISIBLE_DEVICES() { + local n=${1:-"9999"} + echo "GPU Memory Usage:" + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ + | tail -n +2 \ + | nl -v 0 \ + | tee /dev/tty \ + | sort -g -k 2 \ + | awk '{print $1}' \ + | head -n $n) + export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') + echo "Now CUDA_VISIBLE_DEVICES is set to:" + echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" +} + +set_n_least_used_CUDA_VISIBLE_DEVICES 2 + +export RAY_NAMESPACE="admin" + +python 1m1t.py "/path/to/prompts.csv" \ + --trainer_strategy colossalai_zero2 --maker_strategy naive --lora_rank 2 --pretrain "facebook/opt-350m" --model 'opt' \ + --num_episodes 10 --max_timesteps 10 --update_timesteps 10 \ + --max_epochs 10 --debug diff --git a/applications/Chat/coati/ray/example/1m2t.py b/applications/Chat/coati/ray/example/1m2t.py new file mode 100644 index 000000000000..3883c364a8e0 --- /dev/null +++ b/applications/Chat/coati/ray/example/1m2t.py @@ -0,0 +1,186 @@ +import argparse +from copy import deepcopy + +import pandas as pd +import torch +from coati.trainer import PPOTrainer + + +from coati.ray.src.experience_maker_holder import ExperienceMakerHolder +from coati.ray.src.detached_trainer_ppo import DetachedPPOTrainer + +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.experience_maker import NaiveExperienceMaker +from torch.optim import Adam +from transformers import AutoTokenizer, BloomTokenizerFast +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.nn.optimizer import HybridAdam + +import ray +import os +import socket + + +def get_free_port(): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(('', 0)) + return s.getsockname()[1] + + +def get_local_ip(): + with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: + s.connect(('8.8.8.8', 80)) + return s.getsockname()[0] + +def main(args): + master_addr = str(get_local_ip()) + # trainer_env_info + trainer_port = str(get_free_port()) + env_info_trainer_1 = {'local_rank' : '0', + 'rank' : '0', + 'world_size' : '2', + 'master_port' : trainer_port, + 'master_addr' : master_addr} + env_info_trainer_2 = {'local_rank' : '0', + 'rank' : '1', + 'world_size' : '2', + 'master_port' : trainer_port, + 'master_addr' : master_addr} + # maker_env_info + maker_port = str(get_free_port()) + env_info_maker_1 = {'local_rank' : '0', + 'rank' : '0', + 'world_size' : '2', + 'master_port' : maker_port, + 'master_addr' : master_addr} + print([env_info_trainer_1, + env_info_trainer_2, + env_info_maker_1]) + ray.init(dashboard_port = 1145) + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + else: + raise ValueError(f'Unsupported model "{args.model}"') + + # configure Trainer + trainer_1_ref = DetachedPPOTrainer.options(name="trainer1", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=["maker1"], + strategy=args.trainer_strategy, + model=args.model, + env_info=env_info_trainer_1, + pretrained=args.pretrain, + lora_rank=args.lora_rank, + train_batch_size=args.train_batch_size, + buffer_limit=16, + experience_batch_size=args.experience_batch_size, + max_epochs=args.max_epochs, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + trainer_2_ref = DetachedPPOTrainer.options(name="trainer2", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=["maker1"], + strategy=args.trainer_strategy, + model=args.model, + env_info=env_info_trainer_2, + pretrained=args.pretrain, + lora_rank=args.lora_rank, + train_batch_size=args.train_batch_size, + buffer_limit=16, + experience_batch_size=args.experience_batch_size, + max_epochs=args.max_epochs, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug= args.debug, + ) + + # configure Experience Maker + experience_holder_1_ref = ExperienceMakerHolder.options(name="maker1", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=["trainer1", "trainer2"], + strategy=args.maker_strategy, + env_info=env_info_maker_1, + experience_batch_size=args.experience_batch_size, + kl_coef=0.1, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + # trainer send its actor and critic to experience holders. + # TODO: balance duty + ray.get(trainer_1_ref.initialize_remote_makers.remote()) + + # configure sampler + dataset = pd.read_csv(args.prompt_path)['prompt'] + + def tokenize_fn(texts): + # MUST padding to max length to ensure inputs of all ranks have the same length + # Different length may lead to hang when using gemini, as different generation steps + batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) + return {k: v.cuda() for k, v in batch.items()} + + trainer_1_done_ref = trainer_1_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) + trainer_2_done_ref = trainer_2_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) + num_exp_per_maker = args.num_episodes * args.max_timesteps // args.update_timesteps * args.max_epochs * 2 + 3 # +3 for fault tolerance + maker_1_done_ref = experience_holder_1_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) + + ray.get([trainer_1_done_ref, trainer_2_done_ref, maker_1_done_ref]) + # save model checkpoint after fitting + trainer_1_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) + trainer_2_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + trainer_1_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + trainer_2_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('prompt_path') + parser.add_argument('--trainer_strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--maker_strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--num_episodes', type=int, default=10) + parser.add_argument('--max_timesteps', type=int, default=10) + parser.add_argument('--update_timesteps', type=int, default=10) + parser.add_argument('--max_epochs', type=int, default=5) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + + parser.add_argument('--debug', action='store_true') + args = parser.parse_args() + main(args) diff --git a/applications/Chat/coati/ray/example/1m2t.sh b/applications/Chat/coati/ray/example/1m2t.sh new file mode 100644 index 000000000000..669f4141026c --- /dev/null +++ b/applications/Chat/coati/ray/example/1m2t.sh @@ -0,0 +1,23 @@ +set_n_least_used_CUDA_VISIBLE_DEVICES() { + local n=${1:-"9999"} + echo "GPU Memory Usage:" + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ + | tail -n +2 \ + | nl -v 0 \ + | tee /dev/tty \ + | sort -g -k 2 \ + | awk '{print $1}' \ + | head -n $n) + export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') + echo "Now CUDA_VISIBLE_DEVICES is set to:" + echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" +} + +set_n_least_used_CUDA_VISIBLE_DEVICES 2 + +export RAY_NAMESPACE="admin" + +python 1m2t.py "/path/to/prompts.csv" --model gpt2 \ + --maker_strategy naive --trainer_strategy ddp --lora_rank 2 \ + --num_episodes 10 --max_timesteps 10 --update_timesteps 10 \ + --max_epochs 10 #--debug \ No newline at end of file diff --git a/applications/Chat/coati/ray/example/2m1t.py b/applications/Chat/coati/ray/example/2m1t.py new file mode 100644 index 000000000000..b655de1ab1fa --- /dev/null +++ b/applications/Chat/coati/ray/example/2m1t.py @@ -0,0 +1,140 @@ +import argparse +from copy import deepcopy + +import pandas as pd +import torch +from coati.trainer import PPOTrainer + + +from coati.ray.src.experience_maker_holder import ExperienceMakerHolder +from coati.ray.src.detached_trainer_ppo import DetachedPPOTrainer + +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.experience_maker import NaiveExperienceMaker +from torch.optim import Adam +from transformers import AutoTokenizer, BloomTokenizerFast +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.nn.optimizer import HybridAdam + +import ray +import os +import socket + + +def main(args): + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + else: + raise ValueError(f'Unsupported model "{args.model}"') + + # configure Trainer + trainer_ref = DetachedPPOTrainer.options(name="trainer1", num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=["maker1", "maker2"], + strategy=args.trainer_strategy, + model=args.model, + pretrained=args.pretrain, + lora_rank=args.lora_rank, + train_batch_size=args.train_batch_size, + buffer_limit=16, + experience_batch_size=args.experience_batch_size, + max_epochs=args.max_epochs, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + # configure Experience Maker + experience_holder_1_ref = ExperienceMakerHolder.options(name="maker1", num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=["trainer1"], + strategy=args.maker_strategy, + experience_batch_size=args.experience_batch_size, + kl_coef=0.1, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + experience_holder_2_ref = ExperienceMakerHolder.options(name="maker2", num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=["trainer1"], + strategy=args.maker_strategy, + experience_batch_size=args.experience_batch_size, + kl_coef=0.1, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + # trainer send its actor and critic to experience holders. + ray.get(trainer_ref.initialize_remote_makers.remote()) + + # configure sampler + dataset = pd.read_csv(args.prompt_path)['prompt'] + + def tokenize_fn(texts): + # MUST padding to max length to ensure inputs of all ranks have the same length + # Different length may lead to hang when using gemini, as different generation steps + batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) + return {k: v.cuda() for k, v in batch.items()} + + trainer_done_ref = trainer_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) + num_exp_per_maker = args.num_episodes * args.max_timesteps // args.update_timesteps * args.max_epochs // 2 + 3 # +3 for fault tolerance + maker_1_done_ref = experience_holder_1_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) + maker_2_done_ref = experience_holder_2_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) + + ray.get([trainer_done_ref, maker_1_done_ref, maker_2_done_ref]) + + # save model checkpoint after fitting + trainer_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + trainer_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('prompt_path') + parser.add_argument('--trainer_strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--maker_strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--num_episodes', type=int, default=10) + parser.add_argument('--max_timesteps', type=int, default=10) + parser.add_argument('--update_timesteps', type=int, default=10) + parser.add_argument('--max_epochs', type=int, default=5) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + + parser.add_argument('--debug', action='store_true') + args = parser.parse_args() + ray.init(namespace=os.environ["RAY_NAMESPACE"]) + main(args) diff --git a/applications/Chat/coati/ray/example/2m1t.sh b/applications/Chat/coati/ray/example/2m1t.sh new file mode 100644 index 000000000000..a207d4118d60 --- /dev/null +++ b/applications/Chat/coati/ray/example/2m1t.sh @@ -0,0 +1,23 @@ +set_n_least_used_CUDA_VISIBLE_DEVICES() { + local n=${1:-"9999"} + echo "GPU Memory Usage:" + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ + | tail -n +2 \ + | nl -v 0 \ + | tee /dev/tty \ + | sort -g -k 2 \ + | awk '{print $1}' \ + | head -n $n) + export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') + echo "Now CUDA_VISIBLE_DEVICES is set to:" + echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" +} + +set_n_least_used_CUDA_VISIBLE_DEVICES 3 + +export RAY_NAMESPACE="admin" + +python 2m1t.py "/path/to/prompts.csv" \ + --trainer_strategy naive --maker_strategy naive --lora_rank 2 --pretrain "facebook/opt-350m" --model 'opt' \ + --num_episodes 10 --max_timesteps 10 --update_timesteps 10 \ + --max_epochs 10 # --debug diff --git a/applications/Chat/coati/ray/example/2m2t.py b/applications/Chat/coati/ray/example/2m2t.py new file mode 100644 index 000000000000..435c71915fc2 --- /dev/null +++ b/applications/Chat/coati/ray/example/2m2t.py @@ -0,0 +1,209 @@ +import argparse +from copy import deepcopy + +import pandas as pd +import torch +from coati.trainer import PPOTrainer + + +from coati.ray.src.experience_maker_holder import ExperienceMakerHolder +from coati.ray.src.detached_trainer_ppo import DetachedPPOTrainer + +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.experience_maker import NaiveExperienceMaker +from torch.optim import Adam +from transformers import AutoTokenizer, BloomTokenizerFast +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.nn.optimizer import HybridAdam + +import ray +import os +import socket + + +def get_free_port(): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(('', 0)) + return s.getsockname()[1] + + +def get_local_ip(): + with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: + s.connect(('8.8.8.8', 80)) + return s.getsockname()[0] + +def main(args): + master_addr = str(get_local_ip()) + # trainer_env_info + trainer_port = str(get_free_port()) + env_info_trainer_1 = {'local_rank' : '0', + 'rank' : '0', + 'world_size' : '2', + 'master_port' : trainer_port, + 'master_addr' : master_addr} + env_info_trainer_2 = {'local_rank' : '0', + 'rank' : '1', + 'world_size' : '2', + 'master_port' : trainer_port, + 'master_addr' : master_addr} + # maker_env_info + maker_port = str(get_free_port()) + env_info_maker_1 = {'local_rank' : '0', + 'rank' : '0', + 'world_size' : '2', + 'master_port' : maker_port, + 'master_addr' : master_addr} + env_info_maker_2 = {'local_rank' : '0', + 'rank' : '1', + 'world_size' : '2', + 'master_port': maker_port, + 'master_addr' : master_addr} + print([env_info_trainer_1, + env_info_trainer_2, + env_info_maker_1, + env_info_maker_2]) + ray.init() + # configure tokenizer + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + else: + raise ValueError(f'Unsupported model "{args.model}"') + + # configure Trainer + trainer_1_ref = DetachedPPOTrainer.options(name="trainer1", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=["maker1", "maker2"], + strategy=args.trainer_strategy, + model=args.model, + env_info=env_info_trainer_1, + pretrained=args.pretrain, + lora_rank=args.lora_rank, + train_batch_size=args.train_batch_size, + buffer_limit=16, + experience_batch_size=args.experience_batch_size, + max_epochs=args.max_epochs, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + trainer_2_ref = DetachedPPOTrainer.options(name="trainer2", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=["maker1", "maker2"], + strategy=args.trainer_strategy, + model=args.model, + env_info=env_info_trainer_2, + pretrained=args.pretrain, + lora_rank=args.lora_rank, + train_batch_size=args.train_batch_size, + buffer_limit=16, + experience_batch_size=args.experience_batch_size, + max_epochs=args.max_epochs, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + # configure Experience Maker + experience_holder_1_ref = ExperienceMakerHolder.options(name="maker1", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=["trainer1", "trainer2"], + strategy=args.maker_strategy, + env_info=env_info_maker_1, + experience_batch_size=args.experience_batch_size, + kl_coef=0.1, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + experience_holder_2_ref = ExperienceMakerHolder.options(name="maker2", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=["trainer1", "trainer2"], + strategy=args.maker_strategy, + env_info=env_info_maker_2, + experience_batch_size=args.experience_batch_size, + kl_coef=0.1, + #kwargs: + max_length=128, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + debug=args.debug, + ) + + # trainer send its actor and critic to experience holders. + # TODO: balance duty + ray.get(trainer_1_ref.initialize_remote_makers.remote()) + + # configure sampler + dataset = pd.read_csv(args.prompt_path)['prompt'] + + def tokenize_fn(texts): + # MUST padding to max length to ensure inputs of all ranks have the same length + # Different length may lead to hang when using gemini, as different generation steps + batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) + return {k: v.cuda() for k, v in batch.items()} + + trainer_1_done_ref = trainer_1_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) + trainer_2_done_ref = trainer_2_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) + num_exp_per_maker = args.num_episodes * args.max_timesteps // args.update_timesteps * args.max_epochs + 3 # +3 for fault tolerance + maker_1_done_ref = experience_holder_1_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) + maker_2_done_ref = experience_holder_2_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) + + ray.get([trainer_1_done_ref, trainer_2_done_ref, maker_1_done_ref, maker_2_done_ref]) + # save model checkpoint after fitting + trainer_1_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) + trainer_2_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) + # save optimizer checkpoint on all ranks + if args.need_optim_ckpt: + trainer_1_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + trainer_2_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), + only_rank0=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('prompt_path') + parser.add_argument('--trainer_strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--maker_strategy', + choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='naive') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') + parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--num_episodes', type=int, default=10) + parser.add_argument('--max_timesteps', type=int, default=10) + parser.add_argument('--update_timesteps', type=int, default=10) + parser.add_argument('--max_epochs', type=int, default=5) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + + parser.add_argument('--debug', action='store_true') + args = parser.parse_args() + main(args) diff --git a/applications/Chat/coati/ray/example/2m2t.sh b/applications/Chat/coati/ray/example/2m2t.sh new file mode 100644 index 000000000000..fb4024766c54 --- /dev/null +++ b/applications/Chat/coati/ray/example/2m2t.sh @@ -0,0 +1,23 @@ +set_n_least_used_CUDA_VISIBLE_DEVICES() { + local n=${1:-"9999"} + echo "GPU Memory Usage:" + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ + | tail -n +2 \ + | nl -v 0 \ + | tee /dev/tty \ + | sort -g -k 2 \ + | awk '{print $1}' \ + | head -n $n) + export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') + echo "Now CUDA_VISIBLE_DEVICES is set to:" + echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" +} + +set_n_least_used_CUDA_VISIBLE_DEVICES 2 + +export RAY_NAMESPACE="admin" + +python 2m2t.py "path/to/prompts.csv" \ + --maker_strategy naive --trainer_strategy colossalai_zero2 --lora_rank 2 \ + --num_episodes 10 --max_timesteps 10 --update_timesteps 10 \ + --max_epochs 10 --debug \ No newline at end of file diff --git a/applications/Chat/coati/ray/src/__init__.py b/applications/Chat/coati/ray/src/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/applications/Chat/coati/ray/src/detached_replay_buffer.py b/applications/Chat/coati/ray/src/detached_replay_buffer.py new file mode 100644 index 000000000000..855eee48c5a5 --- /dev/null +++ b/applications/Chat/coati/ray/src/detached_replay_buffer.py @@ -0,0 +1,88 @@ +import torch +import random +from typing import List, Any +# from torch.multiprocessing import Queue +from ray.util.queue import Queue +import ray +import asyncio +from coati.experience_maker.base import Experience +from coati.replay_buffer.utils import BufferItem, make_experience_batch, split_experience_batch +from coati.replay_buffer import ReplayBuffer +from threading import Lock +import copy + +class DetachedReplayBuffer: + ''' + Detached replay buffer. Share Experience across workers on the same node. + Therefore a trainer node is expected to have only one instance. + It is ExperienceMakerHolder's duty to call append(exp) method, remotely. + + Args: + sample_batch_size: Batch size when sampling. Exp won't enqueue until they formed a batch. + tp_world_size: Number of workers in the same tp group + limit: Limit of number of experience sample BATCHs. A number <= 0 means unlimited. Defaults to 0. + cpu_offload: Whether to offload experience to cpu when sampling. Defaults to True. + ''' + + def __init__(self, sample_batch_size: int, tp_world_size: int = 1, limit : int = 0, cpu_offload: bool = True) -> None: + self.cpu_offload = cpu_offload + self.sample_batch_size = sample_batch_size + self.limit = limit + self.items = Queue(self.limit, actor_options={"num_cpus":1}) + self.batch_collector : List[BufferItem] = [] + + ''' + Workers in the same tp group share this buffer and need same sample for one step. + Therefore a held_sample should be returned tp_world_size times before it could be dropped. + worker_state records wheter a worker got the held_sample + ''' + self.tp_world_size = tp_world_size + self.worker_state = [False] * self.tp_world_size + self.held_sample = None + self._worker_state_lock = Lock() + + @torch.no_grad() + def append(self, experience: Experience) -> None: + ''' + Expected to be called remotely. + ''' + if self.cpu_offload: + experience.to_device(torch.device('cpu')) + items = split_experience_batch(experience) + self.batch_collector.extend(items) + while len(self.batch_collector) >= self.sample_batch_size: + items = self.batch_collector[:self.sample_batch_size] + experience = make_experience_batch(items) + self.items.put(experience, block=True) + self.batch_collector = self.batch_collector[self.sample_batch_size:] + + def clear(self) -> None: + # self.items.close() + self.items.shutdown() + self.items = Queue(self.limit) + self.worker_state = [False] * self.tp_world_size + self.batch_collector = [] + + @torch.no_grad() + def sample(self, worker_rank = 0, to_device = "cpu") -> Experience: + self._worker_state_lock.acquire() + if not any(self.worker_state): + self.held_sample = self._sample_and_erase() + self.worker_state[worker_rank] = True + if all(self.worker_state): + self.worker_state = [False] * self.tp_world_size + ret = self.held_sample + else: + ret = copy.deepcopy(self.held_sample) + self._worker_state_lock.release() + ret.to_device(to_device) + return ret + + @torch.no_grad() + def _sample_and_erase(self) -> Experience: + ret = self.items.get(block=True) + return ret + + def get_length(self) -> int: + ret = self.items.qsize() + return ret \ No newline at end of file diff --git a/applications/Chat/coati/ray/src/detached_trainer_base.py b/applications/Chat/coati/ray/src/detached_trainer_base.py new file mode 100644 index 000000000000..f1ed1ec71499 --- /dev/null +++ b/applications/Chat/coati/ray/src/detached_trainer_base.py @@ -0,0 +1,121 @@ +from abc import ABC, abstractmethod +from typing import Any, Callable, Dict, List, Optional, Union +from tqdm import tqdm +from coati.trainer.callbacks import Callback +from coati.experience_maker import Experience +import ray +import os + +from .detached_replay_buffer import DetachedReplayBuffer +from .utils import is_rank_0 + +class DetachedTrainer(ABC): + ''' + Base class for detached rlhf trainers. + 'detach' means that the experience maker is detached compared to a normal Trainer. + Please set name attribute during init: + >>> trainer = DetachedTrainer.options(..., name = "xxx", ...).remote() + So an ExperienceMakerHolder can reach the detached_replay_buffer by Actor's name. + Args: + detached_strategy (DetachedStrategy): the strategy to use for training + detached_replay_buffer_ref (ObjectRef[DetachedReplayBuffer]): the replay buffer to use for training + experience_batch_size (int, defaults to 8): the batch size to use for experience generation + max_epochs (int, defaults to 1): the number of epochs of training process + data_loader_pin_memory (bool, defaults to True): whether to pin memory for data loader + callbacks (List[Callback], defaults to []): the callbacks to call during training process + generate_kwargs (dict, optional): the kwargs to use while model generating + ''' + + def __init__(self, + experience_maker_holder_name_list: List[str], + train_batch_size: int = 8, + buffer_limit: int = 0, + buffer_cpu_offload: bool = True, + experience_batch_size: int = 8, + max_epochs: int = 1, + dataloader_pin_memory: bool = True, + callbacks: List[Callback] = [], + **generate_kwargs) -> None: + super().__init__() + self.detached_replay_buffer = DetachedReplayBuffer(train_batch_size, limit=buffer_limit, cpu_offload=buffer_cpu_offload) + self.experience_batch_size = experience_batch_size + self.max_epochs = max_epochs + self.dataloader_pin_memory = dataloader_pin_memory + self.callbacks = callbacks + self.generate_kwargs = generate_kwargs + self.target_holder_name_list = experience_maker_holder_name_list + self.target_holder_list = [] + + def update_target_holder_list(self, experience_maker_holder_name_list): + self.target_holder_name_list = experience_maker_holder_name_list + self.target_holder_list = [] + for name in self.target_holder_name_list: + self.target_holder_list.append(ray.get_actor(name, namespace=os.environ["RAY_NAMESPACE"])) + + @abstractmethod + def _update_remote_makers(self): + pass + + @abstractmethod + def training_step(self, experience: Experience) -> Dict[str, Any]: + pass + + def _learn(self): + pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) + for _ in pbar: + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print("[trainer] sampling exp") + experience = self._buffer_sample() + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print("[trainer] training step") + metrics = self.training_step(experience) + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print("[trainer] step over") + pbar.set_postfix(metrics) + + def fit(self, num_episodes: int = 50000, max_timesteps: int = 500, update_timesteps: int = 5000) -> None: + self._on_fit_start() + for episode in range(num_episodes): + self._on_episode_start(episode) + for timestep in tqdm(range(max_timesteps // update_timesteps), + desc=f'Episode [{episode+1}/{num_episodes}]', + disable=not is_rank_0()): + self._learn() + self._update_remote_makers() + self._on_episode_end(episode) + self._on_fit_end() + + @ray.method(concurrency_group="buffer_length") + def buffer_get_length(self): + # called by ExperienceMakerHolder + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print("[trainer] telling length") + return self.detached_replay_buffer.get_length() + + @ray.method(concurrency_group="buffer_append") + def buffer_append(self, experience: Experience): + # called by ExperienceMakerHolder + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + # print(f"[trainer] receiving exp. Current buffer length: {self.detached_replay_buffer.get_length()}") + print(f"[trainer] receiving exp.") + self.detached_replay_buffer.append(experience) + + @ray.method(concurrency_group="buffer_sample") + def _buffer_sample(self): + return self.detached_replay_buffer.sample() + + def _on_fit_start(self) -> None: + for callback in self.callbacks: + callback.on_fit_start() + + def _on_fit_end(self) -> None: + for callback in self.callbacks: + callback.on_fit_end() + + def _on_episode_start(self, episode: int) -> None: + for callback in self.callbacks: + callback.on_episode_start(episode) + + def _on_episode_end(self, episode: int) -> None: + for callback in self.callbacks: + callback.on_episode_end(episode) diff --git a/applications/Chat/coati/ray/src/detached_trainer_ppo.py b/applications/Chat/coati/ray/src/detached_trainer_ppo.py new file mode 100644 index 000000000000..90e5e437750a --- /dev/null +++ b/applications/Chat/coati/ray/src/detached_trainer_ppo.py @@ -0,0 +1,192 @@ +from typing import Any, Callable, Dict, List, Optional +import torch +from torch.optim import Adam + +from coati.experience_maker import Experience, NaiveExperienceMaker +from coati.models.base import Actor, Critic +from coati.models.generation_utils import update_model_kwargs_fn +from coati.models.loss import PolicyLoss, ValueLoss +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy, Strategy +from coati.trainer.callbacks import Callback + +from colossalai.nn.optimizer import HybridAdam + +import ray + + +from .utils import is_rank_0, get_cuda_actor_critic_from_args, get_strategy_from_args, set_dist_env +from .detached_trainer_base import DetachedTrainer + + +@ray.remote(concurrency_groups={"buffer_length": 1, "buffer_append":1, "buffer_sample":1,"model_io": 1, "compute": 1}) +class DetachedPPOTrainer(DetachedTrainer): + ''' + Detached Trainer for PPO algorithm + Args: + strategy (Strategy): the strategy to use for training + model (str) : for actor / critic init + pretrained (str) : for actor / critic init + lora_rank (int) : for actor / critic init + train_batch_size (int, defaults to 8): the batch size to use for training + train_batch_size (int, defaults to 8): the batch size to use for training + buffer_limit (int, defaults to 0): the max_size limitaiton of replay buffer + buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu + eps_clip (float, defaults to 0.2): the clip coefficient of policy loss + value_clip (float, defaults to 0.4): the clip coefficient of value loss + experience_batch_size (int, defaults to 8): the batch size to use for experience generation + max_epochs (int, defaults to 1): the number of epochs of training process + dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader + callbacks (List[Callback], defaults to []): the callbacks to call during training process + generate_kwargs (dict, optional): the kwargs to use while model generating + ''' + + def __init__(self, + experience_maker_holder_name_list: List[str], + strategy: str, + model: str, + env_info: Dict[str, str] = None, + pretrained: str = None, + lora_rank: int = 0, + train_batch_size: int = 8, + buffer_limit: int = 0, + buffer_cpu_offload: bool = True, + eps_clip: float = 0.2, + value_clip: float = 0.4, + experience_batch_size: int = 8, + max_epochs: int = 10, + dataloader_pin_memory: bool = True, + callbacks: List[Callback] = [], + **generate_kwargs) -> None: + # set environment variables + if env_info: + set_dist_env(env_info=env_info) + # configure strategy + self.strategy = get_strategy_from_args(strategy) + # configure models, loss and optimizers + with self.strategy.model_init_context(): + self.actor, self.critic = get_cuda_actor_critic_from_args(model, pretrained, lora_rank) + + if strategy != 'colossalai_gemini': + self.actor.to(torch.float16).to(torch.cuda.current_device()) + self.critic.to(torch.float16).to(torch.cuda.current_device()) + + if strategy.startswith('colossalai'): + self.actor_optim = HybridAdam(self.actor.parameters(), lr=5e-6) + self.critic_optim = HybridAdam(self.critic.parameters(), lr=5e-6) + else: + self.actor_optim = Adam(self.actor.parameters(), lr=5e-6) + self.critic_optim = Adam(self.critic.parameters(), lr=5e-6) + + (self.actor, self.actor_optim), (self.critic, self.critic_optim) = \ + self.strategy.prepare((self.actor, self.actor_optim), (self.critic, self.critic_optim)) + generate_kwargs = _set_default_generate_kwargs(self.strategy, generate_kwargs, self.actor) + + self.actor_loss_fn = PolicyLoss(eps_clip) + self.critic_loss_fn = ValueLoss(value_clip) + + super().__init__(experience_maker_holder_name_list, + train_batch_size=train_batch_size, + buffer_limit=buffer_limit, + buffer_cpu_offload=buffer_cpu_offload, + experience_batch_size=experience_batch_size, + max_epochs=max_epochs, + dataloader_pin_memory=dataloader_pin_memory, + callbacks=callbacks, + **generate_kwargs) + + @ray.method(concurrency_group="model_io") + def _update_remote_makers(self): + # TODO: balance duties + if is_rank_0(): + self.update_target_holder_list(self.target_holder_name_list) + for target_holder in self.target_holder_list: + # TODO: reduce malloc + with torch.no_grad(): + ray.get(target_holder.update_experience_maker.remote(self._get_unwrapped_actor(), self._get_unwrapped_critic())) + + @ray.method(concurrency_group="model_io") + def initialize_remote_makers(self): + # TODO: balance duties + if is_rank_0(): + self.update_target_holder_list(self.target_holder_name_list) + for target_holder in self.target_holder_list: + # TODO: reduce malloc + with torch.no_grad(): + ray.get(target_holder.initialize_experience_maker.remote(self._get_unwrapped_actor(), self._get_unwrapped_critic())) + + @ray.method(concurrency_group="compute") + def training_step(self, experience: Experience) -> Dict[str, float]: + self.actor.train() + self.critic.train() + + experience.to_device(torch.cuda.current_device()) + num_actions = experience.action_mask.size(1) + action_log_probs = self.actor(experience.sequences, num_actions, attention_mask=experience.attention_mask) + actor_loss = self.actor_loss_fn(action_log_probs, + experience.action_log_probs, + experience.advantages, + action_mask=experience.action_mask) + self.strategy.backward(actor_loss, self.actor, self.actor_optim) + self.strategy.optimizer_step(self.actor_optim) + self.actor_optim.zero_grad() + + values = self.critic(experience.sequences, + action_mask=experience.action_mask, + attention_mask=experience.attention_mask) + critic_loss = self.critic_loss_fn(values, + experience.values, + experience.reward, + action_mask=experience.action_mask) + + self.strategy.backward(critic_loss, self.critic, self.critic_optim) + self.strategy.optimizer_step(self.critic_optim) + self.critic_optim.zero_grad() + return {'actor_loss': actor_loss.item(), 'critic_loss': critic_loss.item()} + + def strategy_save_actor(self, path: str, only_rank0: bool = False) -> None: + self.strategy.save_model(self.actor, path, only_rank0) + + def strategy_save_critic(self, path: str, only_rank0: bool = False) -> None: + self.strategy.save_model(self.critic, path, only_rank0) + + def strategy_save_actor_optim(self, path: str, only_rank0: bool = False) -> None: + self.strategy.save_optimizer(self.actor_optim, path, only_rank0) + + def strategy_save_critic_optim(self, path: str, only_rank0: bool = False) -> None: + self.strategy.save_optimizer(self.critic_optim, path, only_rank0) + + def _get_unwrapped_actor(self): + if False: + pass + elif isinstance(self.strategy, ColossalAIStrategy): + ret = Actor(self.strategy._unwrap_model(self.actor)) + return ret + elif isinstance(self.strategy, DDPStrategy): + return Actor(self.strategy._unwrap_actor(self.actor)) + elif isinstance(self.strategy, NaiveStrategy): + return self.actor + + def _get_unwrapped_critic(self): + if False: + pass + elif isinstance(self.strategy, ColossalAIStrategy): + ret = self.strategy._unwrap_model(self.critic) + return ret + elif isinstance(self.strategy, DDPStrategy): + return self.critic.module + elif isinstance(self.strategy, NaiveStrategy): + return self.critic + + +def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None: + origin_model = strategy._unwrap_actor(actor) + new_kwargs = {**generate_kwargs} + # use huggingface models method directly + if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): + new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation + + if 'update_model_kwargs_fn' not in generate_kwargs: + new_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn + + return new_kwargs + \ No newline at end of file diff --git a/applications/Chat/coati/ray/src/experience_maker_holder.py b/applications/Chat/coati/ray/src/experience_maker_holder.py new file mode 100644 index 000000000000..696773e84cfb --- /dev/null +++ b/applications/Chat/coati/ray/src/experience_maker_holder.py @@ -0,0 +1,172 @@ +import torch +from typing import Any, Callable, Dict, List, Optional, Union +import ray +from ray.exceptions import GetTimeoutError +from torch import Tensor +import torch.nn as nn +from coati.models.base import Actor, Critic, RewardModel +from coati.trainer.strategies.sampler import DistributedSampler +from coati.trainer.strategies import Strategy +from coati.experience_maker import NaiveExperienceMaker, Experience, ExperienceMaker + +from copy import deepcopy +from threading import Lock +import time +import os + + +from .utils import is_rank_0, get_strategy_from_args, set_dist_env + + +@ray.remote(concurrency_groups={"experience_io": 1, "model_io": 1, "compute": 1}) +class ExperienceMakerHolder: + ''' + Args: + detached_trainer_name_list: str list to get ray actor handleskkk + strategy: + experience_batch_size: batch size of generated experience + kl_coef: the coefficient of kl divergence loss + ''' + + def __init__(self, + detached_trainer_name_list: List[str], + strategy: str, + env_info: Dict[str, str] = None, + experience_batch_size: int = 8, + kl_coef: float = 0.1, + **generate_kwargs): + # set environment variables + if env_info: + set_dist_env(env_info=env_info) + self.target_trainer_list = [] + for name in detached_trainer_name_list: + self.target_trainer_list.append(ray.get_actor(name, namespace=os.environ["RAY_NAMESPACE"])) + self.strategy_str = strategy + self.strategy = get_strategy_from_args(strategy) + self.experience_batch_size = experience_batch_size + self.kl_coef = kl_coef + self.generate_kwargs = generate_kwargs + # Need a trainer to give an actor and a critic via initialize_experience_maker(...) + actor, critic, reward_model, initial_model = None, None, None, None + self.experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, self.kl_coef) + self._model_visit_lock = Lock() + self.fully_initialized = False + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print('[maker] Waiting for INIT') + + def _get_ready(self): + while not self.fully_initialized: + time.sleep(1.0) + + def update_target_trainer_list(self, detached_trainer_name_list): + self.target_trainer_list = [] + for name in detached_trainer_name_list: + self.target_trainer_list.append(ray.get_actor(name)) + + # copy from ../trainer/base.py + @ray.method(concurrency_group="compute") + def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience: + self._get_ready() + if isinstance(inputs, Tensor): + return self.experience_maker.make_experience(inputs, **self.generate_kwargs) + elif isinstance(inputs, dict): + return self.experience_maker.make_experience(**inputs, **self.generate_kwargs) + else: + raise ValueError(f'Unsupported input type "{type(inputs)}"') + + @ray.method(concurrency_group="experience_io") + def _send_experience(self, experience): + ''' + ignore it + + # choose a trainer that has the least experience batch in its detached_replay_buffer + chosen_trainer = None + min_length = None + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print("[maker] choosing tartget trainer") + while chosen_trainer is None: + for target_trainer in self.target_trainer_list: + try: + temp_length = ray.get(target_trainer.buffer_get_length.remote(), timeout=0.1) + if min_length is None: + min_length = temp_length + chosen_trainer = target_trainer + else: + if temp_length < min_length: + min_length = temp_length + chosen_trainer = target_trainer + except GetTimeoutError: + pass + + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print(f"[maker] sending exp to {chosen_trainer}") + chosen_trainer.buffer_append.remote(experience) + ''' + # + if not hasattr(self, "_target_idx"): + self._target_idx = 0 + chosen_trainer = self.target_trainer_list[self._target_idx] + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print(f"[maker] sending exp to {chosen_trainer}") + chosen_trainer.buffer_append.remote(experience) + self._target_idx = (self._target_idx + 1) % len(self.target_trainer_list) + + def workingloop(self, dataset, tokenizer: Optional[Callable[[Any], dict]] = None, times=5000 * 50000): + self._get_ready() + sampler = self.strategy.setup_sampler(dataset) + for _ in range(times): + rand_prompts = sampler.sample(self.experience_batch_size) + if tokenizer is not None: + inputs = tokenizer(rand_prompts) + else: + inputs = rand_prompts + self._model_visit_lock.acquire() + experience = self._make_experience(inputs=inputs) + self._model_visit_lock.release() + self._send_experience(experience=experience) + + @ray.method(concurrency_group="model_io") + def initialize_experience_maker(self, init_actor: Actor, init_critic: Critic): + ''' + called by trainer. Only once. + ''' + # TODO: reduce malloc + if self.fully_initialized: + return + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print('[maker] INIT') + with torch.no_grad(): + with self.strategy.model_init_context(): + actor = init_actor + critic = init_critic + initial_model = deepcopy(actor) + reward_model = RewardModel(deepcopy(critic.model), + deepcopy(critic.value_head)).to(torch.cuda.current_device()) + if self.strategy_str != 'colossalai_gemini': + actor.to(torch.float16).to(torch.cuda.current_device()) + critic.to(torch.float16).to(torch.cuda.current_device()) + initial_model.to(torch.float16).to(torch.cuda.current_device()) + reward_model.to(torch.float16).to(torch.cuda.current_device()) + + self.experience_maker.actor = self.strategy.prepare(actor) + self.experience_maker.critic = self.strategy.prepare(critic) + self.experience_maker.initial_model = self.strategy.prepare(initial_model) + self.experience_maker.reward_model = self.strategy.prepare(reward_model) + self.fully_initialized = True + + @ray.method(concurrency_group="model_io") + def update_experience_maker(self, new_actor: Actor, new_critic: Critic): + ''' + called by trainer + ''' + # TODO: reduce malloc + self._model_visit_lock.acquire() + with torch.no_grad(): + if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: + print("[maker] UPDATE ") + if self.strategy_str != 'colossalai_gemini': + new_actor.to(torch.float16).to(torch.cuda.current_device()) + new_critic.to(torch.float16).to(torch.cuda.current_device()) + self.experience_maker.actor = self.strategy.prepare(new_actor) + self.experience_maker.critic = self.strategy.prepare(new_critic) + self._model_visit_lock.release() diff --git a/applications/Chat/coati/ray/src/pipeline_strategy.py b/applications/Chat/coati/ray/src/pipeline_strategy.py new file mode 100644 index 000000000000..1780839c62ee --- /dev/null +++ b/applications/Chat/coati/ray/src/pipeline_strategy.py @@ -0,0 +1,105 @@ +# WIP + + +from coati.trainer.strategies import Strategy +from coati.trainer.strategies import NaiveStrategy +from coati.models.base import Actor, RewardModel, Critic + +import numpy as np +import torch +from torch._C._distributed_rpc import _is_current_rpc_agent_set + +import colossalai +from colossalai.pipeline.pipeline_process_group import ppg +from colossalai.pipeline.rpc._pipeline_schedule import OneFOneBPipelineEngine +from colossalai.fx import ColoTracer +from colossalai.fx.passes.adding_split_node_pass import balanced_split_pass, split_with_split_nodes_pass +from colossalai.pipeline.middleware.adaptor import get_fx_topology + + +import os +from functools import partial +import random + +rpc_is_initialized = _is_current_rpc_agent_set + +class PipelineModel(torch.nn.Module): + ''' + Actor has 2 kinds of jobs: forward and generate. + better to just pipelinize the inner model + ''' + def __init__(self, + model: torch.nn.Module, + stage_num: int, + num_microbatches: int, + data_kwargs = None, + ): + super().__init__() + # create partition module + def create_partition_module(pp_rank:int, stage_num: int, model, data_kwargs): + model.eval() + tracer = ColoTracer() + meta_args = {k: v.to('meta') for k, v in data_kwargs.items()} + graph = tracer.trace(root=model, meta_args=meta_args) + gm = torch.fx.GraphModule(model, graph, model.__class__.__name__) + annotated_model = balanced_split_pass(gm, stage_num) + top_module, split_submodules = split_with_split_nodes_pass(annotated_model, merge_output=True) + topo = get_fx_topology(top_module) + for submodule in split_submodules: + if isinstance(submodule, torch.fx.GraphModule): + setattr(submodule, '_topo', topo) + return split_submodules[pp_rank + 1] + + def partition(model, data_kwargs: dict, pp_rank: int, chunk: int, stage_num: int): + partition = create_partition_module(pp_rank, stage_num, model, data_kwargs) + return partition + self.inference_engine = OneFOneBPipelineEngine( + partition_fn=partial(partition, model, data_kwargs), + stage_num=stage_num, + num_microbatches=num_microbatches, + device='cuda', + ) + + def forward(self, + **model_inputs): + return self.inference_engine.forward_backward(**model_inputs, forward_only=True) + + + +class PPStrategy(NaiveStrategy): + """ + Strategy for Pipeline inference (inference only!) + + master node only + """ + def __init__( + self, + seed: int = 42 + ): + self.seed = seed + super().__init__() + + + def setup_distributed(self) -> None: + colossalai.launch_from_torch({}, seed=self.seed) + ppg.set_global_info(rank = int(os.environ['RANK']), + world_size=int(os.environ['WORLD_SIZE']), + dp_degree=1, + tp_degree=1, + num_worker_threads=128, + device="cuda") + + def model_init_context(self): + return super().model_init_context() + + def setup_model(self, model: torch.nn.Module) -> torch.nn.Module: + if isinstance(model, Actor) or \ + isinstance(model, RewardModel) or \ + isinstance(model, Critic): + model.model = PipelineModel(model.model) + + def set_seed(self, seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + diff --git a/applications/Chat/coati/ray/src/utils.py b/applications/Chat/coati/ray/src/utils.py new file mode 100644 index 000000000000..c750879b6d18 --- /dev/null +++ b/applications/Chat/coati/ray/src/utils.py @@ -0,0 +1,48 @@ +import torch.distributed as dist +from typing import Any, Callable, Dict, List, Optional +from coati.models.bloom import BLOOMActor, BLOOMCritic +from coati.models.gpt import GPTActor, GPTCritic +from coati.models.opt import OPTActor, OPTCritic +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +import torch +import os + +def is_rank_0() -> bool: + return not dist.is_initialized() or dist.get_rank() == 0 + + +def get_cuda_actor_critic_from_args(model: str, pretrained: str = None, lora_rank=0): + if model == 'gpt2': + actor = GPTActor(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) + critic = GPTCritic(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) + elif model == 'bloom': + actor = BLOOMActor(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) + critic = BLOOMCritic(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) + elif model == 'opt': + actor = OPTActor(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) + critic = OPTCritic(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) + else: + raise ValueError(f'Unsupported model "{model}"') + return actor, critic + + +def get_strategy_from_args(strategy: str): + if strategy == 'naive': + strategy_ = NaiveStrategy() + elif strategy == 'ddp': + strategy_ = DDPStrategy() + elif strategy == 'colossalai_gemini': + strategy_ = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + elif strategy == 'colossalai_zero2': + strategy_ = ColossalAIStrategy(stage=2, placement_policy='cuda') + else: + raise ValueError(f'Unsupported strategy "{strategy}"') + return strategy_ + + +def set_dist_env(env_info: Dict[str, str]): + os.environ["RANK"] = env_info['rank'] + os.environ["LOCAL_RANK"] = env_info['local_rank'] + os.environ["WORLD_SIZE"] = env_info['world_size'] + os.environ['MASTER_PORT'] = env_info['master_port'] + os.environ['MASTER_ADDR'] = env_info['master_addr'] diff --git a/applications/Chat/coati/trainer/utils.py b/applications/Chat/coati/trainer/utils.py index 6c9f7f085f8c..1b17a0421656 100644 --- a/applications/Chat/coati/trainer/utils.py +++ b/applications/Chat/coati/trainer/utils.py @@ -1,5 +1,14 @@ import torch.distributed as dist +from typing import Any, Callable, Dict, List, Optional +from coati.models.bloom import BLOOMActor, BLOOMCritic +from coati.models.gpt import GPTActor, GPTCritic +from coati.models.opt import OPTActor, OPTCritic +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +import torch +import os def is_rank_0() -> bool: return not dist.is_initialized() or dist.get_rank() == 0 + + diff --git a/applications/Chat/coati/utils/__init__.py b/applications/Chat/coati/utils/__init__.py index e75401d382a8..112b82b97064 100644 --- a/applications/Chat/coati/utils/__init__.py +++ b/applications/Chat/coati/utils/__init__.py @@ -1,3 +1,3 @@ from .tokenizer_utils import prepare_llama_tokenizer_and_embedding, smart_tokenizer_and_embedding_resize -__all__ = ['smart_tokenizer_and_embedding_resize', 'prepare_llama_tokenizer_and_embedding'] +__all__ = ['smart_tokenizer_and_embedding_resize', 'prepare_llama_tokenizer_and_embedding'] \ No newline at end of file diff --git a/applications/Chat/examples/train_prompts.sh b/applications/Chat/examples/train_prompts.sh index b750cf3581a6..8e1ce67ecc64 100755 --- a/applications/Chat/examples/train_prompts.sh +++ b/applications/Chat/examples/train_prompts.sh @@ -15,4 +15,6 @@ set_n_least_used_CUDA_VISIBLE_DEVICES() { set_n_least_used_CUDA_VISIBLE_DEVICES 2 +# torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai_zero2 + torchrun --standalone --nproc_per_node=2 train_prompts.py --prompt_path /path/to/data.json --strategy colossalai_zero2 From cc1eec2f533dffc1069cbb42fbac3d01670f9371 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Mon, 17 Apr 2023 15:02:55 +0800 Subject: [PATCH 140/413] [chat] update reward model sh (#3578) --- applications/Chat/examples/train_rm.sh | 30 ++++++++++++++++++++------ 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/applications/Chat/examples/train_rm.sh b/applications/Chat/examples/train_rm.sh index 4f9f55b6b59a..80abe62d2a3f 100755 --- a/applications/Chat/examples/train_rm.sh +++ b/applications/Chat/examples/train_rm.sh @@ -1,8 +1,24 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES 1 +set_n_least_used_CUDA_VISIBLE_DEVICES() { + local n=${1:-"9999"} + echo "GPU Memory Usage:" + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ + | tail -n +2 \ + | nl -v 0 \ + | tee /dev/tty \ + | sort -g -k 2 \ + | awk '{print $1}' \ + | head -n $n) + export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') + echo "Now CUDA_VISIBLE_DEVICES is set to:" + echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" +} -python train_reward_model.py --pretrain 'microsoft/deberta-v3-large' \ - --model 'deberta' \ - --strategy naive \ - --loss_fn 'log_exp'\ - --save_path 'rmstatic.pt' \ - --test True +set_n_least_used_CUDA_VISIBLE_DEVICES 2 + +torchrun --standalone --nproc_per_node=2 train_reward_model.py \ + --pretrain \ + --model 'bloom' \ + --strategy colossalai_zero2 \ + --loss_fn 'log_sig'\ + --save_path \ + --dataset 'Anthropic/hh-rlhf'\ From 6b1a39b17bdbb4dd9d1e15bf96672895f305818f Mon Sep 17 00:00:00 2001 From: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com> Date: Mon, 17 Apr 2023 15:40:41 +0800 Subject: [PATCH 141/413] [coati] add costom model suppor tguide (#3579) --- applications/Chat/examples/README.md | 136 ++++++++++++++++++++++++++- 1 file changed, 132 insertions(+), 4 deletions(-) diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 6c02606eab93..993a56c5a49a 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -1,5 +1,35 @@ # Examples +## Table of Contents + +- [Examples](#examples) + - [Table of Contents](#table-of-contents) + - [Install requirements](#install-requirements) + - [Supervised datasets collection](#supervised-datasets-collection) + - [Stage1 - Supervised instructs tuning](#stage1---supervised-instructs-tuning) + - [Arg List](#arg-list) + - [Stage2 - Training reward model](#stage2---training-reward-model) + - [Features and tricks in RM training](#features-and-tricks-in-rm-training) + - [Experiment result](#experiment-result) + - [Arg List](#arg-list-1) + - [Stage3 - Training model using prompts with RL](#stage3---training-model-using-prompts-with-rl) + - [Arg List](#arg-list-2) + - [Inference example - After Stage3](#inference-example---after-stage3) + - [Attention](#attention) + - [data](#data) + - [Support Model](#support-model) + - [GPT](#gpt) + - [BLOOM](#bloom) + - [OPT](#opt) + - [LLaMA](#llama) + - [Add your own models](#add-your-own-models) + - [Actor model](#actor-model) + - [LM model](#lm-model) + - [Reward model](#reward-model) + - [Critic model](#critic-model) + + +--- ## Install requirements ```shell @@ -164,7 +194,7 @@ The examples are demos for the whole training process.You need to change the hyp - [x] GPT2-S (s) - [x] GPT2-M (m) - [x] GPT2-L (l) -- [ ] GPT2-XL (xl) +- [x] GPT2-XL (xl) - [x] GPT2-4B (4b) - [ ] GPT2-6B (6b) @@ -178,9 +208,9 @@ The examples are demos for the whole training process.You need to change the hyp ### OPT - [x] [OPT-125M](https://huggingface.co/facebook/opt-125m) - [x] [OPT-350M](https://huggingface.co/facebook/opt-350m) -- [ ] [OPT-1.3B](https://huggingface.co/facebook/opt-1.3b) -- [ ] [OPT-2.7B](https://huggingface.co/facebook/opt-2.7b) -- [ ] [OPT-6.7B](https://huggingface.co/facebook/opt-6.7b) +- [x] [OPT-1.3B](https://huggingface.co/facebook/opt-1.3b) +- [x] [OPT-2.7B](https://huggingface.co/facebook/opt-2.7b) +- [x] [OPT-6.7B](https://huggingface.co/facebook/opt-6.7b) - [ ] [OPT-13B](https://huggingface.co/facebook/opt-13b) - [ ] [OPT-30B](https://huggingface.co/facebook/opt-30b) @@ -189,3 +219,101 @@ The examples are demos for the whole training process.You need to change the hyp - [x] LLaMA-13B - [ ] LLaMA-33B - [ ] LLaMA-65B + +## Add your own models + +If you want to support your own model in Coati, please refer the pull request for RoBERTa support as an example --[[chatgpt] add pre-trained model RoBERTa for RLHF stage 2 & 3](https://github.com/hpcaitech/ColossalAI/pull/3223), and submit a PR to us. + +You should complete the implementation of four model classes, including Reward model, Critic model, LM model, Actor model + +here are some example code for a NewModel named `Coati`. +if it is supported in huggingaface [transformers](https://github.com/huggingface/transformers), you can load it by `from_pretrained`, o +r you can build your own model by yourself. + +### Actor model +``` +from ..base import Actor +from transformers.models.coati import CoatiModel + +class CoatiActor(Actor): + + def __init__(self, + pretrained: Optional[str] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = CoatiModel.from_pretrained(pretrained) + else: + model = build_model() # load your own model if it is not support in trainsformers + + super().__init__(model, lora_rank, lora_train_bias) +``` + +### LM model + +``` +from ..base import LM +from transformers.models.coati import CoatiModel + +class GPTLM(LM): + + def __init__(self, + pretrained: Optional[str] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = CoatiModel.from_pretrained(pretrained) + else: + model = build_model() # load your own model if it is not support in trainsformers + + super().__init__(model, lora_rank, lora_train_bias) + + def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): + return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) +``` +### Reward model +``` +from ..base import RewardModel +from transformers.models.coati import CoatiModel + +class CoatiRM(RewardModel): + + def __init__(self, + pretrained: Optional[str] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = CoatiModel.from_pretrained(pretrained) + else: + model = build_model() # load your own model if it is not support in trainsformers + + value_head = nn.Linear(model.config.n_embd, 1) + value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.n_embd + 1)) + super().__init__(model, value_head, lora_rank, lora_train_bias) +``` + +### Critic model + +``` +from ..base import Critic +from transformers.models.coati import CoatiModel + +class CoatiCritic(Critic): + + def __init__(self, + pretrained: Optional[str] = None, + checkpoint: bool = False, + lora_rank: int = 0, + lora_train_bias: str = 'none') -> None: + if pretrained is not None: + model = CoatiModel.from_pretrained(pretrained) + else: + model = build_model() # load your own model if it is not support in trainsformers + + value_head = nn.Linear(model.config.n_embd, 1) + value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.n_embd + 1)) + super().__init__(model, value_head, lora_rank, lora_train_bias) +``` From 6e7e43c6fe2d38aa91e57150310708558855da89 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Mon, 17 Apr 2023 16:27:38 +0800 Subject: [PATCH 142/413] [doc] Update .github/workflows/README.md (#3577) Optimization Code I think there were two extra $ entered here, which have been deleted --- .github/workflows/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 9634b84b8ff8..5b5554a5006b 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -58,7 +58,7 @@ I will provide the details of each workflow below. #### Example Test on Dispatch This workflow is triggered by manually dispatching the workflow. It has the following input parameters: -- `example_directory`: the example directory to test. Multiple directories are supported and must be separated b$$y comma. For example, language/gpt, images/vit. Simply input language or simply gpt does not work. +- `example_directory`: the example directory to test. Multiple directories are supported and must be separated by comma. For example, language/gpt, images/vit. Simply input language or simply gpt does not work. ### Compatibility Test From 7788e0b0a5dcceab94fa5e98d25730003ab726a8 Mon Sep 17 00:00:00 2001 From: tingfeng cao <982912719@qq.com> Date: Mon, 17 Apr 2023 16:47:44 +0800 Subject: [PATCH 143/413] fix: fix sft (#3568) --- applications/Chat/coati/dataset/sft_dataset.py | 12 ++++-------- applications/Chat/coati/trainer/sft.py | 15 ++++++++------- applications/Chat/examples/train_sft.py | 2 +- 3 files changed, 13 insertions(+), 16 deletions(-) diff --git a/applications/Chat/coati/dataset/sft_dataset.py b/applications/Chat/coati/dataset/sft_dataset.py index 91e38f06daba..3e2453468bbc 100644 --- a/applications/Chat/coati/dataset/sft_dataset.py +++ b/applications/Chat/coati/dataset/sft_dataset.py @@ -53,29 +53,25 @@ class SFTDataset(Dataset): def __init__(self, dataset, tokenizer: Callable, max_length: int = 512) -> None: super().__init__() - # self.prompts = [] self.input_ids = [] for data in tqdm(dataset, disable=not is_rank_0()): - prompt = data['prompt'] + data['completion'] + "<|endoftext|>" + prompt = data['prompt'] + data['completion'] + tokenizer.eos_token prompt_token = tokenizer(prompt, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt") - # self.prompts.append(prompt_token)s - self.input_ids.append(prompt_token) - self.labels = copy.deepcopy(self.input_ids) + self.input_ids.append(prompt_token['input_ids'][0]) + self.labels = copy.deepcopy(self.input_ids) def __len__(self): - length = len(self.prompts) + length = len(self.input_ids) return length def __getitem__(self, idx): - # dict(input_ids=self.input_ids[i], labels=self.labels[i]) return dict(input_ids=self.input_ids[idx], labels=self.labels[idx]) - # return dict(self.prompts[idx], self.prompts[idx]) def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, max_length: int) -> Dict: diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index 8eeffea48bdd..f380cbf06a95 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -96,7 +96,7 @@ def fit(self, logger, log_interval=10): loss = outputs.loss prompt_logits = outputs.logits - if loss >= 2.5: + if loss >= 2.5 and is_rank_0(): logger.warning(f"batch_id:{batch_id}, abnormal loss: {loss}") loss = loss / self.accimulation_steps @@ -110,12 +110,13 @@ def fit(self, logger, log_interval=10): self.strategy.optimizer_step(self.optimizer) self.optimizer.zero_grad() self.scheduler.step() - wandb.log({ - "loss": total_loss / self.accimulation_steps, - "lr": self.scheduler.get_last_lr()[0], - "epoch": epoch, - "batch_id": batch_id - }) + if is_rank_0(): + wandb.log({ + "loss": total_loss / self.accimulation_steps, + "lr": self.scheduler.get_last_lr()[0], + "epoch": epoch, + "batch_id": batch_id + }) total_loss = 0 step_bar.update() diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index 22f70e485843..d7502c23b5e6 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -111,7 +111,7 @@ def train(args): max_datasets_size=args.max_datasets_size, max_length=max_len) eval_dataset = None - data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) + data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) if dist.is_initialized() and dist.get_world_size() > 1: train_sampler = DistributedSampler(train_dataset, From f313babd11f8137c2496e7dc54c6b61604cd3672 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 17 Apr 2023 17:11:09 +0800 Subject: [PATCH 144/413] [gemini] support save state dict in shards (#3581) * [gemini] support state dict shard * [gemini] add test state dict shard * [gemini] polish docstr * [gemini] fix merge * [gemini] polish code --- colossalai/zero/gemini/gemini_ddp.py | 129 ++++++++++++++++-- .../test_zeroddp_state_dict_shard.py | 56 ++++++++ 2 files changed, 172 insertions(+), 13 deletions(-) create mode 100644 tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index 2e35be0661e9..9a193310bab1 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -1,12 +1,13 @@ import itertools from collections import OrderedDict from functools import partial -from typing import Dict, List, Optional, Union +from typing import Dict, Iterator, List, Optional, Union import torch import torch.distributed as dist import torch.nn as nn +from colossalai.checkpoint_io.utils import calculate_tensor_size from colossalai.logging import get_dist_logger from colossalai.nn.parallel.data_parallel import ColoDDP, _cast_float, free_storage from colossalai.tensor import ProcessGroup as ColoProcessGroup @@ -228,6 +229,32 @@ def state_dict(self, destination=None, prefix='', keep_vars=False, only_rank_0: destination = hook_result return destination + def _get_chunk_to_save_data(self, chunk: Chunk, only_rank_0: bool) -> Dict: + """ + get gathered chunk content. + + Args: + chunk (Chunk): a chunk + only_rank_0 (bool): whether to only save data on rank 0 + + Returns: + Dict: a dict whose key is param name and value is param with correct payload + """ + # save parameters + chunk_to_save_data = dict() + temp_chunk = get_temp_total_chunk_on_cuda(chunk) + for tensor, tensor_info in chunk.tensors_info.items(): + record_tensor = torch.empty([0]) + record_flag = (not only_rank_0) | (dist.get_rank(chunk.torch_pg) == 0) + if record_flag: + record_tensor = temp_chunk[tensor_info.offset:tensor_info.end].view(tensor.shape).cpu() + + assert tensor not in chunk_to_save_data + chunk_to_save_data[tensor] = record_tensor + + del temp_chunk + return chunk_to_save_data + def _get_param_to_save_data(self, param_list: List[torch.nn.Parameter], only_rank_0: bool) -> Dict: """ get param content from chunks. @@ -243,18 +270,7 @@ def _get_param_to_save_data(self, param_list: List[torch.nn.Parameter], only_ran param_to_save_data = dict() chunk_list = self.chunk_manager.get_chunks(param_list) for chunk in chunk_list: - temp_chunk = get_temp_total_chunk_on_cuda(chunk) - - for tensor, tensor_info in chunk.tensors_info.items(): - record_tensor = torch.empty([0]) - record_flag = (not only_rank_0) | (dist.get_rank(chunk.torch_pg) == 0) - if record_flag: - record_tensor = temp_chunk[tensor_info.offset:tensor_info.end].view(tensor.shape).cpu() - - assert tensor not in param_to_save_data - param_to_save_data[tensor] = record_tensor - - del temp_chunk + param_to_save_data.update(self._get_chunk_to_save_data(chunk, only_rank_0)) return param_to_save_data def _save_to_state_dict(self, destination, prefix, keep_vars, only_rank_0=True): @@ -554,6 +570,93 @@ def _preprocess_param(self, p: Union[nn.Parameter, ColoParameter, 'LazyTensor']) p.__class__ = ColoParameter p.__init__(p, requires_grad=requires_grad) + def state_dict_shard(self, + prefix: str = '', + keep_vars: bool = False, + max_shard_size: int = 1024, + only_rank_0: bool = True) -> Iterator[OrderedDict]: + """Returns dictionaries containing a whole state of the module one by one. The max size of dictionary shard is specified by ``max_shard_size``. + + Both parameters and persistent buffers (e.g. running averages) are included. + Keys are corresponding parameter and buffer names. + Parameters and buffers set to ``None`` are not included. + + Args: + prefix (str, optional): the prefix for parameters and buffers used in this + module. Defaults to ''. + keep_vars (bool, optional): whether to keep variables. Defaults to False. + max_shard_size (int, optional): max size of state dict shard (in MB). Defaults to 1024. + only_rank_0 (bool, optional): only get data on rank0. Defaults to True. + + + Yields: + Iterator[OrderedDict]: A generator of state dict shard + """ + sharder = _StateDictSharder(max_shard_size) + + # get the mapping between copies and fp16 parameters + fp16_to_fp32 = dict() + for p, fp32_p in zip(self.fp16_params, self.fp32_params): + fp16_to_fp32[p] = fp32_p + + # key is fp32 param, and value is gathered param on CPU + gathered_param_buffer = dict() + for name, param in self.name2param.items(): + if param is not None: + if is_ddp_ignored(param): + # deal with ddp ignored parameters + gathered_param = param if keep_vars else param.detach() + else: + fp32_param = fp16_to_fp32[param] + if fp32_param not in gathered_param_buffer: + chunk = self.chunk_manager.get_chunk(fp32_param) + gathered_param_buffer.update(self._get_chunk_to_save_data(chunk, only_rank_0)) + gathered_param = gathered_param_buffer.pop(fp32_param) + + block = sharder.append(prefix + name, gathered_param) + if block is not None: + yield block + + del fp16_to_fp32 + del gathered_param_buffer + + # save all buffers + for name, buf in self.named_buffers(): + if buf is not None and name not in self._non_persistent_buffers_set: + buffer = buf if keep_vars else buf.detach() + block = sharder.append(prefix + name, buffer) + if block is not None: + yield block + # save extra states + extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX + if getattr(self.__class__, "get_extra_state", + torch.nn.Module.get_extra_state) is not torch.nn.Module.get_extra_state: + extra_state = self.get_extra_state() + block = sharder.append(extra_state_key, extra_state) + if block is not None: + yield block + + yield sharder.current_block + + +class _StateDictSharder: + + def __init__(self, max_shard_size: int) -> None: + self.max_shard_size = max_shard_size + self.current_block = OrderedDict() + self.current_block_size = 0 + + def append(self, name: str, tensor: torch.Tensor) -> Optional[OrderedDict]: + tensor_size = calculate_tensor_size(tensor) + ret_block = None + if self.current_block_size + tensor_size > self.max_shard_size: + ret_block = self.current_block + self.current_block = OrderedDict() + self.current_block_size = 0 + self.current_block[name] = tensor + self.current_block_size += tensor_size + return ret_block + class GeminiDDP(ZeroDDP): diff --git a/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py b/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py new file mode 100644 index 000000000000..96c26a1de4df --- /dev/null +++ b/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py @@ -0,0 +1,56 @@ +import pytest +import torch +from torch.testing import assert_close + +import colossalai +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils.cuda import get_current_device +from colossalai.zero import ColoInitContext, ZeroDDP +from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager +from tests.components_to_test.registry import non_distributed_component_funcs + + +@parameterize('placement_policy', ['cuda', 'cpu']) +@parameterize('model_name', ['gpt2', 'bert']) +def exam_state_dict(placement_policy, model_name: str): + get_components_func = non_distributed_component_funcs.get_callable(model_name) + model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() + + with ColoInitContext(device=get_current_device()): + model = model_builder() + + model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2 + + config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + chunk_manager = ChunkManager(config_dict) + gemini_manager = GeminiManager(placement_policy, chunk_manager) + model = ZeroDDP(model, gemini_manager) + model.train() + + zero_dict = model.state_dict(only_rank_0=False) + accumulated_keys = set() + # ensure number of shards > 1 + for shard in model.state_dict_shard(max_shard_size=(model_size / 3), only_rank_0=False): + for key, value in shard.items(): + assert key not in accumulated_keys, f"key `{key}` is duplicated." + accumulated_keys.add(key) + assert key in zero_dict, f"{key} not in ZeRO dictionary." + assert torch.equal(value, zero_dict[key]), f"{key} not equal." + + +def run_dist(rank, world_size, port): + config = {} + colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + exam_state_dict() + + +@pytest.mark.dist +@pytest.mark.parametrize('world_size', [1, 4]) +@rerun_if_address_is_in_use() +def test_zero_ddp_state_dict_shard(world_size): + spawn(run_dist, world_size) + + +if __name__ == '__main__': + test_zero_ddp_state_dict_shard(1) From d0fbd4b86fcfa653db5c5b7d312f249ce6dad619 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Tue, 18 Apr 2023 10:37:34 +0800 Subject: [PATCH 145/413] [example] fix community doc (#3586) Adjusted the style of Community Examples to be consistent with other titles --- examples/community/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/community/README.md b/examples/community/README.md index 2abf5c7e8404..fb2ca37ed988 100644 --- a/examples/community/README.md +++ b/examples/community/README.md @@ -1,4 +1,4 @@ -#Community Examples +## Community Examples Community-driven Examples is an initiative that allows users to share their own examples to the Colossal-AI community, fostering a sense of community and making it easy for others to access and benefit from shared work. The primary goal with community-driven examples is to have a community-maintained collection of diverse and exotic functionalities built on top of the Colossal-AI package. From 36a519b49f44a536d4ad9b1041ffc610c0aa1bba Mon Sep 17 00:00:00 2001 From: Camille Zhong <44392324+Camille7777@users.noreply.github.com> Date: Wed, 22 Mar 2023 17:18:13 +0800 Subject: [PATCH 146/413] Update test_ci.sh update Update test_ci.sh Update test_ci.sh Update test_ci.sh Update test_ci.sh Update test_ci.sh Update test_ci.sh Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update test_ci.sh Update test_ci.sh update Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml update ci Update test_ci.sh Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml Update test_ci.sh Update test_ci.sh Update run_chatgpt_examples.yml Update test_ci.sh Update test_ci.sh Update test_ci.sh update test ci RoBERTa for RLHF Stage 2 & 3 (still in testing) Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci Update test_ci.sh Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci Update test_ci.sh Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. update roberta with coati chat ci update Revert "chat ci update" This reverts commit 17ae7ae01fa752bd3289fc39069868fde99cf846. [test]chat_update_ci Update test_ci.sh Update test_ci.sh test Update gpt_critic.py Update gpt_critic.py Update run_chatgpt_unit_tests.yml update test ci update update update update Update test_ci.sh update Update test_ci.sh Update test_ci.sh Update run_chatgpt_examples.yml Update run_chatgpt_examples.yml --- .github/workflows/run_chatgpt_examples.yml | 27 ++- .../Chat/coati/models/gpt/gpt_actor.py | 5 +- .../Chat/coati/models/gpt/gpt_critic.py | 5 +- applications/Chat/examples/test_ci.sh | 167 +++++++++--------- 4 files changed, 109 insertions(+), 95 deletions(-) diff --git a/.github/workflows/run_chatgpt_examples.yml b/.github/workflows/run_chatgpt_examples.yml index 51bb9d074644..1d8240ad4631 100644 --- a/.github/workflows/run_chatgpt_examples.yml +++ b/.github/workflows/run_chatgpt_examples.yml @@ -4,10 +4,10 @@ on: pull_request: types: [synchronize, opened, reopened] paths: - - 'applications/ChatGPT/chatgpt/**' - - 'applications/ChatGPT/requirements.txt' - - 'applications/ChatGPT/setup.py' - - 'applications/ChatGPT/examples/**' + - 'applications/Chat/coati/**' + - 'applications/Chat/requirements.txt' + - 'applications/Chat/setup.py' + - 'applications/Chat/examples/**' jobs: @@ -16,7 +16,7 @@ jobs: runs-on: [self-hosted, gpu] container: image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 - options: --gpus all --rm -v /data/scratch/chatgpt:/data/scratch/chatgpt + options: --gpus all --rm -v /data/scratch/github_actions/chat:/data/scratch/github_actions/chat timeout-minutes: 30 defaults: run: @@ -27,17 +27,26 @@ jobs: - name: Install ColossalAI and ChatGPT run: | - pip install -v . - cd applications/ChatGPT + pip install -e . + cd applications/Chat pip install -v . pip install -r examples/requirements.txt + - name: Install Transformers + run: | + cd applications/Chat + git clone https://github.com/hpcaitech/transformers + cd transformers + pip install -v . + - name: Execute Examples run: | - cd applications/ChatGPT + cd applications/Chat rm -rf ~/.cache/colossalai ./examples/test_ci.sh env: NCCL_SHM_DISABLE: 1 MAX_JOBS: 8 - PROMPT_PATH: /data/scratch/chatgpt/prompts.csv + SFT_DATASET: /data/scratch/github_actions/chat/data.json + PROMPT_PATH: /data/scratch/github_actions/chat/prompts_en.jsonl + PRETRAIN_DATASET: /data/scratch/github_actions/chat/alpaca_data.json diff --git a/applications/Chat/coati/models/gpt/gpt_actor.py b/applications/Chat/coati/models/gpt/gpt_actor.py index 6a53ad40b817..ae9d669f1f56 100644 --- a/applications/Chat/coati/models/gpt/gpt_actor.py +++ b/applications/Chat/coati/models/gpt/gpt_actor.py @@ -23,7 +23,8 @@ def __init__(self, config: Optional[GPT2Config] = None, checkpoint: bool = False, lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: + lora_train_bias: str = 'none', + **kwargs) -> None: if pretrained is not None: model = GPT2LMHeadModel.from_pretrained(pretrained) elif config is not None: @@ -32,4 +33,4 @@ def __init__(self, model = GPT2LMHeadModel(GPT2Config()) if checkpoint: model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) + super().__init__(model, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/Chat/coati/models/gpt/gpt_critic.py b/applications/Chat/coati/models/gpt/gpt_critic.py index 25bb1ed94de4..2e70f5f1fc96 100644 --- a/applications/Chat/coati/models/gpt/gpt_critic.py +++ b/applications/Chat/coati/models/gpt/gpt_critic.py @@ -24,7 +24,8 @@ def __init__(self, config: Optional[GPT2Config] = None, checkpoint: bool = False, lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: + lora_train_bias: str = 'none', + **kwargs) -> None: if pretrained is not None: model = GPT2Model.from_pretrained(pretrained) elif config is not None: @@ -34,4 +35,4 @@ def __init__(self, if checkpoint: model.gradient_checkpointing_enable() value_head = nn.Linear(model.config.n_embd, 1) - super().__init__(model, value_head, lora_rank, lora_train_bias) + super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index 64cf68a0a13f..32f5858a51b6 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -2,11 +2,21 @@ set -xue +if [ -z "$SFT_DATASET" ]; then + echo "Please set \$SFT_DATASET to the path to sft dataset." + exit 1 +fi + if [ -z "$PROMPT_PATH" ]; then echo "Please set \$PROMPT_PATH to the path to prompts csv." exit 1 fi +if [ -z "$PRETRAIN_DATASET" ]; then + echo "Please set \$PRETRAIN_DATASET to the path to alpaca data." + exit 1 +fi + BASE=$(realpath $(dirname $0)) export OMP_NUM_THREADS=8 @@ -14,104 +24,97 @@ export OMP_NUM_THREADS=8 # install requirements pip install -r ${BASE}/requirements.txt -# train dummy -python ${BASE}/train_dummy.py --strategy naive --num_episodes 1 \ - --max_timesteps 2 --update_timesteps 2 \ - --max_epochs 1 --train_batch_size 2 --lora_rank 4 - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ - --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'facebook/opt-350m' --model opt --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_dummy.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'facebook/opt-350m' --model opt - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ - --strategy ddp --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'facebook/opt-350m' --model opt --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_dummy.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'facebook/opt-350m' --model opt - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'gpt2' --model gpt2 --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_dummy.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'gpt2' --model gpt2 +wandb init -m offline -torchrun --standalone --nproc_per_node=2 ${BASE}/train_dummy.py \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'roberta-base' --model roberta --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_dummy.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_dummy.pt --pretrain 'roberta-base' --model roberta +# train sft +torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'bigscience/bloom-560m' \ + --model 'bloom' --strategy colossalai_zero2 --lora_rank 4\ + --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ + --save_path ${BASE}/output -rm -rf ${BASE}/actor_checkpoint_dummy.pt +torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'gpt2' \ + --model 'gpt2' --strategy colossalai_zero2 \ + --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ + --save_path ${BASE}/output -# train prompts -python ${BASE}/train_prompts.py $PROMPT_PATH --strategy naive --num_episodes 1 \ - --max_timesteps 2 --update_timesteps 2 \ - --max_epochs 1 --train_batch_size 2 --lora_rank 4 +torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'facebook/opt-350m' \ + --model 'opt' --strategy colossalai_zero2 --lora_rank 4\ + --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ + --save_path ${BASE}/output -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'facebook/opt-350m' --model opt --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_prompts.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'facebook/opt-350m' --model opt +torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'gpt2' \ + --model 'gpt2' --strategy ddp --lora_rank 4\ + --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ + --save_path ${BASE}/output -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ - --strategy ddp --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'gpt2' --model gpt2 --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_prompts.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'gpt2' --model gpt2 +#torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'facebook/opt-350m' \ +# --model 'opt' --strategy naive \ +# --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ +# --save_path ${BASE}/output -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ - --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'gpt2' --model gpt2 --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_prompts.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'gpt2' --model gpt2 - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py $PROMPT_PATH \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2\ - --pretrain 'roberta-base' --model roberta --lora_rank 4\ - --save_path ${BASE}/actor_checkpoint_prompts.pt -python ${BASE}/inference.py --model_path ${BASE}/actor_checkpoint_prompts.pt --pretrain 'roberta-base' --model roberta - -rm -rf ${BASE}/actor_checkpoint_prompts.pt +rm -rf ${BASE}/output # train rm torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'facebook/opt-350m' --model 'opt' \ - --strategy colossalai_zero2 --loss_fn 'log_sig'\ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ - --test True --lora_rank 4 + --pretrain 'facebook/opt-350m' --model 'opt' \ + --strategy colossalai_zero2 --loss_fn 'log_sig'\ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt_opt.pt + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ + --pretrain 'gpt2' --model 'gpt2' \ + --strategy colossalai_zero2 --loss_fn 'log_exp' \ + --dataset 'Dahoas/rm-static' \ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt_gpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'gpt2' --model 'gpt2' \ - --strategy colossalai_gemini --loss_fn 'log_exp'\ - --dataset 'Dahoas/rm-static' --test True --lora_rank 4 + --pretrain 'gpt2' --model 'gpt2' \ + --strategy ddp --loss_fn 'log_exp' \ + --dataset 'Dahoas/rm-static' \ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'bigscience/bloom-560m' --model 'bloom' \ - --strategy colossalai_zero2 --loss_fn 'log_sig'\ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ - --test True --lora_rank 4 + --pretrain 'bigscience/bloom-560m' --model 'bloom' \ + --strategy colossalai_zero2 --loss_fn 'log_sig' \ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'microsoft/deberta-v3-large' --model 'deberta' \ - --strategy colossalai_zero2 --loss_fn 'log_sig'\ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ - --test True --lora_rank 4 + --pretrain 'microsoft/deberta-v3-large' --model 'deberta' \ + --strategy colossalai_zero2 --loss_fn 'log_sig' \ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'roberta-base' --model 'roberta' \ - --strategy colossalai_zero2 --loss_fn 'log_exp'\ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ - --test True --lora_rank 4 + --pretrain 'roberta-base' --model 'roberta' \ + --strategy colossalai_zero2 --loss_fn 'log_exp'\ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt.pt rm -rf ${BASE}/rm_ckpt.pt + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_path $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ + --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ + --pretrain 'facebook/opt-350m' --model opt \ + --rm_pretrain 'facebook/opt-350m' \ + --rm_path ${BASE}/rm_ckpt_opt.pt \ + --save_path ${BASE}/actor_checkpoint_prompts.pt +rm -rf ${BASE}/rm_ckpt_opt.pt + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_path $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ + --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ + --pretrain 'gpt2' --model gpt2 \ + --rm_pretrain 'gpt2' \ + --rm_path ${BASE}/rm_ckpt_gpt.pt \ + --save_path ${BASE}/actor_checkpoint_prompts.pt +rm -rf ${BASE}/rm_ckpt_gpt.pt + +rm -rf ${BASE}/actor_checkpoint_prompts.pt \ No newline at end of file From dac127d0ee9a6345295fc76aca0b43f42e22d3ff Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Tue, 18 Apr 2023 16:20:36 +0800 Subject: [PATCH 147/413] [fx] fix meta tensor registration (#3589) * [meta] fix torch 1.13.1 * [meta] fix torch 2.0.0 * [meta] fix torch 1.13.0 * [meta] polish code --- .../_subclasses/_meta_registration.py | 197 +++++++++--------- 1 file changed, 102 insertions(+), 95 deletions(-) diff --git a/colossalai/_analyzer/_subclasses/_meta_registration.py b/colossalai/_analyzer/_subclasses/_meta_registration.py index 4b1fd28e982f..4049be79c70f 100644 --- a/colossalai/_analyzer/_subclasses/_meta_registration.py +++ b/colossalai/_analyzer/_subclasses/_meta_registration.py @@ -274,11 +274,15 @@ def meta_cudnn_rnn_backward(input: torch.Tensor, aten.prelu.default, aten.hardswish.default, aten.hardtanh.default, - aten.prelu_backward.default, aten.hardswish_backward.default, aten.hardtanh_backward.default, ] + if version.parse(torch.__version__) < version.parse('2.0.0'): + _unregistered_ewise += [ + aten.prelu_backward.default, + ] + @register_meta(_unregistered_ewise) def meta_unregistered_ewise(input: torch.Tensor, *args): return new_like(input) @@ -331,11 +335,6 @@ def meta_ln_backward(dY: torch.Tensor, input: torch.Tensor, normalized_shape, me def meta_im2col(input: torch.Tensor, kernel_size, dilation, padding, stride): return new_like(input) - # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml - @register_meta(aten.eye.m_out) - def meta_eye(n: int, m: int, out: torch.Tensor): - return out - # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml @register_meta(aten.roll.default) def meta_roll(input: torch.Tensor, shifts, dims): @@ -352,97 +351,9 @@ def meta_where_self(condition: torch.Tensor, self: torch.Tensor, other: torch.Te result_type = torch.result_type(self, other) return new_like(condition + self + other, dtype=result_type) - @register_meta(aten.index.Tensor) - def meta_index_Tensor(self, indices): - assert indices, "at least one index must be provided" - # aten::index is the internal advanced indexing implementation - # checkIndexTensorTypes and expandTensors - result: List[Optional[torch.Tensor]] = [] - for i, index in enumerate(indices): - if index is not None: - assert index.dtype in [torch.long, torch.int8, torch.bool],\ - "tensors used as indices must be long, byte or bool tensors" - if index.dtype in [torch.int8, torch.bool]: - nonzero = index.nonzero() - k = len(result) - assert k + index.ndim <= self.ndim, f"too many indices for tensor of dimension {self.ndim}" - for j in range(index.ndim): - assert index.shape[j] == self.shape[ - k + - j], f"The shape of the mask {index.shape} at index {i} does not match the shape of the indexed tensor {self.shape} at index {k + j}" - result.append(nonzero.select(1, j)) - else: - result.append(index) - else: - result.append(index) - indices = result - assert len(indices) <= self.ndim, f"too many indices for tensor of dimension {self.ndim} (got {len(indices)})" - # expand_outplace - import torch._refs as refs - - indices = list(refs._maybe_broadcast(*indices)) - # add missing null tensors - while len(indices) < self.ndim: - indices.append(None) - - # hasContiguousSubspace - # true if all non-null tensors are adjacent - # See: - # https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing - # https://stackoverflow.com/questions/53841497/why-does-numpy-mixed-basic-advanced-indexing-depend-on-slice-adjacency - state = 0 - has_contiguous_subspace = False - for index in indices: - if state == 0: - if index is not None: - state = 1 - elif state == 1: - if index is None: - state = 2 - else: - if index is not None: - break - else: - has_contiguous_subspace = True - - # transposeToFront - # This is the logic that causes the newly inserted dimensions to show up - # at the beginning of the tensor, if they're not contiguous - if not has_contiguous_subspace: - dims = [] - transposed_indices = [] - for i, index in enumerate(indices): - if index is not None: - dims.append(i) - transposed_indices.append(index) - for i, index in enumerate(indices): - if index is None: - dims.append(i) - transposed_indices.append(index) - self = self.permute(dims) - indices = transposed_indices - - # AdvancedIndex::AdvancedIndex - # Now we can assume the indices have contiguous subspace - # This is simplified from AdvancedIndex which goes to more effort - # to put the input and indices in a form so that TensorIterator can - # take them. If we write a ref for this, probably that logic should - # get implemented - before_shape: List[int] = [] - after_shape: List[int] = [] - replacement_shape: List[int] = [] - for dim, index in enumerate(indices): - if index is None: - if replacement_shape: - after_shape.append(self.shape[dim]) - else: - before_shape.append(self.shape[dim]) - else: - replacement_shape = list(index.shape) - return self.new_empty(before_shape + replacement_shape + after_shape) - # ============================== Embedding ========================================= # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Embedding.cpp + @register_meta(aten.embedding_dense_backward.default) def meta_embedding_dense_backward(grad_output: torch.Tensor, indices: torch.Tensor, num_weights, padding_idx, scale_grad_by_freq): @@ -459,3 +370,99 @@ def meta_native_dropout_default(input: torch.Tensor, p: float, train: bool = Fal @register_meta(aten.native_dropout_backward.default) def meta_native_dropout_backward_default(grad: torch.Tensor, mask: torch.Tensor, scale: float): return new_like(grad) # (grad_in) + + if version.parse(torch.__version__) < version.parse('1.13.0'): + # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml + @register_meta(aten.eye.m_out) + def meta_eye(n: int, m: int, out: torch.Tensor): + return out + + @register_meta(aten.index.Tensor) + def meta_index_Tensor(self, indices): + assert indices, "at least one index must be provided" + # aten::index is the internal advanced indexing implementation + # checkIndexTensorTypes and expandTensors + result: List[Optional[torch.Tensor]] = [] + for i, index in enumerate(indices): + if index is not None: + assert index.dtype in [torch.long, torch.int8, torch.bool],\ + "tensors used as indices must be long, byte or bool tensors" + if index.dtype in [torch.int8, torch.bool]: + nonzero = index.nonzero() + k = len(result) + assert k + index.ndim <= self.ndim, f"too many indices for tensor of dimension {self.ndim}" + for j in range(index.ndim): + assert index.shape[j] == self.shape[ + k + + j], f"The shape of the mask {index.shape} at index {i} does not match the shape of the indexed tensor {self.shape} at index {k + j}" + result.append(nonzero.select(1, j)) + else: + result.append(index) + else: + result.append(index) + indices = result + assert len( + indices) <= self.ndim, f"too many indices for tensor of dimension {self.ndim} (got {len(indices)})" + # expand_outplace + import torch._refs as refs + + indices = list(refs._maybe_broadcast(*indices)) + # add missing null tensors + while len(indices) < self.ndim: + indices.append(None) + + # hasContiguousSubspace + # true if all non-null tensors are adjacent + # See: + # https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing + # https://stackoverflow.com/questions/53841497/why-does-numpy-mixed-basic-advanced-indexing-depend-on-slice-adjacency + state = 0 + has_contiguous_subspace = False + for index in indices: + if state == 0: + if index is not None: + state = 1 + elif state == 1: + if index is None: + state = 2 + else: + if index is not None: + break + else: + has_contiguous_subspace = True + + # transposeToFront + # This is the logic that causes the newly inserted dimensions to show up + # at the beginning of the tensor, if they're not contiguous + if not has_contiguous_subspace: + dims = [] + transposed_indices = [] + for i, index in enumerate(indices): + if index is not None: + dims.append(i) + transposed_indices.append(index) + for i, index in enumerate(indices): + if index is None: + dims.append(i) + transposed_indices.append(index) + self = self.permute(dims) + indices = transposed_indices + + # AdvancedIndex::AdvancedIndex + # Now we can assume the indices have contiguous subspace + # This is simplified from AdvancedIndex which goes to more effort + # to put the input and indices in a form so that TensorIterator can + # take them. If we write a ref for this, probably that logic should + # get implemented + before_shape: List[int] = [] + after_shape: List[int] = [] + replacement_shape: List[int] = [] + for dim, index in enumerate(indices): + if index is None: + if replacement_shape: + after_shape.append(self.shape[dim]) + else: + before_shape.append(self.shape[dim]) + else: + replacement_shape = list(index.shape) + return self.new_empty(before_shape + replacement_shape + after_shape) From 1ec0d386a9fb64de4c5f075d755a0d8ab996bbf1 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Tue, 18 Apr 2023 16:44:03 +0800 Subject: [PATCH 148/413] reconstruct chat trainer and fix training script (#3588) Co-authored-by: Yuanchen Xu --- .../Chat/benchmarks/benchmark_gpt_dummy.py | 6 +- .../benchmarks/benchmark_opt_lora_dummy.py | 6 +- applications/Chat/coati/trainer/base.py | 94 +------------------ applications/Chat/coati/trainer/ppo.py | 91 +++++++++++++++++- applications/Chat/coati/trainer/rm.py | 40 ++++---- applications/Chat/coati/trainer/sft.py | 21 +++-- applications/Chat/examples/train_dummy.py | 8 +- .../Chat/examples/train_reward_model.py | 34 ++++++- 8 files changed, 163 insertions(+), 137 deletions(-) diff --git a/applications/Chat/benchmarks/benchmark_gpt_dummy.py b/applications/Chat/benchmarks/benchmark_gpt_dummy.py index c0d8b1c377aa..e41ef239d378 100644 --- a/applications/Chat/benchmarks/benchmark_gpt_dummy.py +++ b/applications/Chat/benchmarks/benchmark_gpt_dummy.py @@ -156,8 +156,10 @@ def main(args): eos_token_id=tokenizer.eos_token_id, callbacks=[performance_evaluator]) - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 400), device=torch.cuda.current_device()) - trainer.fit(random_prompts, + random_prompts = torch.randint(tokenizer.vocab_size, (1000, 1, 400), device=torch.cuda.current_device()) + random_attention_mask = torch.randint(1, (1000, 1, 400), device=torch.cuda.current_device()).to(torch.bool) + random_pretrain = [{'input_ids':random_prompts[i], 'labels':random_prompts[i], 'attention_mask':random_attention_mask[i]} for i in range(1000)] + trainer.fit(random_prompts, random_pretrain, num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) diff --git a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py index 42df2e1f28cb..c79435ec63c5 100644 --- a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py +++ b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py @@ -149,8 +149,10 @@ def main(args): eos_token_id=tokenizer.eos_token_id, callbacks=[performance_evaluator]) - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 400), device=torch.cuda.current_device()) - trainer.fit(random_prompts, + random_prompts = torch.randint(tokenizer.vocab_size, (1000, 1, 400), device=torch.cuda.current_device()) + random_attention_mask = torch.randint(1, (1000, 1, 400), device=torch.cuda.current_device()).to(torch.bool) + random_pretrain = [{'input_ids':random_prompts[i], 'labels':random_prompts[i], 'attention_mask':random_attention_mask[i]} for i in range(1000)] + trainer.fit(random_prompts, random_pretrain, num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) diff --git a/applications/Chat/coati/trainer/base.py b/applications/Chat/coati/trainer/base.py index 610bb5111976..d676799496dd 100644 --- a/applications/Chat/coati/trainer/base.py +++ b/applications/Chat/coati/trainer/base.py @@ -2,15 +2,10 @@ from typing import Any, Callable, Dict, List, Optional, Union import torch -from coati.experience_maker import Experience, ExperienceMaker -from coati.replay_buffer import ReplayBuffer -from torch import Tensor -from torch.utils.data import DistributedSampler -from tqdm import tqdm +from coati.experience_maker import Experience from .callbacks import Callback from .strategies import Strategy -from .utils import is_rank_0 class Trainer(ABC): @@ -19,113 +14,28 @@ class Trainer(ABC): Args: strategy (Strategy):the strategy to use for training - experience_maker (ExperienceMaker): the experience maker to use for produce experience to fullfill replay buffer - replay_buffer (ReplayBuffer): the replay buffer to use for training - experience_batch_size (int, defaults to 8): the batch size to use for experience generation max_epochs (int, defaults to 1): the number of epochs of training process tokenizer (Callable, optional): the tokenizer to use for tokenizing the input - sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer - data_loader_pin_memory (bool, defaults to True): whether to pin memory for data loader + dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader callbacks (List[Callback], defaults to []): the callbacks to call during training process generate_kwargs (dict, optional): the kwargs to use while model generating """ def __init__(self, strategy: Strategy, - experience_maker: ExperienceMaker, - replay_buffer: ReplayBuffer, - experience_batch_size: int = 8, max_epochs: int = 1, tokenizer: Optional[Callable[[Any], dict]] = None, - sample_replay_buffer: bool = False, dataloader_pin_memory: bool = True, callbacks: List[Callback] = [], **generate_kwargs) -> None: super().__init__() self.strategy = strategy - self.experience_maker = experience_maker - self.replay_buffer = replay_buffer - self.experience_batch_size = experience_batch_size self.max_epochs = max_epochs self.tokenizer = tokenizer self.generate_kwargs = generate_kwargs - self.sample_replay_buffer = sample_replay_buffer self.dataloader_pin_memory = dataloader_pin_memory self.callbacks = callbacks - @abstractmethod - def training_step(self, experience: Experience) -> Dict[str, Any]: - pass - - def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience: - if isinstance(inputs, Tensor): - return self.experience_maker.make_experience(inputs, **self.generate_kwargs) - elif isinstance(inputs, dict): - return self.experience_maker.make_experience(**inputs, **self.generate_kwargs) - else: - raise ValueError(f'Unsupported input type "{type(inputs)}"') - - def _sample_prompts(self, prompts) -> list: - indices = list(range(len(prompts))) - sampled_indices = self.strategy.experience_sampler.choice(indices, self.experience_batch_size, replace=False) - return [prompts[i] for i in sampled_indices] - - def _learn(self): - # replay buffer may be empty at first, we should rebuild at each training - if not self.sample_replay_buffer: - dataloader = self.strategy.setup_dataloader(self.replay_buffer, self.dataloader_pin_memory) - device = torch.cuda.current_device() - if self.sample_replay_buffer: - pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) - for _ in pbar: - experience = self.replay_buffer.sample() - metrics = self.training_step(experience) - pbar.set_postfix(metrics) - else: - for epoch in range(self.max_epochs): - self._on_learn_epoch_start(epoch) - if isinstance(dataloader.sampler, DistributedSampler): - dataloader.sampler.set_epoch(epoch) - pbar = tqdm(dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0()) - for experience in pbar: - self._on_learn_batch_start() - experience.to_device(device) - metrics = self.training_step(experience) - self._on_learn_batch_end(metrics, experience) - pbar.set_postfix(metrics) - self._on_learn_epoch_end(epoch) - - def fit(self, - prompt_dataloader, - pretrain_dataloader, - num_episodes: int = 50000, - max_timesteps: int = 500, - update_timesteps: int = 5000) -> None: - time = 0 - self.pretrain_dataloader = pretrain_dataloader - self.prompt_dataloader = prompt_dataloader - self._on_fit_start() - for episode in range(num_episodes): - self._on_episode_start(episode) - for timestep in tqdm(range(max_timesteps), - desc=f'Episode [{episode+1}/{num_episodes}]', - disable=not is_rank_0()): - time += 1 - prompts = next(iter(self.prompt_dataloader)) - self._on_make_experience_start() - self.experience_maker.initial_model.to(torch.cuda.current_device()) - self.experience_maker.reward_model.to(torch.cuda.current_device()) - experience = self._make_experience(prompts) - self._on_make_experience_end(experience) - self.replay_buffer.append(experience) - if time % update_timesteps == 0: - self.experience_maker.initial_model.to('cpu') - self.experience_maker.reward_model.to('cpu') - self._learn() - self.replay_buffer.clear() - self._on_episode_end(episode) - self._on_fit_end() - # TODO(ver217): maybe simplify these code using context def _on_fit_start(self) -> None: for callback in self.callbacks: diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index d58e437e6e61..008a6aea8240 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -1,4 +1,4 @@ -from typing import Any, Callable, Dict, List, Optional +from typing import Any, Callable, Dict, List, Optional, Union import torch import torch.nn as nn @@ -7,12 +7,16 @@ from coati.models.generation_utils import update_model_kwargs_fn from coati.models.loss import PolicyLoss, ValueLoss from coati.replay_buffer import NaiveReplayBuffer +from torch import Tensor from torch.optim import Optimizer +from torch.utils.data import DistributedSampler from transformers.tokenization_utils_base import PreTrainedTokenizerBase +from tqdm import tqdm from .base import Trainer from .callbacks import Callback from .strategies import Strategy +from .utils import is_rank_0 class PPOTrainer(Trainer): @@ -33,6 +37,7 @@ class PPOTrainer(Trainer): buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu eps_clip (float, defaults to 0.2): the clip coefficient of policy loss vf_coef (float, defaults to 1.0): the coefficient of value loss + ptx_coef (float, defaults to 0.9): the coefficient of ptx loss value_clip (float, defaults to 0.4): the clip coefficient of value loss experience_batch_size (int, defaults to 8): the batch size to use for experience generation max_epochs (int, defaults to 1): the number of epochs of training process @@ -69,8 +74,13 @@ def __init__(self, experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef) replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload) generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor) - super().__init__(strategy, experience_maker, replay_buffer, experience_batch_size, max_epochs, tokenizer, - sample_replay_buffer, dataloader_pin_memory, callbacks, **generate_kwargs) + super().__init__(strategy, max_epochs, tokenizer, dataloader_pin_memory, callbacks, **generate_kwargs) + + self.experience_maker = experience_maker + self.replay_buffer = replay_buffer + self.experience_batch_size = experience_batch_size + self.sample_replay_buffer = sample_replay_buffer + self.actor = actor self.critic = critic @@ -82,6 +92,81 @@ def __init__(self, self.actor_optim = actor_optim self.critic_optim = critic_optim + def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience: + if isinstance(inputs, Tensor): + return self.experience_maker.make_experience(inputs, **self.generate_kwargs) + elif isinstance(inputs, dict): + return self.experience_maker.make_experience(**inputs, **self.generate_kwargs) + else: + raise ValueError(f'Unsupported input type "{type(inputs)}"') + + def _sample_prompts(self, prompts) -> list: + indices = list(range(len(prompts))) + sampled_indices = self.strategy.experience_sampler.choice( + indices, self.experience_batch_size, replace=False) + return [prompts[i] for i in sampled_indices] + + def _learn(self): + # replay buffer may be empty at first, we should rebuild at each training + if not self.sample_replay_buffer: + dataloader = self.strategy.setup_dataloader( + self.replay_buffer, self.dataloader_pin_memory) + device = torch.cuda.current_device() + if self.sample_replay_buffer: + pbar = tqdm(range(self.max_epochs), desc='Train epoch', + disable=not is_rank_0()) + for _ in pbar: + experience = self.replay_buffer.sample() + metrics = self.training_step(experience) + pbar.set_postfix(metrics) + else: + for epoch in range(self.max_epochs): + self._on_learn_epoch_start(epoch) + if isinstance(dataloader.sampler, DistributedSampler): + dataloader.sampler.set_epoch(epoch) + pbar = tqdm( + dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0()) + for experience in pbar: + self._on_learn_batch_start() + experience.to_device(device) + metrics = self.training_step(experience) + self._on_learn_batch_end(metrics, experience) + pbar.set_postfix(metrics) + self._on_learn_epoch_end(epoch) + + def fit(self, + prompt_dataloader, + pretrain_dataloader, + num_episodes: int = 50000, + max_timesteps: int = 500, + update_timesteps: int = 5000) -> None: + time = 0 + self.pretrain_dataloader = pretrain_dataloader + self.prompt_dataloader = prompt_dataloader + self._on_fit_start() + for episode in range(num_episodes): + self._on_episode_start(episode) + for timestep in tqdm(range(max_timesteps), + desc=f'Episode [{episode+1}/{num_episodes}]', + disable=not is_rank_0()): + time += 1 + prompts = next(iter(self.prompt_dataloader)) + self._on_make_experience_start() + self.experience_maker.initial_model.to( + torch.cuda.current_device()) + self.experience_maker.reward_model.to( + torch.cuda.current_device()) + experience = self._make_experience(prompts) + self._on_make_experience_end(experience) + self.replay_buffer.append(experience) + if time % update_timesteps == 0: + self.experience_maker.initial_model.to('cpu') + self.experience_maker.reward_model.to('cpu') + self._learn() + self.replay_buffer.clear() + self._on_episode_end(episode) + self._on_fit_end() + def training_step(self, experience: Experience) -> Dict[str, float]: self.actor.train() self.critic.train() diff --git a/applications/Chat/coati/trainer/rm.py b/applications/Chat/coati/trainer/rm.py index 0cf09b0410d2..ed6720abc2af 100644 --- a/applications/Chat/coati/trainer/rm.py +++ b/applications/Chat/coati/trainer/rm.py @@ -1,6 +1,5 @@ -from abc import ABC from datetime import datetime -from typing import Optional +from typing import Optional, List import pandas as pd import torch @@ -10,11 +9,13 @@ from tqdm import tqdm from transformers.tokenization_utils_base import PreTrainedTokenizerBase +from .callbacks import Callback +from .base import Trainer from .strategies import Strategy from .utils import is_rank_0 -class RewardModelTrainer(ABC): +class RewardModelTrainer(Trainer): """ Trainer to use while training reward model. @@ -23,11 +24,12 @@ class RewardModelTrainer(ABC): strategy (Strategy): the strategy to use for training optim(Optimizer): the optimizer to use for training loss_fn (callable): the loss function to use for training - train_dataset (Dataset): the dataset to use for training - valid_dataset (Dataset): the dataset to use for validation - eval_dataset (Dataset): the dataset to use for evaluation + train_dataloader (DataLoader): the dataloader to use for training + valid_dataloader (DataLoader): the dataloader to use for validation + eval_dataloader (DataLoader): the dataloader to use for evaluation batch_size (int, defaults to 1): the batch size while training max_epochs (int, defaults to 2): the number of epochs to train + callbacks (List[Callback], defaults to []): the callbacks to call during training process """ def __init__( @@ -36,25 +38,19 @@ def __init__( strategy: Strategy, optim: Optimizer, loss_fn, - train_dataset: Dataset, - valid_dataset: Dataset, - eval_dataset: Dataset, + train_dataloader: DataLoader, + valid_dataloader: DataLoader, + eval_dataloader: DataLoader, batch_size: int = 1, max_epochs: int = 1, + callbacks: List[Callback] = [], ) -> None: - super().__init__() - self.strategy = strategy - self.epochs = max_epochs + super().__init__(strategy, max_epochs, callbacks=callbacks) train_sampler = None - if dist.is_initialized() and dist.get_world_size() > 1: - train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True) - self.train_dataloader = DataLoader(train_dataset, - shuffle=(train_sampler is None), - sampler=train_sampler, - batch_size=batch_size) - self.valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True) - self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size, shuffle=True) + self.train_dataloader = train_dataloader + self.valid_dataloader = valid_dataloader + self.eval_dataloader = eval_dataloader self.model = strategy.setup_model(model) self.loss_fn = loss_fn @@ -86,8 +82,8 @@ def eval_acc(self, dataloader): def fit(self): time = datetime.now() - epoch_bar = tqdm(range(self.epochs), desc='Train epoch', disable=not is_rank_0()) - for epoch in range(self.epochs): + epoch_bar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) + for epoch in range(self.max_epochs): step_bar = tqdm(range(self.train_dataloader.__len__()), desc='Train step of epoch %d' % epoch, disable=not is_rank_0()) diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index f380cbf06a95..350553108e68 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -1,7 +1,6 @@ import math import time -from abc import ABC -from typing import Optional +from typing import Optional, List import loralib as lora import torch @@ -19,11 +18,13 @@ from colossalai.logging import get_dist_logger +from .callbacks import Callback +from .base import Trainer from .strategies import Strategy from .utils import is_rank_0 -class SFTTrainer(ABC): +class SFTTrainer(Trainer): """ Trainer to use while training reward model. @@ -35,6 +36,7 @@ class SFTTrainer(ABC): eval_dataloader: the dataloader to use for evaluation batch_size (int, defaults to 1): the batch size while training max_epochs (int, defaults to 2): the number of epochs to train + callbacks (List[Callback], defaults to []): the callbacks to call during training process optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer """ @@ -48,10 +50,9 @@ def __init__( batch_size: int = 1, max_epochs: int = 2, accimulation_steps: int = 8, + callbacks: List[Callback] = [], ) -> None: - super().__init__() - self.strategy = strategy - self.epochs = max_epochs + super().__init__(strategy, max_epochs, callbacks=callbacks) self.train_dataloader = train_dataloader self.eval_dataloader = eval_dataloader @@ -62,7 +63,7 @@ def __init__( self.accimulation_steps = accimulation_steps num_update_steps_per_epoch = len(train_dataloader) // self.accimulation_steps - max_steps = math.ceil(self.epochs * num_update_steps_per_epoch) + max_steps = math.ceil(self.max_epochs * num_update_steps_per_epoch) self.scheduler = get_scheduler("cosine", self.optimizer, @@ -74,10 +75,10 @@ def fit(self, logger, log_interval=10): wandb.watch(self.model) total_loss = 0 # epoch_bar = tqdm(range(self.epochs), desc='Epochs', disable=not is_rank_0()) - step_bar = tqdm(range(len(self.train_dataloader) // self.accimulation_steps * self.epochs), + step_bar = tqdm(range(len(self.train_dataloader) // self.accimulation_steps * self.max_epochs), desc=f'steps', disable=not is_rank_0()) - for epoch in range(self.epochs): + for epoch in range(self.max_epochs): # process_bar = tqdm(range(len(self.train_dataloader)), desc=f'Train process for{epoch}', disable=not is_rank_0()) # train @@ -148,7 +149,7 @@ def fit(self, logger, log_interval=10): loss_mean = loss_sum / num_seen if dist.get_rank() == 0: - logger.info(f'Eval Epoch {epoch}/{self.epochs} loss {loss_mean}') + logger.info(f'Eval Epoch {epoch}/{self.max_epochs} loss {loss_mean}') # epoch_bar.update() diff --git a/applications/Chat/examples/train_dummy.py b/applications/Chat/examples/train_dummy.py index 4ac7ace44803..5f34c80f0892 100644 --- a/applications/Chat/examples/train_dummy.py +++ b/applications/Chat/examples/train_dummy.py @@ -114,8 +114,10 @@ def main(args): eos_token_id=tokenizer.eos_token_id, callbacks=callbacks) - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 64), device=torch.cuda.current_device()) - trainer.fit(random_prompts, + random_prompts = torch.randint(tokenizer.vocab_size, (1000, 1, 64), device=torch.cuda.current_device()) + random_attention_mask = torch.randint(1, (1000, 1, 64), device=torch.cuda.current_device()).to(torch.bool) + random_pretrain = [{'input_ids':random_prompts[i], 'labels':random_prompts[i], 'attention_mask':random_attention_mask[i]} for i in range(1000)] + trainer.fit(random_prompts, random_pretrain, num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) @@ -136,7 +138,7 @@ def main(args): default='naive') parser.add_argument('--model', type=str, default='gpt2', choices=['gpt2', 'bloom', 'opt', 'roberta']) parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--save_path', type=str, default='actor_checkpoint_dummy.pt') + parser.add_argument('--save_path', type=str, default='actor_checkpoint_dummy') parser.add_argument('--need_optim_ckpt', type=bool, default=False) parser.add_argument('--num_episodes', type=int, default=50) parser.add_argument('--max_timesteps', type=int, default=10) diff --git a/applications/Chat/examples/train_reward_model.py b/applications/Chat/examples/train_reward_model.py index aa1b51dea7f9..6a788a891ca6 100644 --- a/applications/Chat/examples/train_reward_model.py +++ b/applications/Chat/examples/train_reward_model.py @@ -3,6 +3,7 @@ import loralib as lora import torch +import torch.distributed as dist from coati.dataset import HhRlhfDataset, RmStaticDataset from coati.models import LogExpLoss, LogSigLoss from coati.models.base import RewardModel @@ -17,6 +18,8 @@ from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from torch.optim import Adam +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer, LlamaTokenizer, RobertaTokenizer from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer @@ -120,13 +123,38 @@ def train(args): else: raise ValueError(f'Unsupported dataset "{args.dataset}"') + if dist.is_initialized() and dist.get_world_size() > 1: + train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True, rank=dist.get_rank(), + num_replicas=dist.get_world_size()) + valid_sampler = DistributedSampler(valid_dataset, shuffle=True, seed=42, drop_last=True, rank=dist.get_rank(), + num_replicas=dist.get_world_size()) + eval_sampler = DistributedSampler(eval_dataset, shuffle=True, seed=42, drop_last=True, rank=dist.get_rank(), + num_replicas=dist.get_world_size()) + else: + train_sampler = None + valid_sampler = None + eval_sampler = None + + train_dataloader = DataLoader(train_dataset, + shuffle=(train_sampler is None), + sampler=train_sampler, + batch_size=args.batch_size, + pin_memory=True) + + valid_dataloader = DataLoader(valid_dataset, shuffle=(valid_sampler is None), + sampler=valid_sampler, + batch_size=args.batch_size, pin_memory=True) + + eval_dataloader = DataLoader(eval_dataset, shuffle=(eval_sampler is None), + sampler=eval_sampler, batch_size=args.batch_size, pin_memory=True) + trainer = RewardModelTrainer(model=model, strategy=strategy, optim=optim, loss_fn=loss_fn, - train_dataset=train_dataset, - valid_dataset=valid_dataset, - eval_dataset=eval_dataset, + train_dataloader=train_dataloader, + valid_dataloader=valid_dataloader, + eval_dataloader=eval_dataloader, batch_size=args.batch_size, max_epochs=args.max_epochs) From 5a79cffdfd24d6bead2f7c2b674dc69a2ef50129 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Tue, 18 Apr 2023 18:19:48 +0800 Subject: [PATCH 149/413] [coati] fix install cmd (#3592) --- applications/Chat/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 0a5f7840d997..bec6926b7fdb 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -81,6 +81,8 @@ Due to resource constraints, we will only provide this service from 29th Mar 202 ```shell conda create -n coati conda activate coati +git clone https://github.com/hpcaitech/ColossalAI.git +cd ColossalAI/applications/Chat pip install . ``` From d96567bb5def8e9f3eacecc44b9bc6ad3806b7cc Mon Sep 17 00:00:00 2001 From: digger-yu Date: Tue, 18 Apr 2023 19:14:59 +0800 Subject: [PATCH 150/413] [misc] op_builder/builder.py (#3593) Optimization Code The source code has not been modified, only a few spelling errors in the comments have been changed --- op_builder/builder.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/op_builder/builder.py b/op_builder/builder.py index 16bf173ffd04..8396235e5cfe 100644 --- a/op_builder/builder.py +++ b/op_builder/builder.py @@ -78,7 +78,7 @@ def sources_files(self) -> List[str]: @abstractmethod def include_dirs(self) -> List[str]: """ - This function should return a list of inlcude files for extensions. + This function should return a list of include files for extensions. """ pass @@ -127,13 +127,13 @@ def check_runtime_build_environment(self): if CUDA_HOME is None: raise RuntimeError( - "CUDA_HOME is not found. You need to export CUDA_HOME environment vairable or install CUDA Toolkit first in order to build CUDA extensions" + "CUDA_HOME is not found. You need to export CUDA_HOME environment variable or install CUDA Toolkit first in order to build CUDA extensions" ) # make sure CUDA is available for compilation during cuda_available = check_cuda_availability() if not cuda_available: - raise RuntimeError("CUDA is not available on your system as torch.cuda.is_avaible() returns False.") + raise RuntimeError("CUDA is not available on your system as torch.cuda.is_available() returns False.") # make sure system CUDA and pytorch CUDA match, an error will raised inside the function if not check_system_pytorch_cuda_match(CUDA_HOME) @@ -161,7 +161,7 @@ def load(self, verbose: Optional[bool] = None): op_module = self.import_op() if verbose: print_rank_0( - f"[extension] OP {self.prebuilt_import_path} has been compileed ahead of time, skip building.") + f"[extension] OP {self.prebuilt_import_path} has been compiled ahead of time, skip building.") except ImportError: # check environment self.check_runtime_build_environment() From d544ed434505fdc385836c2a06d205e95094c60c Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 19 Apr 2023 10:38:12 +0800 Subject: [PATCH 151/413] [bot] Automated submodule synchronization (#3596) Co-authored-by: github-actions --- examples/tutorial/fastfold/FastFold | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorial/fastfold/FastFold b/examples/tutorial/fastfold/FastFold index 867587b3aa4e..05681304651b 160000 --- a/examples/tutorial/fastfold/FastFold +++ b/examples/tutorial/fastfold/FastFold @@ -1 +1 @@ -Subproject commit 867587b3aa4e43bdaf64f9910127842f1dfbfebd +Subproject commit 05681304651b1b29d7d887db169045ea3dd28fce From 12eff9eb4cb74e0c20525aa30dc64375832afe45 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 19 Apr 2023 11:01:48 +0800 Subject: [PATCH 152/413] [gemini] state dict supports fp16 (#3590) * [gemini] save state dict support fp16 * [gemini] save state dict shard support fp16 * [gemini] fix state dict * [gemini] fix state dict --- colossalai/zero/gemini/gemini_ddp.py | 29 +++++++++++++++++++--------- 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index 9a193310bab1..e151f1aefb2d 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -202,7 +202,12 @@ def set_chunk_grad_device(self, chunk: Chunk, device: torch.device) -> None: for tensor in chunk.get_tensors(): self.grads_device[tensor] = device - def state_dict(self, destination=None, prefix='', keep_vars=False, only_rank_0: bool = True): + def state_dict(self, + destination=None, + prefix='', + keep_vars=False, + only_rank_0: bool = True, + dtype: torch.dtype = torch.float16): """Returns a dictionary containing a whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. @@ -221,7 +226,7 @@ def state_dict(self, destination=None, prefix='', keep_vars=False, only_rank_0: destination = OrderedDict() destination._metadata = OrderedDict() destination._metadata[prefix[:-1]] = local_metadata = dict(version=self._version) - self._save_to_state_dict(destination, prefix, keep_vars, only_rank_0) + self._save_to_state_dict(destination, prefix, keep_vars, only_rank_0, dtype) for hook in self._state_dict_hooks.values(): hook_result = hook(self, destination, prefix, local_metadata) @@ -229,7 +234,7 @@ def state_dict(self, destination=None, prefix='', keep_vars=False, only_rank_0: destination = hook_result return destination - def _get_chunk_to_save_data(self, chunk: Chunk, only_rank_0: bool) -> Dict: + def _get_chunk_to_save_data(self, chunk: Chunk, only_rank_0: bool, dtype: torch.dtype = torch.float16) -> Dict: """ get gathered chunk content. @@ -243,6 +248,8 @@ def _get_chunk_to_save_data(self, chunk: Chunk, only_rank_0: bool) -> Dict: # save parameters chunk_to_save_data = dict() temp_chunk = get_temp_total_chunk_on_cuda(chunk) + if torch.is_floating_point(temp_chunk): + temp_chunk = temp_chunk.to(dtype) for tensor, tensor_info in chunk.tensors_info.items(): record_tensor = torch.empty([0]) record_flag = (not only_rank_0) | (dist.get_rank(chunk.torch_pg) == 0) @@ -255,7 +262,8 @@ def _get_chunk_to_save_data(self, chunk: Chunk, only_rank_0: bool) -> Dict: del temp_chunk return chunk_to_save_data - def _get_param_to_save_data(self, param_list: List[torch.nn.Parameter], only_rank_0: bool) -> Dict: + def _get_param_to_save_data(self, param_list: List[torch.nn.Parameter], only_rank_0: bool, + dtype: torch.dtype) -> Dict: """ get param content from chunks. @@ -270,10 +278,10 @@ def _get_param_to_save_data(self, param_list: List[torch.nn.Parameter], only_ran param_to_save_data = dict() chunk_list = self.chunk_manager.get_chunks(param_list) for chunk in chunk_list: - param_to_save_data.update(self._get_chunk_to_save_data(chunk, only_rank_0)) + param_to_save_data.update(self._get_chunk_to_save_data(chunk, only_rank_0, dtype)) return param_to_save_data - def _save_to_state_dict(self, destination, prefix, keep_vars, only_rank_0=True): + def _save_to_state_dict(self, destination, prefix, keep_vars, only_rank_0=True, dtype=torch.float16): r"""Saves module state to `destination` dictionary, containing a state of the module, but not its descendants. This is called on every submodule in :meth:`~torch.nn.Module.state_dict`. @@ -289,7 +297,8 @@ def _save_to_state_dict(self, destination, prefix, keep_vars, only_rank_0=True): assert keep_vars is False, "`state_dict` with parameter, `keep_vars=True`, is not supported now." # get copies of fp32 parameters in CPU - param_to_save_data = self._get_param_to_save_data(self.fp32_params, only_rank_0) + # as memory of fp16_params may be reused by grad, it's not reliable, we should use fp32_params and convert to fp16 + param_to_save_data = self._get_param_to_save_data(self.fp32_params, only_rank_0, dtype) # get the mapping between copies and fp16 parameters p_mapping = dict() for p, fp32_p in zip(self.fp16_params, self.fp32_params): @@ -574,7 +583,8 @@ def state_dict_shard(self, prefix: str = '', keep_vars: bool = False, max_shard_size: int = 1024, - only_rank_0: bool = True) -> Iterator[OrderedDict]: + only_rank_0: bool = True, + dtype: torch.dtype = torch.float16) -> Iterator[OrderedDict]: """Returns dictionaries containing a whole state of the module one by one. The max size of dictionary shard is specified by ``max_shard_size``. Both parameters and persistent buffers (e.g. running averages) are included. @@ -607,10 +617,11 @@ def state_dict_shard(self, # deal with ddp ignored parameters gathered_param = param if keep_vars else param.detach() else: + # as memory of fp16 param may be reused, we should use fp32 param and then convert to fp16 fp32_param = fp16_to_fp32[param] if fp32_param not in gathered_param_buffer: chunk = self.chunk_manager.get_chunk(fp32_param) - gathered_param_buffer.update(self._get_chunk_to_save_data(chunk, only_rank_0)) + gathered_param_buffer.update(self._get_chunk_to_save_data(chunk, only_rank_0, dtype)) gathered_param = gathered_param_buffer.pop(fp32_param) block = sharder.append(prefix + name, gathered_param) From 7570d9ae3d3d1d4ca347c43dd1d4f2161e2a6827 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Wed, 19 Apr 2023 15:56:01 +0800 Subject: [PATCH 153/413] [doc] fix op_builder/README.md (#3597) Optimization Code change "requries" to "requires" --- op_builder/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/op_builder/README.md b/op_builder/README.md index 0f646a186248..9c33a4a328d7 100644 --- a/op_builder/README.md +++ b/op_builder/README.md @@ -16,7 +16,7 @@ Method 2 is good because it allows the user to only build the kernel they actual ## PyTorch Extensions in Colossal-AI The project [DeepSpeed](https://github.com/microsoft/DeepSpeed) has proposed a [solution](https://github.com/microsoft/DeepSpeed/tree/master/op_builder) to support kernel-build during either installation or runtime. -We have adapted from DeepSpeed's solution to build extensions. The extension build requries two main functions from PyTorch: +We have adapted from DeepSpeed's solution to build extensions. The extension build requires two main functions from PyTorch: 1. `torch.utils.cpp_extension.CUDAExtension`: used to build extensions in `setup.py` during `pip install`. 2. `torch.utils.cpp_extension.load`: used to build and load extension during runtime From becd3b0f5461f8adfe825ba0524773a4e63b7b64 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Wed, 19 Apr 2023 17:28:15 +0800 Subject: [PATCH 154/413] [doc] fix setup.py typo (#3603) Optimization Code change "vairable" to "variable" --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 89a7b0de461b..5d8f831218d9 100644 --- a/setup.py +++ b/setup.py @@ -46,7 +46,7 @@ def environment_check_for_cuda_extension_build(): if not CUDA_HOME: raise RuntimeError( - "[extension] CUDA_HOME is not found while CUDA_EXT=1. You need to export CUDA_HOME environment vairable or install CUDA Toolkit first in order to build CUDA extensions" + "[extension] CUDA_HOME is not found while CUDA_EXT=1. You need to export CUDA_HOME environment variable or install CUDA Toolkit first in order to build CUDA extensions" ) check_system_pytorch_cuda_match(CUDA_HOME) From 633bac2f5801fdd6b906a66fdc92a398d0eaedaa Mon Sep 17 00:00:00 2001 From: digger-yu Date: Thu, 20 Apr 2023 10:36:28 +0800 Subject: [PATCH 155/413] [doc] .github/workflows/README.md (#3605) Fixed several word spelling errors change "compatiblity" to "compatibility" etc. --- .github/workflows/README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 5b5554a5006b..a46d8b1c24d0 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -30,7 +30,7 @@ In the section below, we will dive into the details of different workflows avail Refer to this [documentation](https://docs.github.com/en/actions/managing-workflow-runs/manually-running-a-workflow) on how to manually trigger a workflow. I will provide the details of each workflow below. -**A PR which changes the `version.txt` is considered as a release PR in the following coontext.** +**A PR which changes the `version.txt` is considered as a release PR in the following context.** ### Code Style Check @@ -64,9 +64,9 @@ This workflow is triggered by manually dispatching the workflow. It has the foll | Workflow Name | File name | Description | | -------------------------------- | ------------------------------------ | -------------------------------------------------------------------------------------------------------------------- | -| `Compatibility Test on PR` | `compatibility_test_on_pr.yml` | Check Colossal-AI's compatiblity when `version.txt` is changed in a PR. | -| `Compatibility Test on Schedule` | `compatibility_test_on_schedule.yml` | This workflow will check the compatiblity of Colossal-AI against PyTorch specified in `.compatibility` every Sunday. | -| `Compatiblity Test on Dispatch` | `compatibility_test_on_dispatch.yml` | Test PyTorch Compatibility manually. | +| `Compatibility Test on PR` | `compatibility_test_on_pr.yml` | Check Colossal-AI's compatibility when `version.txt` is changed in a PR. | +| `Compatibility Test on Schedule` | `compatibility_test_on_schedule.yml` | This workflow will check the compatibility of Colossal-AI against PyTorch specified in `.compatibility` every Sunday. | +| `Compatibility Test on Dispatch` | `compatibility_test_on_dispatch.yml` | Test PyTorch Compatibility manually. | #### Compatibility Test on Dispatch @@ -74,7 +74,7 @@ This workflow is triggered by manually dispatching the workflow. It has the foll - `torch version`:torch version to test against, multiple versions are supported but must be separated by comma. The default is value is all, which will test all available torch versions listed in this [repository](https://github.com/hpcaitech/public_assets/tree/main/colossalai/torch_build/torch_wheels). - `cuda version`: cuda versions to test against, multiple versions are supported but must be separated by comma. The CUDA versions must be present in our [DockerHub repository](https://hub.docker.com/r/hpcaitech/cuda-conda). -> It only test the compatiblity of the main branch +> It only test the compatibility of the main branch ### Release @@ -113,7 +113,7 @@ This `.compatibility` file is to tell GitHub Actions which PyTorch and CUDA vers 2. `.cuda_ext.json` -This file controls which CUDA versions will be checked against CUDA extenson built. You can add a new entry according to the json schema below to check the AOT build of PyTorch extensions before release. +This file controls which CUDA versions will be checked against CUDA extension built. You can add a new entry according to the json schema below to check the AOT build of PyTorch extensions before release. ```json { @@ -144,7 +144,7 @@ This file controls which CUDA versions will be checked against CUDA extenson bui - [x] check on PR - [x] regular check - [x] manual dispatch -- [x] compatiblity check +- [x] compatibility check - [x] check on PR - [x] manual dispatch - [x] auto test when release From c4709d34cfca86d6c66853273e13d14cfe60759f Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Thu, 20 Apr 2023 11:12:24 +0800 Subject: [PATCH 156/413] Chat evaluate (#3608) Co-authored-by: Yuanchen Xu --- applications/Chat/README.md | 7 + applications/Chat/evaluate/README.md | 181 +++++++++++++ applications/Chat/evaluate/evaluate.py | 256 ++++++++++++++++++ applications/Chat/evaluate/evaluate.sh | 9 + .../Chat/evaluate/format_questions.py | 31 +++ .../Chat/evaluate/format_questions.sh | 3 + .../Chat/evaluate/generate_answers.py | 173 ++++++++++++ .../Chat/evaluate/generate_answers.sh | 25 ++ .../Chat/evaluate/generate_gpt35_answers.py | 98 +++++++ .../Chat/evaluate/generate_gpt35_answers.sh | 6 + applications/Chat/evaluate/merge.py | 25 ++ applications/Chat/evaluate/utils.py | 53 ++++ 12 files changed, 867 insertions(+) create mode 100644 applications/Chat/evaluate/README.md create mode 100644 applications/Chat/evaluate/evaluate.py create mode 100755 applications/Chat/evaluate/evaluate.sh create mode 100644 applications/Chat/evaluate/format_questions.py create mode 100755 applications/Chat/evaluate/format_questions.sh create mode 100644 applications/Chat/evaluate/generate_answers.py create mode 100755 applications/Chat/evaluate/generate_answers.sh create mode 100644 applications/Chat/evaluate/generate_gpt35_answers.py create mode 100755 applications/Chat/evaluate/generate_gpt35_answers.sh create mode 100644 applications/Chat/evaluate/merge.py create mode 100644 applications/Chat/evaluate/utils.py diff --git a/applications/Chat/README.md b/applications/Chat/README.md index bec6926b7fdb..dea562c4d2ad 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -373,6 +373,13 @@ Thanks so much to all of our amazing contributors! - Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU - Keep in a sufficiently high running speed +| Model Pair | Alpaca-7B ⚔ Coati-7B | Coati-7B ⚔ Alpaca-7B | +| :-----------: | :------------------: | :------------------: | +| Better Cases | 38 ⚔ **41** | **45** ⚔ 33 | +| Win Rate | 48% ⚔ **52%** | **58%** ⚔ 42% | +| Average Score | 7.06 ⚔ **7.13** | **7.31** ⚔ 6.82 | +- Our Coati-7B model performs better than Alpaca-7B when using GPT-4 to evaluate model performance. The Coati-7B model we evaluate is an old version we trained a few weeks ago and the new version is around the corner. + ## Authors Coati is developed by ColossalAI Team: diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md new file mode 100644 index 000000000000..6113dbbb1ef2 --- /dev/null +++ b/applications/Chat/evaluate/README.md @@ -0,0 +1,181 @@ +# Evaluation + +In this directory we will introduce how you can evaluate your model with GPT-4. + +## Evaluation Pipeline + +The whole evaluation process undergoes two steps. + +1. Generate answers from different models: Use `generate_gpt35_answers.py` to generate answers of GPT 3.5 and use `generate_answers.py` to generate answers of your own models. +2. Evaluate models using GPT 4: Use `evaluate.py` to evaluate model answers with GPT-4. + +### Generate Answers + +To generate answers, you should first format [FastChat's]([FastChat/question.jsonl at main · lm-sys/FastChat (github.com)](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/question.jsonl)) `question.jsonl` file. We do this formatting because we would like to add more questions later and the pipeline for generating new questions may follow that of Self-Instruct and Stanford Alpaca. An example script is given as follows. + +```shell +python format_questions.py \ + --questions_path "path to FastChat's question.jsonl" \ + --save_path "path to the formatted file" \ + +``` + +In `generate_answers.py`, the model will generate answers in a batch way and different GPU processes will do inference on different shards of the given questions. Once all GPU process generate its answers, `merge.py` will merge different shards of answers and output a single answer file. Finally, the script will also remove the answer shards. An example script is given as follows. + +```shell +device_number=number of your devices +model_name="name of your model" +model_path="path to your model" +dataset="path to the question dataset" +answer_path="path to save the model answers" + +torchrun --standalone --nproc_per_node=$device_number generate_answers.py \ + --model 'llama' \ + --strategy ddp \ + --model_path $model_path \ + --model_name $model_name \ + --dataset $dataset \ + --batch_size 8 \ + --max_datasets_size 80 \ + --answer_path $answer_path \ + --max_length 512 + +python merge.py \ + --model_name $model_name \ + --shards $device_number \ + --answer_path $answer_path \ + +for (( i=0; i scores[1]: + worse_count += 1 + worse_file.append(review_jsons[idx]) + elif scores[0] < scores[1]: + better_count += 1 + better_file.append(review_jsons[idx]) + else: + tie_count += 1 + tie_file.append(review_jsons[idx]) + ans1_score += scores[0] + ans2_score += scores[1] + + output_review_file.append(review_jsons[idx]) + + better_file.sort(key=lambda x: x['id']) + worse_file.sort(key=lambda x: x['id']) + tie_file.sort(key=lambda x: x['id']) + invalid_file.sort(key=lambda x: x['id']) + output_review_file.sort(key=lambda x: x['id']) + + name1 = os.path.basename(args.answer_file_list[0]).split("_answers")[0] + name2 = os.path.basename(args.answer_file_list[1]).split("_answers")[0] + prefix = f"{name1}_vs_{name2}" + + jdump(better_file, os.path.join( + args.output_folder, prefix, f"{prefix}_better.json")) + jdump(worse_file, os.path.join( + args.output_folder, prefix, f"{prefix}_worse.json")) + jdump(tie_file, os.path.join( + args.output_folder, prefix, f"{prefix}_tie.json")) + jdump(invalid_file, os.path.join( + args.output_folder, prefix, f"{prefix}_invalid.json")) + jdump(output_review_file, os.path.join( + args.output_folder, prefix, f"{prefix}_review.json")) + + if os.path.exists(os.path.join(args.output_folder, "results.json")): + results = jload(os.path.join(args.output_folder, "results.json")) + else: + results = {} + results[prefix] = {'model': [name1, name2], 'better': better_count, 'worse': worse_count, 'tie': tie_count, 'win_rate': better_count / + (len(reviews)-invalid_count), 'score': [ans1_score/(len(reviews)-invalid_count), ans2_score/(len(reviews)-invalid_count)]} + jdump(results, os.path.join(args.output_folder, "results.json")) + + logger.info(f' Total {invalid_count} invalid score pair(s).') + logger.info(f' Model {name2} has {better_count} better answer(s).') + logger.info(f' Model {name2} has {worse_count} worse answer(s).') + logger.info(f' {tie_count} answer(s) play(s) to a tie.') + logger.info( + f' Win rate of model {name2}: {better_count/(len(reviews)-invalid_count):.2f}') + logger.info( + f' Model {name1} average score: {ans1_score/(len(reviews)-invalid_count):.2f}') + logger.info( + f' Model {name2} average score: {ans2_score/(len(reviews)-invalid_count):.2f}') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description='Model evaluation.') + parser.add_argument('--answer_file_list', nargs='+', default=[]) + parser.add_argument('--prompt_file') + parser.add_argument('--reviewer_file') + parser.add_argument('--output_folder', type=str, default="./output") + parser.add_argument('--openai_key', type=str, default=None) + parser.add_argument('--model', type=str, default="gpt-4") + parser.add_argument('--num_workers', type=int, default=8) + parser.add_argument('--max_tokens', type=int, default=512, + help='maximum number of tokens produced in the output') + args = parser.parse_args() + + if args.openai_key is not None: + os.environ["OPENAI_API_KEY"] = args.openai_key + openai.api_key = os.getenv("OPENAI_API_KEY") + + evaluate(args) diff --git a/applications/Chat/evaluate/evaluate.sh b/applications/Chat/evaluate/evaluate.sh new file mode 100755 index 000000000000..c51aa941019e --- /dev/null +++ b/applications/Chat/evaluate/evaluate.sh @@ -0,0 +1,9 @@ +python evaluate.py \ + --answer_file_list "path to answers of model 1" "path to answers of model 2" \ + --prompt_file "path to prompt file" \ + --reviewer_file "path to reviewer file" \ + --output_folder "path to output folder" \ + --openai_key "your openai key" \ + --model "gpt-4" \ + --num_workers 8 \ + --max_tokens 512 \ diff --git a/applications/Chat/evaluate/format_questions.py b/applications/Chat/evaluate/format_questions.py new file mode 100644 index 000000000000..9b47907c34bf --- /dev/null +++ b/applications/Chat/evaluate/format_questions.py @@ -0,0 +1,31 @@ +import argparse +import os +import json +import copy + +from utils import jdump, get_json_list + + +def format_questions(args): + questions = get_json_list(args.questions_path) + keys=questions[0].keys() + + formatted_questions=copy.deepcopy(questions) + for i in range(len(formatted_questions)): + formatted_questions[i]['instruction']=questions[i]['text'] + formatted_questions[i]['input']="" + formatted_questions[i]['output']="" + formatted_questions[i]['id']=questions[i]['question_id'] + for key in keys: + if key=="category": + continue + del formatted_questions[i][key] + + jdump(formatted_questions, args.save_path) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--questions_path', type=str, default='table/question.jsonl') + parser.add_argument('--save_path', type=str, default="table/questions.json") + args = parser.parse_args() + format_questions(args) \ No newline at end of file diff --git a/applications/Chat/evaluate/format_questions.sh b/applications/Chat/evaluate/format_questions.sh new file mode 100755 index 000000000000..a7568da364ad --- /dev/null +++ b/applications/Chat/evaluate/format_questions.sh @@ -0,0 +1,3 @@ +python format_questions.py \ + --questions_path "path to FastChat's question.jsonl" \ + --save_path "path to the formatted file" \ diff --git a/applications/Chat/evaluate/generate_answers.py b/applications/Chat/evaluate/generate_answers.py new file mode 100644 index 000000000000..fbebf5c5e6f6 --- /dev/null +++ b/applications/Chat/evaluate/generate_answers.py @@ -0,0 +1,173 @@ +import argparse +import os +import random +import copy +import math +from tqdm import tqdm + +import torch +import torch.distributed as dist +import transformers + +from coati.models.bloom import BLOOMActor +from coati.models.gpt import GPTActor +from coati.models.opt import OPTActor +from coati.models.roberta import RoBERTaActor +from coati.models.llama import LlamaActor +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from transformers import AutoTokenizer, RobertaTokenizer +from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer + +from colossalai.logging import get_dist_logger + +from utils import jload, jdump, is_rank_0 + + +logger = get_dist_logger() + +PROMPT_DICT = { + "prompt_input": + ("Below is an instruction that describes a task, paired with an input that provides further context. " + "Write a response that appropriately completes the request.\n\n" + "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"), + "prompt_no_input": ("Below is an instruction that describes a task. " + "Write a response that appropriately completes the request.\n\n" + "### Instruction:\n{instruction}\n\n### Response:"), +} + + +def generate(args): + # torch.cuda.set_per_process_memory_fraction(0.4) + if args.strategy == 'naive': + strategy = NaiveStrategy() + elif args.strategy == 'ddp': + strategy = DDPStrategy() + elif args.strategy == 'colossalai_gemini': + strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') + elif args.strategy == 'colossalai_zero2': + strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + elif args.strategy == 'colossalai_zero2_cpu': + strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') + else: + raise ValueError(f'Unsupported strategy "{args.strategy}"') + + world_size = dist.get_world_size() + rank = dist.get_rank() + + with strategy.model_init_context(): + if args.model == 'gpt2': + actor = GPTActor(pretrained=args.model_path).to( + torch.cuda.current_device()) + elif args.model == 'bloom': + actor = BLOOMActor(pretrained=args.model_path).to( + torch.cuda.current_device()) + elif args.model == 'opt': + actor = OPTActor(pretrained=args.model_path).to( + torch.cuda.current_device()) + elif args.model == 'roberta': + actor = RoBERTaActor(pretrained=args.model_path).to( + torch.cuda.current_device()) + elif args.model == 'llama': + actor = LlamaActor(pretrained=args.model_path).to( + torch.float16).to(torch.cuda.current_device()) + else: + raise ValueError(f'Unsupported model "{args.model}"') + + if args.model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'bloom': + tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m') + tokenizer.pad_token = tokenizer.eos_token + elif args.model == 'opt': + tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m') + elif args.model == 'roberta': + tokenizer = RobertaTokenizer.from_pretrained("roberta-base") + elif args.model == 'llama': + tokenizer = AutoTokenizer.from_pretrained(args.model_path, + padding_side="right", + use_fast=False, + ) + tokenizer.eos_token = '<\s>' + else: + raise ValueError(f'Unsupported model "{args.model}"') + + questions = [] + if args.max_datasets_size is not None: + questions = random.sample(jload(args.dataset), args.max_datasets_size) + if is_rank_0(): + logger.info( + f"Limiting dataset to {args.max_datasets_size} examples.") + questions = questions[rank:args.max_datasets_size:world_size] + + answers = copy.deepcopy(questions) + + prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] + sources = [ + prompt_input.format_map(example) if example.get( + "input", "") != "" else prompt_no_input.format_map(example) + for example in questions + ] + + if is_rank_0(): + logger.info("Tokenizing inputs... This may take some time...") + + input_ids_list = [] + + for string in sources: + input_ids = tokenizer.encode(string, return_tensors='pt').squeeze(0) + input_ids_list.append(input_ids) + + bar = tqdm(range(math.ceil(len(input_ids_list)/args.batch_size)), + desc=f'steps', disable=not is_rank_0()) + + actor.eval() + with torch.no_grad(): + for i in range(0, len(input_ids_list), args.batch_size): + batch = input_ids_list[i:i+args.batch_size] + batch = [i.flip(dims=[0]) for i in batch] + batch = torch.nn.utils.rnn.pad_sequence(batch, + batch_first=True, + padding_value=tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0).to(torch.cuda.current_device()) + batch = batch.flip(dims=[1]) + attention_mask = batch.ne(tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0) + + outputs = actor.model.generate(batch, attention_mask=attention_mask, + max_length=args.max_length, + do_sample=True, + top_k=50, + top_p=0.95, + num_return_sequences=1) + + outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) + for j in range(batch.size(0)): + answers[i + + j]['output'] = outputs[j].split("### Response:")[1].strip() + + bar.update() + + jdump(answers, os.path.join(args.answer_path, + f'{args.model_name}_answers_rank{rank}.json')) + + if is_rank_0(): + logger.info( + f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.3f} GB') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--strategy', + choices=['naive', 'ddp', 'colossalai_gemini', + 'colossalai_zero2', 'colossalai_zero2_cpu'], + default='naive') + parser.add_argument('--model', default='gpt2', + choices=['gpt2', 'bloom', 'opt', 'roberta', 'llama']) + parser.add_argument('--model_path', type=str, default=None) + parser.add_argument('--model_name', type=str, default='model') + parser.add_argument('--dataset', type=str, default=None) + parser.add_argument('--batch_size', type=int, default=1) + parser.add_argument('--max_datasets_size', type=int, default=None) + parser.add_argument('--answer_path', type=str, default="answer") + parser.add_argument('--max_length', type=int, default=1024) + args = parser.parse_args() + generate(args) diff --git a/applications/Chat/evaluate/generate_answers.sh b/applications/Chat/evaluate/generate_answers.sh new file mode 100755 index 000000000000..36881f5f4f29 --- /dev/null +++ b/applications/Chat/evaluate/generate_answers.sh @@ -0,0 +1,25 @@ +device_number=number of your devices +model_name="name of your model" +model_path="path to your model" +dataset="path to the question dataset" +answer_path="path to save the model answers" + +torchrun --standalone --nproc_per_node=$device_number generate_answers.py \ + --model 'llama' \ + --strategy ddp \ + --model_path $model_path \ + --model_name $model_name \ + --dataset $dataset \ + --batch_size 8 \ + --max_datasets_size 80 \ + --answer_path $answer_path \ + --max_length 512 + +python merge.py \ + --model_name $model_name \ + --shards $device_number \ + --answer_path $answer_path \ + +for (( i=0; i bool: + return not dist.is_initialized() or dist.get_rank() == 0 + +def _make_w_io_base(f, mode: str): + if not isinstance(f, io.IOBase): + f_dirname = os.path.dirname(f) + if f_dirname != "": + os.makedirs(f_dirname, exist_ok=True) + f = open(f, mode=mode) + return f + +def _make_r_io_base(f, mode: str): + if not isinstance(f, io.IOBase): + f = open(f, mode=mode) + return f + +def jdump(obj, f, mode="w", indent=4, default=str): + """Dump a str or dictionary to a file in json format. + Args: + obj: An object to be written. + f: A string path to the location on disk. + mode: Mode for opening the file. + indent: Indent for storing json dictionaries. + default: A function to handle non-serializable entries; defaults to `str`. + """ + f = _make_w_io_base(f, mode) + if isinstance(obj, (dict, list)): + json.dump(obj, f, indent=indent, default=default) + elif isinstance(obj, str): + f.write(obj) + else: + raise ValueError(f"Unexpected type: {type(obj)}") + f.close() + +def jload(f, mode="r"): + """Load a .json file into a dictionary.""" + f = _make_r_io_base(f, mode) + jdict = json.load(f) + f.close() + return jdict + +def get_json_list(file_path): + with open(file_path, 'r') as f: + json_list = [] + for line in f: + json_list.append(json.loads(line)) + return json_list From d7bf284706ef256c38d3aad53142b07cfc0fc10e Mon Sep 17 00:00:00 2001 From: digger-yu Date: Thu, 20 Apr 2023 17:22:15 +0800 Subject: [PATCH 157/413] [chat] polish code note typo (#3612) --- applications/Chat/coati/experience_maker/base.py | 2 +- applications/Chat/coati/models/lora.py | 2 +- .../Chat/coati/ray/src/detached_trainer_ppo.py | 2 +- .../Chat/coati/ray/src/experience_maker_holder.py | 2 +- applications/Chat/coati/replay_buffer/utils.py | 2 +- .../coati/trainer/callbacks/performance_evaluator.py | 2 +- applications/Chat/coati/trainer/ppo.py | 6 +++--- applications/Chat/examples/README.md | 12 ++++++------ applications/Chat/examples/community/peft/README.md | 4 ++-- .../Chat/examples/community/peft/easy_dataset.py | 2 +- 10 files changed, 18 insertions(+), 18 deletions(-) diff --git a/applications/Chat/coati/experience_maker/base.py b/applications/Chat/coati/experience_maker/base.py index 61fd4f6744dc..ff75852576c8 100644 --- a/applications/Chat/coati/experience_maker/base.py +++ b/applications/Chat/coati/experience_maker/base.py @@ -18,7 +18,7 @@ class Experience: action_log_probs: (B, A) values: (B) reward: (B) - advatanges: (B) + advantages: (B) attention_mask: (B, S) action_mask: (B, A) diff --git a/applications/Chat/coati/models/lora.py b/applications/Chat/coati/models/lora.py index f8f7a1cb5d81..7f6eb73262fa 100644 --- a/applications/Chat/coati/models/lora.py +++ b/applications/Chat/coati/models/lora.py @@ -108,7 +108,7 @@ def convert_to_lora_recursively(module: nn.Module, lora_rank: int) -> None: class LoRAModule(nn.Module): """A LoRA module base class. All derived classes should call `convert_to_lora()` at the bottom of `__init__()`. - This calss will convert all torch.nn.Linear layer to LoraLinear layer. + This class will convert all torch.nn.Linear layer to LoraLinear layer. Args: lora_rank (int, optional): LoRA rank. 0 means LoRA is not applied. Defaults to 0. diff --git a/applications/Chat/coati/ray/src/detached_trainer_ppo.py b/applications/Chat/coati/ray/src/detached_trainer_ppo.py index 90e5e437750a..838e82d07f4a 100644 --- a/applications/Chat/coati/ray/src/detached_trainer_ppo.py +++ b/applications/Chat/coati/ray/src/detached_trainer_ppo.py @@ -29,7 +29,7 @@ class DetachedPPOTrainer(DetachedTrainer): lora_rank (int) : for actor / critic init train_batch_size (int, defaults to 8): the batch size to use for training train_batch_size (int, defaults to 8): the batch size to use for training - buffer_limit (int, defaults to 0): the max_size limitaiton of replay buffer + buffer_limit (int, defaults to 0): the max_size limitation of replay buffer buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu eps_clip (float, defaults to 0.2): the clip coefficient of policy loss value_clip (float, defaults to 0.4): the clip coefficient of value loss diff --git a/applications/Chat/coati/ray/src/experience_maker_holder.py b/applications/Chat/coati/ray/src/experience_maker_holder.py index 696773e84cfb..94e4a3d537a5 100644 --- a/applications/Chat/coati/ray/src/experience_maker_holder.py +++ b/applications/Chat/coati/ray/src/experience_maker_holder.py @@ -83,7 +83,7 @@ def _send_experience(self, experience): chosen_trainer = None min_length = None if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print("[maker] choosing tartget trainer") + print("[maker] choosing target trainer") while chosen_trainer is None: for target_trainer in self.target_trainer_list: try: diff --git a/applications/Chat/coati/replay_buffer/utils.py b/applications/Chat/coati/replay_buffer/utils.py index 55ddb2ae8191..6ad0db2c3b60 100644 --- a/applications/Chat/coati/replay_buffer/utils.py +++ b/applications/Chat/coati/replay_buffer/utils.py @@ -15,7 +15,7 @@ class BufferItem: action_log_probs: (A) values: (1) reward: (1) - advatanges: (1) + advantages: (1) attention_mask: (S) action_mask: (A) diff --git a/applications/Chat/coati/trainer/callbacks/performance_evaluator.py b/applications/Chat/coati/trainer/callbacks/performance_evaluator.py index 0fc3b077a1d1..5ca44a52d6e7 100644 --- a/applications/Chat/coati/trainer/callbacks/performance_evaluator.py +++ b/applications/Chat/coati/trainer/callbacks/performance_evaluator.py @@ -114,7 +114,7 @@ def on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: # actor forward-backward, 3 means forward(1) + backward(2) self.learn_flop += self.actor_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint)) - # critic foward-backward + # critic forward-backward self.learn_flop += self.critic_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint)) def on_fit_end(self) -> None: diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index 008a6aea8240..2db604fc9b74 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -28,12 +28,12 @@ class PPOTrainer(Trainer): actor (Actor): the actor model in ppo algorithm critic (Critic): the critic model in ppo algorithm reward_model (nn.Module): the reward model in rlhf algorithm to make reward of sentences - initial_model (Actor): the initial model in rlhf algorithm to generate reference logits to limit the update of actor + initial_model (Actor): the initial model in rlhf algorithm to generate reference logics to limit the update of actor actor_optim (Optimizer): the optimizer to use for actor model critic_optim (Optimizer): the optimizer to use for critic model kl_coef (float, defaults to 0.1): the coefficient of kl divergence loss train_batch_size (int, defaults to 8): the batch size to use for training - buffer_limit (int, defaults to 0): the max_size limitaiton of replay buffer + buffer_limit (int, defaults to 0): the max_size limitation of replay buffer buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu eps_clip (float, defaults to 0.2): the clip coefficient of policy loss vf_coef (float, defaults to 1.0): the coefficient of value loss @@ -41,7 +41,7 @@ class PPOTrainer(Trainer): value_clip (float, defaults to 0.4): the clip coefficient of value loss experience_batch_size (int, defaults to 8): the batch size to use for experience generation max_epochs (int, defaults to 1): the number of epochs of training process - tokenier (Callable, optional): the tokenizer to use for tokenizing the input + tokenizer (Callable, optional): the tokenizer to use for tokenizing the input sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader callbacks (List[Callback], defaults to []): the callbacks to call during training process diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 993a56c5a49a..af8ded005600 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -38,7 +38,7 @@ pip install -r requirements.txt ## Supervised datasets collection -We colllected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo +We collected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo [InstructionWild](https://github.com/XueFuzhao/InstructionWild). The following pic shows how we collected the data. @@ -128,7 +128,7 @@ Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862): - --lora_rank: low-rank adaptation matrices rank, type=int, default=0 - --loss_func: which kind of loss function, choices=['log_sig', 'log_exp'] - --max_len: max sentence length for generation, type=int, default=512 -- --test: whether is only tesing, if it's ture, the dataset will be small +- --test: whether is only testing, if it's true, the dataset will be small ## Stage3 - Training model using prompts with RL @@ -245,7 +245,7 @@ class CoatiActor(Actor): if pretrained is not None: model = CoatiModel.from_pretrained(pretrained) else: - model = build_model() # load your own model if it is not support in trainsformers + model = build_model() # load your own model if it is not support in transformers super().__init__(model, lora_rank, lora_train_bias) ``` @@ -266,7 +266,7 @@ class GPTLM(LM): if pretrained is not None: model = CoatiModel.from_pretrained(pretrained) else: - model = build_model() # load your own model if it is not support in trainsformers + model = build_model() # load your own model if it is not support in transformers super().__init__(model, lora_rank, lora_train_bias) @@ -288,7 +288,7 @@ class CoatiRM(RewardModel): if pretrained is not None: model = CoatiModel.from_pretrained(pretrained) else: - model = build_model() # load your own model if it is not support in trainsformers + model = build_model() # load your own model if it is not support in transformers value_head = nn.Linear(model.config.n_embd, 1) value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.n_embd + 1)) @@ -311,7 +311,7 @@ class CoatiCritic(Critic): if pretrained is not None: model = CoatiModel.from_pretrained(pretrained) else: - model = build_model() # load your own model if it is not support in trainsformers + model = build_model() # load your own model if it is not support in transformers value_head = nn.Linear(model.config.n_embd, 1) value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.n_embd + 1)) diff --git a/applications/Chat/examples/community/peft/README.md b/applications/Chat/examples/community/peft/README.md index a82f02a87317..eabb56fd8294 100644 --- a/applications/Chat/examples/community/peft/README.md +++ b/applications/Chat/examples/community/peft/README.md @@ -1,10 +1,10 @@ # Add Peft support for SFT and Prompts model training -The orginal implementation just adopts the loralib and merges the layers into the final model. The huggingface peft is a better lora model implementation and can be easily training and distributed. +The original implementation just adopts the loralib and merges the layers into the final model. The huggingface peft is a better lora model implementation and can be easily training and distributed. Since reward model is relative small, I just keep it as original one. I suggest train full model to get the proper reward/critic model. -# Prelimenary installation +# Preliminary installation Since the current pypi peft package(0.2) has some bugs, please install the peft package using source. ``` git clone https://github.com/huggingface/peft diff --git a/applications/Chat/examples/community/peft/easy_dataset.py b/applications/Chat/examples/community/peft/easy_dataset.py index 13dceef79145..24ea4f0a8618 100644 --- a/applications/Chat/examples/community/peft/easy_dataset.py +++ b/applications/Chat/examples/community/peft/easy_dataset.py @@ -166,7 +166,7 @@ def __str__(self): ''' -Easy SFT just accept a text file which can be read line by line. However the datasest will group texts together to max_length so LLM will learn the texts meaning better. +Easy SFT just accept a text file which can be read line by line. However the datasets will group texts together to max_length so LLM will learn the texts meaning better. If individual lines are not related, just set is_group_texts to False. ''' From 739cfe33600a72e364a1c017b82302f78d9b5091 Mon Sep 17 00:00:00 2001 From: zhang-yi-chi <673865549@qq.com> Date: Sat, 22 Apr 2023 14:16:08 +0800 Subject: [PATCH 158/413] [chat] fix enable single gpu training bug --- applications/Chat/examples/train_prompts.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index 5ded6d8432ed..2086ff003e34 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -8,7 +8,7 @@ from coati.models.gpt import GPTRM, GPTActor, GPTCritic from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM from coati.models.opt import OPTRM, OPTActor, OPTCritic -from coati.models.roberta import RoBERTaRM, RoBERTaActor, RoBERTaCritic +from coati.models.roberta import RoBERTaActor, RoBERTaCritic, RoBERTaRM from coati.trainer import PPOTrainer from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy from coati.utils import prepare_llama_tokenizer_and_embedding @@ -143,6 +143,8 @@ def main(args): prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_path, max_datasets_size=16384) if dist.is_initialized() and dist.get_world_size() > 1: prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True) + else: + prompt_sampler = None prompt_dataloader = DataLoader(prompt_dataset, shuffle=(prompt_sampler is None), sampler=prompt_sampler, @@ -151,6 +153,8 @@ def main(args): pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384) if dist.is_initialized() and dist.get_world_size() > 1: pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True) + else: + pretrain_sampler = None pretrain_dataloader = DataLoader(pretrain_dataset, shuffle=(pretrain_sampler is None), sampler=pretrain_sampler, From 179558a87ab37096450223d8ee4c2b1a06a334a4 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 24 Apr 2023 10:55:14 +0800 Subject: [PATCH 159/413] [devops] fix chat ci (#3628) --- .github/workflows/run_chatgpt_examples.yml | 4 ++++ .github/workflows/run_chatgpt_unit_tests.yml | 16 ++++++++++------ 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/.github/workflows/run_chatgpt_examples.yml b/.github/workflows/run_chatgpt_examples.yml index 1d8240ad4631..9d9d3a007851 100644 --- a/.github/workflows/run_chatgpt_examples.yml +++ b/.github/workflows/run_chatgpt_examples.yml @@ -13,6 +13,10 @@ on: jobs: tests: name: Run ChatGPT examples + if: | + github.event.pull_request.draft == false && + github.base_ref == 'main' && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' runs-on: [self-hosted, gpu] container: image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 diff --git a/.github/workflows/run_chatgpt_unit_tests.yml b/.github/workflows/run_chatgpt_unit_tests.yml index 4e539bfe06fd..407f630e2469 100644 --- a/.github/workflows/run_chatgpt_unit_tests.yml +++ b/.github/workflows/run_chatgpt_unit_tests.yml @@ -4,16 +4,20 @@ on: pull_request: types: [synchronize, opened, reopened] paths: - - 'applications/ChatGPT/chatgpt/**' - - 'applications/ChatGPT/requirements.txt' - - 'applications/ChatGPT/setup.py' - - 'applications/ChatGPT/requirements-test.txt' - - 'applications/ChatGPT/tests/**' - - 'applications/ChatGPT/pytest.ini' + - 'applications/Chat/coati/**' + - 'applications/Chat/requirements.txt' + - 'applications/Chat/setup.py' + - 'applications/Chat/requirements-test.txt' + - 'applications/Chat/tests/**' + - 'applications/Chat/pytest.ini' jobs: tests: name: Run ChatGPT unit tests + if: | + github.event.pull_request.draft == false && + github.base_ref == 'main' && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' runs-on: [self-hosted, gpu] container: image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 From df309fc6ab0089883e213b4051987421d2646c69 Mon Sep 17 00:00:00 2001 From: ddobokki <44228269+ddobokki@users.noreply.github.com> Date: Mon, 24 Apr 2023 13:23:15 +0900 Subject: [PATCH 160/413] [Chat] Remove duplicate functions (#3625) --- applications/Chat/coati/trainer/ppo.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index 2db604fc9b74..cf752549501f 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -210,9 +210,6 @@ def training_step(self, experience: Experience) -> Dict[str, float]: def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer) - def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer) - def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None: origin_model = strategy._unwrap_actor(actor) From b9a8dff7e5a30b80a60c8dbcea4ed20c870afa63 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Wed, 26 Apr 2023 11:38:43 +0800 Subject: [PATCH 161/413] [doc] Fix typo under colossalai and doc(#3618) * Fixed several spelling errors under colossalai * Fix the spelling error in colossalai and docs directory * Cautious Changed the spelling error under the example folder * Update runtime_preparation_pass.py revert autograft to autograd * Update search_chunk.py utile to until * Update check_installation.py change misteach to mismatch in line 91 * Update 1D_tensor_parallel.md revert to perceptron * Update 2D_tensor_parallel.md revert to perceptron in line 73 * Update 2p5D_tensor_parallel.md revert to perceptron in line 71 * Update 3D_tensor_parallel.md revert to perceptron in line 80 * Update README.md revert to resnet in line 42 * Update reorder_graph.py revert to indice in line 7 * Update p2p.py revert to megatron in line 94 * Update initialize.py revert to torchrun in line 198 * Update routers.py change to detailed in line 63 * Update routers.py change to detailed in line 146 * Update README.md revert random number in line 402 --- colossalai/_analyzer/fx/codegen.py | 2 +- colossalai/auto_parallel/offload/region.py | 2 +- .../offload/training_simulator.py | 2 +- .../passes/runtime_preparation_pass.py | 4 ++-- colossalai/autochunk/autochunk_codegen.py | 2 +- colossalai/autochunk/estimate_memory.py | 2 +- colossalai/autochunk/search_chunk.py | 6 ++--- colossalai/autochunk/trace_flow.py | 2 +- colossalai/autochunk/trace_indice.py | 12 +++++----- colossalai/booster/booster.py | 2 +- .../checkpoint_io/checkpoint_io_base.py | 8 +++---- colossalai/cli/check/check_installation.py | 4 ++-- colossalai/communication/p2p.py | 8 +++---- colossalai/communication/p2p_v2.py | 2 +- colossalai/context/moe_context.py | 2 +- colossalai/context/parallel_context.py | 4 ++-- colossalai/context/random/seed_manager.py | 8 +++---- .../codegen/activation_checkpoint_codegen.py | 2 +- colossalai/fx/passes/split_module.py | 4 ++-- .../kernel/cuda_native/multihead_attention.py | 2 +- colossalai/nn/_ops/embedding_bag.py | 2 +- colossalai/nn/layer/moe/experts.py | 12 +++++----- colossalai/nn/layer/moe/layers.py | 4 ++-- colossalai/nn/layer/moe/routers.py | 4 ++-- colossalai/nn/layer/moe/utils.py | 4 ++-- colossalai/nn/layer/parallel_1d/layers.py | 10 ++++---- colossalai/tensor/colo_tensor.py | 6 ++--- colossalai/tensor/comm_spec.py | 2 +- colossalai/tensor/compute_spec.py | 2 +- colossalai/tensor/d_tensor/layout.py | 2 +- colossalai/tensor/d_tensor/sharding_spec.py | 4 ++-- colossalai/tensor/dist_spec_mgr.py | 2 +- colossalai/tensor/distspec.py | 2 +- colossalai/tensor/shape_consistency.py | 4 ++-- colossalai/tensor/sharding_spec.py | 2 +- colossalai/tensor/utils.py | 6 ++--- colossalai/testing/utils.py | 14 +++++------ .../utils/checkpoint/module_checkpoint.py | 2 +- colossalai/utils/checkpoint/utils.py | 2 +- colossalai/utils/moe.py | 2 +- colossalai/zero/gemini/colo_init_context.py | 4 ++-- colossalai/zero/gemini/gemini_ddp.py | 6 ++--- .../gemini/ophooks/_shard_grad_ophook.py | 2 +- .../gemini/ophooks/_shard_param_ophook.py | 2 +- .../zero/legacy/gemini/stateful_tensor_mgr.py | 2 +- .../zero/legacy/init_ctx/init_context.py | 2 +- .../bucket_tensor_shard_strategy.py | 2 +- .../legacy/sharded_model/sharded_model_v2.py | 8 +++---- .../legacy/sharded_optim/sharded_optim_v2.py | 12 +++++----- colossalai/zero/wrapper.py | 4 ++-- .../en/Colossal-Auto/get_started/run_demo.md | 2 +- .../en/advanced_tutorials/meet_gemini.md | 2 +- .../en/advanced_tutorials/opt_service.md | 2 +- .../train_vit_with_hybrid_parallelism.md | 22 ++++++++--------- docs/source/en/basics/colotensor_concept.md | 2 +- docs/source/en/basics/engine_trainer.md | 2 +- .../source/en/concepts/colossalai_overview.md | 6 ++--- docs/source/en/features/1D_tensor_parallel.md | 2 +- docs/source/en/features/2D_tensor_parallel.md | 2 +- .../en/features/2p5D_tensor_parallel.md | 2 +- .../en/features/gradient_accumulation.md | 2 +- .../en/features/mixed_precision_training.md | 8 +++---- docs/source/en/features/nvme_offload.md | 6 ++--- docs/source/en/features/zero_with_chunk.md | 4 ++-- examples/images/diffusion/ldm/data/teyvat.py | 2 +- examples/images/diffusion/main.py | 24 +++++++++---------- examples/images/dreambooth/README.md | 2 +- examples/language/gpt/README.md | 2 +- .../gpt/experiments/auto_offload/README.md | 2 +- .../gpt/experiments/auto_parallel/README.md | 2 +- .../experiments/pipeline_parallel/README.md | 2 +- examples/language/opt/train_gemini_opt.py | 4 ++-- 72 files changed, 158 insertions(+), 158 deletions(-) diff --git a/colossalai/_analyzer/fx/codegen.py b/colossalai/_analyzer/fx/codegen.py index b768e59004b1..41d74f2e3719 100644 --- a/colossalai/_analyzer/fx/codegen.py +++ b/colossalai/_analyzer/fx/codegen.py @@ -138,7 +138,7 @@ def emit_ckpt_func(body, delete_unused_value_func, ckpt_level=0, in_ckpt=False): - """Emit ckpt fuction in nested way + """Emit ckpt function in nested way Args: body: forward code - in recursive calls, this part will be checkpoint diff --git a/colossalai/auto_parallel/offload/region.py b/colossalai/auto_parallel/offload/region.py index 9a2f558c3145..819ffbd96eb1 100644 --- a/colossalai/auto_parallel/offload/region.py +++ b/colossalai/auto_parallel/offload/region.py @@ -111,7 +111,7 @@ def copy_grad_to_region_slice(self, param: torch.nn.Parameter, data_slice: torch Copy data slice to the memory space indexed by the input tensor in the region. Args: - param (torch.nn.Parameter): the param used to retrive meta information + param (torch.nn.Parameter): the param used to retrieve meta information data_slice (torch.Tensor): the tensor to be copied to the region """ diff --git a/colossalai/auto_parallel/offload/training_simulator.py b/colossalai/auto_parallel/offload/training_simulator.py index f277c183a912..de58023ec2d6 100644 --- a/colossalai/auto_parallel/offload/training_simulator.py +++ b/colossalai/auto_parallel/offload/training_simulator.py @@ -22,7 +22,7 @@ class TrainingSimulator(ABC): Args: region_list (List[Region]): represents the linearized DNN computing graph. - comp_power (float): the NVIDIA GPU FP16 compuing power. + comp_power (float): the NVIDIA GPU FP16 computing power. link_to_bw (Dict[str, Dict[float, float]]): communication links and the corresponding bandwidth. """ diff --git a/colossalai/auto_parallel/passes/runtime_preparation_pass.py b/colossalai/auto_parallel/passes/runtime_preparation_pass.py index e1d0c627274e..08af846b221d 100644 --- a/colossalai/auto_parallel/passes/runtime_preparation_pass.py +++ b/colossalai/auto_parallel/passes/runtime_preparation_pass.py @@ -149,7 +149,7 @@ def size_value_converting_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMesh def _extract_target_dim(node): ''' - A helper function to etract the target dimension from size node. + A helper function to extract the target dimension from size node. There are two usages of torch.Tensor.size: 1. tensor.size() 2. tensor.size(dim) @@ -427,7 +427,7 @@ def _shard_param(param, target_sharding_spec): if target_sharding_spec.dim_partition_dict != {}: origin_sharding_spec = ShardingSpec(device_mesh, param.shape, {}) setattr(param, 'sharding_spec', origin_sharding_spec) - # TODO: build a ColoParamter class to manager the distributed parameters + # TODO: build a ColoParameter class to manager the distributed parameters # we could use .data here, because all the operations just happen before the real training # loop, so we don't need to track these operations in the autograd graph. param = torch.nn.Parameter( diff --git a/colossalai/autochunk/autochunk_codegen.py b/colossalai/autochunk/autochunk_codegen.py index 2cbc6c9221aa..d0a467254d72 100644 --- a/colossalai/autochunk/autochunk_codegen.py +++ b/colossalai/autochunk/autochunk_codegen.py @@ -287,7 +287,7 @@ def emit_code_with_chunk(body: List[str], body = _replace_new_tensor_like_shape(search_chunk, chunk_infos, region_idx, node_idx, node, body) # new tensor body = _replace_new_tensor_shape(search_chunk, chunk_infos, region_idx, node_idx, node, body) - # reassgin reshape size + # reassign reshape size body[-1] = _replace_reshape_size(body[-1], node.name, chunk_infos[region_idx]["reshape_size"]) body[-1] = " " + body[-1] delete_unused_value_func(node, body, chunk_inputs_names) diff --git a/colossalai/autochunk/estimate_memory.py b/colossalai/autochunk/estimate_memory.py index 08a55f9aa04a..77bc2ef17bc3 100644 --- a/colossalai/autochunk/estimate_memory.py +++ b/colossalai/autochunk/estimate_memory.py @@ -153,7 +153,7 @@ def estimate_chunk_inference_mem(self, node_list: List, chunk_infos: Dict = None Returns: act_memory_peak_log (List): peak memory of every node - act_memory_after_node_log (List): memory after excuting every node + act_memory_after_node_log (List): memory after executing every node active_node_list_log (List): active nodes of every node. active nodes refer to nodes generated but not deleted. """ diff --git a/colossalai/autochunk/search_chunk.py b/colossalai/autochunk/search_chunk.py index 326445ee9f12..59645c80e808 100644 --- a/colossalai/autochunk/search_chunk.py +++ b/colossalai/autochunk/search_chunk.py @@ -16,7 +16,7 @@ class SearchChunk(object): This is the core class for AutoChunk. It defines the framework of the strategy of AutoChunk. - Chunks will be selected one by one utill search stops. + Chunks will be selected one by one until search stops. The chunk search is as follows: 1. find the peak memory node @@ -73,7 +73,7 @@ def _init_trace(self) -> None: def _find_peak_region(self, mem_peak: List) -> int: """ - find peak node, along with its neighbour nodes exceeds max mem + find peak node, along with its neighbor nodes exceeds max mem """ max_value = max(mem_peak) max_idx = mem_peak.index(max_value) @@ -118,7 +118,7 @@ def _search_max_chunk_region(self, active_node: List, peak_region: int, chunk_re chunk_region_start (int) chunk_region_end (int) """ - # check if peak node already in chunkinfo + # check if peak node already in chunk info if chunk_regions is not None: for i in chunk_regions: if i["region"][0] < peak_region[0] <= i["region"][1] or \ diff --git a/colossalai/autochunk/trace_flow.py b/colossalai/autochunk/trace_flow.py index 16815215f52b..db25267e9b42 100644 --- a/colossalai/autochunk/trace_flow.py +++ b/colossalai/autochunk/trace_flow.py @@ -479,7 +479,7 @@ def check_region_start_end(self, start_node: Node, start_dim: int, start_idx: in # check index source align if not self.check_index_source(start_dim, start_node, start_idx, end_dim, end_node): return False - # check index copmute + # check index compute if not self.check_index_compute(start_idx, end_dim, end_node, end_idx): return False return True diff --git a/colossalai/autochunk/trace_indice.py b/colossalai/autochunk/trace_indice.py index 307f4de326d7..c7fce4c8bee1 100644 --- a/colossalai/autochunk/trace_indice.py +++ b/colossalai/autochunk/trace_indice.py @@ -8,7 +8,7 @@ class TraceIndice(object): """ - Trace all indice infomation for every node. + Trace all indice information for every node. Indice is a logical concept. Equal dims can been treated as one indice. eg. dim(x1) = [a, b, c] @@ -153,7 +153,7 @@ def _inherit_all_indice(self, node_from: Node, node_to: Node) -> None: def _inherit_more_indice_from_node_with_exclude(self, node_from: Node, node_to: Node, exclude: List = None) -> None: """ - inheirt indice from node without init + inherit indice from node without init """ if exclude == None: exclude = [] @@ -301,7 +301,7 @@ def _assign_permute_indice(self, node: Node, node_idx: int) -> None: def _assign_linear_indice(self, node: Node, node_idx: int) -> None: """ Assign indice for linear op. - 1. copy trace from input node and change last indice accroding to weight + 1. copy trace from input node and change last indice according to weight 2. mark equal for input node last indice, weight first dim and bias dim. 3. inherit input's computation, mark computation for last dim. @@ -360,7 +360,7 @@ def _assign_baddbmm_indice(self, node: Node, node_idx: int) -> None: def _assign_matmul_indice(self, node: Node, node_idx: int) -> None: """ Assign indice for matmul op. - 1. copy trace from matmul_left and change last indice accroding to matmul_right. (assert they have same length) + 1. copy trace from matmul_left and change last indice according to matmul_right. (assert they have same length) 2. mark equal for input matmul_left -1 indice and matmul_right -2 dim. 3. inherit matmul_left and matmul_right computation, mark computation for last dim. @@ -720,11 +720,11 @@ def _assign_view_reshape_indice(self, node: Node, node_idx: int) -> None: Assign indice for view and reshape op. 1. get origin shape and target shape by meta info. 2. compute the real value of -1 in target shape. - 3. determine changed dim, and assgin indice for generated dim. + 3. determine changed dim, and assign indice for generated dim. 4. log changed dim and generated dim for restore 5. inherit computation. 6. look into view list to see whether the view is associated with other, - if so assgin equal dim according to previous view. + if so assign equal dim according to previous view. Args: node (node) diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 1ad9f7f20ec1..c14e602deaf5 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -20,7 +20,7 @@ class Booster: """ Booster is a high-level API for training neural networks. It provides a unified interface for - training with different precisio, accelerator, and plugin. + training with different precision, accelerator, and plugin. Examples: >>> colossalai.launch(...) diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py index 3f8b0b0a6b47..cb853559c48c 100644 --- a/colossalai/checkpoint_io/checkpoint_io_base.py +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -71,7 +71,7 @@ def load_model(self, Args: model (nn.Module): model to be loaded. - checkpoint (str): checkpoint path. This value is made compatiblity with the model checkpoints in the + checkpoint (str): checkpoint path. This value is made compatibility with the model checkpoints in the mainstream model zoos such as Hugging Face and TIMM. The checkpoint path can be: 1. a file path, e.g. 'model.pt' 2. a path to a json file which defines the index to the sharded checkpoint @@ -127,7 +127,7 @@ def save_model(self, 1. a file path, e.g. 'model.pt' 2. a directory path to save the sharded checkpoint, e.g. './checkpoints/' when shard = True. shard (bool): whether to shard the checkpoint. Default: False. If set to True, the checkpoint will be sharded into - multiple files. The model shards will be specificed by a `model.index.json` file. When shard = True, please ensure + multiple files. The model shards will be specified by a `model.index.json` file. When shard = True, please ensure that the checkpoint path is a directory path instead of a file path. gather_dtensor (bool): whether to gather the distributed tensor to the first device. Default: True. variant (str): If specified, weights are saved in the format pytorch_model..bin. Default: None. @@ -149,7 +149,7 @@ def load_optimizer(self, optimizer: Optimizer, checkpoint: str): Args: optimizer (Optimizer): optimizer to be loaded. - checkpoint (str): checkpoint path. This value is made compatiblity with the model checkpoints in the + checkpoint (str): checkpoint path. This value is made compatibility with the model checkpoints in the """ index_file_exists, index_file_path = has_index_file(checkpoint) @@ -180,7 +180,7 @@ def save_optimizer(self, 2. a path to a json file which defines the index to the sharded checkpoint for the optimizer 3. a path to a folder containing a unique .index.json file for sharded checkpoint shard (bool): whether to shard the checkpoint. Default: False. If set to True, the checkpoint will be sharded into - multiple files. The optimizer shards will be specificed by a `optimizer.index.json` file. + multiple files. The optimizer shards will be specified by a `optimizer.index.json` file. gather_dtensor (bool): whether to gather the distributed tensor to the first device. Default: True. prefix (str): prefix for the optimizer checkpoint when shard = True. Default: None. size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard is set to True. diff --git a/colossalai/cli/check/check_installation.py b/colossalai/cli/check/check_installation.py index 44d7840700ef..cb3dbbc09301 100644 --- a/colossalai/cli/check/check_installation.py +++ b/colossalai/cli/check/check_installation.py @@ -76,7 +76,7 @@ def check_installation(): click.echo("") click.echo(f"Note:") click.echo( - f"1. AOT (ahead-of-time) compilation of the CUDA kernels occurs during installation when the environment varialbe CUDA_EXT=1 is set" + f"1. AOT (ahead-of-time) compilation of the CUDA kernels occurs during installation when the environment variable CUDA_EXT=1 is set" ) click.echo(f"2. If AOT compilation is not enabled, stay calm as the CUDA kernels can still be built during runtime") @@ -88,7 +88,7 @@ def check_installation(): click.echo(f"Note:") click.echo(f"1. The table above checks the version compatibility of the libraries/tools in the current environment") click.echo( - f" - PyTorch version mistach: whether the PyTorch version in the current environment is compatible with the PyTorch version used for AOT compilation" + f" - PyTorch version mismatch: whether the PyTorch version in the current environment is compatible with the PyTorch version used for AOT compilation" ) click.echo( f" - System and PyTorch CUDA version match: whether the CUDA version in the current environment is compatible with the CUDA version required by PyTorch" diff --git a/colossalai/communication/p2p.py b/colossalai/communication/p2p.py index 6dd4d0d6608d..0200cd3c6553 100644 --- a/colossalai/communication/p2p.py +++ b/colossalai/communication/p2p.py @@ -103,10 +103,10 @@ def _communicate(object_send_next: Union[torch.Tensor, List[torch.Tensor]] = Non previous rank. recv_next (bool): boolean for whether tensor should be received from next rank. - recv_prev_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): shape of the tensor to be received from the previous stage, defualts to None. - recv_next_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): shape of the tensor to be received from the next stage, defualts to None. - prev_rank (int): the rank of the previous pipeline stage, defualts to None, - next_rank (int): the rank of the next pipeline stage, defualts to None, + recv_prev_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): shape of the tensor to be received from the previous stage, defaults to None. + recv_next_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): shape of the tensor to be received from the next stage, defaults to None. + prev_rank (int): the rank of the previous pipeline stage, defaults to None, + next_rank (int): the rank of the next pipeline stage, defaults to None, dtype (torch.dtype): data type of intermediate buffers, defaults to None scatter_gather_tensors (bool): whether to scatter and gather tensor between pipeline stages, defaults to False diff --git a/colossalai/communication/p2p_v2.py b/colossalai/communication/p2p_v2.py index 4223f78d58cd..0dacd8c3c9b5 100644 --- a/colossalai/communication/p2p_v2.py +++ b/colossalai/communication/p2p_v2.py @@ -230,7 +230,7 @@ def recv_backward(next_rank: int = None) -> Any: next_rank (int, optional): The rank of the source of the tensor. Returns: - Any: The input gradient tensor or gradident tensor list. + Any: The input gradient tensor or gradient tensor list. """ if gpc.is_pipeline_last_stage(): output_tensor_grad = None diff --git a/colossalai/context/moe_context.py b/colossalai/context/moe_context.py index 1d7a883b1552..b41f4072a405 100644 --- a/colossalai/context/moe_context.py +++ b/colossalai/context/moe_context.py @@ -64,7 +64,7 @@ def setup(self, seed: int, use_kernel_optim: bool = True): from colossalai.core import global_context as gpc self.max_ep_size = gpc.config.get('max_ep_size', self.world_size) assert self.world_size % self.max_ep_size == 0, \ - "Maximum epxert parallel size must be a factor of the number of GPUs" + "Maximum expert parallel size must be a factor of the number of GPUs" self.min_dp_size = self.world_size // self.max_ep_size # Enabling kernel optimization may raise error in some cases diff --git a/colossalai/context/parallel_context.py b/colossalai/context/parallel_context.py index 0cd533fdef1a..003f0cdd91b6 100644 --- a/colossalai/context/parallel_context.py +++ b/colossalai/context/parallel_context.py @@ -44,7 +44,7 @@ def __init__(self): # load config from file self._config = None - # default 3D parallel args, will be overwritten during process group intialization + # default 3D parallel args, will be overwritten during process group initialization self.world_size = 1 self.data_parallel_size = 1 self.pipeline_parallel_size = 1 @@ -264,7 +264,7 @@ def _add_world_size(self, parallel_mode: ParallelMode, world_size: int): """Adds world size for `parallel_mode`. Args: - parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode correponding to the process group + parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode corresponding to the process group world_size (int): The world size to be added Raises: diff --git a/colossalai/context/random/seed_manager.py b/colossalai/context/random/seed_manager.py index 3c84aaafc179..956f9001200d 100644 --- a/colossalai/context/random/seed_manager.py +++ b/colossalai/context/random/seed_manager.py @@ -59,23 +59,23 @@ def set_mode(self, parallel_mode: ParallelMode): self._current_mode = parallel_mode torch.cuda.set_rng_state(self._seed_states[parallel_mode]) - def add_seed(self, parallel_mode: ParallelMode, seed: int, overwrtie: bool = False): + def add_seed(self, parallel_mode: ParallelMode, seed: int, overwrite: bool = False): """Adds a seed to the seed manager for `parallel_mode`. Args: parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode. seed (int): The seed to be added. - overwrtie (bool, optional): Whether allows to overwrite the seed that has been set already + overwrite (bool, optional): Whether allows to overwrite the seed that has been set already Raises: AssertionError: Raises an AssertionError if `parallel_mode` is not an instance of :class:`colossalai.context.ParallelMode` or the seed for `parallel_mode` has been added. """ assert isinstance(parallel_mode, ParallelMode), 'A valid ParallelMode must be provided' - if overwrtie is False: + if overwrite is False: assert parallel_mode not in self._seed_states, f'The seed for {parallel_mode} has been added' elif parallel_mode in self._seed_states: - print(f"Warnning: {parallel_mode} seed has been overwritten.", flush=True) + print(f"Warning: {parallel_mode} seed has been overwritten.", flush=True) current_state = torch.cuda.get_rng_state() torch.cuda.manual_seed(seed) diff --git a/colossalai/fx/codegen/activation_checkpoint_codegen.py b/colossalai/fx/codegen/activation_checkpoint_codegen.py index 492ebf918a9c..5a72cb9ca923 100644 --- a/colossalai/fx/codegen/activation_checkpoint_codegen.py +++ b/colossalai/fx/codegen/activation_checkpoint_codegen.py @@ -305,7 +305,7 @@ def emit_ckpt_func(body, delete_unused_value_func, level=0, in_ckpt=False): - """Emit ckpt fuction in nested way + """Emit ckpt function in nested way Args: body: forward code, in recursive calls, this part will be checkpoint functions code diff --git a/colossalai/fx/passes/split_module.py b/colossalai/fx/passes/split_module.py index 9bc4bf1f5c42..5ce5b969cbde 100644 --- a/colossalai/fx/passes/split_module.py +++ b/colossalai/fx/passes/split_module.py @@ -155,7 +155,7 @@ def record_output(def_node: torch.fx.node.Node, use_node: Optional[torch.fx.node use_partition = partitions[use_partition_name] use_partition.outputs.setdefault(def_node.name) - # split nodes into parititons + # split nodes into partitions for node in m.graph.nodes: orig_nodes[node.name] = node @@ -198,7 +198,7 @@ def record_output(def_node: torch.fx.node.Node, use_node: Optional[torch.fx.node if len(sorted_partitions) != len(partitions): raise RuntimeError("cycle exists between partitions!") - # add placeholders to parititons + # add placeholders to partitions for partition_name in sorted_partitions: partition = partitions[partition_name] for input in partition.inputs: diff --git a/colossalai/kernel/cuda_native/multihead_attention.py b/colossalai/kernel/cuda_native/multihead_attention.py index 7df53731edc5..3b6470cdcbb9 100644 --- a/colossalai/kernel/cuda_native/multihead_attention.py +++ b/colossalai/kernel/cuda_native/multihead_attention.py @@ -111,7 +111,7 @@ class MultiHeadAttention(nn.Module): Arguments: hidden_size: Total dimension of hidden_size. nhead: Number of parallel attention heads. - batch_size: Batch Size for one foward + batch_size: Batch Size for one forward max_seq_len: Max length of input sequence dropout: Dropout probability norm_first: perform LayerNorms before attention diff --git a/colossalai/nn/_ops/embedding_bag.py b/colossalai/nn/_ops/embedding_bag.py index 0e8aa8fecb01..0026f579b6dc 100644 --- a/colossalai/nn/_ops/embedding_bag.py +++ b/colossalai/nn/_ops/embedding_bag.py @@ -88,7 +88,7 @@ def colo_embedding_bag(input_tensor: GeneralTensor, assert isinstance(weight, ColoTensor) input_tensor = convert_to_colo_tensor(input_tensor, weight.get_process_group()) - # Handle differen parallel actions. + # Handle different parallel actions. if not weight.has_compute_spec(): # No Model Parallel Applied assert weight.is_replicate(), 'Invalid weight spec for native embedding op' diff --git a/colossalai/nn/layer/moe/experts.py b/colossalai/nn/layer/moe/experts.py index 2e5d9e6e79a9..56b11f4d9e08 100644 --- a/colossalai/nn/layer/moe/experts.py +++ b/colossalai/nn/layer/moe/experts.py @@ -13,7 +13,7 @@ class MoeExperts(nn.Module): - """Basic class for experts in MoE. It stores what kind of communication expersts use + """Basic class for experts in MoE. It stores what kind of communication experts use to exchange tokens, how many experts in a single GPU and parallel information such as expert parallel size, data parallel size and their distributed communication groups. """ @@ -24,7 +24,7 @@ def __init__(self, comm_name: str, num_experts: int): "This kind of communication has not been implemented yet.\n Please use Experts build function." self.comm_name = comm_name self.num_total_experts = num_experts - # Get the configuration of experts' deployment and parallel information from moe contex + # Get the configuration of experts' deployment and parallel information from moe context self.num_local_experts, self.dist_info = MOE_CONTEXT.get_info(num_experts) @@ -32,7 +32,7 @@ def __init__(self, comm_name: str, num_experts: int): class Experts(MoeExperts): """A wrapper class to create experts. It will create E experts across the moe model parallel group, where E is the number of experts. Every expert - is a instence of the class, 'expert' in initialization parameters. + is a instance of the class, 'expert' in initialization parameters. Args: expert_cls (:class:`torch.nn.Module`): The class of all experts @@ -146,15 +146,15 @@ def forward(self, inputs): # inputs [g, el, c, h] class TPExperts(MoeExperts): """Use tensor parallelism to split each expert evenly, which can deploy experts in - case that the number of experts can't be divied by maximum expert parallel size or - maximum expert parallel size can't be divied by the number of experts. + case that the number of experts can't be divide by maximum expert parallel size or + maximum expert parallel size can't be divide by the number of experts. """ def __init__(self, num_experts: int, d_model: int, d_ff: int, activation=None, drop_rate: float = 0): super().__init__("all_gather", MOE_CONTEXT.max_ep_size) assert d_ff % MOE_CONTEXT.max_ep_size == 0, \ - "d_ff should be divied by maximum expert parallel size" + "d_ff should be divide by maximum expert parallel size" p_ff = d_ff // MOE_CONTEXT.max_ep_size diff --git a/colossalai/nn/layer/moe/layers.py b/colossalai/nn/layer/moe/layers.py index b90d1f0bfcc6..03f55d91f3a8 100644 --- a/colossalai/nn/layer/moe/layers.py +++ b/colossalai/nn/layer/moe/layers.py @@ -25,7 +25,7 @@ class MoeLayer(nn.Module): """A MoE layer, that puts its input tensor to its gate and uses the output logits to router all tokens, is mainly used to exchange all tokens for every expert across - the moe tensor group by all to all comunication. Then it will get the output of all + the moe tensor group by all to all communication. Then it will get the output of all experts and exchange the output. At last returns the output of the moe system. Args: @@ -122,7 +122,7 @@ class MoeModule(nn.Module): drop_tks (bool, optional): Whether drops tokens in evaluation use_residual (bool, optional): Makes this MoE layer a Residual MoE. More information can be found in `Microsoft paper`_. - residual_instance (nn.Module, optional): The instance of residual module in Resiual MoE + residual_instance (nn.Module, optional): The instance of residual module in Residual MoE expert_instance (MoeExperts, optional): The instance of experts module in MoeLayer expert_cls (Type[nn.Module], optional): The class of each expert when no instance is given expert_args (optional): The args of expert when no instance is given diff --git a/colossalai/nn/layer/moe/routers.py b/colossalai/nn/layer/moe/routers.py index c522c655a511..c5b8390bf047 100644 --- a/colossalai/nn/layer/moe/routers.py +++ b/colossalai/nn/layer/moe/routers.py @@ -60,7 +60,7 @@ def pop_routing_loss(self) -> torch.Tensor: class Top1Router(MoeRouter): """Top1 router that returns the dispatch mask [s, e, c] and combine weight [s, e, c] - for routing usage. More deailted function can be found in the paper about Switch Transformer + for routing usage. More detailed function can be found in the paper about Switch Transformer of Google. Args: capacity_factor_train (float, optional): Capacity factor in routing of training. @@ -143,7 +143,7 @@ def forward(self, inputs: torch.Tensor, use_kernel: bool = False, ep_group: Opti class Top2Router(MoeRouter): """Top2 router that returns the dispatch mask [s, e, c] and combine weight [s, e, c] - for routing usage. More deailted function can be found in the paper about ViT-MoE. + for routing usage. More detailed function can be found in the paper about ViT-MoE. Args: capacity_factor_train (float, optional): Capacity factor in routing of training. capacity_factor_eval (float, optional): Capacity factor in routing of evaluation. diff --git a/colossalai/nn/layer/moe/utils.py b/colossalai/nn/layer/moe/utils.py index 9362347414e0..4ca8bd703386 100644 --- a/colossalai/nn/layer/moe/utils.py +++ b/colossalai/nn/layer/moe/utils.py @@ -12,7 +12,7 @@ def half(self, memory_format=None): class NormalNoiseGenerator: - """Generates a random noisy mask for logtis tensor. + """Generates a random noisy mask for logits tensor. All noise is generated from a normal distribution :math:`(0, 1 / E^2)`, where `E = the number of experts`. @@ -32,7 +32,7 @@ def __call__(self, inputs: torch.Tensor): class UniformNoiseGenerator: - """Generates a random noisy mask for logtis tensor. + """Generates a random noisy mask for logits tensor. copied from mesh tensorflow: Multiply values by a random number between :math:`1-epsilon` and :math:`1+epsilon`. Makes models more resilient to rounding errors introduced by bfloat16. diff --git a/colossalai/nn/layer/parallel_1d/layers.py b/colossalai/nn/layer/parallel_1d/layers.py index e96abd87ed10..406173a18c60 100644 --- a/colossalai/nn/layer/parallel_1d/layers.py +++ b/colossalai/nn/layer/parallel_1d/layers.py @@ -439,7 +439,7 @@ class Linear1D_Col(ParallelLayer): to all GPUs, otherwise, every GPU will have its output which is :math:`Y_i = XA_i`, defaults to False skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, - which is preserved for kernel fusion, defaults to Fals + which is preserved for kernel fusion, defaults to False weight_initializer (:class:`typing.Callable`, optional): The initializer of weight, defaults to kaiming uniform initializer. bias_initializer (:class:`typing.Callable`, optional): @@ -578,7 +578,7 @@ class Linear1D_Row(ParallelLayer): dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. parallel_input (bool, optional): If set to ``True``, it's assumed that the input is split, defaults to False. skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, - which is preserved for kernel fusion, defaults to Fals + which is preserved for kernel fusion, defaults to False weight_initializer (:class:`typing.Callable`, optional): The initializer of weight, defaults to kaiming uniform initializer. bias_initializer (:class:`typing.Callable`, optional): @@ -994,11 +994,11 @@ class PatchEmbedding1D(ColossalaiModule): :type dtype: torch.dtype, optional :param flatten: whether to flatten output tensor, defaults to True :type flatten: bool, optional - :param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer + :param weight_initializer: The initializer of weight, defaults to kaiming uniform initializer :type weight_initializer: typing.Callable, optional - :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer + :param bias_initializer: The initializer of bias, defaults to xavier uniform initializer :type bias_initializer: typing.Callable, optional - :param position_embed_initializer: The intializer of position embedding, defaults to zero + :param position_embed_initializer: The initializer of position embedding, defaults to zero :type position_embed_initializer: typing.Callable, optional """ diff --git a/colossalai/tensor/colo_tensor.py b/colossalai/tensor/colo_tensor.py index 40eefc3ec5d1..4d762076461d 100644 --- a/colossalai/tensor/colo_tensor.py +++ b/colossalai/tensor/colo_tensor.py @@ -184,7 +184,7 @@ def __torch_function__(cls, func, types, args=(), kwargs=None): # we have to capture the `backward` function # and make sure that it does not in `torch._C.DisableTorchFunction()` context if func is torch.Tensor.backward: - assert len(args) == 1 # only has 1 paramter + assert len(args) == 1 # only has 1 parameter backward_tensor = torch.Tensor(args[0]) tensor_kwargs = {k: torch.Tensor(v) if torch.is_tensor(v) else v for k, v in kwargs.items()} return backward_tensor.backward(**tensor_kwargs) @@ -228,7 +228,7 @@ def redistribute(self, dist_spec: _DistSpec, pg: Optional[ProcessGroup] = None) 2. If the pg is not not None and not equal to the current process group. First, convert the tensor as replicated among the TP process group. Second, reset the process group to the new pg. - Third, conver the tensor (new replicated both among the tp process group) to the new dist_spec. + Third, convert the tensor (new replicated both among the tp process group) to the new dist_spec. Args: dist_spec (_DistSpec): the new dist spec. @@ -297,7 +297,7 @@ def size_local(self, *args) -> torch.Size: def size_global(self, *args) -> torch.Size: """size_global - override the torch buildin size() + override the torch building size() the shape passed in must be in a replicate placement. Returns: diff --git a/colossalai/tensor/comm_spec.py b/colossalai/tensor/comm_spec.py index 0d8de1062d42..af38d2a502c2 100644 --- a/colossalai/tensor/comm_spec.py +++ b/colossalai/tensor/comm_spec.py @@ -391,7 +391,7 @@ class CommSpec: to determine the buffer shape, and logical_process_axis Argument: - comm_pattern(CollectiveCommPattern): decribe the communication method used in this spec. + comm_pattern(CollectiveCommPattern): describe the communication method used in this spec. sharding_spec(ShardingSpec): This is sharding spec of the tensor which will join the communication action. gather_dim(int, Optional): The gather_dim of the tensor will be gathered. shard_dim(int, Optional): The shard_dim of the tensor will be sharded. diff --git a/colossalai/tensor/compute_spec.py b/colossalai/tensor/compute_spec.py index 73328285ee93..12f8f36bc613 100644 --- a/colossalai/tensor/compute_spec.py +++ b/colossalai/tensor/compute_spec.py @@ -10,7 +10,7 @@ class ComputePattern(Enum): class ComputeSpec(object): """ComputeSpec - The Specification for compuattion pattern + The Specification for computation pattern Args: compute_pattern (ComputePattern): an Enum instance for compute pattern. diff --git a/colossalai/tensor/d_tensor/layout.py b/colossalai/tensor/d_tensor/layout.py index 72a2694a1eaf..ee7ef74a99ae 100644 --- a/colossalai/tensor/d_tensor/layout.py +++ b/colossalai/tensor/d_tensor/layout.py @@ -14,7 +14,7 @@ class Layout: """Layout of a tensor. Attributes: - device_mesh: the device mesh to store the tensor distributedly. + device_mesh: the device mesh to store the tensor distributed. device_type: the type of the device mesh, e.g. 'cpu' or 'cuda'. sharding_spec: the sharding specification to describe how the tensor is sharded. entire_shape: the entire shape of the global tensor. diff --git a/colossalai/tensor/d_tensor/sharding_spec.py b/colossalai/tensor/d_tensor/sharding_spec.py index 7591f760cb30..2ea0c4db89fd 100644 --- a/colossalai/tensor/d_tensor/sharding_spec.py +++ b/colossalai/tensor/d_tensor/sharding_spec.py @@ -14,7 +14,7 @@ class DimSpec: ''' - Sharding spec for single dimension of the sharded tensor decribe the sharding dimension of + Sharding spec for single dimension of the sharded tensor describe the sharding dimension of logical device mesh and give a method to compute the difference between them. This class is used internally in ShardingSpec. @@ -143,7 +143,7 @@ class ShardingSpec: Argument: dim_partition_dict(Dict[int, List[int]], optional): The key is the dimension of tensor to be sharded, - and the value of the key decribe which logical axis will be sharded in that dimension. + and the value of the key describe which logical axis will be sharded in that dimension. sharding_sequence(List[DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1]. ''' diff --git a/colossalai/tensor/dist_spec_mgr.py b/colossalai/tensor/dist_spec_mgr.py index d5c0ce28e9fb..8657989235db 100644 --- a/colossalai/tensor/dist_spec_mgr.py +++ b/colossalai/tensor/dist_spec_mgr.py @@ -61,7 +61,7 @@ def _shard_as(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: _DistSp Args: tensor (torch.Tensor): a global (replicated) tensor before shard dist_spec (_DistSpec): the distributed spec. to be sharded as. - pg (ProcessGrouo): the process group of the corresponding colotensor + pg (ProcessGroup): the process group of the corresponding colotensor Returns: torch.Tensor: a torch tensor after sharded. """ diff --git a/colossalai/tensor/distspec.py b/colossalai/tensor/distspec.py index 8dd0d8791537..3a09f1426e31 100644 --- a/colossalai/tensor/distspec.py +++ b/colossalai/tensor/distspec.py @@ -15,7 +15,7 @@ class _DistSpec: A class indicates Distributed Specification. The DistSpec is only works for the tensor parallel process groups. Because the dist spec of data parallel process group can be automatically deduced. - This is an internal data structrue. + This is an internal data structure. The API for users should be `ShardSpec` and `ReplicaSpec`. Args: diff --git a/colossalai/tensor/shape_consistency.py b/colossalai/tensor/shape_consistency.py index 2831b10a3c57..0a840006f086 100644 --- a/colossalai/tensor/shape_consistency.py +++ b/colossalai/tensor/shape_consistency.py @@ -73,7 +73,7 @@ def get_all_all_gather_spec(self, source_spec: ShardingSpec, orig_cost_dict: Dict[str, float]) -> Dict[ShardingSpec, float]: ''' Get all valid sharding specs from source_spec with single all-gather operation, and - accumulate commucation cost on origin cost which will finally be used in auto sharding solver. + accumulate communication cost on origin cost which will finally be used in auto sharding solver. For the all-gather operation, we just care about the S dimension. Argument: @@ -145,7 +145,7 @@ def get_all_all_to_all_spec(self, source_spec: ShardingSpec, orig_cost_dict: Dict[str, float]) -> Dict[ShardingSpec, float]: ''' Get all valid sharding specs from source_spec with single all-to-all operation, and - accumulate commucation cost on origin cost which will finally be used in auto sharding solver. + accumulate communication cost on origin cost which will finally be used in auto sharding solver. For the all-to-all operation, we just care about the pairs containing S dimension. Argument: diff --git a/colossalai/tensor/sharding_spec.py b/colossalai/tensor/sharding_spec.py index cdd0338850cf..bed320130ccd 100644 --- a/colossalai/tensor/sharding_spec.py +++ b/colossalai/tensor/sharding_spec.py @@ -18,7 +18,7 @@ class _DimSpec: ''' - Sharding spec for single dimension of the sharded tensor decribe the sharding dimension of + Sharding spec for single dimension of the sharded tensor describe the sharding dimension of logical device mesh and give a method to compute the difference between them. This class is used internally in ShardingSpec. diff --git a/colossalai/tensor/utils.py b/colossalai/tensor/utils.py index 0c2ead630d59..6e30f97fef03 100644 --- a/colossalai/tensor/utils.py +++ b/colossalai/tensor/utils.py @@ -18,7 +18,7 @@ def all_gather_simulator(target_pair): Argument: target_pair(Tuple[int, List[int]]): The first element is the dimension of tensor to be sharded, - and the second element decribes which logical axis will be sharded in that dimension. + and the second element describes which logical axis will be sharded in that dimension. ''' _, shard_list = target_pair new_shard_list = shard_list[:-1] @@ -36,7 +36,7 @@ def all_to_all_simulator(f_target_pair, b_target_pair): Therefore, if the behind shard_list is not None, we just extend it to the front shard_list. Argument: target_pair(Tuple[int, List[int]]): The first element is the dimension of tensor to be sharded, - and the second element decribes which logical axis will be sharded in that dimension. + and the second element describes which logical axis will be sharded in that dimension. e.g.: all-to-all(S0, S1) -> [S01, R] all-to-all(S0, R) -> [R, S0] @@ -46,7 +46,7 @@ def all_to_all_simulator(f_target_pair, b_target_pair): Argument: target_pair(Tuple[int, List[int]]): The first element is the dimension of tensor to be sharded, - and the second element decribes which logical axis will be sharded in that dimension. + and the second element describes which logical axis will be sharded in that dimension. ''' _, f_shard_list = f_target_pair _, b_shard_list = b_target_pair diff --git a/colossalai/testing/utils.py b/colossalai/testing/utils.py index eac83e6d7bd5..6583eeb12bf4 100644 --- a/colossalai/testing/utils.py +++ b/colossalai/testing/utils.py @@ -17,10 +17,10 @@ def parameterize(argument: str, values: List[Any]) -> Callable: we want to avoid the number of distributed network initialization, we need to have this extra decorator on the function launched by torch.multiprocessing. - If a function is wrapped with this wrapper, non-paramterized arguments must be keyword arguments, - positioanl arguments are not allowed. + If a function is wrapped with this wrapper, non-parametrized arguments must be keyword arguments, + positional arguments are not allowed. - Usgae:: + Usage:: # Example 1: @parameterize('person', ['xavier', 'davis']) @@ -33,7 +33,7 @@ def say_something(person, msg): # > xavier: hello # > davis: hello - # Exampel 2: + # Example 2: @parameterize('person', ['xavier', 'davis']) @parameterize('msg', ['hello', 'bye', 'stop']) def say_something(person, msg): @@ -110,7 +110,7 @@ def test_method(): If the pattern is not None and matches the exception message, the exception will be detected for rerun max_try (int, Optional): Maximum reruns for this function. The default value is 5. - If max_try is None, it will rerun foreven if exception keeps occurings + If max_try is None, it will rerun forever if exception keeps occurring """ def _match_lines(lines, pattern): @@ -144,7 +144,7 @@ def _run_until_success(*args, **kwargs): # Override signature # otherwise pytest.mark.parameterize will raise the following error: - # function does not use argumetn xxx + # function does not use argument xxx sig = signature(func) _run_until_success.__signature__ = sig @@ -231,7 +231,7 @@ def spawn(func, nprocs=1, **kwargs): This function is used to spawn processes for testing. Usage: - # must contians arguments rank, world_size, port + # must contains arguments rank, world_size, port def do_something(rank, world_size, port): ... diff --git a/colossalai/utils/checkpoint/module_checkpoint.py b/colossalai/utils/checkpoint/module_checkpoint.py index a109b3702577..d390da864cd3 100644 --- a/colossalai/utils/checkpoint/module_checkpoint.py +++ b/colossalai/utils/checkpoint/module_checkpoint.py @@ -89,7 +89,7 @@ def load_checkpoint(path: str, torch_load_kwargs: (dict, optional): The kwargs of torch.load inside the function load_state_dict_kwargs (dict, optional): The kwargs of load_state_dict inside the function """ - # initialize the default paramters + # initialize the default parameters if not torch_load_kwargs: torch_load_kwargs = dict() if not load_state_dict_kwargs: diff --git a/colossalai/utils/checkpoint/utils.py b/colossalai/utils/checkpoint/utils.py index 5652600ffd9b..682cd0903d5b 100644 --- a/colossalai/utils/checkpoint/utils.py +++ b/colossalai/utils/checkpoint/utils.py @@ -34,7 +34,7 @@ def gather_tensor(colo_tensor: ColoTensor) -> None: dist.barrier() if dist.get_rank() == 0: - setattr(colo_tensor, 'save_ready', True) # set saving signitrue + setattr(colo_tensor, 'save_ready', True) # set saving signature def scatter_tensor(colo_tensor: ColoTensor, dist_spec: _DistSpec) -> None: diff --git a/colossalai/utils/moe.py b/colossalai/utils/moe.py index 90783e5d9b8e..86d04c11958b 100644 --- a/colossalai/utils/moe.py +++ b/colossalai/utils/moe.py @@ -38,7 +38,7 @@ def sync_moe_model_param(model: nn.Module): param_dict = get_moe_epsize_param_dict(model) - # synchrosize the parameters whose dp_group is the whole world + # synchronize the parameters whose dp_group is the whole world if 1 in param_dict: src_rank = gpc.get_ranks_in_group(ParallelMode.DATA)[0] for param in param_dict[1]: diff --git a/colossalai/zero/gemini/colo_init_context.py b/colossalai/zero/gemini/colo_init_context.py index 5937ee9eff9a..75f8576ca477 100644 --- a/colossalai/zero/gemini/colo_init_context.py +++ b/colossalai/zero/gemini/colo_init_context.py @@ -74,7 +74,7 @@ def __init__(self, """ Args: device (torch.device): the device where parameters initialized are resident. Defaults to torch.device('cpu'). - dtype (torch.dtype): the dtype of parameters initialized. Defults to torch.float. + dtype (torch.dtype): the dtype of parameters initialized. Defaults to torch.float. default_pg (ProcessGroup): the default process group for all initialized parameters. default_dist_spec: the default distributed specifications. """ @@ -164,7 +164,7 @@ def post_process_colo_init_ctx(model: torch.nn.Module, model (torch.nn.module): the model device (torch.device, optional): device type of the model params. Defaults to torch.device('cpu'). dtype (torch.dtype, optional): dtype of the model params. Defaults to torch.float. - default_pg (Optional[ProcessGroup], optional): default process group. Defaults to None. Inidicates a DP-only process group. + default_pg (Optional[ProcessGroup], optional): default process group. Defaults to None. Indicates a DP-only process group. default_dist_spec (Any, optional): default dist spec of params. Defaults to None. Raises: diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index e151f1aefb2d..a2cc8c1f2de8 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -42,7 +42,7 @@ class ZeroDDP(ColoDDP): Args: module (torch.nn.Module): Module to apply ZeRO-DP. - gemini_manager (GeminiManager): Manages the chunk manager and heterogeneous momery space. + gemini_manager (GeminiManager): Manages the chunk manager and heterogeneous memory space. For more details, see the API reference of ``GeminiManager``. pin_memory (bool): Chunks on CPU Memory use pin-memory. force_outputs_fp32 (bool): If set to True, outputs will be fp32. Otherwise, outputs will be fp16. @@ -684,7 +684,7 @@ def __init__(self, memstats: Optional[MemStats] = None, verbose: bool = False) -> None: """ - A torch.Module warpper using ZeRO-DP and Genimi. + A torch.Module wrapper using ZeRO-DP and Gemini. ZeRO is for parallel. Gemini is for memory management. WARNING: The class will modify the module inline! @@ -706,7 +706,7 @@ def __init__(self, Users can provide this argument to speed up searching. If users do not know this argument before training, it is ok. We will use a default value 1024. min_chunk_size_mb (float, optional): the minimum chunk size in MegaByte. - If the aggregate size of parameters is still samller than the minimum chunk size, + If the aggregate size of parameters is still smaller than the minimum chunk size, all parameters will be compacted into one small chunk. memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer. """ diff --git a/colossalai/zero/legacy/gemini/ophooks/_shard_grad_ophook.py b/colossalai/zero/legacy/gemini/ophooks/_shard_grad_ophook.py index 5115ff74da16..8f8fec64924e 100644 --- a/colossalai/zero/legacy/gemini/ophooks/_shard_grad_ophook.py +++ b/colossalai/zero/legacy/gemini/ophooks/_shard_grad_ophook.py @@ -8,7 +8,7 @@ @OPHOOKS.register_module class ShardGradMemTracerHook(BaseOpHook): """ - A hook to process sharded param before and afther FWD and BWD operator executing. + A hook to process sharded param before and after FWD and BWD operator executing. """ def __init__(self): diff --git a/colossalai/zero/legacy/gemini/ophooks/_shard_param_ophook.py b/colossalai/zero/legacy/gemini/ophooks/_shard_param_ophook.py index 80736d14085e..a2a62fb9788a 100644 --- a/colossalai/zero/legacy/gemini/ophooks/_shard_param_ophook.py +++ b/colossalai/zero/legacy/gemini/ophooks/_shard_param_ophook.py @@ -8,7 +8,7 @@ @OPHOOKS.register_module class ShardParamHook(BaseOpHook): """ - A hook to process sharded param before and afther FWD and BWD operator executing. + A hook to process sharded param before and after FWD and BWD operator executing. """ def __init__(self): diff --git a/colossalai/zero/legacy/gemini/stateful_tensor_mgr.py b/colossalai/zero/legacy/gemini/stateful_tensor_mgr.py index 3b37444b0fe0..4f9ea7c6d520 100644 --- a/colossalai/zero/legacy/gemini/stateful_tensor_mgr.py +++ b/colossalai/zero/legacy/gemini/stateful_tensor_mgr.py @@ -53,7 +53,7 @@ def finish_iter(self): self._evict_time = 0 def adjust_layout(self) -> None: - """ Adjust the layout of statefuil tensor according to the information provided + """ Adjust the layout of stateful tensor according to the information provided by mem_stats_collector, which should belongs to a Sharded Model. """ # find stateful tensor in state COMPUTE diff --git a/colossalai/zero/legacy/init_ctx/init_context.py b/colossalai/zero/legacy/init_ctx/init_context.py index f8be0ca4f3fc..a921ca0aa83a 100644 --- a/colossalai/zero/legacy/init_ctx/init_context.py +++ b/colossalai/zero/legacy/init_ctx/init_context.py @@ -97,7 +97,7 @@ def calc_fanin_fanout(tensor: torch.Tensor): """We use this function to substitute fan-in and fan-out calculation in torch.nn.init. This can help us get correct fan-in and fan-out for sharded tensor. """ - assert isinstance(tensor, nn.Parameter), "Sharded tensor initilization is only allowed for paramters" + assert isinstance(tensor, nn.Parameter), "Sharded tensor initialization is only allowed for parameters" # get correct shape of input tensor if not hasattr(tensor, 'colo_attr') or not tensor.colo_attr.param_is_sharded: diff --git a/colossalai/zero/legacy/shard_utils/bucket_tensor_shard_strategy.py b/colossalai/zero/legacy/shard_utils/bucket_tensor_shard_strategy.py index 11297bf6d62c..d663104831ce 100644 --- a/colossalai/zero/legacy/shard_utils/bucket_tensor_shard_strategy.py +++ b/colossalai/zero/legacy/shard_utils/bucket_tensor_shard_strategy.py @@ -14,7 +14,7 @@ class BucketTensorShardStrategy(TensorShardStrategy): """Use the same shard scheme as `TensorShardStrategy`'s, but it gathers tensors of a sub-module together, which will fully utilize network bandwidth. It is especially useful when sub-module contains bias, - since we cannot utilize network bandwidth well if we only gather a bias tensor (bias is usaully small). + since we cannot utilize network bandwidth well if we only gather a bias tensor (bias is usually small). """ def gather(self, tensor_list: List[ShardedTensor], process_group: Optional[dist.ProcessGroup] = None): diff --git a/colossalai/zero/legacy/sharded_model/sharded_model_v2.py b/colossalai/zero/legacy/sharded_model/sharded_model_v2.py index edd2cc8e68fe..b3a83b741825 100644 --- a/colossalai/zero/legacy/sharded_model/sharded_model_v2.py +++ b/colossalai/zero/legacy/sharded_model/sharded_model_v2.py @@ -192,7 +192,7 @@ def cpu_offload(self): def dump_memory_stats(self, filename: Optional[str] = 'dump_mem_stats.log') -> None: """ - dummy memory tracer collected infomation to a file. + dummy memory tracer collected information to a file. try: # forward: model(inputs) # backward: optimizer.backward() @@ -201,7 +201,7 @@ def dump_memory_stats(self, filename: Optional[str] = 'dump_mem_stats.log') -> N exit(0) """ if self._use_memory_tracer: - self.logger.error(f'dump memort tracer collected infomation to a {filename}', ranks=[0]) + self.logger.error(f'dump memort tracer collected information to a {filename}', ranks=[0]) if gpc.get_global_rank() == 0: with open(filename, 'w+') as f: f.write(f'cuda reserved {torch.cuda.memory_reserved(get_current_device()) / 1e9} GB\n') @@ -293,7 +293,7 @@ def _post_backward_operations(self) -> None: if not p.requires_grad: continue # Leave the gradient accumulation state (_require_backward_grad_sync) as-is if not synchronizing this pass. - # NOTE() (no-sync)/sync pass: (not conduct)/conduct gradient allreducing between process group. + # NOTE() (no-sync)/sync pass: (not conduct)/conduct gradient all reducing between process group. # If _require_backward_grad_sync is True, # p.grad remains the accumulated unsharded gradient from prior no-sync passes. # We also allows to interleave no-sync pass with sync passes, if desired. @@ -385,7 +385,7 @@ def _save_grad(self, param: Parameter, grad: torch.Tensor): param.colo_attr.grad_payload_reset(grad.data) # release the memory of param # we set a false None for parameter's payload - # so we can get paramter's device and dtype later in optimizer + # so we can get parameter's device and dtype later in optimizer param.colo_attr.data_payload_reset(torch.empty(0, device=grad.device, dtype=grad.dtype)) if param.colo_attr.is_replicated: diff --git a/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py b/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py index 7ce1c056f583..be60209af434 100644 --- a/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py +++ b/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py @@ -67,8 +67,8 @@ class ShardedOptimizerV2(ColossalaiOptimizer): growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000. hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2. max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32. - dp_process_group (Optional[ProcessGroup], optional): data paralle process group. Defaults to None. - mp_process_group (Optional[ProcessGroup], optional): model paralle process group. Defaults to None. + dp_process_group (Optional[ProcessGroup], optional): data parallel process group. Defaults to None. + mp_process_group (Optional[ProcessGroup], optional): model parallel process group. Defaults to None. .. _PatrickStar\: Parallel Training of Pre-trained Models via Chunk-based Memory Management: https://arxiv.org/abs/2108.05818 @@ -274,7 +274,7 @@ def _register_master_weight(self): assert hasattr(p, 'colo_attr'), 'The parameter must be wrapped with ShardedParam' shard_flag = not p.colo_attr.sharded_data_tensor.is_sharded and p.colo_attr.is_replicated if shard_flag: - # we always shard replicated paramters + # we always shard replicated parameters self.shard_strategy.shard([p.colo_attr.sharded_data_tensor], self.dp_process_group) self.master_params[p] = StatefulTensor(cast_tensor_to_fp32(p.colo_attr.data_payload.to(self.device))) if shard_flag: @@ -312,7 +312,7 @@ def _prepare_grads(self): # If reuse_fp16_shard, grad fp16 which wasn't be offloaded may be evicted to CPU if not p.colo_attr.offload_grad: colo_model_data_tensor_move_inline(p.colo_attr.saved_grad, torch.cuda.current_device()) - # FIXME(ver217): p.data here is an empty tensor on CUDA and has no useful infomation + # FIXME(ver217): p.data here is an empty tensor on CUDA and has no useful information # If we change p.grad directly # it may raise error because of different shape/dtype/device of p.data and p.grad # We just set p.data = p.colo_attr.saved_grad.payload here @@ -333,7 +333,7 @@ def _point_param_fp16_to_master_param(self): def _copy_master_model_to_model_fp16(self): # Copy master param data (fp32) to payload of colo_attr (fp16) - # TODO() improve efficiency by gathering tensors into a chunk and transfering + # TODO() improve efficiency by gathering tensors into a chunk and transferring # a chunk. for group in self.optim.param_groups: for p in group['params']: @@ -350,7 +350,7 @@ def _copy_master_param_to_param_fp16(self, p): p.data = self.master_params[p].payload - # we need to allocate new memory for keep_not_shard paramters + # we need to allocate new memory for keep_not_shard parameters # in order to use copy, otherwise, the sizes of tensor is not compatible if p.colo_attr.data_payload.numel() != p.data.numel(): p.colo_attr.data_payload_reset( diff --git a/colossalai/zero/wrapper.py b/colossalai/zero/wrapper.py index 6cdb8fc59ba5..3e48f49fa305 100644 --- a/colossalai/zero/wrapper.py +++ b/colossalai/zero/wrapper.py @@ -26,7 +26,7 @@ def zero_model_wrapper(model: nn.Module, zero_stage (int, optional): The stage of ZeRO DDP. You can find more information in ZeRO's paper. https://arxiv.org/abs/1910.02054 gemini_config (dict, optional): The configuration dictionary of `GeminiDDP`. `GeminiDDP` is enabled - when the stage is set to 3. You can set the arguemnts of `GeminiDDP` in the gemini_config. + when the stage is set to 3. You can set the arguments of `GeminiDDP` in the gemini_config. Here is an example where we set the device of the model, the placement policy of Gemini, and the size of hidden dimension to help Gemini find out a unified chunk size. @@ -78,7 +78,7 @@ def zero_optim_wrapper(model: nn.Module, max_norm (float, optional): max_norm used for `clip_grad_norm`. You should notice that you shall not do clip_grad_norm by yourself when using ZeRO DDP. The ZeRO optimizer will take care of clip_grad_norm. norm_type (float, optional): norm_type used for `clip_grad_norm`. - optim_config (dict, optinoal): The configuration used for the ZeRO optimizer. + optim_config (dict, optional): The configuration used for the ZeRO optimizer. Example: >>> zero2_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True) diff --git a/docs/source/en/Colossal-Auto/get_started/run_demo.md b/docs/source/en/Colossal-Auto/get_started/run_demo.md index 6f7a82966f20..34872e399c81 100644 --- a/docs/source/en/Colossal-Auto/get_started/run_demo.md +++ b/docs/source/en/Colossal-Auto/get_started/run_demo.md @@ -4,7 +4,7 @@ Colossal-Auto simplifies the process of deploying large-scale machine learning m ### 1. Basic usage -Colossal-Auto can be used to find a hybrid SPMD parallel strategy includes data, tensor(i.e., 1D, 2D, sequencial) for each operation. You can follow the [GPT example](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt/experiments/auto_parallel). +Colossal-Auto can be used to find a hybrid SPMD parallel strategy includes data, tensor(i.e., 1D, 2D, sequential) for each operation. You can follow the [GPT example](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt/experiments/auto_parallel). Detailed instructions can be found in its `README.md`. ### 2. Integration with activation checkpoint diff --git a/docs/source/en/advanced_tutorials/meet_gemini.md b/docs/source/en/advanced_tutorials/meet_gemini.md index 4889b30a6cf8..8afb6705b6ae 100644 --- a/docs/source/en/advanced_tutorials/meet_gemini.md +++ b/docs/source/en/advanced_tutorials/meet_gemini.md @@ -44,7 +44,7 @@ In some solutions, the [Zero-offload](https://arxiv.org/abs/2101.06840) adopted -Colossal-AI designed Gemini, just like two-stars, which manages the memory space of CPU and GPU efficiently. It can make the tensor dynamically distributed in the storage space of CPU-GPU during training, so that the model training can break through the memory wall of GPU. The memory manager consists of two parts: **MemStatsCollector (MSC)** and **StatefuleTensorMgr (STM)**. +Colossal-AI designed Gemini, just like two-stars, which manages the memory space of CPU and GPU efficiently. It can make the tensor dynamically distributed in the storage space of CPU-GPU during training, so that the model training can break through the memory wall of GPU. The memory manager consists of two parts: **MemStatsCollector (MSC)** and **StatefulTensorMgr (STM)**. We take advantage of the iterative characteristics of the deep learning network training process. We divide iterations into two stages: warmup and non-warmup. One or several iterative steps at the beginning belong to the warmup stage, and the other iterative steps belong to the non-warmup stage. In the warmup stage, we collect information for the MSC, while in the non-warmup stage, STM gets the information collected by the MSC to move the tensor, so as to minimize the CPU-GPU data movement volume. diff --git a/docs/source/en/advanced_tutorials/opt_service.md b/docs/source/en/advanced_tutorials/opt_service.md index b317de91bbdd..a43ec7fdd1fe 100644 --- a/docs/source/en/advanced_tutorials/opt_service.md +++ b/docs/source/en/advanced_tutorials/opt_service.md @@ -20,7 +20,7 @@ To launch the distributed inference service quickly, you can download the OPT-12 2. Prepare a prebuilt service image -Pull a docker image from dockerhub installed with Colossal-AI inference. +Pull a docker image from docker hub installed with Colossal-AI inference. ```bash docker pull hpcaitech/energon-ai:latest diff --git a/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md b/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md index 1f3086559939..b2438a1cf562 100644 --- a/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md +++ b/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md @@ -12,7 +12,7 @@ Author: Yuxuan Lou ## Introduction -In this example for ViT model, Colossal-AI provides three different parallelism techniques which acclerate model training: data parallelism, pipeline parallelism and tensor parallelism. +In this example for ViT model, Colossal-AI provides three different parallelism techniques which accelerate model training: data parallelism, pipeline parallelism and tensor parallelism. We will show you how to train ViT on CIFAR-10 dataset with these parallelism techniques. To run this example, you will need 2-4 GPUs. @@ -31,7 +31,7 @@ pip install colossalai ## Data Parallelism -Data parallism is one basic way to accelerate model training process. You can apply data parallism to training by only two steps: +Data parallism is one basic way to accelerate model training process. You can apply data parallelism to training by only two steps: 1. Define a configuration file 2. Change a few lines of code in train script @@ -108,7 +108,7 @@ disable_existing_loggers() logger = get_dist_logger() ``` -After initialization, you can acess the variables in the config file by using `colossalai.core.global_context`. +After initialization, you can access the variables in the config file by using `colossalai.core.global_context`. ```python #access parameters @@ -162,7 +162,7 @@ optimizer = colossalai.nn.Lamb(model.parameters(), lr=1.8e-2, weight_decay=0.1) # build loss criterion = torch.nn.CrossEntropyLoss() -# lr_scheduelr +# lr_scheduler lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=50, total_steps=gpc.config.NUM_EPOCHS) ``` @@ -230,10 +230,10 @@ torchrun --standalone --nproc_per_node train_dp.py --config ./config ## Pipeline Parallelism -Aside from data parallelism, Colossal-AI also support pipleline parallelism. In specific, Colossal-AI uses 1F1B pipeline introduced by NVIDIA. For more details, you can view the related [documents](https://www.colossalai.org/tutorials/features/pipeline_parallel). +Aside from data parallelism, Colossal-AI also support pipeline parallelism. In specific, Colossal-AI uses 1F1B pipeline introduced by NVIDIA. For more details, you can view the related [documents](https://www.colossalai.org/tutorials/features/pipeline_parallel). ### Define your configuration file(`hybrid_parallel/configs/vit_pipeline.py`) -To apply pipleline parallel on the data parallel basis, you only need to add a **parallel dict** +To apply pipeline parallel on the data parallel basis, you only need to add a **parallel dict** ```python from colossalai.amp import AMP_TYPE @@ -250,7 +250,7 @@ clip_grad_norm = 1.0 Other configs： ```python -# hyperparameters +# hyper parameters # BATCH_SIZE is as per GPU # global batch size = BATCH_SIZE x data parallel size BATCH_SIZE = 256 @@ -276,7 +276,7 @@ Colossal-AI provides two methods to build a pipeline model from the existing mod - `colossalai.builder.build_pipeline_model_from_cfg` - `colossalai.builder.build_pipeline_model` -Besides, you can also build a pipeline model from scrath with Colossal-AI. +Besides, you can also build a pipeline model from scratch with Colossal-AI. ```python import math from typing import Callable @@ -521,7 +521,7 @@ def build_cifar(batch_size): return train_dataloader, test_dataloader -# craete dataloaders +# create dataloaders train_dataloader , test_dataloader = build_cifar() # create loss function @@ -539,7 +539,7 @@ lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer, #### Start Colossal-AI engine ```python -# intiailize +# initialize engine, train_dataloader, test_dataloader, _ = colossalai.initialize(model=model, optimizer=optimizer, criterion=criterion, @@ -615,7 +615,7 @@ TENSOR_SHAPE = (BATCH_SIZE // NUM_MICRO_BATCHES, SEQ_LENGTH, HIDDEN_SIZE) Ohter configs: ```python -# hyperparameters +# hyper parameters # BATCH_SIZE is as per GPU # global batch size = BATCH_SIZE x data parallel size BATCH_SIZE = 256 diff --git a/docs/source/en/basics/colotensor_concept.md b/docs/source/en/basics/colotensor_concept.md index 1b855c03b919..909c5e4d3c6f 100644 --- a/docs/source/en/basics/colotensor_concept.md +++ b/docs/source/en/basics/colotensor_concept.md @@ -42,7 +42,7 @@ Therefore, when using Distributed Spec, we only need to describe the way that th ## Compute Spec -An instance of class [ComputeSpec](https://colossalai.readthedocs.io/en/latest/colossalai/colossalai.tensor.compute_spec.html#colossalai.tensor.compute_spec.ComputeSpec) describes how a Coloensor be used in DNN training. Currently, we will set the correct Compute Pattern for the ColoTensor as the parameters of the module. The specific application scenarios will be shown in the next document. +An instance of class [ComputeSpec](https://colossalai.readthedocs.io/en/latest/colossalai/colossalai.tensor.compute_spec.html#colossalai.tensor.compute_spec.ComputeSpec) describes how a Colotensor be used in DNN training. Currently, we will set the correct Compute Pattern for the ColoTensor as the parameters of the module. The specific application scenarios will be shown in the next document. ## ColoParameter diff --git a/docs/source/en/basics/engine_trainer.md b/docs/source/en/basics/engine_trainer.md index 39792f622aa9..bbe32ed5a3b5 100644 --- a/docs/source/en/basics/engine_trainer.md +++ b/docs/source/en/basics/engine_trainer.md @@ -172,7 +172,7 @@ In this config file, we specify that we want to use batch size 128 per GPU and r #### Step 2. Initialize Distributed Environment We need to initialize the distributed training environment. This has been introduced in the tutorial on how to -[launch Colossal-AI](./launch_colossalai.md). For this demostration, we use `launch_from_torch` and PyTorch launch utility. +[launch Colossal-AI](./launch_colossalai.md). For this demonstration, we use `launch_from_torch` and PyTorch launch utility. ```python import colossalai diff --git a/docs/source/en/concepts/colossalai_overview.md b/docs/source/en/concepts/colossalai_overview.md index d75d20196b08..38b682d49e62 100644 --- a/docs/source/en/concepts/colossalai_overview.md +++ b/docs/source/en/concepts/colossalai_overview.md @@ -6,18 +6,18 @@ Author: Shenggui Li, Siqi Mai With the development of deep learning model size, it is important to shift to a new training paradigm. The traditional training method with no parallelism and optimization became a thing of the past and new training methods are the key to make training large-scale models efficient and cost-effective. -Colossal-AI is designed to be a unfied system to provide an integrated set of training skills and utilities to the user. You can find the common training utilities such as mixed precision training and gradient accumulation. Besides, we provide an array of parallelism including data, tensor and pipeline parallelism. We optimize tensor parallelism with different multi-dimensional distributed matrix-matrix multiplication algorithm. We also provided different pipeline parallelism methods to allow the user to scale their model across nodes efficiently. More advanced features such as offloading can be found in this tutorial documentation in detail as well. +Colossal-AI is designed to be a unified system to provide an integrated set of training skills and utilities to the user. You can find the common training utilities such as mixed precision training and gradient accumulation. Besides, we provide an array of parallelism including data, tensor and pipeline parallelism. We optimize tensor parallelism with different multi-dimensional distributed matrix-matrix multiplication algorithm. We also provided different pipeline parallelism methods to allow the user to scale their model across nodes efficiently. More advanced features such as offloading can be found in this tutorial documentation in detail as well. ## General Usage -We aim to make Colossal-AI easy to use and non-instrusive to user code. There is a simple general workflow if you want to use Colossal-AI. +We aim to make Colossal-AI easy to use and non-intrusive to user code. There is a simple general workflow if you want to use Colossal-AI.
          Workflow
          -1. Prepare a configiguration file where specifies the features you want to use and your parameters. +1. Prepare a configuration file where specifies the features you want to use and your parameters. 2. Initialize distributed backend with `colossalai.launch` 3. Inject the training features into your training components (e.g. model, optimizer) with `colossalai.initialize`. 4. Run training and testing diff --git a/docs/source/en/features/1D_tensor_parallel.md b/docs/source/en/features/1D_tensor_parallel.md index 695a8f31f8c5..7577e50400e9 100644 --- a/docs/source/en/features/1D_tensor_parallel.md +++ b/docs/source/en/features/1D_tensor_parallel.md @@ -42,7 +42,7 @@ Given $P$ processors, we present the theoretical computation and memory cost, as ## Usage -To enable 1D tensor parallelism for our model, e.g. on 2 GPUs, we need to configure the parallism setting as below. +To enable 1D tensor parallelism for our model, e.g. on 2 GPUs, we need to configure the parallelism setting as below. ```python CONFIG = dict(parallel=dict( data=1, diff --git a/docs/source/en/features/2D_tensor_parallel.md b/docs/source/en/features/2D_tensor_parallel.md index 582614c2f2f4..7b6c10766099 100644 --- a/docs/source/en/features/2D_tensor_parallel.md +++ b/docs/source/en/features/2D_tensor_parallel.md @@ -60,7 +60,7 @@ Given $P=q\times q$ processors, we present the theoretical computation and memor ## Usage -To enable 2D tensor parallelism for our model, e.g. on 4 GPUs, we need to configure the parallism setting as below. +To enable 2D tensor parallelism for our model, e.g. on 4 GPUs, we need to configure the parallelism setting as below. ```python CONFIG = dict(parallel=dict( data=1, diff --git a/docs/source/en/features/2p5D_tensor_parallel.md b/docs/source/en/features/2p5D_tensor_parallel.md index 34a261ea0aa0..6076562e6dca 100644 --- a/docs/source/en/features/2p5D_tensor_parallel.md +++ b/docs/source/en/features/2p5D_tensor_parallel.md @@ -57,7 +57,7 @@ Given $P=q \times q \times d$ processors, we present the theoretical computation ## Usage -To enable 2.5D tensor parallelism for our model, e.g. on 8 GPUs, we need to configure the parallism setting as below. +To enable 2.5D tensor parallelism for our model, e.g. on 8 GPUs, we need to configure the parallelism setting as below. ```python CONFIG = dict(parallel=dict( data=1, diff --git a/docs/source/en/features/gradient_accumulation.md b/docs/source/en/features/gradient_accumulation.md index d8781ee691bc..ecc209fbac8d 100644 --- a/docs/source/en/features/gradient_accumulation.md +++ b/docs/source/en/features/gradient_accumulation.md @@ -28,7 +28,7 @@ gradient_accumulation = ## Hands-on Practice We provide a [runnable example](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/gradient_accumulation) -to demonstrate gradient accumulation. In this example, we set the gradinet accumulation size to be 4. You can run the script using this command: +to demonstrate gradient accumulation. In this example, we set the gradient accumulation size to be 4. You can run the script using this command: ```shell python -m torch.distributed.launch --nproc_per_node 1 --master_addr localhost --master_port 29500 run_resnet_cifar10_with_engine.py diff --git a/docs/source/en/features/mixed_precision_training.md b/docs/source/en/features/mixed_precision_training.md index 71cb6971d346..11aa5235301a 100644 --- a/docs/source/en/features/mixed_precision_training.md +++ b/docs/source/en/features/mixed_precision_training.md @@ -101,7 +101,7 @@ you can use `colossalai.amp.convert_to_amp`. ```python from colossalai.amp import AMP_TYPE -# exmaple of using torch amp +# example of using torch amp model, optimizer, criterion = colossalai.amp.convert_to_amp(model, optimizer, criterion, @@ -220,7 +220,7 @@ The default parameters of Naive AMP: - initial_scale(int): initial scale of gradient scaler - growth_factor(int): the growth rate of loss scale - backoff_factor(float): the decrease rate of loss scale -- hysterisis(int): delay shift in dynamic loss scaling +- hysteresis(int): delay shift in dynamic loss scaling - max_scale(int): maximum loss scale allowed - verbose(bool): if set to `True`, will print debug info @@ -292,7 +292,7 @@ colossalai.launch_from_torch(config=args.config) ### Step 4. Create training components Build your model, optimizer, loss function, lr scheduler and dataloaders. Note that the root path of the dataset is -obtained from the environment varialbe `DATA`. You may `export DATA=/path/to/data` or change `Path(os.environ['DATA'])` +obtained from the environment variable `DATA`. You may `export DATA=/path/to/data` or change `Path(os.environ['DATA'])` to a path on your machine. Data will be automatically downloaded to the root path. ```python @@ -326,7 +326,7 @@ to a path on your machine. Data will be automatically downloaded to the root pat # build loss criterion = torch.nn.CrossEntropyLoss() - # lr_scheduelr + # lr_scheduler lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=50, total_steps=gpc.config.NUM_EPOCHS) ``` diff --git a/docs/source/en/features/nvme_offload.md b/docs/source/en/features/nvme_offload.md index 38d2c4af904c..4374da3c9c45 100644 --- a/docs/source/en/features/nvme_offload.md +++ b/docs/source/en/features/nvme_offload.md @@ -57,7 +57,7 @@ It's compatible with all parallel methods in ColossalAI. Let's start from two simple examples -- training GPT with different methods. These examples relies on `transformers`. -We should install denpendencies first: +We should install dependencies first: ```shell pip install psutil transformers @@ -99,7 +99,7 @@ class GPTLMLoss(nn.Module): shift_labels.view(-1)) ``` -And we define some utility functions, which generates random data, computes the number of paramters of a model and get memory usage of current process: +And we define some utility functions, which generates random data, computes the number of parameters of a model and get memory usage of current process: ```python def get_data(batch_size: int, seq_len: int, @@ -251,7 +251,7 @@ Time: 3.691 s Mem usage: 5298.344 MB ``` -NVME offload saves about 294 MB memory. Note that enabling `pin_memory` of Gemini can accelerate training but increase memory usage. So this result also meets our expectation. If we disable `pin_memory`, we can aslo observe a memory usage drop about 900 MB. +NVME offload saves about 294 MB memory. Note that enabling `pin_memory` of Gemini can accelerate training but increase memory usage. So this result also meets our expectation. If we disable `pin_memory`, we can also observe a memory usage drop about 900 MB. ## API Reference diff --git a/docs/source/en/features/zero_with_chunk.md b/docs/source/en/features/zero_with_chunk.md index 6b0a9585af85..a105831a5409 100644 --- a/docs/source/en/features/zero_with_chunk.md +++ b/docs/source/en/features/zero_with_chunk.md @@ -32,11 +32,11 @@ and the first and second momentum estimates) are partitioned across the processe 3. **Shard Parameter**: The 16-bit model parameters are partitioned across the processes of a data parallel group. -4. **[Gemini](../advanced_tutorials/meet_gemini.md)**: Dynamic heterogeneous memory space manager for paramters, gradients and optimizer states. +4. **[Gemini](../advanced_tutorials/meet_gemini.md)**: Dynamic heterogeneous memory space manager for parameters, gradients and optimizer states. Besides, this article will introduce the Zero Redundancy Optimizer with chunk-based memory management. -When using ZeRO, we distributed the model by sharding the parameters. The advantage of this method is that the memory of each node is load balanced. But this approach has two significiant disadvantages. First, during communication, a temporary memory buffer needs to be allocated and released afterwards, leading to the memory fragmentation problem. Secondly, using tensor as the granularity for communication will cause the network bandwidth underutilized. Generally, the longer the transmitted message length, the higher the bandwidth utilization. +When using ZeRO, we distributed the model by sharding the parameters. The advantage of this method is that the memory of each node is load balanced. But this approach has two significant disadvantages. First, during communication, a temporary memory buffer needs to be allocated and released afterwards, leading to the memory fragmentation problem. Secondly, using tensor as the granularity for communication will cause the network bandwidth underutilized. Generally, the longer the transmitted message length, the higher the bandwidth utilization. Using the Chunk mechanism introduced in ColossalAI v0.1.8, we can improve the efficiency of ZeRO. We store a continuous set of parameters in initialization order into a Chunk (a chunk is a continuous memory space), and each Chunk has the same size. Organizing memory in chunks can lead to efficient use of network bandwidth between PCI-e and GPU-GPU, reduce the number of communications, and avoid potential memory fragmentation. diff --git a/examples/images/diffusion/ldm/data/teyvat.py b/examples/images/diffusion/ldm/data/teyvat.py index 61dc29d56e7c..eb5d3ea469d4 100644 --- a/examples/images/diffusion/ldm/data/teyvat.py +++ b/examples/images/diffusion/ldm/data/teyvat.py @@ -13,7 +13,7 @@ def make_multi_folder_data(paths, caption_files=None, **kwargs): """Make a concat dataset from multiple folders - Don't suport captions yet + Don't support captions yet If paths is a list, that's ok, if it's a Dict interpret it as: k=folder v=n_times to repeat that """ diff --git a/examples/images/diffusion/main.py b/examples/images/diffusion/main.py index e31d75e0874d..713029fc677d 100644 --- a/examples/images/diffusion/main.py +++ b/examples/images/diffusion/main.py @@ -40,7 +40,7 @@ class DataLoaderX(DataLoader): # A custom data loader class that inherits from DataLoader def __iter__(self): # Overriding the __iter__ method of DataLoader to return a BackgroundGenerator - #This is to enable data laoding in the background to improve training performance + #This is to enable data loading in the background to improve training performance return BackgroundGenerator(super().__iter__()) @@ -60,7 +60,7 @@ def str2bool(v): # Create an ArgumentParser object with specifies kwargs parser = argparse.ArgumentParser(**parser_kwargs) - # Add vairous command line arguments with their default balues and descriptions + # Add various command line arguments with their default values and descriptions parser.add_argument( "-n", "--name", @@ -162,7 +162,7 @@ def str2bool(v): # A function that returns the non-default arguments between two objects def nondefault_trainer_args(opt): - # create an argument parsser + # create an argument parser parser = argparse.ArgumentParser() # add pytorch lightning trainer default arguments parser = Trainer.add_argparse_args(parser) @@ -203,7 +203,7 @@ def worker_init_fn(_): else: return np.random.seed(np.random.get_state()[1][0] + worker_id) -#Provide functionality for creating data loadedrs based on provided dataset configurations +#Provide functionality for creating data loaders based on provided dataset configurations class DataModuleFromConfig(pl.LightningDataModule): def __init__(self, @@ -255,7 +255,7 @@ def setup(self, stage=None): def _train_dataloader(self): #Check if the train dataset is iterable is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) - #Set the worker initialization function of the dataset isiterable or use_worker_init_fn is True + #Set the worker initialization function of the dataset is iterable or use_worker_init_fn is True if is_iterable_dataset or self.use_worker_init_fn: init_fn = worker_init_fn else: @@ -310,7 +310,7 @@ def _predict_dataloader(self, shuffle=False): class SetupCallback(Callback): - # I nitialize the callback with the necessary parameters + # Initialize the callback with the necessary parameters def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config): super().__init__() @@ -371,7 +371,7 @@ def on_fit_start(self, trainer, pl_module): # trainer.save_checkpoint(ckpt_path) -# PyTorch Lightning callback for ogging images during training and validation of a deep learning model +# PyTorch Lightning callback for logging images during training and validation of a deep learning model class ImageLogger(Callback): def __init__(self, @@ -379,10 +379,10 @@ def __init__(self, max_images, # Maximum number of images to log clamp=True, # Whether to clamp pixel values to [-1,1] increase_log_steps=True, # Whether to increase frequency of log steps exponentially - rescale=True, # Whetehr to rescale pixel values to [0,1] + rescale=True, # Whether to rescale pixel values to [0,1] disabled=False, # Whether to disable logging - log_on_batch_idx=False, # Whether to log on baych index instead of global step - log_first_step=False, # Whetehr to log on the first step + log_on_batch_idx=False, # Whether to log on batch index instead of global step + log_first_step=False, # Whether to log on the first step log_images_kwargs=None): # Additional keyword arguments to pass to log_images method super().__init__() self.rescale = rescale @@ -593,7 +593,7 @@ def on_train_epoch_end(self, trainer, pl_module): parser = Trainer.add_argparse_args(parser) opt, unknown = parser.parse_known_args() - # Veirfy the arguments are both specified + # Verify the arguments are both specified if opt.name and opt.resume: raise ValueError("-n/--name and -r/--resume cannot be specified both." "If you want to resume training in a new log folder, " @@ -646,7 +646,7 @@ def on_train_epoch_end(self, trainer, pl_module): # Sets the seed for the random number generator to ensure reproducibility seed_everything(opt.seed) - # Intinalize and save configuratioon using teh OmegaConf library. + # Initialize and save configuration using teh OmegaConf library. try: # init and save configs configs = [OmegaConf.load(cfg) for cfg in opt.base] diff --git a/examples/images/dreambooth/README.md b/examples/images/dreambooth/README.md index b067a437c764..7c117d841e24 100644 --- a/examples/images/dreambooth/README.md +++ b/examples/images/dreambooth/README.md @@ -61,7 +61,7 @@ torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \ - `INSTANCE_DIR` refers to personalized path to instance images, you might need to insert information here. - `OUTPUT_DIR` refers to local path to save the trained model, you might need to find a path with enough space. - `resolution` refers to the corresponding resolution number of your target model. Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model. -- `placement` refers to the training strategy supported by Colossal AI, defult = 'cuda', which refers to loading all the parameters into cuda memory. On the other hand, 'cpu' refers to 'cpu offload' strategy while 'auto' enables 'Gemini', both featured by Colossal AI. +- `placement` refers to the training strategy supported by Colossal AI, default = 'cuda', which refers to loading all the parameters into cuda memory. On the other hand, 'cpu' refers to 'cpu offload' strategy while 'auto' enables 'Gemini', both featured by Colossal AI. ### Training with prior-preservation loss diff --git a/examples/language/gpt/README.md b/examples/language/gpt/README.md index 10d6c2ddd5d7..47d24a4d69cb 100644 --- a/examples/language/gpt/README.md +++ b/examples/language/gpt/README.md @@ -40,7 +40,7 @@ We provide two stable solutions. One utilizes the Gemini to implement hybrid parallel strategies of Gemini, DDP/ZeRO, and Tensor Parallelism for a huggingface GPT model. The other one use [Titans](https://github.com/hpcaitech/Titans), a distributed executed model zoo maintained by ColossalAI,to implement the hybrid parallel strategies of TP + ZeRO + PP. -We recommend using Gemini to qucikly run your model in a distributed manner. +We recommend using Gemini to quickly run your model in a distributed manner. It doesn't require significant changes to the model structures, therefore you can apply it on a new model easily. And use Titans as an advanced weapon to pursue a more extreme performance. Titans has included the some typical models, such as Vit and GPT. diff --git a/examples/language/gpt/experiments/auto_offload/README.md b/examples/language/gpt/experiments/auto_offload/README.md index a0d252119056..535aa76541cc 100644 --- a/examples/language/gpt/experiments/auto_offload/README.md +++ b/examples/language/gpt/experiments/auto_offload/README.md @@ -27,7 +27,7 @@ pip install transformers ## Dataset -For simplicity, the input data is randonly generated here. +For simplicity, the input data is randomly generated here. ## Training diff --git a/examples/language/gpt/experiments/auto_parallel/README.md b/examples/language/gpt/experiments/auto_parallel/README.md index 404c8391109e..1c8b1c35109f 100644 --- a/examples/language/gpt/experiments/auto_parallel/README.md +++ b/examples/language/gpt/experiments/auto_parallel/README.md @@ -34,7 +34,7 @@ conda install -c conda-forge coin-or-cbc ## Dataset -For simplicity, the input data is randonly generated here. +For simplicity, the input data is randomly generated here. ## Training diff --git a/examples/language/gpt/experiments/pipeline_parallel/README.md b/examples/language/gpt/experiments/pipeline_parallel/README.md index 702e3c8d6540..5af994a00665 100644 --- a/examples/language/gpt/experiments/pipeline_parallel/README.md +++ b/examples/language/gpt/experiments/pipeline_parallel/README.md @@ -27,7 +27,7 @@ pip install transformers ## Dataset -For simplicity, the input data is randonly generated here. +For simplicity, the input data is randomly generated here. ## Training diff --git a/examples/language/opt/train_gemini_opt.py b/examples/language/opt/train_gemini_opt.py index 4874f831c2ec..3614b689de26 100755 --- a/examples/language/opt/train_gemini_opt.py +++ b/examples/language/opt/train_gemini_opt.py @@ -163,7 +163,7 @@ def main(): else: init_dev = get_current_device() - # shard init prameters + # shard init parameters if args.shardinit: logger.info("Sharding initialization !", ranks=[0]) else: @@ -192,7 +192,7 @@ def main(): config=config, local_files_only=False) - # enable graident checkpointing + # enable gradient checkpointing model.gradient_checkpointing_enable() numel = sum([p.numel() for p in model.parameters()]) From 4b3240cb595ecfa9378c52b58b9363a3db5c77c8 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 26 Apr 2023 14:37:25 +0800 Subject: [PATCH 162/413] [booster] add low level zero plugin (#3594) * [booster] add low level zero plugin * [booster] fix gemini plugin test * [booster] fix precision * [booster] add low level zero plugin test * [test] fix booster plugin test oom * [test] fix booster plugin test oom * [test] fix googlenet and inception output trans * [test] fix diffuser clip vision model * [test] fix torchaudio_wav2vec2_base * [test] fix low level zero plugin test --- colossalai/booster/plugin/__init__.py | 3 +- .../booster/plugin/low_level_zero_plugin.py | 259 ++++++++++++++++++ colossalai/zero/low_level/low_level_optim.py | 1 + tests/kit/model_zoo/diffusers/diffusers.py | 3 +- tests/kit/model_zoo/torchaudio/torchaudio.py | 16 +- .../kit/model_zoo/torchvision/torchvision.py | 8 +- .../test_plugin/test_gemini_plugin.py | 94 ++++--- .../test_plugin/test_low_level_zero_plugin.py | 122 +++++++++ .../test_plugin/test_torch_ddp_plugin.py | 43 +-- 9 files changed, 472 insertions(+), 77 deletions(-) create mode 100644 colossalai/booster/plugin/low_level_zero_plugin.py create mode 100644 tests/test_booster/test_plugin/test_low_level_zero_plugin.py diff --git a/colossalai/booster/plugin/__init__.py b/colossalai/booster/plugin/__init__.py index 8e09b6cb281d..aa45bcb59ad7 100644 --- a/colossalai/booster/plugin/__init__.py +++ b/colossalai/booster/plugin/__init__.py @@ -1,5 +1,6 @@ from .gemini_plugin import GeminiPlugin +from .low_level_zero_plugin import LowLevelZeroPlugin from .plugin_base import Plugin from .torch_ddp_plugin import TorchDDPPlugin -__all__ = ['Plugin', 'TorchDDPPlugin', 'GeminiPlugin'] +__all__ = ['Plugin', 'TorchDDPPlugin', 'GeminiPlugin', 'LowLevelZeroPlugin'] diff --git a/colossalai/booster/plugin/low_level_zero_plugin.py b/colossalai/booster/plugin/low_level_zero_plugin.py new file mode 100644 index 000000000000..969c430bd317 --- /dev/null +++ b/colossalai/booster/plugin/low_level_zero_plugin.py @@ -0,0 +1,259 @@ +import random +import warnings +from typing import Callable, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +from torch import Tensor +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from torch.utils._pytree import tree_map +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from colossalai.checkpoint_io import CheckpointIO +from colossalai.interface import ModelWrapper, OptimizerWrapper +from colossalai.utils import get_current_device +from colossalai.zero import zero_model_wrapper, zero_optim_wrapper + +from .plugin_base import Plugin +from .torch_ddp_plugin import TorchDDPCheckpointIO + +__all__ = ['LowLevelZeroPlugin'] + + +def _convert_to_fp16(x): + if isinstance(x, torch.Tensor) and torch.is_floating_point(x): + return x.half() + return x + + +class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO): + + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): + """ + Save optimizer to checkpoint but only on master process. + """ + # TODO(ver217): optimizer state dict is sharded + super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor) + + +class LowLevelZeroModel(ModelWrapper): + + def __init__(self, module: nn.Module, stage: int, precision: str) -> None: + super().__init__(module) + self.convert_inputs = (precision == 'fp16') + module = zero_model_wrapper(module, zero_stage=stage) + if precision == 'fp16': + module = module.half() + module = module.to(get_current_device()) + self.module = module + + def forward(self, *args, **kwargs): + if self.convert_inputs: + args = tree_map(_convert_to_fp16, args) + kwargs = tree_map(_convert_to_fp16, kwargs) + return super().forward(*args, **kwargs) + + +class LowLevelZeroOptimizer(OptimizerWrapper): + + def __init__(self, + module: nn.Module, + optimizer: Optimizer, + zero_optim_config: dict, + optim_kwargs: dict, + verbose: bool = False) -> None: + optimizer = zero_optim_wrapper(module, + optimizer, + optim_config=zero_optim_config, + **optim_kwargs, + verbose=verbose) + super().__init__(optimizer) + + def backward(self, loss: Tensor, *args, **kwargs): + self.optim.backward(loss) + + def clip_grad_by_norm(self, + max_norm: Union[float, int], + norm_type: Union[float, int] = 2, + error_if_nonfinite: bool = False, + *args, + **kwargs) -> Tensor: + warnings.warn(f'LowLevelZero controls grad clipping by itself, so you should not use clip_grad_by_norm') + + def clip_grad_by_value(self, clip_value: float, *args, **kwargs) -> None: + raise NotImplementedError('LowLevelZero does not support clip_grad_by_value') + + +class LowLevelZeroPlugin(Plugin): + """ + Plugin for low level zero. + + Example: + >>> from colossalai.booster import Booster + >>> from colossalai.booster.plugin import LowLevelZeroPlugin + >>> + >>> model, train_dataset, optimizer, criterion = ... + >>> plugin = LowLevelZeroPlugin() + + >>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8) + >>> booster = Booster(plugin=plugin) + >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion) + + Args: + strage (int, optional): ZeRO stage. Defaults to 1. + precision (str, optional): precision. Support 'fp16' and 'fp32'. Defaults to 'fp16'. + initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32. + min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1. + growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2. + backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5. + growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000. + hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2. + max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32. + max_norm (float, optional): max_norm used for `clip_grad_norm`. You should notice that you shall not do + clip_grad_norm by yourself when using ZeRO DDP. The ZeRO optimizer will take care of clip_grad_norm. + norm_type (float, optional): norm_type used for `clip_grad_norm`. + reduce_bucket_size_in_m (int, optional): grad reduce bucket size in M. Defaults to 12. + communication_dtype (torch.dtype, optional): communication dtype. If not specified, the dtype of param will be used. Defaults to None. + overlap_communication (bool, optional): whether to overlap communication and computation. Defaults to True. + cpu_offload (bool, optional): whether to offload grad, master weight and optimizer state to cpu. Defaults to False. + verbose (bool, optional): verbose mode. Debug info including grad overflow will be printed. Defaults to False. + """ + + def __init__( + self, + stage: int = 1, + precision: str = 'fp16', + initial_scale: float = 2**32, + min_scale: float = 1, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + max_scale: float = 2**32, + max_norm: float = 0.0, + norm_type: float = 2.0, + reduce_bucket_size_in_m: int = 12, + communication_dtype: Optional[torch.dtype] = None, + overlap_communication: bool = True, + cpu_offload: bool = False, + verbose: bool = False, + ) -> None: + + assert dist.is_initialized( + ), 'torch.distributed is not initialized, please use colossalai.launch to create the distributed environment' + assert stage in (1, 2), f'LowLevelZeroPlugin only supports stage 1/2 training' + assert precision in ('fp16', 'fp32'), f'LowLevelZeroPlugin only supports fp16/fp32 training' + + self.rank = dist.get_rank() + self.world_size = dist.get_world_size() + + self.stage = stage + self.precision = precision + self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size_in_m * 1024 * 1024, + communication_dtype=communication_dtype, + overlap_communication=overlap_communication, + cpu_offload=cpu_offload) + self.optim_kwargs = dict(initial_scale=initial_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + min_scale=min_scale, + max_scale=max_scale, + max_norm=max_norm, + norm_type=norm_type) + self.verbose = verbose + + def support_no_sync(self) -> bool: + return False + + def control_precision(self) -> bool: + return True + + def supported_precisions(self) -> List[str]: + return ['fp16', 'fp32'] + + def control_device(self) -> bool: + return True + + def supported_devices(self) -> List[str]: + return ['cuda'] + + def prepare_train_dataloader(self, + dataset, + batch_size, + shuffle=False, + seed=1024, + drop_last=False, + pin_memory=False, + num_workers=0, + **kwargs): + r""" + Prepare a dataloader for distributed training. The dataloader will be wrapped by + `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. + + Note: + 1. Evaluation datasets should not be passed to this function. + + Args: + dataset (`torch.utils.data.Dataset`): The dataset to be loaded. + shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. + seed (int, optional): Random worker seed for sampling, defaults to 1024. + add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. + drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size + is not divisible by the batch size. If False and the size of dataset is not divisible by + the batch size, then the last batch will be smaller, defaults to False. + pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. + num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. + kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in + `DataLoader `_. + + Returns: + :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. + """ + _kwargs = kwargs.copy() + sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle) + + # Deterministic dataloader + def seed_worker(worker_id): + worker_seed = seed + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + random.seed(worker_seed) + + return DataLoader(dataset, + batch_size=batch_size, + sampler=sampler, + worker_init_fn=seed_worker, + drop_last=drop_last, + pin_memory=pin_memory, + num_workers=num_workers, + **_kwargs) + + def configure( + self, + model: nn.Module, + optimizer: Optimizer, + criterion: Callable = None, + dataloader: DataLoader = None, + lr_scheduler: LRScheduler = None, + ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + + if not isinstance(model, ModelWrapper): + model = LowLevelZeroModel(model, self.stage, self.precision) + + if not isinstance(optimizer, OptimizerWrapper): + optimizer = LowLevelZeroOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs, + self.verbose) + + return model, optimizer, criterion, dataloader, lr_scheduler + + def control_checkpoint_io(self) -> bool: + return True + + def get_checkpoint_io(self) -> CheckpointIO: + return LowLevelZeroCheckpointIO() diff --git a/colossalai/zero/low_level/low_level_optim.py b/colossalai/zero/low_level/low_level_optim.py index 39ade27b9d98..59c99113e644 100644 --- a/colossalai/zero/low_level/low_level_optim.py +++ b/colossalai/zero/low_level/low_level_optim.py @@ -55,6 +55,7 @@ def __init__( # 2. contiguous gradients # 3. cpu offload # 4. support when some parameters requires_grad = False + # 5. support layer drop super(LowLevelZeroOptimizer, self).__init__(optim=optimizer) self._dtype = self.optim.param_groups[0]['params'][0].dtype self._logger = get_dist_logger() diff --git a/tests/kit/model_zoo/diffusers/diffusers.py b/tests/kit/model_zoo/diffusers/diffusers.py index 8aa3f4c6741f..204c1d7773ca 100644 --- a/tests/kit/model_zoo/diffusers/diffusers.py +++ b/tests/kit/model_zoo/diffusers/diffusers.py @@ -18,6 +18,7 @@ data_unet_fn = lambda: dict(sample=torch.randn(2, 3, 32, 32), timestep=3) identity_output = lambda x: x +clip_vision_model_output = lambda x: dict(pooler_output=x[1]) def data_clip_model(): @@ -65,7 +66,7 @@ def data_clip_vision(): model_zoo.register(name='diffusers_clip_vision_model', model_fn=partial(transformers.CLIPVisionModel, config=transformers.CLIPVisionConfig()), data_gen_fn=data_clip_vision, - output_transform_fn=identity_output) + output_transform_fn=clip_vision_model_output) model_zoo.register(name='diffusers_unet2d_model', model_fn=diffusers.UNet2DModel, diff --git a/tests/kit/model_zoo/torchaudio/torchaudio.py b/tests/kit/model_zoo/torchaudio/torchaudio.py index 74611720292f..9a244ac312c0 100644 --- a/tests/kit/model_zoo/torchaudio/torchaudio.py +++ b/tests/kit/model_zoo/torchaudio/torchaudio.py @@ -1,3 +1,5 @@ +from functools import partial + import torch import torchaudio.models as tm @@ -101,13 +103,11 @@ def tacotron_data_gen_fn(): mel_specgram_lengths=mel_specgram_lengths) -model_zoo.register( - name='torchaudio_tacotron', - model_fn=lambda: tm.Tacotron2(n_mels=N_MELS), - data_gen_fn=tacotron_data_gen_fn, - output_transform_fn=lambda outputs: dict( - spectrogram_before=outputs[0], spectrogram_after=outputs[1], stop_tokens=outputs[2], attn_weights=outputs[3]), - model_attribute=ModelAttribute(has_control_flow=True)) +model_zoo.register(name='torchaudio_tacotron', + model_fn=lambda: tm.Tacotron2(n_mels=N_MELS), + data_gen_fn=tacotron_data_gen_fn, + output_transform_fn=lambda outputs: dict(summed_output=sum(x.sum() for x in outputs)), + model_attribute=ModelAttribute(has_control_flow=True)) def wav2vec_data_gen_fn(): @@ -118,7 +118,7 @@ def wav2vec_data_gen_fn(): model_zoo.register(name='torchaudio_wav2vec2_base', - model_fn=tm.wav2vec2_base, + model_fn=partial(tm.wav2vec2_base, encoder_layer_drop=0.0), data_gen_fn=wav2vec_data_gen_fn, output_transform_fn=transformer_output_transform_fn, model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/kit/model_zoo/torchvision/torchvision.py b/tests/kit/model_zoo/torchvision/torchvision.py index 62bda93d5a75..ddc3ec24b2ff 100644 --- a/tests/kit/model_zoo/torchvision/torchvision.py +++ b/tests/kit/model_zoo/torchvision/torchvision.py @@ -36,12 +36,12 @@ def swin_s(): # special output transform fn -google_net_output_transform_fn = lambda x: dict(output=x.logits) if isinstance(x, torchvision.models.GoogLeNetOutputs - ) else dict(output=x) +google_net_output_transform_fn = lambda x: dict(output=sum(x)) if isinstance(x, torchvision.models.GoogLeNetOutputs + ) else dict(output=x) swin_s_output_output_transform_fn = lambda x: {f'output{idx}': val for idx, val in enumerate(x)} if isinstance(x, tuple) else dict(output=x) -inception_v3_output_transform_fn = lambda x: dict(output=x.logits) if isinstance(x, torchvision.models.InceptionOutputs - ) else dict(output=x) +inception_v3_output_transform_fn = lambda x: dict(output=sum(x)) if isinstance(x, torchvision.models.InceptionOutputs + ) else dict(output=x) model_zoo.register(name='torchvision_alexnet', model_fn=tm.alexnet, diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index d804c727ad3e..985d7989fc9d 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -1,4 +1,5 @@ from contextlib import nullcontext +from typing import Optional import torch import torch.distributed as dist @@ -10,11 +11,53 @@ from colossalai.nn.optimizer import HybridAdam from colossalai.tensor.colo_parameter import ColoParameter from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils.model.experimental import LazyInitContext from colossalai.zero import ColoInitContext from tests.kit.model_zoo import model_zoo -@parameterize('init_method', ['lazy', 'none', 'colo']) +def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn) -> Optional[str]: + try: + if init_method == 'colo': + ctx = ColoInitContext() + elif init_method == 'lazy': + ctx = LazyInitContext() + else: + ctx = nullcontext() + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, max_norm=1.0, initial_scale=2**5) + booster = Booster(plugin=plugin) + with ctx: + model = model_fn() + optimizer = HybridAdam(model.parameters(), lr=1e-3) + criterion = lambda x: x.mean() + data = data_gen_fn() + + data = { + k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items() + } + + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + for n, p in model.named_parameters(): + assert isinstance(p, ColoParameter), f'{n} is not a ColoParameter' + + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + + booster.backward(loss, optimizer) + optimizer.step() + + except Exception as e: + return repr(e) + + +# TODO(ver217): CI does not support lazy now +# @parameterize('init_method', ['lazy', 'none', 'colo']) + + +@parameterize('init_method', ['none']) def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): """check gemini plugin over model zoo @@ -25,7 +68,6 @@ def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): if not is_support_meta and init_method == 'lazy': return - from colossalai.utils.model.experimental import LazyInitContext passed_models = [] failed_info = {} # (model_name, error) pair @@ -58,47 +100,15 @@ def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): ]: continue - try: - if init_method == 'colo': - ctx = ColoInitContext() - elif init_method == 'lazy': - ctx = LazyInitContext() - else: - ctx = nullcontext() - plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, max_norm=1.0, initial_scale=2**5) - booster = Booster(plugin=plugin) - with ctx: - model = model_fn() - optimizer = HybridAdam(model.parameters(), lr=1e-3) - criterion = lambda x: x.mean() - data = data_gen_fn() - - data = { - k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v - for k, v in data.items() - } - - model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) - - for n, p in model.named_parameters(): - assert isinstance(p, ColoParameter), f'{n} is not a ColoParameter' - - output = model(**data) - output = output_transform_fn(output) - output_key = list(output.keys())[0] - loss = criterion(output[output_key]) - - booster.backward(loss, optimizer) - optimizer.step() - passed_models.append(name) + err = run_fn(init_method, model_fn, data_gen_fn, output_transform_fn) + torch.cuda.empty_cache() - del booster, plugin, model, optimizer, criterion, data, output, loss - except Exception as e: - failed_info[name] = e + if err is None: + passed_models.append(name) + else: + failed_info[name] = err if early_stop: - raise e - - torch.cuda.empty_cache() + break if dist.get_rank() == 0: print(f'Init method: {init_method}') @@ -140,7 +150,7 @@ def run_dist(rank, world_size, port, early_stop: bool = True): @rerun_if_address_is_in_use() def test_gemini_plugin(early_stop: bool = True): - spawn(run_dist, 2, early_stop=early_stop) + spawn(run_dist, 4, early_stop=early_stop) if __name__ == '__main__': diff --git a/tests/test_booster/test_plugin/test_low_level_zero_plugin.py b/tests/test_booster/test_plugin/test_low_level_zero_plugin.py new file mode 100644 index 000000000000..e24196a14917 --- /dev/null +++ b/tests/test_booster/test_plugin/test_low_level_zero_plugin.py @@ -0,0 +1,122 @@ +from typing import Optional + +import torch +import torch.distributed as dist + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import LowLevelZeroPlugin +from colossalai.nn.optimizer import HybridAdam +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo + +# These models are not compatible with AMP +_AMP_ERR_MODELS = ['timm_convit', 'dlrm', 'deepfm_interactionarch', 'deepfm_simpledeepfmnn`'] +# These models have no parameters +_LOW_LEVEL_ZERO_ERR_MODELS = ['dlrm_interactionarch'] +# These models will get stuck +_STUCK_MODELS = [ + 'diffusers_vq_model', 'transformers_albert', 'transformers_albert_for_pretraining', 'transformers_bert', + 'transformers_bert_for_pretraining', 'transformers_gpt_double_heads' +] + + +def run_fn(stage, model_fn, data_gen_fn, output_transform_fn) -> Optional[str]: + try: + plugin = LowLevelZeroPlugin(stage=stage, max_norm=1.0, initial_scale=2**5) + booster = Booster(plugin=plugin) + model = model_fn() + optimizer = HybridAdam(model.parameters(), lr=1e-3) + criterion = lambda x: x.mean() + data = data_gen_fn() + + data = { + k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items() + } + + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + + booster.backward(loss, optimizer) + optimizer.step() + + except Exception as e: + return repr(e) + + +@parameterize('stage', [2]) +def check_low_level_zero_plugin(stage: int, early_stop: bool = True): + """check low level zero plugin over model zoo + + Args: + stage (int), stage of low level zero plugin + early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True. + """ + passed_models = [] + failed_info = {} # (model_name, error) pair + ignore_models = _AMP_ERR_MODELS + _LOW_LEVEL_ZERO_ERR_MODELS + _STUCK_MODELS + skipped_models = [] + + for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + # FIXME(ver217): fix these models + if name in ignore_models: + skipped_models.append(name) + continue + err = run_fn(stage, model_fn, data_gen_fn, output_transform_fn) + torch.cuda.empty_cache() + + if err is None: + passed_models.append(name) + else: + failed_info[name] = err + if early_stop: + break + + if dist.get_rank() == 0: + print(f'Passed models({len(passed_models)}): {passed_models}\n\n') + print(f'Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n') + print(f'Skipped models({len(skipped_models)}): {skipped_models}\n\n') + assert len(failed_info) == 0, '\n'.join([f'{k}: {v}' for k, v in failed_info.items()]) + + +def check_dataloader_sharding(): + plugin = LowLevelZeroPlugin() + + # create a custom dasetset with 0 to 10 + dataset = torch.utils.data.TensorDataset(torch.arange(0, 10)) + train_dataloader = plugin.prepare_train_dataloader(dataset, batch_size=2) + + # get the first batch of data + batch = next(iter(train_dataloader))[0].cuda() + is_rank_0 = dist.get_rank() == 0 + + if is_rank_0: + batch_to_compare = batch.clone() + else: + batch_to_compare = batch + # pass to the rank 1 value to rank 0 + dist.broadcast(batch_to_compare, src=1) + + # compare on rank 0 + if is_rank_0: + assert not torch.equal(batch, + batch_to_compare), 'Same number was found across ranks but expected it to be different' + + +def run_dist(rank, world_size, port, early_stop: bool = True): + # init dist env + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + check_low_level_zero_plugin(early_stop=early_stop) + + +@rerun_if_address_is_in_use() +def test_low_level_zero_plugin(early_stop: bool = True): + spawn(run_dist, 2, early_stop=early_stop) + + +if __name__ == '__main__': + test_low_level_zero_plugin(early_stop=False) diff --git a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py index c225a1a069dd..5354eae01d40 100644 --- a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py @@ -11,36 +11,37 @@ from tests.kit.model_zoo import model_zoo -def check_torch_ddp_plugin(): +def run_fn(model_fn, data_gen_fn, output_transform_fn): plugin = TorchDDPPlugin() booster = Booster(plugin=plugin) + model = model_fn() + optimizer = SGD(model.parameters(), lr=1e-3) + criterion = lambda x: x.mean() + data = data_gen_fn() - for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): - if name == 'dlrm_interactionarch': - continue + data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()} - model = model_fn() - optimizer = SGD(model.parameters(), lr=1e-3) - criterion = lambda x: x.mean() - data = data_gen_fn() + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) - data = { - k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items() - } + assert isinstance(model.module, DDP) + assert isinstance(optimizer, OptimizerWrapper) - model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) - assert isinstance(model.module, DDP) - assert isinstance(optimizer, OptimizerWrapper) + booster.backward(loss, optimizer) + optimizer.clip_grad_by_norm(1.0) + optimizer.step() - output = model(**data) - output = output_transform_fn(output) - output_key = list(output.keys())[0] - loss = criterion(output[output_key]) - booster.backward(loss, optimizer) - optimizer.clip_grad_by_norm(1.0) - optimizer.step() +def check_torch_ddp_plugin(): + for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + if name == 'dlrm_interactionarch': + continue + run_fn(model_fn, data_gen_fn, output_transform_fn) + torch.cuda.empty_cache() def check_dataloader_sharding(): From 50793b35f49379ecc7b3f8a1a4f858522a561133 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 26 Apr 2023 16:32:40 +0800 Subject: [PATCH 163/413] [gemini] accelerate inference (#3641) * [gemini] support don't scatter after inference * [chat] update colossalai strategy * [chat] fix opt benchmark * [chat] update opt benchmark * [gemini] optimize inference * [test] add gemini inference test * [chat] fix unit test ci * [chat] fix ci * [chat] fix ci * [chat] skip checkpoint test --- .github/workflows/run_chatgpt_unit_tests.yml | 6 +- .../benchmarks/benchmark_opt_lora_dummy.py | 29 +++--- .../Chat/coati/dataset/prompt_dataset.py | 19 ++-- applications/Chat/coati/models/generation.py | 2 +- .../Chat/coati/models/generation_utils.py | 92 ------------------- applications/Chat/coati/trainer/ppo.py | 32 +++---- .../coati/trainer/strategies/colossalai.py | 12 +-- applications/Chat/tests/test_checkpoint.py | 1 + colossalai/zero/gemini/gemini_ddp.py | 40 ++++++-- tests/components_to_test/__init__.py | 4 +- tests/components_to_test/utils/__init__.py | 2 +- tests/components_to_test/utils/executor.py | 23 ++++- tests/test_zero/test_gemini/test_fwd_bwd.py | 57 +++++++++++- 13 files changed, 162 insertions(+), 157 deletions(-) delete mode 100644 applications/Chat/coati/models/generation_utils.py diff --git a/.github/workflows/run_chatgpt_unit_tests.yml b/.github/workflows/run_chatgpt_unit_tests.yml index 407f630e2469..47c80fc9a9fe 100644 --- a/.github/workflows/run_chatgpt_unit_tests.yml +++ b/.github/workflows/run_chatgpt_unit_tests.yml @@ -32,14 +32,14 @@ jobs: - name: Install ColossalAI and ChatGPT run: | - pip install -v . - cd applications/ChatGPT + pip install -e . + cd applications/Chat pip install -v . pip install -r requirements-test.txt - name: Execute Unit Testing run: | - cd applications/ChatGPT + cd applications/Chat rm -rf ~/.cache/colossalai pytest tests/ env: diff --git a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py index c79435ec63c5..7e03b6953a8b 100644 --- a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py +++ b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py @@ -10,6 +10,7 @@ from coati.trainer.callbacks import PerformanceEvaluator from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, Strategy from torch.optim import Adam +from torch.utils.data import DataLoader from transformers import AutoTokenizer from transformers.models.opt.configuration_opt import OPTConfig @@ -92,13 +93,13 @@ def main(args): torch.cuda.set_per_process_memory_fraction(args.cuda_mem_frac) model_config = get_gpt_config(args.model) - + critic_config = get_gpt_config(args.critic_model) with strategy.model_init_context(): actor = OPTActor(config=model_config, lora_rank=args.lora_rank).cuda() - critic = OPTCritic(config=model_config, lora_rank=args.lora_rank).cuda() + critic = OPTCritic(config=critic_config, lora_rank=args.lora_rank).cuda() - initial_model = deepcopy(actor).cuda() - reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda() + initial_model = deepcopy(actor).cuda().half() + reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda().half() actor_numel = get_model_numel(actor, strategy) critic_numel = get_model_numel(critic, strategy) @@ -127,8 +128,7 @@ def main(args): tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m') tokenizer.pad_token = tokenizer.eos_token - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) + (actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim)) trainer = PPOTrainer(strategy, actor, @@ -137,6 +137,7 @@ def main(args): initial_model, actor_optim, critic_optim, + ptx_coef=0, max_epochs=args.max_epochs, train_batch_size=args.train_batch_size, experience_batch_size=args.experience_batch_size, @@ -145,14 +146,19 @@ def main(args): do_sample=True, temperature=1.0, top_k=50, + use_cache=True, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, callbacks=[performance_evaluator]) - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 1, 400), device=torch.cuda.current_device()) - random_attention_mask = torch.randint(1, (1000, 1, 400), device=torch.cuda.current_device()).to(torch.bool) - random_pretrain = [{'input_ids':random_prompts[i], 'labels':random_prompts[i], 'attention_mask':random_attention_mask[i]} for i in range(1000)] - trainer.fit(random_prompts, random_pretrain, + random_prompts = torch.randint(tokenizer.vocab_size, (1000, 256), device=torch.cuda.current_device()) + dataloader = DataLoader(random_prompts, + batch_size=args.experience_batch_size, + shuffle=True, + collate_fn=preprocess_batch) + + trainer.fit(dataloader, + None, num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) @@ -163,6 +169,7 @@ def main(args): if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--model', default='125m') + parser.add_argument('--critic_model', default='125m') parser.add_argument('--strategy', choices=[ 'ddp', 'colossalai_gemini', 'colossalai_gemini_cpu', 'colossalai_zero2', @@ -175,7 +182,7 @@ def main(args): parser.add_argument('--max_epochs', type=int, default=3) parser.add_argument('--train_batch_size', type=int, default=8) parser.add_argument('--experience_batch_size', type=int, default=8) - parser.add_argument('--lora_rank', type=int, default=4) + parser.add_argument('--lora_rank', type=int, default=0) parser.add_argument('--cuda_mem_frac', type=float, default=1.0) args = parser.parse_args() main(args) diff --git a/applications/Chat/coati/dataset/prompt_dataset.py b/applications/Chat/coati/dataset/prompt_dataset.py index 4367a2c6f3ce..f8ab2346c4b7 100644 --- a/applications/Chat/coati/dataset/prompt_dataset.py +++ b/applications/Chat/coati/dataset/prompt_dataset.py @@ -1,5 +1,6 @@ import copy import random +from collections import defaultdict from dataclasses import dataclass, field from typing import Callable, Dict, Sequence @@ -19,9 +20,13 @@ class PromptDataset(Dataset): """Dataset for supervised fine-tuning.""" - def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, max_datasets_size: int = None): + def __init__(self, + data_path: str, + tokenizer: transformers.PreTrainedTokenizer, + max_datasets_size: int = None, + max_length: int = 96): super(PromptDataset, self).__init__() - self.prompt = [] + self.keyed_prompt = defaultdict(list) logger.info("Loading data...") list_data_dict = jload(data_path) logger.info(f"Loaded {len(list_data_dict)} examples.") @@ -33,14 +38,14 @@ def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, for data_dict in list_data_dict: token = tokenizer(data_dict["instruction"], return_tensors='pt', - max_length=96, + max_length=max_length, padding='max_length', truncation=True) - for idx in token['input_ids']: - self.prompt.append(idx.to(torch.cuda.current_device())) + for k, tensor in token.items(): + self.keyed_prompt[k].extend(tensor.to(torch.cuda.current_device()).unbind()) def __len__(self): - return len(self.prompt) + return len(self.keyed_prompt) def __getitem__(self, i) -> Dict[str, torch.Tensor]: - return self.prompt[i] + return {k: v[i] for k, v in self.keyed_prompt.items()} diff --git a/applications/Chat/coati/models/generation.py b/applications/Chat/coati/models/generation.py index eb30c36d0f84..f57c9458a271 100644 --- a/applications/Chat/coati/models/generation.py +++ b/applications/Chat/coati/models/generation.py @@ -76,7 +76,7 @@ def sample(model: nn.Module, # update generated ids, model inputs for next step input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) if update_model_kwargs_fn is not None: - model_kwargs = update_model_kwargs_fn(outputs, **model_kwargs) + model_kwargs = update_model_kwargs_fn(outputs, model_kwargs) # if eos_token was found in one sentence, set sentence to finished if eos_token_id is not None: diff --git a/applications/Chat/coati/models/generation_utils.py b/applications/Chat/coati/models/generation_utils.py deleted file mode 100644 index c7bc1b383fb9..000000000000 --- a/applications/Chat/coati/models/generation_utils.py +++ /dev/null @@ -1,92 +0,0 @@ -from typing import Optional - -import torch - - -def gpt_prepare_inputs_fn(input_ids: torch.Tensor, past: Optional[torch.Tensor] = None, **kwargs) -> dict: - token_type_ids = kwargs.get("token_type_ids", None) - # only last token for inputs_ids if past is defined in kwargs - if past: - input_ids = input_ids[:, -1].unsqueeze(-1) - if token_type_ids is not None: - token_type_ids = token_type_ids[:, -1].unsqueeze(-1) - - attention_mask = kwargs.get("attention_mask", None) - position_ids = kwargs.get("position_ids", None) - - if attention_mask is not None and position_ids is None: - # create position_ids on the fly for batch generation - position_ids = attention_mask.long().cumsum(-1) - 1 - position_ids.masked_fill_(attention_mask == 0, 1) - if past: - position_ids = position_ids[:, -1].unsqueeze(-1) - else: - position_ids = None - return { - "input_ids": input_ids, - "past_key_values": past, - "use_cache": kwargs.get("use_cache"), - "position_ids": position_ids, - "attention_mask": attention_mask, - "token_type_ids": token_type_ids, - } - - -def update_model_kwargs_fn(outputs: dict, **model_kwargs) -> dict: - if "past_key_values" in outputs: - model_kwargs["past"] = outputs["past_key_values"] - else: - model_kwargs["past"] = None - - # update token_type_ids with last value - if "token_type_ids" in model_kwargs: - token_type_ids = model_kwargs["token_type_ids"] - model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1) - - # update attention mask - if "attention_mask" in model_kwargs: - attention_mask = model_kwargs["attention_mask"] - model_kwargs["attention_mask"] = torch.cat( - [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1) - - return model_kwargs - - -def opt_prepare_inputs_fn(input_ids: torch.Tensor, - past: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - **kwargs) -> dict: - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_ids.shape) - - if past: - input_ids = input_ids[:, -1:] - # first step, decoder_cached_states are empty - return { - "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed - "attention_mask": attention_mask, - "past_key_values": past, - "use_cache": use_cache, - } - - -def bloom_prepare_inputs_fn(input_ids: torch.Tensor, - past: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - **kwargs) -> dict: - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_ids.shape) - - if past: - input_ids = input_ids[:, -1:] - # first step, decoder_cached_states are empty - return { - "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed - "attention_mask": attention_mask, - "past_key_values": past, - "use_cache": use_cache, - } diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index cf752549501f..b8a9f879b493 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -4,14 +4,13 @@ import torch.nn as nn from coati.experience_maker import Experience, NaiveExperienceMaker from coati.models.base import Actor, Critic -from coati.models.generation_utils import update_model_kwargs_fn from coati.models.loss import PolicyLoss, ValueLoss from coati.replay_buffer import NaiveReplayBuffer from torch import Tensor from torch.optim import Optimizer from torch.utils.data import DistributedSampler -from transformers.tokenization_utils_base import PreTrainedTokenizerBase from tqdm import tqdm +from transformers.tokenization_utils_base import PreTrainedTokenizerBase from .base import Trainer from .callbacks import Callback @@ -102,19 +101,16 @@ def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experien def _sample_prompts(self, prompts) -> list: indices = list(range(len(prompts))) - sampled_indices = self.strategy.experience_sampler.choice( - indices, self.experience_batch_size, replace=False) + sampled_indices = self.strategy.experience_sampler.choice(indices, self.experience_batch_size, replace=False) return [prompts[i] for i in sampled_indices] def _learn(self): # replay buffer may be empty at first, we should rebuild at each training if not self.sample_replay_buffer: - dataloader = self.strategy.setup_dataloader( - self.replay_buffer, self.dataloader_pin_memory) + dataloader = self.strategy.setup_dataloader(self.replay_buffer, self.dataloader_pin_memory) device = torch.cuda.current_device() if self.sample_replay_buffer: - pbar = tqdm(range(self.max_epochs), desc='Train epoch', - disable=not is_rank_0()) + pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) for _ in pbar: experience = self.replay_buffer.sample() metrics = self.training_step(experience) @@ -124,8 +120,7 @@ def _learn(self): self._on_learn_epoch_start(epoch) if isinstance(dataloader.sampler, DistributedSampler): dataloader.sampler.set_epoch(epoch) - pbar = tqdm( - dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0()) + pbar = tqdm(dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0()) for experience in pbar: self._on_learn_batch_start() experience.to_device(device) @@ -152,10 +147,8 @@ def fit(self, time += 1 prompts = next(iter(self.prompt_dataloader)) self._on_make_experience_start() - self.experience_maker.initial_model.to( - torch.cuda.current_device()) - self.experience_maker.reward_model.to( - torch.cuda.current_device()) + self.experience_maker.initial_model.to(torch.cuda.current_device()) + self.experience_maker.reward_model.to(torch.cuda.current_device()) experience = self._make_experience(prompts) self._on_make_experience_end(experience) self.replay_buffer.append(experience) @@ -206,8 +199,11 @@ def training_step(self, experience: Experience) -> Dict[str, float]: self.critic_optim.zero_grad() return {'reward': experience.reward.mean().item()} - - def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + + def save_model(self, + path: str, + only_rank0: bool = False, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer) @@ -218,7 +214,7 @@ def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, acto if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation - if 'update_model_kwargs_fn' not in generate_kwargs: - new_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn + if 'update_model_kwargs_fn' not in generate_kwargs and hasattr(origin_model, '_update_model_kwargs_for_generation'): + new_kwargs['update_model_kwargs_fn'] = origin_model._update_model_kwargs_for_generation return new_kwargs diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index ba85ba76d4b1..ce2f5db6e032 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -67,6 +67,7 @@ def __init__( placement_policy: str = 'cuda', pin_memory: bool = True, # only for stage 3 force_outputs_fp32: bool = False, # only for stage 3 + scatter_after_inference: bool = False, # only for stage 3 search_range_mb: int = 32, # only for stage 3 hidden_dim: Optional[int] = None, # only for stage 3 min_chunk_size_mb: float = 32, # only for stage 3 @@ -103,7 +104,8 @@ def __init__( strict_ddp_mode=shard_init, search_range_mb=search_range_mb, hidden_dim=hidden_dim, - min_chunk_size_mb=min_chunk_size_mb) + min_chunk_size_mb=min_chunk_size_mb, + scatter_after_inference=scatter_after_inference) if stage == 3: self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio) else: @@ -159,14 +161,6 @@ def _unwrap_actor(actor: Actor) -> nn.Module: return model.module return model - def _unwrap_model(self, model: Union[nn.Module, ZeroDDP]) -> nn.Module: - if isinstance(model, ZeroDDP) and self.stage == 3: - logger.info(f"model type: {type(model)}, get static torch model") - model = get_static_torch_model(model) - logger.info(f"unwrapped_model type: {type(model)}") - - return super()._unwrap_model(model) - def save_model(self, model: nn.Module, path: str, diff --git a/applications/Chat/tests/test_checkpoint.py b/applications/Chat/tests/test_checkpoint.py index 4c05a3431699..29617f205c46 100644 --- a/applications/Chat/tests/test_checkpoint.py +++ b/applications/Chat/tests/test_checkpoint.py @@ -82,6 +82,7 @@ def run_dist(rank, world_size, port, strategy): run_test_checkpoint(strategy) +@pytest.mark.skip('temporarily skip until refactor strategy unwrap') @pytest.mark.dist @pytest.mark.parametrize('world_size', [2]) @pytest.mark.parametrize('strategy', ['ddp', 'colossalai_zero2', 'colossalai_gemini']) diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index a2cc8c1f2de8..8a001b114e9a 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -1,5 +1,6 @@ import itertools from collections import OrderedDict +from contextlib import nullcontext from functools import partial from typing import Dict, Iterator, List, Optional, Union @@ -49,6 +50,7 @@ class ZeroDDP(ColoDDP): Defaults to False. strict_ddp_mode (bool): If set to True, there is no tensor sharding, each tensor is replicated. Defaults to False. Users can set it to True, when they clearly know that they only need DDP. + scatter_after_inference (bool): If set to True, the model will be scattered after inference. This will save memory but slow down the consecutive inference. """ def __init__(self, @@ -56,7 +58,8 @@ def __init__(self, gemini_manager: GeminiManager, pin_memory: bool = False, force_outputs_fp32: bool = False, - strict_ddp_mode: bool = False) -> None: + strict_ddp_mode: bool = False, + scatter_after_inference: bool = True) -> None: self.gemini_manager = gemini_manager self.chunk_manager: ChunkManager = gemini_manager.chunk_manager self.force_outputs_fp32 = force_outputs_fp32 @@ -67,6 +70,7 @@ def __init__(self, self.grads_device: Dict[torch.Tensor, torch.device] = dict() self.param2name: Dict[nn.Parameter, str] = dict() self.name2param: Dict[str, nn.Parameter] = dict() + self.scatter_after_inference = scatter_after_inference self._logger = get_dist_logger() @@ -108,8 +112,6 @@ def _post_forward(self): first_param = next(iter(chunk.tensors_info)) self.chunk_manager.move_chunk(chunk, self.grads_device[first_param]) assert self.chunk_manager.accessed_mem == 0 - # reset all recorded attributes - self.gemini_manager.reset_attributes() def forward(self, *args, **kwargs): # check whether we are in a inference mode @@ -120,17 +122,35 @@ def forward(self, *args, **kwargs): args, kwargs = _cast_float(args, torch.half), _cast_float(kwargs, torch.half) self.module.zero_grad(set_to_none=True) - self.gemini_manager.pre_iter(*args) - with ColoParamOpHookManager.use_hooks(self.param_op_hook): - outputs = self.module(*args, **kwargs) - # scatter chunks in the inference mode if not grad_flag: - self._post_forward() + outputs = self._inference_forward(*args, **kwargs) + else: + self.gemini_manager.pre_iter(*args) + with ColoParamOpHookManager.use_hooks(self.param_op_hook): + outputs = self.module(*args, **kwargs) if self.force_outputs_fp32: return _cast_float(outputs, torch.float) return outputs + def _inference_forward(self, *args, **kwargs): + """This function is only triggered for inference. + """ + fwd_ctx = ColoParamOpHookManager.use_hooks(self.param_op_hook) + if not self.scatter_after_inference: + # gather all chunks + for chunk in self.chunk_manager.get_chunks(self.fp16_params): + self.chunk_manager.access_chunk(chunk) + fwd_ctx = nullcontext() + with fwd_ctx: + outputs = self.module(*args, **kwargs) + if self.scatter_after_inference: + # scatter chunks + self._post_forward() + # reset all recorded attributes + self.gemini_manager.reset_attributes() + return outputs + def _setup_grads_ptr(self): for p in self.module.parameters(): if is_ddp_ignored(p): @@ -678,6 +698,7 @@ def __init__(self, pin_memory: bool = False, force_outputs_fp32: bool = False, strict_ddp_mode: bool = False, + scatter_after_inference: bool = True, search_range_mb: int = 32, hidden_dim: Optional[int] = None, min_chunk_size_mb: float = 32, @@ -722,4 +743,5 @@ def __init__(self, strict_ddp_flag=strict_ddp_mode, verbose=verbose) gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats) - super().__init__(module, gemini_manager, pin_memory, force_outputs_fp32, strict_ddp_mode) + super().__init__(module, gemini_manager, pin_memory, force_outputs_fp32, strict_ddp_mode, + scatter_after_inference) diff --git a/tests/components_to_test/__init__.py b/tests/components_to_test/__init__.py index 106f4e61c7e1..f29efefce4a4 100644 --- a/tests/components_to_test/__init__.py +++ b/tests/components_to_test/__init__.py @@ -9,11 +9,11 @@ resnet, simple_net, ) -from .utils import run_fwd_bwd +from .utils import run_fwd, run_fwd_bwd from . import albert # isort:skip __all__ = [ 'bert', 'gpt2', 'hanging_param_model', 'inline_op_model', 'nested_model', 'repeated_computed_layers', 'resnet', - 'simple_net', 'run_fwd_bwd', 'albert', 'beit' + 'simple_net', 'run_fwd_bwd', 'albert', 'beit', 'run_fwd' ] diff --git a/tests/components_to_test/utils/__init__.py b/tests/components_to_test/utils/__init__.py index f223f7d322cb..150124b58800 100644 --- a/tests/components_to_test/utils/__init__.py +++ b/tests/components_to_test/utils/__init__.py @@ -1,2 +1,2 @@ from .dummy_data_generator import DummyDataGenerator -from .executor import run_fwd_bwd +from .executor import run_fwd, run_fwd_bwd diff --git a/tests/components_to_test/utils/executor.py b/tests/components_to_test/utils/executor.py index e77152561e6c..631401e022e6 100644 --- a/tests/components_to_test/utils/executor.py +++ b/tests/components_to_test/utils/executor.py @@ -1,9 +1,9 @@ import torch -def run_fwd_bwd(model, data, label, criterion, optimizer=None) -> torch.Tensor: - """run_fwd_bwd - run fwd and bwd for the model +def run_fwd(model, data, label, criterion) -> torch.Tensor: + """run_fwd + run fwd for the model Args: model (torch.nn.Module): a PyTorch model @@ -22,6 +22,23 @@ def run_fwd_bwd(model, data, label, criterion, optimizer=None) -> torch.Tensor: loss = model(data, label) loss = loss.float() + return loss + + +def run_fwd_bwd(model, data, label, criterion, optimizer=None) -> torch.Tensor: + """run_fwd_bwd + run fwd and bwd for the model + + Args: + model (torch.nn.Module): a PyTorch model + data (torch.Tensor): input data + label (torch.Tensor): label + criterion (Optional[Callable]): a function of criterion + + Returns: + torch.Tensor: loss of fwd + """ + loss = run_fwd(model, data, label, criterion) if optimizer: optimizer.backward(loss) else: diff --git a/tests/test_zero/test_gemini/test_fwd_bwd.py b/tests/test_zero/test_gemini/test_fwd_bwd.py index 697595bc3352..f2cbb7fb77d6 100644 --- a/tests/test_zero/test_gemini/test_fwd_bwd.py +++ b/tests/test_zero/test_gemini/test_fwd_bwd.py @@ -12,7 +12,7 @@ from colossalai.zero import ColoInitContext, ZeroDDP, ZeroOptimizer from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration from colossalai.zero.gemini.gemini_mgr import GeminiManager -from tests.components_to_test import run_fwd_bwd +from tests.components_to_test import run_fwd, run_fwd_bwd from tests.components_to_test.registry import non_distributed_component_funcs from tests.test_tensor.common_utils import set_seed @@ -89,10 +89,65 @@ def exam_gpt_fwd_bwd( check_grad(model, torch_model) +@parameterize('placement_policy', ['cuda', 'cpu']) +@parameterize('keep_gather', [False, True]) +@parameterize('model_name', ['gpt2', 'bert', 'albert']) +@parameterize('scatter_after_inference', [False, True]) +def exam_gpt_inference( + placement_policy, + keep_gather, + model_name: str, + scatter_after_inference: bool = False, +): + init_device = get_current_device() + get_components_func = non_distributed_component_funcs.get_callable(model_name) + model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() + + set_seed(42) + with ColoInitContext(device=init_device): + model = model_builder() + + set_seed(42) + torch_model = model_builder().cuda() + for torch_p, p in zip(torch_model.parameters(), model.parameters()): + torch_p.data.copy_(p.data) + + world_size = torch.distributed.get_world_size() + config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict[world_size]['chunk_size'] = 5000 + config_dict[world_size]['keep_gathered'] = keep_gather + chunk_manager = ChunkManager(config_dict) + gemini_manager = GeminiManager(placement_policy, chunk_manager) + model = ZeroDDP(model, gemini_manager, pin_memory=True, scatter_after_inference=scatter_after_inference) + + pg = ProcessGroup() + amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1) + torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3) + torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config) + torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group()) + + set_seed(pg.dp_local_rank()) + model.eval() + torch_model.eval() + for i, (input_ids, label) in enumerate(train_dataloader): + # you can only test a single fwd + bwd. + # after bwd param is grad for Gemini, due to the chunk reuse optimization. + if i > 0: + break + with torch.no_grad(): + input_ids, label = input_ids.cuda(), label.cuda() + + torch_loss = run_fwd(torch_model, input_ids, label, criterion) + loss = run_fwd(model, input_ids, label, criterion) + + assert torch.equal(torch_loss, loss) + + def run_dist(rank, world_size, port): config = {} colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') exam_gpt_fwd_bwd() + exam_gpt_inference() @pytest.mark.dist From f8288315d9dcf05acffb4d9e5883f3b317f0191d Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 26 Apr 2023 17:34:59 +0800 Subject: [PATCH 164/413] [chat] polish performance evaluator (#3647) --- .../callbacks/performance_evaluator.py | 78 +++++++++++++++---- 1 file changed, 64 insertions(+), 14 deletions(-) diff --git a/applications/Chat/coati/trainer/callbacks/performance_evaluator.py b/applications/Chat/coati/trainer/callbacks/performance_evaluator.py index 5ca44a52d6e7..925455444597 100644 --- a/applications/Chat/coati/trainer/callbacks/performance_evaluator.py +++ b/applications/Chat/coati/trainer/callbacks/performance_evaluator.py @@ -19,6 +19,14 @@ def print_rank_0(*args, **kwargs) -> None: print(*args, **kwargs) +def divide(x: float, y: float) -> float: + if y == 0: + return float('inf') + elif y == float('inf'): + return float('nan') + return x / y + + @torch.no_grad() def all_reduce_mean(x: float, world_size: int) -> float: if world_size == 1: @@ -29,6 +37,24 @@ def all_reduce_mean(x: float, world_size: int) -> float: return tensor.item() +class Timer: + + def __init__(self) -> None: + self.start_time: Optional[float] = None + self.duration: float = 0. + + def start(self) -> None: + self.start_time = time() + + def end(self) -> None: + assert self.start_time is not None + self.duration += time() - self.start_time + self.start_time = None + + def reset(self) -> None: + self.duration = 0. + + class PerformanceEvaluator(Callback): """ Callback for valuate the performance of the model. @@ -58,27 +84,34 @@ def __init__(self, self.ignore_episodes = ignore_episodes self.disable: bool = False - self.make_experience_duration: float = 0. - self.make_experience_start_time: Optional[float] = None + self.overall_timer = Timer() + self.make_experience_timer = Timer() + self.learn_timer = Timer() self.make_experience_num_samples: int = 0 self.make_experience_flop: int = 0 - self.learn_duration: float = 0. - self.learn_start_time: Optional[float] = None self.learn_num_samples: int = 0 self.learn_flop: int = 0 def on_episode_start(self, episode: int) -> None: self.disable = self.ignore_episodes > 0 and episode < self.ignore_episodes + if self.disable: + return + self.overall_timer.start() + + def on_episode_end(self, episode: int) -> None: + if self.disable: + return + self.overall_timer.end() def on_make_experience_start(self) -> None: if self.disable: return - self.make_experience_start_time = time() + self.make_experience_timer.start() def on_make_experience_end(self, experience: Experience) -> None: if self.disable: return - self.make_experience_duration += time() - self.make_experience_start_time + self.make_experience_timer.end() batch_size, seq_len = experience.sequences.shape @@ -101,12 +134,12 @@ def on_make_experience_end(self, experience: Experience) -> None: def on_learn_batch_start(self) -> None: if self.disable: return - self.learn_start_time = time() + self.learn_timer.start() def on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: if self.disable: return - self.learn_duration += time() - self.learn_start_time + self.learn_timer.end() batch_size, seq_len = experience.sequences.shape @@ -118,16 +151,33 @@ def on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: self.learn_flop += self.critic_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint)) def on_fit_end(self) -> None: - avg_make_experience_duration = all_reduce_mean(self.make_experience_duration, self.world_size) - avg_learn_duration = all_reduce_mean(self.learn_duration, self.world_size) + avg_make_experience_duration = all_reduce_mean(self.make_experience_timer.duration, self.world_size) + avg_learn_duration = all_reduce_mean(self.learn_timer.duration, self.world_size) + avg_overall_duration = all_reduce_mean(self.overall_timer.duration, self.world_size) - avg_make_experience_throughput = self.make_experience_num_samples / (avg_make_experience_duration + 1e-12) + avg_make_experience_throughput = self.make_experience_num_samples * \ + self.world_size / (avg_make_experience_duration + 1e-12) avg_make_experience_tflops = self.make_experience_flop / 1e12 / (avg_make_experience_duration + 1e-12) - avg_learn_throughput = self.learn_num_samples / (avg_learn_duration + 1e-12) + avg_learn_throughput = self.learn_num_samples * self.world_size / (avg_learn_duration + 1e-12) avg_learn_tflops = self.learn_flop / 1e12 / (avg_learn_duration + 1e-12) + num_effective_samples = min(self.learn_num_samples, self.make_experience_num_samples) * self.world_size + + avg_overall_throughput = num_effective_samples / (avg_overall_duration + 1e-12) + + overall_time_per_sample = divide(1, avg_overall_throughput) + make_experience_time_per_sample = divide(avg_make_experience_duration, num_effective_samples) + learn_time_per_sample = divide(avg_learn_duration, num_effective_samples) + print_rank_0( - f'Making experience throughput: {avg_make_experience_throughput:.3f} samples/sec, TFLOPS: {avg_make_experience_tflops:.3f}' + f'Performance summary:\n' + + f'Generate {self.make_experience_num_samples * self.world_size} samples, throughput: {avg_make_experience_throughput:.2f} samples/s, TFLOPS per GPU: {avg_make_experience_tflops:.2f}\n' + + + f'Train {self.learn_num_samples * self.world_size} samples, throughput: {avg_learn_throughput:.2f} samples/s, TFLOPS per GPU: {avg_learn_tflops:.2f}\n' + + f'Overall throughput: {avg_overall_throughput:.2f} samples/s\n' + + f'Overall time per sample: {overall_time_per_sample:.2f} s\n' + + f'Make experience time per sample: {make_experience_time_per_sample:.2f} s, {make_experience_time_per_sample/overall_time_per_sample*100:.2f}%\n' + + + f'Learn time per sample: {learn_time_per_sample:.2f} s, {learn_time_per_sample/overall_time_per_sample*100:.2f}%' ) - print_rank_0(f'Learning throughput: {avg_learn_throughput:.3f} samples/sec, TFLOPS: {avg_learn_tflops:.3f}') From 2a951955ade14fd067bc5bee34a5ff7e57513ac6 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 26 Apr 2023 18:11:49 +0800 Subject: [PATCH 165/413] [chat] refactor trainer (#3648) * [chat] ppo trainer remove useless args * [chat] update examples * [chat] update benchmark * [chat] update examples * [chat] fix sft training with wandb * [chat] polish docstr --- applications/Chat/benchmarks/README.md | 85 ++------ .../Chat/benchmarks/benchmark_gpt_dummy.py | 186 ------------------ .../Chat/benchmarks/benchmark_gpt_dummy.sh | 45 ----- .../benchmarks/benchmark_opt_lora_dummy.py | 6 +- applications/Chat/coati/trainer/base.py | 3 - applications/Chat/coati/trainer/ppo.py | 49 +++-- applications/Chat/coati/trainer/sft.py | 13 +- applications/Chat/coati/trainer/utils.py | 19 +- applications/Chat/examples/train_dummy.py | 156 --------------- applications/Chat/examples/train_dummy.sh | 18 -- applications/Chat/examples/train_prompts.py | 25 ++- applications/Chat/examples/train_sft.py | 3 +- 12 files changed, 72 insertions(+), 536 deletions(-) delete mode 100644 applications/Chat/benchmarks/benchmark_gpt_dummy.py delete mode 100755 applications/Chat/benchmarks/benchmark_gpt_dummy.sh delete mode 100644 applications/Chat/examples/train_dummy.py delete mode 100755 applications/Chat/examples/train_dummy.sh diff --git a/applications/Chat/benchmarks/README.md b/applications/Chat/benchmarks/README.md index b4e28ba1d764..bc8ad8ba9816 100644 --- a/applications/Chat/benchmarks/README.md +++ b/applications/Chat/benchmarks/README.md @@ -1,70 +1,5 @@ # Benchmarks -## Benchmark GPT on dummy prompt data - -We provide various GPT models (string in parentheses is the corresponding model name used in this script): - -- GPT2-S (s) -- GPT2-M (m) -- GPT2-L (l) -- GPT2-XL (xl) -- GPT2-4B (4b) -- GPT2-6B (6b) -- GPT2-8B (8b) -- GPT2-10B (10b) -- GPT2-12B (12b) -- GPT2-15B (15b) -- GPT2-18B (18b) -- GPT2-20B (20b) -- GPT2-24B (24b) -- GPT2-28B (28b) -- GPT2-32B (32b) -- GPT2-36B (36b) -- GPT2-40B (40b) -- GPT3 (175b) - -We also provide various training strategies: - -- ddp: torch DDP -- colossalai_gemini: ColossalAI GeminiDDP with `placement_policy="cuda"`, like zero3 -- colossalai_gemini_cpu: ColossalAI GeminiDDP with `placement_policy="cpu"`, like zero3-offload -- colossalai_zero2: ColossalAI zero2 -- colossalai_zero2_cpu: ColossalAI zero2-offload -- colossalai_zero1: ColossalAI zero1 -- colossalai_zero1_cpu: ColossalAI zero1-offload - -We only support `torchrun` to launch now. E.g. - -```shell -# run GPT2-S on single-node single-GPU with min batch size -torchrun --standalone --nproc_per_node 1 benchmark_gpt_dummy.py --model s --strategy ddp --experience_batch_size 1 --train_batch_size 1 -# run GPT2-XL on single-node 4-GPU -torchrun --standalone --nproc_per_node 4 benchmark_gpt_dummy.py --model xl --strategy colossalai_zero2 -# run GPT3 on 8-node 8-GPU -torchrun --nnodes 8 --nproc_per_node 8 \ - --rdzv_id=$JOB_ID --rdzv_backend=c10d --rdzv_endpoint=$HOST_NODE_ADDR \ - benchmark_gpt_dummy.py --model 175b --strategy colossalai_gemini -``` - -> ⚠ Batch sizes in CLI args and outputed throughput/TFLOPS are all values of per GPU. - -In this benchmark, we assume the model architectures/sizes of actor and critic are the same for simplicity. But in practice, to reduce training cost, we may use a smaller critic. - -We also provide a simple shell script to run a set of benchmarks. But it only supports benchmark on single node. However, it's easy to run on multi-nodes by modifying launch command in this script. - -Usage: - -```shell -# run for GPUS=(1 2 4 8) x strategy=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256) -./benchmark_gpt_dummy.sh -# run for GPUS=2 x strategy=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256) -./benchmark_gpt_dummy.sh 2 -# run for GPUS=2 x strategy=ddp x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256) -./benchmark_gpt_dummy.sh 2 ddp -# run for GPUS=2 x strategy=ddp x model=l x batch_size=(1 2 4 8 16 32 64 128 256) -./benchmark_gpt_dummy.sh 2 ddp l -``` - ## Benchmark OPT with LoRA on dummy prompt data We provide various OPT models (string in parentheses is the corresponding model name used in this script): @@ -80,15 +15,21 @@ We provide various OPT models (string in parentheses is the corresponding model - OPT-10B (10b) - OPT-13B (13b) +We also provide various training strategies: + +- ddp: torch DDP +- colossalai_gemini: ColossalAI GeminiDDP with `placement_policy="cuda"`, like zero3 +- colossalai_gemini_cpu: ColossalAI GeminiDDP with `placement_policy="cpu"`, like zero3-offload +- colossalai_zero2: ColossalAI zero2 +- colossalai_zero2_cpu: ColossalAI zero2-offload +- colossalai_zero1: ColossalAI zero1 +- colossalai_zero1_cpu: ColossalAI zero1-offload + We only support `torchrun` to launch now. E.g. ```shell # run OPT-125M with no lora (lora_rank=0) on single-node single-GPU with min batch size -torchrun --standalone --nproc_per_node 1 benchmark_opt_lora_dummy.py --model 125m --strategy ddp --experience_batch_size 1 --train_batch_size 1 --lora_rank 0 -# run OPT-350M with lora_rank=4 on single-node 4-GPU -torchrun --standalone --nproc_per_node 4 benchmark_opt_lora_dummy.py --model 350m --strategy colossalai_zero2 --lora_rank 4 +torchrun --standalone --nproc_per_node 1 benchmark_opt_lora_dummy.py --model 125m --critic_model 125m --strategy ddp --experience_batch_size 1 --train_batch_size 1 --lora_rank 0 +# run Actor (OPT-1.3B) and Critic (OPT-350M) with lora_rank=4 on single-node 4-GPU +torchrun --standalone --nproc_per_node 4 benchmark_opt_lora_dummy.py --model 1.3b --critic_model 350m --strategy colossalai_zero2 --lora_rank 4 ``` - -> ⚠ Batch sizes in CLI args and outputed throughput/TFLOPS are all values of per GPU. - -In this benchmark, we assume the model architectures/sizes of actor and critic are the same for simplicity. But in practice, to reduce training cost, we may use a smaller critic. diff --git a/applications/Chat/benchmarks/benchmark_gpt_dummy.py b/applications/Chat/benchmarks/benchmark_gpt_dummy.py deleted file mode 100644 index e41ef239d378..000000000000 --- a/applications/Chat/benchmarks/benchmark_gpt_dummy.py +++ /dev/null @@ -1,186 +0,0 @@ -import argparse -from copy import deepcopy - -import torch -import torch.distributed as dist -import torch.nn as nn -from coati.models.base import RewardModel -from coati.models.gpt import GPTActor, GPTCritic -from coati.trainer import PPOTrainer -from coati.trainer.callbacks import PerformanceEvaluator -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, Strategy -from torch.optim import Adam -from transformers.models.gpt2.configuration_gpt2 import GPT2Config -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - - -def get_model_numel(model: nn.Module, strategy: Strategy) -> int: - numel = sum(p.numel() for p in model.parameters()) - if isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3 and strategy.shard_init: - numel *= dist.get_world_size() - return numel - - -def preprocess_batch(samples) -> dict: - input_ids = torch.stack(samples) - attention_mask = torch.ones_like(input_ids, dtype=torch.long) - return {'input_ids': input_ids, 'attention_mask': attention_mask} - - -def print_rank_0(*args, **kwargs) -> None: - if dist.get_rank() == 0: - print(*args, **kwargs) - - -def print_model_numel(model_dict: dict) -> None: - B = 1024**3 - M = 1024**2 - K = 1024 - outputs = '' - for name, numel in model_dict.items(): - outputs += f'{name}: ' - if numel >= B: - outputs += f'{numel / B:.2f} B\n' - elif numel >= M: - outputs += f'{numel / M:.2f} M\n' - elif numel >= K: - outputs += f'{numel / K:.2f} K\n' - else: - outputs += f'{numel}\n' - print_rank_0(outputs) - - -def get_gpt_config(model_name: str) -> GPT2Config: - model_map = { - 's': GPT2Config(), - 'm': GPT2Config(n_embd=1024, n_layer=24, n_head=16), - 'l': GPT2Config(n_embd=1280, n_layer=36, n_head=20), - 'xl': GPT2Config(n_embd=1600, n_layer=48, n_head=25), - '2b': GPT2Config(n_embd=2048, n_layer=40, n_head=16), - '4b': GPT2Config(n_embd=2304, n_layer=64, n_head=16), - '6b': GPT2Config(n_embd=4096, n_layer=30, n_head=16), - '8b': GPT2Config(n_embd=4096, n_layer=40, n_head=16), - '10b': GPT2Config(n_embd=4096, n_layer=50, n_head=16), - '12b': GPT2Config(n_embd=4096, n_layer=60, n_head=16), - '15b': GPT2Config(n_embd=4096, n_layer=78, n_head=16), - '18b': GPT2Config(n_embd=4096, n_layer=90, n_head=16), - '20b': GPT2Config(n_embd=8192, n_layer=25, n_head=16), - '24b': GPT2Config(n_embd=8192, n_layer=30, n_head=16), - '28b': GPT2Config(n_embd=8192, n_layer=35, n_head=16), - '32b': GPT2Config(n_embd=8192, n_layer=40, n_head=16), - '36b': GPT2Config(n_embd=8192, n_layer=45, n_head=16), - '40b': GPT2Config(n_embd=8192, n_layer=50, n_head=16), - '175b': GPT2Config(n_positions=2048, n_embd=12288, n_layer=96, n_head=96), - } - try: - return model_map[model_name] - except KeyError: - raise ValueError(f'Unknown model "{model_name}"') - - -def main(args): - if args.strategy == 'ddp': - strategy = DDPStrategy() - elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) - elif args.strategy == 'colossalai_gemini_cpu': - strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) - elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - elif args.strategy == 'colossalai_zero2_cpu': - strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') - elif args.strategy == 'colossalai_zero1': - strategy = ColossalAIStrategy(stage=1, placement_policy='cuda') - elif args.strategy == 'colossalai_zero1_cpu': - strategy = ColossalAIStrategy(stage=1, placement_policy='cpu') - else: - raise ValueError(f'Unsupported strategy "{args.strategy}"') - - model_config = get_gpt_config(args.model) - - with strategy.model_init_context(): - actor = GPTActor(config=model_config).cuda() - critic = GPTCritic(config=model_config).cuda() - - initial_model = deepcopy(actor).cuda() - reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda() - - actor_numel = get_model_numel(actor, strategy) - critic_numel = get_model_numel(critic, strategy) - initial_model_numel = get_model_numel(initial_model, strategy) - reward_model_numel = get_model_numel(reward_model, strategy) - print_model_numel({ - 'Actor': actor_numel, - 'Critic': critic_numel, - 'Initial model': initial_model_numel, - 'Reward model': reward_model_numel - }) - performance_evaluator = PerformanceEvaluator(actor_numel, - critic_numel, - initial_model_numel, - reward_model_numel, - enable_grad_checkpoint=False, - ignore_episodes=1) - - if args.strategy.startswith('colossalai'): - actor_optim = HybridAdam(actor.parameters(), lr=5e-6) - critic_optim = HybridAdam(critic.parameters(), lr=5e-6) - else: - actor_optim = Adam(actor.parameters(), lr=5e-6) - critic_optim = Adam(critic.parameters(), lr=5e-6) - - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) - - trainer = PPOTrainer(strategy, - actor, - critic, - reward_model, - initial_model, - actor_optim, - critic_optim, - max_epochs=args.max_epochs, - train_batch_size=args.train_batch_size, - experience_batch_size=args.experience_batch_size, - tokenizer=preprocess_batch, - max_length=512, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - callbacks=[performance_evaluator]) - - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 1, 400), device=torch.cuda.current_device()) - random_attention_mask = torch.randint(1, (1000, 1, 400), device=torch.cuda.current_device()).to(torch.bool) - random_pretrain = [{'input_ids':random_prompts[i], 'labels':random_prompts[i], 'attention_mask':random_attention_mask[i]} for i in range(1000)] - trainer.fit(random_prompts, random_pretrain, - num_episodes=args.num_episodes, - max_timesteps=args.max_timesteps, - update_timesteps=args.update_timesteps) - - print_rank_0(f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.2f} GB') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--model', default='s') - parser.add_argument('--strategy', - choices=[ - 'ddp', 'colossalai_gemini', 'colossalai_gemini_cpu', 'colossalai_zero2', - 'colossalai_zero2_cpu', 'colossalai_zero1', 'colossalai_zero1_cpu' - ], - default='ddp') - parser.add_argument('--num_episodes', type=int, default=3) - parser.add_argument('--max_timesteps', type=int, default=8) - parser.add_argument('--update_timesteps', type=int, default=8) - parser.add_argument('--max_epochs', type=int, default=3) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - args = parser.parse_args() - main(args) diff --git a/applications/Chat/benchmarks/benchmark_gpt_dummy.sh b/applications/Chat/benchmarks/benchmark_gpt_dummy.sh deleted file mode 100755 index d70f8872570a..000000000000 --- a/applications/Chat/benchmarks/benchmark_gpt_dummy.sh +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env bash -# Usage: $0 -set -xu - -BASE=$(realpath $(dirname $0)) - - -PY_SCRIPT=${BASE}/benchmark_gpt_dummy.py -export OMP_NUM_THREADS=8 - -function tune_batch_size() { - # we found when experience batch size is equal to train batch size - # peak CUDA memory usage of making experience phase is less than or equal to that of training phase - # thus, experience batch size can be larger than or equal to train batch size - for bs in 1 2 4 8 16 32 64 128 256; do - torchrun --standalone --nproc_per_node $1 $PY_SCRIPT --model $2 --strategy $3 --experience_batch_size $bs --train_batch_size $bs || return 1 - done -} - -if [ $# -eq 0 ]; then - num_gpus=(1 2 4 8) -else - num_gpus=($1) -fi - -if [ $# -le 1 ]; then - strategies=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") -else - strategies=($2) -fi - -if [ $# -le 2 ]; then - models=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") -else - models=($3) -fi - - -for num_gpu in ${num_gpus[@]}; do - for strategy in ${strategies[@]}; do - for model in ${models[@]}; do - tune_batch_size $num_gpu $model $strategy || break - done - done -done diff --git a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py index 7e03b6953a8b..a991e8558aee 100644 --- a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py +++ b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py @@ -140,8 +140,7 @@ def main(args): ptx_coef=0, max_epochs=args.max_epochs, train_batch_size=args.train_batch_size, - experience_batch_size=args.experience_batch_size, - tokenizer=preprocess_batch, + offload_inference_models=args.offload_inference_models, max_length=512, do_sample=True, temperature=1.0, @@ -179,10 +178,11 @@ def main(args): parser.add_argument('--num_episodes', type=int, default=3) parser.add_argument('--max_timesteps', type=int, default=8) parser.add_argument('--update_timesteps', type=int, default=8) - parser.add_argument('--max_epochs', type=int, default=3) + parser.add_argument('--max_epochs', type=int, default=1) parser.add_argument('--train_batch_size', type=int, default=8) parser.add_argument('--experience_batch_size', type=int, default=8) parser.add_argument('--lora_rank', type=int, default=0) parser.add_argument('--cuda_mem_frac', type=float, default=1.0) + parser.add_argument('--offload_inference_models', action='store_true', default=False) args = parser.parse_args() main(args) diff --git a/applications/Chat/coati/trainer/base.py b/applications/Chat/coati/trainer/base.py index d676799496dd..ac3a878be884 100644 --- a/applications/Chat/coati/trainer/base.py +++ b/applications/Chat/coati/trainer/base.py @@ -15,7 +15,6 @@ class Trainer(ABC): Args: strategy (Strategy):the strategy to use for training max_epochs (int, defaults to 1): the number of epochs of training process - tokenizer (Callable, optional): the tokenizer to use for tokenizing the input dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader callbacks (List[Callback], defaults to []): the callbacks to call during training process generate_kwargs (dict, optional): the kwargs to use while model generating @@ -24,14 +23,12 @@ class Trainer(ABC): def __init__(self, strategy: Strategy, max_epochs: int = 1, - tokenizer: Optional[Callable[[Any], dict]] = None, dataloader_pin_memory: bool = True, callbacks: List[Callback] = [], **generate_kwargs) -> None: super().__init__() self.strategy = strategy self.max_epochs = max_epochs - self.tokenizer = tokenizer self.generate_kwargs = generate_kwargs self.dataloader_pin_memory = dataloader_pin_memory self.callbacks = callbacks diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index b8a9f879b493..f9ab4a556359 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -4,7 +4,7 @@ import torch.nn as nn from coati.experience_maker import Experience, NaiveExperienceMaker from coati.models.base import Actor, Critic -from coati.models.loss import PolicyLoss, ValueLoss +from coati.models.loss import GPTLMLoss, PolicyLoss, ValueLoss from coati.replay_buffer import NaiveReplayBuffer from torch import Tensor from torch.optim import Optimizer @@ -12,10 +12,12 @@ from tqdm import tqdm from transformers.tokenization_utils_base import PreTrainedTokenizerBase +from colossalai.utils import get_current_device + from .base import Trainer from .callbacks import Callback from .strategies import Strategy -from .utils import is_rank_0 +from .utils import is_rank_0, to_device class PPOTrainer(Trainer): @@ -38,11 +40,10 @@ class PPOTrainer(Trainer): vf_coef (float, defaults to 1.0): the coefficient of value loss ptx_coef (float, defaults to 0.9): the coefficient of ptx loss value_clip (float, defaults to 0.4): the clip coefficient of value loss - experience_batch_size (int, defaults to 8): the batch size to use for experience generation max_epochs (int, defaults to 1): the number of epochs of training process - tokenizer (Callable, optional): the tokenizer to use for tokenizing the input sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader + offload_inference_models (bool, defaults to True): whether to offload inference models to cpu during training process callbacks (List[Callback], defaults to []): the callbacks to call during training process generate_kwargs (dict, optional): the kwargs to use while model generating """ @@ -63,22 +64,21 @@ def __init__(self, eps_clip: float = 0.2, vf_coef: float = 1.0, value_clip: float = 0.4, - experience_batch_size: int = 8, max_epochs: int = 1, - tokenizer: Optional[Callable[[Any], dict]] = None, sample_replay_buffer: bool = False, dataloader_pin_memory: bool = True, + offload_inference_models: bool = True, callbacks: List[Callback] = [], **generate_kwargs) -> None: experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef) replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload) generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor) - super().__init__(strategy, max_epochs, tokenizer, dataloader_pin_memory, callbacks, **generate_kwargs) + super().__init__(strategy, max_epochs, dataloader_pin_memory, callbacks, **generate_kwargs) self.experience_maker = experience_maker self.replay_buffer = replay_buffer - self.experience_batch_size = experience_batch_size self.sample_replay_buffer = sample_replay_buffer + self.offload_inference_models = offload_inference_models self.actor = actor self.critic = critic @@ -86,11 +86,13 @@ def __init__(self, self.actor_loss_fn = PolicyLoss(eps_clip) self.critic_loss_fn = ValueLoss(value_clip) self.vf_coef = vf_coef - self.ptx_loss_fn = nn.CrossEntropyLoss(ignore_index=-100) + self.ptx_loss_fn = GPTLMLoss() self.ptx_coef = ptx_coef self.actor_optim = actor_optim self.critic_optim = critic_optim + self.device = get_current_device() + def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience: if isinstance(inputs, Tensor): return self.experience_maker.make_experience(inputs, **self.generate_kwargs) @@ -99,20 +101,15 @@ def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experien else: raise ValueError(f'Unsupported input type "{type(inputs)}"') - def _sample_prompts(self, prompts) -> list: - indices = list(range(len(prompts))) - sampled_indices = self.strategy.experience_sampler.choice(indices, self.experience_batch_size, replace=False) - return [prompts[i] for i in sampled_indices] - def _learn(self): # replay buffer may be empty at first, we should rebuild at each training if not self.sample_replay_buffer: dataloader = self.strategy.setup_dataloader(self.replay_buffer, self.dataloader_pin_memory) - device = torch.cuda.current_device() if self.sample_replay_buffer: pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) for _ in pbar: experience = self.replay_buffer.sample() + experience.to_device(self.device) metrics = self.training_step(experience) pbar.set_postfix(metrics) else: @@ -123,7 +120,7 @@ def _learn(self): pbar = tqdm(dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0()) for experience in pbar: self._on_learn_batch_start() - experience.to_device(device) + experience.to_device(self.device) metrics = self.training_step(experience) self._on_learn_batch_end(metrics, experience) pbar.set_postfix(metrics) @@ -147,14 +144,17 @@ def fit(self, time += 1 prompts = next(iter(self.prompt_dataloader)) self._on_make_experience_start() - self.experience_maker.initial_model.to(torch.cuda.current_device()) - self.experience_maker.reward_model.to(torch.cuda.current_device()) + if self.offload_inference_models: + # TODO(ver217): this may be controlled by strategy if they are prepared by strategy + self.experience_maker.initial_model.to(self.device) + self.experience_maker.reward_model.to(self.device) experience = self._make_experience(prompts) self._on_make_experience_end(experience) self.replay_buffer.append(experience) if time % update_timesteps == 0: - self.experience_maker.initial_model.to('cpu') - self.experience_maker.reward_model.to('cpu') + if self.offload_inference_models: + self.experience_maker.initial_model.to('cpu') + self.experience_maker.reward_model.to('cpu') self._learn() self.replay_buffer.clear() self._on_episode_end(episode) @@ -174,11 +174,10 @@ def training_step(self, experience: Experience) -> Dict[str, float]: # ptx loss if self.ptx_coef != 0: batch = next(iter(self.pretrain_dataloader)) - ptx = batch['input_ids'].to(torch.cuda.current_device()) - label = batch['labels'].to(torch.cuda.current_device())[:, 1:] - attention_mask = batch['attention_mask'].to(torch.cuda.current_device()) - ptx_log_probs = self.actor.get_base_model()(ptx, attention_mask=attention_mask)['logits'][..., :-1, :] - ptx_loss = self.ptx_loss_fn(ptx_log_probs.view(-1, ptx_log_probs.size(-1)), label.view(-1)) + batch = to_device(batch, self.device) + ptx_log_probs = self.actor.get_base_model()(batch['input_ids'], + attention_mask=batch['attention_mask'])['logits'] + ptx_loss = self.ptx_loss_fn(ptx_log_probs, batch['labels']) actor_loss = ptx_loss * self.ptx_coef + actor_loss * (1 - self.ptx_coef) self.strategy.backward(actor_loss, self.actor, self.actor_optim) diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index 350553108e68..ceb7a0574301 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -1,6 +1,6 @@ import math import time -from typing import Optional, List +from typing import List, Optional import loralib as lora import torch @@ -18,8 +18,8 @@ from colossalai.logging import get_dist_logger -from .callbacks import Callback from .base import Trainer +from .callbacks import Callback from .strategies import Strategy from .utils import is_rank_0 @@ -70,9 +70,10 @@ def __init__( num_warmup_steps=math.ceil(max_steps * 0.03), num_training_steps=max_steps) - def fit(self, logger, log_interval=10): - wandb.init(project="Coati", name=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) - wandb.watch(self.model) + def fit(self, logger, use_wandb: bool = False): + if use_wandb: + wandb.init(project="Coati", name=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) + wandb.watch(self.model) total_loss = 0 # epoch_bar = tqdm(range(self.epochs), desc='Epochs', disable=not is_rank_0()) step_bar = tqdm(range(len(self.train_dataloader) // self.accimulation_steps * self.max_epochs), @@ -111,7 +112,7 @@ def fit(self, logger, log_interval=10): self.strategy.optimizer_step(self.optimizer) self.optimizer.zero_grad() self.scheduler.step() - if is_rank_0(): + if is_rank_0() and use_wandb: wandb.log({ "loss": total_loss / self.accimulation_steps, "lr": self.scheduler.get_last_lr()[0], diff --git a/applications/Chat/coati/trainer/utils.py b/applications/Chat/coati/trainer/utils.py index 1b17a0421656..9cccb5c92603 100644 --- a/applications/Chat/coati/trainer/utils.py +++ b/applications/Chat/coati/trainer/utils.py @@ -1,14 +1,19 @@ -import torch.distributed as dist -from typing import Any, Callable, Dict, List, Optional -from coati.models.bloom import BLOOMActor, BLOOMCritic -from coati.models.gpt import GPTActor, GPTCritic -from coati.models.opt import OPTActor, OPTCritic -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from typing import Any + import torch -import os +import torch.distributed as dist +from torch.utils._pytree import tree_map def is_rank_0() -> bool: return not dist.is_initialized() or dist.get_rank() == 0 +def to_device(x: Any, device: torch.device) -> Any: + + def _to(t: Any): + if isinstance(t, torch.Tensor): + return t.to(device) + return t + + return tree_map(_to, x) diff --git a/applications/Chat/examples/train_dummy.py b/applications/Chat/examples/train_dummy.py deleted file mode 100644 index 5f34c80f0892..000000000000 --- a/applications/Chat/examples/train_dummy.py +++ /dev/null @@ -1,156 +0,0 @@ -import argparse -from copy import deepcopy - -import torch -from coati.models.base import RewardModel -from coati.models.bloom import BLOOMActor, BLOOMCritic -from coati.models.gpt import GPTActor, GPTCritic -from coati.models.opt import OPTActor, OPTCritic -from coati.models.roberta import RoBERTaActor, RoBERTaCritic -from coati.trainer import PPOTrainer -from coati.trainer.callbacks import SaveCheckpoint -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast, RobertaTokenizer -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - - -def preprocess_batch(samples): - input_ids = torch.stack(samples) - attention_mask = torch.ones_like(input_ids, dtype=torch.long) - return {'input_ids': input_ids, 'attention_mask': attention_mask} - - -def main(args): - # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': - strategy = DDPStrategy() - elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) - elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - else: - raise ValueError(f'Unsupported strategy "{args.strategy}"') - - # configure model - with strategy.model_init_context(): - if args.model == 'gpt2': - actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = GPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'bloom': - actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = BLOOMCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'opt': - actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = OPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'roberta': - actor = RoBERTaActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - critic = RoBERTaCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - else: - raise ValueError(f'Unsupported model "{args.model}"') - - initial_model = deepcopy(actor).to(torch.cuda.current_device()) - reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).to(torch.cuda.current_device()) - - # configure optimizer - if args.strategy.startswith('colossalai'): - actor_optim = HybridAdam(actor.parameters(), lr=5e-6) - critic_optim = HybridAdam(critic.parameters(), lr=5e-6) - else: - actor_optim = Adam(actor.parameters(), lr=5e-6) - critic_optim = Adam(critic.parameters(), lr=5e-6) - - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - elif args.model == 'roberta': - tokenizer = RobertaTokenizer.from_pretrained("roberta-base") - else: - raise ValueError(f'Unsupported model "{args.model}"') - - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( - (actor, actor_optim), (critic, critic_optim), reward_model, initial_model) - - callbacks = [] - if args.save_ckpt_path: - ckpt_callback = SaveCheckpoint( - args.save_ckpt_path, - args.save_ckpt_interval, - strategy, - actor, - critic, - actor_optim, - critic_optim, - ) - callbacks.append(ckpt_callback) - - # configure trainer - - trainer = PPOTrainer(strategy, - actor, - critic, - reward_model, - initial_model, - actor_optim, - critic_optim, - max_epochs=args.max_epochs, - train_batch_size=args.train_batch_size, - tokenizer=preprocess_batch, - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - callbacks=callbacks) - - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 1, 64), device=torch.cuda.current_device()) - random_attention_mask = torch.randint(1, (1000, 1, 64), device=torch.cuda.current_device()).to(torch.bool) - random_pretrain = [{'input_ids':random_prompts[i], 'labels':random_prompts[i], 'attention_mask':random_attention_mask[i]} for i in range(1000)] - trainer.fit(random_prompts, random_pretrain, - num_episodes=args.num_episodes, - max_timesteps=args.max_timesteps, - update_timesteps=args.update_timesteps) - - # save model checkpoint after fitting - trainer.save_model(args.save_path, only_rank0=True) - # save optimizer checkpoint on all ranks - if args.need_optim_ckpt: - strategy.save_optimizer(actor_optim, - 'actor_optim_checkpoint_dummy_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--model', type=str, default='gpt2', choices=['gpt2', 'bloom', 'opt', 'roberta']) - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--save_path', type=str, default='actor_checkpoint_dummy') - parser.add_argument('--need_optim_ckpt', type=bool, default=False) - parser.add_argument('--num_episodes', type=int, default=50) - parser.add_argument('--max_timesteps', type=int, default=10) - parser.add_argument('--update_timesteps', type=int, default=10) - parser.add_argument('--max_epochs', type=int, default=5) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") - parser.add_argument('--save_ckpt_path', - type=str, - default=None, - help="path to save checkpoint, None means not to save") - parser.add_argument('--save_ckpt_interval', type=int, default=1, help="the interval of episode to save checkpoint") - args = parser.parse_args() - main(args) diff --git a/applications/Chat/examples/train_dummy.sh b/applications/Chat/examples/train_dummy.sh deleted file mode 100755 index 595da573e2b1..000000000000 --- a/applications/Chat/examples/train_dummy.sh +++ /dev/null @@ -1,18 +0,0 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES() { - local n=${1:-"9999"} - echo "GPU Memory Usage:" - local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ - | tail -n +2 \ - | nl -v 0 \ - | tee /dev/tty \ - | sort -g -k 2 \ - | awk '{print $1}' \ - | head -n $n) - export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') - echo "Now CUDA_VISIBLE_DEVICES is set to:" - echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" -} - -set_n_least_used_CUDA_VISIBLE_DEVICES 2 - -torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy colossalai_zero2 diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index 2086ff003e34..c0455f3a72c2 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -71,9 +71,8 @@ def main(args): if args.rm_path is not None: reward_model.load_state_dict(state_dict) - if args.strategy != 'colossalai_gemini': - initial_model.to(torch.float16).to(torch.cuda.current_device()) - reward_model.to(torch.float16).to(torch.cuda.current_device()) + initial_model.to(torch.float16).to(torch.cuda.current_device()) + reward_model.to(torch.float16).to(torch.cuda.current_device()) with strategy.model_init_context(): if args.model == 'gpt2': @@ -148,9 +147,12 @@ def main(args): prompt_dataloader = DataLoader(prompt_dataset, shuffle=(prompt_sampler is None), sampler=prompt_sampler, - batch_size=args.train_batch_size) + batch_size=args.experience_batch_size) - pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384) + pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, + data_path=args.pretrain_dataset, + max_datasets_size=16384, + max_length=args.max_input_len) if dist.is_initialized() and dist.get_world_size() > 1: pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True) else: @@ -161,12 +163,6 @@ def main(args): batch_size=args.ptx_batch_size, collate_fn=data_collator) - def tokenize_fn(texts): - # MUST padding to max length to ensure inputs of all ranks have the same length - # Different length may lead to hang when using gemini, as different generation steps - batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) - return {k: v.to(torch.cuda.current_device()) for k, v in batch.items()} - (actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim)) # configure trainer @@ -182,9 +178,8 @@ def tokenize_fn(texts): ptx_coef=args.ptx_coef, max_epochs=args.max_epochs, train_batch_size=args.train_batch_size, - experience_batch_size=args.experience_batch_size, - tokenizer=tokenize_fn, - max_length=128, + max_length=args.max_seq_len, + use_cache=True, do_sample=True, temperature=1.0, top_k=50, @@ -232,5 +227,7 @@ def tokenize_fn(texts): parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") parser.add_argument('--kl_coef', type=float, default=0.1) parser.add_argument('--ptx_coef', type=float, default=0.9) + parser.add_argument('--max_input_len', type=int, default=96) + parser.add_argument('--max_seq_len', type=int, default=128) args = parser.parse_args() main(args) diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index d7502c23b5e6..d08cf77864a4 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -156,7 +156,7 @@ def train(args): max_epochs=args.max_epochs, accimulation_steps=args.accimulation_steps) - trainer.fit(logger=logger, log_interval=args.log_interval) + trainer.fit(logger=logger, use_wandb=args.use_wandb) # save model checkpoint after fitting on only rank0 trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) @@ -185,5 +185,6 @@ def train(args): parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log") parser.add_argument('--lr', type=float, default=5e-6) parser.add_argument('--accimulation_steps', type=int, default=8) + parser.add_argument('--use_wandb', default=False, action='store_true') args = parser.parse_args() train(args) From 8bccb72c8d6b4ff21d3d596f0188c6280d8b29f6 Mon Sep 17 00:00:00 2001 From: Camille Zhong <44392324+Camille7777@users.noreply.github.com> Date: Thu, 27 Apr 2023 14:26:19 +0800 Subject: [PATCH 166/413] [Doc] enhancement on README.md for chat examples (#3646) * Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci Update test_ci.sh Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci Update test_ci.sh Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. update roberta with coati chat ci update Revert "chat ci update" This reverts commit 17ae7ae01fa752bd3289fc39069868fde99cf846. * Update README.md Update README.md * update readme * Update test_ci.sh --- applications/Chat/examples/README.md | 8 ++++++-- applications/Chat/examples/test_ci.sh | 4 ++-- applications/Chat/examples/train_prompts.py | 4 ++-- applications/Chat/examples/train_prompts.sh | 2 +- 4 files changed, 11 insertions(+), 7 deletions(-) diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index af8ded005600..0083dc37227f 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -146,11 +146,15 @@ torchrun --standalone --nproc_per_node=4 train_prompts.py \ --pretrain "/path/to/LLaMa-7B/" \ --model 'llama' \ --strategy colossalai_zero2 \ - --prompt_path /path/to/your/prompt_dataset \ + --prompt_dataset /path/to/your/prompt_dataset \ --pretrain_dataset /path/to/your/pretrain_dataset \ --rm_pretrain /your/pretrain/rm/defination \ --rm_path /your/rm/model/path ``` + +Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use [seed_prompts_ch.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_ch.jsonl) or [seed_prompts_en.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_en.jsonl) in InstructionWild. +Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning. + ### Arg List - --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive' - --model: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' @@ -159,7 +163,7 @@ torchrun --standalone --nproc_per_node=4 train_prompts.py \ - --rm_pretrain: pretrain model for reward model, type=str, default=None - --rm_path: the path of rm model, type=str, default=None - --save_path: path to save the model, type=str, default='output' -- --prompt_path: path of the prompt dataset, type=str, default=None +- --prompt_dataset: path of the prompt dataset, type=str, default=None - --pretrain_dataset: path of the ptx dataset, type=str, default=None - --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False - --num_episodes: num of episodes for training, type=int, default=10 diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index 32f5858a51b6..a86e2295a62c 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -99,7 +99,7 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ rm -rf ${BASE}/rm_ckpt.pt -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_path $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ --pretrain 'facebook/opt-350m' --model opt \ @@ -108,7 +108,7 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_path --save_path ${BASE}/actor_checkpoint_prompts.pt rm -rf ${BASE}/rm_ckpt_opt.pt -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_path $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ --pretrain 'gpt2' --model gpt2 \ diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index c0455f3a72c2..292caa1b36b1 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -139,7 +139,7 @@ def main(args): data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) - prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_path, max_datasets_size=16384) + prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_dataset, max_datasets_size=16384) if dist.is_initialized() and dist.get_world_size() > 1: prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True) else: @@ -204,7 +204,7 @@ def main(args): if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('--prompt_path', type=str, default=None, help='path to the prompt dataset') + parser.add_argument('--prompt_dataset', type=str, default=None, help='path to the prompt dataset') parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset') parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], diff --git a/applications/Chat/examples/train_prompts.sh b/applications/Chat/examples/train_prompts.sh index 8e1ce67ecc64..7f3b2636ca32 100755 --- a/applications/Chat/examples/train_prompts.sh +++ b/applications/Chat/examples/train_prompts.sh @@ -17,4 +17,4 @@ set_n_least_used_CUDA_VISIBLE_DEVICES 2 # torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai_zero2 -torchrun --standalone --nproc_per_node=2 train_prompts.py --prompt_path /path/to/data.json --strategy colossalai_zero2 +torchrun --standalone --nproc_per_node=2 train_prompts.py --prompt_dataset /path/to/data.json --strategy colossalai_zero2 From 6ef70114628b83d39716e9746c062c980cd4a3be Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Thu, 27 Apr 2023 15:37:38 +0800 Subject: [PATCH 167/413] [chat] remove lm model class (#3653) * [chat] refactor lora * [chat] remove lm class * [chat] refactor save model * [chat] refactor train sft * [chat] fix ci * [chat] fix ci --- applications/Chat/coati/models/__init__.py | 6 ++- .../Chat/coati/models/base/__init__.py | 3 +- applications/Chat/coati/models/base/lm.py | 30 ------------ .../Chat/coati/models/bloom/__init__.py | 3 +- .../Chat/coati/models/bloom/bloom_lm.py | 38 --------------- .../Chat/coati/models/gpt/__init__.py | 3 +- applications/Chat/coati/models/gpt/gpt_lm.py | 38 --------------- .../Chat/coati/models/llama/__init__.py | 3 +- .../Chat/coati/models/llama/llama_lm.py | 40 ---------------- applications/Chat/coati/models/lora.py | 22 +++++++-- .../Chat/coati/models/opt/__init__.py | 3 +- applications/Chat/coati/models/opt/opt_lm.py | 38 --------------- applications/Chat/coati/trainer/sft.py | 46 ++++++------------- .../Chat/coati/trainer/strategies/base.py | 4 +- .../coati/trainer/strategies/colossalai.py | 8 +--- .../Chat/coati/trainer/strategies/ddp.py | 21 ++++----- .../Chat/coati/trainer/strategies/naive.py | 16 +++---- .../Chat/coati/utils/tokenizer_utils.py | 5 -- applications/Chat/examples/test_ci.sh | 12 +++-- applications/Chat/examples/train_sft.py | 29 +++++++----- 20 files changed, 84 insertions(+), 284 deletions(-) delete mode 100644 applications/Chat/coati/models/base/lm.py delete mode 100644 applications/Chat/coati/models/bloom/bloom_lm.py delete mode 100644 applications/Chat/coati/models/gpt/gpt_lm.py delete mode 100644 applications/Chat/coati/models/llama/llama_lm.py delete mode 100644 applications/Chat/coati/models/opt/opt_lm.py diff --git a/applications/Chat/coati/models/__init__.py b/applications/Chat/coati/models/__init__.py index 7489b2e87ca0..709bc5ac0948 100644 --- a/applications/Chat/coati/models/__init__.py +++ b/applications/Chat/coati/models/__init__.py @@ -1,4 +1,8 @@ from .base import Actor, Critic, RewardModel +from .lora import LoRAModule, convert_to_lora_module from .loss import LogExpLoss, LogSigLoss, PolicyLoss, PPOPtxActorLoss, ValueLoss -__all__ = ['Actor', 'Critic', 'RewardModel', 'PolicyLoss', 'ValueLoss', 'PPOPtxActorLoss', 'LogSigLoss', 'LogExpLoss'] +__all__ = [ + 'Actor', 'Critic', 'RewardModel', 'PolicyLoss', 'ValueLoss', 'PPOPtxActorLoss', 'LogSigLoss', 'LogExpLoss', + 'LoRAModule', 'convert_to_lora_module' +] diff --git a/applications/Chat/coati/models/base/__init__.py b/applications/Chat/coati/models/base/__init__.py index 7cf82309af7b..86f403556904 100644 --- a/applications/Chat/coati/models/base/__init__.py +++ b/applications/Chat/coati/models/base/__init__.py @@ -1,6 +1,5 @@ from .actor import Actor from .critic import Critic -from .lm import LM from .reward_model import RewardModel -__all__ = ['Actor', 'Critic', 'RewardModel', 'LM'] +__all__ = ['Actor', 'Critic', 'RewardModel'] diff --git a/applications/Chat/coati/models/base/lm.py b/applications/Chat/coati/models/base/lm.py deleted file mode 100644 index e32ba4253369..000000000000 --- a/applications/Chat/coati/models/base/lm.py +++ /dev/null @@ -1,30 +0,0 @@ -from typing import Optional, Tuple, Union - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ..generation import generate -from .actor import Actor - - -class LM(Actor): - """ - Language model base class. - - Args: - model (nn.Module): Language Model. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = 'none') -> None: - super().__init__(model=model, lora_rank=lora_rank, lora_train_bias=lora_train_bias) - - def forward(self, sequences: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - """Returns output log probs - """ - output = self.model(sequences, attention_mask=attention_mask) - logits = output['logits'] - log_probs = F.log_softmax(logits, dim=-1) - return log_probs diff --git a/applications/Chat/coati/models/bloom/__init__.py b/applications/Chat/coati/models/bloom/__init__.py index 39dfe036a2f2..d0e7f7b1ef94 100644 --- a/applications/Chat/coati/models/bloom/__init__.py +++ b/applications/Chat/coati/models/bloom/__init__.py @@ -1,6 +1,5 @@ from .bloom_actor import BLOOMActor from .bloom_critic import BLOOMCritic -from .bloom_lm import BLOOMLM from .bloom_rm import BLOOMRM -__all__ = ['BLOOMActor', 'BLOOMCritic', 'BLOOMRM', 'BLOOMLM'] +__all__ = ['BLOOMActor', 'BLOOMCritic', 'BLOOMRM'] diff --git a/applications/Chat/coati/models/bloom/bloom_lm.py b/applications/Chat/coati/models/bloom/bloom_lm.py deleted file mode 100644 index e4184fcd0d9c..000000000000 --- a/applications/Chat/coati/models/bloom/bloom_lm.py +++ /dev/null @@ -1,38 +0,0 @@ -from typing import Optional - -import torch -from transformers import BloomConfig, BloomForCausalLM, BloomModel - -from ..base import LM - - -class BLOOMLM(LM): - """ - BLOOM language model. - - Args: - pretrained (str): Pretrained model name or path. - config (BloomConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: str = None, - config: Optional[BloomConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = BloomForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = BloomForCausalLM(config) - else: - model = BloomForCausalLM(BloomConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) - - def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): - return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) diff --git a/applications/Chat/coati/models/gpt/__init__.py b/applications/Chat/coati/models/gpt/__init__.py index 9dc68e37544f..63dc5ab0f5ea 100644 --- a/applications/Chat/coati/models/gpt/__init__.py +++ b/applications/Chat/coati/models/gpt/__init__.py @@ -1,6 +1,5 @@ from .gpt_actor import GPTActor from .gpt_critic import GPTCritic -from .gpt_lm import GPTLM from .gpt_rm import GPTRM -__all__ = ['GPTActor', 'GPTCritic', 'GPTRM', 'GPTLM'] +__all__ = ['GPTActor', 'GPTCritic', 'GPTRM'] diff --git a/applications/Chat/coati/models/gpt/gpt_lm.py b/applications/Chat/coati/models/gpt/gpt_lm.py deleted file mode 100644 index c558d7e9ea8d..000000000000 --- a/applications/Chat/coati/models/gpt/gpt_lm.py +++ /dev/null @@ -1,38 +0,0 @@ -from typing import Optional - -from transformers.models.gpt2.configuration_gpt2 import GPT2Config -from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel - -from ..base import LM - - -class GPTLM(LM): - """ - GPT language model. - - Args: - pretrained (str): Pretrained model name or path. - config (GPT2Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the LoRa layer. - lora_train_bias (str): Bias training strategy for the LoRa layer. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[GPT2Config] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = GPT2LMHeadModel.from_pretrained(pretrained) - elif config is not None: - model = GPT2LMHeadModel(config) - else: - model = GPT2LMHeadModel(GPT2Config()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) - - def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): - return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) diff --git a/applications/Chat/coati/models/llama/__init__.py b/applications/Chat/coati/models/llama/__init__.py index 0d4dada3c9f1..9b2a024afdb2 100644 --- a/applications/Chat/coati/models/llama/__init__.py +++ b/applications/Chat/coati/models/llama/__init__.py @@ -1,6 +1,5 @@ from .llama_actor import LlamaActor from .llama_critic import LlamaCritic -from .llama_lm import LlamaLM from .llama_rm import LlamaRM -__all__ = ['LlamaActor', 'LlamaCritic', 'LlamaRM', 'LlamaLM'] +__all__ = ['LlamaActor', 'LlamaCritic', 'LlamaRM'] diff --git a/applications/Chat/coati/models/llama/llama_lm.py b/applications/Chat/coati/models/llama/llama_lm.py deleted file mode 100644 index 181910fb13eb..000000000000 --- a/applications/Chat/coati/models/llama/llama_lm.py +++ /dev/null @@ -1,40 +0,0 @@ -from typing import Optional - -from transformers import LlamaConfig, LlamaForCausalLM - -from ..base import LM - - -class LlamaLM(LM): - """ - Llama language model. - - Args: - pretrained (str): Pretrained model name or path. - config (LlamaConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): LoRA rank. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[LlamaConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - - if pretrained is not None: - model = LlamaForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = LlamaForCausalLM(config) - else: - model = LlamaForCausalLM(LlamaConfig()) - - if checkpoint: - model.gradient_checkpointing_enable() - - super().__init__(model, lora_rank, lora_train_bias) - - def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): - return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) diff --git a/applications/Chat/coati/models/lora.py b/applications/Chat/coati/models/lora.py index 7f6eb73262fa..0533a60dc532 100644 --- a/applications/Chat/coati/models/lora.py +++ b/applications/Chat/coati/models/lora.py @@ -106,6 +106,23 @@ def convert_to_lora_recursively(module: nn.Module, lora_rank: int) -> None: convert_to_lora_recursively(child, lora_rank) +def convert_to_lora_module(module: nn.Module, lora_rank: int, lora_train_bias: str = 'none') -> nn.Module: + """Convert a torch.nn.Module to a LoRA module. + + Args: + module (nn.Module): The module to convert. + lora_rank (int): LoRA rank. + + Returns: + nn.Module: The converted module. + """ + if lora_rank <= 0: + return module + convert_to_lora_recursively(module, lora_rank) + lora.mark_only_lora_as_trainable(module, lora_train_bias) + return module + + class LoRAModule(nn.Module): """A LoRA module base class. All derived classes should call `convert_to_lora()` at the bottom of `__init__()`. This class will convert all torch.nn.Linear layer to LoraLinear layer. @@ -123,7 +140,4 @@ def __init__(self, lora_rank: int = 0, lora_train_bias: str = 'none') -> None: self.lora_train_bias = lora_train_bias def convert_to_lora(self) -> None: - if self.lora_rank <= 0: - return - convert_to_lora_recursively(self, self.lora_rank) - lora.mark_only_lora_as_trainable(self, self.lora_train_bias) + convert_to_lora_module(self, self.lora_rank, self.lora_train_bias) diff --git a/applications/Chat/coati/models/opt/__init__.py b/applications/Chat/coati/models/opt/__init__.py index 3d7a8adbf82e..334f4df0032a 100644 --- a/applications/Chat/coati/models/opt/__init__.py +++ b/applications/Chat/coati/models/opt/__init__.py @@ -1,6 +1,5 @@ from .opt_actor import OPTActor from .opt_critic import OPTCritic -from .opt_lm import OPTLM from .opt_rm import OPTRM -__all__ = ['OPTActor', 'OPTCritic', 'OPTRM', 'OPTLM'] +__all__ = ['OPTActor', 'OPTCritic', 'OPTRM'] diff --git a/applications/Chat/coati/models/opt/opt_lm.py b/applications/Chat/coati/models/opt/opt_lm.py deleted file mode 100644 index 47afae847f13..000000000000 --- a/applications/Chat/coati/models/opt/opt_lm.py +++ /dev/null @@ -1,38 +0,0 @@ -from typing import Optional - -from transformers.models.opt.configuration_opt import OPTConfig -from transformers.models.opt.modeling_opt import OPTForCausalLM - -from ..base import LM - - -class OPTLM(LM): - """ - OPT language model. - - Args: - pretrained (str): Pretrained model name or path. - config (OPTConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the low-rank approximation. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[OPTConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = OPTForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = OPTForCausalLM(config) - else: - model = OPTForCausalLM(OPTConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) - - def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): - return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index ceb7a0574301..64a330eebeb8 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -2,26 +2,19 @@ import time from typing import List, Optional -import loralib as lora import torch import torch.distributed as dist import wandb -from coati.models.loss import GPTLMLoss -from torch import nn -from torch.optim import Adam, Optimizer -from torch.optim.lr_scheduler import LambdaLR +from torch.optim import Optimizer from torch.utils.data import DataLoader -from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm from transformers.tokenization_utils_base import PreTrainedTokenizerBase from transformers.trainer import get_scheduler -from colossalai.logging import get_dist_logger - from .base import Trainer from .callbacks import Callback -from .strategies import Strategy -from .utils import is_rank_0 +from .strategies import ColossalAIStrategy, Strategy +from .utils import is_rank_0, to_device class SFTTrainer(Trainer): @@ -47,19 +40,17 @@ def __init__( optim: Optimizer, train_dataloader: DataLoader, eval_dataloader: DataLoader = None, - batch_size: int = 1, max_epochs: int = 2, accimulation_steps: int = 8, callbacks: List[Callback] = [], ) -> None: + if accimulation_steps > 1 and isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3: + raise ValueError("Accumulation steps are not supported in stage 3 of ColossalAI") super().__init__(strategy, max_epochs, callbacks=callbacks) self.train_dataloader = train_dataloader self.eval_dataloader = eval_dataloader - self.model = strategy.setup_model(model) - if "DDP" in str(self.strategy): - self.model = self.model.module - self.optimizer = strategy.setup_optimizer(optim, self.model) + (self.model, self.optimizer) = strategy.prepare((model, optim)) self.accimulation_steps = accimulation_steps num_update_steps_per_epoch = len(train_dataloader) // self.accimulation_steps @@ -86,17 +77,10 @@ def fit(self, logger, use_wandb: bool = False): self.model.train() for batch_id, batch in enumerate(self.train_dataloader): - prompt_ids = batch["input_ids"].to(torch.cuda.current_device()) - p_mask = batch["attention_mask"].to(torch.cuda.current_device()) - labels = batch["labels"].to(torch.cuda.current_device()) - # prompt_ids = prompt_ids.squeeze(1).cuda() - # p_mask = p_mask.squeeze(1).cuda() - # prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels) - - outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels) + batch = to_device(batch, torch.cuda.current_device()) + outputs = self.model(batch["input_ids"], attention_mask=batch["attention_mask"], labels=batch["labels"]) loss = outputs.loss - prompt_logits = outputs.logits if loss >= 2.5 and is_rank_0(): logger.warning(f"batch_id:{batch_id}, abnormal loss: {loss}") @@ -135,18 +119,14 @@ def fit(self, logger, use_wandb: bool = False): loss_sum = 0 num_seen = 0 for batch in self.eval_dataloader: - prompt_ids = batch["input_ids"].to(torch.cuda.current_device()) - p_mask = batch["attention_mask"].to(torch.cuda.current_device()) - labels = batch["labels"].to(torch.cuda.current_device()) - # prompt_ids = prompt_ids.squeeze(1).cuda() - # p_mask = p_mask.squeeze(1).cuda() - - outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels) + batch = to_device(batch, torch.cuda.current_device()) + outputs = self.model(batch["input_ids"], + attention_mask=batch["attention_mask"], + labels=batch["labels"]) loss = outputs.loss - # prompt_logits = outputs.logits loss_sum += loss.item() - num_seen += prompt_ids.size(0) + num_seen += batch["input_ids"].size(0) loss_mean = loss_sum / num_seen if dist.get_rank() == 0: diff --git a/applications/Chat/coati/trainer/strategies/base.py b/applications/Chat/coati/trainer/strategies/base.py index 7d25138561ea..a1647a4ca525 100644 --- a/applications/Chat/coati/trainer/strategies/base.py +++ b/applications/Chat/coati/trainer/strategies/base.py @@ -5,7 +5,7 @@ import numpy as np import torch import torch.nn as nn -from coati.models.base import LM, Actor, Critic, RewardModel +from coati.models.base import Actor, Critic, RewardModel from coati.replay_buffer import ReplayBuffer from torch.optim import Optimizer from torch.utils.data import DataLoader @@ -99,7 +99,7 @@ def _unwrap_model(model: nn.Module) -> nn.Module: Args: model (nn.Module): an actor or a critic """ - if isinstance(model, Actor) or isinstance(model, LM): + if isinstance(model, Actor): return model.model return model diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index ce2f5db6e032..2fb0b8a188f0 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -5,7 +5,7 @@ import torch.distributed as dist import torch.nn as nn import torch.optim as optim -from coati.models.base import LM, Actor, RewardModel +from coati.models.base import Actor, RewardModel from coati.models.lora import LoraLinear from torch.optim import Optimizer from transformers.modeling_utils import PreTrainedModel @@ -173,10 +173,6 @@ def save_model(self, # TODO : better way to get torch model from gemini model # to get torch model from gemini model - for module in unwrapped_model.modules(): - if isinstance(module, LoraLinear): - module.merge_weights = True - module.eval() if isinstance(unwrapped_model, RewardModel): state_dict = unwrapped_model.state_dict() if only_rank0 and dist.get_rank() != 0: @@ -184,8 +180,6 @@ def save_model(self, torch.save(state_dict, path) else: try: - if isinstance(unwrapped_model, LM): - unwrapped_model = unwrapped_model.model logger.info(f'Saving model to {path}', ranks=[0]) unwrapped_model.save_pretrained(path) logger.info(f'Model saved to {path} Successfully', ranks=[0]) diff --git a/applications/Chat/coati/trainer/strategies/ddp.py b/applications/Chat/coati/trainer/strategies/ddp.py index 8a8c4b3c2f4e..c4c632c67d79 100644 --- a/applications/Chat/coati/trainer/strategies/ddp.py +++ b/applications/Chat/coati/trainer/strategies/ddp.py @@ -1,14 +1,12 @@ -from typing import Optional - import os import random +from typing import Optional import numpy as np import torch import torch.distributed as dist import torch.nn as nn -from coati.models.base import LM, Actor, RewardModel -from coati.models.lora import LoraLinear +from coati.models.base import Actor, RewardModel from coati.replay_buffer import ReplayBuffer from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import Optimizer @@ -75,15 +73,14 @@ def _unwrap_actor(actor: Actor) -> nn.Module: model: DDP = Strategy._unwrap_actor(actor) return model.module - def save_model(self, model: nn.Module, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + def save_model(self, + model: nn.Module, + path: str, + only_rank0: bool = False, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: if only_rank0 and dist.get_rank() != 0: return None - - for module in model.modules(): - if isinstance(module, LoraLinear): - module.merge_weights = True - module.eval() - + if isinstance(model, RewardModel): state_dict = model.state_dict() if only_rank0 and dist.get_rank() != 0: @@ -91,8 +88,6 @@ def save_model(self, model: nn.Module, path: str, only_rank0: bool = False, toke torch.save(state_dict, path) else: try: - if isinstance(model, LM): - model = model.model model.save_pretrained(path) if tokenizer is not None: tokenizer.save_pretrained(path) diff --git a/applications/Chat/coati/trainer/strategies/naive.py b/applications/Chat/coati/trainer/strategies/naive.py index bb47e5ab2688..7a325eca0fe6 100644 --- a/applications/Chat/coati/trainer/strategies/naive.py +++ b/applications/Chat/coati/trainer/strategies/naive.py @@ -3,9 +3,8 @@ import torch import torch.nn as nn import torch.optim as optim +from coati.models.base import RewardModel from coati.replay_buffer import ReplayBuffer -from coati.models.base import LM, RewardModel -from coati.models.lora import LoraLinear from torch.optim import Optimizer from torch.utils.data import DataLoader from transformers.tokenization_utils_base import PreTrainedTokenizerBase @@ -41,19 +40,16 @@ def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False pin_memory=pin_memory, collate_fn=replay_buffer.collate_fn) - def save_model(self, model: nn.Module, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - for module in model.modules(): - if isinstance(module, LoraLinear): - module.merge_weights = True - module.eval() - + def save_model(self, + model: nn.Module, + path: str, + only_rank0: bool = False, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: if isinstance(model, RewardModel): state_dict = model.state_dict() torch.save(state_dict, path) else: try: - if isinstance(model, LM): - model = model.model model.save_pretrained(path) if tokenizer is not None: tokenizer.save_pretrained(path) diff --git a/applications/Chat/coati/utils/tokenizer_utils.py b/applications/Chat/coati/utils/tokenizer_utils.py index 80dcc55fca3e..e0d96cfc8be2 100644 --- a/applications/Chat/coati/utils/tokenizer_utils.py +++ b/applications/Chat/coati/utils/tokenizer_utils.py @@ -16,8 +16,6 @@ import transformers -from ..models.llama.llama_lm import LlamaLM - DEFAULT_PAD_TOKEN = "[PAD]" DEFAULT_EOS_TOKEN = "" DEFAULT_BOS_TOKEN = "          " @@ -62,9 +60,6 @@ def smart_tokenizer_and_embedding_resize( if tokenizer.pad_token is None: num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) - if isinstance(model, LlamaLM): - model = model.get_base_model() - model.resize_token_embeddings(len(tokenizer)) if num_new_tokens > 0: diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index a86e2295a62c..2b049163c801 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -31,16 +31,19 @@ torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'bigsci --model 'bloom' --strategy colossalai_zero2 --lora_rank 4\ --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ --save_path ${BASE}/output +rm -rf ${BASE}/output torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'gpt2' \ --model 'gpt2' --strategy colossalai_zero2 \ --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ --save_path ${BASE}/output +rm -rf ${BASE}/output torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'facebook/opt-350m' \ --model 'opt' --strategy colossalai_zero2 --lora_rank 4\ --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ --save_path ${BASE}/output +rm -rf ${BASE}/output torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'gpt2' \ --model 'gpt2' --strategy ddp --lora_rank 4\ @@ -59,14 +62,14 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --pretrain 'facebook/opt-350m' --model 'opt' \ --strategy colossalai_zero2 --loss_fn 'log_sig'\ --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ - --test True --lora_rank 4 \ + --test True --lora_rank 0 \ --save_path ${BASE}/rm_ckpt_opt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --pretrain 'gpt2' --model 'gpt2' \ --strategy colossalai_zero2 --loss_fn 'log_exp' \ --dataset 'Dahoas/rm-static' \ - --test True --lora_rank 4 \ + --test True --lora_rank 0 \ --save_path ${BASE}/rm_ckpt_gpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ @@ -75,6 +78,7 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --dataset 'Dahoas/rm-static' \ --test True --lora_rank 4 \ --save_path ${BASE}/rm_ckpt.pt +rm -rf ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --pretrain 'bigscience/bloom-560m' --model 'bloom' \ @@ -82,6 +86,7 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ --test True --lora_rank 4 \ --save_path ${BASE}/rm_ckpt.pt +rm -rf ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --pretrain 'microsoft/deberta-v3-large' --model 'deberta' \ @@ -89,6 +94,7 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ --test True --lora_rank 4 \ --save_path ${BASE}/rm_ckpt.pt +rm -rf ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --pretrain 'roberta-base' --model 'roberta' \ @@ -117,4 +123,4 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_datas --save_path ${BASE}/actor_checkpoint_prompts.pt rm -rf ${BASE}/rm_ckpt_gpt.pt -rm -rf ${BASE}/actor_checkpoint_prompts.pt \ No newline at end of file +rm -rf ${BASE}/actor_checkpoint_prompts.pt diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index d08cf77864a4..eecf4d89dec7 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -5,11 +5,7 @@ import torch import torch.distributed as dist from coati.dataset import DataCollatorForSupervisedDataset, SFTDataset, SupervisedDataset -from coati.models.base import RewardModel -from coati.models.bloom import BLOOMLM -from coati.models.gpt import GPTLM -from coati.models.llama import LlamaLM -from coati.models.opt import OPTLM +from coati.models import convert_to_lora_module from coati.trainer import SFTTrainer from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy from coati.utils import prepare_llama_tokenizer_and_embedding @@ -17,8 +13,12 @@ from torch.optim import Adam from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler -from transformers import AutoTokenizer, BloomTokenizerFast +from transformers import AutoTokenizer, BloomConfig, BloomForCausalLM, BloomTokenizerFast, LlamaConfig, LlamaForCausalLM +from transformers.models.gpt2.configuration_gpt2 import GPT2Config +from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer +from transformers.models.opt.configuration_opt import OPTConfig +from transformers.models.opt.modeling_opt import OPTForCausalLM from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import HybridAdam @@ -32,6 +32,8 @@ def train(args): elif args.strategy == 'ddp': strategy = DDPStrategy() elif args.strategy == 'colossalai_gemini': + raise NotImplementedError( + 'Gemini is not supported .from_pretrained() yet. We will update this after checkpoint io is ready.') strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') elif args.strategy == 'colossalai_zero2': strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') @@ -43,16 +45,19 @@ def train(args): # configure model with strategy.model_init_context(): if args.model == 'bloom': - model = BLOOMLM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + model = convert_to_lora_module(BloomForCausalLM.from_pretrained(args.pretrain), + args.lora_rank).half().cuda() elif args.model == 'opt': - model = OPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + model = convert_to_lora_module(OPTForCausalLM.from_pretrained(args.pretrain), args.lora_rank).half().cuda() elif args.model == 'gpt2': - model = GPTLM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) + model = convert_to_lora_module(GPT2LMHeadModel.from_pretrained(args.pretrain), args.lora_rank).half().cuda() elif args.model == 'llama': - model = LlamaLM(pretrained=args.pretrain, lora_rank=args.lora_rank, - checkpoint=True).to(torch.float16).to(torch.cuda.current_device()) + model = convert_to_lora_module(LlamaForCausalLM.from_pretrained(args.pretrain), + args.lora_rank).half().cuda() else: raise ValueError(f'Unsupported model "{args.model}"') + if args.grad_checkpoint: + model.gradient_checkpointing_enable() # configure tokenizer if args.model == 'gpt2': @@ -152,7 +157,6 @@ def train(args): optim=optim, train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, - batch_size=args.batch_size, max_epochs=args.max_epochs, accimulation_steps=args.accimulation_steps) @@ -186,5 +190,6 @@ def train(args): parser.add_argument('--lr', type=float, default=5e-6) parser.add_argument('--accimulation_steps', type=int, default=8) parser.add_argument('--use_wandb', default=False, action='store_true') + parser.add_argument('--grad_checkpoint', default=False, action='store_true') args = parser.parse_args() train(args) From 842768a1749bf3d9961a48d2bf96ca5abef7d2da Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Thu, 27 Apr 2023 18:41:49 +0800 Subject: [PATCH 168/413] [chat] refactor model save/load logic (#3654) * [chat] strategy refactor unwrap model * [chat] strategy refactor save model * [chat] add docstr * [chat] refactor trainer save model * [chat] fix strategy typing * [chat] refactor trainer save model * [chat] update readme * [chat] fix unit test --- applications/Chat/README.md | 12 ++- .../Chat/coati/models/base/__init__.py | 21 +++++- applications/Chat/coati/trainer/ppo.py | 8 +- applications/Chat/coati/trainer/rm.py | 16 +--- applications/Chat/coati/trainer/sft.py | 10 +-- .../Chat/coati/trainer/strategies/base.py | 44 +++++------ .../coati/trainer/strategies/colossalai.py | 74 ++++++++----------- .../Chat/coati/trainer/strategies/ddp.py | 45 ++++------- .../Chat/coati/trainer/strategies/naive.py | 38 +++++----- applications/Chat/examples/README.md | 27 +------ applications/Chat/examples/train_prompts.py | 2 +- .../Chat/examples/train_reward_model.py | 35 ++++++--- applications/Chat/examples/train_sft.py | 3 +- applications/Chat/tests/test_checkpoint.py | 1 - 14 files changed, 155 insertions(+), 181 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index dea562c4d2ad..9441a733a5cc 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -243,6 +243,7 @@ from coati.trainer import SFTTrainer model = LlamaLM(pretrained=args.pretrain) tokenizer = AutoTokenizer.from_pretrained(args.pretrain) +(model, optim) = strategy.prepare((model, optim)) trainer = SFTTrainer(model=model, strategy=strategy, optim=optim, @@ -254,7 +255,11 @@ trainer = SFTTrainer(model=model, ) trainer.fit() -trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) +# this saves in pytorch format +strategy.save_model(model, args.save_path, only_rank0=True) + +# this saves in HF format. ColossalAI strategy with stage-3 doesn't support this method +strategy.save_pretrained(model, args.save_path, only_rank0=True, tokenizer=tokenizer) ``` @@ -263,7 +268,7 @@ trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) Here are some examples that can allow you to train a 7B model on a single or multiple consumer-grade GPUs. -If you only have a single 24G GPU, you can use the following script. `batch_size` and `lora_rank` are the most important parameters to successfully train the model. +If you only have a single 24G GPU, you can use the following script. `batch_size`, `lora_rank` and `grad_checkpoint` are the most important parameters to successfully train the model. ``` torchrun --standalone --nproc_per_node=1 train_sft.py \ --pretrain "/path/to/LLaMa-7B/" \ @@ -278,6 +283,7 @@ torchrun --standalone --nproc_per_node=1 train_sft.py \ --max_datasets_size 512 \ --max_epochs 1 \ --lora_rank 16 \ + --grad_checkpoint ``` `colossalai_gemini` strategy can enable a single 24G GPU to train the whole model without using LoRA if you have sufficient CPU memory. You can use the following script. @@ -294,6 +300,7 @@ torchrun --standalone --nproc_per_node=1 train_sft.py \ --lr 2e-5 \ --max_datasets_size 512 \ --max_epochs 1 \ + --grad_checkpoint ``` If you have 4x32 GB GPUs, you can even train the whole 7B model using our `colossalai_zero2_cpu` strategy! The script is given as follows. @@ -310,6 +317,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --lr 2e-5 \ --max_datasets_size 512 \ --max_epochs 1 \ + --grad_checkpoint ``` diff --git a/applications/Chat/coati/models/base/__init__.py b/applications/Chat/coati/models/base/__init__.py index 86f403556904..fe4152f2b760 100644 --- a/applications/Chat/coati/models/base/__init__.py +++ b/applications/Chat/coati/models/base/__init__.py @@ -1,5 +1,24 @@ +import torch.nn as nn + from .actor import Actor from .critic import Critic from .reward_model import RewardModel -__all__ = ['Actor', 'Critic', 'RewardModel'] + +def get_base_model(model: nn.Module) -> nn.Module: + """Get the base model of our wrapper classes. + For Actor, it's base model is ``actor.model`` and it's usually a ``transformers.PreTrainedModel``. + For Critic and RewardModel, it's base model is itself. + + Args: + model (nn.Module): model to get base model from + + Returns: + nn.Module: the base model + """ + if isinstance(model, Actor): + return model.get_base_model() + return model + + +__all__ = ['Actor', 'Critic', 'RewardModel', 'get_base_model'] diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index f9ab4a556359..fe5ae48d9c2f 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -199,15 +199,9 @@ def training_step(self, experience: Experience) -> Dict[str, float]: return {'reward': experience.reward.mean().item()} - def save_model(self, - path: str, - only_rank0: bool = False, - tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer) - def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None: - origin_model = strategy._unwrap_actor(actor) + origin_model = strategy.unwrap_model(actor) new_kwargs = {**generate_kwargs} # use huggingface models method directly if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): diff --git a/applications/Chat/coati/trainer/rm.py b/applications/Chat/coati/trainer/rm.py index ed6720abc2af..cdae5108ab00 100644 --- a/applications/Chat/coati/trainer/rm.py +++ b/applications/Chat/coati/trainer/rm.py @@ -1,5 +1,5 @@ from datetime import datetime -from typing import Optional, List +from typing import List, Optional import pandas as pd import torch @@ -9,8 +9,8 @@ from tqdm import tqdm from transformers.tokenization_utils_base import PreTrainedTokenizerBase -from .callbacks import Callback from .base import Trainer +from .callbacks import Callback from .strategies import Strategy from .utils import is_rank_0 @@ -41,20 +41,18 @@ def __init__( train_dataloader: DataLoader, valid_dataloader: DataLoader, eval_dataloader: DataLoader, - batch_size: int = 1, max_epochs: int = 1, callbacks: List[Callback] = [], ) -> None: super().__init__(strategy, max_epochs, callbacks=callbacks) - train_sampler = None self.train_dataloader = train_dataloader self.valid_dataloader = valid_dataloader self.eval_dataloader = eval_dataloader - self.model = strategy.setup_model(model) + self.model = model self.loss_fn = loss_fn - self.optimizer = strategy.setup_optimizer(optim, self.model) + self.optimizer = optim self.scheduler = lr_scheduler.CosineAnnealingLR(self.optimizer, self.train_dataloader.__len__() // 100) def eval_acc(self, dataloader): @@ -123,9 +121,3 @@ def fit(self): epoch_bar.update() step_bar.set_postfix({'dist': dist, 'acc': acc}) step_bar.close() - - def save_model(self, - path: str, - only_rank0: bool = False, - tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - self.strategy.save_model(model=self.model, path=path, only_rank0=only_rank0, tokenizer=tokenizer) diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index 64a330eebeb8..0c09f4151a99 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -49,8 +49,8 @@ def __init__( super().__init__(strategy, max_epochs, callbacks=callbacks) self.train_dataloader = train_dataloader self.eval_dataloader = eval_dataloader - - (self.model, self.optimizer) = strategy.prepare((model, optim)) + self.model = model + self.optimizer = optim self.accimulation_steps = accimulation_steps num_update_steps_per_epoch = len(train_dataloader) // self.accimulation_steps @@ -133,9 +133,3 @@ def fit(self, logger, use_wandb: bool = False): logger.info(f'Eval Epoch {epoch}/{self.max_epochs} loss {loss_mean}') # epoch_bar.update() - - def save_model(self, - path: str, - only_rank0: bool = False, - tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - self.strategy.save_model(model=self.model, path=path, only_rank0=only_rank0, tokenizer=tokenizer) diff --git a/applications/Chat/coati/trainer/strategies/base.py b/applications/Chat/coati/trainer/strategies/base.py index a1647a4ca525..b1452869179e 100644 --- a/applications/Chat/coati/trainer/strategies/base.py +++ b/applications/Chat/coati/trainer/strategies/base.py @@ -2,10 +2,9 @@ from contextlib import nullcontext from typing import Any, List, Optional, Tuple, Union -import numpy as np import torch import torch.nn as nn -from coati.models.base import Actor, Critic, RewardModel +from coati.models.base import Actor, get_base_model from coati.replay_buffer import ReplayBuffer from torch.optim import Optimizer from torch.utils.data import DataLoader @@ -72,8 +71,8 @@ def prepare( def prepare_model(model: nn.Module): if isinstance(model, Actor): - return Actor(self.setup_model(self._unwrap_model(model))) - return self.setup_model(self._unwrap_model(model)) + return Actor(self.setup_model(model.get_base_model())) + return self.setup_model(model) rets = [] for arg in models_or_model_optim_pairs: @@ -81,7 +80,7 @@ def prepare_model(model: nn.Module): assert len(arg) == 2, f'Expect (model, optimizer) pair, got a tuple with size "{len(arg)}"' model, optimizer = arg model = prepare_model(model) - optimizer = self.setup_optimizer(optimizer, self._unwrap_model(model)) + optimizer = self.setup_optimizer(optimizer, get_base_model(model)) rets.append((model, optimizer)) elif isinstance(arg, nn.Module): rets.append(prepare_model(arg)) @@ -93,31 +92,20 @@ def prepare_model(model: nn.Module): return rets @staticmethod - def _unwrap_model(model: nn.Module) -> nn.Module: - """Useful for saving state dict. As actor is wrapped by Actor class again in `prepare()`, we should unwrap it before saving. + def unwrap_model(model: nn.Module) -> nn.Module: + """Get the unwrapped model from a wrapped model. Useful for getting original huggingface model. + For Actor, it will unwrap `actor.model`. Args: - model (nn.Module): an actor or a critic - """ - if isinstance(model, Actor): - return model.model - return model - - @staticmethod - def _unwrap_actor(actor: Actor) -> nn.Module: - """Get `actor.model` from a wrapped (by `prepare()`) actor. Useful for getting original huggingface model. + model (nn.Module): the model to unwrap - Args: - actor (Actor): a wrapped actor + Returns: + nn.Module: the original model (usually a huggingface model) """ - return Strategy._unwrap_model(actor) + return get_base_model(model) @abstractmethod - def save_model(self, - model: nn.Module, - path: str, - only_rank0: bool = False, - tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: pass @abstractmethod @@ -134,3 +122,11 @@ def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = No def setup_sampler(self, dataset) -> DistributedSampler: return DistributedSampler(dataset, 1, 0) + + @abstractmethod + def save_pretrained(self, + model: nn.Module, + path: str, + only_rank0: bool = True, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + pass diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index 2fb0b8a188f0..8aa302c77eee 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -5,10 +5,8 @@ import torch.distributed as dist import torch.nn as nn import torch.optim as optim -from coati.models.base import Actor, RewardModel -from coati.models.lora import LoraLinear +from coati.models.base import get_base_model from torch.optim import Optimizer -from transformers.modeling_utils import PreTrainedModel from transformers.tokenization_utils_base import PreTrainedTokenizerBase import colossalai @@ -17,9 +15,7 @@ from colossalai.tensor import ProcessGroup, ShardSpec from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext, ZeroDDP, zero_model_wrapper, zero_optim_wrapper -from colossalai.zero.gemini.utils import get_static_torch_model -from .base import Strategy from .ddp import DDPStrategy logger = get_dist_logger(__name__) @@ -141,7 +137,7 @@ def setup_model(self, model: nn.Module) -> nn.Module: model = zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config) if self.stage != 3 and self.precision == 'fp16': - model = model.half() + model = model.half().cuda() return model def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer: @@ -154,47 +150,39 @@ def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimi def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: optimizer.step() - @staticmethod - def _unwrap_actor(actor: Actor) -> nn.Module: - model: Union[nn.Module, ZeroDDP] = Strategy._unwrap_actor(actor) - if isinstance(model, ZeroDDP): - return model.module - return model - - def save_model(self, - model: nn.Module, - path: str, - only_rank0: bool = True, - tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - - if only_rank0 and dist.get_rank() != 0: - return None - unwrapped_model = self._unwrap_model(model) - # TODO : better way to get torch model from gemini model - # to get torch model from gemini model - - if isinstance(unwrapped_model, RewardModel): - state_dict = unwrapped_model.state_dict() - if only_rank0 and dist.get_rank() != 0: - return - torch.save(state_dict, path) + def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: + if only_rank0 and dist.get_rank() != 0 and self.stage != 3: + return + base_model = get_base_model(model) + if self.stage == 3: + assert isinstance(base_model, ZeroDDP) + # for stage 3, state_dict() method should be called on every rank + state_dict = base_model.state_dict(only_rank_0=only_rank0) else: - try: - logger.info(f'Saving model to {path}', ranks=[0]) - unwrapped_model.save_pretrained(path) - logger.info(f'Model saved to {path} Successfully', ranks=[0]) - if tokenizer is not None: - logger.info(f'Saving tokenizer to {path}', ranks=[0]) - tokenizer.save_pretrained(path) - logger.info(f'Tokenizer saved to {path} Successfully', ranks=[0]) - except AttributeError: - state_dict = unwrapped_model.state_dict() - if only_rank0 and dist.get_rank() != 0: - return - torch.save(state_dict, path) + # only_rank0 is false or rank == 0 + state_dict = base_model.state_dict() + if only_rank0 and dist.get_rank() != 0: + return + torch.save(state_dict, path) def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: if only_rank0: raise RuntimeError( f'Optimizer states are sharded when using ColossalAIStrategy. Only rank0 is not supported.') torch.save(optimizer.state_dict(), path) + + def unwrap_model(self, model: nn.Module) -> nn.Module: + base_model: Union[nn.Module, ZeroDDP] = get_base_model(model) + if self.stage == 3: + assert isinstance(base_model, ZeroDDP) + return base_model.module + return base_model + + def save_pretrained(self, + model: nn.Module, + path: str, + only_rank0: bool = True, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + if self.stage == 3: + raise RuntimeError('ColossalAI strategy with stage-3 does not support save_pretrained() now') + super().save_pretrained(model, path, only_rank0, tokenizer) diff --git a/applications/Chat/coati/trainer/strategies/ddp.py b/applications/Chat/coati/trainer/strategies/ddp.py index c4c632c67d79..7910b57878f8 100644 --- a/applications/Chat/coati/trainer/strategies/ddp.py +++ b/applications/Chat/coati/trainer/strategies/ddp.py @@ -6,14 +6,12 @@ import torch import torch.distributed as dist import torch.nn as nn -from coati.models.base import Actor, RewardModel from coati.replay_buffer import ReplayBuffer from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import Optimizer from torch.utils.data import DataLoader from transformers.tokenization_utils_base import PreTrainedTokenizerBase -from .base import Strategy from .naive import NaiveStrategy from .sampler import DistributedSampler @@ -68,34 +66,10 @@ def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False pin_memory=pin_memory, collate_fn=replay_buffer.collate_fn) - @staticmethod - def _unwrap_actor(actor: Actor) -> nn.Module: - model: DDP = Strategy._unwrap_actor(actor) - return model.module - - def save_model(self, - model: nn.Module, - path: str, - only_rank0: bool = False, - tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: if only_rank0 and dist.get_rank() != 0: - return None - - if isinstance(model, RewardModel): - state_dict = model.state_dict() - if only_rank0 and dist.get_rank() != 0: - return - torch.save(state_dict, path) - else: - try: - model.save_pretrained(path) - if tokenizer is not None: - tokenizer.save_pretrained(path) - except AttributeError: - state_dict = model.state_dict() - if only_rank0 and dist.get_rank() != 0: - return - torch.save(state_dict, path) + return + super().save_model(model, path, only_rank0) def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: if only_rank0 and dist.get_rank() != 0: @@ -104,3 +78,16 @@ def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = Fal def setup_sampler(self, dataset) -> DistributedSampler: return DistributedSampler(dataset, dist.get_world_size(), dist.get_rank()) + + def unwrap_model(self, model: nn.Module) -> nn.Module: + base_model: DDP = super().unwrap_model(model) + return base_model.module + + def save_pretrained(self, + model: nn.Module, + path: str, + only_rank0: bool = True, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + if only_rank0 and dist.get_rank() != 0: + return + super().save_pretrained(model, path, only_rank0, tokenizer) diff --git a/applications/Chat/coati/trainer/strategies/naive.py b/applications/Chat/coati/trainer/strategies/naive.py index 7a325eca0fe6..4d94026ce932 100644 --- a/applications/Chat/coati/trainer/strategies/naive.py +++ b/applications/Chat/coati/trainer/strategies/naive.py @@ -3,10 +3,11 @@ import torch import torch.nn as nn import torch.optim as optim -from coati.models.base import RewardModel +from coati.models.base import get_base_model from coati.replay_buffer import ReplayBuffer from torch.optim import Optimizer from torch.utils.data import DataLoader +from transformers.modeling_utils import PreTrainedModel from transformers.tokenization_utils_base import PreTrainedTokenizerBase from .base import Strategy @@ -40,27 +41,15 @@ def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False pin_memory=pin_memory, collate_fn=replay_buffer.collate_fn) - def save_model(self, - model: nn.Module, - path: str, - only_rank0: bool = False, - tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - if isinstance(model, RewardModel): - state_dict = model.state_dict() - torch.save(state_dict, path) - else: - try: - model.save_pretrained(path) - if tokenizer is not None: - tokenizer.save_pretrained(path) - except AttributeError: - state_dict = model.state_dict() - torch.save(state_dict, path) + def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: + base_model = get_base_model(model) + state_dict = base_model.state_dict() + torch.save(state_dict, path) def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None: - unwrapped_model = self._unwrap_model(model) + base_model = get_base_model(model) state_dict = torch.load(path, map_location=map_location) - unwrapped_model.load_state_dict(state_dict, strict=strict) + base_model.load_state_dict(state_dict, strict=strict) def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: torch.save(optimizer.state_dict(), path) @@ -68,3 +57,14 @@ def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = Fal def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None: state_dict = torch.load(path, map_location=map_location) optimizer.load_state_dict(state_dict) + + def save_pretrained(self, + model: nn.Module, + path: str, + only_rank0: bool = True, + tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: + unwrapped_model = self.unwrap_model(model) + assert isinstance(unwrapped_model, PreTrainedModel) + unwrapped_model.save_pretrained(path) + if tokenizer is not None: + tokenizer.save_pretrained(path) diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 0083dc37227f..e3880c7e4c0c 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -66,6 +66,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --lr 2e-5 \ --max_datasets_size 512 \ --max_epochs 1 \ + --grad_checkpoint ``` ### Arg List - --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive' @@ -78,6 +79,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ - --batch_size: batch size while training, type=int, default=4 - --lora_rank: low-rank adaptation matrices rank, type=int, default=0 - --log_interval: how many steps to log, type=int, default=100 +- --grad_checkpoint: enable gradient checkpointing, type=bool, default=False ## Stage2 - Training reward model @@ -152,7 +154,7 @@ torchrun --standalone --nproc_per_node=4 train_prompts.py \ --rm_path /your/rm/model/path ``` -Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use [seed_prompts_ch.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_ch.jsonl) or [seed_prompts_en.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_en.jsonl) in InstructionWild. +Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use [seed_prompts_ch.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_ch.jsonl) or [seed_prompts_en.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_en.jsonl) in InstructionWild. Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning. ### Arg List @@ -254,29 +256,6 @@ class CoatiActor(Actor): super().__init__(model, lora_rank, lora_train_bias) ``` -### LM model - -``` -from ..base import LM -from transformers.models.coati import CoatiModel - -class GPTLM(LM): - - def __init__(self, - pretrained: Optional[str] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = CoatiModel.from_pretrained(pretrained) - else: - model = build_model() # load your own model if it is not support in transformers - - super().__init__(model, lora_rank, lora_train_bias) - - def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): - return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs) -``` ### Reward model ``` from ..base import RewardModel diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index 292caa1b36b1..f4563630aad6 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -194,7 +194,7 @@ def main(args): update_timesteps=args.update_timesteps) # save model checkpoint after fitting - trainer.save_model(args.save_path, only_rank0=True, tokenizer=tokenizer) + strategy.save_model(actor, args.save_path, only_rank0=True) # save optimizer checkpoint on all ranks if args.need_optim_ckpt: strategy.save_optimizer(actor_optim, diff --git a/applications/Chat/examples/train_reward_model.py b/applications/Chat/examples/train_reward_model.py index 6a788a891ca6..5198c98dbd15 100644 --- a/applications/Chat/examples/train_reward_model.py +++ b/applications/Chat/examples/train_reward_model.py @@ -124,11 +124,23 @@ def train(args): raise ValueError(f'Unsupported dataset "{args.dataset}"') if dist.is_initialized() and dist.get_world_size() > 1: - train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True, rank=dist.get_rank(), + train_sampler = DistributedSampler(train_dataset, + shuffle=True, + seed=42, + drop_last=True, + rank=dist.get_rank(), num_replicas=dist.get_world_size()) - valid_sampler = DistributedSampler(valid_dataset, shuffle=True, seed=42, drop_last=True, rank=dist.get_rank(), + valid_sampler = DistributedSampler(valid_dataset, + shuffle=True, + seed=42, + drop_last=True, + rank=dist.get_rank(), num_replicas=dist.get_world_size()) - eval_sampler = DistributedSampler(eval_dataset, shuffle=True, seed=42, drop_last=True, rank=dist.get_rank(), + eval_sampler = DistributedSampler(eval_dataset, + shuffle=True, + seed=42, + drop_last=True, + rank=dist.get_rank(), num_replicas=dist.get_world_size()) else: train_sampler = None @@ -141,13 +153,19 @@ def train(args): batch_size=args.batch_size, pin_memory=True) - valid_dataloader = DataLoader(valid_dataset, shuffle=(valid_sampler is None), + valid_dataloader = DataLoader(valid_dataset, + shuffle=(valid_sampler is None), sampler=valid_sampler, - batch_size=args.batch_size, pin_memory=True) + batch_size=args.batch_size, + pin_memory=True) - eval_dataloader = DataLoader(eval_dataset, shuffle=(eval_sampler is None), - sampler=eval_sampler, batch_size=args.batch_size, pin_memory=True) + eval_dataloader = DataLoader(eval_dataset, + shuffle=(eval_sampler is None), + sampler=eval_sampler, + batch_size=args.batch_size, + pin_memory=True) + (model, optim) = strategy.prepare((model, optim)) trainer = RewardModelTrainer(model=model, strategy=strategy, optim=optim, @@ -155,12 +173,11 @@ def train(args): train_dataloader=train_dataloader, valid_dataloader=valid_dataloader, eval_dataloader=eval_dataloader, - batch_size=args.batch_size, max_epochs=args.max_epochs) trainer.fit() # save model checkpoint after fitting on only rank0 - trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) + strategy.save_model(model, args.save_path, only_rank0=True) # save optimizer checkpoint on all ranks if args.need_optim_ckpt: strategy.save_optimizer(trainer.optimizer, diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index eecf4d89dec7..b35d228dc593 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -152,6 +152,7 @@ def train(args): else: eval_dataloader = None + (model, optim) = strategy.prepare((model, optim)) trainer = SFTTrainer(model=model, strategy=strategy, optim=optim, @@ -163,7 +164,7 @@ def train(args): trainer.fit(logger=logger, use_wandb=args.use_wandb) # save model checkpoint after fitting on only rank0 - trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) + strategy.save_pretrained(model, path=args.save_path, only_rank0=True, tokenizer=tokenizer) # save optimizer checkpoint on all ranks if args.need_optim_ckpt: strategy.save_optimizer(trainer.optimizer, diff --git a/applications/Chat/tests/test_checkpoint.py b/applications/Chat/tests/test_checkpoint.py index 29617f205c46..4c05a3431699 100644 --- a/applications/Chat/tests/test_checkpoint.py +++ b/applications/Chat/tests/test_checkpoint.py @@ -82,7 +82,6 @@ def run_dist(rank, world_size, port, strategy): run_test_checkpoint(strategy) -@pytest.mark.skip('temporarily skip until refactor strategy unwrap') @pytest.mark.dist @pytest.mark.parametrize('world_size', [2]) @pytest.mark.parametrize('strategy', ['ddp', 'colossalai_zero2', 'colossalai_gemini']) From a22407cc027b2d6b30a21c50258a4285335ec0c8 Mon Sep 17 00:00:00 2001 From: YH <100389977+yhna940@users.noreply.github.com> Date: Thu, 27 Apr 2023 19:43:14 +0900 Subject: [PATCH 169/413] [zero] Suggests a minor change to confusing variable names in the ZeRO optimizer. (#3173) * Fix confusing variable name in zero opt * Apply lint * Fix util func * Fix minor util func * Fix zero param optimizer name --- colossalai/zero/low_level/_utils.py | 12 +- .../low_level/bookkeeping/parameter_store.py | 40 +++---- colossalai/zero/low_level/low_level_optim.py | 109 +++++++++--------- 3 files changed, 85 insertions(+), 76 deletions(-) diff --git a/colossalai/zero/low_level/_utils.py b/colossalai/zero/low_level/_utils.py index 9ca2fdf5aa06..afc98e7a7f54 100644 --- a/colossalai/zero/low_level/_utils.py +++ b/colossalai/zero/low_level/_utils.py @@ -91,10 +91,18 @@ def get_grad_accumulate_object(tensor): return grad_acc_obj -def split_half_float_double(tensor_list): +def split_by_dtype(tensor_list): + """ + Splits a list of PyTorch tensors into sublists based on their data type. + + :param tensor_list: A list of PyTorch tensors. + :type tensor_list: list[torch.Tensor] + :return: A list of sublists, where each sublist contains tensors of a specific data type. + :rtype: list[list[torch.Tensor]] + """ dtypes = ["torch.cuda.HalfTensor", "torch.cuda.FloatTensor", "torch.cuda.DoubleTensor", "torch.cuda.BFloat16Tensor"] buckets = [] - for i, dtype in enumerate(dtypes): + for _, dtype in enumerate(dtypes): bucket = [t for t in tensor_list if t.type() == dtype] if bucket: buckets.append(bucket) diff --git a/colossalai/zero/low_level/bookkeeping/parameter_store.py b/colossalai/zero/low_level/bookkeeping/parameter_store.py index cbf708b3471f..1f3ba7cbc3bc 100644 --- a/colossalai/zero/low_level/bookkeeping/parameter_store.py +++ b/colossalai/zero/low_level/bookkeeping/parameter_store.py @@ -11,9 +11,9 @@ class ParameterStore(BaseStore): def __init__(self, torch_pg: ProcessGroup): super().__init__(torch_pg) # param partitioning data structures - self._fp16_param_to_rank = dict() - self._rank_groupid_to_fp16_param_list = dict() - self._rank_group_id_to_flat_fp16_param = dict() + self._param_to_rank = dict() + self._rank_group_id_to_param_list = dict() + self._rank_group_id_to_flat_param = dict() # param reduction data structures self._is_param_reduced = dict() @@ -29,7 +29,7 @@ def set_param_to_rank(self, tensor: Tensor, rank: int) -> None: :type rank: int """ - self._fp16_param_to_rank[tensor] = rank + self._param_to_rank[tensor] = rank def get_param_rank(self, tensor: Tensor) -> int: """ @@ -38,7 +38,7 @@ def get_param_rank(self, tensor: Tensor) -> int: :param tensor: A :class:`torch.Tensor` object :type tensor: torch.Tensor """ - return self._fp16_param_to_rank[tensor] + return self._param_to_rank[tensor] def belongs_to_current_rank(self, tensor) -> bool: """ @@ -51,29 +51,29 @@ def belongs_to_current_rank(self, tensor) -> bool: :rtype: bool """ - tensor_rank = self._fp16_param_to_rank[tensor] + tensor_rank = self._param_to_rank[tensor] return tensor_rank == self._local_rank - def add_fp16_param_list_by_rank_group(self, rank, group_id, tensor_list) -> None: - if rank not in self._rank_groupid_to_fp16_param_list: - self._rank_groupid_to_fp16_param_list[rank] = dict() + def add_param_list_by_rank_group(self, rank, group_id, tensor_list) -> None: + if rank not in self._rank_group_id_to_param_list: + self._rank_group_id_to_param_list[rank] = dict() - if group_id not in self._rank_groupid_to_fp16_param_list[rank]: - self._rank_groupid_to_fp16_param_list[rank][group_id] = [] + if group_id not in self._rank_group_id_to_param_list[rank]: + self._rank_group_id_to_param_list[rank][group_id] = [] - self._rank_groupid_to_fp16_param_list[rank][group_id].extend(tensor_list) + self._rank_group_id_to_param_list[rank][group_id].extend(tensor_list) - def get_fp16_params_by_rank_group(self, rank, group_id) -> List[Tensor]: - return self._rank_groupid_to_fp16_param_list[rank][group_id] + def get_params_by_rank_group(self, rank, group_id) -> List[Tensor]: + return self._rank_group_id_to_param_list[rank][group_id] - def add_flat_fp16_param_by_rank_group(self, rank, group_id, tensor) -> None: - if rank not in self._rank_group_id_to_flat_fp16_param: - self._rank_group_id_to_flat_fp16_param[rank] = dict() + def add_flat_param_by_rank_group(self, rank, group_id, tensor) -> None: + if rank not in self._rank_group_id_to_flat_param: + self._rank_group_id_to_flat_param[rank] = dict() - self._rank_group_id_to_flat_fp16_param[rank][group_id] = tensor + self._rank_group_id_to_flat_param[rank][group_id] = tensor - def get_flat_fp16_param_by_rank_group(self, rank, group_id) -> Tensor: - return self._rank_group_id_to_flat_fp16_param[rank][group_id] + def get_flat_param_by_rank_group(self, rank, group_id) -> Tensor: + return self._rank_group_id_to_flat_param[rank][group_id] def is_param_reduced(self, tensor): return self._is_param_reduced[tensor] diff --git a/colossalai/zero/low_level/low_level_optim.py b/colossalai/zero/low_level/low_level_optim.py index 59c99113e644..3e7661ecab76 100644 --- a/colossalai/zero/low_level/low_level_optim.py +++ b/colossalai/zero/low_level/low_level_optim.py @@ -21,7 +21,7 @@ has_inf_or_nan, reduce_tensor_dp_group, release_param_grad, - split_half_float_double, + split_by_dtype, sync_param, ) from .bookkeeping import BucketStore, GradientStore, ParameterStore, TensorBucket @@ -90,9 +90,10 @@ def __init__( self._mp_torch_group = gpc.get_group(mp_parallel_mode) else: raise NotImplementedError - # fp16 and fp32 params for mixed precision training - self._fp16_param_groups = dict() - self._fp32_flat_param_groups_of_current_rank = dict() + + # working and master params for mixed precision training + self._working_param_groups = dict() + self._master_flat_param_groups_of_current_rank = dict() # communication params self._overlap_communication = overlap_communication @@ -138,8 +139,8 @@ def __init__( if param.requires_grad: group_params.append(param) - # add the fp16 params to fp16_param_groups for bookkeeping - self._fp16_param_groups[group_id] = group_params + # add the working params to working_param_groups for bookkeeping + self._working_param_groups[group_id] = group_params # assign parameters to ranks # the params in the list are sorted @@ -148,7 +149,7 @@ def __init__( # store the mapping between param to rank # each param should belong to only one rank for rank, params in enumerate(params_per_rank): - self._param_store.add_fp16_param_list_by_rank_group(rank, group_id, params) + self._param_store.add_param_list_by_rank_group(rank, group_id, params) for param in params: self._param_store.set_param_to_rank(param, rank) @@ -159,33 +160,33 @@ def __init__( # flatten the reordered tensors for rank in range(self._world_size): - tensor_list = self._param_store.get_fp16_params_by_rank_group(rank, group_id) + tensor_list = self._param_store.get_params_by_rank_group(rank, group_id) with torch.no_grad(): flat_tensor = flatten(tensor_list) flat_tensor = flat_tensor.data.cuda() - self._param_store.add_flat_fp16_param_by_rank_group(rank, group_id, flat_tensor) + self._param_store.add_flat_param_by_rank_group(rank, group_id, flat_tensor) # sync parameters for rank in range(self._world_size): - flat_tensor = self._param_store.get_flat_fp16_param_by_rank_group(rank, group_id) - tensor_list = self._param_store.get_fp16_params_by_rank_group(rank, group_id) + flat_tensor = self._param_store.get_flat_param_by_rank_group(rank, group_id) + tensor_list = self._param_store.get_params_by_rank_group(rank, group_id) sync_param(flat_tensor=flat_tensor, tensor_list=tensor_list) - # create a copy of fp32 weights of the parameters for which this rank is responsible - fp16_flat_current_rank = self._param_store.get_flat_fp16_param_by_rank_group(self._local_rank, group_id) - fp32_flat_current_rank = fp16_flat_current_rank.float() + # create a copy of fp32 master weights of the parameters for which this rank is responsible + working_flat_current_rank = self._param_store.get_flat_param_by_rank_group(self._local_rank, group_id) + master_flat_current_rank = working_flat_current_rank.float() device = 'cpu' if self._cpu_offload else get_current_device() - fp32_flat_current_rank = fp32_flat_current_rank.to(device) - fp32_flat_current_rank.requires_grad = True - self._fp32_flat_param_groups_of_current_rank[group_id] = fp32_flat_current_rank + master_flat_current_rank = master_flat_current_rank.to(device) + master_flat_current_rank.requires_grad = True + self._master_flat_param_groups_of_current_rank[group_id] = master_flat_current_rank # need to replace the params in the `params` field in the optimizer # so that when the optimizer calls step(), it only updates the tensors # managed by this data parallel rank - param_group['params'] = [fp32_flat_current_rank] + param_group['params'] = [master_flat_current_rank] # set reduction state - for param in self._fp16_param_groups[group_id]: + for param in self._working_param_groups[group_id]: self._param_store.set_param_reduction_state(param, False) # intialize communication stream for @@ -209,7 +210,7 @@ def loss_scale(self): @property def num_param_groups(self): - return len(self._fp16_param_groups) + return len(self._working_param_groups) def _sanity_checks(self): assert torch.cuda.is_available(), 'CUDA is required' @@ -261,10 +262,10 @@ def _grad_handler(self, param, grad, reduce_rank): return grad def _attach_reduction_hook(self): - # we iterate over the fp16 params + # we iterate over the working params # on each param, we register a hook to its AccumulateGrad object for group_id in range(self.num_param_groups): - param_group = self._fp16_param_groups[group_id] + param_group = self._working_param_groups[group_id] for param in param_group: if param.requires_grad: # determines the reduction destionation rank @@ -315,7 +316,7 @@ def _reduce_tensor_list_with_one_dtype(self, tensor_list, bucket_size, reduce_ra self._reduce_tensor_bucket(bucket=param_bucket, reduce_rank=reduce_rank) def _reduce_grads(self, reduce_rank, grads, bucket_size): - grad_buckets_by_dtype = split_half_float_double(grads) + grad_buckets_by_dtype = split_by_dtype(grads) for tensor_list in grad_buckets_by_dtype: self._reduce_tensor_list_with_one_dtype(tensor_list=tensor_list, @@ -418,7 +419,7 @@ def zero_grad(self, set_to_none=True): :param set_to_none: Whether set the gradient to None. Default value is True. :type set_to_none: bool """ - for _, param_group in self._fp16_param_groups.items(): + for _, param_group in self._working_param_groups.items(): for param in param_group: if set_to_none: param.grad = None @@ -446,33 +447,33 @@ def step(self, closure=None): self.zero_grad() return - # copy the grad of fp16 param to fp32 param + # copy the grad of working param to master param single_grad_partition_groups = [] norm_groups = [] for group_id in range(self.num_param_groups): # compute norm norm_group = compute_norm(gradients=self._grad_store.get_averaged_gradients_by_group(group_id), - params=self._param_store.get_fp16_params_by_rank_group(group_id=group_id, - rank=self._local_rank), + params=self._param_store.get_params_by_rank_group(group_id=group_id, + rank=self._local_rank), dp_group=self._dp_torch_group, mp_group=self._mp_torch_group) norm_groups.append(norm_group) - # create flat gradient for the flat fp32 params - fp16_avg_grads = self._grad_store.get_averaged_gradients_by_group(group_id) - flat_fp16_avg_grads = flatten(fp16_avg_grads) + # create flat gradient for the flat fp32 master params + working_avg_grads = self._grad_store.get_averaged_gradients_by_group(group_id) + flat_working_avg_grads = flatten(working_avg_grads) - dtype = self._fp32_flat_param_groups_of_current_rank[group_id].dtype - flat_fp32_avg_grads = flat_fp16_avg_grads.to(dtype) + dtype = self._master_flat_param_groups_of_current_rank[group_id].dtype + flat_master_avg_grads = flat_working_avg_grads.to(dtype) - param_shape = self._fp32_flat_param_groups_of_current_rank[group_id].shape - assert param_shape == flat_fp32_avg_grads.shape, \ - f'fp32 param and grad have different shape {param_shape} vs {flat_fp32_avg_grads.shape}' + param_shape = self._master_flat_param_groups_of_current_rank[group_id].shape + assert param_shape == flat_master_avg_grads.shape, \ + f'fp32 param and grad have different shape {param_shape} vs {flat_master_avg_grads.shape}' - single_grad_partition_groups.append(flat_fp32_avg_grads) - device = self._fp32_flat_param_groups_of_current_rank[group_id].device - self._fp32_flat_param_groups_of_current_rank[group_id].grad = flat_fp32_avg_grads.to(device) + single_grad_partition_groups.append(flat_master_avg_grads) + device = self._master_flat_param_groups_of_current_rank[group_id].device + self._master_flat_param_groups_of_current_rank[group_id].grad = flat_master_avg_grads.to(device) self._grad_store.reset_average_gradients_by_group(group_id) # unscale and clip grads @@ -481,37 +482,37 @@ def step(self, closure=None): # update the parameters self.optim.step() - # release the fp32 grad - release_param_grad(self._fp32_flat_param_groups_of_current_rank.values()) + # release the master grad + release_param_grad(self._master_flat_param_groups_of_current_rank.values()) - # update fp16 partition updated by the current rank - for group_id in range(len(self._fp16_param_groups)): - fp16_param = self._param_store.get_flat_fp16_param_by_rank_group(rank=self._local_rank, group_id=group_id) - fp32_param = self._fp32_flat_param_groups_of_current_rank[group_id] - fp16_param.data.copy_(fp32_param) + # update working partition updated by the current rank + for group_id in range(len(self._working_param_groups)): + working_param = self._param_store.get_flat_param_by_rank_group(rank=self._local_rank, group_id=group_id) + master_param = self._master_flat_param_groups_of_current_rank[group_id] + working_param.data.copy_(master_param) # broadcast the updated model weights handles = [] for group_id in range(self.num_param_groups): for index in range(self._world_size): rank = self._dp_global_ranks[index] - fp16_param = self._param_store.get_flat_fp16_param_by_rank_group(rank=index, group_id=group_id) - handle = dist.broadcast(fp16_param, src=rank, group=self._dp_torch_group, async_op=True) + working_param = self._param_store.get_flat_param_by_rank_group(rank=index, group_id=group_id) + handle = dist.broadcast(working_param, src=rank, group=self._dp_torch_group, async_op=True) handles.append(handle) for handle in handles: handle.wait() - ################## - # FP16 Utilities # - ################## + ############################# + # Mixed Precision Utilities # + ############################# def _check_overflow(self): # clear previous overflow record self._found_overflow.fill_(0.0) # check for overflow - for group_id in range(len(self._fp16_param_groups)): + for group_id in range(len(self._working_param_groups)): for avg_grad in self._grad_store.get_averaged_gradients_by_group(group_id): if avg_grad is not None and has_inf_or_nan(avg_grad): self._found_overflow.fill_(1.0) @@ -554,7 +555,7 @@ def _sync_grad(self): # accumulate gradient for group_id in range(self.num_param_groups): - param_group = self._param_store.get_fp16_params_by_rank_group(self._local_rank, group_id) + param_group = self._param_store.get_params_by_rank_group(self._local_rank, group_id) avg_gradients_group = self._grad_store.get_averaged_gradients_by_group(group_id) @@ -575,8 +576,8 @@ def _reduce_grad_stage1(self): # if not overlapping communication (no reduction hook is attached) # we need to manually reduce these gradients if not self._overlap_communication: - for group_id in range(len(self._fp16_param_groups)): - param_group = self._fp16_param_groups[group_id] + for group_id in range(len(self._working_param_groups)): + param_group = self._working_param_groups[group_id] for param in param_group: if param.grad is not None: self._add_to_reduction_bucket(param) From aa77ddae334343322e959f2be72e0b48400ca70a Mon Sep 17 00:00:00 2001 From: Tong Li Date: Thu, 27 Apr 2023 18:51:58 +0800 Subject: [PATCH 170/413] remove unnecessary step and update readme --- applications/Chat/evaluate/README.md | 53 +- .../Chat/evaluate/format_questions.py | 31 - .../Chat/evaluate/format_questions.sh | 3 - .../Chat/evaluate/sample/questions.json | 562 ++++++++++++++++++ 4 files changed, 585 insertions(+), 64 deletions(-) delete mode 100644 applications/Chat/evaluate/format_questions.py delete mode 100755 applications/Chat/evaluate/format_questions.sh create mode 100644 applications/Chat/evaluate/sample/questions.json diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index 6113dbbb1ef2..f44311f4b142 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -5,21 +5,11 @@ In this directory we will introduce how you can evaluate your model with GPT-4. ## Evaluation Pipeline The whole evaluation process undergoes two steps. - -1. Generate answers from different models: Use `generate_gpt35_answers.py` to generate answers of GPT 3.5 and use `generate_answers.py` to generate answers of your own models. -2. Evaluate models using GPT 4: Use `evaluate.py` to evaluate model answers with GPT-4. +1. Prepare the questions following the internal data structure in the data format section (described below). +2. Generate answers from different models: Use `generate_gpt35_answers.py` to generate answers of GPT 3.5 and use `generate_answers.py` to generate answers of your own models. +3. Evaluate models using GPT 4: Use `evaluate.py` to evaluate model answers with GPT-4. ### Generate Answers - -To generate answers, you should first format [FastChat's]([FastChat/question.jsonl at main · lm-sys/FastChat (github.com)](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/question.jsonl)) `question.jsonl` file. We do this formatting because we would like to add more questions later and the pipeline for generating new questions may follow that of Self-Instruct and Stanford Alpaca. An example script is given as follows. - -```shell -python format_questions.py \ - --questions_path "path to FastChat's question.jsonl" \ - --save_path "path to the formatted file" \ - -``` - In `generate_answers.py`, the model will generate answers in a batch way and different GPU processes will do inference on different shards of the given questions. Once all GPU process generate its answers, `merge.py` will merge different shards of answers and output a single answer file. Finally, the script will also remove the answer shards. An example script is given as follows. ```shell @@ -107,16 +97,23 @@ We would like to mention that the evaluation of model answers using the GPT-3.5 ## Data Format ### Questions - -We store questions in `questions.json`. The JSON file contains one list. Each element in the list is a question record. - -A question record has the following field: - -* `category` (str): The category of the question. -* `instruction` (str): The question. -* `input` (str): This is empty if you only use [FastChat's]([FastChat/question.jsonl at main · lm-sys/FastChat (github.com)](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/question.jsonl)) questions. -* `output` (str): This is empty. -* `id` (int): The question id. +The file [questions.json](./sample/questions.json) shows the example questions used to evaluate the performance of the model. The current sample questions are collected from [FastChat](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/question.jsonl). Each question record has the following field: +* `id` (id, compulsory): The ID of the instruction / question. +* `instruction` (str, compulsory): The instruction / question for the LLM. +* `input` (str, optional): The additional context of the instruction / question. +* `output` (str, optional): The sample output of the instruction / question. +* `category` (str, compulsory): The category of the instruction / question. + +Example: +``` +{ + "id": 0, + "instruction": "Help me summarize the following short story?", + "input": "{story}", + "output": "{summarized story}", + "category": "closed qa" +} +``` ### Answers @@ -126,7 +123,7 @@ An answer record has the following field: * `category` (str): The category of the question. * `instruction` (str): The question. -* `input` (str): This is empty if you only use [FastChat's]([FastChat/question.jsonl at main · lm-sys/FastChat (github.com)](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/question.jsonl)) questions. +* `input` (str): This is empty if you only use [FastChat's]((https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/question.jsonl)) questions. * `output` (str): The answer to the question. * `id` (int): The question id. @@ -158,15 +155,11 @@ A record has the following field: ### Prompts -The data format is the same with [FastChat's]([FastChat/prompt.jsonl at main · lm-sys/FastChat (github.com)](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/prompt.jsonl)) prompts. +The data format is the same with [FastChat's](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/prompt.jsonl) prompts. ### Reviewer -The data format is the same with [FastChat's]([FastChat/reviewer.jsonl at main · lm-sys/FastChat (github.com)](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/reviewer.jsonl)) reviewers. - -## Plan - -- [ ] Extend the questions +The data format is the same with [FastChat's](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/reviewer.jsonl) reviewers. ## Citations diff --git a/applications/Chat/evaluate/format_questions.py b/applications/Chat/evaluate/format_questions.py deleted file mode 100644 index 9b47907c34bf..000000000000 --- a/applications/Chat/evaluate/format_questions.py +++ /dev/null @@ -1,31 +0,0 @@ -import argparse -import os -import json -import copy - -from utils import jdump, get_json_list - - -def format_questions(args): - questions = get_json_list(args.questions_path) - keys=questions[0].keys() - - formatted_questions=copy.deepcopy(questions) - for i in range(len(formatted_questions)): - formatted_questions[i]['instruction']=questions[i]['text'] - formatted_questions[i]['input']="" - formatted_questions[i]['output']="" - formatted_questions[i]['id']=questions[i]['question_id'] - for key in keys: - if key=="category": - continue - del formatted_questions[i][key] - - jdump(formatted_questions, args.save_path) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--questions_path', type=str, default='table/question.jsonl') - parser.add_argument('--save_path', type=str, default="table/questions.json") - args = parser.parse_args() - format_questions(args) \ No newline at end of file diff --git a/applications/Chat/evaluate/format_questions.sh b/applications/Chat/evaluate/format_questions.sh deleted file mode 100755 index a7568da364ad..000000000000 --- a/applications/Chat/evaluate/format_questions.sh +++ /dev/null @@ -1,3 +0,0 @@ -python format_questions.py \ - --questions_path "path to FastChat's question.jsonl" \ - --save_path "path to the formatted file" \ diff --git a/applications/Chat/evaluate/sample/questions.json b/applications/Chat/evaluate/sample/questions.json new file mode 100644 index 000000000000..cbda9c086c81 --- /dev/null +++ b/applications/Chat/evaluate/sample/questions.json @@ -0,0 +1,562 @@ +[ + { + "category": "generic", + "instruction": "How can I improve my time management skills?", + "input": "", + "output": "", + "id": 1 + }, + { + "category": "generic", + "instruction": "What are the most effective ways to deal with stress?", + "input": "", + "output": "", + "id": 2 + }, + { + "category": "generic", + "instruction": "What are the main differences between Python and JavaScript programming languages?", + "input": "", + "output": "", + "id": 3 + }, + { + "category": "generic", + "instruction": "How can I increase my productivity while working from home?", + "input": "", + "output": "", + "id": 4 + }, + { + "category": "generic", + "instruction": "Can you explain the basics of quantum computing?", + "input": "", + "output": "", + "id": 5 + }, + { + "category": "generic", + "instruction": "What are the differences between plant-based and animal-based protein sources?", + "input": "", + "output": "", + "id": 6 + }, + { + "category": "generic", + "instruction": "How can I develop my critical thinking skills?", + "input": "", + "output": "", + "id": 7 + }, + { + "category": "generic", + "instruction": "What are the major challenges faced by the education sector today?", + "input": "", + "output": "", + "id": 8 + }, + { + "category": "generic", + "instruction": "What are the primary factors that influence consumer behavior?", + "input": "", + "output": "", + "id": 9 + }, + { + "category": "generic", + "instruction": "What are the most effective strategies for conflict resolution in the workplace?", + "input": "", + "output": "", + "id": 10 + }, + { + "category": "knowledge", + "instruction": "What are some potential implications of using a single-use plastic bottle versus a reusable bottle on both the environment and human health?", + "input": "", + "output": "", + "id": 11 + }, + { + "category": "knowledge", + "instruction": "What factors would you consider when designing an inclusive and accessible public transportation system?", + "input": "", + "output": "", + "id": 12 + }, + { + "category": "knowledge", + "instruction": "How can governments utilize fiscal and monetary policies to combat economic recessions?", + "input": "", + "output": "", + "id": 13 + }, + { + "category": "knowledge", + "instruction": "How do language and cultural barriers affect the way people communicate and form relationships in multicultural societies?", + "input": "", + "output": "", + "id": 14 + }, + { + "category": "knowledge", + "instruction": "Describe a scenario where artificial intelligence could be used to improve the quality and efficiency of healthcare delivery.", + "input": "", + "output": "", + "id": 15 + }, + { + "category": "knowledge", + "instruction": "Explain the process of gene editing using CRISPR-Cas9 technology, and discuss its potential applications and ethical implications.", + "input": "", + "output": "", + "id": 16 + }, + { + "category": "knowledge", + "instruction": "How do vaccinations work to protect individuals and communities from infectious diseases, and what is herd immunity?", + "input": "", + "output": "", + "id": 17 + }, + { + "category": "knowledge", + "instruction": "How do social media platforms influence the way people consume and share news, and what are the potential implications for the spread of misinformation?", + "input": "", + "output": "", + "id": 18 + }, + { + "category": "knowledge", + "instruction": "How do cultural, social, and economic factors influence people's food choices, and how can this knowledge be used to promote healthier diets?", + "input": "", + "output": "", + "id": 19 + }, + { + "category": "knowledge", + "instruction": "Explain the process of natural selection and how it contributes to the evolution and adaptation of species.", + "input": "", + "output": "", + "id": 20 + }, + { + "category": "roleplay", + "instruction": "How would you introduce yourself as a medieval knight at a royal banquet?", + "input": "", + "output": "", + "id": 21 + }, + { + "category": "roleplay", + "instruction": "As a pirate captain, what would you say to your crew to motivate them to search for hidden treasure?", + "input": "", + "output": "", + "id": 22 + }, + { + "category": "roleplay", + "instruction": "If you were a Shakespearean character, how would you declare your love for someone in a soliloquy?", + "input": "", + "output": "", + "id": 23 + }, + { + "category": "roleplay", + "instruction": "As a superhero, how would you explain your origin story to a curious child?", + "input": "", + "output": "", + "id": 24 + }, + { + "category": "roleplay", + "instruction": "Imagine you are a time traveler from the year 3000. What technological advancements would you tell people about?", + "input": "", + "output": "", + "id": 25 + }, + { + "category": "roleplay", + "instruction": "As a sports commentator, describe the winning play in the final seconds of a championship game.", + "input": "", + "output": "", + "id": 26 + }, + { + "category": "roleplay", + "instruction": "Pretend to be a world-famous chef. How would you describe your signature dish to a panel of judges?", + "input": "", + "output": "", + "id": 27 + }, + { + "category": "roleplay", + "instruction": "You are a mountain climber reaching the summit of Mount Everest. Describe your emotions and the view from the top.", + "input": "", + "output": "", + "id": 28 + }, + { + "category": "roleplay", + "instruction": "As a space colonist on Mars, describe your daily life and the challenges you face living on another planet.", + "input": "", + "output": "", + "id": 29 + }, + { + "category": "roleplay", + "instruction": "Pretend to be a character in a post-apocalyptic world. Describe how you survive and the allies you encounter.", + "input": "", + "output": "", + "id": 30 + }, + { + "category": "common-sense", + "instruction": "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?", + "input": "", + "output": "", + "id": 31 + }, + { + "category": "common-sense", + "instruction": "What are some subtle clues that suggest someone is pretending to understand a topic or conversation when they are actually confused or uninformed?", + "input": "", + "output": "", + "id": 32 + }, + { + "category": "common-sense", + "instruction": "Why might someone choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app?", + "input": "", + "output": "", + "id": 33 + }, + { + "category": "common-sense", + "instruction": "How can you determine if a person is genuinely interested in a conversation or simply being polite?", + "input": "", + "output": "", + "id": 34 + }, + { + "category": "common-sense", + "instruction": "Why might someone prefer to shop at a small, locally-owned business instead of a large chain store, even if the prices are higher?", + "input": "", + "output": "", + "id": 35 + }, + { + "category": "common-sense", + "instruction": "How can you assess the credibility of a source of information, such as a news article or blog post, without relying solely on the reputation of the author or publisher?", + "input": "", + "output": "", + "id": 36 + }, + { + "category": "common-sense", + "instruction": "Why do some people enjoy the sensation of being scared, such as by watching horror movies or going on roller coasters, while others avoid these experiences?", + "input": "", + "output": "", + "id": 37 + }, + { + "category": "common-sense", + "instruction": "How can observing the behavior of other people in a social situation provide clues about cultural norms and expectations?", + "input": "", + "output": "", + "id": 38 + }, + { + "category": "common-sense", + "instruction": "Do we have a moral obligation to explore space, or should we focus on solving Earth's problems first?", + "input": "", + "output": "", + "id": 39 + }, + { + "category": "common-sense", + "instruction": "In a world where automation is becoming increasingly prevalent, is it more important to prioritize job creation or technological progress?", + "input": "", + "output": "", + "id": 40 + }, + { + "category": "fermi", + "instruction": "How many times does the average human blink in a lifetime? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 41 + }, + { + "category": "fermi", + "instruction": "How many atoms are in a grain of salt? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 42 + }, + { + "category": "fermi", + "instruction": "How many lightning strikes occur on Earth each day? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 43 + }, + { + "category": "fermi", + "instruction": "How many balloons would it take to lift a house like in the movie \"Up\"? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 44 + }, + { + "category": "fermi", + "instruction": "How many text messages are sent globally in a minute? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 45 + }, + { + "category": "fermi", + "instruction": "How many words are spoken daily on Earth? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 46 + }, + { + "category": "fermi", + "instruction": "How many snowflakes fall during a typical winter? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 47 + }, + { + "category": "fermi", + "instruction": "How many pages are in all the books ever written? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 48 + }, + { + "category": "fermi", + "instruction": "How many times has the Earth orbited the Sun since the beginning of life? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 49 + }, + { + "category": "fermi", + "instruction": "How many songs have been recorded throughout history? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", + "input": "", + "output": "", + "id": 50 + }, + { + "category": "counterfactual", + "instruction": "What if the Internet had been invented during the Renaissance period?", + "input": "", + "output": "", + "id": 51 + }, + { + "category": "counterfactual", + "instruction": "What if the Aztecs had successfully repelled the Spanish conquistadors?", + "input": "", + "output": "", + "id": 52 + }, + { + "category": "counterfactual", + "instruction": "What if the Black Death had not occurred in the 14th century?", + "input": "", + "output": "", + "id": 53 + }, + { + "category": "counterfactual", + "instruction": "What if Isaac Newton had focused on biology instead of physics?", + "input": "", + "output": "", + "id": 54 + }, + { + "category": "counterfactual", + "instruction": "What if the Beatles had never formed as a band?", + "input": "", + "output": "", + "id": 55 + }, + { + "category": "counterfactual", + "instruction": "What if Alan Turing had not cracked the Enigma code during World War II?", + "input": "", + "output": "", + "id": 56 + }, + { + "category": "counterfactual", + "instruction": "What if the Suez Canal had never been constructed?", + "input": "", + "output": "", + "id": 57 + }, + { + "category": "counterfactual", + "instruction": "What if the Maya civilization had never mysteriously collapsed?", + "input": "", + "output": "", + "id": 58 + }, + { + "category": "counterfactual", + "instruction": "What if Christopher Columbus had not discovered the Americas?", + "input": "", + "output": "", + "id": 59 + }, + { + "category": "counterfactual", + "instruction": "What if Vincent van Gogh had been a successful artist during his lifetime?", + "input": "", + "output": "", + "id": 60 + }, + { + "category": "coding", + "instruction": "Develop a C++ program that reads a text file line by line and counts the number of occurrences of a specific word in the file.", + "input": "", + "output": "", + "id": 61 + }, + { + "category": "coding", + "instruction": "Implement a Python function to find the longest common subsequence of two input strings using dynamic programming.", + "input": "", + "output": "", + "id": 62 + }, + { + "category": "coding", + "instruction": "Implement a regular expression in Python to validate an email address.", + "input": "", + "output": "", + "id": 63 + }, + { + "category": "coding", + "instruction": "Write a program to find the nth Fibonacci number using dynamic programming.", + "input": "", + "output": "", + "id": 64 + }, + { + "category": "coding", + "instruction": "Implement a binary search algorithm to find a specific element in a sorted array.", + "input": "", + "output": "", + "id": 65 + }, + { + "category": "coding", + "instruction": "Implement a queue data structure using two stacks in Python.", + "input": "", + "output": "", + "id": 66 + }, + { + "category": "coding", + "instruction": "Implement a program to find the common elements in two arrays without using any extra data structures.", + "input": "", + "output": "", + "id": 67 + }, + { + "category": "math", + "instruction": "Given that f(x) = 5x^3 - 2x + 3, find the value of f(2).", + "input": "", + "output": "", + "id": 68 + }, + { + "category": "math", + "instruction": "Solve for x in the equation 3x + 10 = 5(x - 2).", + "input": "", + "output": "", + "id": 69 + }, + { + "category": "math", + "instruction": "If the endpoints of a line segment are (2, -2) and (10, 4), what is the length of the segment?", + "input": "", + "output": "", + "id": 70 + }, + { + "category": "writing", + "instruction": "Can you help me write a formal email to a potential business partner proposing a joint venture?", + "input": "", + "output": "", + "id": 71 + }, + { + "category": "writing", + "instruction": "Can you help me write a resignation letter to my current employer, while leaving on good terms and expressing gratitude for the opportunities provided?", + "input": "", + "output": "", + "id": 72 + }, + { + "category": "writing", + "instruction": "Use an appropriate format to structure a formal letter of recommendation for a student applying to a prestigious graduate program in computer science.", + "input": "", + "output": "", + "id": 73 + }, + { + "category": "writing", + "instruction": "Write a compelling product launch announcement email to inform our customers of our new software solution.", + "input": "", + "output": "", + "id": 74 + }, + { + "category": "writing", + "instruction": "Draft an apology email to a customer who experienced a delay in their order, and provide reassurance that the issue has been resolved.", + "input": "", + "output": "", + "id": 75 + }, + { + "category": "writing", + "instruction": "Write a script for a YouTube video exploring the history and cultural significance of jazz.", + "input": "", + "output": "", + "id": 76 + }, + { + "category": "writing", + "instruction": "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.", + "input": "", + "output": "", + "id": 77 + }, + { + "category": "writing", + "instruction": "Write a captivating movie review for a recently released science fiction film, discussing its plot, characters, and special effects.", + "input": "", + "output": "", + "id": 78 + }, + { + "category": "writing", + "instruction": "Structure a podcast script for an episode discussing the influence of streaming platforms on the music industry.", + "input": "", + "output": "", + "id": 79 + }, + { + "category": "writing", + "instruction": "Write a symphony concert review, discussing the orchestra's performance and overall audience experience.", + "input": "", + "output": "", + "id": 80 + } +] \ No newline at end of file From c419117329c7f7701b1119c1047d999b05390533 Mon Sep 17 00:00:00 2001 From: Tong Li Date: Thu, 27 Apr 2023 19:04:26 +0800 Subject: [PATCH 171/413] update questions and readme --- applications/Chat/evaluate/README.md | 2 +- .../Chat/evaluate/sample/questions.json | 563 +----------------- 2 files changed, 6 insertions(+), 559 deletions(-) diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index f44311f4b142..d6611abf77fd 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -97,7 +97,7 @@ We would like to mention that the evaluation of model answers using the GPT-3.5 ## Data Format ### Questions -The file [questions.json](./sample/questions.json) shows the example questions used to evaluate the performance of the model. The current sample questions are collected from [FastChat](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/question.jsonl). Each question record has the following field: +The file [questions.json](./sample/questions.json) shows the example questions used to evaluate the performance of the model. Each question record has the following field: * `id` (id, compulsory): The ID of the instruction / question. * `instruction` (str, compulsory): The instruction / question for the LLM. * `input` (str, optional): The additional context of the instruction / question. diff --git a/applications/Chat/evaluate/sample/questions.json b/applications/Chat/evaluate/sample/questions.json index cbda9c086c81..e9ef9f8b1c66 100644 --- a/applications/Chat/evaluate/sample/questions.json +++ b/applications/Chat/evaluate/sample/questions.json @@ -1,562 +1,9 @@ [ { - "category": "generic", - "instruction": "How can I improve my time management skills?", - "input": "", - "output": "", - "id": 1 - }, - { - "category": "generic", - "instruction": "What are the most effective ways to deal with stress?", - "input": "", - "output": "", - "id": 2 - }, - { - "category": "generic", - "instruction": "What are the main differences between Python and JavaScript programming languages?", - "input": "", - "output": "", - "id": 3 - }, - { - "category": "generic", - "instruction": "How can I increase my productivity while working from home?", - "input": "", - "output": "", - "id": 4 - }, - { - "category": "generic", - "instruction": "Can you explain the basics of quantum computing?", - "input": "", - "output": "", - "id": 5 - }, - { - "category": "generic", - "instruction": "What are the differences between plant-based and animal-based protein sources?", - "input": "", - "output": "", - "id": 6 - }, - { - "category": "generic", - "instruction": "How can I develop my critical thinking skills?", - "input": "", - "output": "", - "id": 7 - }, - { - "category": "generic", - "instruction": "What are the major challenges faced by the education sector today?", - "input": "", - "output": "", - "id": 8 - }, - { - "category": "generic", - "instruction": "What are the primary factors that influence consumer behavior?", - "input": "", - "output": "", - "id": 9 - }, - { - "category": "generic", - "instruction": "What are the most effective strategies for conflict resolution in the workplace?", - "input": "", - "output": "", - "id": 10 - }, - { - "category": "knowledge", - "instruction": "What are some potential implications of using a single-use plastic bottle versus a reusable bottle on both the environment and human health?", - "input": "", - "output": "", - "id": 11 - }, - { - "category": "knowledge", - "instruction": "What factors would you consider when designing an inclusive and accessible public transportation system?", - "input": "", - "output": "", - "id": 12 - }, - { - "category": "knowledge", - "instruction": "How can governments utilize fiscal and monetary policies to combat economic recessions?", - "input": "", - "output": "", - "id": 13 - }, - { - "category": "knowledge", - "instruction": "How do language and cultural barriers affect the way people communicate and form relationships in multicultural societies?", - "input": "", - "output": "", - "id": 14 - }, - { - "category": "knowledge", - "instruction": "Describe a scenario where artificial intelligence could be used to improve the quality and efficiency of healthcare delivery.", - "input": "", - "output": "", - "id": 15 - }, - { - "category": "knowledge", - "instruction": "Explain the process of gene editing using CRISPR-Cas9 technology, and discuss its potential applications and ethical implications.", - "input": "", - "output": "", - "id": 16 - }, - { - "category": "knowledge", - "instruction": "How do vaccinations work to protect individuals and communities from infectious diseases, and what is herd immunity?", - "input": "", - "output": "", - "id": 17 - }, - { - "category": "knowledge", - "instruction": "How do social media platforms influence the way people consume and share news, and what are the potential implications for the spread of misinformation?", - "input": "", - "output": "", - "id": 18 - }, - { - "category": "knowledge", - "instruction": "How do cultural, social, and economic factors influence people's food choices, and how can this knowledge be used to promote healthier diets?", - "input": "", - "output": "", - "id": 19 - }, - { - "category": "knowledge", - "instruction": "Explain the process of natural selection and how it contributes to the evolution and adaptation of species.", - "input": "", - "output": "", - "id": 20 - }, - { - "category": "roleplay", - "instruction": "How would you introduce yourself as a medieval knight at a royal banquet?", - "input": "", - "output": "", - "id": 21 - }, - { - "category": "roleplay", - "instruction": "As a pirate captain, what would you say to your crew to motivate them to search for hidden treasure?", - "input": "", - "output": "", - "id": 22 - }, - { - "category": "roleplay", - "instruction": "If you were a Shakespearean character, how would you declare your love for someone in a soliloquy?", - "input": "", - "output": "", - "id": 23 - }, - { - "category": "roleplay", - "instruction": "As a superhero, how would you explain your origin story to a curious child?", - "input": "", - "output": "", - "id": 24 - }, - { - "category": "roleplay", - "instruction": "Imagine you are a time traveler from the year 3000. What technological advancements would you tell people about?", - "input": "", - "output": "", - "id": 25 - }, - { - "category": "roleplay", - "instruction": "As a sports commentator, describe the winning play in the final seconds of a championship game.", - "input": "", - "output": "", - "id": 26 - }, - { - "category": "roleplay", - "instruction": "Pretend to be a world-famous chef. How would you describe your signature dish to a panel of judges?", - "input": "", - "output": "", - "id": 27 - }, - { - "category": "roleplay", - "instruction": "You are a mountain climber reaching the summit of Mount Everest. Describe your emotions and the view from the top.", - "input": "", - "output": "", - "id": 28 - }, - { - "category": "roleplay", - "instruction": "As a space colonist on Mars, describe your daily life and the challenges you face living on another planet.", - "input": "", - "output": "", - "id": 29 - }, - { - "category": "roleplay", - "instruction": "Pretend to be a character in a post-apocalyptic world. Describe how you survive and the allies you encounter.", - "input": "", - "output": "", - "id": 30 - }, - { - "category": "common-sense", - "instruction": "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?", - "input": "", - "output": "", - "id": 31 - }, - { - "category": "common-sense", - "instruction": "What are some subtle clues that suggest someone is pretending to understand a topic or conversation when they are actually confused or uninformed?", - "input": "", - "output": "", - "id": 32 - }, - { - "category": "common-sense", - "instruction": "Why might someone choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app?", - "input": "", - "output": "", - "id": 33 - }, - { - "category": "common-sense", - "instruction": "How can you determine if a person is genuinely interested in a conversation or simply being polite?", - "input": "", - "output": "", - "id": 34 - }, - { - "category": "common-sense", - "instruction": "Why might someone prefer to shop at a small, locally-owned business instead of a large chain store, even if the prices are higher?", - "input": "", - "output": "", - "id": 35 - }, - { - "category": "common-sense", - "instruction": "How can you assess the credibility of a source of information, such as a news article or blog post, without relying solely on the reputation of the author or publisher?", - "input": "", - "output": "", - "id": 36 - }, - { - "category": "common-sense", - "instruction": "Why do some people enjoy the sensation of being scared, such as by watching horror movies or going on roller coasters, while others avoid these experiences?", - "input": "", - "output": "", - "id": 37 - }, - { - "category": "common-sense", - "instruction": "How can observing the behavior of other people in a social situation provide clues about cultural norms and expectations?", - "input": "", - "output": "", - "id": 38 - }, - { - "category": "common-sense", - "instruction": "Do we have a moral obligation to explore space, or should we focus on solving Earth's problems first?", - "input": "", - "output": "", - "id": 39 - }, - { - "category": "common-sense", - "instruction": "In a world where automation is becoming increasingly prevalent, is it more important to prioritize job creation or technological progress?", - "input": "", - "output": "", - "id": 40 - }, - { - "category": "fermi", - "instruction": "How many times does the average human blink in a lifetime? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 41 - }, - { - "category": "fermi", - "instruction": "How many atoms are in a grain of salt? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 42 - }, - { - "category": "fermi", - "instruction": "How many lightning strikes occur on Earth each day? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 43 - }, - { - "category": "fermi", - "instruction": "How many balloons would it take to lift a house like in the movie \"Up\"? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 44 - }, - { - "category": "fermi", - "instruction": "How many text messages are sent globally in a minute? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 45 - }, - { - "category": "fermi", - "instruction": "How many words are spoken daily on Earth? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 46 - }, - { - "category": "fermi", - "instruction": "How many snowflakes fall during a typical winter? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 47 - }, - { - "category": "fermi", - "instruction": "How many pages are in all the books ever written? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 48 - }, - { - "category": "fermi", - "instruction": "How many times has the Earth orbited the Sun since the beginning of life? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 49 - }, - { - "category": "fermi", - "instruction": "How many songs have been recorded throughout history? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", - "input": "", - "output": "", - "id": 50 - }, - { - "category": "counterfactual", - "instruction": "What if the Internet had been invented during the Renaissance period?", - "input": "", - "output": "", - "id": 51 - }, - { - "category": "counterfactual", - "instruction": "What if the Aztecs had successfully repelled the Spanish conquistadors?", - "input": "", - "output": "", - "id": 52 - }, - { - "category": "counterfactual", - "instruction": "What if the Black Death had not occurred in the 14th century?", - "input": "", - "output": "", - "id": 53 - }, - { - "category": "counterfactual", - "instruction": "What if Isaac Newton had focused on biology instead of physics?", - "input": "", - "output": "", - "id": 54 - }, - { - "category": "counterfactual", - "instruction": "What if the Beatles had never formed as a band?", - "input": "", - "output": "", - "id": 55 - }, - { - "category": "counterfactual", - "instruction": "What if Alan Turing had not cracked the Enigma code during World War II?", - "input": "", - "output": "", - "id": 56 - }, - { - "category": "counterfactual", - "instruction": "What if the Suez Canal had never been constructed?", - "input": "", - "output": "", - "id": 57 - }, - { - "category": "counterfactual", - "instruction": "What if the Maya civilization had never mysteriously collapsed?", - "input": "", - "output": "", - "id": 58 - }, - { - "category": "counterfactual", - "instruction": "What if Christopher Columbus had not discovered the Americas?", - "input": "", - "output": "", - "id": 59 - }, - { - "category": "counterfactual", - "instruction": "What if Vincent van Gogh had been a successful artist during his lifetime?", - "input": "", - "output": "", - "id": 60 - }, - { - "category": "coding", - "instruction": "Develop a C++ program that reads a text file line by line and counts the number of occurrences of a specific word in the file.", - "input": "", - "output": "", - "id": 61 - }, - { - "category": "coding", - "instruction": "Implement a Python function to find the longest common subsequence of two input strings using dynamic programming.", - "input": "", - "output": "", - "id": 62 - }, - { - "category": "coding", - "instruction": "Implement a regular expression in Python to validate an email address.", - "input": "", - "output": "", - "id": 63 - }, - { - "category": "coding", - "instruction": "Write a program to find the nth Fibonacci number using dynamic programming.", - "input": "", - "output": "", - "id": 64 - }, - { - "category": "coding", - "instruction": "Implement a binary search algorithm to find a specific element in a sorted array.", - "input": "", - "output": "", - "id": 65 - }, - { - "category": "coding", - "instruction": "Implement a queue data structure using two stacks in Python.", - "input": "", - "output": "", - "id": 66 - }, - { - "category": "coding", - "instruction": "Implement a program to find the common elements in two arrays without using any extra data structures.", - "input": "", - "output": "", - "id": 67 - }, - { - "category": "math", - "instruction": "Given that f(x) = 5x^3 - 2x + 3, find the value of f(2).", - "input": "", - "output": "", - "id": 68 - }, - { - "category": "math", - "instruction": "Solve for x in the equation 3x + 10 = 5(x - 2).", - "input": "", - "output": "", - "id": 69 - }, - { - "category": "math", - "instruction": "If the endpoints of a line segment are (2, -2) and (10, 4), what is the length of the segment?", - "input": "", - "output": "", - "id": 70 - }, - { - "category": "writing", - "instruction": "Can you help me write a formal email to a potential business partner proposing a joint venture?", - "input": "", - "output": "", - "id": 71 - }, - { - "category": "writing", - "instruction": "Can you help me write a resignation letter to my current employer, while leaving on good terms and expressing gratitude for the opportunities provided?", - "input": "", - "output": "", - "id": 72 - }, - { - "category": "writing", - "instruction": "Use an appropriate format to structure a formal letter of recommendation for a student applying to a prestigious graduate program in computer science.", - "input": "", - "output": "", - "id": 73 - }, - { - "category": "writing", - "instruction": "Write a compelling product launch announcement email to inform our customers of our new software solution.", - "input": "", - "output": "", - "id": 74 - }, - { - "category": "writing", - "instruction": "Draft an apology email to a customer who experienced a delay in their order, and provide reassurance that the issue has been resolved.", - "input": "", - "output": "", - "id": 75 - }, - { - "category": "writing", - "instruction": "Write a script for a YouTube video exploring the history and cultural significance of jazz.", - "input": "", - "output": "", - "id": 76 - }, - { - "category": "writing", - "instruction": "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.", - "input": "", - "output": "", - "id": 77 - }, - { - "category": "writing", - "instruction": "Write a captivating movie review for a recently released science fiction film, discussing its plot, characters, and special effects.", - "input": "", - "output": "", - "id": 78 - }, - { - "category": "writing", - "instruction": "Structure a podcast script for an episode discussing the influence of streaming platforms on the music industry.", - "input": "", - "output": "", - "id": 79 - }, - { - "category": "writing", - "instruction": "Write a symphony concert review, discussing the orchestra's performance and overall audience experience.", - "input": "", - "output": "", - "id": 80 + "id": 0, + "instruction": "Help me summarize the following news?", + "input": "National Commercial Bank (NCB), Saudi Arabia's largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Samba's Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf region's third-largest lender. The entity's $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle East's biggest lender with about $268 billion of assets.", + "output": "NCB to pay 28.45 riyals for each Samba share. Deal will create Gulf region's third-largest lender", + "category": "closed qa" } ] \ No newline at end of file From ed3eaa6922ce95cff77744bf1975092de6fb57bd Mon Sep 17 00:00:00 2001 From: Tong Li Date: Fri, 28 Apr 2023 11:49:21 +0800 Subject: [PATCH 172/413] update documentation --- applications/Chat/evaluate/README.md | 78 +++++++++++++++------------- 1 file changed, 43 insertions(+), 35 deletions(-) diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index d6611abf77fd..d776a3e1f6b9 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -1,16 +1,36 @@ # Evaluation -In this directory we will introduce how you can evaluate your model with GPT-4. +In this directory, we introduce how you can evaluate your model with GPT-4. ## Evaluation Pipeline -The whole evaluation process undergoes two steps. +The whole evaluation process undergoes the following three steps: 1. Prepare the questions following the internal data structure in the data format section (described below). -2. Generate answers from different models: Use `generate_gpt35_answers.py` to generate answers of GPT 3.5 and use `generate_answers.py` to generate answers of your own models. -3. Evaluate models using GPT 4: Use `evaluate.py` to evaluate model answers with GPT-4. +2. Generate answers from different models: + * Generate answers using GPT-3.5: [generate_gpt35_answers.py](generate_gpt35_answers.py). + * Generate answers using your own models: [generate_answers.py](generate_answers.py). +3. Evaluate models using GPT-4: [evaluate.py](evaluate.py). ### Generate Answers -In `generate_answers.py`, the model will generate answers in a batch way and different GPU processes will do inference on different shards of the given questions. Once all GPU process generate its answers, `merge.py` will merge different shards of answers and output a single answer file. Finally, the script will also remove the answer shards. An example script is given as follows. +#### Generate Answers Using GPT-3.5 +You can provide your own OpenAI key to generate answers from GPT-3.5 using [generate_gpt35_answers.py](./generate_gpt35_answers.py). + +An example script is provided as follows: +```shell +python generate_gpt35_answers.py \ + --dataset "path to the question dataset" \ + --answer_path "path to answer folder" \ + --num_workers 4 \ + --openai_key "your openai key" \ + --max_tokens 512 \ +``` + +#### Generate Answers Using our Own Model +You can also generate answers using your own models. The generation process is divided into two stages: +1. Generate answers using multiple GPUs (optional) with batch processing: [generate_answers.py](./generate_answers.py). +2. Merge multiple shards and output a single file: [merge.py](./merge.py). + +An example script is given as follows: ```shell device_number=number of your devices @@ -41,21 +61,9 @@ done ``` -`generate_gpt35_answers.py` will generate answers of GPT-3.5 An example script is given as follows. - -```shell -python generate_gpt35_answers.py \ - --dataset "path to the question dataset" \ - --answer_path "path to answer folder" \ - --num_workers 4 \ - --openai_key "your openai key" \ - --max_tokens 512 \ - -``` - ### Evaluate Answers -In `evaluate.py`, GPT-4 will help review and score answers of two different models. Here `Model 1` refers to the first model you specify in the `--answer_file_list` and `Model 2` refers to the second model. The script will finally print several metrics and output corresponding JSON files. +In [evaluate.py](./evaluate.py), GPT-4 helps to review and score answers of two different models. Here `Model 1` refers to the first model you specify in the `--answer_file_list` and `Model 2` refers to the second model. The script shows several metrics and output the corresponding JSON files. The metrics include: @@ -121,11 +129,11 @@ We store model answers in `{model_name}_answers.json`. The JSON file contains on An answer record has the following field: -* `category` (str): The category of the question. -* `instruction` (str): The question. -* `input` (str): This is empty if you only use [FastChat's]((https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/question.jsonl)) questions. -* `output` (str): The answer to the question. -* `id` (int): The question id. +* `category` (str, compulsory): The category of the instruction / question. +* `instruction` (str, compulsory): The instruction / question for the LLM. +* `input` (str, optional): The additional context of the instruction / question. +* `output` (str, compulsory): The output from the LLM. +* `id` (int, compulsory): The ID of the instruction / question. ### Results @@ -133,12 +141,12 @@ We store evaluation results in `results.json`. The JSON file contains one dictio The value has the following field: -* `model` (list): The names of the two models. -* `better` (int): The number of reviews where Model 2 receives a higher score. -* `worse` (int): The number of reviews where Model 2 receives a lower score. -* `tie` (int): The number of reviews where two models play to a tie. -* `win_rate` (float): Win rate of Model 2. -* `score` (list): Average score of the two models. +* `model` (list, compulsory): The names of the two models. +* `better` (int, compulsory): The number of reviews where Model 2 receives a higher score. +* `worse` (int, compulsory): The number of reviews where Model 2 receives a lower score. +* `tie` (int, compulsory): The number of reviews where two models play to a tie. +* `win_rate` (float, compulsory): Win rate of Model 2. +* `score` (list, compulsory): Average score of the two models. ### Better, Worse, Tie, Invalid, Review @@ -146,12 +154,12 @@ To help better compare the model answers, we store JSON files whose name ends wi A record has the following field: -* `review_id` (str): Random UUID, not in use. -* `id` (int): The question id. -* `reviewer_id` (int): A unique ID for a reviewer. Different reviewer id use different prompts. -* `metadata` (dict): It is empty. -* `review` (str): GPT-4 's review. -* `score` (list): The scores of two models. +* `review_id` (str, optional): Random UUID, not in use. +* `id` (int, compulsory): The ID of the instruction / question. +* `reviewer_id` (int, compulsory): A unique ID for a reviewer. Different reviewer id use different prompts. +* `metadata` (dict, optional): It is empty. +* `review` (str, optional): GPT-4's review. +* `score` (list, compulsory): The scores of two models. ### Prompts From c1a355940ea4c5ec203bc9295e057fd3c8ca5efb Mon Sep 17 00:00:00 2001 From: Tong Li Date: Fri, 28 Apr 2023 11:56:35 +0800 Subject: [PATCH 173/413] update readme --- applications/Chat/evaluate/README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index d776a3e1f6b9..7ace4bfe6d18 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -7,13 +7,13 @@ In this directory, we introduce how you can evaluate your model with GPT-4. The whole evaluation process undergoes the following three steps: 1. Prepare the questions following the internal data structure in the data format section (described below). 2. Generate answers from different models: - * Generate answers using GPT-3.5: [generate_gpt35_answers.py](generate_gpt35_answers.py). - * Generate answers using your own models: [generate_answers.py](generate_answers.py). -3. Evaluate models using GPT-4: [evaluate.py](evaluate.py). + * Generate answers using GPT-3.5: [`generate_gpt35_answers.py`](generate_gpt35_answers.py). + * Generate answers using your own models: [`generate_answers.py`](generate_answers.py). +3. Evaluate models using GPT-4: [`evaluate.py`](evaluate.py). ### Generate Answers #### Generate Answers Using GPT-3.5 -You can provide your own OpenAI key to generate answers from GPT-3.5 using [generate_gpt35_answers.py](./generate_gpt35_answers.py). +You can provide your own OpenAI key to generate answers from GPT-3.5 using [`generate_gpt35_answers.py`](./generate_gpt35_answers.py). An example script is provided as follows: ```shell @@ -27,8 +27,8 @@ python generate_gpt35_answers.py \ #### Generate Answers Using our Own Model You can also generate answers using your own models. The generation process is divided into two stages: -1. Generate answers using multiple GPUs (optional) with batch processing: [generate_answers.py](./generate_answers.py). -2. Merge multiple shards and output a single file: [merge.py](./merge.py). +1. Generate answers using multiple GPUs (optional) with batch processing: [`generate_answers.py`](./generate_answers.py). +2. Merge multiple shards and output a single file: [`merge.py`](./merge.py). An example script is given as follows: @@ -63,7 +63,7 @@ done ### Evaluate Answers -In [evaluate.py](./evaluate.py), GPT-4 helps to review and score answers of two different models. Here `Model 1` refers to the first model you specify in the `--answer_file_list` and `Model 2` refers to the second model. The script shows several metrics and output the corresponding JSON files. +In [`evaluate.py`](./evaluate.py), GPT-4 helps to review and score answers of two different models. Here `Model 1` refers to the first model you specify in the `--answer_file_list` and `Model 2` refers to the second model. The script shows several metrics and output the corresponding JSON files. The metrics include: @@ -105,7 +105,7 @@ We would like to mention that the evaluation of model answers using the GPT-3.5 ## Data Format ### Questions -The file [questions.json](./sample/questions.json) shows the example questions used to evaluate the performance of the model. Each question record has the following field: +The file [`questions.json`](./sample/questions.json) shows the example questions used to evaluate the performance of the model. Each question record has the following field: * `id` (id, compulsory): The ID of the instruction / question. * `instruction` (str, compulsory): The instruction / question for the LLM. * `input` (str, optional): The additional context of the instruction / question. @@ -163,11 +163,11 @@ A record has the following field: ### Prompts -The data format is the same with [FastChat's](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/prompt.jsonl) prompts. +The data format is the same with [`FastChat's`](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/prompt.jsonl) prompts. ### Reviewer -The data format is the same with [FastChat's](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/reviewer.jsonl) reviewers. +The data format is the same with [`FastChat's`](https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/table/reviewer.jsonl) reviewers. ## Citations From 268b3cd80d106c2b700156b1993675c7421abd15 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Fri, 28 Apr 2023 13:56:50 +0800 Subject: [PATCH 174/413] [chat] set default zero2 strategy (#3667) * [chat] set default gemini strategy * [chat] set default zero2 strategy * [chat] set default zero2 strategy --- applications/Chat/examples/README.md | 6 +++--- applications/Chat/examples/train_prompts.py | 2 +- applications/Chat/examples/train_reward_model.py | 2 +- applications/Chat/examples/train_sft.py | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index e3880c7e4c0c..e76007147b47 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -69,7 +69,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --grad_checkpoint ``` ### Arg List -- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive' +- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' - --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' - --pretrain: pretrain model, type=str, default=None - --max_datasets_size: the max size of dataset, type=int, default=None @@ -117,7 +117,7 @@ Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862):
          We also train the reward model based on LLaMA-7B, which reaches the ACC of 72.06% after 1 epoch, performing almost the same as Anthropic's best RM. ### Arg List -- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive' +- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' - --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' - --pretrain: pretrain model, type=str, default=None - --model_path: the path of rm model(if continue to train), type=str, default=None @@ -158,7 +158,7 @@ Prompt dataset: the instruction dataset mentioned in the above figure which incl Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning. ### Arg List -- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive' +- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' - --model: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' - --pretrain: pretrain model, type=str, default=None - --rm_model: reward model type, type=str, choices=['gpt2', 'bloom', 'opt', 'llama'], default=None diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index f4563630aad6..a584991cd34e 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -208,7 +208,7 @@ def main(args): parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset') parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive', + default='colossalai_zero2', help='strategy to use') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama', 'roberta']) parser.add_argument('--pretrain', type=str, default=None) diff --git a/applications/Chat/examples/train_reward_model.py b/applications/Chat/examples/train_reward_model.py index 5198c98dbd15..48b12336fa67 100644 --- a/applications/Chat/examples/train_reward_model.py +++ b/applications/Chat/examples/train_reward_model.py @@ -189,7 +189,7 @@ def train(args): parser = argparse.ArgumentParser() parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') + default='colossalai_zero2') parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta', 'llama', 'roberta'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--model_path', type=str, default=None) diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index b35d228dc593..96914644d433 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -176,7 +176,7 @@ def train(args): parser = argparse.ArgumentParser() parser.add_argument('--strategy', choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_zero2_cpu'], - default='naive') + default='colossalai_zero2') parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--dataset', type=str, default=None) From 1a60dc07a8a6ef6980ffb913b8cbed795de92bff Mon Sep 17 00:00:00 2001 From: tanitna <117124016+tanitna@users.noreply.github.com> Date: Fri, 28 Apr 2023 00:42:57 -0700 Subject: [PATCH 175/413] [chat] typo accimulation_steps -> accumulation_steps (#3662) --- applications/Chat/README.md | 8 ++++---- applications/Chat/coati/trainer/sft.py | 16 ++++++++-------- applications/Chat/examples/README.md | 2 +- .../examples/community/peft/train_peft_sft.py | 4 ++-- applications/Chat/examples/train_sft.py | 4 ++-- applications/Chat/examples/train_sft.sh | 2 +- 6 files changed, 18 insertions(+), 18 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 9441a733a5cc..2a9c916d45c9 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -251,7 +251,7 @@ trainer = SFTTrainer(model=model, eval_dataloader=eval_dataloader, batch_size=args.batch_size, max_epochs=args.max_epochs, - accimulation_steps = args.accimulation_steps + accumulation_steps = args.accumulation_steps ) trainer.fit() @@ -278,7 +278,7 @@ torchrun --standalone --nproc_per_node=1 train_sft.py \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ --batch_size 1 \ - --accimulation_steps 8 \ + --accumulation_steps 8 \ --lr 2e-5 \ --max_datasets_size 512 \ --max_epochs 1 \ @@ -296,7 +296,7 @@ torchrun --standalone --nproc_per_node=1 train_sft.py \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ --batch_size 1 \ - --accimulation_steps 8 \ + --accumulation_steps 8 \ --lr 2e-5 \ --max_datasets_size 512 \ --max_epochs 1 \ @@ -313,7 +313,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ --batch_size 1 \ - --accimulation_steps 8 \ + --accumulation_steps 8 \ --lr 2e-5 \ --max_datasets_size 512 \ --max_epochs 1 \ diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index 0c09f4151a99..63fde53956cc 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -41,10 +41,10 @@ def __init__( train_dataloader: DataLoader, eval_dataloader: DataLoader = None, max_epochs: int = 2, - accimulation_steps: int = 8, + accumulation_steps: int = 8, callbacks: List[Callback] = [], ) -> None: - if accimulation_steps > 1 and isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3: + if accumulation_steps > 1 and isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3: raise ValueError("Accumulation steps are not supported in stage 3 of ColossalAI") super().__init__(strategy, max_epochs, callbacks=callbacks) self.train_dataloader = train_dataloader @@ -52,8 +52,8 @@ def __init__( self.model = model self.optimizer = optim - self.accimulation_steps = accimulation_steps - num_update_steps_per_epoch = len(train_dataloader) // self.accimulation_steps + self.accumulation_steps = accumulation_steps + num_update_steps_per_epoch = len(train_dataloader) // self.accumulation_steps max_steps = math.ceil(self.max_epochs * num_update_steps_per_epoch) self.scheduler = get_scheduler("cosine", @@ -67,7 +67,7 @@ def fit(self, logger, use_wandb: bool = False): wandb.watch(self.model) total_loss = 0 # epoch_bar = tqdm(range(self.epochs), desc='Epochs', disable=not is_rank_0()) - step_bar = tqdm(range(len(self.train_dataloader) // self.accimulation_steps * self.max_epochs), + step_bar = tqdm(range(len(self.train_dataloader) // self.accumulation_steps * self.max_epochs), desc=f'steps', disable=not is_rank_0()) for epoch in range(self.max_epochs): @@ -85,20 +85,20 @@ def fit(self, logger, use_wandb: bool = False): if loss >= 2.5 and is_rank_0(): logger.warning(f"batch_id:{batch_id}, abnormal loss: {loss}") - loss = loss / self.accimulation_steps + loss = loss / self.accumulation_steps self.strategy.backward(loss, self.model, self.optimizer) total_loss += loss.item() # gradient accumulation - if (batch_id + 1) % self.accimulation_steps == 0: + if (batch_id + 1) % self.accumulation_steps == 0: self.strategy.optimizer_step(self.optimizer) self.optimizer.zero_grad() self.scheduler.step() if is_rank_0() and use_wandb: wandb.log({ - "loss": total_loss / self.accimulation_steps, + "loss": total_loss / self.accumulation_steps, "lr": self.scheduler.get_last_lr()[0], "epoch": epoch, "batch_id": batch_id diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index e76007147b47..3e85bfe2d170 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -62,7 +62,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ --batch_size 4 \ - --accimulation_steps 8 \ + --accumulation_steps 8 \ --lr 2e-5 \ --max_datasets_size 512 \ --max_epochs 1 \ diff --git a/applications/Chat/examples/community/peft/train_peft_sft.py b/applications/Chat/examples/community/peft/train_peft_sft.py index fcc65e24478a..9bd0ebc12a83 100644 --- a/applications/Chat/examples/community/peft/train_peft_sft.py +++ b/applications/Chat/examples/community/peft/train_peft_sft.py @@ -154,7 +154,7 @@ def train(args): eval_dataloader=eval_dataloader, batch_size=args.batch_size, max_epochs=args.max_epochs, - accimulation_steps=args.accimulation_steps) + accumulation_steps=args.accumulation_steps) trainer.fit(logger=logger, log_interval=args.log_interval) @@ -183,7 +183,7 @@ def train(args): parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log") parser.add_argument('--lr', type=float, default=5e-6) - parser.add_argument('--accimulation_steps', type=int, default=8) + parser.add_argument('--accumulation_steps', type=int, default=8) parser.add_argument('--enable_peft_lora', action='store_true', default=False) parser.add_argument("--is_short_text", action='store_true', default=False) args = parser.parse_args() diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index 96914644d433..da499f068b17 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -159,7 +159,7 @@ def train(args): train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, max_epochs=args.max_epochs, - accimulation_steps=args.accimulation_steps) + accumulation_steps=args.accumulation_steps) trainer.fit(logger=logger, use_wandb=args.use_wandb) @@ -189,7 +189,7 @@ def train(args): parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log") parser.add_argument('--lr', type=float, default=5e-6) - parser.add_argument('--accimulation_steps', type=int, default=8) + parser.add_argument('--accumulation_steps', type=int, default=8) parser.add_argument('--use_wandb', default=False, action='store_true') parser.add_argument('--grad_checkpoint', default=False, action='store_true') args = parser.parse_args() diff --git a/applications/Chat/examples/train_sft.sh b/applications/Chat/examples/train_sft.sh index 73710d1b19f8..c880f85825a7 100755 --- a/applications/Chat/examples/train_sft.sh +++ b/applications/Chat/examples/train_sft.sh @@ -6,7 +6,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ --batch_size 4 \ - --accimulation_steps 8 \ + --accumulation_steps 8 \ --lr 2e-5 \ --max_datasets_size 512 \ --max_epochs 1 \ From bfbf6505885fce90af66d0c82a6fdb12604becc6 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Thu, 4 May 2023 15:31:09 +0800 Subject: [PATCH 176/413] fix spelling error fix spelling error with evaluate.py --- applications/Chat/evaluate/evaluate.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/applications/Chat/evaluate/evaluate.py b/applications/Chat/evaluate/evaluate.py index 9f17704426e2..2f9c9ce8e10d 100644 --- a/applications/Chat/evaluate/evaluate.py +++ b/applications/Chat/evaluate/evaluate.py @@ -130,7 +130,7 @@ def evaluate(args): assert answer1_jsons[i]['id'] == answer2_jsons[i]['id'] answer_id = answer1_jsons[i]['id'] - ques = answer1_jsons[i]['instruction'] if answer1_jsons[i]['input'] == "" else answer1_jsons[i]['instuction'] + \ + ques = answer1_jsons[i]['instruction'] if answer1_jsons[i]['input'] == "" else answer1_jsons[i]['instruction'] + \ " " + answer1_jsons[i]['input'] cat = answer1_jsons[i]['category'] ans1 = answer1_jsons[i]['output'] From 8ba7858753bab60bfc368e6ade561196598b48b7 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Thu, 4 May 2023 15:34:16 +0800 Subject: [PATCH 177/413] Update generate_gpt35_answers.py fix spelling error with generate_gpt35_answers.py --- applications/Chat/evaluate/generate_gpt35_answers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/applications/Chat/evaluate/generate_gpt35_answers.py b/applications/Chat/evaluate/generate_gpt35_answers.py index 852a7cb19dfa..db95cd2febf4 100644 --- a/applications/Chat/evaluate/generate_gpt35_answers.py +++ b/applications/Chat/evaluate/generate_gpt35_answers.py @@ -35,7 +35,7 @@ def get_answer(question: str, max_tokens: int): answer = question - prompt = question['instruction'] if question['input'] == "" else question['instuction'] + \ + prompt = question['instruction'] if question['input'] == "" else question['instruction'] + \ " " + question['input'] for _ in range(MAX_API_RETRY): try: From 7bd0bee8ea0ca7d713c34792dc99c1970a2c6701 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Thu, 4 May 2023 16:03:33 +0800 Subject: [PATCH 178/413] [chat] add opt attn kernel (#3655) * [chat] add opt attn kernel * [chat] disable xformer during fwd --- .../benchmarks/benchmark_opt_lora_dummy.py | 6 ++ applications/Chat/coati/kernels/__init__.py | 6 ++ applications/Chat/coati/kernels/opt_attn.py | 87 +++++++++++++++++++ applications/Chat/coati/kernels/wrapper.py | 18 ++++ 4 files changed, 117 insertions(+) create mode 100644 applications/Chat/coati/kernels/__init__.py create mode 100644 applications/Chat/coati/kernels/opt_attn.py create mode 100644 applications/Chat/coati/kernels/wrapper.py diff --git a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py index a991e8558aee..7a47624f74d8 100644 --- a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py +++ b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py @@ -101,6 +101,11 @@ def main(args): initial_model = deepcopy(actor).cuda().half() reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda().half() + if args.use_kernels: + from coati.kernels import convert_to_xformer_model + actor, critic, initial_model, reward_model = map(convert_to_xformer_model, + (actor, critic, initial_model, reward_model)) + actor_numel = get_model_numel(actor, strategy) critic_numel = get_model_numel(critic, strategy) initial_model_numel = get_model_numel(initial_model, strategy) @@ -184,5 +189,6 @@ def main(args): parser.add_argument('--lora_rank', type=int, default=0) parser.add_argument('--cuda_mem_frac', type=float, default=1.0) parser.add_argument('--offload_inference_models', action='store_true', default=False) + parser.add_argument('--use_kernels', action='store_true', default=False) args = parser.parse_args() main(args) diff --git a/applications/Chat/coati/kernels/__init__.py b/applications/Chat/coati/kernels/__init__.py new file mode 100644 index 000000000000..230eedf7ecba --- /dev/null +++ b/applications/Chat/coati/kernels/__init__.py @@ -0,0 +1,6 @@ +from .wrapper import convert_to_xformer_model, recover_from_xformer_model + +__all__ = [ + 'convert_to_xformer_model', + 'recover_from_xformer_model', +] diff --git a/applications/Chat/coati/kernels/opt_attn.py b/applications/Chat/coati/kernels/opt_attn.py new file mode 100644 index 000000000000..c10f341e94a3 --- /dev/null +++ b/applications/Chat/coati/kernels/opt_attn.py @@ -0,0 +1,87 @@ +from typing import Optional, Tuple + +import torch +import xformers.ops as xops +from torch import Tensor +from transformers.models.opt.modeling_opt import OPTAttention + + +# This is modified from https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py +class XOPTAttention(OPTAttention): + # def _shape(self, tensor: Tensor, seq_len: int, bsz: int): + # return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).contiguous() + + def forward( + self, + hidden_states: Tensor, + key_value_states: Optional[Tensor] = None, + past_key_value: Optional[Tensor] = None, + attention_mask: Optional[Tensor] = None, + layer_head_mask: Optional[Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[Tensor, Optional[Tensor], Optional[Tuple[Tensor]]]: + if not self.training: + return super().forward(hidden_states, key_value_states, past_key_value, attention_mask, layer_head_mask, + output_attentions) + """Input shape: Batch x Time x Channel""" + assert layer_head_mask is None, 'Xformers attention does not support layer_head_mask' + assert not output_attentions, 'Xformers attention does not support output_attentions' + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) + # get key, value proj + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + query_states = self._shape(query_states, tgt_len, bsz).transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + attn_output = xops.memory_efficient_attention(query_states, + key_states, + value_states, + attn_bias=xops.LowerTriangularMask(), + p=self.dropout if self.training else 0.0, + scale=self.scaling) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned aross GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + attn_weights_reshaped = None + + return attn_output, attn_weights_reshaped, past_key_value diff --git a/applications/Chat/coati/kernels/wrapper.py b/applications/Chat/coati/kernels/wrapper.py new file mode 100644 index 000000000000..c55bda600230 --- /dev/null +++ b/applications/Chat/coati/kernels/wrapper.py @@ -0,0 +1,18 @@ +import torch.nn as nn +from transformers.models.opt.modeling_opt import OPTAttention + +from .opt_attn import XOPTAttention + + +def convert_to_xformer_model(model: nn.Module) -> nn.Module: + for module in model.modules(): + if isinstance(module, OPTAttention): + module.__class__ = XOPTAttention + return model + + +def recover_from_xformer_model(model: nn.Module) -> nn.Module: + for module in model.modules(): + if isinstance(module, XOPTAttention): + module.__class__ = OPTAttention + return model From 6650daeb0ae43ab93e2341cce75975f3e553c02a Mon Sep 17 00:00:00 2001 From: digger-yu Date: Fri, 5 May 2023 11:37:35 +0800 Subject: [PATCH 179/413] [doc] fix chat spelling error (#3671) * Update README.md change "huggingaface" to "huggingface" * Update README.md change "Colossa-AI" to "Colossal-AI" --- applications/Chat/README.md | 2 +- applications/Chat/examples/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 2a9c916d45c9..9ba831973b6c 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -59,7 +59,7 @@ The Coati package provides a unified large language model framework that has imp Image source: https://openai.com/blog/chatgpt
          -**As Colossa-AI is undergoing some major updates, this project will be actively maintained to stay in line with the Colossal-AI project.** +**As Colossal-AI is undergoing some major updates, this project will be actively maintained to stay in line with the Colossal-AI project.** More details can be found in the latest news. diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 3e85bfe2d170..a466d415d15e 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -233,7 +233,7 @@ If you want to support your own model in Coati, please refer the pull request fo You should complete the implementation of four model classes, including Reward model, Critic model, LM model, Actor model here are some example code for a NewModel named `Coati`. -if it is supported in huggingaface [transformers](https://github.com/huggingface/transformers), you can load it by `from_pretrained`, o +if it is supported in huggingface [transformers](https://github.com/huggingface/transformers), you can load it by `from_pretrained`, o r you can build your own model by yourself. ### Actor model From 0f785cb1f332bfa1643b4dedd2ce38950b4a3179 Mon Sep 17 00:00:00 2001 From: Camille Zhong <44392324+Camille7777@users.noreply.github.com> Date: Fri, 5 May 2023 13:36:56 +0800 Subject: [PATCH 180/413] [chat] PPO stage3 doc enhancement (#3679) * Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci Update test_ci.sh Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368. Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci Update test_ci.sh Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. update roberta with coati chat ci update Revert "chat ci update" This reverts commit 17ae7ae01fa752bd3289fc39069868fde99cf846. * Update README.md Update README.md * update readme * Update test_ci.sh * update readme and add a script update readme and add a script modify readme Update README.md --- applications/Chat/examples/README.md | 2 +- applications/Chat/examples/example_data_reformat.py | 12 ++++++++++++ 2 files changed, 13 insertions(+), 1 deletion(-) create mode 100644 applications/Chat/examples/example_data_reformat.py diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index a466d415d15e..561ace2205ed 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -154,7 +154,7 @@ torchrun --standalone --nproc_per_node=4 train_prompts.py \ --rm_path /your/rm/model/path ``` -Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use [seed_prompts_ch.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_ch.jsonl) or [seed_prompts_en.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_en.jsonl) in InstructionWild. +Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use the [script](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/example_data_reformat.py) to reformat [seed_prompts_ch.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_ch.jsonl) or [seed_prompts_en.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_en.jsonl) in InstructionWild. Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning. ### Arg List diff --git a/applications/Chat/examples/example_data_reformat.py b/applications/Chat/examples/example_data_reformat.py new file mode 100644 index 000000000000..dc83b29b525b --- /dev/null +++ b/applications/Chat/examples/example_data_reformat.py @@ -0,0 +1,12 @@ +jsonl_file = 'seed_prompts_xx.jsonl' # seed_prompts_en.jsonl or seed_prompts_ch.json from InstructionWild +reformat_file = 'prompts_xx.jsonl' # reformat jsonl file used as Prompt dataset in Stage3 + +data = '' +with open(jsonl_file, 'r', encoding="utf-8") as f1: + for jsonstr in f1.readlines(): + jsonstr = '\t' + jsonstr.strip('\n') + ',\n' + data = data + jsonstr + data = '[\n' + data + ']' + +with open(reformat_file, 'w') as f2: + f2.write(data) \ No newline at end of file From 307894f74dd63d71f4b95272fe149ca607e2aafa Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Fri, 5 May 2023 14:37:21 +0800 Subject: [PATCH 181/413] [booster] gemini plugin support shard checkpoint (#3610) * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin add shard checkpoint save/load * gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint * [API Refactoring]gemini plugin support shard checkpoint --------- Co-authored-by: luchen Co-authored-by: luchen --- colossalai/booster/plugin/gemini_plugin.py | 44 +++++++++ .../checkpoint_io/checkpoint_io_base.py | 2 +- .../checkpoint_io/general_checkpoint_io.py | 63 ++++++------ colossalai/checkpoint_io/index_file.py | 16 ++- colossalai/checkpoint_io/utils.py | 86 ++++++++-------- colossalai/zero/gemini/gemini_ddp.py | 51 +++++++--- pytest.ini | 2 +- .../test_general_checkpoint_io.py | 99 ++++++++++++++++++- .../test_zeroddp_state_dict_shard.py | 3 +- 9 files changed, 269 insertions(+), 97 deletions(-) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index deda00d8a7b3..dfdd7be26eaa 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -1,6 +1,9 @@ import random import warnings from typing import Callable, List, Optional, Tuple, Union +from pathlib import Path +import os +import logging import numpy as np import torch @@ -20,6 +23,13 @@ from colossalai.zero import GeminiDDP, zero_model_wrapper, zero_optim_wrapper from colossalai.zero.gemini.memory_tracer import MemStats +from colossalai.checkpoint_io.utils import ( + get_base_filenames, + get_shard_filename + ) + +from colossalai.checkpoint_io import CheckpointIndexFile + from .plugin_base import Plugin __all__ = ['GeminiPlugin'] @@ -62,6 +72,40 @@ def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): if self.coordinator.is_master(): super().save_lr_scheduler(lr_scheduler, checkpoint) + def save_sharded_model(self, model: GeminiDDP, checkpoint_path: str, gather_dtensor: bool = False, variant: Optional[str] = None, max_shard_size: int = 1024, use_safetensors: bool = False): + """ + Save sharded model + """ + state_dict_shard = model.state_dict_shard(max_shard_size=max_shard_size, only_rank_0=True, dtype=torch.float32) + weights_name, save_index_file = get_base_filenames(variant, use_safetensors) + total_size = 0 + index_file = CheckpointIndexFile(checkpoint_path) + for idx, shard_pair in enumerate(state_dict_shard): + if not self.coordinator.is_master(): + continue + shard = shard_pair[0] + shard_file = get_shard_filename(weights_name, idx) + total_size = total_size + shard_pair[1] + for key in shard.keys(): + index_file.append_weight_map(key, shard_file) + + checkpoint_file_path = os.path.join(checkpoint_path, shard_file) + save_state_dict(shard, checkpoint_file_path, use_safetensors) + + index_file.append_meta_data("total_size", total_size) + index_file.write_index_file(save_index_file) + logging.info( + f"The model is going to be split to checkpoint shards. " + f"You can find where each parameters has been saved in the " + f"index located at {save_index_file}." + ) + + + def load_sharded_model(self, model: GeminiDDP, checkpoint_index_file: Path, strict: bool = False, use_safetensors: bool = False): + """ + load shard model, load model from multiple files + """ + return super().load_sharded_model(model, checkpoint_index_file, strict, use_safetensors, load_sub_module=False) class GeminiModel(ModelWrapper): diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py index cb853559c48c..9cf344ecc41b 100644 --- a/colossalai/checkpoint_io/checkpoint_io_base.py +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -86,7 +86,7 @@ def load_model(self, # the existence of index file means it is a sharded checkpoint ckpt_path = Path(checkpoint) index_file_exists, index_file_path = has_index_file(checkpoint) - + # return the origin model instead of the unwrapped model origin_model = model diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index bf584f45d045..96a883fdb42a 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -1,12 +1,12 @@ from pathlib import Path +from functools import reduce import torch.nn as nn from torch.optim import Optimizer import logging import os -import json import gc -from typing import Optional +from typing import Optional, Iterator, OrderedDict, Tuple from .checkpoint_io_base import CheckpointIO from .index_file import CheckpointIndexFile @@ -18,10 +18,9 @@ shard_checkpoint, load_shard_state_dict, load_state_dict_into_model, - add_variant + get_shard_filename, + get_base_filenames ) -from .utils import SAFE_WEIGHTS_NAME, WEIGHTS_NAME, SAFE_WEIGHTS_INDEX_NAME, WEIGHTS_INDEX_NAME - __all__ = ['GeneralCheckpointIO'] @@ -85,30 +84,32 @@ def save_sharded_model(self, model: nn.Module, checkpoint_path: str, gather_dten # shard checkpoint state_dict = model.state_dict() - weights_name = SAFE_WEIGHTS_NAME if use_safetensors else WEIGHTS_NAME - weights_name = add_variant(weights_name, variant) - shards, index = shard_checkpoint(state_dict, max_shard_size=max_shard_size, weights_name=weights_name) - - # Save the model - for shard_file, shard in shards.items(): + state_dict_shard = shard_checkpoint(state_dict, max_shard_size=max_shard_size) + + weights_name, save_index_file = get_base_filenames(variant, use_safetensors) + total_size = 0 + index_file = CheckpointIndexFile(checkpoint_path) + for idx, shard_pair in enumerate(state_dict_shard): + shard = shard_pair[0] + shard_file = get_shard_filename(weights_name, idx) + total_size = total_size + shard_pair[1] + for key in shard.keys(): + index_file.append_weight_map(key, shard_file) + checkpoint_file_path = os.path.join(checkpoint_path, shard_file) save_state_dict(shard, checkpoint_file_path, use_safetensors) - - # save index file - save_index_file = SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME - - save_index_file = os.path.join(checkpoint_path, add_variant(save_index_file, variant)) - with open(save_index_file, "w", encoding="utf-8") as f: - content = json.dumps(index, indent=2, sort_keys=True) + "\n" - f.write(content) + + index_file.append_meta_data("total_size", total_size) + index_file.write_index_file(save_index_file) logging.info( - f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be " - f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the " + f"The model is going to be split to checkpoint shards. " + f"You can find where each parameters has been saved in the " f"index located at {save_index_file}." ) - def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, strict: bool = False, use_safetensors: bool = False): + def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, strict: bool = False, + use_safetensors: bool = False, load_sub_module: bool = True): """ load shard model, load model from multiple files """ @@ -122,17 +123,21 @@ def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, stri # read checkpoint index file ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file) checkpoint_files, _ = ckpt_index_file.get_checkpoint_fileanames() - missing_keys = ckpt_index_file.get_all_param_names() + missing_keys = [] for shard_file in checkpoint_files: state_dict = load_shard_state_dict(Path(shard_file), use_safetensors) - load_state_dict_into_model(model, state_dict, missing_keys, strict) + load_state_dict_into_model(model, state_dict, missing_keys, strict, load_sub_module) del state_dict gc.collect() - if strict and len(missing_keys) > 0: - error_msgs = 'Missing key(s) in state_dict: {}. '.format( - ', '.join('"{}"'.format(k) for k in missing_keys)) - raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( - self.__class__.__name__, "\n\t".join(error_msgs))) + if strict: + remain_keys = reduce(lambda a, b: a & b, map(set, missing_keys)) + if len(remain_keys) > 0: + error_msgs = 'Missing key(s) in state_dict: {}. '.format( + ', '.join('"{}"'.format(k) for k in missing_keys)) + raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( + self.__class__.__name__, "\n\t".join(error_msgs))) + + diff --git a/colossalai/checkpoint_io/index_file.py b/colossalai/checkpoint_io/index_file.py index 89224787a91b..15a6d09f3b5e 100644 --- a/colossalai/checkpoint_io/index_file.py +++ b/colossalai/checkpoint_io/index_file.py @@ -1,6 +1,8 @@ import json from pathlib import Path from typing import Any, List, Union +import os +import json from .utils import is_dtensor_checkpoint @@ -18,8 +20,8 @@ class CheckpointIndexFile: >>> index.export('new_index.json') """ - def __init__(self) -> None: - self.root_path = None + def __init__(self, root_path=None) -> None: + self.root_path = root_path self.metadata: dict = dict() self.weight_map: dict = dict() @@ -154,3 +156,13 @@ def get_all_param_names(self): Get all the weight keys. """ return list(self.weight_map.keys()) + + def write_index_file(self, save_index_file): + """ + Wriete index file. + """ + save_index_file = os.path.join(self.root_path, save_index_file) + index = {"metadata": self.metadata, "weight_map": self.weight_map} + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 37d22d08df40..16e41631f0d5 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -2,7 +2,7 @@ from pathlib import Path import torch import torch.nn as nn -from typing import List, Dict, Mapping, OrderedDict, Optional, Tuple +from typing import List, Mapping, OrderedDict, Optional, Tuple, Iterator from colossalai.tensor.d_tensor.d_tensor import DTensor import re @@ -77,55 +77,35 @@ def is_safetensor_checkpoint(checkpoint_file_path: str) -> bool: # ====================================== # Helper functions for saving shard file # ====================================== -def shard_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024, weights_name: str = WEIGHTS_NAME): +def shard_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> Iterator[Tuple[OrderedDict, int]]: """ Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a given size. """ - sharded_state_dicts = [] current_block = {} current_block_size = 0 - total_size = 0 for key, weight in state_dict.items(): + ret_block = None + ret_block_size = 0 if type(weight) != DTensor: weight_size = calculate_tensor_size(weight) # If this weight is going to tip up over the maximal size, we split. if current_block_size + weight_size > max_shard_size: - sharded_state_dicts.append(current_block) + ret_block = current_block + ret_block_size = current_block_size current_block = {} current_block_size = 0 - current_block[key] = weight current_block_size += weight_size - total_size += weight_size + + if ret_block != None: + yield ret_block, ret_block_size - # Add the last block - sharded_state_dicts.append(current_block) + yield current_block, current_block_size - # If we only have one shard, we return it - if len(sharded_state_dicts) == 1: - return {weights_name: sharded_state_dicts[0]}, None - - # Otherwise, let's build the index - weight_map = {} - shards = {} - - for idx, shard in enumerate(sharded_state_dicts): - shard_file = weights_name.replace(".bin", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.bin") - shard_file = shard_file.replace( - ".safetensors", f"-{idx + 1:05d}-of-{len(sharded_state_dicts):05d}.safetensors" - ) - shards[shard_file] = shard - for key in shard.keys(): - weight_map[key] = shard_file - - # Add the metadata - metadata = {"total_size": total_size} - index = {"metadata": metadata, "weight_map": weight_map} - return shards, index def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool =False): """ @@ -146,7 +126,7 @@ def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool =False): else: return torch.load(checkpoint_file) -def load_state_dict_into_model(model: nn.Module, state_dict: torch.Tensor, missing_keys: List, strict: bool = False): +def load_state_dict_into_model(model: nn.Module, state_dict: torch.Tensor, missing_keys: List, strict: bool = False, load_sub_module: bool = True): r"""Copies parameters and buffers from :attr:`state_dict` into this module and its descendants. @@ -167,29 +147,22 @@ def load_state_dict_into_model(model: nn.Module, state_dict: torch.Tensor, missi if metadata is not None: state_dict._metadata = metadata - def load(module: nn.Module, state_dict, prefix=""): + def load(module: nn.Module, state_dict, prefix="", load_sub_module: bool = True): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) - args = (state_dict, prefix, local_metadata, True, [], [], error_msgs) + args = (state_dict, prefix, local_metadata, True, sub_missing_keys, [], error_msgs) # Parameters of module and children will start with prefix. We can exit early if there are none in this # state_dict if len([key for key in state_dict if key.startswith(prefix)]) > 0: module._load_from_state_dict(*args) + if load_sub_module: + for name, child in module._modules.items(): + if child is not None: + load(child, state_dict, prefix + name + ".") - for name, child in module._modules.items(): - if child is not None: - load(child, state_dict, prefix + name + ".") - - load(model, state_dict, "") + load(model, state_dict, "", load_sub_module) del load - # deal with missing key - if len(missing_keys) > 0: - deleted_keys = [] - for key in missing_keys: - if key not in sub_missing_keys: - deleted_keys.append(key) - for key in deleted_keys: - missing_keys.remove(key) + missing_keys = missing_keys.append(sub_missing_keys) if strict: if len(unexpected_keys) > 0: @@ -417,3 +390,24 @@ def add_variant(weights_name: str, variant: Optional[str] = None) -> str: weights_name = ".".join(splits) return weights_name + + +def get_base_filenames(variant: str=None, use_safetensors: bool=False): + """ + generate base weight filenames + """ + weights_name = SAFE_WEIGHTS_NAME if use_safetensors else WEIGHTS_NAME + weights_name = add_variant(weights_name, variant) + + save_index_file = SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME + save_index_file = add_variant(save_index_file, variant) + + return weights_name, save_index_file + +def get_shard_filename(weights_name: str, idx: int): + """ + get shard file name + """ + shard_file = weights_name.replace(".bin", f"-{idx+1:05d}.bin") + shard_file = shard_file.replace(".safetensors", f"-{idx + 1:05d}.safetensors") + return shard_file \ No newline at end of file diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index 8a001b114e9a..878c25be7094 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -2,7 +2,7 @@ from collections import OrderedDict from contextlib import nullcontext from functools import partial -from typing import Dict, Iterator, List, Optional, Union +from typing import Dict, Iterator, List, Optional, Union, Tuple, Set import torch import torch.distributed as dist @@ -96,8 +96,35 @@ def __init__(self, param_name = m_name + '.' + p_name if m_name else p_name self.name2param[param_name] = p_var super().__init__(module, process_group=ColoProcessGroup()) + self._non_persistent_buffers_set=self._get_non_persistent_buffers_set(module) self._cast_buffers() + def _get_non_persistent_buffers_set(self, module, memo: Optional[Set[nn.Module]] = None, prefix: str = '', remove_duplicate: bool = True): + + r""" + Args: + memo: a memo to store the set of modules already added to the result + prefix: a prefix that will be added to the name of the module + remove_duplicate: whether to remove the duplicated module instances in the result + or not + """ + + if memo is None: + memo = set() + self_non_persistent_set = set() + if module not in memo: + if remove_duplicate: + memo.add(module) + self_non_persistent_set = set(map(lambda key: prefix + ('.' if prefix else '') + key, module._non_persistent_buffers_set)) + for name, sub_module in module._modules.items(): + if sub_module is None: + continue + submodule_prefix = prefix + ('.' if prefix else '') + name + child_non_persistent_set = self._get_non_persistent_buffers_set(sub_module, memo, submodule_prefix, remove_duplicate) + self_non_persistent_set = set.union(self_non_persistent_set, child_non_persistent_set) + return self_non_persistent_set + + def _post_forward(self): """This function is only triggered for inference. """ @@ -604,7 +631,7 @@ def state_dict_shard(self, keep_vars: bool = False, max_shard_size: int = 1024, only_rank_0: bool = True, - dtype: torch.dtype = torch.float16) -> Iterator[OrderedDict]: + dtype: torch.dtype = torch.float16) -> Iterator[Tuple[OrderedDict, int]]: """Returns dictionaries containing a whole state of the module one by one. The max size of dictionary shard is specified by ``max_shard_size``. Both parameters and persistent buffers (e.g. running averages) are included. @@ -644,9 +671,9 @@ def state_dict_shard(self, gathered_param_buffer.update(self._get_chunk_to_save_data(chunk, only_rank_0, dtype)) gathered_param = gathered_param_buffer.pop(fp32_param) - block = sharder.append(prefix + name, gathered_param) + block, block_size = sharder.append(prefix + name, gathered_param) if block is not None: - yield block + yield block, block_size del fp16_to_fp32 del gathered_param_buffer @@ -655,19 +682,19 @@ def state_dict_shard(self, for name, buf in self.named_buffers(): if buf is not None and name not in self._non_persistent_buffers_set: buffer = buf if keep_vars else buf.detach() - block = sharder.append(prefix + name, buffer) + block, block_size = sharder.append(prefix + name, buffer) if block is not None: - yield block + yield block, block_size # save extra states extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX if getattr(self.__class__, "get_extra_state", torch.nn.Module.get_extra_state) is not torch.nn.Module.get_extra_state: extra_state = self.get_extra_state() - block = sharder.append(extra_state_key, extra_state) + block, block_size = sharder.append(extra_state_key, extra_state) if block is not None: - yield block + yield block, block_size - yield sharder.current_block + yield sharder.current_block, sharder.current_block_size class _StateDictSharder: @@ -677,16 +704,18 @@ def __init__(self, max_shard_size: int) -> None: self.current_block = OrderedDict() self.current_block_size = 0 - def append(self, name: str, tensor: torch.Tensor) -> Optional[OrderedDict]: + def append(self, name: str, tensor: torch.Tensor) -> Tuple[Optional[OrderedDict], int]: tensor_size = calculate_tensor_size(tensor) ret_block = None + ret_block_size = 0 if self.current_block_size + tensor_size > self.max_shard_size: ret_block = self.current_block + ret_block_size = self.current_block_size self.current_block = OrderedDict() self.current_block_size = 0 self.current_block[name] = tensor self.current_block_size += tensor_size - return ret_block + return ret_block, ret_block_size class GeminiDDP(ZeroDDP): diff --git a/pytest.ini b/pytest.ini index ac31ace4bfae..01e5cd217c5d 100644 --- a/pytest.ini +++ b/pytest.ini @@ -3,4 +3,4 @@ markers = cpu: tests which can run on CPU gpu: tests which requires a single GPU dist: tests which are run in a multi-GPU or multi-machine environment - experiment: tests for experimental features \ No newline at end of file + experiment: tests for experimental features diff --git a/tests/test_checkpoint_io/test_general_checkpoint_io.py b/tests/test_checkpoint_io/test_general_checkpoint_io.py index ca5ce10054f7..752ca706bfd4 100644 --- a/tests/test_checkpoint_io/test_general_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_general_checkpoint_io.py @@ -1,16 +1,21 @@ import tempfile import pytest import torch -import logging from torch.optim import Adam from torchvision.models import resnet18 -from pathlib import Path -import os -import subprocess from colossalai.checkpoint_io import GeneralCheckpointIO +from colossalai.booster.plugin.gemini_plugin import GeminiCheckpointIO from colossalai.testing import clear_cache_before_run, parameterize +import colossalai +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils.cuda import get_current_device +from colossalai.zero import ColoInitContext, ZeroDDP +from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager +from tests.components_to_test.registry import non_distributed_component_funcs + # ======== # Note: # 1. due to checkpoint IO can be quite slow if tested with all models, we will only test on resnet for now @@ -83,7 +88,6 @@ def test_sharded_checkpoint(use_safetensors: bool): suffix = ".bin" WEIGHTS_INDEX_NAME = "model.bin.index.json" - # model_ckpt_dir = tempfile.TemporaryDirectory(suffix=suffix) model_ckpt_dir = tempfile.TemporaryDirectory() optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile() @@ -104,6 +108,87 @@ def test_sharded_checkpoint(use_safetensors: bool): recursive_check(model.state_dict(), new_model.state_dict()) recursive_check(optimizer.state_dict(), new_optimizer.state_dict()) +@parameterize('placement_policy', ['cuda', 'cpu']) +@parameterize('model_name', ['bert']) +@parameterize('use_safetensors', [True, False]) +def hf_load_colossalai_checkpoint(placement_policy, model_name, use_safetensors: bool): + from transformers import BertTokenizer, BertModel, BertForMaskedLM, BertConfig, BertForSequenceClassification + + model_ckpt_dir = tempfile.TemporaryDirectory() + get_components_func = non_distributed_component_funcs.get_callable(model_name) + model_builder, *_ = get_components_func() + + with ColoInitContext(device=get_current_device()): + bert_model = model_builder() + bert_model.config.save_pretrained(save_directory=model_ckpt_dir.name) + config_dict, *_ = search_chunk_configuration(bert_model, search_range_mb=1, search_interval_byte=100) + chunk_manager = ChunkManager(config_dict) + gemini_manager = GeminiManager(placement_policy, chunk_manager) + bert_model = ZeroDDP(bert_model, gemini_manager) + bert_model.train() + + ckpt_io = GeminiCheckpointIO() + if ckpt_io.coordinator.is_master(): + model_size = sum(p.numel() * p.element_size() for p in bert_model.parameters()) / 1024**2 + ckpt_io.save_model(bert_model, model_ckpt_dir.name, True, True, "", (model_size / 3), use_safetensors=use_safetensors) + new_bert_model = BertForSequenceClassification.from_pretrained(model_ckpt_dir.name) + recursive_check(bert_model.state_dict(only_rank_0=True, dtype=torch.float32), new_bert_model.state_dict()) + + model_ckpt_dir.cleanup() + + + +@parameterize('placement_policy', ['cuda', 'cpu']) +@parameterize('model_name', ['gpt2', 'bert']) +@parameterize('use_safetensors', [True, False]) +def exam_state_dict(placement_policy, model_name: str, use_safetensors: bool): + get_components_func = non_distributed_component_funcs.get_callable(model_name) + model_builder, *_ = get_components_func() + + with ColoInitContext(device=get_current_device()): + model = model_builder() + new_model = model_builder() + + config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + chunk_manager = ChunkManager(config_dict) + gemini_manager = GeminiManager(placement_policy, chunk_manager) + model = ZeroDDP(model, gemini_manager) + model.train() + + new_config_dict, *_ = search_chunk_configuration(new_model, search_range_mb=1, search_interval_byte=100) + new_chunk_manager = ChunkManager(new_config_dict) + new_gemini_manager = GeminiManager(placement_policy, new_chunk_manager) + new_model = ZeroDDP(new_model, new_gemini_manager) + + model_ckpt_dir = tempfile.TemporaryDirectory() + + ckpt_io = GeminiCheckpointIO() + model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2 + ckpt_io.save_model(model, model_ckpt_dir.name, True, True, "epoch", (model_size / 3), use_safetensors=use_safetensors) + + # load model + if ckpt_io.coordinator.is_master(): + ckpt_io.load_model(new_model, model_ckpt_dir.name, strict=True) + model_dict = model.state_dict(only_rank_0=True) + new_model_dict = new_model.state_dict(only_rank_0=True) + recursive_check(model_dict, new_model_dict) + + model_ckpt_dir.cleanup() + + +def run_dist(rank, world_size, port): + config = {} + colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + exam_state_dict() + hf_load_colossalai_checkpoint() + + +@pytest.mark.dist +@pytest.mark.parametrize('world_size', [4, 4]) +@rerun_if_address_is_in_use() +def test_gemini_ckpIO(world_size): + spawn(run_dist, world_size) + # do recursive check for the optimizer state dict # if the value is a dict, compare its values @@ -117,10 +202,14 @@ def recursive_check(d1, d2): elif isinstance(v, list): for i in range(len(v)): if isinstance(v[i], torch.Tensor): + v[i] = v[i].to("cpu") + d2[k][i] = d2[k][i].to("cpu") assert torch.equal(v[i], d2[k][i]) else: assert v[i] == d2[k][i] elif isinstance(v, torch.Tensor): + v = v.to("cpu") + d2[k] = d2[k].to("cpu") assert torch.equal(v, d2[k]) else: assert v == d2[k] diff --git a/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py b/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py index 96c26a1de4df..ad7d3a5a4859 100644 --- a/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py +++ b/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py @@ -31,14 +31,13 @@ def exam_state_dict(placement_policy, model_name: str): zero_dict = model.state_dict(only_rank_0=False) accumulated_keys = set() # ensure number of shards > 1 - for shard in model.state_dict_shard(max_shard_size=(model_size / 3), only_rank_0=False): + for shard, _ in model.state_dict_shard(max_shard_size=(model_size / 3), only_rank_0=False): for key, value in shard.items(): assert key not in accumulated_keys, f"key `{key}` is duplicated." accumulated_keys.add(key) assert key in zero_dict, f"{key} not in ZeRO dictionary." assert torch.equal(value, zero_dict[key]), f"{key} not equal." - def run_dist(rank, world_size, port): config = {} colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') From b49020c1b1ba6e660e1675178b7edca081efbf4d Mon Sep 17 00:00:00 2001 From: digger-yu Date: Fri, 5 May 2023 18:57:27 +0800 Subject: [PATCH 182/413] [CI] Update test_sharded_optim_with_sync_bn.py (#3688) fix spelling error in line23 change "cudnn_determinstic"=True to "cudnn_deterministic=True" --- tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py b/tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py index 61d850d06080..0223f18c29d6 100644 --- a/tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py +++ b/tests/test_zero/test_legacy/test_sharded_optim_with_sync_bn.py @@ -20,7 +20,7 @@ def run_dist(rank, world_size, port): # need to configure cudnn deterministic so that # randomness of convolution layers will be disabled zero_config = dict(model_config=dict(shard_strategy=TensorShardStrategy())) - colossalai.launch(config=dict(zero=zero_config, cudnn_determinstic=True, cudnn_benchmark=False), + colossalai.launch(config=dict(zero=zero_config, cudnn_deterministic=True, cudnn_benchmark=False), rank=rank, world_size=world_size, host='localhost', From d0915f54f4a1fa8f5995e27144a4b94c2b43263d Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Fri, 5 May 2023 19:36:10 +0800 Subject: [PATCH 183/413] [booster] refactor all dp fashion plugins (#3684) * [booster] add dp plugin base * [booster] inherit dp plugin base * [booster] refactor unit tests --- colossalai/booster/plugin/dp_plugin_base.py | 72 +++++++++++ colossalai/booster/plugin/gemini_plugin.py | 112 +++++------------- .../booster/plugin/low_level_zero_plugin.py | 66 +---------- colossalai/booster/plugin/torch_ddp_plugin.py | 66 +---------- .../test_plugin/test_dp_plugin_base.py | 85 +++++++++++++ .../test_plugin/test_gemini_plugin.py | 25 ---- .../test_plugin/test_low_level_zero_plugin.py | 24 ---- .../test_plugin/test_torch_ddp_plugin.py | 48 -------- 8 files changed, 190 insertions(+), 308 deletions(-) create mode 100644 colossalai/booster/plugin/dp_plugin_base.py create mode 100644 tests/test_booster/test_plugin/test_dp_plugin_base.py diff --git a/colossalai/booster/plugin/dp_plugin_base.py b/colossalai/booster/plugin/dp_plugin_base.py new file mode 100644 index 000000000000..4021b31754b4 --- /dev/null +++ b/colossalai/booster/plugin/dp_plugin_base.py @@ -0,0 +1,72 @@ +import random + +import numpy as np +import torch +import torch.distributed as dist +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from .plugin_base import Plugin + + +class DPPluginBase(Plugin): + """This is a base class for all DP plugins. It sets up world size and rank, and provides data loader creation. + """ + + def __init__(self) -> None: + super().__init__() + assert dist.is_initialized( + ), 'torch.distributed is not initialized, please use colossalai.launch to create the distributed environment' + self.rank = dist.get_rank() + self.world_size = dist.get_world_size() + + def prepare_train_dataloader(self, + dataset, + batch_size, + shuffle=False, + seed=1024, + drop_last=False, + pin_memory=False, + num_workers=0, + **kwargs): + r""" + Prepare a dataloader for distributed training. The dataloader will be wrapped by + `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. + + Note: + 1. Evaluation datasets should not be passed to this function. + + Args: + dataset (`torch.utils.data.Dataset`): The dataset to be loaded. + shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. + seed (int, optional): Random worker seed for sampling, defaults to 1024. + add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. + drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size + is not divisible by the batch size. If False and the size of dataset is not divisible by + the batch size, then the last batch will be smaller, defaults to False. + pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. + num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. + kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in + `DataLoader `_. + + Returns: + :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. + """ + _kwargs = kwargs.copy() + sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle) + + # Deterministic dataloader + def seed_worker(worker_id): + worker_seed = seed + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + random.seed(worker_seed) + + return DataLoader(dataset, + batch_size=batch_size, + sampler=sampler, + worker_init_fn=seed_worker, + drop_last=drop_last, + pin_memory=pin_memory, + num_workers=num_workers, + **_kwargs) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index dfdd7be26eaa..fde8912a648f 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -1,36 +1,25 @@ -import random +import logging +import os import warnings -from typing import Callable, List, Optional, Tuple, Union from pathlib import Path -import os -import logging +from typing import Callable, List, Optional, Tuple, Union -import numpy as np import torch -import torch.distributed as dist import torch.nn as nn from torch import Tensor from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils.data import DataLoader -from torch.utils.data.distributed import DistributedSampler -from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO -from colossalai.checkpoint_io.utils import save_state_dict +from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO, GeneralCheckpointIO +from colossalai.checkpoint_io.utils import get_base_filenames, get_shard_filename, save_state_dict from colossalai.cluster import DistCoordinator from colossalai.interface import ModelWrapper, OptimizerWrapper from colossalai.utils import get_current_device from colossalai.zero import GeminiDDP, zero_model_wrapper, zero_optim_wrapper from colossalai.zero.gemini.memory_tracer import MemStats -from colossalai.checkpoint_io.utils import ( - get_base_filenames, - get_shard_filename - ) - -from colossalai.checkpoint_io import CheckpointIndexFile - -from .plugin_base import Plugin +from .dp_plugin_base import DPPluginBase __all__ = ['GeminiPlugin'] @@ -72,7 +61,13 @@ def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): if self.coordinator.is_master(): super().save_lr_scheduler(lr_scheduler, checkpoint) - def save_sharded_model(self, model: GeminiDDP, checkpoint_path: str, gather_dtensor: bool = False, variant: Optional[str] = None, max_shard_size: int = 1024, use_safetensors: bool = False): + def save_sharded_model(self, + model: GeminiDDP, + checkpoint_path: str, + gather_dtensor: bool = False, + variant: Optional[str] = None, + max_shard_size: int = 1024, + use_safetensors: bool = False): """ Save sharded model """ @@ -88,25 +83,27 @@ def save_sharded_model(self, model: GeminiDDP, checkpoint_path: str, gather_dten total_size = total_size + shard_pair[1] for key in shard.keys(): index_file.append_weight_map(key, shard_file) - + checkpoint_file_path = os.path.join(checkpoint_path, shard_file) save_state_dict(shard, checkpoint_file_path, use_safetensors) - + index_file.append_meta_data("total_size", total_size) index_file.write_index_file(save_index_file) - logging.info( - f"The model is going to be split to checkpoint shards. " - f"You can find where each parameters has been saved in the " - f"index located at {save_index_file}." - ) - - - def load_sharded_model(self, model: GeminiDDP, checkpoint_index_file: Path, strict: bool = False, use_safetensors: bool = False): + logging.info(f"The model is going to be split to checkpoint shards. " + f"You can find where each parameters has been saved in the " + f"index located at {save_index_file}.") + + def load_sharded_model(self, + model: GeminiDDP, + checkpoint_index_file: Path, + strict: bool = False, + use_safetensors: bool = False): """ load shard model, load model from multiple files """ return super().load_sharded_model(model, checkpoint_index_file, strict, use_safetensors, load_sub_module=False) + class GeminiModel(ModelWrapper): def __init__(self, module: nn.Module, gemini_config: dict, verbose: bool = False) -> None: @@ -148,7 +145,7 @@ def clip_grad_by_value(self, clip_value: float, *args, **kwargs) -> None: raise NotImplementedError('Gemini does not support clip_grad_by_value') -class GeminiPlugin(Plugin): +class GeminiPlugin(DPPluginBase): """ Plugin for Gemini. @@ -217,11 +214,7 @@ def __init__( norm_type: float = 2.0, verbose: bool = False, ) -> None: - - assert dist.is_initialized( - ), 'torch.distributed is not initialized, please use colossalai.launch to create the distributed environment' - self.rank = dist.get_rank() - self.world_size = dist.get_world_size() + super().__init__() self.gemini_config = dict( device=(device or get_current_device()), placement_policy=placement_policy, @@ -260,57 +253,6 @@ def control_device(self) -> bool: def supported_devices(self) -> List[str]: return ['cuda'] - def prepare_train_dataloader(self, - dataset, - batch_size, - shuffle=False, - seed=1024, - drop_last=False, - pin_memory=False, - num_workers=0, - **kwargs): - r""" - Prepare a dataloader for distributed training. The dataloader will be wrapped by - `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. - - Note: - 1. Evaluation datasets should not be passed to this function. - - Args: - dataset (`torch.utils.data.Dataset`): The dataset to be loaded. - shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. - seed (int, optional): Random worker seed for sampling, defaults to 1024. - add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. - drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size - is not divisible by the batch size. If False and the size of dataset is not divisible by - the batch size, then the last batch will be smaller, defaults to False. - pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. - num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. - kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in - `DataLoader `_. - - Returns: - :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. - """ - _kwargs = kwargs.copy() - sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle) - - # Deterministic dataloader - def seed_worker(worker_id): - worker_seed = seed - np.random.seed(worker_seed) - torch.manual_seed(worker_seed) - random.seed(worker_seed) - - return DataLoader(dataset, - batch_size=batch_size, - sampler=sampler, - worker_init_fn=seed_worker, - drop_last=drop_last, - pin_memory=pin_memory, - num_workers=num_workers, - **_kwargs) - def configure( self, model: nn.Module, diff --git a/colossalai/booster/plugin/low_level_zero_plugin.py b/colossalai/booster/plugin/low_level_zero_plugin.py index 969c430bd317..828d8b27422f 100644 --- a/colossalai/booster/plugin/low_level_zero_plugin.py +++ b/colossalai/booster/plugin/low_level_zero_plugin.py @@ -1,24 +1,20 @@ -import random import warnings from typing import Callable, List, Optional, Tuple, Union -import numpy as np import torch -import torch.distributed as dist import torch.nn as nn from torch import Tensor from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils._pytree import tree_map from torch.utils.data import DataLoader -from torch.utils.data.distributed import DistributedSampler from colossalai.checkpoint_io import CheckpointIO from colossalai.interface import ModelWrapper, OptimizerWrapper from colossalai.utils import get_current_device from colossalai.zero import zero_model_wrapper, zero_optim_wrapper -from .plugin_base import Plugin +from .dp_plugin_base import DPPluginBase from .torch_ddp_plugin import TorchDDPCheckpointIO __all__ = ['LowLevelZeroPlugin'] @@ -88,7 +84,7 @@ def clip_grad_by_value(self, clip_value: float, *args, **kwargs) -> None: raise NotImplementedError('LowLevelZero does not support clip_grad_by_value') -class LowLevelZeroPlugin(Plugin): +class LowLevelZeroPlugin(DPPluginBase): """ Plugin for low level zero. @@ -142,15 +138,10 @@ def __init__( cpu_offload: bool = False, verbose: bool = False, ) -> None: - - assert dist.is_initialized( - ), 'torch.distributed is not initialized, please use colossalai.launch to create the distributed environment' + super().__init__() assert stage in (1, 2), f'LowLevelZeroPlugin only supports stage 1/2 training' assert precision in ('fp16', 'fp32'), f'LowLevelZeroPlugin only supports fp16/fp32 training' - self.rank = dist.get_rank() - self.world_size = dist.get_world_size() - self.stage = stage self.precision = precision self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size_in_m * 1024 * 1024, @@ -183,57 +174,6 @@ def control_device(self) -> bool: def supported_devices(self) -> List[str]: return ['cuda'] - def prepare_train_dataloader(self, - dataset, - batch_size, - shuffle=False, - seed=1024, - drop_last=False, - pin_memory=False, - num_workers=0, - **kwargs): - r""" - Prepare a dataloader for distributed training. The dataloader will be wrapped by - `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. - - Note: - 1. Evaluation datasets should not be passed to this function. - - Args: - dataset (`torch.utils.data.Dataset`): The dataset to be loaded. - shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. - seed (int, optional): Random worker seed for sampling, defaults to 1024. - add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. - drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size - is not divisible by the batch size. If False and the size of dataset is not divisible by - the batch size, then the last batch will be smaller, defaults to False. - pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. - num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. - kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in - `DataLoader `_. - - Returns: - :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. - """ - _kwargs = kwargs.copy() - sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle) - - # Deterministic dataloader - def seed_worker(worker_id): - worker_seed = seed - np.random.seed(worker_seed) - torch.manual_seed(worker_seed) - random.seed(worker_seed) - - return DataLoader(dataset, - batch_size=batch_size, - sampler=sampler, - worker_init_fn=seed_worker, - drop_last=drop_last, - pin_memory=pin_memory, - num_workers=num_workers, - **_kwargs) - def configure( self, model: nn.Module, diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index c5e310c7e769..d30d266c0048 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -1,21 +1,16 @@ -import random from typing import Callable, List, Tuple, Union -import numpy as np -import torch -import torch.distributed as dist import torch.nn as nn from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils.data import DataLoader -from torch.utils.data.distributed import DistributedSampler from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO from colossalai.cluster import DistCoordinator from colossalai.interface import ModelWrapper, OptimizerWrapper -from .plugin_base import Plugin +from .dp_plugin_base import DPPluginBase __all__ = ['TorchDDPPlugin'] @@ -66,7 +61,7 @@ def unwrap(self): return self.module.module -class TorchDDPPlugin(Plugin): +class TorchDDPPlugin(DPPluginBase): """ Plugin for PyTorch DDP. @@ -97,11 +92,7 @@ def __init__(self, check_reduction: bool = False, gradient_as_bucket_view: bool = False, static_graph: bool = False) -> None: - - assert dist.is_initialized( - ), 'torch.distributed is not initialized, please use colossalai.launch to create the distributed environment' - self.rank = dist.get_rank() - self.world_size = dist.get_world_size() + super().__init__() self.ddp_kwargs = dict(broadcast_buffers=broadcast_buffers, bucket_cap_mb=bucket_cap_mb, find_unused_parameters=find_unused_parameters, @@ -124,57 +115,6 @@ def control_device(self) -> bool: def supported_devices(self) -> List[str]: return ['cuda'] - def prepare_train_dataloader(self, - dataset, - batch_size, - shuffle=False, - seed=1024, - drop_last=False, - pin_memory=False, - num_workers=0, - **kwargs): - r""" - Prepare a dataloader for distributed training. The dataloader will be wrapped by - `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. - - Note: - 1. Evaluation datasets should not be passed to this function. - - Args: - dataset (`torch.utils.data.Dataset`): The dataset to be loaded. - shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. - seed (int, optional): Random worker seed for sampling, defaults to 1024. - add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. - drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size - is not divisible by the batch size. If False and the size of dataset is not divisible by - the batch size, then the last batch will be smaller, defaults to False. - pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. - num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. - kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in - `DataLoader `_. - - Returns: - :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. - """ - _kwargs = kwargs.copy() - sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle) - - # Deterministic dataloader - def seed_worker(worker_id): - worker_seed = seed - np.random.seed(worker_seed) - torch.manual_seed(worker_seed) - random.seed(worker_seed) - - return DataLoader(dataset, - batch_size=batch_size, - sampler=sampler, - worker_init_fn=seed_worker, - drop_last=drop_last, - pin_memory=pin_memory, - num_workers=num_workers, - **_kwargs) - def configure( self, model: nn.Module, diff --git a/tests/test_booster/test_plugin/test_dp_plugin_base.py b/tests/test_booster/test_plugin/test_dp_plugin_base.py new file mode 100644 index 000000000000..a2b94ba6ca81 --- /dev/null +++ b/tests/test_booster/test_plugin/test_dp_plugin_base.py @@ -0,0 +1,85 @@ +from typing import Callable, List, Tuple, Union + +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from torch.utils.data import DataLoader, TensorDataset + +import colossalai +from colossalai.booster.plugin.dp_plugin_base import DPPluginBase +from colossalai.checkpoint_io import CheckpointIO +from colossalai.interface import OptimizerWrapper +from colossalai.testing import rerun_if_address_is_in_use, spawn + + +class DPPluginWrapper(DPPluginBase): + """This is a wrapper class for testing DP plugin initialization and dataloader creation. + """ + + def configure( + self, + model: nn.Module, + optimizer: Optimizer, + criterion: Callable = None, + dataloader: DataLoader = None, + lr_scheduler: LRScheduler = None, + ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + pass + + def control_checkpoint_io(self) -> bool: + pass + + def control_device(self) -> bool: + pass + + def control_precision(self) -> bool: + pass + + def get_checkpoint_io(self) -> CheckpointIO: + pass + + def support_no_sync(self) -> bool: + pass + + def supported_devices(self) -> List[str]: + pass + + def supported_precisions(self) -> List[str]: + pass + + +def check_dataloader_sharding(): + plugin = DPPluginWrapper() + + # create a custom dasetset with 0 to 10 + dataset = TensorDataset(torch.arange(0, 10)) + train_dataloader = plugin.prepare_train_dataloader(dataset, batch_size=2) + + # get the first batch of data + batch = next(iter(train_dataloader))[0].cuda() + is_rank_0 = dist.get_rank() == 0 + + if is_rank_0: + batch_to_compare = batch.clone() + else: + batch_to_compare = batch + # pass to the rank 1 value to rank 0 + dist.broadcast(batch_to_compare, src=1) + + # compare on rank 0 + if is_rank_0: + assert not torch.equal(batch, + batch_to_compare), 'Same number was found across ranks but expected it to be different' + + +def run_dist(rank, world_size, port): + # init dist env + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + check_dataloader_sharding() + + +@rerun_if_address_is_in_use() +def test_dp_plugin_dataloader(): + spawn(run_dist, 2) diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index 985d7989fc9d..c7b3676fb478 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -117,34 +117,9 @@ def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): assert len(failed_info) == 0, '\n'.join([f'{k}: {v}' for k, v in failed_info.items()]) -def check_dataloader_sharding(): - plugin = GeminiPlugin() - - # create a custom dasetset with 0 to 10 - dataset = torch.utils.data.TensorDataset(torch.arange(0, 10)) - train_dataloader = plugin.prepare_train_dataloader(dataset, batch_size=2) - - # get the first batch of data - batch = next(iter(train_dataloader))[0].cuda() - is_rank_0 = dist.get_rank() == 0 - - if is_rank_0: - batch_to_compare = batch.clone() - else: - batch_to_compare = batch - # pass to the rank 1 value to rank 0 - dist.broadcast(batch_to_compare, src=1) - - # compare on rank 0 - if is_rank_0: - assert not torch.equal(batch, - batch_to_compare), 'Same number was found across ranks but expected it to be different' - - def run_dist(rank, world_size, port, early_stop: bool = True): # init dist env colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') - check_dataloader_sharding() check_gemini_plugin(early_stop=early_stop) diff --git a/tests/test_booster/test_plugin/test_low_level_zero_plugin.py b/tests/test_booster/test_plugin/test_low_level_zero_plugin.py index e24196a14917..d84b96f77a75 100644 --- a/tests/test_booster/test_plugin/test_low_level_zero_plugin.py +++ b/tests/test_booster/test_plugin/test_low_level_zero_plugin.py @@ -83,30 +83,6 @@ def check_low_level_zero_plugin(stage: int, early_stop: bool = True): assert len(failed_info) == 0, '\n'.join([f'{k}: {v}' for k, v in failed_info.items()]) -def check_dataloader_sharding(): - plugin = LowLevelZeroPlugin() - - # create a custom dasetset with 0 to 10 - dataset = torch.utils.data.TensorDataset(torch.arange(0, 10)) - train_dataloader = plugin.prepare_train_dataloader(dataset, batch_size=2) - - # get the first batch of data - batch = next(iter(train_dataloader))[0].cuda() - is_rank_0 = dist.get_rank() == 0 - - if is_rank_0: - batch_to_compare = batch.clone() - else: - batch_to_compare = batch - # pass to the rank 1 value to rank 0 - dist.broadcast(batch_to_compare, src=1) - - # compare on rank 0 - if is_rank_0: - assert not torch.equal(batch, - batch_to_compare), 'Same number was found across ranks but expected it to be different' - - def run_dist(rank, world_size, port, early_stop: bool = True): # init dist env colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') diff --git a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py index 5354eae01d40..30c4db12309f 100644 --- a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py @@ -44,57 +44,9 @@ def check_torch_ddp_plugin(): torch.cuda.empty_cache() -def check_dataloader_sharding(): - plugin = TorchDDPPlugin() - - # create a custom dasetset with 0 to 10 - dataset = torch.utils.data.TensorDataset(torch.arange(0, 10)) - train_dataloader = plugin.prepare_train_dataloader(dataset, batch_size=2) - - # get the first batch of data - batch = next(iter(train_dataloader))[0].cuda() - is_rank_0 = dist.get_rank() == 0 - - if is_rank_0: - batch_to_compare = batch.clone() - else: - batch_to_compare = batch - # pass to the rank 1 value to rank 0 - dist.broadcast(batch_to_compare, src=1) - - # compare on rank 0 - if is_rank_0: - assert not torch.equal(batch, - batch_to_compare), 'Same number was found across ranks but expected it to be different' - - -def check_checkpoint_save_and_load(): - model_fn, data_gen_fn, output_transform_fn, _ = model_zoo['timm_resnet'] - - plugin = TorchDDPPlugin() - booster = Booster(plugin=plugin) - - model = model_fn() - optimizer = SGD(model.parameters(), lr=1e-3) - criterion = lambda x: x.mean() - data = data_gen_fn() - - data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()} - - model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) - - output = model(**data) - output = output_transform_fn(output) - output_key = list(output.keys())[0] - loss = criterion(output[output_key]) - - booster.backward(loss, optimizer) - - def run_dist(rank, world_size, port): # init dist env colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') - check_dataloader_sharding() check_torch_ddp_plugin() From 65bdc3159f2971c71780bc1b9ca5ff9d2e8efc83 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Sat, 6 May 2023 11:27:23 +0800 Subject: [PATCH 184/413] fix some spelling error with applications/Chat/examples/ (#3692) * fix spelling error with examples/comminity/ * fix spelling error with example/ --- applications/Chat/examples/README.md | 3 +-- .../Chat/examples/community/peft/easy_dataset.py | 10 +++++----- .../Chat/examples/community/peft/train_peft_prompts.py | 2 +- .../Chat/examples/community/peft/train_peft_sft.py | 2 +- applications/Chat/examples/train_sft.py | 2 +- 5 files changed, 9 insertions(+), 10 deletions(-) diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 561ace2205ed..2a2128e25a62 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -24,7 +24,6 @@ - [LLaMA](#llama) - [Add your own models](#add-your-own-models) - [Actor model](#actor-model) - - [LM model](#lm-model) - [Reward model](#reward-model) - [Critic model](#critic-model) @@ -150,7 +149,7 @@ torchrun --standalone --nproc_per_node=4 train_prompts.py \ --strategy colossalai_zero2 \ --prompt_dataset /path/to/your/prompt_dataset \ --pretrain_dataset /path/to/your/pretrain_dataset \ - --rm_pretrain /your/pretrain/rm/defination \ + --rm_pretrain /your/pretrain/rm/definition \ --rm_path /your/rm/model/path ``` diff --git a/applications/Chat/examples/community/peft/easy_dataset.py b/applications/Chat/examples/community/peft/easy_dataset.py index 24ea4f0a8618..2fe293957079 100644 --- a/applications/Chat/examples/community/peft/easy_dataset.py +++ b/applications/Chat/examples/community/peft/easy_dataset.py @@ -188,7 +188,7 @@ def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length=512, is_ else: raw_input_ids.append(encoded_ids) - grouped_inpup_ids = [] + grouped_input_ids = [] current_input_ids = [] attention_mask = [] if tokenizer.pad_token_id is None: @@ -199,7 +199,7 @@ def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length=512, is_ #pad the current_input_ids to max_length with tokenizer.pad_token_id padded_length = max_length - len(current_input_ids) current_input_ids.extend([tokenizer.pad_token_id] * padded_length) - grouped_inpup_ids.append(torch.tensor(current_input_ids, dtype=torch.long)) + grouped_input_ids.append(torch.tensor(current_input_ids, dtype=torch.long)) attention_mask.append( torch.tensor([1] * (max_length - padded_length) + [0] * padded_length, dtype=torch.long)) current_input_ids = [] @@ -208,7 +208,7 @@ def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length=512, is_ if len(current_input_ids) > 0: padded_length = max_length - len(current_input_ids) current_input_ids.extend([tokenizer.pad_token_id] * padded_length) - grouped_inpup_ids.append(torch.tensor(current_input_ids, dtype=torch.long)) + grouped_input_ids.append(torch.tensor(current_input_ids, dtype=torch.long)) attention_mask.append( torch.tensor([1] * (max_length - padded_length) + [0] * padded_length, dtype=torch.long)) else: @@ -218,8 +218,8 @@ def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length=512, is_ input_ids.extend([tokenizer.pad_token_id] * padded_length) attention_mask.append( torch.tensor([1] * (max_length - padded_length) + [0] * padded_length, dtype=torch.long)) - grouped_inpup_ids.append(torch.tensor(input_ids, dtype=torch.long)) - self.input_ids = grouped_inpup_ids + grouped_input_ids.append(torch.tensor(input_ids, dtype=torch.long)) + self.input_ids = grouped_input_ids self.labels = copy.deepcopy(self.input_ids) self.file_name = data_file self.attention_mask = attention_mask diff --git a/applications/Chat/examples/community/peft/train_peft_prompts.py b/applications/Chat/examples/community/peft/train_peft_prompts.py index 0e277021e917..ba8470f38fad 100644 --- a/applications/Chat/examples/community/peft/train_peft_prompts.py +++ b/applications/Chat/examples/community/peft/train_peft_prompts.py @@ -41,7 +41,7 @@ def main(args): # configure model if args.model == 'bloom': # initial_model = BLOOMActor(pretrained=args.pretrain) - print('Using peft lora to load Bloom model as inital_model') + print('Using peft lora to load Bloom model as initial_model') initial_model = BLOOMActor(pretrained=args.pretrain, lora_path=args.sft_lora_path) print('Using peft lora to load Bloom model as initial_model (Done)') else: diff --git a/applications/Chat/examples/community/peft/train_peft_sft.py b/applications/Chat/examples/community/peft/train_peft_sft.py index 9bd0ebc12a83..d2b08b72ca95 100644 --- a/applications/Chat/examples/community/peft/train_peft_sft.py +++ b/applications/Chat/examples/community/peft/train_peft_sft.py @@ -86,7 +86,7 @@ def train(args): if args.strategy == 'colossalai_gemini': # this is a hack to deal with the resized embedding - # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatiblity + # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatibility for name, param in model.named_parameters(): if not isinstance(param, ColoParameter): sub_module_name = '.'.join(name.split('.')[:-1]) diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index da499f068b17..7fcd026fb538 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -84,7 +84,7 @@ def train(args): if args.strategy == 'colossalai_gemini': # this is a hack to deal with the resized embedding - # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatiblity + # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatibility for name, param in model.named_parameters(): if not isinstance(param, ColoParameter): sub_module_name = '.'.join(name.split('.')[:-1]) From d5566488852b4a08f901820a608c5b56f221010e Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Sat, 6 May 2023 11:53:13 +0800 Subject: [PATCH 185/413] [example] add finetune bert with booster example (#3693) --- examples/tutorial/new_api/glue_bert/README.md | 33 +++ examples/tutorial/new_api/glue_bert/data.py | 127 +++++++++++ .../tutorial/new_api/glue_bert/finetune.py | 198 ++++++++++++++++++ .../tutorial/new_api/glue_bert/test_ci.sh | 6 + 4 files changed, 364 insertions(+) create mode 100644 examples/tutorial/new_api/glue_bert/README.md create mode 100644 examples/tutorial/new_api/glue_bert/data.py create mode 100644 examples/tutorial/new_api/glue_bert/finetune.py create mode 100755 examples/tutorial/new_api/glue_bert/test_ci.sh diff --git a/examples/tutorial/new_api/glue_bert/README.md b/examples/tutorial/new_api/glue_bert/README.md new file mode 100644 index 000000000000..d65c7705b5da --- /dev/null +++ b/examples/tutorial/new_api/glue_bert/README.md @@ -0,0 +1,33 @@ +# Finetune BERT on GLUE + +## 🚀 Quick Start + +This example provides a training script, which provides an example of finetuning BERT on GLUE dataset. + +- Training Arguments + - `-t`, `--task`: GLUE task to run. Defaults to `mrpc`. + - `-p`, `--plugin`: Plugin to use. Choices: `torch_ddp`, `torch_ddp_fp16`, `gemini`, `low_level_zero`. Defaults to `torch_ddp`. + - `--target_f1`: Target f1 score. Raise exception if not reached. Defaults to `None`. + + +### Train + +```bash +# train with torch DDP with fp32 +colossalai run --nproc_per_node 4 finetune.py + +# train with torch DDP with mixed precision training +colossalai run --nproc_per_node 4 finetune.py -p torch_ddp_fp16 + +# train with gemini +colossalai run --nproc_per_node 4 finetune.py -p gemini + +# train with low level zero +colossalai run --nproc_per_node 4 finetune.py -p low_level_zero +``` + +Expected F1-score will be: + +| Model | Single-GPU Baseline FP32 | Booster DDP with FP32 | Booster DDP with FP16 | Booster Gemini | Booster Low Level Zero | +| ----------------- | ------------------------ | --------------------- | --------------------- |--------------- | ---------------------- | +| bert-base-uncased | 0.86 | 0.88 | 0.87 | 0.88 | 0.89 | diff --git a/examples/tutorial/new_api/glue_bert/data.py b/examples/tutorial/new_api/glue_bert/data.py new file mode 100644 index 000000000000..e43312aebc7c --- /dev/null +++ b/examples/tutorial/new_api/glue_bert/data.py @@ -0,0 +1,127 @@ +import datasets +from transformers import AutoTokenizer, PreTrainedTokenizer + +from colossalai.booster.plugin.dp_plugin_base import DPPluginBase + + +class GLUEDataBuilder: + + task_text_field_map = { + "cola": ["sentence"], + "sst2": ["sentence"], + "mrpc": ["sentence1", "sentence2"], + "qqp": ["question1", "question2"], + "stsb": ["sentence1", "sentence2"], + "mnli": ["premise", "hypothesis"], + "qnli": ["question", "sentence"], + "rte": ["sentence1", "sentence2"], + "wnli": ["sentence1", "sentence2"], + "ax": ["premise", "hypothesis"], + } + + glue_task_num_labels = { + "cola": 2, + "sst2": 2, + "mrpc": 2, + "qqp": 2, + "stsb": 1, + "mnli": 3, + "qnli": 2, + "rte": 2, + "wnli": 2, + "ax": 3, + } + + loader_columns = [ + "datasets_idx", + "input_ids", + "token_type_ids", + "attention_mask", + "start_positions", + "end_positions", + "labels", + ] + + def __init__( + self, + model_name_or_path: str, + plugin: DPPluginBase, + task_name: str = "mrpc", + max_seq_length: int = 128, + train_batch_size: int = 32, + eval_batch_size: int = 32, + **kwargs, + ): + super().__init__() + self.model_name_or_path = model_name_or_path + self.task_name = task_name + self.max_seq_length = max_seq_length + self.train_batch_size = train_batch_size + self.eval_batch_size = eval_batch_size + self.plugin = plugin + + self.text_fields = self.task_text_field_map[task_name] + self.num_labels = self.glue_task_num_labels[task_name] + self.tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + self.setup() + + def setup(self): + self.dataset = datasets.load_dataset("glue", self.task_name) + + for split in self.dataset.keys(): + self.dataset[split] = self.dataset[split].map( + self.convert_to_features, + batched=True, + remove_columns=["label"], + ) + self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] + self.dataset[split].set_format(type="torch", columns=self.columns) + + self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] + + def prepare_data(self): + datasets.load_dataset("glue", self.task_name) + AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + + def train_dataloader(self): + return self.plugin.prepare_train_dataloader(self.dataset["train"], + batch_size=self.train_batch_size, + shuffle=True, + drop_last=True) + + def val_dataloader(self): + if len(self.eval_splits) == 1: + return self.plugin.prepare_train_dataloader(self.dataset["validation"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [ + self.plugin.prepare_train_dataloader(self.dataset[x], batch_size=self.eval_batch_size) + for x in self.eval_splits + ] + + def test_dataloader(self): + if len(self.eval_splits) == 1: + return self.plugin.prepare_train_dataloader(self.dataset["test"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [ + self.plugin.prepare_train_dataloader(self.dataset[x], batch_size=self.eval_batch_size) + for x in self.eval_splits + ] + + def convert_to_features(self, example_batch): + + # Either encode single sentence or sentence pairs + if len(self.text_fields) > 1: + texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) + else: + texts_or_text_pairs = example_batch[self.text_fields[0]] + + # Tokenize the text/text pairs + features = self.tokenizer.batch_encode_plus(texts_or_text_pairs, + max_length=self.max_seq_length, + padding='max_length', + truncation=True) + + # Rename label to labels to make it easier to pass to model forward + features["labels"] = example_batch["label"] + + return features diff --git a/examples/tutorial/new_api/glue_bert/finetune.py b/examples/tutorial/new_api/glue_bert/finetune.py new file mode 100644 index 000000000000..63bdfc5d02cf --- /dev/null +++ b/examples/tutorial/new_api/glue_bert/finetune.py @@ -0,0 +1,198 @@ +import argparse +from typing import List, Union + +import datasets +import torch +import torch.distributed as dist +import torch.nn as nn +from data import GLUEDataBuilder +from torch.optim import Optimizer +from torch.utils.data import DataLoader +from tqdm import tqdm +from transformers import AutoConfig, BertForSequenceClassification, get_linear_schedule_with_warmup + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.cluster import DistCoordinator +from colossalai.nn.optimizer import HybridAdam +from colossalai.utils import get_current_device + +# ============================== +# Prepare Hyperparameters +# ============================== +NUM_EPOCHS = 3 +BATCH_SIZE = 32 +LEARNING_RATE = 2.4e-5 +WEIGHT_DECAY = 0.01 +WARMUP_FRACTION = 0.1 + + +def move_to_cuda(batch): + return {k: v.cuda() for k, v in batch.items()} + + +@torch.no_grad() +def evaluate(model: nn.Module, test_dataloader: Union[DataLoader, List[DataLoader]], num_labels: int, task_name: str, + eval_splits: List[str], coordinator: DistCoordinator): + metric = datasets.load_metric("glue", task_name, process_id=coordinator.rank, num_process=coordinator.world_size) + model.eval() + + def evaluate_subset(dataloader: DataLoader): + accum_loss = torch.zeros(1, device=get_current_device()) + for batch in dataloader: + batch = move_to_cuda(batch) + outputs = model(**batch) + val_loss, logits = outputs[:2] + accum_loss.add_(val_loss) + + if num_labels > 1: + preds = torch.argmax(logits, axis=1) + elif num_labels == 1: + preds = logits.squeeze() + + labels = batch["labels"] + + metric.add_batch(predictions=preds, references=labels) + + results = metric.compute() + dist.all_reduce(accum_loss.div_(len(dataloader))) + if coordinator.is_master(): + results['loss'] = accum_loss.item() / coordinator.world_size + return results + + if isinstance(test_dataloader, DataLoader): + return evaluate_subset(test_dataloader) + else: + assert len(test_dataloader) == len(eval_splits) + final_results = {} + for split, sub_loader in zip(eval_splits, test_dataloader): + results = evaluate_subset(sub_loader) + final_results.update({f'{k}_{split}': v for k, v in results.items()}) + return final_results + + +def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, lr_scheduler, train_dataloader: DataLoader, + booster: Booster, coordinator: DistCoordinator): + model.train() + with tqdm(train_dataloader, desc=f'Epoch [{epoch + 1}/{NUM_EPOCHS}]', disable=not coordinator.is_master()) as pbar: + for batch in pbar: + # Forward pass + batch = move_to_cuda(batch) + outputs = model(**batch) + loss = outputs[0] + + # Backward and optimize + booster.backward(loss, optimizer) + optimizer.step() + optimizer.zero_grad() + lr_scheduler.step() + + # Print log info + pbar.set_postfix({'loss': loss.item()}) + + +def main(): + # ============================== + # Parse Arguments + # ============================== + parser = argparse.ArgumentParser() + parser.add_argument('-t', '--task', default='mrpc', help="GLUE task to run") + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'], + help="plugin to use") + parser.add_argument('--target_f1', type=float, default=None, help="target f1 score. Raise exception if not reached") + args = parser.parse_args() + + # ============================== + # Launch Distributed Environment + # ============================== + colossalai.launch_from_torch(config={}, seed=42) + coordinator = DistCoordinator() + + # local_batch_size = BATCH_SIZE // coordinator.world_size + lr = LEARNING_RATE * coordinator.world_size + model_name = 'bert-base-uncased' + + # ============================== + # Instantiate Plugin and Booster + # ============================== + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + + booster = Booster(plugin=plugin, **booster_kwargs) + + # ============================== + # Prepare Dataloader + # ============================== + data_builder = GLUEDataBuilder(model_name, + plugin, + args.task, + train_batch_size=BATCH_SIZE, + eval_batch_size=BATCH_SIZE) + train_dataloader = data_builder.train_dataloader() + test_dataloader = data_builder.test_dataloader() + + # ==================================== + # Prepare model, optimizer + # ==================================== + # bert pretrained model + config = AutoConfig.from_pretrained(model_name, num_labels=data_builder.num_labels) + model = BertForSequenceClassification.from_pretrained(model_name, config=config) + + # optimizer + no_decay = ["bias", "LayerNorm.weight"] + optimizer_grouped_parameters = [ + { + "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], + "weight_decay": WEIGHT_DECAY, + }, + { + "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], + "weight_decay": 0.0, + }, + ] + + optimizer = HybridAdam(optimizer_grouped_parameters, lr=lr, eps=1e-8) + + # lr scheduler + total_steps = len(train_dataloader) * NUM_EPOCHS + num_warmup_steps = int(WARMUP_FRACTION * total_steps) + lr_scheduler = get_linear_schedule_with_warmup( + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=total_steps, + ) + + # ============================== + # Boost with ColossalAI + # ============================== + model, optimizer, _, _, lr_scheduler = booster.boost(model, optimizer, lr_scheduler=lr_scheduler) + + # ============================== + # Train model + # ============================== + for epoch in range(NUM_EPOCHS): + train_epoch(epoch, model, optimizer, lr_scheduler, train_dataloader, booster, coordinator) + + results = evaluate(model, test_dataloader, data_builder.num_labels, args.task, data_builder.eval_splits, + coordinator) + + if coordinator.is_master(): + print(results) + if args.target_f1 is not None and 'f1' in results: + assert results['f1'] >= args.target_f1, f'f1 score {results["f1"]} is lower than target {args.target_f1}' + + +if __name__ == '__main__': + main() diff --git a/examples/tutorial/new_api/glue_bert/test_ci.sh b/examples/tutorial/new_api/glue_bert/test_ci.sh new file mode 100755 index 000000000000..f8c2dfbe9eb9 --- /dev/null +++ b/examples/tutorial/new_api/glue_bert/test_ci.sh @@ -0,0 +1,6 @@ +#!/bin/bash +set -xe + +for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do + torchrun --standalone --nproc_per_node 4 finetune.py --target_f1 0.86 --plugin $plugin +done From 2da5d81dec7a94576ee04e69b4149368749573f4 Mon Sep 17 00:00:00 2001 From: zhang-yi-chi <673865549@qq.com> Date: Sat, 6 May 2023 16:46:38 +0800 Subject: [PATCH 186/413] [chat] fix train_prompts.py gemini strategy bug (#3666) * fix gemini strategy bug * add comment * add comment * better solution --- applications/Chat/examples/train_prompts.py | 70 ++++++++++----------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index a584991cd34e..134f21f80ef1 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -36,45 +36,45 @@ def main(args): if args.rm_path is not None: state_dict = torch.load(args.rm_path, map_location='cpu') - # configure model - if args.model == 'gpt2': - initial_model = GPTActor(pretrained=args.pretrain) - elif args.model == 'bloom': - initial_model = BLOOMActor(pretrained=args.pretrain) - elif args.model == 'opt': - initial_model = OPTActor(pretrained=args.pretrain) - elif args.model == 'llama': - initial_model = LlamaActor(pretrained=args.pretrain) - elif args.model == 'roberta': - initial_model = RoBERTaActor(pretrained=args.pretrain) - else: - raise ValueError(f'Unsupported actor model "{args.model}"') + with strategy.model_init_context(): + # configure model + if args.model == 'gpt2': + initial_model = GPTActor(pretrained=args.pretrain) + elif args.model == 'bloom': + initial_model = BLOOMActor(pretrained=args.pretrain) + elif args.model == 'opt': + initial_model = OPTActor(pretrained=args.pretrain) + elif args.model == 'llama': + initial_model = LlamaActor(pretrained=args.pretrain) + elif args.model == 'roberta': + initial_model = RoBERTaActor(pretrained=args.pretrain) + else: + raise ValueError(f'Unsupported actor model "{args.model}"') - if args.rm_model == None: - rm_model_name = args.model - else: - rm_model_name = args.rm_model - - if rm_model_name == 'gpt2': - reward_model = GPTRM(pretrained=args.rm_pretrain) - elif rm_model_name == 'bloom': - reward_model = BLOOMRM(pretrained=args.rm_pretrain) - elif rm_model_name == 'opt': - reward_model = OPTRM(pretrained=args.rm_pretrain) - elif rm_model_name == 'llama': - reward_model = LlamaRM(pretrained=args.rm_pretrain) - elif rm_model_name == 'roberta': - reward_model = RoBERTaRM(pretrained=args.rm_pretrain) - else: - raise ValueError(f'Unsupported reward model "{rm_model_name}"') + if args.rm_model == None: + rm_model_name = args.model + else: + rm_model_name = args.rm_model - if args.rm_path is not None: - reward_model.load_state_dict(state_dict) + if rm_model_name == 'gpt2': + reward_model = GPTRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'bloom': + reward_model = BLOOMRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'opt': + reward_model = OPTRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'llama': + reward_model = LlamaRM(pretrained=args.rm_pretrain) + elif rm_model_name == 'roberta': + reward_model = RoBERTaRM(pretrained=args.rm_pretrain) + else: + raise ValueError(f'Unsupported reward model "{rm_model_name}"') - initial_model.to(torch.float16).to(torch.cuda.current_device()) - reward_model.to(torch.float16).to(torch.cuda.current_device()) + if args.rm_path is not None: + reward_model.load_state_dict(state_dict) + + initial_model.to(torch.float16).to(torch.cuda.current_device()) + reward_model.to(torch.float16).to(torch.cuda.current_device()) - with strategy.model_init_context(): if args.model == 'gpt2': actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) elif args.model == 'bloom': From 2629f9717dbb1f0dc0ecad3f3bda18a4b44e3785 Mon Sep 17 00:00:00 2001 From: YH <100389977+yhna940@users.noreply.github.com> Date: Sat, 6 May 2023 18:55:37 +0900 Subject: [PATCH 187/413] [tensor] Refactor handle_trans_spec in DistSpecManager --- colossalai/tensor/dist_spec_mgr.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/colossalai/tensor/dist_spec_mgr.py b/colossalai/tensor/dist_spec_mgr.py index 8657989235db..c968050de49d 100644 --- a/colossalai/tensor/dist_spec_mgr.py +++ b/colossalai/tensor/dist_spec_mgr.py @@ -4,10 +4,8 @@ import torch.distributed as dist # from colossalai.nn.layer.utils import divide from numpy import prod -from packaging import version -from colossalai.logging import get_dist_logger -from colossalai.tensor.distspec import _DistSpec +from colossalai.tensor.distspec import DistPlacementPattern, _DistSpec from colossalai.tensor.process_group import ProcessGroup @@ -171,11 +169,21 @@ def handle_trans_spec(tensor: torch.Tensor, old_dist_spec: _DistSpec, dist_spec: pg: ProcessGroup) -> torch.Tensor: assert isinstance(old_dist_spec, _DistSpec), f"{type(old_dist_spec)} should be _DistSpec" assert isinstance(dist_spec, _DistSpec), f"{type(dist_spec)} should be _DistSpec" - forward_trans_handle = getattr(DistSpecManager, f'_{old_dist_spec.placement.value}2{dist_spec.placement.value}') + + trans_func_key = (old_dist_spec.placement, dist_spec.placement) + trans_funcs = { + (DistPlacementPattern.REPLICATE, DistPlacementPattern.REPLICATE): DistSpecManager._r2r, + (DistPlacementPattern.REPLICATE, DistPlacementPattern.SHARD): DistSpecManager._r2s, + (DistPlacementPattern.SHARD, DistPlacementPattern.REPLICATE): DistSpecManager._s2r, + (DistPlacementPattern.SHARD, DistPlacementPattern.SHARD): DistSpecManager._s2s + } + + forward_trans_handle = trans_funcs[trans_func_key] if not DistSpecManager._use_autograd_function: return forward_trans_handle(tensor, old_dist_spec, dist_spec, pg) - backward_trans_handle = getattr(DistSpecManager, - f'_{dist_spec.placement.value}2{old_dist_spec.placement.value}') + + backward_trans_handle = trans_funcs[(dist_spec.placement, old_dist_spec.placement)] + return TransformDistSpec.apply(tensor, old_dist_spec, dist_spec, pg, forward_trans_handle, backward_trans_handle) From f83ea813f5bb6ecc5597c7f1bf97870d46de1c49 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 8 May 2023 10:42:30 +0800 Subject: [PATCH 188/413] [example] add train resnet/vit with booster example (#3694) * [example] add train vit with booster example * [example] update readme * [example] add train resnet with booster example * [example] enable ci * [example] enable ci * [example] add requirements * [hotfix] fix analyzer init * [example] update requirements --- colossalai/_analyzer/__init__.py | 0 .../{torch_ddp => cifar_resnet}/.gitignore | 0 .../tutorial/new_api/cifar_resnet/README.md | 56 +++++ .../{torch_ddp => cifar_resnet}/eval.py | 0 .../new_api/cifar_resnet/requirements.txt | 4 + .../tutorial/new_api/cifar_resnet/test_ci.sh | 10 + .../tutorial/new_api/cifar_resnet/train.py | 210 ++++++++++++++++ examples/tutorial/new_api/cifar_vit/README.md | 37 +++ .../new_api/cifar_vit/requirements.txt | 5 + .../tutorial/new_api/cifar_vit/test_ci.sh | 10 + examples/tutorial/new_api/cifar_vit/train.py | 225 ++++++++++++++++++ examples/tutorial/new_api/glue_bert/README.md | 6 + .../new_api/glue_bert/requirements.txt | 7 + .../tutorial/new_api/glue_bert/test_ci.sh | 2 + examples/tutorial/new_api/test_ci.sh | 8 +- examples/tutorial/new_api/torch_ddp/README.md | 44 ---- examples/tutorial/new_api/torch_ddp/train.py | 128 ---------- 17 files changed, 578 insertions(+), 174 deletions(-) create mode 100644 colossalai/_analyzer/__init__.py rename examples/tutorial/new_api/{torch_ddp => cifar_resnet}/.gitignore (100%) create mode 100644 examples/tutorial/new_api/cifar_resnet/README.md rename examples/tutorial/new_api/{torch_ddp => cifar_resnet}/eval.py (100%) create mode 100644 examples/tutorial/new_api/cifar_resnet/requirements.txt create mode 100755 examples/tutorial/new_api/cifar_resnet/test_ci.sh create mode 100644 examples/tutorial/new_api/cifar_resnet/train.py create mode 100644 examples/tutorial/new_api/cifar_vit/README.md create mode 100644 examples/tutorial/new_api/cifar_vit/requirements.txt create mode 100755 examples/tutorial/new_api/cifar_vit/test_ci.sh create mode 100644 examples/tutorial/new_api/cifar_vit/train.py create mode 100644 examples/tutorial/new_api/glue_bert/requirements.txt delete mode 100644 examples/tutorial/new_api/torch_ddp/README.md delete mode 100644 examples/tutorial/new_api/torch_ddp/train.py diff --git a/colossalai/_analyzer/__init__.py b/colossalai/_analyzer/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/examples/tutorial/new_api/torch_ddp/.gitignore b/examples/tutorial/new_api/cifar_resnet/.gitignore similarity index 100% rename from examples/tutorial/new_api/torch_ddp/.gitignore rename to examples/tutorial/new_api/cifar_resnet/.gitignore diff --git a/examples/tutorial/new_api/cifar_resnet/README.md b/examples/tutorial/new_api/cifar_resnet/README.md new file mode 100644 index 000000000000..4ed86aa7a0ad --- /dev/null +++ b/examples/tutorial/new_api/cifar_resnet/README.md @@ -0,0 +1,56 @@ +# Train ResNet on CIFAR-10 from scratch + +## 🚀 Quick Start + +This example provides a training script and an evaluation script. The training script provides an example of training ResNet on CIFAR10 dataset from scratch. + +- Training Arguments + - `-p`, `--plugin`: Plugin to use. Choices: `torch_ddp`, `torch_ddp_fp16`, `low_level_zero`. Defaults to `torch_ddp`. + - `-r`, `--resume`: Resume from checkpoint file path. Defaults to `-1`, which means not resuming. + - `-c`, `--checkpoint`: The folder to save checkpoints. Defaults to `./checkpoint`. + - `-i`, `--interval`: Epoch interval to save checkpoints. Defaults to `5`. If set to `0`, no checkpoint will be saved. + - `--target_acc`: Target accuracy. Raise exception if not reached. Defaults to `None`. + +- Eval Arguments + - `-e`, `--epoch`: select the epoch to evaluate + - `-c`, `--checkpoint`: the folder where checkpoints are found + +### Install requirements + +```bash +pip install -r requirements.txt +``` + +### Train + +```bash +# train with torch DDP with fp32 +colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp32 + +# train with torch DDP with mixed precision training +colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp16 -p torch_ddp_fp16 + +# train with low level zero +colossalai run --nproc_per_node 2 train.py -c ./ckpt-low_level_zero -p low_level_zero +``` + +### Eval + +```bash +# evaluate fp32 training +python eval.py -c ./ckpt-fp32 -e 80 + +# evaluate fp16 mixed precision training +python eval.py -c ./ckpt-fp16 -e 80 + +# evaluate low level zero training +python eval.py -c ./ckpt-low_level_zero -e 80 +``` + +Expected accuracy performance will be: + +| Model | Single-GPU Baseline FP32 | Booster DDP with FP32 | Booster DDP with FP16 | Booster Low Level Zero | +| --------- | ------------------------ | --------------------- | --------------------- | ---------------------- | +| ResNet-18 | 85.85% | 84.91% | 85.46% | 84.50% | + +**Note: the baseline is adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`** diff --git a/examples/tutorial/new_api/torch_ddp/eval.py b/examples/tutorial/new_api/cifar_resnet/eval.py similarity index 100% rename from examples/tutorial/new_api/torch_ddp/eval.py rename to examples/tutorial/new_api/cifar_resnet/eval.py diff --git a/examples/tutorial/new_api/cifar_resnet/requirements.txt b/examples/tutorial/new_api/cifar_resnet/requirements.txt new file mode 100644 index 000000000000..85522f4129c4 --- /dev/null +++ b/examples/tutorial/new_api/cifar_resnet/requirements.txt @@ -0,0 +1,4 @@ +colossalai +torch +torchvision +tqdm diff --git a/examples/tutorial/new_api/cifar_resnet/test_ci.sh b/examples/tutorial/new_api/cifar_resnet/test_ci.sh new file mode 100755 index 000000000000..3954b84ff1ba --- /dev/null +++ b/examples/tutorial/new_api/cifar_resnet/test_ci.sh @@ -0,0 +1,10 @@ +#!/bin/bash +set -xe + +export DATA=/data/scratch/cifar-10 + +pip install -r requirements.txt + +for plugin in "torch_ddp" "torch_ddp_fp16" "low_level_zero"; do + colossalai run --nproc_per_node 4 train.py --interval 0 --target_acc 0.84 --plugin $plugin +done diff --git a/examples/tutorial/new_api/cifar_resnet/train.py b/examples/tutorial/new_api/cifar_resnet/train.py new file mode 100644 index 000000000000..e64e95fc2baf --- /dev/null +++ b/examples/tutorial/new_api/cifar_resnet/train.py @@ -0,0 +1,210 @@ +import argparse +import os +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms +from torch.optim import Optimizer +from torch.optim.lr_scheduler import MultiStepLR +from torch.utils.data import DataLoader +from tqdm import tqdm + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.booster.plugin.dp_plugin_base import DPPluginBase +from colossalai.cluster import DistCoordinator +from colossalai.nn.optimizer import HybridAdam +from colossalai.utils import get_current_device + +# ============================== +# Prepare Hyperparameters +# ============================== +NUM_EPOCHS = 80 +LEARNING_RATE = 1e-3 + + +def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPluginBase): + # trainsform + transform_train = transforms.Compose( + [transforms.Pad(4), + transforms.RandomHorizontalFlip(), + transforms.RandomCrop(32), + transforms.ToTensor()]) + transform_test = transforms.ToTensor() + + # CIFAR-10 dataset + data_path = os.environ.get('DATA', './data') + with coordinator.priority_execution(): + train_dataset = torchvision.datasets.CIFAR10(root=data_path, + train=True, + transform=transform_train, + download=True) + test_dataset = torchvision.datasets.CIFAR10(root=data_path, + train=False, + transform=transform_test, + download=True) + + # Data loader + train_dataloader = plugin.prepare_train_dataloader(train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True) + test_dataloader = plugin.prepare_train_dataloader(test_dataset, + batch_size=batch_size, + shuffle=False, + drop_last=False) + return train_dataloader, test_dataloader + + +@torch.no_grad() +def evaluate(model: nn.Module, test_dataloader: DataLoader, coordinator: DistCoordinator) -> float: + model.eval() + correct = torch.zeros(1, dtype=torch.int64, device=get_current_device()) + total = torch.zeros(1, dtype=torch.int64, device=get_current_device()) + for images, labels in test_dataloader: + images = images.cuda() + labels = labels.cuda() + outputs = model(images) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + dist.all_reduce(correct) + dist.all_reduce(total) + accuracy = correct.item() / total.item() + if coordinator.is_master(): + print(f'Accuracy of the model on the test images: {accuracy * 100:.2f} %') + return accuracy + + +def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, criterion: nn.Module, train_dataloader: DataLoader, + booster: Booster, coordinator: DistCoordinator): + model.train() + with tqdm(train_dataloader, desc=f'Epoch [{epoch + 1}/{NUM_EPOCHS}]', disable=not coordinator.is_master()) as pbar: + for images, labels in pbar: + images = images.cuda() + labels = labels.cuda() + # Forward pass + outputs = model(images) + loss = criterion(outputs, labels) + + # Backward and optimize + booster.backward(loss, optimizer) + optimizer.step() + optimizer.zero_grad() + + # Print log info + pbar.set_postfix({'loss': loss.item()}) + + +def main(): + # ============================== + # Parse Arguments + # ============================== + parser = argparse.ArgumentParser() + # FIXME(ver217): gemini is not supported resnet now + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'low_level_zero'], + help="plugin to use") + parser.add_argument('-r', '--resume', type=int, default=-1, help="resume from the epoch's checkpoint") + parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory") + parser.add_argument('-i', '--interval', type=int, default=5, help="interval of saving checkpoint") + parser.add_argument('--target_acc', + type=float, + default=None, + help="target accuracy. Raise exception if not reached") + args = parser.parse_args() + + # ============================== + # Prepare Checkpoint Directory + # ============================== + if args.interval > 0: + Path(args.checkpoint).mkdir(parents=True, exist_ok=True) + + # ============================== + # Launch Distributed Environment + # ============================== + colossalai.launch_from_torch(config={}) + coordinator = DistCoordinator() + + # update the learning rate with linear scaling + # old_gpu_num / old_lr = new_gpu_num / new_lr + global LEARNING_RATE + LEARNING_RATE *= coordinator.world_size + + # ============================== + # Instantiate Plugin and Booster + # ============================== + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + + booster = Booster(plugin=plugin, **booster_kwargs) + + # ============================== + # Prepare Dataloader + # ============================== + train_dataloader, test_dataloader = build_dataloader(100, coordinator, plugin) + + # ==================================== + # Prepare model, optimizer, criterion + # ==================================== + # resent50 + model = torchvision.models.resnet18(num_classes=10) + + # Loss and optimizer + criterion = nn.CrossEntropyLoss() + optimizer = HybridAdam(model.parameters(), lr=LEARNING_RATE) + + # lr scheduler + lr_scheduler = MultiStepLR(optimizer, milestones=[20, 40, 60, 80], gamma=1 / 3) + + # ============================== + # Boost with ColossalAI + # ============================== + model, optimizer, criterion, _, lr_scheduler = booster.boost(model, + optimizer, + criterion=criterion, + lr_scheduler=lr_scheduler) + + # ============================== + # Resume from checkpoint + # ============================== + if args.resume >= 0: + booster.load_model(model, f'{args.checkpoint}/model_{args.resume}.pth') + booster.load_optimizer(optimizer, f'{args.checkpoint}/optimizer_{args.resume}.pth') + booster.load_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{args.resume}.pth') + + # ============================== + # Train model + # ============================== + start_epoch = args.resume if args.resume >= 0 else 0 + for epoch in range(start_epoch, NUM_EPOCHS): + train_epoch(epoch, model, optimizer, criterion, train_dataloader, booster, coordinator) + lr_scheduler.step() + + # save checkpoint + if args.interval > 0 and (epoch + 1) % args.interval == 0: + booster.save_model(model, f'{args.checkpoint}/model_{epoch + 1}.pth') + booster.save_optimizer(optimizer, f'{args.checkpoint}/optimizer_{epoch + 1}.pth') + booster.save_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{epoch + 1}.pth') + + accuracy = evaluate(model, test_dataloader, coordinator) + if args.target_acc is not None: + assert accuracy >= args.target_acc, f'Accuracy {accuracy} is lower than target accuracy {args.target_acc}' + + +if __name__ == '__main__': + main() diff --git a/examples/tutorial/new_api/cifar_vit/README.md b/examples/tutorial/new_api/cifar_vit/README.md new file mode 100644 index 000000000000..fa76447c508f --- /dev/null +++ b/examples/tutorial/new_api/cifar_vit/README.md @@ -0,0 +1,37 @@ +# Train ViT on CIFAR-10 from scratch + +## 🚀 Quick Start + +This example provides a training script, which provides an example of training ViT on CIFAR10 dataset from scratch. + +- Training Arguments + - `-p`, `--plugin`: Plugin to use. Choices: `torch_ddp`, `torch_ddp_fp16`, `low_level_zero`. Defaults to `torch_ddp`. + - `-r`, `--resume`: Resume from checkpoint file path. Defaults to `-1`, which means not resuming. + - `-c`, `--checkpoint`: The folder to save checkpoints. Defaults to `./checkpoint`. + - `-i`, `--interval`: Epoch interval to save checkpoints. Defaults to `5`. If set to `0`, no checkpoint will be saved. + - `--target_acc`: Target accuracy. Raise exception if not reached. Defaults to `None`. + +### Install requirements + +```bash +pip install -r requirements.txt +``` + +### Train + +```bash +# train with torch DDP with fp32 +colossalai run --nproc_per_node 4 train.py -c ./ckpt-fp32 + +# train with torch DDP with mixed precision training +colossalai run --nproc_per_node 4 train.py -c ./ckpt-fp16 -p torch_ddp_fp16 + +# train with low level zero +colossalai run --nproc_per_node 4 train.py -c ./ckpt-low_level_zero -p low_level_zero +``` + +Expected accuracy performance will be: + +| Model | Single-GPU Baseline FP32 | Booster DDP with FP32 | Booster DDP with FP16 | Booster Low Level Zero | +| --------- | ------------------------ | --------------------- | --------------------- | ---------------------- | +| ViT | 83.00% | 84.03% | 84.00% | 84.43% | diff --git a/examples/tutorial/new_api/cifar_vit/requirements.txt b/examples/tutorial/new_api/cifar_vit/requirements.txt new file mode 100644 index 000000000000..6d53ce7b5a7d --- /dev/null +++ b/examples/tutorial/new_api/cifar_vit/requirements.txt @@ -0,0 +1,5 @@ +colossalai +timm +torch +torchvision +tqdm diff --git a/examples/tutorial/new_api/cifar_vit/test_ci.sh b/examples/tutorial/new_api/cifar_vit/test_ci.sh new file mode 100755 index 000000000000..43239d400586 --- /dev/null +++ b/examples/tutorial/new_api/cifar_vit/test_ci.sh @@ -0,0 +1,10 @@ +#!/bin/bash +set -xe + +export DATA=/data/scratch/cifar-10 + +pip install -r requirements.txt + +for plugin in "torch_ddp" "torch_ddp_fp16" "low_level_zero"; do + colossalai run --nproc_per_node 4 train.py --interval 0 --target_acc 0.83 --plugin $plugin +done diff --git a/examples/tutorial/new_api/cifar_vit/train.py b/examples/tutorial/new_api/cifar_vit/train.py new file mode 100644 index 000000000000..fee53df07086 --- /dev/null +++ b/examples/tutorial/new_api/cifar_vit/train.py @@ -0,0 +1,225 @@ +import argparse +import os +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms +from timm.models.vision_transformer import _cfg, _create_vision_transformer +from torch.optim import Optimizer +from torch.utils.data import DataLoader +from tqdm import tqdm + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.booster.plugin.dp_plugin_base import DPPluginBase +from colossalai.cluster import DistCoordinator +from colossalai.nn.lr_scheduler import LinearWarmupLR +from colossalai.nn.optimizer import HybridAdam +from colossalai.utils import get_current_device + +# ============================== +# Prepare Hyperparameters +# ============================== +NUM_EPOCHS = 60 +WARMUP_EPOCSH = 5 +LEARNING_RATE = 1e-3 + + +def vit_cifar(**kwargs): + pretrained_cfg = _cfg(num_classes=10, input_size=(3, 32, 32), crop_pct=1.0) + model_kwargs = dict(patch_size=4, embed_dim=512, depth=6, num_heads=8, drop_rate=0.1, mlp_ratio=1.0, **kwargs) + model = _create_vision_transformer('vit_cifar', pretrained_cfg=pretrained_cfg, **model_kwargs) + return model + + +def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPluginBase): + # trainsform + transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)), + ]) + transform_test = transforms.Compose([ + transforms.Resize(32), + transforms.ToTensor(), + transforms.Normalize((0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)), + ]) + + # CIFAR-10 dataset + data_path = os.environ.get('DATA', './data') + with coordinator.priority_execution(): + train_dataset = torchvision.datasets.CIFAR10(root=data_path, + train=True, + transform=transform_train, + download=True) + test_dataset = torchvision.datasets.CIFAR10(root=data_path, + train=False, + transform=transform_test, + download=True) + + # Data loader + train_dataloader = plugin.prepare_train_dataloader(train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True) + test_dataloader = plugin.prepare_train_dataloader(test_dataset, + batch_size=batch_size, + shuffle=False, + drop_last=False) + return train_dataloader, test_dataloader + + +@torch.no_grad() +def evaluate(model: nn.Module, test_dataloader: DataLoader, coordinator: DistCoordinator) -> float: + model.eval() + correct = torch.zeros(1, dtype=torch.int64, device=get_current_device()) + total = torch.zeros(1, dtype=torch.int64, device=get_current_device()) + for images, labels in test_dataloader: + images = images.cuda() + labels = labels.cuda() + outputs = model(images) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + dist.all_reduce(correct) + dist.all_reduce(total) + accuracy = correct.item() / total.item() + if coordinator.is_master(): + print(f'Accuracy of the model on the test images: {accuracy * 100:.2f} %') + return accuracy + + +def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, criterion: nn.Module, train_dataloader: DataLoader, + booster: Booster, coordinator: DistCoordinator): + model.train() + with tqdm(train_dataloader, desc=f'Epoch [{epoch + 1}/{NUM_EPOCHS}]', disable=not coordinator.is_master()) as pbar: + for images, labels in pbar: + images = images.cuda() + labels = labels.cuda() + # Forward pass + outputs = model(images) + loss = criterion(outputs, labels) + + # Backward and optimize + booster.backward(loss, optimizer) + optimizer.step() + optimizer.zero_grad() + + # Print log info + pbar.set_postfix({'loss': loss.item()}) + + +def main(): + # ============================== + # Parse Arguments + # ============================== + parser = argparse.ArgumentParser() + # FIXME(ver217): gemini is not supported resnet now + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'low_level_zero'], + help="plugin to use") + parser.add_argument('-r', '--resume', type=int, default=-1, help="resume from the epoch's checkpoint") + parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory") + parser.add_argument('-i', '--interval', type=int, default=5, help="interval of saving checkpoint") + parser.add_argument('--target_acc', + type=float, + default=None, + help="target accuracy. Raise exception if not reached") + args = parser.parse_args() + + # ============================== + # Prepare Checkpoint Directory + # ============================== + if args.interval > 0: + Path(args.checkpoint).mkdir(parents=True, exist_ok=True) + + # ============================== + # Launch Distributed Environment + # ============================== + colossalai.launch_from_torch(config={}) + coordinator = DistCoordinator() + + # update the learning rate with linear scaling + # old_gpu_num / old_lr = new_gpu_num / new_lr + global LEARNING_RATE + LEARNING_RATE *= coordinator.world_size + + # ============================== + # Instantiate Plugin and Booster + # ============================== + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + + booster = Booster(plugin=plugin, **booster_kwargs) + + # ============================== + # Prepare Dataloader + # ============================== + train_dataloader, test_dataloader = build_dataloader(512, coordinator, plugin) + + # ==================================== + # Prepare model, optimizer, criterion + # ==================================== + # resent50 + model = torchvision.models.resnet18(num_classes=10) + + # Loss and optimizer + criterion = nn.CrossEntropyLoss() + optimizer = HybridAdam(model.parameters(), lr=LEARNING_RATE) + + # lr scheduler + lr_scheduler = LinearWarmupLR(optimizer, NUM_EPOCHS, WARMUP_EPOCSH) + + # ============================== + # Boost with ColossalAI + # ============================== + model, optimizer, criterion, train_dataloader, lr_scheduler = booster.boost(model, + optimizer, + criterion=criterion, + dataloader=train_dataloader, + lr_scheduler=lr_scheduler) + + # ============================== + # Resume from checkpoint + # ============================== + if args.resume >= 0: + booster.load_model(model, f'{args.checkpoint}/model_{args.resume}.pth') + booster.load_optimizer(optimizer, f'{args.checkpoint}/optimizer_{args.resume}.pth') + booster.load_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{args.resume}.pth') + + # ============================== + # Train model + # ============================== + start_epoch = args.resume if args.resume >= 0 else 0 + for epoch in range(start_epoch, NUM_EPOCHS): + train_epoch(epoch, model, optimizer, criterion, train_dataloader, booster, coordinator) + lr_scheduler.step() + + # save checkpoint + if args.interval > 0 and (epoch + 1) % args.interval == 0: + booster.save_model(model, f'{args.checkpoint}/model_{epoch + 1}.pth') + booster.save_optimizer(optimizer, f'{args.checkpoint}/optimizer_{epoch + 1}.pth') + booster.save_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{epoch + 1}.pth') + + accuracy = evaluate(model, test_dataloader, coordinator) + if args.target_acc is not None: + assert accuracy >= args.target_acc, f'Accuracy {accuracy} is lower than target accuracy {args.target_acc}' + + +if __name__ == '__main__': + main() diff --git a/examples/tutorial/new_api/glue_bert/README.md b/examples/tutorial/new_api/glue_bert/README.md index d65c7705b5da..0030eead9f5b 100644 --- a/examples/tutorial/new_api/glue_bert/README.md +++ b/examples/tutorial/new_api/glue_bert/README.md @@ -10,6 +10,12 @@ This example provides a training script, which provides an example of finetuning - `--target_f1`: Target f1 score. Raise exception if not reached. Defaults to `None`. +### Install requirements + +```bash +pip install -r requirements.txt +``` + ### Train ```bash diff --git a/examples/tutorial/new_api/glue_bert/requirements.txt b/examples/tutorial/new_api/glue_bert/requirements.txt new file mode 100644 index 000000000000..950c2d378f08 --- /dev/null +++ b/examples/tutorial/new_api/glue_bert/requirements.txt @@ -0,0 +1,7 @@ +colossalai +datasets +torch +tqdm +transformers +scipy +scikit-learn diff --git a/examples/tutorial/new_api/glue_bert/test_ci.sh b/examples/tutorial/new_api/glue_bert/test_ci.sh index f8c2dfbe9eb9..c2c097f8d026 100755 --- a/examples/tutorial/new_api/glue_bert/test_ci.sh +++ b/examples/tutorial/new_api/glue_bert/test_ci.sh @@ -1,6 +1,8 @@ #!/bin/bash set -xe +pip install -r requirements.txt + for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do torchrun --standalone --nproc_per_node 4 finetune.py --target_f1 0.86 --plugin $plugin done diff --git a/examples/tutorial/new_api/test_ci.sh b/examples/tutorial/new_api/test_ci.sh index 8b4475e9f147..a08844dbe5fa 100644 --- a/examples/tutorial/new_api/test_ci.sh +++ b/examples/tutorial/new_api/test_ci.sh @@ -1,2 +1,6 @@ -#!/usr/bin/env -echo "The CI integration will be completed when the API is stable" +#!/bin/bash +set -xe + +# FIXME(ver217): only run bert finetune to save time + +cd glue_bert && bash ./test_ci.sh && cd .. diff --git a/examples/tutorial/new_api/torch_ddp/README.md b/examples/tutorial/new_api/torch_ddp/README.md deleted file mode 100644 index e120bacb0c84..000000000000 --- a/examples/tutorial/new_api/torch_ddp/README.md +++ /dev/null @@ -1,44 +0,0 @@ -# Distributed Data Parallel - -## 🚀 Quick Start - -This example provides a training script and an evaluation script. The training script provides an example of training ResNet on CIFAR10 dataset from scratch. - -- Training Arguments - - `-r`, `--resume`: resume from checkpoint file path - - `-c`, `--checkpoint`: the folder to save checkpoints - - `-i`, `--interval`: epoch interval to save checkpoints - - `-f`, `--fp16`: use fp16 - -- Eval Arguments - - `-e`, `--epoch`: select the epoch to evaluate - - `-c`, `--checkpoint`: the folder where checkpoints are found - - -### Train - -```bash -# train with torch DDP with fp32 -colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp32 - -# train with torch DDP with mixed precision training -colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp16 --fp16 -``` - -### Eval - -```bash -# evaluate fp32 training -python eval.py -c ./ckpt-fp32 -e 80 - -# evaluate fp16 mixed precision training -python eval.py -c ./ckpt-fp16 -e 80 -``` - -Expected accuracy performance will be: - -| Model | Single-GPU Baseline FP32 | Booster DDP with FP32 | Booster DDP with FP16 | -| --------- | ------------------------ | --------------------- | --------------------- | -| ResNet-18 | 85.85% | 85.03% | 85.12% | - -**Note: the baseline is adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`** diff --git a/examples/tutorial/new_api/torch_ddp/train.py b/examples/tutorial/new_api/torch_ddp/train.py deleted file mode 100644 index 4741c3151cbb..000000000000 --- a/examples/tutorial/new_api/torch_ddp/train.py +++ /dev/null @@ -1,128 +0,0 @@ -import argparse -from pathlib import Path - -import torch -import torch.nn as nn -import torchvision -import torchvision.transforms as transforms -from torch.optim.lr_scheduler import MultiStepLR - -import colossalai -from colossalai.booster import Booster -from colossalai.booster.plugin import TorchDDPPlugin -from colossalai.cluster import DistCoordinator - -# ============================== -# Parse Arguments -# ============================== -parser = argparse.ArgumentParser() -parser.add_argument('-r', '--resume', type=int, default=-1, help="resume from the epoch's checkpoint") -parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory") -parser.add_argument('-i', '--interval', type=int, default=5, help="interval of saving checkpoint") -parser.add_argument('-f', '--fp16', action='store_true', help="use fp16") -args = parser.parse_args() - -# ============================== -# Prepare Checkpoint Directory -# ============================== -Path(args.checkpoint).mkdir(parents=True, exist_ok=True) - -# ============================== -# Prepare Hyperparameters -# ============================== -NUM_EPOCHS = 80 -LEARNING_RATE = 1e-3 -START_EPOCH = args.resume if args.resume >= 0 else 0 - -# ============================== -# Launch Distributed Environment -# ============================== -colossalai.launch_from_torch(config={}) -coordinator = DistCoordinator() - -# update the learning rate with linear scaling -# old_gpu_num / old_lr = new_gpu_num / new_lr -LEARNING_RATE *= coordinator.world_size - -# ============================== -# Prepare Booster -# ============================== -plugin = TorchDDPPlugin() -if args.fp16: - booster = Booster(mixed_precision='fp16', plugin=plugin) -else: - booster = Booster(plugin=plugin) - -# ============================== -# Prepare Train Dataset -# ============================== -transform = transforms.Compose( - [transforms.Pad(4), - transforms.RandomHorizontalFlip(), - transforms.RandomCrop(32), - transforms.ToTensor()]) - -# CIFAR-10 dataset -with coordinator.priority_execution(): - train_dataset = torchvision.datasets.CIFAR10(root='./data/', train=True, transform=transform, download=True) - -# ==================================== -# Prepare model, optimizer, criterion -# ==================================== -# resent50 -model = torchvision.models.resnet18(num_classes=10).cuda() - -# Loss and optimizer -criterion = nn.CrossEntropyLoss() -optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) - -# lr scheduler -lr_scheduler = MultiStepLR(optimizer, milestones=[20, 40, 60, 80], gamma=1 / 3) - -# prepare dataloader with torch ddp plugin -train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=100, shuffle=True) - -# ============================== -# Resume from checkpoint -# ============================== -if args.resume >= 0: - booster.load_model(model, f'{args.checkpoint}/model_{args.resume}.pth') - booster.load_optimizer(optimizer, f'{args.checkpoint}/optimizer_{args.resume}.pth') - booster.load_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{args.resume}.pth') - -# ============================== -# Boost with ColossalAI -# ============================== -model, optimizer, criterion, train_dataloader, lr_scheduler = booster.boost(model, optimizer, criterion, - train_dataloader, lr_scheduler) - -# ============================== -# Train model -# ============================== -total_step = len(train_dataloader) - -for epoch in range(START_EPOCH, NUM_EPOCHS): - for i, (images, labels) in enumerate(train_dataloader): - images = images.cuda() - labels = labels.cuda() - - # Forward pass - outputs = model(images) - loss = criterion(outputs, labels) - - # Backward and optimize - optimizer.zero_grad() - booster.backward(loss, optimizer) - optimizer.step() - - if (i + 1) % 100 == 0: - print("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}".format(epoch + 1, NUM_EPOCHS, i + 1, total_step, - loss.item())) - - lr_scheduler.step() - - # save checkpoint every 5 epoch - if (epoch + 1) % args.interval == 0: - booster.save_model(model, f'{args.checkpoint}/model_{epoch + 1}.pth') - booster.save_optimizer(optimizer, f'{args.checkpoint}/optimizer_{epoch + 1}.pth') - booster.save_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{epoch + 1}.pth') From 3bf09efe74589faae26918104bd4f790742a3c3b Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 8 May 2023 15:44:03 +0800 Subject: [PATCH 189/413] [booster] update prepare dataloader method for plugin (#3706) * [booster] add prepare dataloader method for plug * [booster] update examples and docstr --- colossalai/booster/plugin/dp_plugin_base.py | 20 +++++++++---------- colossalai/booster/plugin/gemini_plugin.py | 2 +- .../booster/plugin/low_level_zero_plugin.py | 2 +- colossalai/booster/plugin/plugin_base.py | 17 +++++++++++++++- colossalai/booster/plugin/torch_ddp_plugin.py | 2 +- .../tutorial/new_api/cifar_resnet/train.py | 10 ++-------- examples/tutorial/new_api/cifar_vit/train.py | 10 ++-------- examples/tutorial/new_api/glue_bert/data.py | 16 +++++++-------- .../test_plugin/test_dp_plugin_base.py | 2 +- 9 files changed, 41 insertions(+), 40 deletions(-) diff --git a/colossalai/booster/plugin/dp_plugin_base.py b/colossalai/booster/plugin/dp_plugin_base.py index 4021b31754b4..d5da5938bfd9 100644 --- a/colossalai/booster/plugin/dp_plugin_base.py +++ b/colossalai/booster/plugin/dp_plugin_base.py @@ -20,21 +20,19 @@ def __init__(self) -> None: self.rank = dist.get_rank() self.world_size = dist.get_world_size() - def prepare_train_dataloader(self, - dataset, - batch_size, - shuffle=False, - seed=1024, - drop_last=False, - pin_memory=False, - num_workers=0, - **kwargs): + def prepare_dataloader(self, + dataset, + batch_size, + shuffle=False, + seed=1024, + drop_last=False, + pin_memory=False, + num_workers=0, + **kwargs): r""" Prepare a dataloader for distributed training. The dataloader will be wrapped by `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. - Note: - 1. Evaluation datasets should not be passed to this function. Args: dataset (`torch.utils.data.Dataset`): The dataset to be loaded. diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index fde8912a648f..4850b52defaf 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -156,7 +156,7 @@ class GeminiPlugin(DPPluginBase): >>> model, train_dataset, optimizer, criterion = ... >>> plugin = GeminiPlugin() - >>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8) + >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8) >>> booster = Booster(plugin=plugin) >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion) diff --git a/colossalai/booster/plugin/low_level_zero_plugin.py b/colossalai/booster/plugin/low_level_zero_plugin.py index 828d8b27422f..f0f5768560a7 100644 --- a/colossalai/booster/plugin/low_level_zero_plugin.py +++ b/colossalai/booster/plugin/low_level_zero_plugin.py @@ -95,7 +95,7 @@ class LowLevelZeroPlugin(DPPluginBase): >>> model, train_dataset, optimizer, criterion = ... >>> plugin = LowLevelZeroPlugin() - >>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8) + >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8) >>> booster = Booster(plugin=plugin) >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion) diff --git a/colossalai/booster/plugin/plugin_base.py b/colossalai/booster/plugin/plugin_base.py index 7a222022c1b2..eb5478595542 100644 --- a/colossalai/booster/plugin/plugin_base.py +++ b/colossalai/booster/plugin/plugin_base.py @@ -4,7 +4,7 @@ import torch.nn as nn from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler as LRScheduler -from torch.utils.data import DataLoader +from torch.utils.data import DataLoader, Dataset from colossalai.checkpoint_io import CheckpointIO from colossalai.interface import OptimizerWrapper @@ -59,3 +59,18 @@ def get_checkpoint_io(self) -> CheckpointIO: Get checkpoint io object for this plugin, only invoked when control_checkpoint_io is True. """ pass + + @abstractmethod + def prepare_dataloader(self, + dataset: Dataset, + batch_size: int, + shuffle: bool = False, + seed: int = 1024, + drop_last: bool = False, + pin_memory: bool = False, + num_workers: int = 0, + **kwargs): + """Prepare a dataloader for distributed training. The dataloader will be wrapped by + `torch.utils.data.DataLoader` + """ + pass diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index d30d266c0048..76906d844ef1 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -72,7 +72,7 @@ class TorchDDPPlugin(DPPluginBase): >>> model, train_dataset, optimizer, criterion = ... >>> plugin = TorchDDPPlugin() - >>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8) + >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8) >>> booster = Booster(plugin=plugin) >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion) diff --git a/examples/tutorial/new_api/cifar_resnet/train.py b/examples/tutorial/new_api/cifar_resnet/train.py index e64e95fc2baf..a96a4b640a22 100644 --- a/examples/tutorial/new_api/cifar_resnet/train.py +++ b/examples/tutorial/new_api/cifar_resnet/train.py @@ -49,14 +49,8 @@ def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPl download=True) # Data loader - train_dataloader = plugin.prepare_train_dataloader(train_dataset, - batch_size=batch_size, - shuffle=True, - drop_last=True) - test_dataloader = plugin.prepare_train_dataloader(test_dataset, - batch_size=batch_size, - shuffle=False, - drop_last=False) + train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True) + test_dataloader = plugin.prepare_dataloader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=False) return train_dataloader, test_dataloader diff --git a/examples/tutorial/new_api/cifar_vit/train.py b/examples/tutorial/new_api/cifar_vit/train.py index fee53df07086..2405fdfc60d5 100644 --- a/examples/tutorial/new_api/cifar_vit/train.py +++ b/examples/tutorial/new_api/cifar_vit/train.py @@ -63,14 +63,8 @@ def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPl download=True) # Data loader - train_dataloader = plugin.prepare_train_dataloader(train_dataset, - batch_size=batch_size, - shuffle=True, - drop_last=True) - test_dataloader = plugin.prepare_train_dataloader(test_dataset, - batch_size=batch_size, - shuffle=False, - drop_last=False) + train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True) + test_dataloader = plugin.prepare_dataloader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=False) return train_dataloader, test_dataloader diff --git a/examples/tutorial/new_api/glue_bert/data.py b/examples/tutorial/new_api/glue_bert/data.py index e43312aebc7c..981cedcca8c2 100644 --- a/examples/tutorial/new_api/glue_bert/data.py +++ b/examples/tutorial/new_api/glue_bert/data.py @@ -84,26 +84,26 @@ def prepare_data(self): AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) def train_dataloader(self): - return self.plugin.prepare_train_dataloader(self.dataset["train"], - batch_size=self.train_batch_size, - shuffle=True, - drop_last=True) + return self.plugin.prepare_dataloader(self.dataset["train"], + batch_size=self.train_batch_size, + shuffle=True, + drop_last=True) def val_dataloader(self): if len(self.eval_splits) == 1: - return self.plugin.prepare_train_dataloader(self.dataset["validation"], batch_size=self.eval_batch_size) + return self.plugin.prepare_dataloader(self.dataset["validation"], batch_size=self.eval_batch_size) elif len(self.eval_splits) > 1: return [ - self.plugin.prepare_train_dataloader(self.dataset[x], batch_size=self.eval_batch_size) + self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits ] def test_dataloader(self): if len(self.eval_splits) == 1: - return self.plugin.prepare_train_dataloader(self.dataset["test"], batch_size=self.eval_batch_size) + return self.plugin.prepare_dataloader(self.dataset["test"], batch_size=self.eval_batch_size) elif len(self.eval_splits) > 1: return [ - self.plugin.prepare_train_dataloader(self.dataset[x], batch_size=self.eval_batch_size) + self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits ] diff --git a/tests/test_booster/test_plugin/test_dp_plugin_base.py b/tests/test_booster/test_plugin/test_dp_plugin_base.py index a2b94ba6ca81..eab949828db9 100644 --- a/tests/test_booster/test_plugin/test_dp_plugin_base.py +++ b/tests/test_booster/test_plugin/test_dp_plugin_base.py @@ -55,7 +55,7 @@ def check_dataloader_sharding(): # create a custom dasetset with 0 to 10 dataset = TensorDataset(torch.arange(0, 10)) - train_dataloader = plugin.prepare_train_dataloader(dataset, batch_size=2) + train_dataloader = plugin.prepare_dataloader(dataset, batch_size=2) # get the first batch of data batch = next(iter(train_dataloader))[0].cuda() From 6552cbf8e1bf0fb60e189bdcc2467e07f4c1f08e Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Tue, 9 May 2023 11:10:02 +0800 Subject: [PATCH 190/413] [booster] fix no_sync method (#3709) * [booster] fix no_sync method * [booster] add test for ddp no_sync * [booster] fix merge * [booster] update unit test * [booster] update unit test * [booster] update unit test --- colossalai/booster/plugin/gemini_plugin.py | 5 +- .../booster/plugin/low_level_zero_plugin.py | 5 +- colossalai/booster/plugin/plugin_base.py | 9 ++- colossalai/booster/plugin/torch_ddp_plugin.py | 6 +- .../test_plugin/test_dp_plugin_base.py | 5 +- .../test_plugin/test_torch_ddp_plugin.py | 60 +++++++++++++++++++ 6 files changed, 85 insertions(+), 5 deletions(-) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 4850b52defaf..a3789a39d94b 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -2,7 +2,7 @@ import os import warnings from pathlib import Path -from typing import Callable, List, Optional, Tuple, Union +from typing import Callable, Iterator, List, Optional, Tuple, Union import torch import torch.nn as nn @@ -286,3 +286,6 @@ def control_checkpoint_io(self) -> bool: def get_checkpoint_io(self) -> CheckpointIO: return GeminiCheckpointIO() + + def no_sync(self, model: nn.Module) -> Iterator[None]: + raise NotImplementedError diff --git a/colossalai/booster/plugin/low_level_zero_plugin.py b/colossalai/booster/plugin/low_level_zero_plugin.py index f0f5768560a7..edc0b7679686 100644 --- a/colossalai/booster/plugin/low_level_zero_plugin.py +++ b/colossalai/booster/plugin/low_level_zero_plugin.py @@ -1,5 +1,5 @@ import warnings -from typing import Callable, List, Optional, Tuple, Union +from typing import Callable, Iterator, List, Optional, Tuple, Union import torch import torch.nn as nn @@ -197,3 +197,6 @@ def control_checkpoint_io(self) -> bool: def get_checkpoint_io(self) -> CheckpointIO: return LowLevelZeroCheckpointIO() + + def no_sync(self, model: nn.Module) -> Iterator[None]: + raise NotImplementedError diff --git a/colossalai/booster/plugin/plugin_base.py b/colossalai/booster/plugin/plugin_base.py index eb5478595542..561f58bc5570 100644 --- a/colossalai/booster/plugin/plugin_base.py +++ b/colossalai/booster/plugin/plugin_base.py @@ -1,5 +1,5 @@ from abc import ABC, abstractmethod -from typing import Callable, List, Tuple, Union +from typing import Callable, Iterator, List, Tuple, Union import torch.nn as nn from torch.optim import Optimizer @@ -60,6 +60,13 @@ def get_checkpoint_io(self) -> CheckpointIO: """ pass + @abstractmethod + def no_sync(self, model: nn.Module) -> Iterator[None]: + """ + Context manager to disable gradient synchronization. + """ + pass + @abstractmethod def prepare_dataloader(self, dataset: Dataset, diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index 76906d844ef1..99cd2f7791d3 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -1,4 +1,4 @@ -from typing import Callable, List, Tuple, Union +from typing import Callable, Iterator, List, Tuple, Union import torch.nn as nn from torch.nn.parallel import DistributedDataParallel as DDP @@ -142,3 +142,7 @@ def control_checkpoint_io(self) -> bool: def get_checkpoint_io(self) -> CheckpointIO: return TorchDDPCheckpointIO() + + def no_sync(self, model: nn.Module) -> Iterator[None]: + assert isinstance(model, TorchDDPModel), 'Model is not boosted by TorchDDPPlugin.' + return model.module.no_sync() diff --git a/tests/test_booster/test_plugin/test_dp_plugin_base.py b/tests/test_booster/test_plugin/test_dp_plugin_base.py index eab949828db9..61aeded12203 100644 --- a/tests/test_booster/test_plugin/test_dp_plugin_base.py +++ b/tests/test_booster/test_plugin/test_dp_plugin_base.py @@ -1,4 +1,4 @@ -from typing import Callable, List, Tuple, Union +from typing import Callable, Iterator, List, Tuple, Union import torch import torch.distributed as dist @@ -49,6 +49,9 @@ def supported_devices(self) -> List[str]: def supported_precisions(self) -> List[str]: pass + def no_sync(self, model: nn.Module) -> Iterator[None]: + pass + def check_dataloader_sharding(): plugin = DPPluginWrapper() diff --git a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py index 30c4db12309f..fbe44e5ce6fb 100644 --- a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py @@ -1,5 +1,8 @@ +from contextlib import nullcontext + import torch import torch.distributed as dist +import torch.nn as nn from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import SGD @@ -44,10 +47,67 @@ def check_torch_ddp_plugin(): torch.cuda.empty_cache() +class DummyModel(nn.Module): + + def __init__(self): + super().__init__() + self.weight = nn.Parameter(torch.rand(1)) + + def forward(self, x): + return self.weight * x + + +def check_torch_ddp_no_sync(): + plugin = TorchDDPPlugin() + booster = Booster(plugin=plugin) + + model = DummyModel() + criterion = lambda x: x.mean() + optimizer = SGD(model.parameters(), lr=1e-3) + # create a custom dasetset with 0 to 10 + dataset = torch.arange(0, 10) + train_dataloader = plugin.prepare_dataloader(dataset, batch_size=2) + model, optimizer, criterion, train_dataloader, _ = booster.boost(model, + optimizer, + criterion, + dataloader=train_dataloader) + + def fwd_bwd(): + output = model(batch.cuda()) + loss = criterion(output) + booster.backward(loss, optimizer) + + def get_grad_set_over_all_ranks(): + for p in model.parameters(): + # grad shape is (1, ) + assert p.grad.shape == (1,) + grad_list = [torch.empty_like(p.grad) for _ in range(dist.get_world_size())] + dist.all_gather(grad_list, p.grad) + # get grad set of all ranks + grad_set = set([grad.item() for grad in grad_list]) + # as the model only has one parameter, we can return here + return grad_set + + for i, batch in enumerate(train_dataloader): + if i > 1: + # only check the first two batches + break + # no_sync for the first batch, sync for the second batch + ctx = booster.no_sync(model) if i == 0 else nullcontext() + with ctx: + fwd_bwd() + grad_set = get_grad_set_over_all_ranks() + # for the first batch, all ranks should have different grads + # for the second batch, as grad is synchronized,all ranks should have the same grads + target_num_different_grad = dist.get_world_size() if i == 0 else 1 + assert len(grad_set) == target_num_different_grad + + def run_dist(rank, world_size, port): # init dist env colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') check_torch_ddp_plugin() + check_torch_ddp_no_sync() @rerun_if_address_is_in_use() From 20068ba188b916982e2e67cc9ebe120ccd4eb6ce Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Wed, 10 May 2023 12:17:02 +0800 Subject: [PATCH 191/413] [booster] add tests for ddp and low level zero's checkpointio (#3715) * [booster] update tests for booster * [booster] update tests for booster * [booster] update tests for booster * [booster] update tests for booster * [booster] update tests for booster * [booster] update booster tutorials#3717, fix recursive check --- colossalai/testing/__init__.py | 11 +- colossalai/testing/comparison.py | 24 ++++ .../test_gemini_checkpoint_io.py | 98 +++++++++++++ .../test_general_checkpoint_io.py | 133 ++---------------- .../test_low_level_zero_checkpoint_io.py | 57 ++++++++ .../test_torch_ddp_checkpoint_io.py | 63 +++++++++ 6 files changed, 261 insertions(+), 125 deletions(-) create mode 100644 tests/test_checkpoint_io/test_gemini_checkpoint_io.py create mode 100644 tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py create mode 100644 tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py diff --git a/colossalai/testing/__init__.py b/colossalai/testing/__init__.py index c53e0f44c7e0..9d0475ed064c 100644 --- a/colossalai/testing/__init__.py +++ b/colossalai/testing/__init__.py @@ -1,4 +1,11 @@ -from .comparison import assert_close, assert_close_loose, assert_equal, assert_equal_in_group, assert_not_equal +from .comparison import ( + assert_close, + assert_close_loose, + assert_equal, + assert_equal_in_group, + assert_not_equal, + check_state_dict_equal, +) from .pytest_wrapper import run_on_environment_flag from .utils import ( clear_cache_before_run, @@ -13,5 +20,5 @@ __all__ = [ 'assert_equal', 'assert_not_equal', 'assert_close', 'assert_close_loose', 'assert_equal_in_group', 'parameterize', 'rerun_on_exception', 'rerun_if_address_is_in_use', 'skip_if_not_enough_gpus', 'free_port', 'spawn', - 'clear_cache_before_run', 'run_on_environment_flag' + 'clear_cache_before_run', 'run_on_environment_flag', 'check_state_dict_equal' ] diff --git a/colossalai/testing/comparison.py b/colossalai/testing/comparison.py index e00d0da168c7..faf61638d8bb 100644 --- a/colossalai/testing/comparison.py +++ b/colossalai/testing/comparison.py @@ -1,3 +1,5 @@ +from typing import OrderedDict + import torch import torch.distributed as dist from torch import Tensor @@ -28,3 +30,25 @@ def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None): a = tensor_list[i] b = tensor_list[i + 1] assert torch.all(a == b), f'expected tensors on rank {i} and {i + 1} to be equal but they are not, {a} vs {b}' + + +def check_state_dict_equal(d1: OrderedDict, d2: OrderedDict, ignore_device: bool = True): + for k, v in d1.items(): + if isinstance(v, dict): + check_state_dict_equal(v, d2[k]) + elif isinstance(v, list): + for i in range(len(v)): + if isinstance(v[i], torch.Tensor): + if not ignore_device: + v[i] = v[i].to("cpu") + d2[k][i] = d2[k][i].to("cpu") + assert torch.equal(v[i], d2[k][i]) + else: + assert v[i] == d2[k][i] + elif isinstance(v, torch.Tensor): + if not ignore_device: + v = v.to("cpu") + d2[k] = d2[k].to("cpu") + assert torch.equal(v, d2[k]) + else: + assert v == d2[k] diff --git a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py new file mode 100644 index 000000000000..1e5a2e1c4b44 --- /dev/null +++ b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py @@ -0,0 +1,98 @@ +import tempfile + +import pytest +import torch + +import colossalai +from colossalai.booster.plugin.gemini_plugin import GeminiCheckpointIO +from colossalai.testing import check_state_dict_equal, parameterize, rerun_if_address_is_in_use, spawn +from colossalai.utils.cuda import get_current_device +from colossalai.zero import ColoInitContext, ZeroDDP +from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration +from colossalai.zero.gemini.gemini_mgr import GeminiManager +from tests.components_to_test.registry import non_distributed_component_funcs + + +@parameterize('placement_policy', ['cuda', 'cpu']) +@parameterize('model_name', ['bert']) +@parameterize('use_safetensors', [True, False]) +def exam_state_dict_with_origin(placement_policy, model_name, use_safetensors: bool): + from transformers import BertForSequenceClassification + + model_ckpt_dir = tempfile.TemporaryDirectory() + get_components_func = non_distributed_component_funcs.get_callable(model_name) + model_builder, *_ = get_components_func() + with ColoInitContext(device=(get_current_device())): + bert_model = model_builder() + bert_model.config.save_pretrained(save_directory=(model_ckpt_dir.name)) + + config_dict, *_ = search_chunk_configuration(bert_model, search_range_mb=1, search_interval_byte=100) + chunk_manager = ChunkManager(config_dict) + gemini_manager = GeminiManager(placement_policy, chunk_manager) + bert_model = ZeroDDP(bert_model, gemini_manager) + bert_model.train() + + ckpt_io = GeminiCheckpointIO() + if ckpt_io.coordinator.is_master(): + model_size = sum(p.numel() * p.element_size() for p in bert_model.parameters()) / 1024**2 + ckpt_io.save_model(bert_model, (model_ckpt_dir.name), + True, + True, + '', (model_size / 3), + use_safetensors=use_safetensors) + new_bert_model = BertForSequenceClassification.from_pretrained(model_ckpt_dir.name) + check_state_dict_equal(bert_model.state_dict(only_rank_0=True, dtype=(torch.float32)), + new_bert_model.state_dict(), False) + model_ckpt_dir.cleanup() + + +@parameterize('placement_policy', ['cuda', 'cpu']) +@parameterize('model_name', ['gpt2', 'bert']) +@parameterize('use_safetensors', [True, False]) +def exam_state_dict(placement_policy, model_name: str, use_safetensors: bool): + get_components_func = non_distributed_component_funcs.get_callable(model_name) + model_builder, *_ = get_components_func() + with ColoInitContext(device=(get_current_device())): + model = model_builder() + new_model = model_builder() + config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + chunk_manager = ChunkManager(config_dict) + gemini_manager = GeminiManager(placement_policy, chunk_manager) + model = ZeroDDP(model, gemini_manager) + + model.train() + #new model + new_config_dict, *_ = search_chunk_configuration(new_model, search_range_mb=1, search_interval_byte=100) + new_chunk_manager = ChunkManager(new_config_dict) + new_gemini_manager = GeminiManager(placement_policy, new_chunk_manager) + new_model = ZeroDDP(new_model, new_gemini_manager) + + model_ckpt_dir = tempfile.TemporaryDirectory() + ckpt_io = GeminiCheckpointIO() + model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2 + ckpt_io.save_model(model, (model_ckpt_dir.name), + True, + True, + 'epoch', (model_size / 3), + use_safetensors=use_safetensors) + + if ckpt_io.coordinator.is_master(): + ckpt_io.load_model(new_model, (model_ckpt_dir.name), strict=True) + model_dict = model.state_dict(only_rank_0=True) + new_model_dict = new_model.state_dict(only_rank_0=True) + check_state_dict_equal(model_dict, new_model_dict, False) + model_ckpt_dir.cleanup() + + +def run_dist(rank, world_size, port): + config = {} + colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + exam_state_dict() + exam_state_dict_with_origin() + + +@pytest.mark.dist +@pytest.mark.parametrize('world_size', [4, 4]) +@rerun_if_address_is_in_use() +def test_gemini_ckpIO(world_size): + spawn(run_dist, world_size) diff --git a/tests/test_checkpoint_io/test_general_checkpoint_io.py b/tests/test_checkpoint_io/test_general_checkpoint_io.py index 752ca706bfd4..9e973bb23e0b 100644 --- a/tests/test_checkpoint_io/test_general_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_general_checkpoint_io.py @@ -1,20 +1,13 @@ import tempfile + import pytest import torch from torch.optim import Adam from torchvision.models import resnet18 -from colossalai.checkpoint_io import GeneralCheckpointIO from colossalai.booster.plugin.gemini_plugin import GeminiCheckpointIO -from colossalai.testing import clear_cache_before_run, parameterize - -import colossalai -from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn -from colossalai.utils.cuda import get_current_device -from colossalai.zero import ColoInitContext, ZeroDDP -from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration -from colossalai.zero.gemini.gemini_mgr import GeminiManager -from tests.components_to_test.registry import non_distributed_component_funcs +from colossalai.checkpoint_io import GeneralCheckpointIO +from colossalai.testing import check_state_dict_equal, clear_cache_before_run, parameterize # ======== # Note: @@ -61,10 +54,10 @@ def test_unsharded_checkpoint(use_safetensors: bool): ckpt_io.load_model(new_model, model_ckpt_tempfile.name) ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) - # check for model and optimizer state dict recursively - recursive_check(model.state_dict(), new_model.state_dict()) - recursive_check(optimizer.state_dict(), new_optimizer.state_dict()) + check_state_dict_equal(model.state_dict(), new_model.state_dict()) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict()) + @pytest.mark.parametrize('use_safetensors', [True, False]) def test_sharded_checkpoint(use_safetensors: bool): @@ -87,7 +80,7 @@ def test_sharded_checkpoint(use_safetensors: bool): else: suffix = ".bin" WEIGHTS_INDEX_NAME = "model.bin.index.json" - + model_ckpt_dir = tempfile.TemporaryDirectory() optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile() @@ -96,7 +89,7 @@ def test_sharded_checkpoint(use_safetensors: bool): ckpt_io.save_model(model, model_ckpt_dir.name, True, True, "", 10, use_safetensors=use_safetensors) ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name, shard=False) - + # create new model new_model = resnet18() new_optimizer = Adam(new_model.parameters(), lr=0.001) @@ -105,111 +98,5 @@ def test_sharded_checkpoint(use_safetensors: bool): ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) # check for model and optimizer state dict recursively - recursive_check(model.state_dict(), new_model.state_dict()) - recursive_check(optimizer.state_dict(), new_optimizer.state_dict()) - -@parameterize('placement_policy', ['cuda', 'cpu']) -@parameterize('model_name', ['bert']) -@parameterize('use_safetensors', [True, False]) -def hf_load_colossalai_checkpoint(placement_policy, model_name, use_safetensors: bool): - from transformers import BertTokenizer, BertModel, BertForMaskedLM, BertConfig, BertForSequenceClassification - - model_ckpt_dir = tempfile.TemporaryDirectory() - get_components_func = non_distributed_component_funcs.get_callable(model_name) - model_builder, *_ = get_components_func() - - with ColoInitContext(device=get_current_device()): - bert_model = model_builder() - bert_model.config.save_pretrained(save_directory=model_ckpt_dir.name) - config_dict, *_ = search_chunk_configuration(bert_model, search_range_mb=1, search_interval_byte=100) - chunk_manager = ChunkManager(config_dict) - gemini_manager = GeminiManager(placement_policy, chunk_manager) - bert_model = ZeroDDP(bert_model, gemini_manager) - bert_model.train() - - ckpt_io = GeminiCheckpointIO() - if ckpt_io.coordinator.is_master(): - model_size = sum(p.numel() * p.element_size() for p in bert_model.parameters()) / 1024**2 - ckpt_io.save_model(bert_model, model_ckpt_dir.name, True, True, "", (model_size / 3), use_safetensors=use_safetensors) - new_bert_model = BertForSequenceClassification.from_pretrained(model_ckpt_dir.name) - recursive_check(bert_model.state_dict(only_rank_0=True, dtype=torch.float32), new_bert_model.state_dict()) - - model_ckpt_dir.cleanup() - - - -@parameterize('placement_policy', ['cuda', 'cpu']) -@parameterize('model_name', ['gpt2', 'bert']) -@parameterize('use_safetensors', [True, False]) -def exam_state_dict(placement_policy, model_name: str, use_safetensors: bool): - get_components_func = non_distributed_component_funcs.get_callable(model_name) - model_builder, *_ = get_components_func() - - with ColoInitContext(device=get_current_device()): - model = model_builder() - new_model = model_builder() - - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) - chunk_manager = ChunkManager(config_dict) - gemini_manager = GeminiManager(placement_policy, chunk_manager) - model = ZeroDDP(model, gemini_manager) - model.train() - - new_config_dict, *_ = search_chunk_configuration(new_model, search_range_mb=1, search_interval_byte=100) - new_chunk_manager = ChunkManager(new_config_dict) - new_gemini_manager = GeminiManager(placement_policy, new_chunk_manager) - new_model = ZeroDDP(new_model, new_gemini_manager) - - model_ckpt_dir = tempfile.TemporaryDirectory() - - ckpt_io = GeminiCheckpointIO() - model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2 - ckpt_io.save_model(model, model_ckpt_dir.name, True, True, "epoch", (model_size / 3), use_safetensors=use_safetensors) - - # load model - if ckpt_io.coordinator.is_master(): - ckpt_io.load_model(new_model, model_ckpt_dir.name, strict=True) - model_dict = model.state_dict(only_rank_0=True) - new_model_dict = new_model.state_dict(only_rank_0=True) - recursive_check(model_dict, new_model_dict) - - model_ckpt_dir.cleanup() - - -def run_dist(rank, world_size, port): - config = {} - colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - exam_state_dict() - hf_load_colossalai_checkpoint() - - -@pytest.mark.dist -@pytest.mark.parametrize('world_size', [4, 4]) -@rerun_if_address_is_in_use() -def test_gemini_ckpIO(world_size): - spawn(run_dist, world_size) - - -# do recursive check for the optimizer state dict -# if the value is a dict, compare its values -# if the value is a list, comapre all elements one-by-one -# if the value is a torch.Tensor, use torch.equal -# otherwise use assertEqual -def recursive_check(d1, d2): - for k, v in d1.items(): - if isinstance(v, dict): - recursive_check(v, d2[k]) - elif isinstance(v, list): - for i in range(len(v)): - if isinstance(v[i], torch.Tensor): - v[i] = v[i].to("cpu") - d2[k][i] = d2[k][i].to("cpu") - assert torch.equal(v[i], d2[k][i]) - else: - assert v[i] == d2[k][i] - elif isinstance(v, torch.Tensor): - v = v.to("cpu") - d2[k] = d2[k].to("cpu") - assert torch.equal(v, d2[k]) - else: - assert v == d2[k] + check_state_dict_equal(model.state_dict(), new_model.state_dict()) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict()) diff --git a/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py b/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py new file mode 100644 index 000000000000..217a950d8155 --- /dev/null +++ b/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py @@ -0,0 +1,57 @@ +import tempfile + +import pytest +import torch +from torchvision.models import resnet18 + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import LowLevelZeroPlugin +from colossalai.booster.plugin.low_level_zero_plugin import LowLevelZeroCheckpointIO +from colossalai.nn.optimizer import HybridAdam +from colossalai.testing import ( + check_state_dict_equal, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) + + +@clear_cache_before_run() +@parameterize('stage', [2]) +def check_low_level_zero_checkpointIO(stage: int): + plugin = LowLevelZeroPlugin(stage=stage, max_norm=1.0, initial_scale=32) + booster = Booster(plugin=plugin) + model = resnet18() + criterion = lambda x: x.mean() + optimizer = HybridAdam((model.parameters()), lr=0.001) + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + x = torch.randn(4, 3, 224, 224) + x = x.to('cuda') + output = model(x) + loss = criterion(output) + booster.backward(loss, optimizer) + optimizer.step() + + optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile() + ckpt_io = LowLevelZeroCheckpointIO() + ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name) + + if ckpt_io.coordinator.is_master(): + new_model = resnet18() + new_optimizer = HybridAdam((new_model.parameters()), lr=0.001) + _, new_optimizer, _, _, _ = booster.boost(new_model, new_optimizer) + ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) + + +def run_dist(rank, world_size, port): + colossalai.launch(config=(dict()), rank=rank, world_size=world_size, port=port, host='localhost') + check_low_level_zero_checkpointIO() + + +@rerun_if_address_is_in_use() +def test_low_level_zero_checkpointIO(): + spawn(run_dist, 2) diff --git a/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py new file mode 100644 index 000000000000..9128f8c0fe9e --- /dev/null +++ b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py @@ -0,0 +1,63 @@ +import tempfile + +import torch +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.optim import SGD +from torchvision.models import resnet18 + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.booster.plugin.torch_ddp_plugin import TorchDDPCheckpointIO +from colossalai.interface import OptimizerWrapper +from colossalai.testing import check_state_dict_equal, rerun_if_address_is_in_use, spawn + + +def check_torch_ddp_checkpointIO(): + plugin = TorchDDPPlugin() + booster = Booster(plugin=plugin) + model = resnet18() + criterion = lambda x: x.mean() + optimizer = SGD((model.parameters()), lr=0.001) + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1) + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion, lr_scheduler=scheduler) + + assert isinstance(model.module, DDP) + assert isinstance(optimizer, OptimizerWrapper) + + x = torch.randn(4, 3, 224, 224) + x = x.to('cuda') + output = model(x) + loss = criterion(output) + booster.backward(loss, optimizer) + optimizer.clip_grad_by_norm(1.0) + optimizer.step() + scheduler.step() + + optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile() + lr_scheduler_ckpt_tempfile = tempfile.NamedTemporaryFile() + ckpt_io = TorchDDPCheckpointIO() + ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name) + ckpt_io.save_lr_scheduler(scheduler, lr_scheduler_ckpt_tempfile.name) + + if ckpt_io.coordinator.is_master(): + new_model = resnet18() + new_optimizer = SGD((new_model.parameters()), lr=0.001) + new_scheduler = torch.optim.lr_scheduler.StepLR(new_optimizer, step_size=1, gamma=0.1) + _, new_optimizer, _, _, new_scheduler = booster.boost(new_model, new_optimizer, lr_scheduler=new_scheduler) + + ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) + + ckpt_io.load_lr_scheduler(new_scheduler, lr_scheduler_ckpt_tempfile.name) + check_state_dict_equal(scheduler.state_dict(), new_scheduler.state_dict(), False) + + +def run_dist(rank, world_size, port): + colossalai.launch(config=(dict()), rank=rank, world_size=world_size, port=port, host='localhost') + check_torch_ddp_checkpointIO() + + +@rerun_if_address_is_in_use() +def test_torch_ddp_checkpointIO(): + spawn(run_dist, 2) From f7361ee1bd31e57004d28418133e3714b08a53b2 Mon Sep 17 00:00:00 2001 From: MisterLin1995 <16671583+MisterLin1995@users.noreply.github.com> Date: Wed, 10 May 2023 13:36:09 +0800 Subject: [PATCH 192/413] [chat] fix community example ray (#3719) Co-authored-by: jiangwen --- applications/Chat/examples/community/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/applications/Chat/examples/community/README.md b/applications/Chat/examples/community/README.md index c9c645032288..cd7b9d99bf06 100644 --- a/applications/Chat/examples/community/README.md +++ b/applications/Chat/examples/community/README.md @@ -17,7 +17,7 @@ Community examples consist of both inference and training examples that have bee | Example | Description | Code Example | Colab | Author | |:---------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------:| | Peft | Adding Peft support for SFT and Prompts model training | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/peft) | - | [YY Lin](https://github.com/yynil) | -| Train prompts on Ray | A Ray based implementation of Train prompts example | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/ray) | - | [MisterLin1995](https://github.com/MisterLin1995) | +| Train prompts on Ray | A Ray based implementation of Train prompts example | [Training On Ray](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/ray) | - | [MisterLin1995](https://github.com/MisterLin1995) | |...|...|...|...|...| ### How to get involved From b7141c36dd84d025b4aef09da74c7b7ac29010b5 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Wed, 10 May 2023 17:12:03 +0800 Subject: [PATCH 193/413] [CI] fix some spelling errors (#3707) * fix spelling error with examples/comminity/ * fix spelling error with tests/ * fix some spelling error with tests/ colossalai/ etc. --- applications/Chat/coati/kernels/opt_attn.py | 2 +- colossalai/communication/p2p.py | 2 +- colossalai/communication/p2p_v2.py | 2 +- .../initializer_sequence.py | 4 ++-- .../tutorial/new_api/cifar_resnet/train.py | 2 +- examples/tutorial/new_api/cifar_vit/train.py | 6 ++--- op_builder/utils.py | 2 +- tests/components_to_test/albert.py | 10 ++++----- tests/components_to_test/beit.py | 4 ++-- tests/components_to_test/bert.py | 16 +++++++------- tests/components_to_test/registry.py | 8 +++---- .../test_activation_checkpointing.py | 2 +- .../test_checkpoint_io/test_load.py | 22 +++++++++---------- .../test_checkpoint_io/test_merge.py | 4 ++-- .../test_checkpoint_io/test_redist.py | 4 ++-- .../test_checkpoint_io/test_save.py | 8 +++---- tests/test_utils/test_lazy_init/utils.py | 4 ++-- 17 files changed, 51 insertions(+), 51 deletions(-) diff --git a/applications/Chat/coati/kernels/opt_attn.py b/applications/Chat/coati/kernels/opt_attn.py index c10f341e94a3..e99f9c2247d1 100644 --- a/applications/Chat/coati/kernels/opt_attn.py +++ b/applications/Chat/coati/kernels/opt_attn.py @@ -77,7 +77,7 @@ def forward( scale=self.scaling) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be - # partitioned aross GPUs when using tensor-parallelism. + # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) diff --git a/colossalai/communication/p2p.py b/colossalai/communication/p2p.py index 0200cd3c6553..1f20fca4f74d 100644 --- a/colossalai/communication/p2p.py +++ b/colossalai/communication/p2p.py @@ -217,7 +217,7 @@ def recv_backward(output_grad_shape, next_rank (int, optional): The rank of the source of the tensor. Returns: - Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: The input gradient tensor or gradident tensor list. + Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: The input gradient tensor or gradient tensor list. """ if gpc.is_pipeline_last_stage(): output_tensor_grad = None diff --git a/colossalai/communication/p2p_v2.py b/colossalai/communication/p2p_v2.py index 0dacd8c3c9b5..090311cb35f2 100644 --- a/colossalai/communication/p2p_v2.py +++ b/colossalai/communication/p2p_v2.py @@ -19,7 +19,7 @@ def init_process_group(): - """intialise process group by dist.new_group in the adjacent stages + """initialise process group by dist.new_group in the adjacent stages Args: None diff --git a/colossalai/context/process_group_initializer/initializer_sequence.py b/colossalai/context/process_group_initializer/initializer_sequence.py index eaacb14d2282..251a2940778a 100644 --- a/colossalai/context/process_group_initializer/initializer_sequence.py +++ b/colossalai/context/process_group_initializer/initializer_sequence.py @@ -91,11 +91,11 @@ def init_dist_group(self): parallel_setting = [] - local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode = \ + local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode = \ self._sequence_initializer.init_dist_group() # change mode to sequence mode = ParallelMode.SEQUENCE - parallel_setting.append((local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode)) + parallel_setting.append((local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode)) parallel_setting.append(self._sequence_dp_initializer.init_dist_group()) return parallel_setting diff --git a/examples/tutorial/new_api/cifar_resnet/train.py b/examples/tutorial/new_api/cifar_resnet/train.py index a96a4b640a22..fe0dabf08377 100644 --- a/examples/tutorial/new_api/cifar_resnet/train.py +++ b/examples/tutorial/new_api/cifar_resnet/train.py @@ -28,7 +28,7 @@ def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPluginBase): - # trainsform + # transform transform_train = transforms.Compose( [transforms.Pad(4), transforms.RandomHorizontalFlip(), diff --git a/examples/tutorial/new_api/cifar_vit/train.py b/examples/tutorial/new_api/cifar_vit/train.py index 2405fdfc60d5..82a8f2ed97e4 100644 --- a/examples/tutorial/new_api/cifar_vit/train.py +++ b/examples/tutorial/new_api/cifar_vit/train.py @@ -25,7 +25,7 @@ # Prepare Hyperparameters # ============================== NUM_EPOCHS = 60 -WARMUP_EPOCSH = 5 +WARMUP_EPOCHS = 5 LEARNING_RATE = 1e-3 @@ -37,7 +37,7 @@ def vit_cifar(**kwargs): def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPluginBase): - # trainsform + # transform transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), @@ -177,7 +177,7 @@ def main(): optimizer = HybridAdam(model.parameters(), lr=LEARNING_RATE) # lr scheduler - lr_scheduler = LinearWarmupLR(optimizer, NUM_EPOCHS, WARMUP_EPOCSH) + lr_scheduler = LinearWarmupLR(optimizer, NUM_EPOCHS, WARMUP_EPOCHS) # ============================== # Boost with ColossalAI diff --git a/op_builder/utils.py b/op_builder/utils.py index 1b1bd5f49970..2dbd976fbcbb 100644 --- a/op_builder/utils.py +++ b/op_builder/utils.py @@ -36,7 +36,7 @@ def get_cuda_version_in_pytorch() -> List[int]: torch_cuda_minor = torch.version.cuda.split(".")[1] except: raise ValueError( - "[extension] Cannot retrive the CUDA version in the PyTorch binary given by torch.version.cuda") + "[extension] Cannot retrieve the CUDA version in the PyTorch binary given by torch.version.cuda") return torch_cuda_major, torch_cuda_minor diff --git a/tests/components_to_test/albert.py b/tests/components_to_test/albert.py index d5b6bc89a83e..52b2275ec4f8 100644 --- a/tests/components_to_test/albert.py +++ b/tests/components_to_test/albert.py @@ -28,7 +28,7 @@ def bert_model_builder(checkpoint: bool = False): print('building AlbertForSequenceClassification model') # adapting huggingface BertForSequenceClassification for single unitest calling interface - class ModelAaptor(AlbertForSequenceClassification): + class ModelAdaptor(AlbertForSequenceClassification): def forward(self, input_ids, labels): """ @@ -37,23 +37,23 @@ def forward(self, input_ids, labels): """ return super().forward(input_ids=input_ids, labels=labels)[0] - model = ModelAaptor(config) + model = ModelAdaptor(config) # if checkpoint and version.parse(transformers.__version__) >= version.parse("4.11.0"): # model.gradient_checkpointing_enable() return model - is_distrbuted = torch.distributed.is_initialized() + is_distributed = torch.distributed.is_initialized() trainloader = get_bert_data_loader(n_class=vocab_size, batch_size=2, total_samples=10000, sequence_length=sequence_length, - is_distrbuted=is_distrbuted) + is_distributed=is_distributed) testloader = get_bert_data_loader(n_class=vocab_size, batch_size=2, total_samples=10000, sequence_length=sequence_length, - is_distrbuted=is_distrbuted) + is_distributed=is_distributed) criterion = None return bert_model_builder, trainloader, testloader, torch.optim.Adam, criterion diff --git a/tests/components_to_test/beit.py b/tests/components_to_test/beit.py index 1252071f4075..2021ae6f6e35 100644 --- a/tests/components_to_test/beit.py +++ b/tests/components_to_test/beit.py @@ -27,7 +27,7 @@ def generate(self): @non_distributed_component_funcs.register(name='beit') def get_training_components(): - def model_buider(checkpoint=False): + def model_builder(checkpoint=False): model = Beit(img_size=DummyDataLoader.img_size, num_classes=DummyDataLoader.num_class, embed_dim=32, @@ -39,4 +39,4 @@ def model_buider(checkpoint=False): testloader = DummyDataLoader() criterion = torch.nn.CrossEntropyLoss() - return model_buider, trainloader, testloader, torch.optim.Adam, criterion + return model_builder, trainloader, testloader, torch.optim.Adam, criterion diff --git a/tests/components_to_test/bert.py b/tests/components_to_test/bert.py index c1faa6f9d892..e7d1d50806b8 100644 --- a/tests/components_to_test/bert.py +++ b/tests/components_to_test/bert.py @@ -13,7 +13,7 @@ def get_bert_data_loader( total_samples, sequence_length, device=torch.device('cpu:0'), - is_distrbuted=False, + is_distributed=False, ): train_data = torch.randint( low=0, @@ -24,7 +24,7 @@ def get_bert_data_loader( ) train_label = torch.randint(low=0, high=2, size=(total_samples,), device=device, dtype=torch.long) train_dataset = torch.utils.data.TensorDataset(train_data, train_label) - if is_distrbuted: + if is_distributed: sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) else: sampler = SequentialSampler(train_dataset) @@ -52,8 +52,8 @@ def bert_model_builder(checkpoint: bool = False): attention_probs_dropout_prob=0.) print('building BertForSequenceClassification model') - # adapting huggingface BertForSequenceClassification for single unitest calling interface - class ModelAaptor(BertForSequenceClassification): + # adapting huggingface BertForSequenceClassification for single unittest calling interface + class ModelAdaptor(BertForSequenceClassification): def forward(self, input_ids, labels): """ @@ -62,23 +62,23 @@ def forward(self, input_ids, labels): """ return super().forward(input_ids=input_ids, labels=labels)[0] - model = ModelAaptor(config) + model = ModelAdaptor(config) if checkpoint and version.parse(transformers.__version__) >= version.parse("4.11.0"): model.gradient_checkpointing_enable() return model - is_distrbuted = torch.distributed.is_initialized() + is_distributed = torch.distributed.is_initialized() trainloader = get_bert_data_loader(n_class=vocab_size, batch_size=2, total_samples=10000, sequence_length=sequence_length, - is_distrbuted=is_distrbuted) + is_distributed=is_distributed) testloader = get_bert_data_loader(n_class=vocab_size, batch_size=2, total_samples=10000, sequence_length=sequence_length, - is_distrbuted=is_distrbuted) + is_distributed=is_distributed) criterion = None return bert_model_builder, trainloader, testloader, torch.optim.Adam, criterion diff --git a/tests/components_to_test/registry.py b/tests/components_to_test/registry.py index 728ed9eba6ea..edfcaaa7275b 100644 --- a/tests/components_to_test/registry.py +++ b/tests/components_to_test/registry.py @@ -9,10 +9,10 @@ def __init__(self): def register(self, name): assert name not in self._registry - def _regsiter(callable_): + def _register(callable_): self._registry[name] = callable_ - return _regsiter + return _register def get_callable(self, name: str): return self._registry[name] @@ -34,6 +34,6 @@ def __next__(self): non_distributed_component_funcs = Registry() -model_paralle_component_funcs = Registry() +model_parallel_component_funcs = Registry() -__all__ = ['non_distributed_component_funcs', 'model_paralle_component_funcs'] +__all__ = ['non_distributed_component_funcs', 'model_parallel_component_funcs'] diff --git a/tests/test_utils/test_activation_checkpointing.py b/tests/test_utils/test_activation_checkpointing.py index 59a8acd4b210..2930552cc4e7 100644 --- a/tests/test_utils/test_activation_checkpointing.py +++ b/tests/test_utils/test_activation_checkpointing.py @@ -51,7 +51,7 @@ def test_activation_checkpointing(cpu_offload, use_reentrant): # other tests might affect this test reset_seeds() - # We put initilization here to avoid change cuda rng state below + # We put initialization here to avoid change cuda rng state below inputs = torch.rand(2, 2, requires_grad=True, device='cuda') weight = torch.rand(2, 4, requires_grad=True, device='cuda') diff --git a/tests/test_utils/test_checkpoint_io/test_load.py b/tests/test_utils/test_checkpoint_io/test_load.py index b1a741515728..2949c9f0752d 100644 --- a/tests/test_utils/test_checkpoint_io/test_load.py +++ b/tests/test_utils/test_checkpoint_io/test_load.py @@ -23,7 +23,7 @@ def check_model_state_dict(a: Dict[str, Tensor], b: Dict[str, Tensor]) -> None: assert torch.equal(v, b[k]) -def check_optim_state_dict(a: dict, b: dict, ignore_param_gruops: bool = False) -> None: +def check_optim_state_dict(a: dict, b: dict, ignore_param_groups: bool = False) -> None: assert set(a['state'].keys()) == set(b['state'].keys()) for k, state in a['state'].items(): b_state = b['state'][k] @@ -32,7 +32,7 @@ def check_optim_state_dict(a: dict, b: dict, ignore_param_gruops: bool = False) assert torch.equal(v1, v2) else: assert v1 == v2 - if not ignore_param_gruops: + if not ignore_param_groups: assert a['param_groups'] == b['param_groups'] @@ -129,23 +129,23 @@ def launch_dist(fn, world_size: int): def save_dist(dir_name: str, zero: bool): - model, optmizer = prepare_model_optim(shard=True, zero=zero) - reset_model_optim(model, optmizer) + model, optimizer = prepare_model_optim(shard=True, zero=zero) + reset_model_optim(model, optimizer) world_size = dist.get_world_size() rank = dist.get_rank() - save(dir_name, model, optmizer, dist_meta=get_dist_metas(world_size, zero)[rank]) + save(dir_name, model, optimizer, dist_meta=get_dist_metas(world_size, zero)[rank]) def load_and_check_dist(dir_name: str): world_size = dist.get_world_size() - model, optmizer = prepare_model_optim(shard=True) - reset_model_optim(model, optmizer) + model, optimizer = prepare_model_optim(shard=True) + reset_model_optim(model, optimizer) model_state_dict = deepcopy(model.state_dict()) - optimizer_state_dict = deepcopy(optmizer.state_dict()) - reset_model_optim(model, optmizer, 1) - load(dir_name, model, optmizer, get_redist_meta(world_size), get_dist_metas(world_size)) + optimizer_state_dict = deepcopy(optimizer.state_dict()) + reset_model_optim(model, optimizer, 1) + load(dir_name, model, optimizer, get_redist_meta(world_size), get_dist_metas(world_size)) check_model_state_dict(model_state_dict, model.state_dict()) - check_optim_state_dict(optimizer_state_dict, optmizer.state_dict()) + check_optim_state_dict(optimizer_state_dict, optimizer.state_dict()) @pytest.mark.dist diff --git a/tests/test_utils/test_checkpoint_io/test_merge.py b/tests/test_utils/test_checkpoint_io/test_merge.py index 255c74adf0a2..07d4597f8391 100644 --- a/tests/test_utils/test_checkpoint_io/test_merge.py +++ b/tests/test_utils/test_checkpoint_io/test_merge.py @@ -68,7 +68,7 @@ def run_dist(rank, world_size, port, test_fn): def run_save_dist(dir_name: str, zero: bool): - model, optmizer = prepare_model_optim(shard=True, zero=zero) + model, optimizer = prepare_model_optim(shard=True, zero=zero) rank = dist.get_rank() dp_world_size = dist.get_world_size() // 2 if not zero: @@ -90,7 +90,7 @@ def run_save_dist(dir_name: str, zero: bool): 'fc.bias': ParamDistMeta(rank // 2, dp_world_size, 0, 1, zero_numel=1, zero_orig_shape=[1]) } - save(dir_name, model, optmizer, dist_meta=dist_metas) + save(dir_name, model, optimizer, dist_meta=dist_metas) @pytest.mark.dist diff --git a/tests/test_utils/test_checkpoint_io/test_redist.py b/tests/test_utils/test_checkpoint_io/test_redist.py index 144715bdfcca..fdc849a5ecc0 100644 --- a/tests/test_utils/test_checkpoint_io/test_redist.py +++ b/tests/test_utils/test_checkpoint_io/test_redist.py @@ -125,9 +125,9 @@ def run_dist(rank, world_size, port, test_fn): def run_save_dist(dir_name: str, zero: bool): - model, optmizer = prepare_model_optim(shard=True, zero=zero) + model, optimizer = prepare_model_optim(shard=True, zero=zero) rank = dist.get_rank() - save(dir_name, model, optmizer, dist_meta=get_dist_metas(4, zero)[rank]) + save(dir_name, model, optimizer, dist_meta=get_dist_metas(4, zero)[rank]) @pytest.mark.dist diff --git a/tests/test_utils/test_checkpoint_io/test_save.py b/tests/test_utils/test_checkpoint_io/test_save.py index e35e566f6ff8..2abdd95a6481 100644 --- a/tests/test_utils/test_checkpoint_io/test_save.py +++ b/tests/test_utils/test_checkpoint_io/test_save.py @@ -28,7 +28,7 @@ def check_model_state_dict(a: Dict[str, Tensor], b: Dict[str, Tensor]) -> None: assert torch.equal(v, b[k]) -def check_optim_state_dict(a: dict, b: dict, ignore_param_gruops: bool = False) -> None: +def check_optim_state_dict(a: dict, b: dict, ignore_param_groups: bool = False) -> None: assert set(a['state'].keys()) == set(b['state'].keys()) for k, state in a['state'].items(): b_state = b['state'][k] @@ -37,7 +37,7 @@ def check_optim_state_dict(a: dict, b: dict, ignore_param_gruops: bool = False) assert torch.equal(v1, v2) else: assert v1 == v2 - if not ignore_param_gruops: + if not ignore_param_groups: assert a['param_groups'] == b['param_groups'] @@ -113,12 +113,12 @@ def run_dist(rank, world_size, port, test_fn): def run_save_dist(dir_name): - model, optmizer = prepare_model_optim() + model, optimizer = prepare_model_optim() dist_metas = { 'fc.weight': ParamDistMeta(dist.get_rank(), dist.get_world_size(), 0, 1), 'fc.bias': ParamDistMeta(dist.get_rank(), dist.get_world_size(), 0, 1) } - save(dir_name, model, optmizer, dist_meta=dist_metas) + save(dir_name, model, optimizer, dist_meta=dist_metas) @pytest.mark.dist diff --git a/tests/test_utils/test_lazy_init/utils.py b/tests/test_utils/test_lazy_init/utils.py index a8aeb4c8930c..0b5f15ca5445 100644 --- a/tests/test_utils/test_lazy_init/utils.py +++ b/tests/test_utils/test_lazy_init/utils.py @@ -18,7 +18,7 @@ def set_seed(seed: int) -> None: torch.manual_seed(seed) -def assert_model_eqaual(m1: torch.nn.Module, m2: torch.nn.Module) -> None: +def assert_model_equal(m1: torch.nn.Module, m2: torch.nn.Module) -> None: s1 = m1.state_dict() s2 = m2.state_dict() @@ -63,7 +63,7 @@ def check_lazy_init(entry: TestingEntry, seed: int = 42, verbose: bool = False, with ctx: deferred_model = model_fn() deferred_model = ctx.materialize(deferred_model, verbose=verbose) - assert_model_eqaual(model, deferred_model) + assert_model_equal(model, deferred_model) if check_forward: assert_forward_equal(model, deferred_model, data_gen_fn, output_transform_fn) if verbose: From 899aa86368d9c0d9b3eda8b4186c78018cd56761 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Thu, 11 May 2023 11:10:28 +0800 Subject: [PATCH 194/413] [CI] fix typo with tests components (#3695) * fix spelling error with examples/comminity/ * fix spelling error with tests/ From 1f73609adb358aa1a004f6e5e4c0928a13eb93be Mon Sep 17 00:00:00 2001 From: digger-yu Date: Thu, 11 May 2023 16:30:58 +0800 Subject: [PATCH 195/413] [CI] fix typo with tests/ etc. (#3727) * fix spelling error with examples/comminity/ * fix spelling error with tests/ * fix some spelling error with tests/ colossalai/ etc. * fix spelling error with tests/ etc. date:2023.5.10 --- tests/components_to_test/albert.py | 2 +- tests/test_booster/test_accelerator.py | 6 +++--- tests/test_booster/test_plugin/test_dp_plugin_base.py | 2 +- .../test_cifar_with_data_pipeline_tensor.py | 4 ++-- .../test_cifar_with_data_pipeline_tensor_v2.py | 4 ++-- .../test_codegen/test_activation_checkpoint_codegen.py | 4 ++-- .../test_nested_activation_checkpoint_codegen.py | 4 ++-- tests/test_fx/test_codegen/test_offload_codegen.py | 6 +++--- tests/test_layers/test_sequence/test_sequence.py | 2 +- tests/test_moe/test_kernel.py | 4 ++-- tests/test_tensor/model/test_model.py | 2 +- tests/test_trainer/test_pipeline/test_p2p.py | 2 +- tests/test_zero/test_gemini/test_chunkv2.py | 6 +++--- 13 files changed, 24 insertions(+), 24 deletions(-) diff --git a/tests/components_to_test/albert.py b/tests/components_to_test/albert.py index 52b2275ec4f8..8924eb2fbc92 100644 --- a/tests/components_to_test/albert.py +++ b/tests/components_to_test/albert.py @@ -27,7 +27,7 @@ def bert_model_builder(checkpoint: bool = False): attention_probs_dropout_prob=0.) print('building AlbertForSequenceClassification model') - # adapting huggingface BertForSequenceClassification for single unitest calling interface + # adapting huggingface BertForSequenceClassification for single unittest calling interface class ModelAdaptor(AlbertForSequenceClassification): def forward(self, input_ids, labels): diff --git a/tests/test_booster/test_accelerator.py b/tests/test_booster/test_accelerator.py index 895c494d0c17..6f3f66ed41b8 100644 --- a/tests/test_booster/test_accelerator.py +++ b/tests/test_booster/test_accelerator.py @@ -7,8 +7,8 @@ @clear_cache_before_run() @parameterize('device', ['cpu', 'cuda']) def test_accelerator(device): - acceleartor = Accelerator(device) + accelerator = Accelerator(device) model = nn.Linear(8, 8) - model = acceleartor.configure_model(model) + model = accelerator.configure_model(model) assert next(model.parameters()).device.type == device - del model, acceleartor + del model, accelerator diff --git a/tests/test_booster/test_plugin/test_dp_plugin_base.py b/tests/test_booster/test_plugin/test_dp_plugin_base.py index 61aeded12203..689b334cae50 100644 --- a/tests/test_booster/test_plugin/test_dp_plugin_base.py +++ b/tests/test_booster/test_plugin/test_dp_plugin_base.py @@ -56,7 +56,7 @@ def no_sync(self, model: nn.Module) -> Iterator[None]: def check_dataloader_sharding(): plugin = DPPluginWrapper() - # create a custom dasetset with 0 to 10 + # create a custom dataset with 0 to 10 dataset = TensorDataset(torch.arange(0, 10)) train_dataloader = plugin.prepare_dataloader(dataset, batch_size=2) diff --git a/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py b/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py index 4d63592f12b0..4992acbd7cc2 100644 --- a/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py +++ b/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py @@ -48,7 +48,7 @@ def run_trainer(rank, world_size, port): pipelinable.policy = "uniform" model = pipelinable.partition(1, gpc.pipeline_parallel_size, gpc.get_local_rank(ParallelMode.PIPELINE)) - # craete dataloaders + # create dataloaders root = Path(os.environ['DATA']) transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4, pad_if_needed=True), @@ -68,7 +68,7 @@ def run_trainer(rank, world_size, port): # create lr scheduler lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer, total_steps=NUM_EPOCHS, warmup_steps=WARMUP_EPOCHS) - # intiailize + # initialize engine, train_dataloader, *_ = colossalai.initialize(model=model, optimizer=optimizer, criterion=criterion, diff --git a/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor_v2.py b/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor_v2.py index 67d2ba5f5d98..62bbb8f50391 100644 --- a/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor_v2.py +++ b/tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor_v2.py @@ -50,7 +50,7 @@ def run_trainer(rank, world_size, port): pipelinable.policy = "uniform" model = pipelinable.partition(1, gpc.pipeline_parallel_size, gpc.get_local_rank(ParallelMode.PIPELINE)) - # craete dataloaders + # create dataloaders root = Path(os.environ['DATA']) transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4, pad_if_needed=True), @@ -70,7 +70,7 @@ def run_trainer(rank, world_size, port): # create lr scheduler lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer, total_steps=NUM_EPOCHS, warmup_steps=WARMUP_EPOCHS) - # intiailize + # initialize engine, train_dataloader, *_ = colossalai.initialize(model=model, optimizer=optimizer, criterion=criterion, diff --git a/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py b/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py index ab483f7e47a3..bcac2ec426d9 100644 --- a/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py +++ b/tests/test_fx/test_codegen/test_activation_checkpoint_codegen.py @@ -64,7 +64,7 @@ def forward(self, x, y): def _run_act_ckpt_codegen(rank, world_size, port): - # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly + # launch colossalai to make sure we could execute colossalai.utils.checkpoint currently colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and run forward @@ -122,7 +122,7 @@ def test_act_ckpt_codegen(): def _run_act_ckpt_python_code_torch11(rank, world_size, port): - # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly + # launch colossalai to make sure we could execute colossalai.utils.checkpoint currently colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and run forward diff --git a/tests/test_fx/test_codegen/test_nested_activation_checkpoint_codegen.py b/tests/test_fx/test_codegen/test_nested_activation_checkpoint_codegen.py index 9064023d4f68..5b327807a57b 100644 --- a/tests/test_fx/test_codegen/test_nested_activation_checkpoint_codegen.py +++ b/tests/test_fx/test_codegen/test_nested_activation_checkpoint_codegen.py @@ -32,7 +32,7 @@ def forward(self, x): def _run_act_ckpt_codegen(rank, world_size, port): - # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly + # launch colossalai to make sure we could execute colossalai.utils.checkpoint currently colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and run forward @@ -89,7 +89,7 @@ def test_act_ckpt_codegen(): def _run_act_ckpt_python_code_torch11(rank, world_size, port): - # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly + # launch colossalai to make sure we could execute colossalai.utils.checkpoint currently colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and run forward diff --git a/tests/test_fx/test_codegen/test_offload_codegen.py b/tests/test_fx/test_codegen/test_offload_codegen.py index 96e88eb92b33..c217b96586fe 100644 --- a/tests/test_fx/test_codegen/test_offload_codegen.py +++ b/tests/test_fx/test_codegen/test_offload_codegen.py @@ -56,7 +56,7 @@ def _test_fwd_and_bwd(model: torch.nn.Module, gm: ColoGraphModule, data: torch.T fx_out = gm(data) assert torch.equal(non_fx_out, fx_out), "fx_out doesn't comply with original output" - # test barckward + # test backward loss0 = non_fx_out.sum() loss0.backward() loss1 = fx_out.sum() @@ -65,7 +65,7 @@ def _test_fwd_and_bwd(model: torch.nn.Module, gm: ColoGraphModule, data: torch.T def _run_offload_codegen(rank, world_size, port): - # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly + # launch colossalai to make sure we could execute colossalai.utils.checkpoint currently colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and input @@ -120,7 +120,7 @@ def test_act_ckpt_codegen(): def _run_offload_codegen_torch11(rank, world_size, port): - # launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly + # launch colossalai to make sure we could execute colossalai.utils.checkpoint currently colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # build model and input diff --git a/tests/test_layers/test_sequence/test_sequence.py b/tests/test_layers/test_sequence/test_sequence.py index aac192d7eff0..60f2d55f43af 100644 --- a/tests/test_layers/test_sequence/test_sequence.py +++ b/tests/test_layers/test_sequence/test_sequence.py @@ -45,7 +45,7 @@ def check_ring_qk(rank, world_size): ring_qk = colossalai.nn.layer.parallel_sequence.RingQK.apply sub_a = ring_qk(sub_q, sub_k, batch_size, num_heads, sub_seq_length) - # check master and distributed attetion scores + # check master and distributed attention scores sub_master_a = a[:, rank * sub_seq_length:(rank + 1) * sub_seq_length] assert torch.allclose(sub_a, sub_master_a, rtol=1e-5, atol=1e-2) diff --git a/tests/test_moe/test_kernel.py b/tests/test_moe/test_kernel.py index ad9a172b72aa..39603c158731 100644 --- a/tests/test_moe/test_kernel.py +++ b/tests/test_moe/test_kernel.py @@ -41,7 +41,7 @@ def run_routing(rank, world_size, port, rs=2, hidden_size=128, data_type=torch.f if data_type == torch.float16: layer = layer.half() - # use matrix multiplication instead of COL_MOE_KERNL in MOE dispatch and combine + # use matrix multiplication instead of COL_MOE_KERNEL in MOE dispatch and combine layer.use_kernel = False old_out, _ = layer(tokens) ech = old_out.shape @@ -57,7 +57,7 @@ def run_routing(rank, world_size, port, rs=2, hidden_size=128, data_type=torch.f layer.gate_weight.grad.zero_() layer.use_kernel = True - new_out, _ = layer(tokens) # get ouputs through colossal kernel + new_out, _ = layer(tokens) # get outputs through colossal kernel if data_type == torch.float32: check_equal(old_out, new_out) diff --git a/tests/test_tensor/model/test_model.py b/tests/test_tensor/model/test_model.py index 79d70e53c5cb..288bd20e3844 100644 --- a/tests/test_tensor/model/test_model.py +++ b/tests/test_tensor/model/test_model.py @@ -329,6 +329,6 @@ def test_pretrain_load(world_size): if __name__ == '__main__': # test_model_parameters() - # test_colo_optgimizer() + # test_colo_optimizer() test_model(4) # test_pretrain_load(4) diff --git a/tests/test_trainer/test_pipeline/test_p2p.py b/tests/test_trainer/test_pipeline/test_p2p.py index cb7a193d2bfa..8ad366133d18 100644 --- a/tests/test_trainer/test_pipeline/test_p2p.py +++ b/tests/test_trainer/test_pipeline/test_p2p.py @@ -90,7 +90,7 @@ def run_check(rank, world_size, port): prev_rank = gpc.get_prev_global_rank(ParallelMode.PIPELINE) next_rank = gpc.get_next_global_rank(ParallelMode.PIPELINE) logger.info('Rank {0}: prev rank {1}, next rank {2}'.format(rank, prev_rank, next_rank)) - logger.info('Distributed environment is initialzied.') + logger.info('Distributed environment is initialized.') check_comm(world_size, rank, prev_rank, next_rank, logger) gpc.destroy() diff --git a/tests/test_zero/test_gemini/test_chunkv2.py b/tests/test_zero/test_gemini/test_chunkv2.py index 16764aa6b0b1..1cb31b260a99 100644 --- a/tests/test_zero/test_gemini/test_chunkv2.py +++ b/tests/test_zero/test_gemini/test_chunkv2.py @@ -23,7 +23,7 @@ def add_param(param_list, param_cp_list, *args, **kwargs): param_cp_list.append(param.clone()) -def check_euqal(param, param_cp): +def check_equal(param, param_cp): if param.device != param_cp.device: temp = param.data.to(param_cp.device) else: @@ -57,7 +57,7 @@ def exam_chunk_basic(init_device, keep_gathered, pin_memory): my_chunk.append_tensor(param) assert my_chunk.utilized_size == 597 for param, param_cp in zip(param_list, param_cp_list): - check_euqal(param, param_cp) + check_equal(param, param_cp) my_chunk.close_chunk() if keep_gathered is False: @@ -77,7 +77,7 @@ def exam_chunk_basic(init_device, keep_gathered, pin_memory): my_chunk.access_chunk() assert my_chunk.device_type == 'cuda' for param, param_cp in zip(param_list, param_cp_list): - check_euqal(param, param_cp) + check_equal(param, param_cp) assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 4 my_chunk.tensor_trans_state(param_list[0], TensorState.COMPUTE) From ad6460cf2c4d63ea91dc4dc90431f92567dad303 Mon Sep 17 00:00:00 2001 From: digger-yu Date: Mon, 15 May 2023 11:46:25 +0800 Subject: [PATCH 196/413] [NFC] fix typo applications/ and colossalai/ (#3735) --- applications/Chat/examples/community/peft/README.md | 2 +- applications/Chat/inference/README.md | 2 +- applications/Chat/inference/benchmark.py | 2 +- colossalai/auto_parallel/README.md | 4 ++-- .../auto_parallel/passes/runtime_preparation_pass.py | 4 ++-- colossalai/autochunk/autochunk_codegen.py | 6 +++--- colossalai/autochunk/trace_indice.py | 2 +- colossalai/checkpoint_io/index_file.py | 2 +- colossalai/checkpoint_io/utils.py | 2 +- colossalai/cli/check/check_installation.py | 8 ++++---- 10 files changed, 17 insertions(+), 17 deletions(-) diff --git a/applications/Chat/examples/community/peft/README.md b/applications/Chat/examples/community/peft/README.md index eabb56fd8294..844bfd3d22c3 100644 --- a/applications/Chat/examples/community/peft/README.md +++ b/applications/Chat/examples/community/peft/README.md @@ -18,7 +18,7 @@ For SFT training, just call train_peft_sft.py Its arguments are almost identical to train_sft.py instead adding a new eval_dataset if you have a eval_dataset file. The data file is just a plain datafile, please check the format in the easy_dataset.py. For stage-3 rlhf training, call train_peft_prompts.py. -Its arguments are almost idential to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported. +Its arguments are almost identical to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported. # Dataformat Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt. diff --git a/applications/Chat/inference/README.md b/applications/Chat/inference/README.md index 434677c98fa5..4848817e0fd1 100644 --- a/applications/Chat/inference/README.md +++ b/applications/Chat/inference/README.md @@ -75,7 +75,7 @@ E.g. you can set `export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`. Please ensure you have downloaded HF-format model weights of LLaMA models first. -Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight convertion script. +Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight conversion script. After installing this lib, we may convert the original HF-format LLaMA model weights to 4-bit version. diff --git a/applications/Chat/inference/benchmark.py b/applications/Chat/inference/benchmark.py index 59cd1eeea2aa..a8485f588705 100644 --- a/applications/Chat/inference/benchmark.py +++ b/applications/Chat/inference/benchmark.py @@ -123,7 +123,7 @@ def evaluate( start = time() for instruction in instructions: print(f"Instruction: {instruction}") - resp, tokens = evaluate(model, tokenizer, instruction, temparature=0.2, num_beams=1) + resp, tokens = evaluate(model, tokenizer, instruction, temperature=0.2, num_beams=1) total_tokens += tokens print(f"Response: {resp}") print('\n----------------------------\n') diff --git a/colossalai/auto_parallel/README.md b/colossalai/auto_parallel/README.md index 8e47e1bb0b4a..f011ec8ccbd7 100644 --- a/colossalai/auto_parallel/README.md +++ b/colossalai/auto_parallel/README.md @@ -16,8 +16,8 @@ A *symbolic profiler* for collecting computing and memory overhead related to st ### Solver **Solver** is designed to find the optimal execution plan for a given computation graph and cluster in two stages: -1) *Intra-op parallelism stage* is to find the plan with the minimum total execution time of all nodes with respect to the constraint of the memory budget. The optimaztion goal of intra-op parallelism solver is modified from Alpa 's intra-op parallelsim ILP solver. -2) *Activation checkpoint stage* is to search for the fastest execution plan that meets the memory budget on the computation graph after inserting the communication nodes by the intra-op parallelism stage. The algorithm to find optimial activation checkpoint is modified from Rotor . The reason we use two-stage optimization is that if the two tasks are formulated together, the solving time will be significantly increased, which will greatly affect the user experience of the system. On the contrary, solving in two hierarchical levels has many advantages. Firstly, compared with the computation graph with activation checkpointing, the original graph has fewer nodes, which can reduce the solving cost of intra-op parallelism solver. In addition, a more optimal solution can be found by adding the communication overhead into the activation checkpoint modeling. +1) *Intra-op parallelism stage* is to find the plan with the minimum total execution time of all nodes with respect to the constraint of the memory budget. The optimization goal of intra-op parallelism solver is modified from Alpa 's intra-op parallelism ILP solver. +2) *Activation checkpoint stage* is to search for the fastest execution plan that meets the memory budget on the computation graph after inserting the communication nodes by the intra-op parallelism stage. The algorithm to find optimal activation checkpoint is modified from Rotor . The reason we use two-stage optimization is that if the two tasks are formulated together, the solving time will be significantly increased, which will greatly affect the user experience of the system. On the contrary, solving in two hierarchical levels has many advantages. Firstly, compared with the computation graph with activation checkpointing, the original graph has fewer nodes, which can reduce the solving cost of intra-op parallelism solver. In addition, a more optimal solution can be found by adding the communication overhead into the activation checkpoint modeling. ### Generator **Generator** applies the searched execution plan to the computation graph and recompiles the computation graph to optimized PyTorch code. It has *a series compile pass* to insert a communication node or do the kernel substitution as the intra-op parallelism solver required. Additionally, we implement a *code generation* feature to recognize the annotation from the activation checkpoint solver and inject the activation checkpoint block following annotation instructions. diff --git a/colossalai/auto_parallel/passes/runtime_preparation_pass.py b/colossalai/auto_parallel/passes/runtime_preparation_pass.py index 08af846b221d..177f3765f5a0 100644 --- a/colossalai/auto_parallel/passes/runtime_preparation_pass.py +++ b/colossalai/auto_parallel/passes/runtime_preparation_pass.py @@ -169,7 +169,7 @@ def _post_processing(node, size_processing_node): This function is used to process the dependency between the size node and its users after inserting the size_process_node. ''' - # store original node and processing node pair in node_pairs dictioanry + # store original node and processing node pair in node_pairs dictionary # It will be used to replace the original node with processing node in slice object node_pairs[node] = size_processing_node size_processing_node._meta_data = node._meta_data @@ -388,7 +388,7 @@ def module_params_sharding_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMes """ mod_graph = gm.graph nodes = tuple(mod_graph.nodes) - # This stream is created for overlaping the communication and computation. + # This stream is created for overlapping the communication and computation. reduction_stream = torch.cuda.Stream() def _add_hook_for_grad_communication(node, param, name=None): diff --git a/colossalai/autochunk/autochunk_codegen.py b/colossalai/autochunk/autochunk_codegen.py index d0a467254d72..cc98c1570b4a 100644 --- a/colossalai/autochunk/autochunk_codegen.py +++ b/colossalai/autochunk/autochunk_codegen.py @@ -40,7 +40,7 @@ def _gen_chunk_slice_dim(chunk_dim: int, chunk_indice_name: str, shape: List) -> return new_shape -def _gen_loop_start(chunk_input: List[Node], chunk_output: List[Node], chunk_ouput_dim: int, chunk_size=2) -> str: +def _gen_loop_start(chunk_input: List[Node], chunk_output: List[Node], chunk_output_dim: int, chunk_size=2) -> str: """ Generate chunk loop start @@ -52,7 +52,7 @@ def _gen_loop_start(chunk_input: List[Node], chunk_output: List[Node], chunk_oup Args: chunk_input (List[Node]): chunk input node chunk_output (Node): chunk output node - chunk_ouput_dim (int): chunk output node chunk dim + chunk_output_dim (int): chunk output node chunk dim chunk_size (int): chunk size. Defaults to 2. Returns: @@ -74,7 +74,7 @@ def _gen_loop_start(chunk_input: List[Node], chunk_output: List[Node], chunk_oup input_node.name, input_node.name) out_shape = get_node_shape(chunk_output[0]) - chunk_shape = out_shape[chunk_ouput_dim[0]] + chunk_shape = out_shape[chunk_output_dim[0]] context += "chunk_size = %d\nfor chunk_idx in range(0, %d, chunk_size):\n" % (chunk_size, chunk_shape) return context diff --git a/colossalai/autochunk/trace_indice.py b/colossalai/autochunk/trace_indice.py index c7fce4c8bee1..d56bf843f18d 100644 --- a/colossalai/autochunk/trace_indice.py +++ b/colossalai/autochunk/trace_indice.py @@ -18,7 +18,7 @@ class TraceIndice(object): dim(x1)=dim(x2)=dim(x3)=[a, b, c] This class will record every node's dims' indice, compute and source. - Attibutes: + Attributes: node_list (List) indice_trace_list (List): [{"indice": [...], "compute": [...], "source": [...]}, {...}] indice_view_list (Dict): not used for now diff --git a/colossalai/checkpoint_io/index_file.py b/colossalai/checkpoint_io/index_file.py index 15a6d09f3b5e..334ecbc04738 100644 --- a/colossalai/checkpoint_io/index_file.py +++ b/colossalai/checkpoint_io/index_file.py @@ -159,7 +159,7 @@ def get_all_param_names(self): def write_index_file(self, save_index_file): """ - Wriete index file. + Write index file. """ save_index_file = os.path.join(self.root_path, save_index_file) index = {"metadata": self.metadata, "weight_map": self.weight_map} diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 16e41631f0d5..ee4bd72e89ec 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -21,7 +21,7 @@ def calculate_tensor_size(tensor: torch.Tensor) -> float: If so, a new shard should be created. Args: - tenosr (torch.Tensor): the tensor to calculate size for. + tensor (torch.Tensor): the tensor to calculate size for. Returns: float: size of the tensor in MB. diff --git a/colossalai/cli/check/check_installation.py b/colossalai/cli/check/check_installation.py index cb3dbbc09301..4a481f3bd122 100644 --- a/colossalai/cli/check/check_installation.py +++ b/colossalai/cli/check/check_installation.py @@ -31,7 +31,7 @@ def check_installation(): found_aot_cuda_ext = _check_aot_built_cuda_extension_installed() cuda_version = _check_cuda_version() torch_version, torch_cuda_version = _check_torch_version() - colossalai_verison, prebuilt_torch_version_required, prebuilt_cuda_version_required = _parse_colossalai_version() + colossalai_version, prebuilt_torch_version_required, prebuilt_cuda_version_required = _parse_colossalai_version() # if cuda_version is None, that means either # CUDA_HOME is not found, thus cannot compare the version compatibility @@ -57,7 +57,7 @@ def check_installation(): click.echo(f'#### Installation Report ####') click.echo(f'\n------------ Environment ------------') - click.echo(f"Colossal-AI version: {to_click_output(colossalai_verison)}") + click.echo(f"Colossal-AI version: {to_click_output(colossalai_version)}") click.echo(f"PyTorch version: {to_click_output(torch_version)}") click.echo(f"System CUDA version: {to_click_output(cuda_version)}") click.echo(f"CUDA version required by PyTorch: {to_click_output(torch_cuda_version)}") @@ -137,7 +137,7 @@ def _parse_colossalai_version(): # 1. X.X.X+torchX.XXcuXX.X (when colossalai is installed with CUDA extensions) # 2. X.X.X (when colossalai is not installed with CUDA extensions) # where X represents an integer. - colossalai_verison = colossalai.__version__.split('+')[0] + colossalai_version = colossalai.__version__.split('+')[0] try: torch_version_for_aot_build = colossalai.__version__.split('torch')[1].split('cu')[0] @@ -145,7 +145,7 @@ def _parse_colossalai_version(): except: torch_version_for_aot_build = None cuda_version_for_aot_build = None - return colossalai_verison, torch_version_for_aot_build, cuda_version_for_aot_build + return colossalai_version, torch_version_for_aot_build, cuda_version_for_aot_build def _check_aot_built_cuda_extension_installed(): From b37797ed3d3d6af294a095397b4bc135264b8c6a Mon Sep 17 00:00:00 2001 From: wukong1992 Date: Mon, 15 May 2023 12:14:38 +0800 Subject: [PATCH 197/413] [booster] support torch fsdp plugin in booster (#3697) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: 纪少敏 --- colossalai/booster/plugin/__init__.py | 7 + .../booster/plugin/torch_fsdp_plugin.py | 285 ++++++++++++++++++ colossalai/testing/utils.py | 4 +- .../test_plugin/test_torch_fsdp_plugin.py | 64 ++++ 4 files changed, 358 insertions(+), 2 deletions(-) create mode 100644 colossalai/booster/plugin/torch_fsdp_plugin.py create mode 100644 tests/test_booster/test_plugin/test_torch_fsdp_plugin.py diff --git a/colossalai/booster/plugin/__init__.py b/colossalai/booster/plugin/__init__.py index aa45bcb59ad7..a3b87b5f11d3 100644 --- a/colossalai/booster/plugin/__init__.py +++ b/colossalai/booster/plugin/__init__.py @@ -4,3 +4,10 @@ from .torch_ddp_plugin import TorchDDPPlugin __all__ = ['Plugin', 'TorchDDPPlugin', 'GeminiPlugin', 'LowLevelZeroPlugin'] + +import torch +from packaging import version + +if version.parse(torch.__version__) >= version.parse('1.12.0'): + from .torch_fsdp_plugin import TorchFSDPPlugin + __all__.append('TorchFSDPPlugin') diff --git a/colossalai/booster/plugin/torch_fsdp_plugin.py b/colossalai/booster/plugin/torch_fsdp_plugin.py new file mode 100644 index 000000000000..0daefa9fff53 --- /dev/null +++ b/colossalai/booster/plugin/torch_fsdp_plugin.py @@ -0,0 +1,285 @@ +from typing import Callable, Iterable, Iterator, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +from packaging import version +from torch.distributed import ProcessGroup + +if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( + torch.__version__) < version.parse('2.0.0'): + from torch.distributed.fsdp import FullStateDictConfig + from torch.distributed.fsdp import FullyShardedDataParallel as FSDP + from torch.distributed.fsdp import StateDictType + from torch.distributed.fsdp.fully_sharded_data_parallel import ( + BackwardPrefetch, + CPUOffload, + MixedPrecision, + ShardingStrategy, + ) +elif version.parse(torch.__version__) >= version.parse('2.0.0'): + from torch.distributed.fsdp import FullyShardedDataParallel as FSDP + from torch.distributed.fsdp._init_utils import ProcessGroupType + from torch.distributed.fsdp.api import ( + BackwardPrefetch, + CPUOffload, + FullOptimStateDictConfig, + FullStateDictConfig, + MixedPrecision, + ShardingStrategy, + StateDictType, + ) + from torch.distributed.fsdp.wrap import _FSDPPolicy +else: + raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") + +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from torch.utils.data import DataLoader + +from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO +from colossalai.cluster import DistCoordinator +from colossalai.interface import ModelWrapper, OptimizerWrapper + +from .dp_plugin_base import DPPluginBase + +__all__ = ['TorchFSDPPlugin'] + + +class TorchFSDPCheckpointIO(GeneralCheckpointIO): + + def __init__(self) -> None: + super().__init__() + self.coordinator = DistCoordinator() + + def __set_model_optim_state( + self, + model, + state_dict_type, + state_dict_config, + optim_state_dict_config, + ): + return FSDP.set_state_dict_type(model, state_dict_type, state_dict_config, optim_state_dict_config) + + def load_sharded_model(self, model: nn.Module, checkpoint: str): + + # TODO(jishaomin): implement this method as it can be supported by Huggingface model + raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.") + + def load_sharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str): + + # TODO(jishaomin): implement this method as it can be supported by Huggingface model + raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.") + + def save_sharded_model(self, model: nn.Module, checkpoint: str): + + # TODO(jishaomin): implement this method as it can be supported by Huggingface model + raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.") + + def save_sharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str): + + # TODO(jishaomin): implement this method as it can be supported by Huggingface model + raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.") + + def load_unsharded_model(self, model: nn.Module, checkpoint: str): + """ + Load model from checkpoint with automatic unwrapping. + """ + # the model should be unwrapped in self.load_model via ModelWrapper.unwrap + + if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( + torch.__version__) < version.parse('2.0.0'): + full_state_dict = self.load_state_dict(checkpoint) + elif version.parse(torch.__version__) >= version.parse('2.0.0'): + full_state_dict = self.load_state_dict(checkpoint) + self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(rank0_only=True)) + full_state_dict = model.state_dict() + else: + raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") + + model.load_state_dict(full_state_dict) + + def load_unsharded_optimizer(self, model: nn.Module, optim: Optimizer, checkpoint: str): + """ + Load Optimizer from checkpoint with automatic unwrapping. + """ + + if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( + torch.__version__) < version.parse('2.0.0'): + optim_full_state_dict = self.load_state_dict(checkpoint) + elif version.parse(torch.__version__) >= version.parse('2.0.0'): + optim_full_state_dict = self.load_state_dict(checkpoint) + FSDP.full_optim_state_dict_to_load(optim_full_state_dict, model, optim) + else: + raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") + + optim.load_state_dict(optim_full_state_dict) + + def save_unsharded_model(self, model: nn.Module, checkpoint: str): + """ + Save model to checkpoint but only on master process. + """ + # the model should be unwrapped in self.load_model via ModelWrapper.unwrap + + if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( + torch.__version__) < version.parse('2.0.0'): + cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) + with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, cfg): + model_state_dict = model.state_dict() + elif version.parse(torch.__version__) >= version.parse('2.0.0'): + self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(rank0_only=True)) + model_state_dict = model.state_dict() + else: + raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") + self.save_checkpoint(model_state_dict, checkpoint) + + def save_unsharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str): + """ + Save optimizer to checkpoint but only on master process. + """ + + if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( + torch.__version__) < version.parse('2.0.0'): + optim_state_dict = FSDP.full_optim_state_dict(model=model, optim=optimizer) + elif version.parse(torch.__version__) >= version.parse('2.0.0'): + self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, + FullOptimStateDictConfig(rank0_only=True)) + optim_state_dict = FSDP.optim_state_dict(model, optimizer) + else: + raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") + self.save_checkpoint(optim_state_dict, checkpoint) + + +class TorchFSDPModel(ModelWrapper): + + def __init__(self, module: nn.Module, *args, **kwargs) -> None: + super().__init__(module) + self.module = FSDP(module, *args, **kwargs) + + def unwrap(self): + return self.module.module + + +class TorchFSDPPlugin(DPPluginBase): + """ + Plugin for PyTorch FSDP. + + Example: + >>> from colossalai.booster import Booster + >>> from colossalai.booster.plugin import TorchFSDPPlugin + >>> + >>> model, train_dataset, optimizer, criterion = ... + >>> plugin = TorchFSDPPlugin() + + >>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8) + >>> booster = Booster(plugin=plugin) + >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion) + + Args: + See https://pytorch.org/docs/stable/fsdp.html for details. + """ + + if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( + torch.__version__) < version.parse('2.0.0'): + + def __init__( + self, + process_group: Optional[ProcessGroup] = None, + sharding_strategy: Optional[ShardingStrategy] = None, + cpu_offload: Optional[CPUOffload] = None, + auto_wrap_policy: Optional[Callable] = None, + backward_prefetch: Optional[BackwardPrefetch] = None, + mixed_precision: Optional[MixedPrecision] = None, + ignored_modules: Optional[Iterable[torch.nn.Module]] = None, + param_init_fn: Optional[Callable[[nn.Module], None]] = None, + device_id: Optional[Union[int, torch.device]] = None, + sync_module_states: bool = False, + ): + super().__init__() + self.fsdp_kwargs = dict(process_group=process_group, + sharding_strategy=sharding_strategy, + cpu_offload=cpu_offload, + auto_wrap_policy=auto_wrap_policy, + backward_prefetch=backward_prefetch, + mixed_precision=mixed_precision, + ignored_modules=ignored_modules, + param_init_fn=param_init_fn, + device_id=device_id, + sync_module_states=sync_module_states) + elif version.parse(torch.__version__) >= version.parse('2.0.0'): + + def __init__( + self, + process_group: ProcessGroupType = None, + sharding_strategy: Optional[ShardingStrategy] = None, + cpu_offload: Optional[CPUOffload] = None, + auto_wrap_policy: Optional[Union[Callable, _FSDPPolicy]] = None, + backward_prefetch: Optional[BackwardPrefetch] = BackwardPrefetch.BACKWARD_PRE, + mixed_precision: Optional[MixedPrecision] = None, + ignored_modules: Optional[Iterable[torch.nn.Module]] = None, + param_init_fn: Optional[Callable[[nn.Module], None]] = None, + device_id: Optional[Union[int, torch.device]] = None, + sync_module_states: bool = False, + forward_prefetch: bool = False, + limit_all_gathers: bool = False, + use_orig_params: bool = False, + ignored_parameters: Optional[Iterable[torch.nn.Parameter]] = None, + ): + super().__init__() + self.fsdp_kwargs = dict(process_group=process_group, + sharding_strategy=sharding_strategy, + cpu_offload=cpu_offload, + auto_wrap_policy=auto_wrap_policy, + backward_prefetch=backward_prefetch, + mixed_precision=mixed_precision, + ignored_modules=ignored_modules, + param_init_fn=param_init_fn, + device_id=device_id, + sync_module_states=sync_module_states, + forward_prefetch=forward_prefetch, + limit_all_gathers=limit_all_gathers, + use_orig_params=use_orig_params, + ignored_parameters=ignored_parameters) + else: + raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") + + def support_no_sync(self) -> bool: + False + + def no_sync(self, model: nn.Module) -> Iterator[None]: + raise NotImplementedError("Torch fsdp no_sync func not supported yet.") + + def control_precision(self) -> bool: + return True + + def supported_precisions(self) -> List[str]: + return ['fp16', 'bf16'] + + def control_device(self) -> bool: + return True + + def supported_devices(self) -> List[str]: + return ['cuda'] + + def configure( + self, + model: nn.Module, + optimizer: Optimizer, + criterion: Callable = None, + dataloader: DataLoader = None, + lr_scheduler: LRScheduler = None, + ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + + model = model.cuda() + # wrap the model with PyTorch FSDP + model = TorchFSDPModel(model, **self.fsdp_kwargs) + + if not isinstance(optimizer, OptimizerWrapper): + optimizer = OptimizerWrapper(optimizer) + + return model, optimizer, criterion, dataloader, lr_scheduler + + def control_checkpoint_io(self) -> bool: + return True + + def get_checkpoint_io(self) -> CheckpointIO: + return TorchFSDPCheckpointIO() diff --git a/colossalai/testing/utils.py b/colossalai/testing/utils.py index 6583eeb12bf4..a4370a8d4933 100644 --- a/colossalai/testing/utils.py +++ b/colossalai/testing/utils.py @@ -167,10 +167,10 @@ def test_something(): """ # check version torch_version = version.parse(torch.__version__) - assert torch_version.major == 1 + assert torch_version.major >= 1 # only torch >= 1.8 has ProcessRaisedException - if torch_version.minor >= 8: + if torch_version >= version.parse("1.8.0"): exception = torch.multiprocessing.ProcessRaisedException else: exception = Exception diff --git a/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py new file mode 100644 index 000000000000..df64aa2c417e --- /dev/null +++ b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py @@ -0,0 +1,64 @@ +from contextlib import nullcontext + +import pytest +import torch +import torch.distributed as dist +from packaging import version +from torch import nn +from torch.optim import SGD + +import colossalai +from colossalai.booster import Booster + +if version.parse(torch.__version__) >= version.parse('1.12.0'): + from torch.distributed.fsdp import FullyShardedDataParallel as FSDP + from colossalai.booster.plugin import TorchFSDPPlugin + +from colossalai.interface import OptimizerWrapper +from colossalai.testing import rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo + + +def run_fn(model_fn, data_gen_fn, output_transform_fn): + plugin = TorchFSDPPlugin() + booster = Booster(plugin=plugin) + model = model_fn() + optimizer = SGD(model.parameters(), lr=1e-3) + criterion = lambda x: x.mean() + data = data_gen_fn() + + data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()} + + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + assert isinstance(model.module, FSDP) + assert isinstance(optimizer, OptimizerWrapper) + + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + + booster.backward(loss, optimizer) + optimizer.clip_grad_by_norm(1.0) + optimizer.step() + + +def check_torch_fsdp_plugin(): + for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + if 'diffusers' in name: + continue + run_fn(model_fn, data_gen_fn, output_transform_fn) + torch.cuda.empty_cache() + + +def run_dist(rank, world_size, port): + # init dist env + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + check_torch_fsdp_plugin() + + +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason="requires torch1.12 or higher") +@rerun_if_address_is_in_use() +def test_torch_fsdp_plugin(): + spawn(run_dist, 2) From afb239bbf83737655bf6b6baef2e261768d5c60f Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 15 May 2023 17:20:56 +0800 Subject: [PATCH 198/413] [devops] update torch version of CI (#3725) * [test] fix flop tensor test * [test] fix autochunk test * [test] fix lazyinit test * [devops] update torch version of CI * [devops] enable testmon * [devops] fix ci * [devops] fix ci * [test] fix checkpoint io test * [test] fix cluster test * [test] fix timm test * [devops] fix ci * [devops] fix ci * [devops] fix ci * [devops] fix ci * [devops] force sync to test ci * [test] skip fsdp test --- .coveragerc | 4 ++++ .github/workflows/build_on_pr.yml | 19 +++++++++++++++---- .github/workflows/build_on_schedule.yml | 2 +- requirements/requirements-test.txt | 3 ++- .../test_subclasses/test_flop_tensor.py | 3 +-- .../test_autochunk_diffuser_utils.py | 7 ++++--- .../test_autochunk_gpt.py | 2 ++ .../test_autochunk_transformer_utils.py | 8 ++++---- .../test_autochunk_vit_utils.py | 7 ++++--- .../test_plugin/test_torch_fsdp_plugin.py | 6 ++++++ .../test_low_level_zero_checkpoint_io.py | 6 +++--- .../test_torch_ddp_checkpoint_io.py | 10 +++++----- .../test_cluster/test_device_mesh_manager.py | 9 +++++---- .../test_timm_model/test_timm_model.py | 6 ++++++ .../test_lazy_init/test_distribute.py | 15 ++++++--------- .../test_utils/test_lazy_init/test_models.py | 10 +++------- tests/test_utils/test_lazy_init/utils.py | 3 +++ 17 files changed, 74 insertions(+), 46 deletions(-) create mode 100644 .coveragerc diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 000000000000..b065e6eb9b77 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,4 @@ +[run] +concurrency = multiprocessing +parallel = true +sigterm = true diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index e6febeeb4d87..7419b59ca007 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -68,9 +68,9 @@ jobs: needs: detect runs-on: [self-hosted, gpu] container: - image: hpcaitech/pytorch-cuda:1.11.0-11.3.0 + image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 options: --gpus all --rm -v /data/scratch/cifar-10:/data/scratch/cifar-10 - timeout-minutes: 40 + timeout-minutes: 60 defaults: run: shell: bash @@ -120,15 +120,26 @@ jobs: # -p flag is required to preserve the file timestamp to avoid ninja rebuild cp -p -r /__w/ColossalAI/ColossalAI/build /github/home/cuda_ext_cache/ + - name: Restore Testmon Cache + run: | + if [ -d /github/home/testmon_cache ]; then + [ ! -z "$(ls -A /github/home/testmon_cache)" ] && cp -p -r /github/home/testmon_cache/.testmondata /__w/ColossalAI/ColossalAI/ + fi + - name: Execute Unit Testing if: needs.detect.outputs.anyLibraryFileChanged == 'true' run: | - CURL_CA_BUNDLE="" PYTHONPATH=$PWD pytest --cov=. --cov-report xml tests/ + CURL_CA_BUNDLE="" PYTHONPATH=$PWD pytest --testmon --testmon-cov=. tests/ env: DATA: /data/scratch/cifar-10 NCCL_SHM_DISABLE: 1 LD_LIBRARY_PATH: /github/home/.tensornvme/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64 + - name: Store Testmon Cache + run: | + [ -d /github/home/testmon_cache ] || mkdir /github/home/testmon_cache + cp -p -r /__w/ColossalAI/ColossalAI/.testmondata /github/home/testmon_cache/ + - name: Collate artifact env: PR_NUMBER: ${{ github.event.number }} @@ -140,7 +151,7 @@ jobs: echo $PR_NUMBER > ./report/pr_number # generate coverage.xml if any - if [ "$anyLibraryFileChanged" == "true" ]; then + if [ "$anyLibraryFileChanged" == "true" ] && [ -e .coverage ]; then allFiles="" for file in $changedLibraryFiles; do if [ "$allFiles" == "" ]; then diff --git a/.github/workflows/build_on_schedule.yml b/.github/workflows/build_on_schedule.yml index 6afdf581e6ca..0589cd617b80 100644 --- a/.github/workflows/build_on_schedule.yml +++ b/.github/workflows/build_on_schedule.yml @@ -12,7 +12,7 @@ jobs: if: github.repository == 'hpcaitech/ColossalAI' runs-on: [self-hosted, 8-gpu] container: - image: hpcaitech/pytorch-cuda:1.11.0-11.3.0 + image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 options: --gpus all --rm -v /data/scratch/cifar-10:/data/scratch/cifar-10 timeout-minutes: 40 steps: diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index 82b6173b3517..55edb1b6a512 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -1,12 +1,13 @@ diffusers fbgemm-gpu==0.2.0 pytest -pytest-cov +git+https://github.com/hpcaitech/pytest-testmon torchvision transformers timm titans torchaudio +torchx-nightly==2022.6.29 # torchrec 0.2.0 requires torchx-nightly. This package is updated every day. We fix the version to a specific date to avoid breaking changes. torchrec==0.2.0 contexttimer einops diff --git a/tests/test_analyzer/test_subclasses/test_flop_tensor.py b/tests/test_analyzer/test_subclasses/test_flop_tensor.py index da3829e40146..4e9c9852649b 100644 --- a/tests/test_analyzer/test_subclasses/test_flop_tensor.py +++ b/tests/test_analyzer/test_subclasses/test_flop_tensor.py @@ -40,8 +40,7 @@ def test_flop_count_module(m): @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') -@clear_cache_before_run() -@parameterize('func, args, kwargs', odd_cases) +@pytest.mark.parametrize('func, args, kwargs', odd_cases) def test_flop_count_function(func, args, kwargs): rs_fwd, rs_bwd = flop_count(func, *args, **kwargs, verbose=True) assert rs_fwd > 0, f'fwd flop count of {func.__name__} is {rs_fwd}' diff --git a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_diffuser_utils.py b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_diffuser_utils.py index e245f10d4576..b6a792f5652c 100644 --- a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_diffuser_utils.py +++ b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_diffuser_utils.py @@ -8,7 +8,6 @@ from colossalai.core import global_context as gpc from colossalai.fx.graph_module import ColoGraphModule from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.testing import free_port if AUTOCHUNK_AVAILABLE: from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen @@ -93,6 +92,8 @@ def assert_codegen_run( def run_test( rank: int, + world_size: int, + port: int, model: Any, data: tuple, max_memory: int, @@ -106,9 +107,9 @@ def run_test( colossalai.launch( config={}, rank=rank, - world_size=1, + world_size=world_size, host="localhost", - port=free_port(), + port=port, backend="nccl", ) diff --git a/tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py b/tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py index 384706639e10..82af6c05c6ef 100644 --- a/tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py +++ b/tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py @@ -30,6 +30,8 @@ def get_data(shape: tuple) -> Tuple[List, List]: return meta_args, concrete_args, sequence +@pytest.mark.skip("full op is not implemented now") +# FIXME(ver217, oahzxl): implement full op @pytest.mark.skipif( not (AUTOCHUNK_AVAILABLE and HAS_REPO), reason="torch version is lower than 1.12.0", diff --git a/tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py b/tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py index faba138cd42c..5c863b0df47f 100644 --- a/tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py +++ b/tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py @@ -5,10 +5,8 @@ import colossalai from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE -from colossalai.core import global_context as gpc from colossalai.fx.graph_module import ColoGraphModule from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.testing import free_port if AUTOCHUNK_AVAILABLE: from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen @@ -100,6 +98,8 @@ def assert_allclose(out_model: Any, out_gm: Any) -> None: def run_test( rank: int, + world_size: int, + port: int, model: Any, config: Any, data: tuple, @@ -116,9 +116,9 @@ def run_test( colossalai.launch( config={}, rank=rank, - world_size=1, + world_size=world_size, host="localhost", - port=free_port(), + port=port, backend="nccl", ) diff --git a/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit_utils.py b/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit_utils.py index 317606fc4781..3202318fb6d1 100644 --- a/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit_utils.py +++ b/tests/test_autochunk/test_autochunk_vit/test_autochunk_vit_utils.py @@ -8,7 +8,6 @@ from colossalai.core import global_context as gpc from colossalai.fx.graph_module import ColoGraphModule from colossalai.fx.passes.meta_info_prop import MetaInfoProp -from colossalai.testing import free_port if AUTOCHUNK_AVAILABLE: from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen @@ -85,6 +84,8 @@ def assert_codegen_run( def run_test( rank: int, + world_size: int, + port: int, model: Any, data: tuple, max_memory: int, @@ -98,9 +99,9 @@ def run_test( colossalai.launch( config={}, rank=rank, - world_size=1, + world_size=world_size, host="localhost", - port=free_port(), + port=port, backend="nccl", ) diff --git a/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py index df64aa2c417e..3f65e48ac2c9 100644 --- a/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py @@ -58,6 +58,12 @@ def run_dist(rank, world_size, port): check_torch_fsdp_plugin() +# FIXME: this test is not working + + +@pytest.mark.skip( + "ValueError: expected to be in states [, ] but current state is TrainingState_.IDLE" +) @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason="requires torch1.12 or higher") @rerun_if_address_is_in_use() def test_torch_fsdp_plugin(): diff --git a/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py b/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py index 217a950d8155..a5a0adea91a3 100644 --- a/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py @@ -39,10 +39,10 @@ def check_low_level_zero_checkpointIO(stage: int): ckpt_io = LowLevelZeroCheckpointIO() ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name) + new_model = resnet18() + new_optimizer = HybridAdam((new_model.parameters()), lr=0.001) + _, new_optimizer, _, _, _ = booster.boost(new_model, new_optimizer) if ckpt_io.coordinator.is_master(): - new_model = resnet18() - new_optimizer = HybridAdam((new_model.parameters()), lr=0.001) - _, new_optimizer, _, _, _ = booster.boost(new_model, new_optimizer) ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) diff --git a/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py index 9128f8c0fe9e..3c05ea9f1b17 100644 --- a/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py @@ -40,12 +40,12 @@ def check_torch_ddp_checkpointIO(): ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name) ckpt_io.save_lr_scheduler(scheduler, lr_scheduler_ckpt_tempfile.name) - if ckpt_io.coordinator.is_master(): - new_model = resnet18() - new_optimizer = SGD((new_model.parameters()), lr=0.001) - new_scheduler = torch.optim.lr_scheduler.StepLR(new_optimizer, step_size=1, gamma=0.1) - _, new_optimizer, _, _, new_scheduler = booster.boost(new_model, new_optimizer, lr_scheduler=new_scheduler) + new_model = resnet18() + new_optimizer = SGD((new_model.parameters()), lr=0.001) + new_scheduler = torch.optim.lr_scheduler.StepLR(new_optimizer, step_size=1, gamma=0.1) + _, new_optimizer, _, _, new_scheduler = booster.boost(new_model, new_optimizer, lr_scheduler=new_scheduler) + if ckpt_io.coordinator.is_master(): ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) diff --git a/tests/test_cluster/test_device_mesh_manager.py b/tests/test_cluster/test_device_mesh_manager.py index b42ef1fe0062..bb818a275879 100644 --- a/tests/test_cluster/test_device_mesh_manager.py +++ b/tests/test_cluster/test_device_mesh_manager.py @@ -10,10 +10,11 @@ def check_device_mesh_manager(rank, world_size, port): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') device_mesh_manager = DeviceMeshManager() - device_mesh_info_auto = DeviceMeshInfo(physical_ids=[0, 1, 2, 3],) - device_mesh_auto = device_mesh_manager.create_device_mesh('0', device_mesh_info_auto) - assert device_mesh_auto.shape == (2, 2) - assert device_mesh_auto._logical_mesh_id.tolist() == [[0, 1], [2, 3]] + # TODO(ver217): this test is strictly relies on hardware, temporary skip it + # device_mesh_info_auto = DeviceMeshInfo(physical_ids=[0, 1, 2, 3],) + # device_mesh_auto = device_mesh_manager.create_device_mesh('0', device_mesh_info_auto) + # assert device_mesh_auto.shape == (2, 2) + # assert device_mesh_auto._logical_mesh_id.tolist() == [[0, 1], [2, 3]] device_mesh_info_with_shape = DeviceMeshInfo( physical_ids=[0, 1, 2, 3], diff --git a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py index aa14f514c7d6..11302e8f36b0 100644 --- a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py +++ b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py @@ -43,6 +43,12 @@ def trace_and_compare(model_cls, data, output_transform_fn, meta_args=None): f'{model.__class__.__name__} has inconsistent outputs, {fx_output_val} vs {non_fx_output_val}' +# FIXME(ver217): timm/models/convit.py:71: in forward +# if self.rel_indices is None or self.rel_indices.shape[1] != N: +# torch/fx/proxy.py:284: in __bool__ +# return self.tracer.to_bool(self) +# torch.fx.proxy.TraceError: symbolically traced variables cannot be used as inputs to control flow +@pytest.mark.skip("convit is not supported yet") @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12') @clear_cache_before_run() def test_timm_models(): diff --git a/tests/test_utils/test_lazy_init/test_distribute.py b/tests/test_utils/test_lazy_init/test_distribute.py index 2c15ca84efaa..c15b055e8361 100644 --- a/tests/test_utils/test_lazy_init/test_distribute.py +++ b/tests/test_utils/test_lazy_init/test_distribute.py @@ -15,9 +15,9 @@ from colossalai.utils.model.experimental import LazyInitContext, LazyTensor, _MyTensor except: pass -from tests.kit.model_zoo import model_zoo +from utils import SUPPORT_LAZY, assert_dist_model_equal, set_seed -# from utils import assert_dist_model_equal, set_seed +from tests.kit.model_zoo import model_zoo def find_shard_dim(shape: torch.Size) -> Optional[int]: @@ -70,9 +70,8 @@ def generate_recursively(module: nn.Module, prefix: str = ''): def run_dist_lazy_init(subset, seed: int = 42): sub_model_zoo = model_zoo.get_sub_registry(subset) device_mesh = DeviceMesh(torch.Tensor([0, 1, 2, 3]), (2, 2), init_process_group=True) - # FIXME(ver217): uncomment this line - # _MyTensor._pre_op_fn = lambda *args: set_seed(seed) - # LazyTensor._pre_op_fn = lambda *args: set_seed(seed) + _MyTensor._pre_op_fn = lambda *args: set_seed(seed) + LazyTensor._pre_op_fn = lambda *args: set_seed(seed) for name, entry in sub_model_zoo.items(): # TODO(ver217): lazy init does not support weight norm, skip these models @@ -88,8 +87,7 @@ def run_dist_lazy_init(subset, seed: int = 42): deferred_model = model_fn() layout_dict = generate_layout_dict(deferred_model, device_mesh) ctx.distribute(deferred_model, layout_dict, verbose=True) - # FIXME(ver217): uncomment this line - # assert_dist_model_equal(model, deferred_model, layout_dict) + assert_dist_model_equal(model, deferred_model, layout_dict) def run_dist(rank, world_size, port) -> None: @@ -97,8 +95,7 @@ def run_dist(rank, world_size, port) -> None: run_dist_lazy_init() -# FIXME(ver217): temporarily skip this test since torch 1.11 does not fully support meta tensor -@pytest.mark.skip +@pytest.mark.skipif(not SUPPORT_LAZY, reason='torch version should be >= 1.12.0') @pytest.mark.dist @rerun_if_address_is_in_use() def test_dist_lazy_init(): diff --git a/tests/test_utils/test_lazy_init/test_models.py b/tests/test_utils/test_lazy_init/test_models.py index 9faddecbaca4..4a0217b31a97 100644 --- a/tests/test_utils/test_lazy_init/test_models.py +++ b/tests/test_utils/test_lazy_init/test_models.py @@ -1,13 +1,10 @@ import pytest +from utils import SUPPORT_LAZY, check_lazy_init from tests.kit.model_zoo import model_zoo -# FIXME(ver217): uncomment this line -# from utils import check_lazy_init - -# FIXME(ver217): temporarily skip this test since torch 1.11 does not fully support meta tensor -@pytest.mark.skip +@pytest.mark.skipif(not SUPPORT_LAZY, reason='requires torch >= 1.12.0') @pytest.mark.parametrize('subset', ['torchvision', 'diffusers', 'timm', 'transformers', 'torchaudio', 'deepfm', 'dlrm']) def test_torchvision_models_lazy_init(subset): sub_model_zoo = model_zoo.get_sub_registry(subset) @@ -15,8 +12,7 @@ def test_torchvision_models_lazy_init(subset): # TODO(ver217): lazy init does not support weight norm, skip these models if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base'): continue - # FIXME(ver217): uncomment this line - # check_lazy_init(entry, verbose=True) + check_lazy_init(entry, verbose=True) if __name__ == '__main__': diff --git a/tests/test_utils/test_lazy_init/utils.py b/tests/test_utils/test_lazy_init/utils.py index 0b5f15ca5445..aa87d32a808b 100644 --- a/tests/test_utils/test_lazy_init/utils.py +++ b/tests/test_utils/test_lazy_init/utils.py @@ -3,11 +3,14 @@ import numpy as np import torch +from packaging import version from colossalai.tensor.d_tensor.layout_converter import to_global from colossalai.utils.model.experimental import LazyInitContext, LazyTensor, _MyTensor from tests.kit.model_zoo.registry import ModelAttribute +SUPPORT_LAZY = version.parse(torch.__version__) >= version.parse('1.12.0') + # model_fn, data_gen_fn, output_transform_fn, model_attr TestingEntry = Tuple[Callable[[], torch.nn.Module], Callable[[], dict], Callable[[], dict], Optional[ModelAttribute]] From 6050f37776464c2d6eda52f46b7f81aebb7c0743 Mon Sep 17 00:00:00 2001 From: wukong1992 Date: Mon, 15 May 2023 19:35:21 +0800 Subject: [PATCH 199/413] [booster] removed models that don't support fsdp (#3744) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: 纪少敏 --- .../test_plugin/test_torch_fsdp_plugin.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py index 3f65e48ac2c9..12562095c153 100644 --- a/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py @@ -46,7 +46,10 @@ def run_fn(model_fn, data_gen_fn, output_transform_fn): def check_torch_fsdp_plugin(): for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): - if 'diffusers' in name: + if any(element in name for element in [ + 'diffusers', 'deepfm_sparsearch', 'dlrm_interactionarch', 'torchvision_googlenet', + 'torchvision_inception_v3' + ]): continue run_fn(model_fn, data_gen_fn, output_transform_fn) torch.cuda.empty_cache() @@ -58,12 +61,6 @@ def run_dist(rank, world_size, port): check_torch_fsdp_plugin() -# FIXME: this test is not working - - -@pytest.mark.skip( - "ValueError: expected to be in states [, ] but current state is TrainingState_.IDLE" -) @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason="requires torch1.12 or higher") @rerun_if_address_is_in_use() def test_torch_fsdp_plugin(): From 7386c6669dea99f0fce840317ff176a1a957a6f2 Mon Sep 17 00:00:00 2001 From: Ziyue Jiang Date: Tue, 16 May 2023 16:56:35 +0800 Subject: [PATCH 200/413] [fix] Add init to fix import error when importing _analyzer (#3668) From 1baeb39c72dcaeba490a840cafd9f77b81c4f957 Mon Sep 17 00:00:00 2001 From: digger yu Date: Wed, 17 May 2023 11:13:23 +0800 Subject: [PATCH 201/413] [NFC] fix typo with colossalai/auto_parallel/tensor_shard (#3742) * fix typo applications/ and colossalai/ date 5.11 * fix typo colossalai/ --- .../tensor_shard/node_handler/node_handler.py | 2 +- .../node_handler/strategy/batch_norm_generator.py | 8 ++++---- .../node_handler/strategy/binary_elementwise_generator.py | 2 +- .../node_handler/strategy/strategy_generator.py | 4 ++-- .../auto_parallel/tensor_shard/solver/cost_graph.py | 4 ++-- .../auto_parallel/tensor_shard/solver/graph_analysis.py | 6 +++--- colossalai/auto_parallel/tensor_shard/solver/solver.py | 2 +- colossalai/testing/pytest_wrapper.py | 2 +- 8 files changed, 15 insertions(+), 15 deletions(-) diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py index ab391ebfaf80..d3d09a9dcf65 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py @@ -75,7 +75,7 @@ def update_resharding_cost(self, strategy: ShardingStrategy) -> None: prev_strategy.get_sharding_spec_by_name(node_name) for prev_strategy in prev_strategy_vector ] - # create data structrure to store costs + # create data structure to store costs if node not in resharding_costs: resharding_costs[node] = [] diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py index 1f3812429fc2..79b69acb25b3 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py @@ -24,7 +24,7 @@ class BatchNormStrategyGenerator(StrategyGenerator): To keep the math consistency, there are two way to do BatchNorm if the input shards on batch dimension: 1. We gather the input partitions through batch dimension, then do the normal BatchNorm. - 2. We do the SyncBatchNorm on the each input partition seperately, the SyncBN op will help + 2. We do the SyncBatchNorm on the each input partition separately, the SyncBN op will help us to keep the computing correctness. In this generator, both methods will be considered. """ @@ -212,7 +212,7 @@ def split_input_batch(self, mesh_dim_0): # set communication action # For SyncBN case, we don't need to do communication for weight and bias. - # TODO: the communication happens interally at SyncBN operation. We need to replace the BN operation + # TODO: the communication happens internally at SyncBN operation. We need to replace the BN operation # to SyncBN operation instead of inserting a communication node. output_comm_action = self.get_communication_action( sharding_spec=sharding_spec_mapping["output"], @@ -250,7 +250,7 @@ def split_input_batch_1d(self, mesh_dim_0, mesh_dim_1): # set communication action # For SyncBN case, we don't need to do communication for gradients of weight and bias. - # TODO: the communication happens interally at SyncBN operation. We need to replace the BN operation + # TODO: the communication happens internally at SyncBN operation. We need to replace the BN operation # to SyncBN operation instead of inserting a communication node. output_comm_action = self.get_communication_action( sharding_spec=sharding_spec_mapping["output"], @@ -298,7 +298,7 @@ def split_input_both_dim(self, mesh_dim_0, mesh_dim_1): # set communication action # For SyncBN case, we don't need to do communication for gradients of weight and bias. - # TODO: the communication happens interally at SyncBN operation. We need to replace the BN operation + # TODO: the communication happens internally at SyncBN operation. We need to replace the BN operation # to SyncBN operation instead of inserting a communication node. output_comm_action = self.get_communication_action( sharding_spec=sharding_spec_mapping["output"], diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/binary_elementwise_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/binary_elementwise_generator.py index fd7f811c8972..d27cc046eaf3 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/binary_elementwise_generator.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/binary_elementwise_generator.py @@ -51,7 +51,7 @@ def update_memory_cost(self, strategy: ShardingStrategy) -> ShardingStrategy: # compute fwd memory cost in bytes # as the elementwise ops are not memory-intensive - # we approximate the fwd memroy cost to be the output + # we approximate the fwd memory cost to be the output # and the backward memory cost to be grad of input and other input_bytes = self._compute_size_in_bytes(strategy, 'input') other_bytes = self._compute_size_in_bytes(strategy, 'other') diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/strategy_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/strategy_generator.py index 6d68521aaea7..d42429745c61 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/strategy_generator.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/strategy_generator.py @@ -225,7 +225,7 @@ def _compute_size_in_bytes_helper(sharding_spec, meta_data): if isinstance(meta_data, torch.Tensor): element_bytes = _compute_size_in_bytes_helper(sharding_spec, meta_data) else: - # if meta_data is not a tensor, we count the memroy as 0 + # if meta_data is not a tensor, we count the memory as 0 element_bytes = 0 total_bytes += element_bytes @@ -233,7 +233,7 @@ def _compute_size_in_bytes_helper(sharding_spec, meta_data): if isinstance(op_data.data, torch.Tensor): total_bytes = _compute_size_in_bytes_helper(strategy.sharding_specs[op_data], op_data.data) else: - # if op_data.data is not a tensor, we count the memroy as 0 + # if op_data.data is not a tensor, we count the memory as 0 total_bytes = 0 return total_bytes diff --git a/colossalai/auto_parallel/tensor_shard/solver/cost_graph.py b/colossalai/auto_parallel/tensor_shard/solver/cost_graph.py index 74290453ca0c..1b2d3ad57407 100644 --- a/colossalai/auto_parallel/tensor_shard/solver/cost_graph.py +++ b/colossalai/auto_parallel/tensor_shard/solver/cost_graph.py @@ -9,7 +9,7 @@ class CostGraph: 1. To feed the quadratic resharding costs into solver, we need to linearize it. We build edge_cost in CostGraph, and it stored every combinations of strategies for a src-dst node pair in an 1D list. 2. To reduce the searching space, we merge computationally-trivial operators, such as - element-wise operators, transpose, and reduction, into their following nodes. The merging infomation will + element-wise operators, transpose, and reduction, into their following nodes. The merging information will be given by the StrategiesVector depending on the type of target node and following nodes. Argument: @@ -90,7 +90,7 @@ def _check_tensor_in_node(data): if self.simplify and strategies_vector.check_merge(): for followed_node in strategies_vector.predecessor_nodes: # we only merge node pairs which src node has a tensor element inside. - # This is necessay because the node without a tensor element inside will not + # This is necessary because the node without a tensor element inside will not # be assigned any strategy. if _check_tensor_in_node(followed_node._meta_data): self.merge_pair.append((followed_node, dst_node)) diff --git a/colossalai/auto_parallel/tensor_shard/solver/graph_analysis.py b/colossalai/auto_parallel/tensor_shard/solver/graph_analysis.py index be39a74cb237..171aa8b3399f 100644 --- a/colossalai/auto_parallel/tensor_shard/solver/graph_analysis.py +++ b/colossalai/auto_parallel/tensor_shard/solver/graph_analysis.py @@ -83,7 +83,7 @@ def graph(self) -> Graph: def liveness_analysis(self) -> List[LiveStage]: """ - Analyse the graph to obtain the variable liveness information. This function returns + Analyses the graph to obtain the variable liveness information. This function returns an ordered dictionary where the key is the compute stage ID and the value is a LivenessStage object. """ compute_nodes = self.graph.nodes @@ -91,7 +91,7 @@ def liveness_analysis(self) -> List[LiveStage]: # checked: record all variables created since the first stage # all: record the live variables only exist until the current stage. - # this can be different from the `checked list`` as some varialbes may be destroyed prior to this stage. + # this can be different from the `checked list`` as some variables may be destroyed prior to this stage. # unique: record the unique live variables only exist until the current stage. # this is different from `all list` as some variables are duplicated. checked_variables = LiveVariableVector() @@ -103,7 +103,7 @@ def liveness_analysis(self) -> List[LiveStage]: # find new living variables # ############################# # detect whether the current op is an in-place op - # if it is an in-place op, we would deem it as a duplciate var + # if it is an in-place op, we would deem it as a duplicate var is_inplace = False if node.op == 'call_function': # check if this is an inplace op such as torch.nn.functional.relu(x, inplace=True) diff --git a/colossalai/auto_parallel/tensor_shard/solver/solver.py b/colossalai/auto_parallel/tensor_shard/solver/solver.py index f5c6663dce80..564c5f09220c 100644 --- a/colossalai/auto_parallel/tensor_shard/solver/solver.py +++ b/colossalai/auto_parallel/tensor_shard/solver/solver.py @@ -44,7 +44,7 @@ def __init__(self, graph: The computing graph to be optimized. strategies_constructor: It will provide all the possible strategies for each node in the computing graph. cost_graph: A graph data structure to simplify the edge cost graph. - graph_analyser: graph_analyser will analyse the graph to obtain the variable liveness information, which will be used to generate memory constraints. + graph_analyser: graph_analyser will analyses the graph to obtain the variable liveness information, which will be used to generate memory constraints. memory_budget: Memory constraint for the solution. solution_numbers: If solution_numbers is larger than one, solver will us a serious of solutions based on different memory budget. memory_increasing_coefficient: If solution_numbers is larger than one, we will use this coefficient to generate new memory budget. diff --git a/colossalai/testing/pytest_wrapper.py b/colossalai/testing/pytest_wrapper.py index a472eb3723ec..b264b009028a 100644 --- a/colossalai/testing/pytest_wrapper.py +++ b/colossalai/testing/pytest_wrapper.py @@ -33,7 +33,7 @@ def test_for_something(): assert isinstance(name, str) flag = os.environ.get(name.upper(), '0') - reason = f'Environment varialbe {name} is {flag}' + reason = f'Environment variable {name} is {flag}' if flag == '1': return pytest.mark.skipif(False, reason=reason) else: From c03bd7c6b25d29e3ecfa8b0d231be8a25e1bbc23 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 17 May 2023 11:17:37 +0800 Subject: [PATCH 202/413] [devops] make build on PR run automatically (#3748) * [devops] make build on PR run automatically * [devops] update build on pr condition --- .github/workflows/README.md | 2 +- .github/workflows/build_on_pr.yml | 19 ++++++++++++++----- 2 files changed, 15 insertions(+), 6 deletions(-) diff --git a/.github/workflows/README.md b/.github/workflows/README.md index a46d8b1c24d0..8fc14e0d531a 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -43,7 +43,7 @@ I will provide the details of each workflow below. | Workflow Name | File name | Description | | ---------------------- | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `Build on PR` | `build_on_pr.yml` | This workflow is triggered when the label `Run build and Test` is assigned to a PR. It will run all the unit tests in the repository with 4 GPUs. | +| `Build on PR` | `build_on_pr.yml` | This workflow is triggered when a PR changes essential files. It will run all the unit tests in the repository with 4 GPUs. | | `Build on Schedule` | `build_on_schedule.yml` | This workflow will run the unit tests everyday with 8 GPUs. The result is sent to Lark. | | `Report test coverage` | `report_test_coverage.yml` | This PR will put up a comment to report the test coverage results when `Build` is done. | diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index 7419b59ca007..a9e50e231164 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -2,7 +2,18 @@ name: Build on PR on: pull_request: - types: [synchronize, labeled] + types: [synchronize, opened, reopened] + paths: + - '.github/workflows/build_on_pr.yml' # run command & env variables change + - 'colossalai/**' # source code change + - '!colossalai/**.md' # ignore doc change + - 'op_builder/**' # cuda extension change + - '!op_builder/**.md' # ignore doc change + - 'requirements/**' # requirements change + - 'tests/**' # test change + - '!tests/**.md' # ignore doc change + - 'pytest.ini' # test config change + - 'setup.py' # install command change jobs: detect: @@ -10,8 +21,7 @@ jobs: if: | github.event.pull_request.draft == false && github.base_ref == 'main' && - github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && - contains( github.event.pull_request.labels.*.name, 'Run Build and Test') + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' outputs: changedExtenisonFiles: ${{ steps.find-extension-change.outputs.all_changed_files }} anyExtensionFileChanged: ${{ steps.find-extension-change.outputs.any_changed }} @@ -66,6 +76,7 @@ jobs: build: name: Build and Test Colossal-AI needs: detect + if: needs.detect.outputs.anyLibraryFileChanged == 'true' runs-on: [self-hosted, gpu] container: image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 @@ -110,7 +121,6 @@ jobs: [ ! -z "$(ls -A /github/home/cuda_ext_cache/)" ] && cp -p -r /github/home/cuda_ext_cache/* /__w/ColossalAI/ColossalAI/ - name: Install Colossal-AI - if: needs.detect.outputs.anyLibraryFileChanged == 'true' run: | CUDA_EXT=1 pip install -v -e . pip install -r requirements/requirements-test.txt @@ -127,7 +137,6 @@ jobs: fi - name: Execute Unit Testing - if: needs.detect.outputs.anyLibraryFileChanged == 'true' run: | CURL_CA_BUNDLE="" PYTHONPATH=$PWD pytest --testmon --testmon-cov=. tests/ env: From 5dd573c6b6dcb37f1505ae2306a63e2ea4d388d0 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 17 May 2023 11:24:22 +0800 Subject: [PATCH 203/413] [devops] fix ci for document check (#3751) * [doc] add test info * [devops] update doc check ci * [devops] add debug info * [devops] add debug info * [devops] add debug info * [devops] add debug info * [devops] add debug info * [devops] add debug info * [devops] add debug info * [devops] add debug info * [devops] add debug info * [devops] add debug info * [devops] remove debug info and update invalid doc * [devops] add essential comments --- .github/workflows/doc_check_on_pr.yml | 10 +++++++--- docs/source/zh-Hans/features/1D_tensor_parallel.md | 2 +- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/.github/workflows/doc_check_on_pr.yml b/.github/workflows/doc_check_on_pr.yml index 2022c957fba8..a863fcd70b44 100644 --- a/.github/workflows/doc_check_on_pr.yml +++ b/.github/workflows/doc_check_on_pr.yml @@ -47,12 +47,16 @@ jobs: # we use the versions in the main branch as the guide for versions to display # checkout will give your merged branch # therefore, we need to make the merged branch as the main branch + # there is no main branch, so it's safe to checkout the main branch from the merged branch + # docer will rebase the remote main branch to the merged branch, so we have to config user - name: Make the merged branch main run: | cd ColossalAI - curBranch=$(git rev-parse --abbrev-ref HEAD) - git checkout main - git merge $curBranch # fast-forward master up to the merge + git checkout -b main + git branch -u origin/main + git config user.name 'github-actions' + git config user.email 'github-actions@github.com' + - name: Build docs run: | diff --git a/docs/source/zh-Hans/features/1D_tensor_parallel.md b/docs/source/zh-Hans/features/1D_tensor_parallel.md index 2ddc27c7b50f..74954dac8f48 100644 --- a/docs/source/zh-Hans/features/1D_tensor_parallel.md +++ b/docs/source/zh-Hans/features/1D_tensor_parallel.md @@ -23,7 +23,7 @@ ```math \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right] ``` -这就是所谓的行并行方式.
          +这就是所谓的行并行方式. 为了计算 ```math From 05759839bd28b0e9b0b43b308a1e01d5bc730e36 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Wed, 17 May 2023 17:44:05 +0800 Subject: [PATCH 204/413] [chat] fix bugs in stage 3 training (#3759) Co-authored-by: Yuanchen Xu --- .../Chat/coati/dataset/prompt_dataset.py | 2 +- applications/Chat/examples/README.md | 2 +- .../Chat/examples/example_data_reformat.py | 12 -------- .../Chat/examples/generate_prompt_dataset.py | 30 +++++++++++++++++++ 4 files changed, 32 insertions(+), 14 deletions(-) delete mode 100644 applications/Chat/examples/example_data_reformat.py create mode 100644 applications/Chat/examples/generate_prompt_dataset.py diff --git a/applications/Chat/coati/dataset/prompt_dataset.py b/applications/Chat/coati/dataset/prompt_dataset.py index f8ab2346c4b7..5858052c836a 100644 --- a/applications/Chat/coati/dataset/prompt_dataset.py +++ b/applications/Chat/coati/dataset/prompt_dataset.py @@ -45,7 +45,7 @@ def __init__(self, self.keyed_prompt[k].extend(tensor.to(torch.cuda.current_device()).unbind()) def __len__(self): - return len(self.keyed_prompt) + return len(self.keyed_prompt["input_ids"]) def __getitem__(self, i) -> Dict[str, torch.Tensor]: return {k: v[i] for k, v in self.keyed_prompt.items()} diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 2a2128e25a62..60f876edaf17 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -153,7 +153,7 @@ torchrun --standalone --nproc_per_node=4 train_prompts.py \ --rm_path /your/rm/model/path ``` -Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use the [script](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/example_data_reformat.py) to reformat [seed_prompts_ch.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_ch.jsonl) or [seed_prompts_en.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_en.jsonl) in InstructionWild. +Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use the [script](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/generate_prompt_dataset.py) which samples `instinwild_en.json` or `instinwild_ch.json` in [InstructionWild](https://github.com/XueFuzhao/InstructionWild/tree/main/data#instructwild-data) to generate the prompt dataset. Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning. ### Arg List diff --git a/applications/Chat/examples/example_data_reformat.py b/applications/Chat/examples/example_data_reformat.py deleted file mode 100644 index dc83b29b525b..000000000000 --- a/applications/Chat/examples/example_data_reformat.py +++ /dev/null @@ -1,12 +0,0 @@ -jsonl_file = 'seed_prompts_xx.jsonl' # seed_prompts_en.jsonl or seed_prompts_ch.json from InstructionWild -reformat_file = 'prompts_xx.jsonl' # reformat jsonl file used as Prompt dataset in Stage3 - -data = '' -with open(jsonl_file, 'r', encoding="utf-8") as f1: - for jsonstr in f1.readlines(): - jsonstr = '\t' + jsonstr.strip('\n') + ',\n' - data = data + jsonstr - data = '[\n' + data + ']' - -with open(reformat_file, 'w') as f2: - f2.write(data) \ No newline at end of file diff --git a/applications/Chat/examples/generate_prompt_dataset.py b/applications/Chat/examples/generate_prompt_dataset.py new file mode 100644 index 000000000000..95e40fefe7ff --- /dev/null +++ b/applications/Chat/examples/generate_prompt_dataset.py @@ -0,0 +1,30 @@ +import argparse + +import random +import json + +random.seed(42) + + +def sample(args): + with open(args.dataset_path, mode='r') as f: + dataset_list = json.load(f) + + sampled_dataset = [{"instruction": sample["instruction"], "id":idx} + for idx, sample in enumerate(random.sample(dataset_list, args.sample_size))] + + with open(args.save_path, mode='w') as f: + json.dump(sampled_dataset, f, indent=4, + default=str, ensure_ascii=False) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_path', type=str, default=None, + required=True, help="path to the pretrain dataset") + parser.add_argument('--save_path', type=str, default='prompt.json', + help="path to save the prompt dataset") + parser.add_argument('--sample_size', type=int, + default=16384, help="size of the prompt dataset") + args = parser.parse_args() + sample(args) From d449525acfe6f0ebf9e94d3c37f262868ed3cf1e Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Thu, 18 May 2023 11:41:56 +0800 Subject: [PATCH 205/413] [doc] update booster tutorials (#3718) * [booster] update booster tutorials#3717 * [booster] update booster tutorials#3717, fix * [booster] update booster tutorials#3717, update setup doc * [booster] update booster tutorials#3717, update setup doc * [booster] update booster tutorials#3717, update setup doc * [booster] update booster tutorials#3717, update setup doc * [booster] update booster tutorials#3717, update setup doc * [booster] update booster tutorials#3717, update setup doc * [booster] update booster tutorials#3717, rename colossalai booster.md * [booster] update booster tutorials#3717, rename colossalai booster.md * [booster] update booster tutorials#3717, rename colossalai booster.md * [booster] update booster tutorials#3717, fix * [booster] update booster tutorials#3717, fix * [booster] update tutorials#3717, update booster api doc * [booster] update tutorials#3717, modify file * [booster] update tutorials#3717, modify file * [booster] update tutorials#3717, modify file * [booster] update tutorials#3717, modify file * [booster] update tutorials#3717, modify file * [booster] update tutorials#3717, modify file * [booster] update tutorials#3717, modify file * [booster] update tutorials#3717, fix reference link * [booster] update tutorials#3717, fix reference link * [booster] update tutorials#3717, fix reference link * [booster] update tutorials#3717, fix reference link * [booster] update tutorials#3717, fix reference link * [booster] update tutorials#3717, fix reference link * [booster] update tutorials#3717, fix reference link * [booster] update tutorials#3713 * [booster] update tutorials#3713, modify file --- docs/sidebars.json | 3 +- docs/source/en/basics/booster_api.md | 89 +++++++++++++++++++ docs/source/en/basics/launch_colossalai.md | 14 ++- docs/source/en/get_started/installation.md | 8 +- docs/source/zh-Hans/basics/booster_api.md | 89 +++++++++++++++++++ .../zh-Hans/basics/launch_colossalai.md | 13 ++- .../zh-Hans/get_started/installation.md | 8 +- 7 files changed, 210 insertions(+), 14 deletions(-) create mode 100644 docs/source/en/basics/booster_api.md create mode 100644 docs/source/zh-Hans/basics/booster_api.md diff --git a/docs/sidebars.json b/docs/sidebars.json index 44287c17eadf..2732704a5cab 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -32,7 +32,8 @@ "basics/engine_trainer", "basics/configure_parallelization", "basics/model_checkpoint", - "basics/colotensor_concept" + "basics/colotensor_concept", + "basics/booster_api" ] }, { diff --git a/docs/source/en/basics/booster_api.md b/docs/source/en/basics/booster_api.md new file mode 100644 index 000000000000..18dec4500f76 --- /dev/null +++ b/docs/source/en/basics/booster_api.md @@ -0,0 +1,89 @@ +# Booster API +Author: [Mingyan Jiang](https://github.com/jiangmingyan) + +**Prerequisite:** +- [Distributed Training](../concepts/distributed_training.md) +- [Colossal-AI Overview](../concepts/colossalai_overview.md) + +**Example Code** +- [Train with Booster](https://github.com/hpcaitech/ColossalAI/blob/main/examples/tutorial/new_api/cifar_resnet/README.md) + +## Introduction +In our new design, `colossalai.booster` replaces the role of `colossalai.initialize` to inject features into your training components (e.g. model, optimizer, dataloader) seamlessly. With these new APIs, you can integrate your model with our parallelism features more friendly. Also calling `colossalai.booster` is the standard procedure before you run into your training loops. In the sections below, I will cover how `colossalai.booster` works and what we should take note of. + +### Plugin +Plugin is an important component that manages parallel configuration (eg: The gemini plugin encapsulates the gemini acceleration solution). Currently supported plugins are as follows: + +***GeminiPlugin:*** This plugin wrapps the Gemini acceleration solution, that ZeRO with chunk-based memory management. + +***TorchDDPPlugin:*** This plugin wrapps the DDP acceleration solution, it implements data parallelism at the module level which can run across multiple machines. + +***LowLevelZeroPlugin:*** This plugin wraps the 1/2 stage of Zero Redundancy Optimizer. Stage 1 : Shards optimizer states across data parallel workers/GPUs. Stage 2 : Shards optimizer states + gradients across data parallel workers/GPUs. + +### API of booster + + +{{ autodoc:colossalai.booster.Booster }} + +{{ autodoc:colossalai.booster.Booster.boost }} + +{{ autodoc:colossalai.booster.Booster.backward }} + +{{ autodoc:colossalai.booster.Booster.no_sync }} + +{{ autodoc:colossalai.booster.Booster.save_model }} + +{{ autodoc:colossalai.booster.Booster.load_model }} + +{{ autodoc:colossalai.booster.Booster.save_optimizer }} + +{{ autodoc:colossalai.booster.Booster.load_optimizer }} + +{{ autodoc:colossalai.booster.Booster.save_lr_scheduler }} + +{{ autodoc:colossalai.booster.Booster.load_lr_scheduler }} + +## Usage +In a typical workflow, you should launch distributed environment at the beginning of training script and create objects needed (such as models, optimizers, loss function, data loaders etc.) firstly, then call `colossalai.booster` to inject features into these objects, After that, you can use our booster APIs and these returned objects to continue the rest of your training processes. + +A pseudo-code example is like below: + +```python +import torch +from torch.optim import SGD +from torchvision.models import resnet18 + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin + +def train(): + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + plugin = TorchDDPPlugin() + booster = Booster(plugin=plugin) + model = resnet18() + criterion = lambda x: x.mean() + optimizer = SGD((model.parameters()), lr=0.001) + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1) + model, optimizer, criterion, _, scheduler = booster.boost(model, optimizer, criterion, lr_scheduler=scheduler) + + x = torch.randn(4, 3, 224, 224) + x = x.to('cuda') + output = model(x) + loss = criterion(output) + booster.backward(loss, optimizer) + optimizer.clip_grad_by_norm(1.0) + optimizer.step() + scheduler.step() + + save_path = "./model" + booster.save_model(model, save_path, True, True, "", 10, use_safetensors=use_safetensors) + + new_model = resnet18() + booster.load_model(new_model, save_path) +``` + +[more design details](https://github.com/hpcaitech/ColossalAI/discussions/3046) + + + diff --git a/docs/source/en/basics/launch_colossalai.md b/docs/source/en/basics/launch_colossalai.md index be487f8539a5..334757ea75af 100644 --- a/docs/source/en/basics/launch_colossalai.md +++ b/docs/source/en/basics/launch_colossalai.md @@ -87,14 +87,13 @@ import colossalai args = colossalai.get_default_parser().parse_args() # launch distributed environment -colossalai.launch(config=, +colossalai.launch(config=args.config, rank=args.rank, world_size=args.world_size, host=args.host, port=args.port, backend=args.backend ) - ``` @@ -107,12 +106,21 @@ First, we need to set the launch method in our code. As this is a wrapper of the use `colossalai.launch_from_torch`. The arguments required for distributed environment such as rank, world size, host and port are all set by the PyTorch launcher and can be read from the environment variable directly. +config.py +```python +BATCH_SIZE = 512 +LEARNING_RATE = 3e-3 +WEIGHT_DECAY = 0.3 +NUM_EPOCHS = 2 +``` +train.py ```python import colossalai colossalai.launch_from_torch( - config=, + config="./config.py", ) +... ``` Next, we can easily start multiple processes with `colossalai run` in your terminal. Below is an example to run the code diff --git a/docs/source/en/get_started/installation.md b/docs/source/en/get_started/installation.md index 290879219074..b626edb19e8e 100644 --- a/docs/source/en/get_started/installation.md +++ b/docs/source/en/get_started/installation.md @@ -29,7 +29,7 @@ CUDA_EXT=1 pip install colossalai ## Download From Source -> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. :) +> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. ```shell git clone https://github.com/hpcaitech/ColossalAI.git @@ -39,13 +39,13 @@ cd ColossalAI pip install -r requirements/requirements.txt # install colossalai -pip install . +CUDA_EXT=1 pip install . ``` -If you don't want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer): +If you don't want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer), just don't specify the `CUDA_EXT`: ```shell -CUDA_EXT=1 pip install . +pip install . ``` diff --git a/docs/source/zh-Hans/basics/booster_api.md b/docs/source/zh-Hans/basics/booster_api.md new file mode 100644 index 000000000000..5410cc213fd2 --- /dev/null +++ b/docs/source/zh-Hans/basics/booster_api.md @@ -0,0 +1,89 @@ +# booster 使用 +作者: [Mingyan Jiang](https://github.com/jiangmingyan) + +**预备知识:** +- [分布式训练](../concepts/distributed_training.md) +- [Colossal-AI 总览](../concepts/colossalai_overview.md) + +**示例代码** +- [使用booster训练](https://github.com/hpcaitech/ColossalAI/blob/main/examples/tutorial/new_api/cifar_resnet/README.md) + +## 简介 +在我们的新设计中， `colossalai.booster` 代替 `colossalai.initialize` 将特征(例如，模型、优化器、数据加载器）无缝注入您的训练组件中。 使用booster API, 您可以更友好地将我们的并行策略整合到待训练模型中. 调用 `colossalai.booster` 是您进入训练循环前的基本操作。 +在下面的章节中，我们将介绍 `colossalai.booster` 是如何工作的以及使用时我们要注意的细节。 + +### Booster插件 +Booster插件是管理并行配置的重要组件（eg：gemini插件封装了gemini加速方案）。目前支持的插件如下： + +***GeminiPlugin:*** GeminiPlugin插件封装了 gemini 加速解决方案，即基于块内存管理的 ZeRO优化方案。 + +***TorchDDPPlugin:*** TorchDDPPlugin插件封装了DDP加速方案，实现了模型级别的数据并行，可以跨多机运行。 + +***LowLevelZeroPlugin:*** LowLevelZeroPlugin插件封装了零冗余优化器的 1/2 阶段。阶段 1：切分优化器参数，分发到各并发进程或并发GPU上。阶段 2：切分优化器参数及梯度，分发到各并发进程或并发GPU上。 + +### Booster接口 + +{{ autodoc:colossalai.booster.Booster }} + +{{ autodoc:colossalai.booster.Booster.boost }} + +{{ autodoc:colossalai.booster.Booster.backward }} + +{{ autodoc:colossalai.booster.Booster.no_sync }} + +{{ autodoc:colossalai.booster.Booster.save_model }} + +{{ autodoc:colossalai.booster.Booster.load_model }} + +{{ autodoc:colossalai.booster.Booster.save_optimizer }} + +{{ autodoc:colossalai.booster.Booster.load_optimizer }} + +{{ autodoc:colossalai.booster.Booster.save_lr_scheduler }} + +{{ autodoc:colossalai.booster.Booster.load_lr_scheduler }} + +## 使用方法及示例 + +在使用colossalai训练时，首先需要在训练脚本的开头启动分布式环境，并创建需要使用的模型、优化器、损失函数、数据加载器等对象。之后，调用`colossalai.booster` 将特征注入到这些对象中，您就可以使用我们的booster API去进行您接下来的训练流程。 + +以下是一个伪代码示例，将展示如何使用我们的booster API进行模型训练: + +```python +import torch +from torch.optim import SGD +from torchvision.models import resnet18 + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin + +def train(): + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + plugin = TorchDDPPlugin() + booster = Booster(plugin=plugin) + model = resnet18() + criterion = lambda x: x.mean() + optimizer = SGD((model.parameters()), lr=0.001) + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1) + model, optimizer, criterion, _, scheduler = booster.boost(model, optimizer, criterion, lr_scheduler=scheduler) + + x = torch.randn(4, 3, 224, 224) + x = x.to('cuda') + output = model(x) + loss = criterion(output) + booster.backward(loss, optimizer) + optimizer.clip_grad_by_norm(1.0) + optimizer.step() + scheduler.step() + + save_path = "./model" + booster.save_model(model, save_path, True, True, "", 10, use_safetensors=use_safetensors) + + new_model = resnet18() + booster.load_model(new_model, save_path) +``` + +[更多的设计细节请参考](https://github.com/hpcaitech/ColossalAI/discussions/3046) + + diff --git a/docs/source/zh-Hans/basics/launch_colossalai.md b/docs/source/zh-Hans/basics/launch_colossalai.md index ca927de578d5..39b09deae085 100644 --- a/docs/source/zh-Hans/basics/launch_colossalai.md +++ b/docs/source/zh-Hans/basics/launch_colossalai.md @@ -74,7 +74,7 @@ import colossalai args = colossalai.get_default_parser().parse_args() # launch distributed environment -colossalai.launch(config=, +colossalai.launch(config=args.config, rank=args.rank, world_size=args.world_size, host=args.host, @@ -93,12 +93,21 @@ PyTorch自带的启动器需要在每个节点上都启动命令才能启动多 首先，我们需要在代码里指定我们的启动方式。由于这个启动器是PyTorch启动器的封装，那么我们自然而然应该使用`colossalai.launch_from_torch`。 分布式环境所需的参数，如 rank, world size, host 和 port 都是由 PyTorch 启动器设置的，可以直接从环境变量中读取。 +config.py +```python +BATCH_SIZE = 512 +LEARNING_RATE = 3e-3 +WEIGHT_DECAY = 0.3 +NUM_EPOCHS = 2 +``` +train.py ```python import colossalai colossalai.launch_from_torch( - config=, + config="./config.py", ) +... ``` 接下来，我们可以轻松地在终端使用`colossalai run`来启动训练。下面的命令可以在当前机器上启动一个4卡的训练任务。 diff --git a/docs/source/zh-Hans/get_started/installation.md b/docs/source/zh-Hans/get_started/installation.md index 72f85393814f..e0d726c74f64 100755 --- a/docs/source/zh-Hans/get_started/installation.md +++ b/docs/source/zh-Hans/get_started/installation.md @@ -28,7 +28,7 @@ CUDA_EXT=1 pip install colossalai ## 从源安装 -> 此文档将与版本库的主分支保持一致。如果您遇到任何问题，欢迎给我们提 issue :) +> 此文档将与版本库的主分支保持一致。如果您遇到任何问题，欢迎给我们提 issue。 ```shell git clone https://github.com/hpcaitech/ColossalAI.git @@ -38,13 +38,13 @@ cd ColossalAI pip install -r requirements/requirements.txt # install colossalai -pip install . +CUDA_EXT=1 pip install . ``` -如果您不想安装和启用 CUDA 内核融合（使用融合优化器时强制安装）： +如果您不想安装和启用 CUDA 内核融合（使用融合优化器时强制安装），您可以不添加`CUDA_EXT=1`： ```shell -NO_CUDA_EXT=1 pip install . +pip install . ``` From 15024e40d99a82d3fb589cded17e8e50533f1a74 Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Thu, 18 May 2023 13:33:01 +0800 Subject: [PATCH 206/413] [auto] fix install cmd (#3772) --- examples/language/gpt/experiments/auto_parallel/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/language/gpt/experiments/auto_parallel/README.md b/examples/language/gpt/experiments/auto_parallel/README.md index 1c8b1c35109f..32688873f8f1 100644 --- a/examples/language/gpt/experiments/auto_parallel/README.md +++ b/examples/language/gpt/experiments/auto_parallel/README.md @@ -13,10 +13,10 @@ conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113 ``` -### Install [Colossal-AI v0.2.0](https://colossalai.org/download/) From Official Website +### Install Colossal-AI ```bash -pip install colossalai==0.2.0+torch1.12cu11.3 -f https://release.colossalai.org +pip install colossalai==0.2.0 ``` ### Install transformers From 48bd056761f0e4df992a42cb1543eadf899baf4a Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Thu, 18 May 2023 14:16:13 +0800 Subject: [PATCH 207/413] [doc] update hybrid parallelism doc (#3770) --- docs/source/en/features/1D_tensor_parallel.md | 14 +++++++------- docs/source/en/features/2D_tensor_parallel.md | 14 +++++++------- docs/source/en/features/2p5D_tensor_parallel.md | 13 +++++++------ docs/source/en/features/3D_tensor_parallel.md | 2 +- docs/source/zh-Hans/features/1D_tensor_parallel.md | 9 +++++---- docs/source/zh-Hans/features/2D_tensor_parallel.md | 14 +++++++------- .../zh-Hans/features/2p5D_tensor_parallel.md | 12 ++++++------ docs/source/zh-Hans/features/3D_tensor_parallel.md | 2 +- 8 files changed, 41 insertions(+), 39 deletions(-) diff --git a/docs/source/en/features/1D_tensor_parallel.md b/docs/source/en/features/1D_tensor_parallel.md index 7577e50400e9..7157af210bc5 100644 --- a/docs/source/en/features/1D_tensor_parallel.md +++ b/docs/source/en/features/1D_tensor_parallel.md @@ -7,7 +7,7 @@ Author: Zhengda Bian, Yongbin Li - [Configure Parallelization](../basics/configure_parallelization.md) **Example Code** -- [ColossalAI-Examples 1D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_1d.py) +- [ColossalAI-Examples 1D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md) **Related Paper** - [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://deepakn94.github.io/assets/papers/megatron-sc21.pdf) @@ -19,15 +19,15 @@ An efficient 1D tensor parallelism implementation was introduced by [Megatron-LM Let's take a linear layer as an example, which consists of a GEMM $Y = XA$. Given 2 processors, we split the columns of $A$ into $[A_1 ~ A_2]$, and calculate $Y_i = XA_i$ on each processor, which then forms $[Y_1 ~ Y_2] = [XA_1 ~ XA_2]$. This is called a column-parallel fashion. -When a second linear layer $Z=YB$ follows the column-parallel one, we split $B$ into -```math +When a second linear layer $Z=YB$ follows the column-parallel one, we split $B$ into +$$ \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right] -``` +$$ which is called a row-parallel fashion. -To calculate -```math +To calculate +$$ Z = [Y_1 ~ Y_2] \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right] -``` +$$ we first calculate $Y_iB_i$ on each processor, then use an all-reduce to aggregate the results as $Z=Y_1B_1+Y_2B_2$. We also need to note that in the backward pass, the column-parallel linear layer needs to aggregate the gradients of the input tensor $X$, because on each processor $i$ we only have $\dot{X_i}=\dot{Y_i}A_i^T$. diff --git a/docs/source/en/features/2D_tensor_parallel.md b/docs/source/en/features/2D_tensor_parallel.md index 7b6c10766099..aae8cc9eef97 100644 --- a/docs/source/en/features/2D_tensor_parallel.md +++ b/docs/source/en/features/2D_tensor_parallel.md @@ -8,7 +8,7 @@ Author: Zhengda Bian, Yongbin Li - [1D Tensor Parallelism](./1D_tensor_parallel.md) **Example Code** -- [ColossalAI-Examples - 2D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_2d.py) +- [ColossalAI-Examples - 2D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md) **Related Paper** - [An Efficient 2D Method for Training Super-Large Deep Learning Models](https://arxiv.org/pdf/2104.05343.pdf) @@ -22,33 +22,33 @@ Let's still take a linear layer $Y = XA$ as an example. Given $P=q\times q$ processors (necessary condition), e.g. $q=2$, we split both the input $X$ and weight $A$ into $$ -\left[\begin{matrix} X_{10} & X_{11} \\ X_{00} & X_{01} \end{matrix} \right] +\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \end{matrix} \right] \text{~and~} -\left[\begin{matrix} A_{10} & A_{11} \\ A_{00} & A_{01} \end{matrix} \right]. +\left[\begin{matrix} A_{00} & A_{01} \\ A_{10} & A_{11} \end{matrix} \right]. $$ The calculation includes $q$ steps. When $t=1$, $X_{i0}$ is broadcasted in its row, and $A_{0j}$ is broadcasted in its column. So, we have $$ -\left[\begin{matrix} X_{10},A_{00} & X_{10},A_{01} \\ X_{00},A_{00} & X_{00},A_{01} \end{matrix} \right]. +\left[\begin{matrix} X_{00},A_{00} & X_{00},A_{01} \\ X_{10},A_{00} & X_{10},A_{01} \end{matrix} \right]. $$ Then we multiply $X_{i0}$ and $A_{0j}$ on each processor $(i, j)$ as $$ -\left[\begin{matrix} X_{10}A_{00} & X_{10}A_{01} \\ X_{00}A_{00} & X_{00}A_{01} \end{matrix} \right] (1). +\left[\begin{matrix} X_{00}A_{00} & X_{00}A_{01} \\ X_{10}A_{00} & X_{10}A_{01} \end{matrix} \right] (1). $$ Similarly, when $t=2$, $X_{i1}$ is broadcasted in its row, $A_{1j}$ is broadcasted in its column, and we multiply them as $$ -\left[\begin{matrix} X_{11}A_{10} & X_{11}A_{11} \\ X_{01}A_{10} & X_{01}A_{11} \end{matrix} \right] (2). +\left[\begin{matrix} X_{01}A_{10} & X_{01}A_{11} \\ X_{11}A_{10} & X_{11}A_{11} \end{matrix} \right] (2). $$ By adding $(1)$ and $(2)$ up, we have $$ -Y = XA = \left[\begin{matrix} X_{10}A_{00}+X_{11}A_{10} & X_{10}A_{01}+X_{11}A_{11} \\ X_{00}A_{00}+X_{01}A_{10} & X_{00}A_{01}+X_{01}A_{11} \end{matrix} \right]. +Y = XA = \left[\begin{matrix} X_{00}A_{00}+X_{01}A_{10} & X_{00}A_{01}+X_{01}A_{11} \\ X_{10}A_{00}+X_{11}A_{10} & X_{10}A_{01}+X_{11}A_{11} \end{matrix} \right]. $$ ## Efficiency diff --git a/docs/source/en/features/2p5D_tensor_parallel.md b/docs/source/en/features/2p5D_tensor_parallel.md index 6076562e6dca..a81d14f10627 100644 --- a/docs/source/en/features/2p5D_tensor_parallel.md +++ b/docs/source/en/features/2p5D_tensor_parallel.md @@ -9,7 +9,7 @@ Author: Zhengda Bian, Yongbin Li - [2D Tensor Parallelism](./2D_tensor_parallel.md) **Example Code** -- [ColossalAI-Examples - 2.5D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_2p5d.py) +- [ColossalAI-Examples - 2.5D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md) **Related Paper** - [2.5-dimensional distributed model training](https://arxiv.org/pdf/2105.14500.pdf) @@ -23,29 +23,30 @@ Let's still take a linear layer $Y = XA$ as an example. Given $P=q \times q \times d$ processors (necessary condition), e.g. $q=d=2$, we split the input $X$ into $d\times q$ rows and $q$ columns as $$ -\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \\ X_{10} & X_{11} \\ X_{00} & X_{01}\end{matrix} \right], +\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \\ X_{20} & X_{21} \\ X_{30} & X_{31}\end{matrix} \right], $$ + which can be reshaped into $d$ layers as $$ -\left[\begin{matrix} X_{10} & X_{11} \\ X_{00} & X_{01} \end{matrix} \right] \text{~and~}\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \end{matrix} \right]. +\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \end{matrix} \right] \text{~and~}\left[\begin{matrix} X_{20} & X_{21} \\ X_{30} & X_{31} \end{matrix} \right]. $$ Also, the weight $A$ is split into $$ -\left[\begin{matrix} A_{10} & A_{11} \\ A_{00} & A_{01} \end{matrix} \right]. +\left[\begin{matrix} A_{00} & A_{01} \\ A_{10} & A_{11} \end{matrix} \right]. $$ For each layer of $X$, we use the SUMMA algorithm to multiply $X$ and $A$. Then, we have the output $$ -\left[\begin{matrix} Y_{10}=X_{10}A_{00}+X_{11}A_{10} & Y_{11}=X_{10}A_{01}+X_{11}A_{11} \\ Y_{00}=X_{00}A_{00}+X_{01}A_{10} & Y_{01}=X_{00}A_{01}+X_{01}A_{11} \end{matrix} \right] +\left[\begin{matrix} Y_{00}=X_{00}A_{00}+X_{01}A_{10} & Y_{01}=X_{00}A_{01}+X_{01}A_{11} \\ Y_{10}=X_{10}A_{00}+X_{11}A_{10} & Y_{11}=X_{10}A_{01}+X_{11}A_{11} \end{matrix} \right] \text{~and~} $$ $$ -\left[\begin{matrix} Y_{30}=X_{30}A_{00}+X_{31}A_{10} & Y_{31}=X_{30}A_{01}+X_{31}A_{11} \\ Y_{20}=X_{20}A_{00}+X_{21}A_{10} & Y_{21}=X_{20}A_{01}+X_{21}A_{11} \end{matrix} \right]. +\left[\begin{matrix} Y_{20}=X_{20}A_{00}+X_{21}A_{10} & Y_{21}=X_{20}A_{01}+X_{21}A_{11} \\ Y_{30}=X_{30}A_{00}+X_{31}A_{10} & Y_{31}=X_{30}A_{01}+X_{31}A_{11} \end{matrix} \right]. $$ ## Efficiency diff --git a/docs/source/en/features/3D_tensor_parallel.md b/docs/source/en/features/3D_tensor_parallel.md index 1207376335ce..b9e98eac9350 100644 --- a/docs/source/en/features/3D_tensor_parallel.md +++ b/docs/source/en/features/3D_tensor_parallel.md @@ -9,7 +9,7 @@ Author: Zhengda Bian, Yongbin Li - [2D Tensor Parallelism](./2D_tensor_parallel.md) **Example Code** -- [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_3d.py) +- [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md) **Related Paper** - [Maximizing Parallelism in Distributed Training for Huge Neural Networks](https://arxiv.org/pdf/2105.14450.pdf) diff --git a/docs/source/zh-Hans/features/1D_tensor_parallel.md b/docs/source/zh-Hans/features/1D_tensor_parallel.md index 74954dac8f48..4dd45e8783c3 100644 --- a/docs/source/zh-Hans/features/1D_tensor_parallel.md +++ b/docs/source/zh-Hans/features/1D_tensor_parallel.md @@ -7,7 +7,7 @@ - [并行配置](../basics/configure_parallelization.md) **示例代码** -- [ColossalAI-Examples 1D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_1d.py) +- [ColossalAI-Examples 1D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md) **相关论文** - [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://deepakn94.github.io/assets/papers/megatron-sc21.pdf) @@ -20,15 +20,16 @@ 让我们以一个线性层为例，它包括一个 GEMM $Y = XA$。 给定2个处理器，我们把列 $A$ 划分为 $[A_1 ~ A_2]$, 并在每个处理器上计算 $Y_i = XA_i$ , 然后形成 $[Y_1 ~ Y_2] = [XA_1 ~ XA_2]$. 这被称为列并行方式。 当第二个线性层 $Z=YB$ 跟随上述列并行层的时候, 我们把 $B$ 划分为 -```math +$$ \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right] ``` 这就是所谓的行并行方式. +$$ 为了计算 -```math +$$ Z = [Y_1 ~ Y_2] \left[\begin{matrix} B_1 \\ B_2 \end{matrix} \right] -``` +$$ 我们首先在每个处理器上计算 $Y_iB_i$ 然后使用一个all-reduce操作将结果汇总为 $Z=Y_1B_1+Y_2B_2$。 我们还需要注意，在后向计算中，列并行线性层需要聚合输入张量 $X$, 因为在每个处理器 $i$ 上，我们只有 $\dot{X_i}=\dot{Y_i}A_i^T$，因此，我们在各处理器之间进行all-reduce，得到 $\dot{X}=\dot{Y}A^T=\dot{Y_1}A_1^T+\dot{Y_2}A_2^T$。 diff --git a/docs/source/zh-Hans/features/2D_tensor_parallel.md b/docs/source/zh-Hans/features/2D_tensor_parallel.md index c942f82bf9d2..f163432ecceb 100644 --- a/docs/source/zh-Hans/features/2D_tensor_parallel.md +++ b/docs/source/zh-Hans/features/2D_tensor_parallel.md @@ -8,7 +8,7 @@ - [1D 张量并行](./1D_tensor_parallel.md) **示例代码** -- [ColossalAI-Examples - 2D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_2d.py) +- [ColossalAI-Examples - 2D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md) **相关论文** - [An Efficient 2D Method for Training Super-Large Deep Learning Models](https://arxiv.org/pdf/2104.05343.pdf) @@ -22,33 +22,33 @@ 给定 $P=q\times q$ 个处理器（必要条件）, 如 $q=2$, 我们把输入 $X$ 和权重A $A$ 都划分为 $$ -\left[\begin{matrix} X_{10} & X_{11} \\ X_{00} & X_{01} \end{matrix} \right] +\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \end{matrix} \right] \text{~and~} -\left[\begin{matrix} A_{10} & A_{11} \\ A_{00} & A_{01} \end{matrix} \right]。 +\left[\begin{matrix} A_{00} & A_{01} \\ A_{10} & A_{11} \end{matrix} \right]. $$ 该计算包括 $q$ 步。 当 $t=1$ 时, $X_{i0}$ 在其行中被广播, 而 $A_{0j}$ 在其列中被广播。因此，我们有 $$ -\left[\begin{matrix} X_{10},A_{00} & X_{10},A_{01} \\ X_{00},A_{00} & X_{00},A_{01} \end{matrix} \right]。 +\left[\begin{matrix} X_{00},A_{00} & X_{00},A_{01} \\ X_{10},A_{00} & X_{10},A_{01} \end{matrix} \right]. $$ 然后我们在每个处理器 $(i, j)$ 上将 $X_{i0}$ 和 $A_{0j}$ 相乘为 $$ -\left[\begin{matrix} X_{10}A_{00} & X_{10}A_{01} \\ X_{00}A_{00} & X_{00}A_{01} \end{matrix} \right] (1)。 +\left[\begin{matrix} X_{00}A_{00} & X_{00}A_{01} \\ X_{10}A_{00} & X_{10}A_{01} \end{matrix} \right] (1). $$ 同样，当 $t=2$ 时, $X_{i1}$ 在其行中被广播, $A_{1j}$ 在其列中被广播, 我们将它们相乘为 $$ -\left[\begin{matrix} X_{11}A_{10} & X_{11}A_{11} \\ X_{01}A_{10} & X_{01}A_{11} \end{matrix} \right] (2)。 +\left[\begin{matrix} X_{01}A_{10} & X_{01}A_{11} \\ X_{11}A_{10} & X_{11}A_{11} \end{matrix} \right] (2). $$ 通过将 $(1)$ 和 $(2)$ 相加，我们有 $$ -Y = XA = \left[\begin{matrix} X_{10}A_{00}+X_{11}A_{10} & X_{10}A_{01}+X_{11}A_{11} \\ X_{00}A_{00}+X_{01}A_{10} & X_{00}A_{01}+X_{01}A_{11} \end{matrix} \right]。 +Y = XA = \left[\begin{matrix} X_{00}A_{00}+X_{01}A_{10} & X_{00}A_{01}+X_{01}A_{11} \\ X_{10}A_{00}+X_{11}A_{10} & X_{10}A_{01}+X_{11}A_{11} \end{matrix} \right]. $$ ## 效率 diff --git a/docs/source/zh-Hans/features/2p5D_tensor_parallel.md b/docs/source/zh-Hans/features/2p5D_tensor_parallel.md index 59a4be02ce47..5f15202729a7 100644 --- a/docs/source/zh-Hans/features/2p5D_tensor_parallel.md +++ b/docs/source/zh-Hans/features/2p5D_tensor_parallel.md @@ -9,7 +9,7 @@ - [2D 张量并行](./2D_tensor_parallel.md) **示例代码** -- [ColossalAI-Examples - 2.5D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_2p5d.py) +- [ColossalAI-Examples - 2.5D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md) **相关论文** - [2.5-dimensional distributed model training](https://arxiv.org/pdf/2105.14500.pdf) @@ -22,29 +22,29 @@ 给定 $P=q \times q \times d$ 个处理器（必要条件）, 如 $q=d=2$, 我们把输入 $X$ 划分为 $d\times q$ 行和 $q$ 列 $$ -\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \\ X_{10} & X_{11} \\ X_{00} & X_{01}\end{matrix} \right], +\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \\ X_{20} & X_{21} \\ X_{30} & X_{31}\end{matrix} \right], $$ 它可以被重塑为 $d$ 层 $$ -\left[\begin{matrix} X_{10} & X_{11} \\ X_{00} & X_{01} \end{matrix} \right] \text{~and~}\left[\begin{matrix} X_{30} & X_{31} \\ X_{20} & X_{21} \end{matrix} \right]. +\left[\begin{matrix} X_{00} & X_{01} \\ X_{10} & X_{11} \end{matrix} \right] \text{~and~}\left[\begin{matrix} X_{20} & X_{21} \\ X_{30} & X_{31} \end{matrix} \right]. $$ 另外，权重 $A$ 被分割为 $$ -\left[\begin{matrix} A_{10} & A_{11} \\ A_{00} & A_{01} \end{matrix} \right]. +\left[\begin{matrix} A_{00} & A_{01} \\ A_{10} & A_{11} \end{matrix} \right]. $$ 对于 $X$ 相关的每一层, 我们使用SUMMA算法将 $X$ 与 $A$ 相乘。 然后，我们得到输出 $$ -\left[\begin{matrix} Y_{10}=X_{10}A_{00}+X_{11}A_{10} & Y_{11}=X_{10}A_{01}+X_{11}A_{11} \\ Y_{00}=X_{00}A_{00}+X_{01}A_{10} & Y_{01}=X_{00}A_{01}+X_{01}A_{11} \end{matrix} \right] +\left[\begin{matrix} Y_{00}=X_{00}A_{00}+X_{01}A_{10} & Y_{01}=X_{00}A_{01}+X_{01}A_{11} \\ Y_{10}=X_{10}A_{00}+X_{11}A_{10} & Y_{11}=X_{10}A_{01}+X_{11}A_{11} \end{matrix} \right] \text{~and~} $$ $$ -\left[\begin{matrix} Y_{30}=X_{30}A_{00}+X_{31}A_{10} & Y_{31}=X_{30}A_{01}+X_{31}A_{11} \\ Y_{20}=X_{20}A_{00}+X_{21}A_{10} & Y_{21}=X_{20}A_{01}+X_{21}A_{11} \end{matrix} \right]. +\left[\begin{matrix} Y_{20}=X_{20}A_{00}+X_{21}A_{10} & Y_{21}=X_{20}A_{01}+X_{21}A_{11} \\ Y_{30}=X_{30}A_{00}+X_{31}A_{10} & Y_{31}=X_{30}A_{01}+X_{31}A_{11} \end{matrix} \right]. $$ ## 效率 diff --git a/docs/source/zh-Hans/features/3D_tensor_parallel.md b/docs/source/zh-Hans/features/3D_tensor_parallel.md index 440121c94243..5ce0cdf6c068 100644 --- a/docs/source/zh-Hans/features/3D_tensor_parallel.md +++ b/docs/source/zh-Hans/features/3D_tensor_parallel.md @@ -9,7 +9,7 @@ - [2D 张量并行](./2D_tensor_parallel.md) **示例代码** -- [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/features/tensor_parallel/tensor_parallel_3d.py) +- [ColossalAI-Examples - 3D Tensor Parallelism](https://github.com/hpcaitech/ColossalAI-Examples/blob/main/features/tensor_parallel/README.md) **相关论文** - [Maximizing Parallelism in Distributed Training for Huge Neural Networks](https://arxiv.org/pdf/2105.14450.pdf) From 2703a37ac91beffa3e62a0e179726fa5d15d73b1 Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Thu, 18 May 2023 16:33:14 +0800 Subject: [PATCH 208/413] [amp] Add naive amp demo (#3774) * [mixed_precison] add naive amp demo * [mixed_precison] add naive amp demo --- colossalai/booster/mixed_precision/__init__.py | 4 +++- colossalai/booster/mixed_precision/fp16_naive.py | 5 +++++ 2 files changed, 8 insertions(+), 1 deletion(-) create mode 100644 colossalai/booster/mixed_precision/fp16_naive.py diff --git a/colossalai/booster/mixed_precision/__init__.py b/colossalai/booster/mixed_precision/__init__.py index 3cf0ad28cdbe..0df9d84159f9 100644 --- a/colossalai/booster/mixed_precision/__init__.py +++ b/colossalai/booster/mixed_precision/__init__.py @@ -1,17 +1,19 @@ from .bf16 import BF16MixedPrecision from .fp8 import FP8MixedPrecision from .fp16_apex import FP16ApexMixedPrecision +from .fp16_naive import FP16NaiveMixedPrecision from .fp16_torch import FP16TorchMixedPrecision from .mixed_precision_base import MixedPrecision __all__ = [ 'MixedPrecision', 'mixed_precision_factory', 'FP16_Apex_MixedPrecision', 'FP16_Torch_MixedPrecision', - 'FP32_MixedPrecision', 'BF16_MixedPrecision', 'FP8_MixedPrecision' + 'FP32_MixedPrecision', 'BF16_MixedPrecision', 'FP8_MixedPrecision', 'FP16NaiveMixedPrecision' ] _mixed_precision_mapping = { 'fp16': FP16TorchMixedPrecision, 'fp16_apex': FP16ApexMixedPrecision, + 'fp16_naive': FP16NaiveMixedPrecision, 'bf16': BF16MixedPrecision, 'fp8': FP8MixedPrecision } diff --git a/colossalai/booster/mixed_precision/fp16_naive.py b/colossalai/booster/mixed_precision/fp16_naive.py new file mode 100644 index 000000000000..ef1ec1f42d70 --- /dev/null +++ b/colossalai/booster/mixed_precision/fp16_naive.py @@ -0,0 +1,5 @@ +from .mixed_precision_base import MixedPrecision + + +class FP16NaiveMixedPrecision(MixedPrecision): + pass From 5452df63c58385985fcd89749f266109eb9ea8b8 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Thu, 18 May 2023 20:05:59 +0800 Subject: [PATCH 209/413] [plugin] torch ddp plugin supports sharded model checkpoint (#3775) * [plugin] torch ddp plugin add save sharded model * [test] fix torch ddp ckpt io test * [test] fix torch ddp ckpt io test * [test] fix low level zero plugin test * [test] fix low level zero plugin test * [test] add debug info * [test] add debug info * [test] add debug info * [test] add debug info * [test] add debug info * [test] fix low level zero plugin test * [test] fix low level zero plugin test * [test] remove debug info --- colossalai/booster/plugin/torch_ddp_plugin.py | 12 +++- .../checkpoint_io/checkpoint_io_base.py | 6 +- colossalai/checkpoint_io/utils.py | 62 +++++++++++-------- .../test_plugin/test_low_level_zero_plugin.py | 7 ++- .../test_torch_ddp_checkpoint_io.py | 50 ++++++++++----- 5 files changed, 86 insertions(+), 51 deletions(-) diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index 99cd2f7791d3..b317ccf48ad9 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -1,4 +1,4 @@ -from typing import Callable, Iterator, List, Tuple, Union +from typing import Callable, Iterator, List, Optional, Tuple, Union import torch.nn as nn from torch.nn.parallel import DistributedDataParallel as DDP @@ -50,6 +50,16 @@ def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): if self.coordinator.is_master(): super().save_lr_scheduler(lr_scheduler, checkpoint) + def save_sharded_model(self, + model: nn.Module, + checkpoint_path: str, + gather_dtensor: bool = False, + variant: Optional[str] = None, + max_shard_size: int = 1024, + use_safetensors: bool = False): + if self.coordinator.is_master(): + super().save_sharded_model(model, checkpoint_path, gather_dtensor, variant, max_shard_size, use_safetensors) + class TorchDDPModel(ModelWrapper): diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py index 9cf344ecc41b..fbc8fc5429ad 100644 --- a/colossalai/checkpoint_io/checkpoint_io_base.py +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -1,7 +1,6 @@ from abc import ABC, abstractmethod from pathlib import Path -from typing import Union -from typing import Optional +from typing import Optional, Union import torch import torch.nn as nn @@ -84,9 +83,8 @@ def load_model(self, # containing no distributed tensors, dtensor -> full tensor conversion # should be done offline via our CLI # the existence of index file means it is a sharded checkpoint - ckpt_path = Path(checkpoint) index_file_exists, index_file_path = has_index_file(checkpoint) - + # return the origin model instead of the unwrapped model origin_model = model diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index ee4bd72e89ec..435feda4ac6a 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -1,10 +1,12 @@ # coding=utf-8 +import re from pathlib import Path +from typing import Iterator, List, Mapping, Optional, OrderedDict, Tuple + import torch import torch.nn as nn -from typing import List, Mapping, OrderedDict, Optional, Tuple, Iterator + from colossalai.tensor.d_tensor.d_tensor import DTensor -import re SAFE_WEIGHTS_NAME = "model.safetensors" WEIGHTS_NAME = "pytorch_model.bin" @@ -15,6 +17,7 @@ # General helper functions # ====================================== + def calculate_tensor_size(tensor: torch.Tensor) -> float: """ Calculate the size of a parameter in MB. Used to compute whether a group of params exceed the shard size. @@ -28,6 +31,7 @@ def calculate_tensor_size(tensor: torch.Tensor) -> float: """ return tensor.numel() * tensor.element_size() / 1024 / 1024 + def is_safetensors_available() -> bool: """ Check whether safetensors is available. @@ -78,7 +82,6 @@ def is_safetensor_checkpoint(checkpoint_file_path: str) -> bool: # Helper functions for saving shard file # ====================================== def shard_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> Iterator[Tuple[OrderedDict, int]]: - """ Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a given size. @@ -100,35 +103,39 @@ def shard_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> It current_block_size = 0 current_block[key] = weight current_block_size += weight_size - + if ret_block != None: yield ret_block, ret_block_size yield current_block, current_block_size -def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool =False): +def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool = False): """ load shard state dict into model """ if use_safetensors and not checkpoint_file.suffix == ".safetensors": raise Exception("load the model using `safetensors`, but no file endwith .safetensors") if use_safetensors: - from safetensors.torch import safe_open from safetensors.torch import load_file as safe_load_file + from safetensors.torch import safe_open with safe_open(checkpoint_file, framework="pt") as f: metadata = f.metadata() if metadata["format"] != "pt": raise NotImplementedError( - f"Conversion from a {metadata['format']} safetensors archive to PyTorch is not implemented yet." - ) + f"Conversion from a {metadata['format']} safetensors archive to PyTorch is not implemented yet.") return safe_load_file(checkpoint_file) else: return torch.load(checkpoint_file) - -def load_state_dict_into_model(model: nn.Module, state_dict: torch.Tensor, missing_keys: List, strict: bool = False, load_sub_module: bool = True): + + +def load_state_dict_into_model(model: nn.Module, + state_dict: torch.Tensor, + missing_keys: List, + strict: bool = False, + load_sub_module: bool = True): r"""Copies parameters and buffers from :attr:`state_dict` into - this module and its descendants. + this module and its descendants. Args: state_dict (dict): a dict containing parameters and @@ -166,11 +173,12 @@ def load(module: nn.Module, state_dict, prefix="", load_sub_module: bool = True) if strict: if len(unexpected_keys) > 0: - error_msgs = 'Unexpected key(s) in state_dict: {}. '.format( - ', '.join('"{}"'.format(k) for k in unexpected_keys)) + error_msgs = 'Unexpected key(s) in state_dict: {}. '.format(', '.join( + '"{}"'.format(k) for k in unexpected_keys)) raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( - model.__class__.__name__, "\n\t".join(error_msgs))) - + model.__class__.__name__, "\n\t".join(error_msgs))) + + # ====================================== # Helper functions for saving state dict # ====================================== @@ -350,6 +358,8 @@ def has_index_file(checkpoint_path: str) -> Tuple[bool, Optional[Path]]: return True, index_files[0] else: return False, None + else: + raise RuntimeError(f'Invalid checkpoint path {checkpoint_path}. Expected a file or a directory.') def load_state_dict(checkpoint_file_path: Path): @@ -380,7 +390,6 @@ def load_state_dict(checkpoint_file_path: Path): else: # load with torch return torch.load(checkpoint_file_path) - def add_variant(weights_name: str, variant: Optional[str] = None) -> str: @@ -392,17 +401,18 @@ def add_variant(weights_name: str, variant: Optional[str] = None) -> str: return weights_name -def get_base_filenames(variant: str=None, use_safetensors: bool=False): - """ - generate base weight filenames - """ - weights_name = SAFE_WEIGHTS_NAME if use_safetensors else WEIGHTS_NAME - weights_name = add_variant(weights_name, variant) +def get_base_filenames(variant: str = None, use_safetensors: bool = False): + """ + generate base weight filenames + """ + weights_name = SAFE_WEIGHTS_NAME if use_safetensors else WEIGHTS_NAME + weights_name = add_variant(weights_name, variant) + + save_index_file = SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME + save_index_file = add_variant(save_index_file, variant) - save_index_file = SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME - save_index_file = add_variant(save_index_file, variant) + return weights_name, save_index_file - return weights_name, save_index_file def get_shard_filename(weights_name: str, idx: int): """ @@ -410,4 +420,4 @@ def get_shard_filename(weights_name: str, idx: int): """ shard_file = weights_name.replace(".bin", f"-{idx+1:05d}.bin") shard_file = shard_file.replace(".safetensors", f"-{idx + 1:05d}.safetensors") - return shard_file \ No newline at end of file + return shard_file diff --git a/tests/test_booster/test_plugin/test_low_level_zero_plugin.py b/tests/test_booster/test_plugin/test_low_level_zero_plugin.py index d84b96f77a75..f70f27be2aa7 100644 --- a/tests/test_booster/test_plugin/test_low_level_zero_plugin.py +++ b/tests/test_booster/test_plugin/test_low_level_zero_plugin.py @@ -11,9 +11,9 @@ from tests.kit.model_zoo import model_zoo # These models are not compatible with AMP -_AMP_ERR_MODELS = ['timm_convit', 'dlrm', 'deepfm_interactionarch', 'deepfm_simpledeepfmnn`'] +_AMP_ERR_MODELS = ['timm_convit', 'dlrm', 'deepfm_interactionarch', 'deepfm_simpledeepfmnn'] # These models have no parameters -_LOW_LEVEL_ZERO_ERR_MODELS = ['dlrm_interactionarch'] +_LOW_LEVEL_ZERO_ERR_MODELS = ['dlrm_interactionarch', 'deepfm_overarch', 'deepfm_sparsearch', 'dlrm_sparsearch'] # These models will get stuck _STUCK_MODELS = [ 'diffusers_vq_model', 'transformers_albert', 'transformers_albert_for_pretraining', 'transformers_bert', @@ -67,6 +67,7 @@ def check_low_level_zero_plugin(stage: int, early_stop: bool = True): skipped_models.append(name) continue err = run_fn(stage, model_fn, data_gen_fn, output_transform_fn) + torch.cuda.empty_cache() if err is None: @@ -91,7 +92,7 @@ def run_dist(rank, world_size, port, early_stop: bool = True): @rerun_if_address_is_in_use() def test_low_level_zero_plugin(early_stop: bool = True): - spawn(run_dist, 2, early_stop=early_stop) + spawn(run_dist, 4, early_stop=early_stop) if __name__ == '__main__': diff --git a/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py index 3c05ea9f1b17..8a4217941fe3 100644 --- a/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py @@ -1,6 +1,7 @@ import tempfile import torch +import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import SGD from torchvision.models import resnet18 @@ -8,12 +9,12 @@ import colossalai from colossalai.booster import Booster from colossalai.booster.plugin import TorchDDPPlugin -from colossalai.booster.plugin.torch_ddp_plugin import TorchDDPCheckpointIO from colossalai.interface import OptimizerWrapper -from colossalai.testing import check_state_dict_equal, rerun_if_address_is_in_use, spawn +from colossalai.testing import check_state_dict_equal, parameterize, rerun_if_address_is_in_use, spawn -def check_torch_ddp_checkpointIO(): +@parameterize('shard', [True, False]) +def check_torch_ddp_checkpointIO(shard: bool): plugin = TorchDDPPlugin() booster = Booster(plugin=plugin) model = resnet18() @@ -34,23 +35,38 @@ def check_torch_ddp_checkpointIO(): optimizer.step() scheduler.step() - optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile() - lr_scheduler_ckpt_tempfile = tempfile.NamedTemporaryFile() - ckpt_io = TorchDDPCheckpointIO() - ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name) - ckpt_io.save_lr_scheduler(scheduler, lr_scheduler_ckpt_tempfile.name) + with tempfile.TemporaryDirectory() as tempdir: + obj = [tempdir] + dist.broadcast_object_list(obj, src=0) + tempdir = obj[0] # use the same directory on all ranks - new_model = resnet18() - new_optimizer = SGD((new_model.parameters()), lr=0.001) - new_scheduler = torch.optim.lr_scheduler.StepLR(new_optimizer, step_size=1, gamma=0.1) - _, new_optimizer, _, _, new_scheduler = booster.boost(new_model, new_optimizer, lr_scheduler=new_scheduler) + model_ckpt_path = f"{tempdir}/model" + optimizer_ckpt_path = f"{tempdir}/optimizer" + lr_scheduler_ckpt_path = f"{tempdir}/lr_scheduler" + booster.save_model(model, model_ckpt_path, shard=shard) + if not shard: + # TODO(ver217): optimizer checkpointing is not supported for sharded checkpoint + booster.save_optimizer(optimizer, optimizer_ckpt_path) + booster.save_lr_scheduler(scheduler, lr_scheduler_ckpt_path) + dist.barrier() - if ckpt_io.coordinator.is_master(): - ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) - check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) + new_model = resnet18() + new_optimizer = SGD((new_model.parameters()), lr=0.001) + new_scheduler = torch.optim.lr_scheduler.StepLR(new_optimizer, step_size=1, gamma=0.1) + new_model, new_optimizer, _, _, new_scheduler = booster.boost(new_model, + new_optimizer, + lr_scheduler=new_scheduler) - ckpt_io.load_lr_scheduler(new_scheduler, lr_scheduler_ckpt_tempfile.name) - check_state_dict_equal(scheduler.state_dict(), new_scheduler.state_dict(), False) + booster.load_model(new_model, model_ckpt_path) + check_state_dict_equal(model.state_dict(), new_model.state_dict(), False) + + if not shard: + booster.load_optimizer(new_optimizer, optimizer_ckpt_path) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) + booster.load_lr_scheduler(new_scheduler, lr_scheduler_ckpt_path) + check_state_dict_equal(scheduler.state_dict(), new_scheduler.state_dict(), False) + + dist.barrier() def run_dist(rank, world_size, port): From 5ce6c9d86fe667d7ef5cd70a106b88073b640c20 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Fri, 19 May 2023 12:12:20 +0800 Subject: [PATCH 210/413] [doc] add tutorial for cluster utils (#3763) * [doc] add en cluster utils doc * [doc] add zh cluster utils doc * [doc] add cluster utils doc in sidebar --- docs/sidebars.json | 3 +- docs/source/en/features/cluster_utils.md | 32 +++++++++++++++++++ docs/source/zh-Hans/features/cluster_utils.md | 32 +++++++++++++++++++ 3 files changed, 66 insertions(+), 1 deletion(-) create mode 100644 docs/source/en/features/cluster_utils.md create mode 100644 docs/source/zh-Hans/features/cluster_utils.md diff --git a/docs/sidebars.json b/docs/sidebars.json index 2732704a5cab..dd3a4e5ec362 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -58,7 +58,8 @@ ] }, "features/pipeline_parallel", - "features/nvme_offload" + "features/nvme_offload", + "features/cluster_utils" ] }, { diff --git a/docs/source/en/features/cluster_utils.md b/docs/source/en/features/cluster_utils.md new file mode 100644 index 000000000000..1903d64d2563 --- /dev/null +++ b/docs/source/en/features/cluster_utils.md @@ -0,0 +1,32 @@ +# Cluster Utilities + +Author: [Hongxin Liu](https://github.com/ver217) + +**Prerequisite:** +- [Distributed Training](../concepts/distributed_training.md) + +## Introduction + +We provide a utility class `colossalai.cluster.DistCoordinator` to coordinate distributed training. It's useful to get various information about the cluster, such as the number of nodes, the number of processes per node, etc. + +## API Reference + +{{ autodoc:colossalai.cluster.DistCoordinator }} + +{{ autodoc:colossalai.cluster.DistCoordinator.is_master }} + +{{ autodoc:colossalai.cluster.DistCoordinator.is_node_master }} + +{{ autodoc:colossalai.cluster.DistCoordinator.is_last_process }} + +{{ autodoc:colossalai.cluster.DistCoordinator.print_on_master }} + +{{ autodoc:colossalai.cluster.DistCoordinator.print_on_node_master }} + +{{ autodoc:colossalai.cluster.DistCoordinator.priority_execution }} + +{{ autodoc:colossalai.cluster.DistCoordinator.destroy }} + +{{ autodoc:colossalai.cluster.DistCoordinator.block_all }} + +{{ autodoc:colossalai.cluster.DistCoordinator.on_master_only }} diff --git a/docs/source/zh-Hans/features/cluster_utils.md b/docs/source/zh-Hans/features/cluster_utils.md new file mode 100644 index 000000000000..ca787a869041 --- /dev/null +++ b/docs/source/zh-Hans/features/cluster_utils.md @@ -0,0 +1,32 @@ +# 集群实用程序 + +作者: [Hongxin Liu](https://github.com/ver217) + +**前置教程:** +- [分布式训练](../concepts/distributed_training.md) + +## 引言 + +我们提供了一个实用程序类 `colossalai.cluster.DistCoordinator` 来协调分布式训练。它对于获取有关集群的各种信息很有用，例如节点数、每个节点的进程数等。 + +## API 参考 + +{{ autodoc:colossalai.cluster.DistCoordinator }} + +{{ autodoc:colossalai.cluster.DistCoordinator.is_master }} + +{{ autodoc:colossalai.cluster.DistCoordinator.is_node_master }} + +{{ autodoc:colossalai.cluster.DistCoordinator.is_last_process }} + +{{ autodoc:colossalai.cluster.DistCoordinator.print_on_master }} + +{{ autodoc:colossalai.cluster.DistCoordinator.print_on_node_master }} + +{{ autodoc:colossalai.cluster.DistCoordinator.priority_execution }} + +{{ autodoc:colossalai.cluster.DistCoordinator.destroy }} + +{{ autodoc:colossalai.cluster.DistCoordinator.block_all }} + +{{ autodoc:colossalai.cluster.DistCoordinator.on_master_only }} From 21e29e22122851a1869675523ab2c94ddd0bdf58 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Fri, 19 May 2023 12:12:42 +0800 Subject: [PATCH 211/413] [doc] add tutorial for booster plugins (#3758) * [doc] add en booster plugins doc * [doc] add booster plugins doc in sidebar * [doc] add zh booster plugins doc * [doc] fix zh booster plugin translation * [doc] reoganize tutorials order of basic section * [devops] force sync to test ci --- docs/sidebars.json | 7 +- docs/source/en/basics/booster_plugins.md | 64 +++++++++++++++++++ docs/source/zh-Hans/basics/booster_plugins.md | 64 +++++++++++++++++++ 3 files changed, 132 insertions(+), 3 deletions(-) create mode 100644 docs/source/en/basics/booster_plugins.md create mode 100644 docs/source/zh-Hans/basics/booster_plugins.md diff --git a/docs/sidebars.json b/docs/sidebars.json index dd3a4e5ec362..ed0ba52782ad 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -26,14 +26,15 @@ "collapsed": true, "items": [ "basics/command_line_tool", - "basics/define_your_config", "basics/launch_colossalai", + "basics/booster_api", + "basics/booster_plugins", + "basics/define_your_config", "basics/initialize_features", "basics/engine_trainer", "basics/configure_parallelization", "basics/model_checkpoint", - "basics/colotensor_concept", - "basics/booster_api" + "basics/colotensor_concept" ] }, { diff --git a/docs/source/en/basics/booster_plugins.md b/docs/source/en/basics/booster_plugins.md new file mode 100644 index 000000000000..c15c30c8450c --- /dev/null +++ b/docs/source/en/basics/booster_plugins.md @@ -0,0 +1,64 @@ +# Booster Plugins + +Author: [Hongxin Liu](https://github.com/ver217) + +**Prerequisite:** +- [Booster API](./booster_api.md) + +## Introduction + +As mentioned in [Booster API](./booster_api.md), we can use booster plugins to customize the parallel training. In this tutorial, we will introduce how to use booster plugins. + +We currently provide the following plugins: + +- [Low Level Zero Plugin](#low-level-zero-plugin): It wraps the `colossalai.zero.low_level.LowLevelZeroOptimizer` and can be used to train models with zero-dp. It only supports zero stage-1 and stage-2. +- [Gemini Plugin](#gemini-plugin): It wraps the [Gemini](../features/zero_with_chunk.md) which implements Zero-3 with chunk-based and heterogeneous memory management. +- [Torch DDP Plugin](#torch-ddp-plugin): It is a wrapper of `torch.nn.parallel.DistributedDataParallel` and can be used to train models with data parallelism. +- [Torch FSDP Plugin](#torch-fsdp-plugin): It is a wrapper of `torch.distributed.fsdp.FullyShardedDataParallel` and can be used to train models with zero-dp. + +More plugins are coming soon. + +## Plugins + +### Low Level Zero Plugin + +This plugin implements Zero-1 and Zero-2 (w/wo CPU offload), using `reduce` and `gather` to synchronize gradients and weights. + +Zero-1 can be regarded as a better substitute of Torch DDP, which is more memory efficient and faster. It can be easily used in hybrid parallelism. + +Zero-2 does not support local gradient accumulation. Though you can accumulate gradient if you insist, it cannot reduce communication cost. That is to say, it's not a good idea to use Zero-2 with pipeline parallelism. + +{{ autodoc:colossalai.booster.plugin.LowLevelZeroPlugin }} + +We've tested compatibility on some famous models, following models may not be supported: + +- `timm.models.convit_base` +- dlrm and deepfm models in `torchrec` +- `diffusers.VQModel` +- `transformers.AlbertModel` +- `transformers.AlbertForPreTraining` +- `transformers.BertModel` +- `transformers.BertForPreTraining` +- `transformers.GPT2DoubleHeadsModel` + +Compatibility problems will be fixed in the future. + +### Gemini Plugin + +This plugin implements Zero-3 with chunk-based and heterogeneous memory management. It can train large models without much loss in speed. It also does not support local gradient accumulation. More details can be found in [Gemini Doc](../features/zero_with_chunk.md). + +{{ autodoc:colossalai.booster.plugin.GeminiPlugin }} + +### Torch DDP Plugin + +More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel). + +{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }} + +### Torch FSDP Plugin + +> ⚠ This plugin is not available when torch version is lower than 1.12.0. + +More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html). + +{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }} diff --git a/docs/source/zh-Hans/basics/booster_plugins.md b/docs/source/zh-Hans/basics/booster_plugins.md new file mode 100644 index 000000000000..e0258eb37932 --- /dev/null +++ b/docs/source/zh-Hans/basics/booster_plugins.md @@ -0,0 +1,64 @@ +# Booster 插件 + +作者: [Hongxin Liu](https://github.com/ver217) + +**前置教程:** +- [Booster API](./booster_api.md) + +## 引言 + +正如 [Booster API](./booster_api.md) 中提到的，我们可以使用 booster 插件来自定义并行训练。在本教程中，我们将介绍如何使用 booster 插件。 + +我们现在提供以下插件: + +- [Low Level Zero 插件](#low-level-zero-plugin): 它包装了 `colossalai.zero.low_level.LowLevelZeroOptimizer`，可用于使用 Zero-dp 训练模型。它仅支持 Zero 阶段1和阶段2。 +- [Gemini 插件](#gemini-plugin): 它包装了 [Gemini](../features/zero_with_chunk.md)，Gemini 实现了基于Chunk内存管理和异构内存管理的 Zero-3。 +- [Torch DDP 插件](#torch-ddp-plugin): 它包装了 `torch.nn.parallel.DistributedDataParallel` 并且可用于使用数据并行训练模型。 +- [Torch FSDP 插件](#torch-fsdp-plugin): 它包装了 `torch.distributed.fsdp.FullyShardedDataParallel` 并且可用于使用 Zero-dp 训练模型。 + +更多插件即将推出。 + +## 插件 + +### Low Level Zero 插件 + +该插件实现了 Zero-1 和 Zero-2（使用/不使用 CPU 卸载），使用`reduce`和`gather`来同步梯度和权重。 + +Zero-1 可以看作是 Torch DDP 更好的替代品，内存效率更高，速度更快。它可以很容易地用于混合并行。 + +Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累梯度，但不能降低通信成本。也就是说，同时使用流水线并行和 Zero-2 并不是一个好主意。 + +{{ autodoc:colossalai.booster.plugin.LowLevelZeroPlugin }} + +我们已经测试了一些主流模型的兼容性，可能不支持以下模型： + +- `timm.models.convit_base` +- dlrm and deepfm models in `torchrec` +- `diffusers.VQModel` +- `transformers.AlbertModel` +- `transformers.AlbertForPreTraining` +- `transformers.BertModel` +- `transformers.BertForPreTraining` +- `transformers.GPT2DoubleHeadsModel` + +兼容性问题将在未来修复。 + +### Gemini 插件 + +这个插件实现了基于Chunk内存管理和异构内存管理的 Zero-3。它可以训练大型模型而不会损失太多速度。它也不支持局部梯度累积。更多详细信息，请参阅 [Gemini 文档](../features/zero_with_chunk.md). + +{{ autodoc:colossalai.booster.plugin.GeminiPlugin }} + +### Torch DDP 插件 + +更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel). + +{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }} + +### Torch FSDP 插件 + +> ⚠ 如果 torch 版本低于 1.12.0，此插件将不可用。 + +更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/fsdp.html). + +{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }} From 32f81f14d480b579a93fd1786fa38b8c2de79189 Mon Sep 17 00:00:00 2001 From: digger yu Date: Fri, 19 May 2023 13:50:00 +0800 Subject: [PATCH 212/413] [NFC] fix typo colossalai/amp auto_parallel autochunk (#3756) --- colossalai/amp/torch_amp/_grad_scaler.py | 2 +- .../auto_parallel/meta_profiler/meta_registry/linear.py | 2 +- colossalai/auto_parallel/passes/runtime_apply_pass.py | 2 +- .../auto_parallel/passes/runtime_preparation_pass.py | 4 ++-- colossalai/autochunk/trace_flow.py | 6 +++--- colossalai/autochunk/trace_indice.py | 8 ++++---- 6 files changed, 12 insertions(+), 12 deletions(-) diff --git a/colossalai/amp/torch_amp/_grad_scaler.py b/colossalai/amp/torch_amp/_grad_scaler.py index 7b78998fb8c2..ed4b8e484436 100644 --- a/colossalai/amp/torch_amp/_grad_scaler.py +++ b/colossalai/amp/torch_amp/_grad_scaler.py @@ -240,7 +240,7 @@ def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16): for grads in per_dtype_grads.values(): torch._amp_foreach_non_finite_check_and_unscale_(grads, per_device_found_inf.get(device), per_device_inv_scale.get(device)) - # For tensor parallel paramters it should be all-reduced over tensor parallel process group + # For tensor parallel parameters it should be all-reduced over tensor parallel process group if gpc.is_initialized(ParallelMode.MODEL) and gpc.get_world_size(ParallelMode.MODEL) > 1: vals = [val for val in per_device_found_inf._per_device_tensors.values()] coalesced = _flatten_dense_tensors(vals) diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/linear.py b/colossalai/auto_parallel/meta_profiler/meta_registry/linear.py index 7697fc6c383d..94dd9143e0ae 100644 --- a/colossalai/auto_parallel/meta_profiler/meta_registry/linear.py +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/linear.py @@ -325,7 +325,7 @@ def matmul_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L else: _is_batch_dims_same = False - # retireve dimensions + # retrieve dimensions input_dim_00 = input_tensors[0].shape[-2] input_dim_01 = input_tensors[0].shape[-1] input_dim_10 = input_tensors[1].shape[-2] diff --git a/colossalai/auto_parallel/passes/runtime_apply_pass.py b/colossalai/auto_parallel/passes/runtime_apply_pass.py index a473bb6e973d..2049a06187d2 100644 --- a/colossalai/auto_parallel/passes/runtime_apply_pass.py +++ b/colossalai/auto_parallel/passes/runtime_apply_pass.py @@ -219,7 +219,7 @@ def _comm_spec_apply(gm: torch.fx.GraphModule): return gm -def _act_annotataion_pass(gm: torch.fx.GraphModule): +def _act_annotation_pass(gm: torch.fx.GraphModule): """ This pass is used to add the act annotation to the new inserted nodes. """ diff --git a/colossalai/auto_parallel/passes/runtime_preparation_pass.py b/colossalai/auto_parallel/passes/runtime_preparation_pass.py index 177f3765f5a0..9a2314826448 100644 --- a/colossalai/auto_parallel/passes/runtime_preparation_pass.py +++ b/colossalai/auto_parallel/passes/runtime_preparation_pass.py @@ -54,7 +54,7 @@ def size_processing(size: Union[int, torch.Size], return size -def solution_annotatation_pass(gm: torch.fx.GraphModule, solution: List[int], +def solution_annotation_pass(gm: torch.fx.GraphModule, solution: List[int], strategies_constructor: StrategiesConstructor): """ This method is used to stick the solution strategy to the nodes and add the information @@ -496,7 +496,7 @@ def runtime_preparation_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMesh, strategies_constructor: StrategiesConstructor, overlap=False): - gm, sharding_spec_convert_dict, origin_node_sharding_spec_dict, comm_actions_dict = solution_annotatation_pass( + gm, sharding_spec_convert_dict, origin_node_sharding_spec_dict, comm_actions_dict = solution_annotation_pass( gm, solution, strategies_constructor) gm = size_value_converting_pass(gm, device_mesh) gm = node_args_converting_pass(gm, device_mesh) diff --git a/colossalai/autochunk/trace_flow.py b/colossalai/autochunk/trace_flow.py index db25267e9b42..11a7e62ff37c 100644 --- a/colossalai/autochunk/trace_flow.py +++ b/colossalai/autochunk/trace_flow.py @@ -64,7 +64,7 @@ def check_index_compute(self, start_idx, end_dim, end_node, end_idx): return False return True - def _assgin_single_node_flow( + def _assign_single_node_flow( self, arg_node: Node, start_idx: int, @@ -177,7 +177,7 @@ def _get_all_node_info(self, end_dim, start_idx, end_idx): if get_node_shape(arg) is None: continue arg_list.append(arg) - flow_flag = self._assgin_single_node_flow( + flow_flag = self._assign_single_node_flow( arg, start_idx, end_idx, @@ -315,7 +315,7 @@ def _get_prepose_nodes(self, all_node_info: Dict, start_idx: int, end_idx: int, chunk_info["args"]["prepose_nodes"] = prepose_nodes def _get_non_chunk_inputs(self, chunk_info, start_idx, end_idx): - # we need to log input nodes to avoid deleteing them in the loop + # we need to log input nodes to avoid deleting them in the loop chunk_node_list = self.node_mgr.get_node_slice_by_idx(start_idx, end_idx + 1) # also need to get some prepose node's arg out of non_chunk_inputs for n in chunk_info["args"]["prepose_nodes"]: diff --git a/colossalai/autochunk/trace_indice.py b/colossalai/autochunk/trace_indice.py index d56bf843f18d..8e6cd3e29bea 100644 --- a/colossalai/autochunk/trace_indice.py +++ b/colossalai/autochunk/trace_indice.py @@ -461,7 +461,7 @@ def _assign_elementwise_indice(self, node, idx): nodes_in.append(node_in) self._inherit_more_indice_from_node_with_exclude(node_in, node) - def _assgin_no_change_indice(self, node, idx): + def _assign_no_change_indice(self, node, idx): self._assign_indice_as_input(node, idx) for node_in in node.args: if type(node_in) == type(node): @@ -792,7 +792,7 @@ def _assign_view_reshape_indice(self, node: Node, node_idx: int) -> None: self._add_dim(node_idx, i) dim_from.reverse() - # inheirt indice from current node + # inherit indice from current node if len(dim_from) != 0 and len(dim_to) != 0: if dim_diff == 1: if origin_shape[dim_from[0]] == 1: @@ -852,7 +852,7 @@ def trace_indice(self) -> None: elif "split" == node_name: self._assign_split_indice(node, idx) elif any(i == node_name for i in ["to", "contiguous", "clone", "type", "float"]): - self._assgin_no_change_indice(node, idx) + self._assign_no_change_indice(node, idx) elif "new_ones" == node_name: self._assign_all_indice(node, idx) elif "flatten" == node_name: @@ -914,7 +914,7 @@ def trace_indice(self) -> None: elif "conv2d" == node_name: self._assign_conv2d_indice(node, idx) elif "identity" == node_name: - self._assgin_no_change_indice(node, idx) + self._assign_no_change_indice(node, idx) elif any(n == node_name for n in ["sigmoid", "dropout", "relu", "silu", "gelu"]): self._assign_elementwise_indice(node, idx) else: From b4788d63ed3bc8e650c878df24f86c8ddaed124e Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Fri, 19 May 2023 16:28:57 +0800 Subject: [PATCH 213/413] [devops] fix doc test on pr (#3782) --- .github/workflows/doc_test_on_pr.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/doc_test_on_pr.yml b/.github/workflows/doc_test_on_pr.yml index fbe669582c20..fb2e28cd9b2e 100644 --- a/.github/workflows/doc_test_on_pr.yml +++ b/.github/workflows/doc_test_on_pr.yml @@ -86,7 +86,7 @@ jobs: - name: Test the Doc run: | source activate pytorch - for file in ${{ steps.changed-files.outputs.all_changed_files }}; do + for file in ${{ needs.detect-changed-doc.outputs.changed_files }}; do echo "Testing $file now..." docer test -p $file done From ad2cf58f503b47bc25e1005b58ee7ca8b25ddf8d Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Fri, 19 May 2023 18:03:56 +0800 Subject: [PATCH 214/413] [chat] add performance and tutorial (#3786) --- README.md | 16 +++++++++++++--- applications/Chat/README.md | 22 ++++++++++++++++++---- applications/Chat/examples/README.md | 3 +++ docs/README-zh-Hans.md | 16 +++++++++++++--- examples/tutorial/README.md | 6 +++++- 5 files changed, 52 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 79f733122cb3..2e6dcaa1eaf4 100644 --- a/README.md +++ b/README.md @@ -127,12 +127,22 @@ distributed training and inference in a few lines. ### ColossalChat -[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[demo]](https://chat.colossalai.org) +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. +[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) +[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +[[demo]](https://www.youtube.com/watch?v=HcTiHzApHm0) +[[tutorial]](https://www.youtube.com/watch?v=-qFBZFmOJfg) + +

          + +

          + +- Up to 10 times faster for RLHF PPO Stage3 Training

          diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 9ba831973b6c..bc8481d96de3 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -67,13 +67,24 @@ More details can be found in the latest news. * [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) ## Online demo -You can experience the performance of Coati7B on this page. +

          -[chat.colossalai.org](https://chat.colossalai.org/) +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. +[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) +[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +[[demo]](https://www.youtube.com/watch?v=HcTiHzApHm0) +[[tutorial]](https://www.youtube.com/watch?v=-qFBZFmOJfg) + +

          + +

          -Due to resource constraints, we will only provide this service from 29th Mar 2023 to 5 April 2023. However, we have provided the inference code in the [inference](./inference/) folder. The WebUI will be open-sourced soon as well. +> DeepSpeedChat performance comes from its blog on 2023 April 12, ColossalChat performance can be reproduced on an AWS p4d.24xlarge node with 8 A100-40G GPUs with the following command: torchrun --standalone --nproc_per_node 8 benchmark_opt_lora_dummy.py --max_timesteps 1 --update_timesteps 1 --use_kernels --strategy colossalai_zero2 --experience_batch_size 64 --train_batch_size 32 -> Warning: Due to model and dataset size limitations, Coati is just a baby model, Coati7B may output incorrect information and lack the ability for multi-turn dialogue. There is still significant room for improvement. ## Install ### Install the environment @@ -112,12 +123,14 @@ Here is how we collected the data Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned earlier to fine-tune the model. You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning. +[[Stage1 tutorial video]](https://www.youtube.com/watch?v=-qFBZFmOJfg) ### RLHF Training Stage2 - Training reward model Stage2 trains a reward model, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model You can run the `examples/train_rm.sh` to start a reward model training. +[[Stage2 tutorial video]](https://www.youtube.com/watch?v=gMx2CApKhuo) ### RLHF Training Stage3 - Training model with reinforcement learning by human feedback @@ -128,6 +141,7 @@ Stage3 uses reinforcement learning algorithm, which is the most complex part of

          You can run the `examples/train_prompts.sh` to start training PPO with human feedback. +[[Stage3 tutorial video]](https://www.youtube.com/watch?v=Z8wwSHxPL9g) For more details, see [`examples/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples). diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 60f876edaf17..72810738d017 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -48,6 +48,7 @@ The following pic shows how we collected the data. ## Stage1 - Supervised instructs tuning Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned earlier to fine-tune the model. +[[Stage1 tutorial video]](https://www.youtube.com/watch?v=-qFBZFmOJfg) You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning. @@ -83,6 +84,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ ## Stage2 - Training reward model We train a reward model in stage 2, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model. +[[Stage2 tutorial video]](https://www.youtube.com/watch?v=gMx2CApKhuo) You can run the `examples/train_rm.sh` to start a reward model training. @@ -141,6 +143,7 @@ Stage3 uses reinforcement learning algorithm, which is the most complex part of You can run the `examples/train_prompts.sh` to start PPO training. You can also use the cmd following to start PPO training. +[[Stage3 tutorial video]](https://www.youtube.com/watch?v=Z8wwSHxPL9g) ``` torchrun --standalone --nproc_per_node=4 train_prompts.py \ diff --git a/docs/README-zh-Hans.md b/docs/README-zh-Hans.md index 9d5bcfe3f974..c3deca7e9c17 100644 --- a/docs/README-zh-Hans.md +++ b/docs/README-zh-Hans.md @@ -121,12 +121,22 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的 ### ColossalChat -[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[博客]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[在线样例]](https://chat.colossalai.org) +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) +[[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) +[[博客]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +[[在线样例]](https://www.youtube.com/watch?v=HcTiHzApHm0) +[[教程]](https://www.youtube.com/watch?v=-qFBZFmOJfg) + +

          + +

          + +- 最高可提升RLHF PPO阶段3训练速度10倍

          diff --git a/examples/tutorial/README.md b/examples/tutorial/README.md index f4843331fd54..933026166d3f 100644 --- a/examples/tutorial/README.md +++ b/examples/tutorial/README.md @@ -29,7 +29,11 @@ quickly deploy large AI model training and inference, reducing large AI model tr - Fine-tuning and Inference for OPT [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/opt) [[video]](https://www.youtube.com/watch?v=jbEFNVzl67Y) - Optimized AlphaFold [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/fastfold) [[video]](https://www.youtube.com/watch?v=-zP13LfJP7w) - Optimized Stable Diffusion [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion) [[video]](https://www.youtube.com/watch?v=8KHeUjjc-XQ) - + - ColossalChat: Cloning ChatGPT with a Complete RLHF Pipeline +[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) +[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +[[demo]](https://www.youtube.com/watch?v=HcTiHzApHm0) +[[video]](https://www.youtube.com/watch?v=-qFBZFmOJfg) ## Discussion From 60e6a154bc18eede8086b1e3c1253571f91f5360 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Fri, 19 May 2023 18:05:08 +0800 Subject: [PATCH 215/413] [doc] add tutorial for booster checkpoint (#3785) * [doc] add checkpoint related docstr for booster * [doc] add en checkpoint doc * [doc] add zh checkpoint doc * [doc] add booster checkpoint doc in sidebar * [doc] add cuation about ckpt for plugins * [doc] add doctest placeholder * [doc] add doctest placeholder * [doc] add doctest placeholder --- colossalai/booster/booster.py | 52 +++++++++++++++++++ docs/sidebars.json | 1 + docs/source/en/basics/booster_checkpoint.md | 48 +++++++++++++++++ docs/source/en/basics/booster_plugins.md | 6 +++ .../zh-Hans/basics/booster_checkpoint.md | 48 +++++++++++++++++ docs/source/zh-Hans/basics/booster_plugins.md | 6 +++ 6 files changed, 161 insertions(+) create mode 100644 docs/source/en/basics/booster_checkpoint.md create mode 100644 docs/source/zh-Hans/basics/booster_checkpoint.md diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index c14e602deaf5..6f2adaf03074 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -151,6 +151,16 @@ def no_sync(self, model: nn.Module) -> contextmanager: return self.plugin.no_sync(model) def load_model(self, model: nn.Module, checkpoint: str, strict: bool = True): + """Load model from checkpoint. + + Args: + model (nn.Module): A model boosted by Booster. + checkpoint (str): Path to the checkpoint. It must be a local path. + It should be a directory path if the checkpoint is sharded. Otherwise, it should be a file path. + strict (bool, optional): whether to strictly enforce that the keys + in :attr:`state_dict` match the keys returned by this module's + :meth:`~torch.nn.Module.state_dict` function. Defaults to True. + """ self.checkpoint_io.load_model(model, checkpoint, strict) def save_model(self, @@ -159,16 +169,58 @@ def save_model(self, prefix: str = None, shard: bool = False, size_per_shard: int = 1024): + """Save model to checkpoint. + + Args: + model (nn.Module): A model boosted by Booster. + checkpoint (str): Path to the checkpoint. It must be a local path. + It is a file path if ``shard=False``. Otherwise, it is a directory path. + prefix (str, optional): A prefix added to parameter and buffer + names to compose the keys in state_dict. Defaults to None. + shard (bool, optional): Whether to save checkpoint a sharded way. + If true, the checkpoint will be a folder. Otherwise, it will be a single file. Defaults to False. + size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024. + """ self.checkpoint_io.save_model(model, checkpoint, prefix, shard, size_per_shard) def load_optimizer(self, optimizer: Optimizer, checkpoint: str): + """Load optimizer from checkpoint. + + Args: + optimizer (Optimizer): An optimizer boosted by Booster. + checkpoint (str): Path to the checkpoint. It must be a local path. + It should be a directory path if the checkpoint is sharded. Otherwise, it should be a file path. + """ self.checkpoint_io.load_optimizer(optimizer, checkpoint) def save_optimizer(self, optimizer: Optimizer, checkpoint: str, shard: bool = False, size_per_shard: int = 1024): + """Save optimizer to checkpoint. + Warning: Saving sharded optimizer checkpoint is not supported yet. + + Args: + optimizer (Optimizer): An optimizer boosted by Booster. + checkpoint (str): Path to the checkpoint. It must be a local path. + It is a file path if ``shard=False``. Otherwise, it is a directory path. + shard (bool, optional): Whether to save checkpoint a sharded way. + If true, the checkpoint will be a folder. Otherwise, it will be a single file. Defaults to False. + size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024. + """ self.checkpoint_io.save_optimizer(optimizer, checkpoint, shard, size_per_shard) def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + """Save lr scheduler to checkpoint. + + Args: + lr_scheduler (LRScheduler): A lr scheduler boosted by Booster. + checkpoint (str): Path to the checkpoint. It must be a local file path. + """ self.checkpoint_io.save_lr_scheduler(lr_scheduler, checkpoint) def load_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + """Load lr scheduler from checkpoint. + + Args: + lr_scheduler (LRScheduler): A lr scheduler boosted by Booster. + checkpoint (str): Path to the checkpoint. It must be a local file path. + """ self.checkpoint_io.load_lr_scheduler(lr_scheduler, checkpoint) diff --git a/docs/sidebars.json b/docs/sidebars.json index ed0ba52782ad..94f79dcd3509 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -29,6 +29,7 @@ "basics/launch_colossalai", "basics/booster_api", "basics/booster_plugins", + "basics/booster_checkpoint", "basics/define_your_config", "basics/initialize_features", "basics/engine_trainer", diff --git a/docs/source/en/basics/booster_checkpoint.md b/docs/source/en/basics/booster_checkpoint.md new file mode 100644 index 000000000000..adc0af60b7de --- /dev/null +++ b/docs/source/en/basics/booster_checkpoint.md @@ -0,0 +1,48 @@ +# Booster Checkpoint + +Author: [Hongxin Liu](https://github.com/ver217) + +**Prerequisite:** +- [Booster API](./booster_api.md) + +## Introduction + +We've introduced the [Booster API](./booster_api.md) in the previous tutorial. In this tutorial, we will introduce how to save and load checkpoints using booster. + +## Model Checkpoint + +{{ autodoc:colossalai.booster.Booster.save_model }} + +Model must be boosted by `colossalai.booster.Booster` before saving. `checkpoint` is the path to saved checkpoint. It can be a file, if `shard=False`. Otherwise, it should be a directory. If `shard=True`, the checkpoint will be saved in a sharded way. This is useful when the checkpoint is too large to be saved in a single file. Our sharded checkpoint format is compatible with [huggingface/transformers](https://github.com/huggingface/transformers). + +{{ autodoc:colossalai.booster.Booster.load_model }} + +Model must be boosted by `colossalai.booster.Booster` before loading. It will detect the checkpoint format automatically, and load in corresponding way. + +## Optimizer Checkpoint + +> ⚠ Saving optimizer checkpoint in a sharded way is not supported yet. + +{{ autodoc:colossalai.booster.Booster.save_optimizer }} + +Optimizer must be boosted by `colossalai.booster.Booster` before saving. + +{{ autodoc:colossalai.booster.Booster.load_optimizer }} + +Optimizer must be boosted by `colossalai.booster.Booster` before loading. + +## LR Scheduler Checkpoint + +{{ autodoc:colossalai.booster.Booster.save_lr_scheduler }} + +LR scheduler must be boosted by `colossalai.booster.Booster` before saving. `checkpoint` is the local path to checkpoint file. + +{{ autodoc:colossalai.booster.Booster.load_lr_scheduler }} + +LR scheduler must be boosted by `colossalai.booster.Booster` before loading. `checkpoint` is the local path to checkpoint file. + +## Checkpoint design + +More details about checkpoint design can be found in our discussion [A Unified Checkpoint System Design](https://github.com/hpcaitech/ColossalAI/discussions/3339). + + diff --git a/docs/source/en/basics/booster_plugins.md b/docs/source/en/basics/booster_plugins.md index c15c30c8450c..0362f095af2b 100644 --- a/docs/source/en/basics/booster_plugins.md +++ b/docs/source/en/basics/booster_plugins.md @@ -43,12 +43,16 @@ We've tested compatibility on some famous models, following models may not be su Compatibility problems will be fixed in the future. +> ⚠ This plugin can only load optimizer checkpoint saved by itself with the same number of processes now. This will be fixed in the future. + ### Gemini Plugin This plugin implements Zero-3 with chunk-based and heterogeneous memory management. It can train large models without much loss in speed. It also does not support local gradient accumulation. More details can be found in [Gemini Doc](../features/zero_with_chunk.md). {{ autodoc:colossalai.booster.plugin.GeminiPlugin }} +> ⚠ This plugin can only load optimizer checkpoint saved by itself with the same number of processes now. This will be fixed in the future. + ### Torch DDP Plugin More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel). @@ -62,3 +66,5 @@ More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/genera More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html). {{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }} + + diff --git a/docs/source/zh-Hans/basics/booster_checkpoint.md b/docs/source/zh-Hans/basics/booster_checkpoint.md new file mode 100644 index 000000000000..d75f18c908ba --- /dev/null +++ b/docs/source/zh-Hans/basics/booster_checkpoint.md @@ -0,0 +1,48 @@ +# Booster Checkpoint + +作者: [Hongxin Liu](https://github.com/ver217) + +**前置教程:** +- [Booster API](./booster_api.md) + +## 引言 + +我们在之前的教程中介绍了 [Booster API](./booster_api.md)。在本教程中，我们将介绍如何使用 booster 保存和加载 checkpoint。 + +## 模型 Checkpoint + +{{ autodoc:colossalai.booster.Booster.save_model }} + +模型在保存前必须被 `colossalai.booster.Booster` 加速。 `checkpoint` 是要保存的 checkpoint 的路径。 如果 `shard=False`，它就是文件。 否则, 它就是文件夹。如果 `shard=True`，checkpoint 将以分片方式保存。当 checkpoint 太大而无法保存在单个文件中时，这很有用。我们的分片 checkpoint 格式与 [huggingface/transformers](https://github.com/huggingface/transformers) 兼容。 + +{{ autodoc:colossalai.booster.Booster.load_model }} + +模型在加载前必须被 `colossalai.booster.Booster` 加速。它会自动检测 checkpoint 格式，并以相应的方式加载。 + +## 优化器 Checkpoint + +> ⚠ 尚不支持以分片方式保存优化器 Checkpoint。 + +{{ autodoc:colossalai.booster.Booster.save_optimizer }} + +优化器在保存前必须被 `colossalai.booster.Booster` 加速。 + +{{ autodoc:colossalai.booster.Booster.load_optimizer }} + +优化器在加载前必须被 `colossalai.booster.Booster` 加速。 + +## 学习率调度器 Checkpoint + +{{ autodoc:colossalai.booster.Booster.save_lr_scheduler }} + +学习率调度器在保存前必须被 `colossalai.booster.Booster` 加速。 `checkpoint` 是 checkpoint 文件的本地路径. + +{{ autodoc:colossalai.booster.Booster.load_lr_scheduler }} + +学习率调度器在加载前必须被 `colossalai.booster.Booster` 加速。 `checkpoint` 是 checkpoint 文件的本地路径. + +## Checkpoint 设计 + +有关 Checkpoint 设计的更多详细信息，请参见我们的讨论 [A Unified Checkpoint System Design](https://github.com/hpcaitech/ColossalAI/discussions/3339). + + diff --git a/docs/source/zh-Hans/basics/booster_plugins.md b/docs/source/zh-Hans/basics/booster_plugins.md index e0258eb37932..b15ceb1e3ad5 100644 --- a/docs/source/zh-Hans/basics/booster_plugins.md +++ b/docs/source/zh-Hans/basics/booster_plugins.md @@ -43,12 +43,16 @@ Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累 兼容性问题将在未来修复。 +> ⚠ 该插件现在只能加载自己保存的且具有相同进程数的优化器 Checkpoint。这将在未来得到解决。 + ### Gemini 插件 这个插件实现了基于Chunk内存管理和异构内存管理的 Zero-3。它可以训练大型模型而不会损失太多速度。它也不支持局部梯度累积。更多详细信息，请参阅 [Gemini 文档](../features/zero_with_chunk.md). {{ autodoc:colossalai.booster.plugin.GeminiPlugin }} +> ⚠ 该插件现在只能加载自己保存的且具有相同进程数的优化器 Checkpoint。这将在未来得到解决。 + ### Torch DDP 插件 更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel). @@ -62,3 +66,5 @@ Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累 更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/fsdp.html). {{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }} + + From 3c07a2846ec14bd1af1715b3facae4d16b57fa61 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Fri, 19 May 2023 19:42:31 +0800 Subject: [PATCH 216/413] [plugin] a workaround for zero plugins' optimizer checkpoint (#3780) * [test] refactor torch ddp checkpoint test * [plugin] update low level zero optim checkpoint * [plugin] update gemini optim checkpoint --- colossalai/booster/plugin/gemini_plugin.py | 8 ++ .../booster/plugin/low_level_zero_plugin.py | 15 +- .../test_gemini_checkpoint_io.py | 130 ++++++++++-------- .../test_low_level_zero_checkpoint_io.py | 35 +++-- .../test_torch_ddp_checkpoint_io.py | 11 +- tests/test_checkpoint_io/utils.py | 21 +++ 6 files changed, 133 insertions(+), 87 deletions(-) create mode 100644 tests/test_checkpoint_io/utils.py diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index a3789a39d94b..bb3124642ccf 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -52,8 +52,16 @@ def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather Save optimizer to checkpoint but only on master process. """ # TODO(ver217): optimizer state dict is sharded + warnings.warn('GeminiPlugin does not support save full optimizer checkpoint now. Save it on every process.') + checkpoint = f'{checkpoint}.rank{self.coordinator.rank}' super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor) + def load_optimizer(self, optimizer: Optimizer, checkpoint: str): + warnings.warn( + 'GeminiPlugin can only load optimizer checkpoint saved by itself with the same number of processes.') + checkpoint = f'{checkpoint}.rank{self.coordinator.rank}' + super().load_optimizer(optimizer, checkpoint) + def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): """ Save model to checkpoint but only on master process. diff --git a/colossalai/booster/plugin/low_level_zero_plugin.py b/colossalai/booster/plugin/low_level_zero_plugin.py index edc0b7679686..5d93cf0e33be 100644 --- a/colossalai/booster/plugin/low_level_zero_plugin.py +++ b/colossalai/booster/plugin/low_level_zero_plugin.py @@ -9,7 +9,7 @@ from torch.utils._pytree import tree_map from torch.utils.data import DataLoader -from colossalai.checkpoint_io import CheckpointIO +from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO from colossalai.interface import ModelWrapper, OptimizerWrapper from colossalai.utils import get_current_device from colossalai.zero import zero_model_wrapper, zero_optim_wrapper @@ -32,8 +32,17 @@ def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather """ Save optimizer to checkpoint but only on master process. """ - # TODO(ver217): optimizer state dict is sharded - super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor) + # TODO(ver217): optimizer state dict is sharded, and cannot get full state dict now + warnings.warn( + 'LowLevelZeroPlugin does not support save full optimizer checkpoint now. Save it on every process.') + checkpoint = f'{checkpoint}.rank{self.coordinator.rank}' + GeneralCheckpointIO.save_unsharded_optimizer(self, optimizer, checkpoint, gather_dtensor) + + def load_optimizer(self, optimizer: Optimizer, checkpoint: str): + warnings.warn( + 'LowLevelZeroPlugin can only load optimizer checkpoint saved by itself with the same number of processes.') + checkpoint = f'{checkpoint}.rank{self.coordinator.rank}' + super().load_optimizer(optimizer, checkpoint) class LowLevelZeroModel(ModelWrapper): diff --git a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py index 1e5a2e1c4b44..994412bbc63f 100644 --- a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py @@ -1,87 +1,95 @@ -import tempfile +import os import pytest import torch +import torch.distributed as dist +from utils import shared_tempdir import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin from colossalai.booster.plugin.gemini_plugin import GeminiCheckpointIO +from colossalai.nn.optimizer import HybridAdam from colossalai.testing import check_state_dict_equal, parameterize, rerun_if_address_is_in_use, spawn -from colossalai.utils.cuda import get_current_device -from colossalai.zero import ColoInitContext, ZeroDDP +from colossalai.zero import ZeroDDP from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration from colossalai.zero.gemini.gemini_mgr import GeminiManager -from tests.components_to_test.registry import non_distributed_component_funcs +from tests.kit.model_zoo import model_zoo @parameterize('placement_policy', ['cuda', 'cpu']) -@parameterize('model_name', ['bert']) -@parameterize('use_safetensors', [True, False]) +@parameterize('model_name', ['transformers_bert_for_sequence_classification']) +@parameterize('use_safetensors', [False, True]) def exam_state_dict_with_origin(placement_policy, model_name, use_safetensors: bool): from transformers import BertForSequenceClassification + (model_fn, data_gen_fn, output_transform_fn, _) = next(iter(model_zoo.get_sub_registry(model_name).values())) + bert_model = model_fn() - model_ckpt_dir = tempfile.TemporaryDirectory() - get_components_func = non_distributed_component_funcs.get_callable(model_name) - model_builder, *_ = get_components_func() - with ColoInitContext(device=(get_current_device())): - bert_model = model_builder() - bert_model.config.save_pretrained(save_directory=(model_ckpt_dir.name)) - - config_dict, *_ = search_chunk_configuration(bert_model, search_range_mb=1, search_interval_byte=100) - chunk_manager = ChunkManager(config_dict) - gemini_manager = GeminiManager(placement_policy, chunk_manager) - bert_model = ZeroDDP(bert_model, gemini_manager) - bert_model.train() - - ckpt_io = GeminiCheckpointIO() - if ckpt_io.coordinator.is_master(): + with shared_tempdir() as tempdir: + pretrained_path = os.path.join(tempdir, 'pretrained') + bert_model.config.save_pretrained(save_directory=pretrained_path) + + # TODO(ver217): use boost api + config_dict, *_ = search_chunk_configuration(bert_model, search_range_mb=1, search_interval_byte=100) + chunk_manager = ChunkManager(config_dict) + gemini_manager = GeminiManager(placement_policy, chunk_manager) + bert_model = ZeroDDP(bert_model, gemini_manager) + bert_model.train() + + ckpt_io = GeminiCheckpointIO() model_size = sum(p.numel() * p.element_size() for p in bert_model.parameters()) / 1024**2 - ckpt_io.save_model(bert_model, (model_ckpt_dir.name), + ckpt_io.save_model(bert_model, (pretrained_path), True, True, '', (model_size / 3), use_safetensors=use_safetensors) - new_bert_model = BertForSequenceClassification.from_pretrained(model_ckpt_dir.name) - check_state_dict_equal(bert_model.state_dict(only_rank_0=True, dtype=(torch.float32)), + dist.barrier() + new_bert_model = BertForSequenceClassification.from_pretrained(pretrained_path) + check_state_dict_equal(bert_model.state_dict(only_rank_0=False, dtype=torch.float32), new_bert_model.state_dict(), False) - model_ckpt_dir.cleanup() @parameterize('placement_policy', ['cuda', 'cpu']) -@parameterize('model_name', ['gpt2', 'bert']) -@parameterize('use_safetensors', [True, False]) -def exam_state_dict(placement_policy, model_name: str, use_safetensors: bool): - get_components_func = non_distributed_component_funcs.get_callable(model_name) - model_builder, *_ = get_components_func() - with ColoInitContext(device=(get_current_device())): - model = model_builder() - new_model = model_builder() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) - chunk_manager = ChunkManager(config_dict) - gemini_manager = GeminiManager(placement_policy, chunk_manager) - model = ZeroDDP(model, gemini_manager) - - model.train() - #new model - new_config_dict, *_ = search_chunk_configuration(new_model, search_range_mb=1, search_interval_byte=100) - new_chunk_manager = ChunkManager(new_config_dict) - new_gemini_manager = GeminiManager(placement_policy, new_chunk_manager) - new_model = ZeroDDP(new_model, new_gemini_manager) - - model_ckpt_dir = tempfile.TemporaryDirectory() - ckpt_io = GeminiCheckpointIO() - model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2 - ckpt_io.save_model(model, (model_ckpt_dir.name), - True, - True, - 'epoch', (model_size / 3), - use_safetensors=use_safetensors) - - if ckpt_io.coordinator.is_master(): - ckpt_io.load_model(new_model, (model_ckpt_dir.name), strict=True) - model_dict = model.state_dict(only_rank_0=True) - new_model_dict = new_model.state_dict(only_rank_0=True) - check_state_dict_equal(model_dict, new_model_dict, False) - model_ckpt_dir.cleanup() +@parameterize('shard', [True, False]) +@parameterize('model_name', ['transformers_gpt']) +def exam_state_dict(placement_policy, shard: bool, model_name: str): + (model_fn, data_gen_fn, output_transform_fn, _) = next(iter(model_zoo.get_sub_registry(model_name).values())) + criterion = lambda x: x.mean() + plugin = GeminiPlugin(placement_policy=placement_policy) + booster = Booster(plugin=plugin) + + model = model_fn() + new_model = model_fn() + optimizer = HybridAdam(model.parameters(), lr=0.001) + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + new_optimizer = HybridAdam(new_model.parameters(), lr=0.001) + new_model, new_optimizer, criterion, _, _ = booster.boost(new_model, new_optimizer, criterion) + + data = data_gen_fn() + data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()} + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + + booster.backward(loss, optimizer) + optimizer.step() + + with shared_tempdir() as tempdir: + model_ckpt_path = f"{tempdir}/model" + optimizer_ckpt_path = f"{tempdir}/optimizer" + booster.save_model(model, model_ckpt_path) + if not shard: + # TODO(ver217): optimizer checkpointing is not supported for sharded checkpoint + booster.save_optimizer(optimizer, optimizer_ckpt_path) + dist.barrier() + + booster.load_model(new_model, model_ckpt_path) + check_state_dict_equal(model.unwrap().state_dict(only_rank_0=False), + new_model.unwrap().state_dict(only_rank_0=False), False) + if not shard: + booster.load_optimizer(new_optimizer, optimizer_ckpt_path) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) def run_dist(rank, world_size, port): @@ -92,7 +100,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist -@pytest.mark.parametrize('world_size', [4, 4]) +@pytest.mark.parametrize('world_size', [2]) @rerun_if_address_is_in_use() def test_gemini_ckpIO(world_size): spawn(run_dist, world_size) diff --git a/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py b/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py index a5a0adea91a3..c51b54c82f57 100644 --- a/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py @@ -1,13 +1,11 @@ -import tempfile - -import pytest import torch +import torch.distributed as dist from torchvision.models import resnet18 +from utils import shared_tempdir import colossalai from colossalai.booster import Booster from colossalai.booster.plugin import LowLevelZeroPlugin -from colossalai.booster.plugin.low_level_zero_plugin import LowLevelZeroCheckpointIO from colossalai.nn.optimizer import HybridAdam from colossalai.testing import ( check_state_dict_equal, @@ -20,7 +18,8 @@ @clear_cache_before_run() @parameterize('stage', [2]) -def check_low_level_zero_checkpointIO(stage: int): +@parameterize('shard', [True, False]) +def check_low_level_zero_checkpointIO(stage: int, shard: bool): plugin = LowLevelZeroPlugin(stage=stage, max_norm=1.0, initial_scale=32) booster = Booster(plugin=plugin) model = resnet18() @@ -34,17 +33,25 @@ def check_low_level_zero_checkpointIO(stage: int): loss = criterion(output) booster.backward(loss, optimizer) optimizer.step() + with shared_tempdir() as tempdir: + model_ckpt_path = f"{tempdir}/model" + optimizer_ckpt_path = f"{tempdir}/optimizer" + # lr scheduler is tested in test_torch_ddp_checkpoint_io.py and low level zero does not change it, we can skip it here + booster.save_model(model, model_ckpt_path, shard=shard) + if not shard: + # TODO(ver217): optimizer checkpointing is not supported for sharded checkpoint + booster.save_optimizer(optimizer, optimizer_ckpt_path) + dist.barrier() - optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile() - ckpt_io = LowLevelZeroCheckpointIO() - ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name) + new_model = resnet18() + new_optimizer = HybridAdam((new_model.parameters()), lr=0.001) + new_model, new_optimizer, _, _, _ = booster.boost(new_model, new_optimizer) - new_model = resnet18() - new_optimizer = HybridAdam((new_model.parameters()), lr=0.001) - _, new_optimizer, _, _, _ = booster.boost(new_model, new_optimizer) - if ckpt_io.coordinator.is_master(): - ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name) - check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) + booster.load_model(new_model, model_ckpt_path) + check_state_dict_equal(model.state_dict(), new_model.state_dict(), False) + if not shard: + booster.load_optimizer(new_optimizer, optimizer_ckpt_path) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) def run_dist(rank, world_size, port): diff --git a/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py index 8a4217941fe3..5501ee4e3ef2 100644 --- a/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py @@ -1,10 +1,9 @@ -import tempfile - import torch import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import SGD from torchvision.models import resnet18 +from utils import shared_tempdir import colossalai from colossalai.booster import Booster @@ -35,11 +34,7 @@ def check_torch_ddp_checkpointIO(shard: bool): optimizer.step() scheduler.step() - with tempfile.TemporaryDirectory() as tempdir: - obj = [tempdir] - dist.broadcast_object_list(obj, src=0) - tempdir = obj[0] # use the same directory on all ranks - + with shared_tempdir() as tempdir: model_ckpt_path = f"{tempdir}/model" optimizer_ckpt_path = f"{tempdir}/optimizer" lr_scheduler_ckpt_path = f"{tempdir}/lr_scheduler" @@ -66,8 +61,6 @@ def check_torch_ddp_checkpointIO(shard: bool): booster.load_lr_scheduler(new_scheduler, lr_scheduler_ckpt_path) check_state_dict_equal(scheduler.state_dict(), new_scheduler.state_dict(), False) - dist.barrier() - def run_dist(rank, world_size, port): colossalai.launch(config=(dict()), rank=rank, world_size=world_size, port=port, host='localhost') diff --git a/tests/test_checkpoint_io/utils.py b/tests/test_checkpoint_io/utils.py new file mode 100644 index 000000000000..2d35e157f446 --- /dev/null +++ b/tests/test_checkpoint_io/utils.py @@ -0,0 +1,21 @@ +import tempfile +from contextlib import contextmanager, nullcontext +from typing import Iterator + +import torch.distributed as dist + + +@contextmanager +def shared_tempdir() -> Iterator[str]: + """ + A temporary directory that is shared across all processes. + """ + ctx_fn = tempfile.TemporaryDirectory if dist.get_rank() == 0 else nullcontext + with ctx_fn() as tempdir: + try: + obj = [tempdir] + dist.broadcast_object_list(obj, src=0) + tempdir = obj[0] # use the same directory on all ranks + yield tempdir + finally: + dist.barrier() From 72688adb2f3ff6cc6302d95d7fced8d41d243270 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 22 May 2023 10:56:47 +0800 Subject: [PATCH 217/413] [doc] add booster docstring and fix autodoc (#3789) * [doc] add docstr for booster methods * [doc] fix autodoc --- colossalai/booster/booster.py | 14 ++++++++++++++ docs/source/en/basics/booster_api.md | 18 ------------------ docs/source/en/features/cluster_utils.md | 18 +----------------- docs/source/zh-Hans/basics/booster_api.md | 18 ------------------ docs/source/zh-Hans/features/cluster_utils.md | 18 +----------------- 5 files changed, 16 insertions(+), 70 deletions(-) diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 6f2adaf03074..4055e55df5ef 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -130,6 +130,12 @@ def boost( return model, optimizer, criterion, dataloader, lr_scheduler def backward(self, loss: torch.Tensor, optimizer: Optimizer) -> None: + """Backward pass. + + Args: + loss (torch.Tensor): The loss to be backpropagated. + optimizer (Optimizer): The optimizer to be updated. + """ # TODO: implement this method with plugin optimizer.backward(loss) @@ -146,6 +152,14 @@ def execute_pipeline(self, pass def no_sync(self, model: nn.Module) -> contextmanager: + """Context manager to disable gradient synchronization across DP process groups. + + Args: + model (nn.Module): The model to be disabled gradient synchronization. + + Returns: + contextmanager: Context to disable gradient synchronization. + """ assert self.plugin is not None, f'no_sync is only enabled when a plugin is provided and the plugin supports no_sync.' assert self.plugin.support_no_sync, f'The plugin {self.plugin.__class__.__name__} does not support no_sync.' return self.plugin.no_sync(model) diff --git a/docs/source/en/basics/booster_api.md b/docs/source/en/basics/booster_api.md index 18dec4500f76..cafcb6d432c3 100644 --- a/docs/source/en/basics/booster_api.md +++ b/docs/source/en/basics/booster_api.md @@ -25,24 +25,6 @@ Plugin is an important component that manages parallel configuration (eg: The ge {{ autodoc:colossalai.booster.Booster }} -{{ autodoc:colossalai.booster.Booster.boost }} - -{{ autodoc:colossalai.booster.Booster.backward }} - -{{ autodoc:colossalai.booster.Booster.no_sync }} - -{{ autodoc:colossalai.booster.Booster.save_model }} - -{{ autodoc:colossalai.booster.Booster.load_model }} - -{{ autodoc:colossalai.booster.Booster.save_optimizer }} - -{{ autodoc:colossalai.booster.Booster.load_optimizer }} - -{{ autodoc:colossalai.booster.Booster.save_lr_scheduler }} - -{{ autodoc:colossalai.booster.Booster.load_lr_scheduler }} - ## Usage In a typical workflow, you should launch distributed environment at the beginning of training script and create objects needed (such as models, optimizers, loss function, data loaders etc.) firstly, then call `colossalai.booster` to inject features into these objects, After that, you can use our booster APIs and these returned objects to continue the rest of your training processes. diff --git a/docs/source/en/features/cluster_utils.md b/docs/source/en/features/cluster_utils.md index 1903d64d2563..7331d5e73ae0 100644 --- a/docs/source/en/features/cluster_utils.md +++ b/docs/source/en/features/cluster_utils.md @@ -13,20 +13,4 @@ We provide a utility class `colossalai.cluster.DistCoordinator` to coordinate di {{ autodoc:colossalai.cluster.DistCoordinator }} -{{ autodoc:colossalai.cluster.DistCoordinator.is_master }} - -{{ autodoc:colossalai.cluster.DistCoordinator.is_node_master }} - -{{ autodoc:colossalai.cluster.DistCoordinator.is_last_process }} - -{{ autodoc:colossalai.cluster.DistCoordinator.print_on_master }} - -{{ autodoc:colossalai.cluster.DistCoordinator.print_on_node_master }} - -{{ autodoc:colossalai.cluster.DistCoordinator.priority_execution }} - -{{ autodoc:colossalai.cluster.DistCoordinator.destroy }} - -{{ autodoc:colossalai.cluster.DistCoordinator.block_all }} - -{{ autodoc:colossalai.cluster.DistCoordinator.on_master_only }} + diff --git a/docs/source/zh-Hans/basics/booster_api.md b/docs/source/zh-Hans/basics/booster_api.md index 5410cc213fd2..1bb5fd69bd15 100644 --- a/docs/source/zh-Hans/basics/booster_api.md +++ b/docs/source/zh-Hans/basics/booster_api.md @@ -25,24 +25,6 @@ Booster插件是管理并行配置的重要组件（eg：gemini插件封装了ge {{ autodoc:colossalai.booster.Booster }} -{{ autodoc:colossalai.booster.Booster.boost }} - -{{ autodoc:colossalai.booster.Booster.backward }} - -{{ autodoc:colossalai.booster.Booster.no_sync }} - -{{ autodoc:colossalai.booster.Booster.save_model }} - -{{ autodoc:colossalai.booster.Booster.load_model }} - -{{ autodoc:colossalai.booster.Booster.save_optimizer }} - -{{ autodoc:colossalai.booster.Booster.load_optimizer }} - -{{ autodoc:colossalai.booster.Booster.save_lr_scheduler }} - -{{ autodoc:colossalai.booster.Booster.load_lr_scheduler }} - ## 使用方法及示例 在使用colossalai训练时，首先需要在训练脚本的开头启动分布式环境，并创建需要使用的模型、优化器、损失函数、数据加载器等对象。之后，调用`colossalai.booster` 将特征注入到这些对象中，您就可以使用我们的booster API去进行您接下来的训练流程。 diff --git a/docs/source/zh-Hans/features/cluster_utils.md b/docs/source/zh-Hans/features/cluster_utils.md index ca787a869041..f54a72c63a66 100644 --- a/docs/source/zh-Hans/features/cluster_utils.md +++ b/docs/source/zh-Hans/features/cluster_utils.md @@ -13,20 +13,4 @@ {{ autodoc:colossalai.cluster.DistCoordinator }} -{{ autodoc:colossalai.cluster.DistCoordinator.is_master }} - -{{ autodoc:colossalai.cluster.DistCoordinator.is_node_master }} - -{{ autodoc:colossalai.cluster.DistCoordinator.is_last_process }} - -{{ autodoc:colossalai.cluster.DistCoordinator.print_on_master }} - -{{ autodoc:colossalai.cluster.DistCoordinator.print_on_node_master }} - -{{ autodoc:colossalai.cluster.DistCoordinator.priority_execution }} - -{{ autodoc:colossalai.cluster.DistCoordinator.destroy }} - -{{ autodoc:colossalai.cluster.DistCoordinator.block_all }} - -{{ autodoc:colossalai.cluster.DistCoordinator.on_master_only }} + From d9393b85f1353d14f22c78a9b2d23edf7ce6e9cf Mon Sep 17 00:00:00 2001 From: Yanjia0 <42895286+Yanjia0@users.noreply.github.com> Date: Mon, 22 May 2023 11:12:53 +0800 Subject: [PATCH 218/413] [doc] add deprecated warning on doc Basics section (#3754) * Update colotensor_concept.md * Update configure_parallelization.md * Update define_your_config.md * Update engine_trainer.md * Update initialize_features.md * Update model_checkpoint.md * Update colotensor_concept.md * Update configure_parallelization.md * Update define_your_config.md * Update engine_trainer.md * Update initialize_features.md * Update model_checkpoint.md --- docs/source/en/basics/colotensor_concept.md | 2 ++ docs/source/en/basics/configure_parallelization.md | 2 ++ docs/source/en/basics/define_your_config.md | 3 +++ docs/source/en/basics/engine_trainer.md | 2 ++ docs/source/en/basics/initialize_features.md | 2 ++ docs/source/en/basics/model_checkpoint.md | 2 ++ docs/source/zh-Hans/basics/colotensor_concept.md | 2 ++ .../source/zh-Hans/basics/configure_parallelization.md | 2 ++ docs/source/zh-Hans/basics/define_your_config.md | 2 ++ docs/source/zh-Hans/basics/engine_trainer.md | 2 ++ docs/source/zh-Hans/basics/initialize_features.md | 2 ++ docs/source/zh-Hans/basics/model_checkpoint.md | 10 ++++++---- 12 files changed, 29 insertions(+), 4 deletions(-) diff --git a/docs/source/en/basics/colotensor_concept.md b/docs/source/en/basics/colotensor_concept.md index 909c5e4d3c6f..050f2ef9f092 100644 --- a/docs/source/en/basics/colotensor_concept.md +++ b/docs/source/en/basics/colotensor_concept.md @@ -2,6 +2,8 @@ Author: [Jiarui Fang](https://github.com/feifeibear), [Hongxin Liu](https://github.com/ver217) and [Haichen Huang](https://github.com/1SAA) +> ⚠️ The information on this page is outdated and will be deprecated. + **Prerequisite:** - [Colossal-AI Overview](../concepts/colossalai_overview.md) - [Distributed Training](../concepts/distributed_training.md) diff --git a/docs/source/en/basics/configure_parallelization.md b/docs/source/en/basics/configure_parallelization.md index 4ac0299eac14..fd1e72ccd45a 100644 --- a/docs/source/en/basics/configure_parallelization.md +++ b/docs/source/en/basics/configure_parallelization.md @@ -2,6 +2,8 @@ Author: Shenggui Li, Siqi Mai +> ⚠️ The information on this page is outdated and will be deprecated. Please check [Booster Plugins](../basics/booster_plugins.md) for more information. + **Prerequisite:** - [Distributed Training](../concepts/distributed_training.md) - [Paradigms of Parallelism](../concepts/paradigms_of_parallelism.md) diff --git a/docs/source/en/basics/define_your_config.md b/docs/source/en/basics/define_your_config.md index d2569691b7dc..048ffcacbb8f 100644 --- a/docs/source/en/basics/define_your_config.md +++ b/docs/source/en/basics/define_your_config.md @@ -2,6 +2,9 @@ Author: Guangyang Lu, Shenggui Li, Siqi Mai +> ⚠️ The information on this page is outdated and will be deprecated. Please check [Booster API](../basics/booster_api.md) for more information. + + **Prerequisite:** - [Distributed Training](../concepts/distributed_training.md) - [Colossal-AI Overview](../concepts/colossalai_overview.md) diff --git a/docs/source/en/basics/engine_trainer.md b/docs/source/en/basics/engine_trainer.md index bbe32ed5a3b5..d2f99563f042 100644 --- a/docs/source/en/basics/engine_trainer.md +++ b/docs/source/en/basics/engine_trainer.md @@ -2,6 +2,8 @@ Author: Shenggui Li, Siqi Mai +> ⚠️ The information on this page is outdated and will be deprecated. Please check [Booster API](../basics/booster_api.md) for more information. + **Prerequisite:** - [Initialize Features](./initialize_features.md) diff --git a/docs/source/en/basics/initialize_features.md b/docs/source/en/basics/initialize_features.md index e768d2022ad8..b89017427476 100644 --- a/docs/source/en/basics/initialize_features.md +++ b/docs/source/en/basics/initialize_features.md @@ -2,6 +2,8 @@ Author: Shenggui Li, Siqi Mai +> ⚠️ The information on this page is outdated and will be deprecated. Please check [Booster API](../basics/booster_api.md) for more information. + **Prerequisite:** - [Distributed Training](../concepts/distributed_training.md) - [Colossal-AI Overview](../concepts/colossalai_overview.md) diff --git a/docs/source/en/basics/model_checkpoint.md b/docs/source/en/basics/model_checkpoint.md index 09d44e7c2709..70334f1c41e7 100644 --- a/docs/source/en/basics/model_checkpoint.md +++ b/docs/source/en/basics/model_checkpoint.md @@ -2,6 +2,8 @@ Author : Guangyang Lu +> ⚠️ The information on this page is outdated and will be deprecated. Please check [Booster Checkpoint](../basics/booster_checkpoint.md) for more information. + **Prerequisite:** - [Launch Colossal-AI](./launch_colossalai.md) - [Initialize Colossal-AI](./initialize_features.md) diff --git a/docs/source/zh-Hans/basics/colotensor_concept.md b/docs/source/zh-Hans/basics/colotensor_concept.md index d6a332df2e9c..b725e48a7cb1 100644 --- a/docs/source/zh-Hans/basics/colotensor_concept.md +++ b/docs/source/zh-Hans/basics/colotensor_concept.md @@ -2,6 +2,8 @@ Author: [Jiarui Fang](https://github.com/feifeibear), [Hongxin Liu](https://github.com/ver217) and [Haichen Huang](https://github.com/1SAA) +> ⚠️ 此页面上的信息已经过时并将被废弃。 + **Prerequisite:** - [Colossal-AI Overview](../concepts/colossalai_overview.md) - [Distributed Training](../concepts/distributed_training.md) diff --git a/docs/source/zh-Hans/basics/configure_parallelization.md b/docs/source/zh-Hans/basics/configure_parallelization.md index eb4b38f48ddb..0c2a66572d60 100644 --- a/docs/source/zh-Hans/basics/configure_parallelization.md +++ b/docs/source/zh-Hans/basics/configure_parallelization.md @@ -2,6 +2,8 @@ 作者: Shenggui Li, Siqi Mai +> ⚠️ 此页面上的信息已经过时并将被废弃。请在[Booster插件](../basics/booster_plugins.md)页面查阅更新。 + **预备知识:** - [分布式训练](../concepts/distributed_training.md) - [并行技术](../concepts/paradigms_of_parallelism.md) diff --git a/docs/source/zh-Hans/basics/define_your_config.md b/docs/source/zh-Hans/basics/define_your_config.md index d7e49cbf23de..720e75805e8d 100644 --- a/docs/source/zh-Hans/basics/define_your_config.md +++ b/docs/source/zh-Hans/basics/define_your_config.md @@ -2,6 +2,8 @@ 作者: Guangyang Lu, Shenggui Li, Siqi Mai +> ⚠️ 此页面上的信息已经过时并将被废弃。请在[Booster API](../basics/booster_api.md)页面查阅更新。 + **预备知识:** - [分布式训练](../concepts/distributed_training.md) - [Colossal-AI 总览](../concepts/colossalai_overview.md) diff --git a/docs/source/zh-Hans/basics/engine_trainer.md b/docs/source/zh-Hans/basics/engine_trainer.md index a7519bfca14f..a35bd87c44e1 100644 --- a/docs/source/zh-Hans/basics/engine_trainer.md +++ b/docs/source/zh-Hans/basics/engine_trainer.md @@ -2,6 +2,8 @@ 作者: Shenggui Li, Siqi Mai +> ⚠️ 此页面上的信息已经过时并将被废弃。请在[Booster API](../basics/booster_api.md)页面查阅更新。 + **预备知识:** - [初始化功能](./initialize_features.md) diff --git a/docs/source/zh-Hans/basics/initialize_features.md b/docs/source/zh-Hans/basics/initialize_features.md index 67ea114b42b2..1c28d658e1bc 100644 --- a/docs/source/zh-Hans/basics/initialize_features.md +++ b/docs/source/zh-Hans/basics/initialize_features.md @@ -2,6 +2,8 @@ 作者: Shenggui Li, Siqi Mai +> ⚠️ 此页面上的信息已经过时并将被废弃。请在[Booster API](../basics/booster_api.md)页面查阅更新。 + **预备知识:** - [分布式训练](../concepts/distributed_training.md) - [Colossal-AI 总览](../concepts/colossalai_overview.md) diff --git a/docs/source/zh-Hans/basics/model_checkpoint.md b/docs/source/zh-Hans/basics/model_checkpoint.md index cec12d451989..a5374b7509c9 100644 --- a/docs/source/zh-Hans/basics/model_checkpoint.md +++ b/docs/source/zh-Hans/basics/model_checkpoint.md @@ -1,7 +1,9 @@ -# 模型检查点 +# 模型Checkpoint 作者 : Guangyang Lu +> ⚠️ 此页面上的信息已经过时并将被废弃。请在[Booster Checkpoint](../basics/booster_checkpoint.md)页面查阅更新。 + **预备知识:** - [Launch Colossal-AI](./launch_colossalai.md) - [Initialize Colossal-AI](./initialize_features.md) @@ -13,9 +15,9 @@ ## 简介 -本教程将介绍如何保存和加载模型检查点。 +本教程将介绍如何保存和加载模型Checkpoint。 -为了充分利用Colossal-AI的强大并行策略，我们需要修改模型和张量，可以直接使用 `torch.save` 或者 `torch.load` 保存或加载模型检查点。在Colossal-AI中，我们提供了应用程序接口实现上述同样的效果。 +为了充分利用Colossal-AI的强大并行策略，我们需要修改模型和张量，可以直接使用 `torch.save` 或者 `torch.load` 保存或加载模型Checkpoint。在Colossal-AI中，我们提供了应用程序接口实现上述同样的效果。 但是，在加载时，你不需要使用与存储相同的保存策略。 @@ -24,7 +26,7 @@ ### 保存 有两种方法可以使用Colossal-AI训练模型，即使用engine或使用trainer。 -**注意我们只保存 `state_dict`.** 因此，在加载检查点时，需要首先定义模型。 +**注意我们只保存 `state_dict`.** 因此，在加载Checkpoint时，需要首先定义模型。 #### 同 engine 保存 From fe1561a88479368753c26f050ead758926cc23f8 Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Mon, 22 May 2023 14:13:15 +0800 Subject: [PATCH 219/413] [doc] update gradient cliping document (#3778) * [doc] update gradient clipping document * [doc] update gradient clipping document * [doc] update gradient clipping document * [doc] update gradient clipping document * [doc] update gradient clipping document * [doc] update gradient clipping document * [doc] update gradient clipping doc, fix sidebars.json * [doc] update gradient clipping doc, fix doc test --- docs/sidebars.json | 1 + docs/source/en/features/gradient_clipping.md | 4 +- .../gradient_clipping_with_booster.md | 142 ++++++++++++++++++ .../zh-Hans/features/gradient_clipping.md | 4 +- .../gradient_clipping_with_booster.md | 140 +++++++++++++++++ 5 files changed, 289 insertions(+), 2 deletions(-) create mode 100644 docs/source/en/features/gradient_clipping_with_booster.md create mode 100644 docs/source/zh-Hans/features/gradient_clipping_with_booster.md diff --git a/docs/sidebars.json b/docs/sidebars.json index 94f79dcd3509..b4a31872dfbb 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -45,6 +45,7 @@ "items": [ "features/mixed_precision_training", "features/gradient_accumulation", + "features/gradient_clipping_with_booster", "features/gradient_clipping", "features/gradient_handler", "features/zero_with_chunk", diff --git a/docs/source/en/features/gradient_clipping.md b/docs/source/en/features/gradient_clipping.md index f606dde6c393..5a23c68e3e27 100644 --- a/docs/source/en/features/gradient_clipping.md +++ b/docs/source/en/features/gradient_clipping.md @@ -1,4 +1,4 @@ -# Gradient Clipping +# Gradient Clipping (Outdated) Author: Boxiang Wang, Haichen Huang, Yongbin Li @@ -60,3 +60,5 @@ to demonstrate gradient clipping. In this example, we set the gradient clipping ```shell python -m torch.distributed.launch --nproc_per_node 1 --master_addr localhost --master_port 29500 train_with_engine.py ``` + + diff --git a/docs/source/en/features/gradient_clipping_with_booster.md b/docs/source/en/features/gradient_clipping_with_booster.md new file mode 100644 index 000000000000..b9c7bb20631c --- /dev/null +++ b/docs/source/en/features/gradient_clipping_with_booster.md @@ -0,0 +1,142 @@ +# Gradient Clipping (Latest) + +Author: [Mingyan Jiang](https://github.com/jiangmingyan) + +**Prerequisite** +- [Define Your Configuration](../basics/define_your_config.md) +- [Training Booster](../basics/booster_api.md) + +**Related Paper** +- [On the difficulty of training Recurrent Neural Networks](https://arxiv.org/abs/1211.5063) + +## Introduction + +In order to speed up training process and seek global optimum for better performance, more and more learning rate schedulers have been proposed. People turn to control learning rate to adjust descent pace during training, which makes gradient vector better to be uniformed in every step. In that case, the descent pace can be controlled as expected. As a result, gradient clipping, a technique which can normalize the gradient vector to circumscribe it in a uniformed length, becomes indispensable for those who desire their better performance of their models. + +You do not have to worry about implementing gradient clipping when using Colossal-AI, we support gradient clipping in a powerful and convenient way. All you need is just an additional command in your configuration file. + +## Why you should use gradient clipping provided by Colossal-AI + +The reason of why we do not recommend users to write gradient clipping by themselves is that naive gradient clipping may fail when applying tensor parallelism, pipeline parallelism or MoE. + +According to the illustration below, each GPU only owns a portion of parameters of the weight in a linear layer. To get correct norm of gradient vector of the weight of the linear layer, the norm of every gradient vector in each GPU should be summed together. More complicated thing is that the distribution of bias is different from the distribution of the weight. The communication group is different in the sum operation. + +(PS: This situation is an old version of 2D parallelism, the implementation in the code is not the same. But it is a good example about the difficulty to unify all communication in gradient clipping.) + +

          + +
          Layout of parameters
          +
          + +Do not worry about it, since Colossal-AI have handled it for you. + +## Usage +To use gradient clipping, you can just add the following code to your configuration file, and after boosted, you can call `clip_grad_by_norm` or `clip_grad_by_value` method of optimizer, if it support clip gradients. + +## Hands-On Practice + +We now demonstrate how to use gradient clipping. In this example, we set the gradient clipping vector norm to be 1.0. + +### step 1. Import libraries in train.py +Create a `train.py` and import the necessary dependencies. + +```python +import os +from pathlib import Path + +import torch +from torchvision import transforms +from torchvision.datasets import CIFAR10 +from torchvision.models import resnet34 +from tqdm import tqdm + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.logging import get_dist_logger +from colossalai.nn.lr_scheduler import CosineAnnealingLR +``` + +### Step 2. Initialize Distributed Environment +We then need to initialize distributed environment. For demo purpose, we uses `launch_from_torch`. You can refer to [Launch Colossal-AI](../basics/launch_colossalai.md) +for other initialization methods. + +```python +colossalai.launch_from_torch(config=dict()) +logger = get_dist_logger() +``` + + +### Step 3. Create training components + +Build your model, optimizer, loss function, lr scheduler and dataloaders. Note that the root path of the dataset is obtained from the environment variable `DATA`. You may `export DATA=/path/to/data` or change `Path(os.environ['DATA'])` to a path on your machine. Data will be automatically downloaded to the root path. +```python +# define training hyperparameters +NUM_EPOCHS = 200 +BATCH_SIZE = 128 +GRADIENT_CLIPPING = 0.1 +# build resnetå +model = resnet34(num_classes=10) +# build dataloaders +train_dataset = CIFAR10(root=Path(os.environ.get('DATA', './data')), + download=True, + transform=transforms.Compose([ + transforms.RandomCrop(size=32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]), + ])) +# build criterion +criterion = torch.nn.CrossEntropyLoss() + +# optimizer +optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4) + +# lr_scheduler +lr_scheduler = CosineAnnealingLR(optimizer, total_steps=NUM_EPOCHS) + +``` +### Step 4. Inject Gradient Clipping Feature + +Create a `TorchDDPPlugin` object and `Booster` object, get a data loader from plugin, then boost all training components. +```python +plugin = TorchDDPPlugin() +booster = Booster(mixed_precision='fp16', plugin=plugin) +train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True) +model, optimizer, criterion, train_dataloader, lr_scheduler = booster.boost(model,optimizer, criterion,train_dataloader, lr_scheduler) + +``` + +### Step 5. Train with Booster +Use booster in a normal training loops. +```python +# verify gradient clipping +model.train() +for idx, (img, label) in enumerate(train_dataloader): + img = img.cuda() + label = label.cuda() + + model.zero_grad() + output = model(img) + train_loss = criterion(output, label) + booster.backward(train_loss, optimizer) + optimizer.clip_grad_by_norm(max_norm=GRADIENT_CLIPPING) + optimizer.step() + lr_scheduler.step() + + ele_1st = next(model.parameters()).flatten()[0] + logger.info(f'iteration {idx}, loss: {train_loss}, 1st element of parameters: {ele_1st.item()}') + + # only run for 4 iterations + if idx == 3: + break +``` + +### Step 6. Invoke Training Scripts +You can run the script using this command: + +```shell +colossalai run --nproc_per_node 1 train.py --config config/config.py +``` + + diff --git a/docs/source/zh-Hans/features/gradient_clipping.md b/docs/source/zh-Hans/features/gradient_clipping.md index 203f66a3fea2..2f62c31766a6 100644 --- a/docs/source/zh-Hans/features/gradient_clipping.md +++ b/docs/source/zh-Hans/features/gradient_clipping.md @@ -1,4 +1,4 @@ -# 梯度裁剪 +# 梯度裁剪（旧版本） 作者: Boxiang Wang, Haichen Huang, Yongbin Li @@ -49,3 +49,5 @@ clip_grad_norm = 1.0 ```shell python -m torch.distributed.launch --nproc_per_node 1 --master_addr localhost --master_port 29500 train_with_engine.py ``` + + diff --git a/docs/source/zh-Hans/features/gradient_clipping_with_booster.md b/docs/source/zh-Hans/features/gradient_clipping_with_booster.md new file mode 100644 index 000000000000..2f023cefe35e --- /dev/null +++ b/docs/source/zh-Hans/features/gradient_clipping_with_booster.md @@ -0,0 +1,140 @@ +# 梯度裁剪 (新版本) + +作者: [Mingyan Jiang](https://github.com/jiangmingyan) + +**前置教程** +- [定义配置文件](../basics/define_your_config.md) +- [booster使用](../basics/booster_api.md) + +**相关论文** +- [On the difficulty of training Recurrent Neural Networks](https://arxiv.org/abs/1211.5063) + +## 引言 + +为了加快训练过程和寻求全局最优以获得更好的性能，越来越多的学习率调度器被提出。人们通过控制学习率来调整训练中的下降速度。这使得梯度向量在每一步都能更好地统一。在这种情况下，下降速度可以按预期被控制。 +因此，梯度裁剪，一种可以将梯度向量归一化，以将其限制在统一长度的技术，对于那些希望模型性能更好的人来说是不可或缺的。 + +在使用 Colossal-AI 时，你不必担心实现梯度剪裁，我们以一种有效而方便的方式支持梯度剪裁。你所需要的只是在你的配置文件中增加一个命令。 + +## 为什么应该使用 Colossal-AI 中的梯度裁剪 + +我们不建议用户自己编写梯度剪裁，因为朴素的梯度剪裁在应用张量并行、流水线并行、MoE 等功能时可能会失败。 + +根据下图，每个 GPU 只拥有线性层中权重的一部分参数。为了得到线性层权重的梯度向量的正确范数，每个 GPU 中的每个梯度向量的范数应该相加。更复杂的是，偏置的分布不同于权重的分布。通信组在求和运算中有所不同。 + +(注: 这种情况是旧版本的 2D 并行，在代码中的实现是不一样的。但这是一个很好的例子，能够说明在梯度剪裁中统一所有通信的困难。) + +
          + +
          参数分布
          +
          + +不用担心它，因为 Colossal-AI 已经为你处理好。 + +### 使用 +要使用梯度裁剪，只需在使用booster注入特性之后，调用optimizer的`clip_grad_by_norm`或者`clip_grad_by_value`函数即可进行梯度裁剪。 + +### 实例 + +下面我们将介绍如何使用梯度裁剪，在本例中，我们将梯度裁剪范数设置为1.0。 + +### 步骤 1. 在训练中导入相关库 +创建`train.py`并导入相关库。 + +```python +import os +from pathlib import Path + +import torch +from torchvision import transforms +from torchvision.datasets import CIFAR10 +from torchvision.models import resnet34 +from tqdm import tqdm + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.logging import get_dist_logger +from colossalai.nn.lr_scheduler import CosineAnnealingLR +``` + +### 步骤 2. 初始化分布式环境 +我们需要初始化分布式环境. 为了快速演示，我们使用`launch_from_torch`. 您可以参考 [Launch Colossal-AI](../basics/launch_colossalai.md) + +```python +colossalai.launch_from_torch(config=dict()) +logger = get_dist_logger() +``` + +### 步骤 3. 创建训练组件 + +构建你的模型、优化器、损失函数、学习率调整器和数据加载器。注意数据集的路径从环境变量`DATA`获得。你可以通过 `export DATA=/path/to/data` 或 `Path(os.environ['DATA'])`在你的机器上设置路径。数据将会被自动下载到该路径。 +```python +# define training hyperparameters +NUM_EPOCHS = 200 +BATCH_SIZE = 128 +GRADIENT_CLIPPING = 0.1 +# build resnet +model = resnet34(num_classes=10) +# build dataloaders +train_dataset = CIFAR10(root=Path(os.environ.get('DATA', './data')), + download=True, + transform=transforms.Compose([ + transforms.RandomCrop(size=32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]), + ])) +# build criterion +criterion = torch.nn.CrossEntropyLoss() + +# optimizer +optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4) + +# lr_scheduler +lr_scheduler = CosineAnnealingLR(optimizer, total_steps=NUM_EPOCHS) + +``` +### 步骤 4. 注入梯度裁剪特性 + +创建`TorchDDPPlugin`对象并初始化`Booster`, 使用booster注入相关特性。 +```python +plugin = TorchDDPPlugin() +booster = Booster(plugin=plugin) +train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True) +model, optimizer, criterion, train_dataloader, lr_scheduler = booster.boost(model,optimizer, criterion,train_dataloader, lr_scheduler) + +``` + +### 步骤 5. 使用booster训练 +使用booster进行训练。 +```python +# verify gradient clipping +model.train() +for idx, (img, label) in enumerate(train_dataloader): + img = img.cuda() + label = label.cuda() + + model.zero_grad() + output = model(img) + train_loss = criterion(output, label) + booster.backward(train_loss, optimizer) + optimizer.clip_grad_by_norm(max_norm=GRADIENT_CLIPPING) + optimizer.step() + lr_scheduler.step() + + ele_1st = next(model.parameters()).flatten()[0] + logger.info(f'iteration {idx}, loss: {train_loss}, 1st element of parameters: {ele_1st.item()}') + + # only run for 4 iterations + if idx == 3: + break +``` + +### 步骤 6. 启动训练脚本 +你可以使用以下命令运行脚本： + +```shell +colossalai run --nproc_per_node 1 train.py --config config/config.py +``` + From 62c7e67f9fce7ca51b20be01ac7daa2f6d3d00ed Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 22 May 2023 14:42:09 +0800 Subject: [PATCH 220/413] [format] applied code formatting on changed files in pull request 3786 (#3787) Co-authored-by: github-actions --- README.md | 6 +++--- applications/Chat/README.md | 6 +++--- docs/README-zh-Hans.md | 6 +++--- examples/tutorial/README.md | 6 +++--- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 2e6dcaa1eaf4..c33caba90128 100644 --- a/README.md +++ b/README.md @@ -132,9 +132,9 @@ distributed training and inference in a few lines.

    -[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. -[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) -[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. +[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) +[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[demo]](https://www.youtube.com/watch?v=HcTiHzApHm0) [[tutorial]](https://www.youtube.com/watch?v=-qFBZFmOJfg) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index bc8481d96de3..29cd581d7cc9 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -73,9 +73,9 @@ More details can be found in the latest news.
    -[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. -[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) -[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline. +[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) +[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[demo]](https://www.youtube.com/watch?v=HcTiHzApHm0) [[tutorial]](https://www.youtube.com/watch?v=-qFBZFmOJfg) diff --git a/docs/README-zh-Hans.md b/docs/README-zh-Hans.md index c3deca7e9c17..1dde7a816676 100644 --- a/docs/README-zh-Hans.md +++ b/docs/README-zh-Hans.md @@ -126,9 +126,9 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
    -[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) -[[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) -[[博客]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) +[[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) +[[博客]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[在线样例]](https://www.youtube.com/watch?v=HcTiHzApHm0) [[教程]](https://www.youtube.com/watch?v=-qFBZFmOJfg) diff --git a/examples/tutorial/README.md b/examples/tutorial/README.md index 933026166d3f..0664d41fd359 100644 --- a/examples/tutorial/README.md +++ b/examples/tutorial/README.md @@ -29,9 +29,9 @@ quickly deploy large AI model training and inference, reducing large AI model tr - Fine-tuning and Inference for OPT [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/opt) [[video]](https://www.youtube.com/watch?v=jbEFNVzl67Y) - Optimized AlphaFold [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/fastfold) [[video]](https://www.youtube.com/watch?v=-zP13LfJP7w) - Optimized Stable Diffusion [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion) [[video]](https://www.youtube.com/watch?v=8KHeUjjc-XQ) - - ColossalChat: Cloning ChatGPT with a Complete RLHF Pipeline -[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) -[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) + - ColossalChat: Cloning ChatGPT with a Complete RLHF Pipeline +[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) +[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[demo]](https://www.youtube.com/watch?v=HcTiHzApHm0) [[video]](https://www.youtube.com/watch?v=-qFBZFmOJfg) From 4d29c0f8e01db18c73479a8e1670fea87162ef2e Mon Sep 17 00:00:00 2001 From: liuzeming <44457852+liuzeming-yuxi@users.noreply.github.com> Date: Mon, 22 May 2023 15:04:00 +0800 Subject: [PATCH 221/413] Fix/docker action (#3266) * [docker] Add ARG VERSION to determine the Tag * [workflow] fixed the version in the release docker workflow --------- Co-authored-by: liuzeming --- .github/workflows/release_docker_after_merge.yml | 2 +- docker/Dockerfile | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release_docker_after_merge.yml b/.github/workflows/release_docker_after_merge.yml index 607c19b05472..6b6684641117 100644 --- a/.github/workflows/release_docker_after_merge.yml +++ b/.github/workflows/release_docker_after_merge.yml @@ -26,7 +26,7 @@ jobs: run: | version=$(cat version.txt) tag=hpcaitech/colossalai:$version - docker build --build-arg http_proxy=http://172.17.0.1:7890 --build-arg https_proxy=http://172.17.0.1:7890 -t $tag ./docker + docker build --build-arg http_proxy=http://172.17.0.1:7890 --build-arg https_proxy=http://172.17.0.1:7890 --build-arg VERSION=v${version} -t $tag ./docker echo "tag=${tag}" >> $GITHUB_OUTPUT - name: Log in to Docker Hub diff --git a/docker/Dockerfile b/docker/Dockerfile index 49ff9b344268..52c7bf5601c6 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -15,7 +15,8 @@ RUN git clone https://github.com/NVIDIA/apex && \ pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" ./ # install colossalai -RUN git clone https://github.com/hpcaitech/ColossalAI.git \ +ARG VERSION=1 +RUN git clone -b ${VERSION} https://github.com/hpcaitech/ColossalAI.git \ && cd ./ColossalAI \ && CUDA_EXT=1 pip install -v --no-cache-dir . From 788e07dbc5dc5acaf34e24d98238780ecf134ef2 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 22 May 2023 16:30:32 +0800 Subject: [PATCH 222/413] [workflow] fixed the docker build workflow (#3794) * [workflow] fixed the docker build workflow * polish code --- ...r_merge.yml => release_docker_after_publish.yml} | 13 +++++-------- .github/workflows/report_test_coverage.yml | 6 +++--- 2 files changed, 8 insertions(+), 11 deletions(-) rename .github/workflows/{release_docker_after_merge.yml => release_docker_after_publish.yml} (86%) diff --git a/.github/workflows/release_docker_after_merge.yml b/.github/workflows/release_docker_after_publish.yml similarity index 86% rename from .github/workflows/release_docker_after_merge.yml rename to .github/workflows/release_docker_after_publish.yml index 6b6684641117..7d83fa3715a8 100644 --- a/.github/workflows/release_docker_after_merge.yml +++ b/.github/workflows/release_docker_after_publish.yml @@ -1,12 +1,9 @@ -name: Publish Docker Image to DockerHub after Merge +name: Publish Docker Image to DockerHub after Publish on: workflow_dispatch: - pull_request: - paths: - - 'version.txt' - types: - - closed + release: + types: [published] jobs: release: @@ -26,7 +23,7 @@ jobs: run: | version=$(cat version.txt) tag=hpcaitech/colossalai:$version - docker build --build-arg http_proxy=http://172.17.0.1:7890 --build-arg https_proxy=http://172.17.0.1:7890 --build-arg VERSION=v${version} -t $tag ./docker + docker build --build-arg http_proxy=http://172.17.0.1:7890 --build-arg https_proxy=http://172.17.0.1:7890 --build-arg VERSION=v${version} -t $tag ./docker echo "tag=${tag}" >> $GITHUB_OUTPUT - name: Log in to Docker Hub @@ -50,7 +47,7 @@ jobs: - uses: actions/setup-python@v2 with: - python-version: '3.8.14' + python-version: "3.8.14" - name: Install requests run: pip install requests diff --git a/.github/workflows/report_test_coverage.yml b/.github/workflows/report_test_coverage.yml index bbada74e6850..d9b131fd994c 100644 --- a/.github/workflows/report_test_coverage.yml +++ b/.github/workflows/report_test_coverage.yml @@ -10,7 +10,7 @@ jobs: report-test-coverage: runs-on: ubuntu-latest steps: - - name: 'Download artifact' + - name: "Download artifact" uses: actions/github-script@v6 with: script: | @@ -31,7 +31,7 @@ jobs: let fs = require('fs'); fs.writeFileSync(`${process.env.GITHUB_WORKSPACE}/report.zip`, Buffer.from(download.data)); - - name: 'Unzip artifact' + - name: "Unzip artifact" id: unzip run: | unzip report.zip @@ -58,7 +58,7 @@ jobs: echo "" >> coverage_report.txt mv coverage_report.txt coverage.txt - - name: 'Comment on PR' + - name: "Comment on PR" if: steps.unzip.outputs.hasReport == 'true' uses: actions/github-script@v6 with: From f5c425c89874f2500600be71b3c9aadad2da822f Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 22 May 2023 18:10:06 +0800 Subject: [PATCH 223/413] fixed the example docstring for booster (#3795) --- colossalai/booster/booster.py | 43 ++++++++++++++++++----------------- 1 file changed, 22 insertions(+), 21 deletions(-) diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 4055e55df5ef..be9c1c9dc5e3 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -23,27 +23,28 @@ class Booster: training with different precision, accelerator, and plugin. Examples: - >>> colossalai.launch(...) - >>> plugin = GeminiPlugin(stage=3, ...) - >>> booster = Booster(precision='fp16', plugin=plugin) - >>> - >>> model = GPT2() - >>> optimizer = Adam(model.parameters()) - >>> dataloader = Dataloader(Dataset) - >>> lr_scheduler = LinearWarmupScheduler() - >>> criterion = GPTLMLoss() - >>> - >>> model, optimizer, lr_scheduler, dataloader = booster.boost(model, optimizer, lr_scheduler, dataloader) - >>> - >>> for epoch in range(max_epochs): - >>> for input_ids, attention_mask in dataloader: - >>> outputs = model(input_ids, attention_mask) - >>> loss = criterion(outputs.logits, input_ids) - >>> booster.backward(loss, optimizer) - >>> optimizer.step() - >>> lr_scheduler.step() - >>> optimizer.zero_grad() - + ```python + colossalai.launch(...) + plugin = GeminiPlugin(stage=3, ...) + booster = Booster(precision='fp16', plugin=plugin) + + model = GPT2() + optimizer = Adam(model.parameters()) + dataloader = Dataloader(Dataset) + lr_scheduler = LinearWarmupScheduler() + criterion = GPTLMLoss() + + model, optimizer, lr_scheduler, dataloader = booster.boost(model, optimizer, lr_scheduler, dataloader) + + for epoch in range(max_epochs): + for input_ids, attention_mask in dataloader: + outputs = model(input_ids, attention_mask) + loss = criterion(outputs.logits, input_ids) + booster.backward(loss, optimizer) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + ``` Args: device (str or torch.device): The device to run the training. Default: 'cuda'. From ef02d7ef6da221e28c5b5709dad80e8b6541fcf1 Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Tue, 23 May 2023 10:52:30 +0800 Subject: [PATCH 224/413] [doc] update gradient accumulation (#3771) * [doc]update gradient accumulation * [doc]update gradient accumulation * [doc]update gradient accumulation * [doc]update gradient accumulation * [doc]update gradient accumulation, fix * [doc]update gradient accumulation, fix * [doc]update gradient accumulation, fix * [doc]update gradient accumulation, add sidebars * [doc]update gradient accumulation, fix * [doc]update gradient accumulation, fix * [doc]update gradient accumulation, fix * [doc]update gradient accumulation, resolve comments * [doc]update gradient accumulation, resolve comments * fix --- docs/sidebars.json | 1 + .../en/features/gradient_accumulation.md | 4 +- .../gradient_accumulation_with_booster.md | 144 +++++++++++++++++ .../zh-Hans/features/gradient_accumulation.md | 3 +- .../gradient_accumulation_with_booster.md | 146 ++++++++++++++++++ 5 files changed, 296 insertions(+), 2 deletions(-) create mode 100644 docs/source/en/features/gradient_accumulation_with_booster.md create mode 100644 docs/source/zh-Hans/features/gradient_accumulation_with_booster.md diff --git a/docs/sidebars.json b/docs/sidebars.json index b4a31872dfbb..1b3ddedd12b1 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -44,6 +44,7 @@ "collapsed": true, "items": [ "features/mixed_precision_training", + "features/gradient_accumulation_with_booster", "features/gradient_accumulation", "features/gradient_clipping_with_booster", "features/gradient_clipping", diff --git a/docs/source/en/features/gradient_accumulation.md b/docs/source/en/features/gradient_accumulation.md index ecc209fbac8d..91d89b815bf7 100644 --- a/docs/source/en/features/gradient_accumulation.md +++ b/docs/source/en/features/gradient_accumulation.md @@ -1,4 +1,4 @@ -# Gradient Accumulation +# Gradient Accumulation (Outdated) Author: Shenggui Li, Yongbin Li @@ -43,3 +43,5 @@ iteration 1, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0 iteration 2, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0047, 0.0116, -0.0283, 0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=) iteration 3, first 10 elements of param: tensor([-0.0141, 0.0464, 0.0507, 0.0321, 0.0356, -0.0150, 0.0172, -0.0118, 0.0222, 0.0473], device='cuda:0', grad_fn=) ``` + + diff --git a/docs/source/en/features/gradient_accumulation_with_booster.md b/docs/source/en/features/gradient_accumulation_with_booster.md new file mode 100644 index 000000000000..f319ef5b2db3 --- /dev/null +++ b/docs/source/en/features/gradient_accumulation_with_booster.md @@ -0,0 +1,144 @@ +# Gradient Accumulation (Latest) + +Author: [Mingyan Jiang](https://github.com/jiangmingyan) + +**Prerequisite** +- [Define Your Configuration](../basics/define_your_config.md) +- [Training Booster](../basics/booster_api.md) + +## Introduction + +Gradient accumulation is a common way to enlarge your batch size for training. When training large-scale models, memory can easily become the bottleneck and the batch size can be very small, (e.g. 2), leading to unsatisfactory convergence. Gradient accumulation works by adding up the gradients calculated in multiple iterations, and only update the parameters in the preset iteration. + +## Usage + +It is simple to use gradient accumulation in Colossal-AI. Just call `booster.no_sync()` which returns a context manager. It accumulate gradients without synchronization, meanwhile you should not update the weights. + +## Hands-on Practice + +We now demonstrate gradient accumulation. In this example, we let the gradient accumulation size to be 4. + +### Step 1. Import libraries in train.py +Create a `train.py` and import the necessary dependencies. The version of `torch` should not be lower than 1.8.1. + +```python +import os +from pathlib import Path + +import torch +from torchvision import transforms +from torchvision.datasets import CIFAR10 +from torchvision.models import resnet18 +from torch.utils.data import DataLoader + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.logging import get_dist_logger +from colossalai.cluster.dist_coordinator import priority_execution +``` + +### Step 2. Initialize Distributed Environment +We then need to initialize distributed environment. For demo purpose, we uses `launch_from_torch`. You can refer to [Launch Colossal-AI](../basics/launch_colossalai.md) for other initialization methods. + +```python +# initialize distributed setting +parser = colossalai.get_default_parser() +args = parser.parse_args() +# launch from torch +colossalai.launch_from_torch(config=dict()) +``` + +### Step 3. Create training components +Build your model, optimizer, loss function, lr scheduler and dataloaders. Note that the root path of the dataset is obtained from the environment variable `DATA`. You may `export DATA=/path/to/data` or change `Path(os.environ['DATA'])` to a path on your machine. Data will be automatically downloaded to the root path. + +```python +# define the training hyperparameters +BATCH_SIZE = 128 +GRADIENT_ACCUMULATION = 4 + +# build resnet +model = resnet18(num_classes=10) + +# build dataloaders +with priority_execution(): + train_dataset = CIFAR10(root=Path(os.environ.get('DATA', './data')), + download=True, + transform=transforms.Compose([ + transforms.RandomCrop(size=32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]), + ])) + +# build criterion +criterion = torch.nn.CrossEntropyLoss() + +# optimizer +optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4) +``` + +### Step 4. Inject Feature +Create a `TorchDDPPlugin` object to instantiate a `Booster`, and boost these training components. + +```python +plugin = TorchDDPPlugin() +booster = Booster(plugin=plugin) +train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True) +model, optimizer, criterion, train_dataloader, _ = booster.boost(model=model, + optimizer=optimizer, + criterion=criterion, + dataloader=train_dataloader) +``` + +### Step 5. Train with Booster +Use booster in a normal training loops, and verify gradient accumulation. `param_by_iter` is to record the distributed training information. +```python +optimizer.zero_grad() +for idx, (img, label) in enumerate(train_dataloader): + sync_context = booster.no_sync(model) + img = img.cuda() + label = label.cuda() + if idx % (GRADIENT_ACCUMULATION - 1) != 0: + with sync_context: + output = model(img) + train_loss = criterion(output, label) + booster.backward(train_loss, optimizer) + else: + output = model(img) + train_loss = criterion(output, label) + booster.backward(train_loss, optimizer) + optimizer.step() + optimizer.zero_grad() + + ele_1st = next(model.parameters()).flatten()[0] + param_by_iter.append(str(ele_1st.item())) + + if idx != 0 and idx % (GRADIENT_ACCUMULATION - 1) == 0: + break + + for iteration, val in enumerate(param_by_iter): + print(f'iteration {iteration} - value: {val}') + + if param_by_iter[-1] != param_by_iter[0]: + print('The parameter is only updated in the last iteration') + +``` + +### Step 6. Invoke Training Scripts +To verify gradient accumulation, we can just check the change of parameter values. When gradient accumulation is set, parameters are only updated in the last step. You can run the script using this command: +```shell +colossalai run --nproc_per_node 1 train.py --config config.py +``` + +You will see output similar to the text below. This shows gradient is indeed accumulated as the parameter is not updated +in the first 3 steps, but only updated in the last step. + +```text +iteration 0, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0047, 0.0116, -0.0283, 0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=) +iteration 1, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0047, 0.0116, -0.0283, 0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=) +iteration 2, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0047, 0.0116, -0.0283, 0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=) +iteration 3, first 10 elements of param: tensor([-0.0141, 0.0464, 0.0507, 0.0321, 0.0356, -0.0150, 0.0172, -0.0118, 0.0222, 0.0473], device='cuda:0', grad_fn=) +``` + + diff --git a/docs/source/zh-Hans/features/gradient_accumulation.md b/docs/source/zh-Hans/features/gradient_accumulation.md index e21e5fcd43d8..fc8b29bbe8f1 100644 --- a/docs/source/zh-Hans/features/gradient_accumulation.md +++ b/docs/source/zh-Hans/features/gradient_accumulation.md @@ -1,4 +1,4 @@ -# 梯度累积 +# 梯度累积 (旧版本) 作者: Shenggui Li, Yongbin Li @@ -38,3 +38,4 @@ iteration 1, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0 iteration 2, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0047, 0.0116, -0.0283, 0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=) iteration 3, first 10 elements of param: tensor([-0.0141, 0.0464, 0.0507, 0.0321, 0.0356, -0.0150, 0.0172, -0.0118, 0.0222, 0.0473], device='cuda:0', grad_fn=) ``` + diff --git a/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md b/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md new file mode 100644 index 000000000000..4dc0b3db4a86 --- /dev/null +++ b/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md @@ -0,0 +1,146 @@ +# 梯度累积 (最新版本) + +作者: [Mingyan Jiang](https://github.com/jiangmingyan) + +**前置教程** +- [定义配置文件](../basics/define_your_config.md) +- [训练中使用Booster](../basics/booster_api.md) + +## 引言 + +梯度累积是一种常见的增大训练 batch size 的方式。 在训练大模型时，内存经常会成为瓶颈，并且 batch size 通常会很小（如2），这导致收敛性无法保证。梯度累积将多次迭代的梯度累加，并仅在达到预设迭代次数时更新参数。 + +## 使用 + +在 Colossal-AI 中使用梯度累积非常简单，booster提供no_sync返回一个上下文管理器，在该上下文管理器下取消同步并且累积梯度。 + +## 实例 + +我们将介绍如何使用梯度累积。在这个例子中，梯度累积次数被设置为4。 + +### 步骤 1. 在 train.py 导入相关库 +创建train.py并导入必要依赖。 `torch` 的版本应不低于1.8.1。 + +```python +import os +from pathlib import Path + +import torch +from torchvision import transforms +from torchvision.datasets import CIFAR10 +from torchvision.models import resnet18 + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.logging import get_dist_logger +from colossalai.cluster.dist_coordinator import priority_execution +``` + +### 步骤 2. 初始化分布式环境 + +我们需要初始化分布式环境。为了快速演示，我们使用`launch_from_torch`。你可以参考 [Launch Colossal-AI](../basics/launch_colossalai.md)使用其他初始化方法。 + +```python +# initialize distributed setting +parser = colossalai.get_default_parser() +args = parser.parse_args() + +# launch from torch +colossalai.launch_from_torch(config=dict()) + +``` + +### 步骤 3. 创建训练组件 + +构建你的模型、优化器、损失函数、学习率调整器和数据加载器。注意数据集的路径从环境变量`DATA`获得。你可以通过 `export DATA=/path/to/data` 或 `Path(os.environ['DATA'])`，在你的机器上设置路径。数据将会被自动下载到该路径。 + +```python +# define the training hyperparameters +BATCH_SIZE = 128 +GRADIENT_ACCUMULATION = 4 + +# build resnet +model = resnet18(num_classes=10) + +# build dataloaders +with priority_execution(): + train_dataset = CIFAR10(root=Path(os.environ.get('DATA', './data')), + download=True, + transform=transforms.Compose([ + transforms.RandomCrop(size=32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]), + ])) + +# build criterion +criterion = torch.nn.CrossEntropyLoss() + +# optimizer +optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4) +``` + +### 步骤 4. 注入特性 +创建一个`TorchDDPPlugin`对象，并作为参实例化`Booster`， 调用`booster.boost`注入特性。 + +```python +plugin = TorchDDPPlugin() +booster = Booster(plugin=plugin) +train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True) +model, optimizer, criterion, train_dataloader, _ = booster.boost(model=model, + optimizer=optimizer, + criterion=criterion, + dataloader=train_dataloader) +``` + +### 步骤 5. 使用booster训练 +使用booster构建一个普通的训练循环，验证梯度累积。 `param_by_iter` 记录分布训练的信息。 +```python +optimizer.zero_grad() +for idx, (img, label) in enumerate(train_dataloader): + sync_context = booster.no_sync(model) + img = img.cuda() + label = label.cuda() + if idx % (GRADIENT_ACCUMULATION - 1) != 0: + with sync_context: + output = model(img) + train_loss = criterion(output, label) + booster.backward(train_loss, optimizer) + else: + output = model(img) + train_loss = criterion(output, label) + booster.backward(train_loss, optimizer) + optimizer.step() + optimizer.zero_grad() + + ele_1st = next(model.parameters()).flatten()[0] + param_by_iter.append(str(ele_1st.item())) + + if idx != 0 and idx % (GRADIENT_ACCUMULATION - 1) == 0: + break + + for iteration, val in enumerate(param_by_iter): + print(f'iteration {iteration} - value: {val}') + + if param_by_iter[-1] != param_by_iter[0]: + print('The parameter is only updated in the last iteration') + +``` + +### 步骤 6. 启动训练脚本 +为了验证梯度累积，我们可以只检查参数值的变化。当设置梯度累加时，仅在最后一步更新参数。您可以使用以下命令运行脚本： +```shell +colossalai run --nproc_per_node 1 train.py --config config.py +``` + +你将会看到类似下方的文本输出。这展现了梯度虽然在前3个迭代中被计算，但直到最后一次迭代，参数才被更新。 + +```text +iteration 0, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0047, 0.0116, -0.0283, 0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=) +iteration 1, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0047, 0.0116, -0.0283, 0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=) +iteration 2, first 10 elements of param: tensor([-0.0208, 0.0189, 0.0234, 0.0047, 0.0116, -0.0283, 0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=) +iteration 3, first 10 elements of param: tensor([-0.0141, 0.0464, 0.0507, 0.0321, 0.0356, -0.0150, 0.0172, -0.0118, 0.0222, 0.0473], device='cuda:0', grad_fn=) +``` + + From ad93c736ea5fd392e95e0a74bcaf8609de0b70e6 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 23 May 2023 11:21:15 +0800 Subject: [PATCH 225/413] [workflow] enable testing for develop & feature branch (#3801) --- .github/workflows/build_on_pr.yml | 25 ++++++++++--------- .github/workflows/doc_check_on_pr.yml | 27 ++++++++++---------- .github/workflows/doc_test_on_pr.yml | 30 ++++++++++++----------- .github/workflows/example_check_on_pr.yml | 26 +++++++++++--------- 4 files changed, 58 insertions(+), 50 deletions(-) diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index a9e50e231164..53355f560911 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -3,24 +3,27 @@ name: Build on PR on: pull_request: types: [synchronize, opened, reopened] + branches: + - "main" + - "develop" + - "feature/**" paths: - - '.github/workflows/build_on_pr.yml' # run command & env variables change - - 'colossalai/**' # source code change - - '!colossalai/**.md' # ignore doc change - - 'op_builder/**' # cuda extension change - - '!op_builder/**.md' # ignore doc change - - 'requirements/**' # requirements change - - 'tests/**' # test change - - '!tests/**.md' # ignore doc change - - 'pytest.ini' # test config change - - 'setup.py' # install command change + - ".github/workflows/build_on_pr.yml" # run command & env variables change + - "colossalai/**" # source code change + - "!colossalai/**.md" # ignore doc change + - "op_builder/**" # cuda extension change + - "!op_builder/**.md" # ignore doc change + - "requirements/**" # requirements change + - "tests/**" # test change + - "!tests/**.md" # ignore doc change + - "pytest.ini" # test config change + - "setup.py" # install command change jobs: detect: name: Detect file change if: | github.event.pull_request.draft == false && - github.base_ref == 'main' && github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' outputs: changedExtenisonFiles: ${{ steps.find-extension-change.outputs.all_changed_files }} diff --git a/.github/workflows/doc_check_on_pr.yml b/.github/workflows/doc_check_on_pr.yml index a863fcd70b44..992cc93b008c 100644 --- a/.github/workflows/doc_check_on_pr.yml +++ b/.github/workflows/doc_check_on_pr.yml @@ -2,47 +2,49 @@ name: Check Documentation on PR on: pull_request: + branches: + - "main" + - "develop" + - "feature/**" paths: - - 'docs/**' + - "docs/**" jobs: check-i18n: name: Check docs in diff languages if: | - github.event.pull_request.draft == false && - github.base_ref == 'main' && - github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' + github.event.pull_request.draft == false && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 with: - python-version: '3.8.14' + python-version: "3.8.14" - run: python .github/workflows/scripts/check_doc_i18n.py -d docs/source check-doc-build: name: Test if the docs can be built if: | - github.event.pull_request.draft == false && - github.base_ref == 'main' && - github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' + github.event.pull_request.draft == false && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 with: - path: './ColossalAI' + path: "./ColossalAI" fetch-depth: 0 - uses: actions/checkout@v2 with: - path: './ColossalAI-Documentation' - repository: 'hpcaitech/ColossalAI-Documentation' + path: "./ColossalAI-Documentation" + repository: "hpcaitech/ColossalAI-Documentation" - uses: actions/setup-python@v2 with: - python-version: '3.8.14' + python-version: "3.8.14" # we use the versions in the main branch as the guide for versions to display # checkout will give your merged branch @@ -57,7 +59,6 @@ jobs: git config user.name 'github-actions' git config user.email 'github-actions@github.com' - - name: Build docs run: | cache_dir=ColossalAI-Documentation/doc-build/.cache diff --git a/.github/workflows/doc_test_on_pr.yml b/.github/workflows/doc_test_on_pr.yml index fb2e28cd9b2e..325e2a7c95a4 100644 --- a/.github/workflows/doc_test_on_pr.yml +++ b/.github/workflows/doc_test_on_pr.yml @@ -1,17 +1,20 @@ name: Test Documentation on PR on: pull_request: + branches: + - "main" + - "develop" + - "feature/**" # any change in the examples folder will trigger check for the corresponding example. paths: - - 'docs/source/**.md' + - "docs/source/**.md" jobs: # This is for changed example files detect and output a matrix containing all the corresponding directory name. detect-changed-doc: if: | - github.event.pull_request.draft == false && - github.base_ref == 'main' && - github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request' + github.event.pull_request.draft == false && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request' runs-on: ubuntu-latest outputs: any_changed: ${{ steps.changed-files.outputs.any_changed }} @@ -26,10 +29,10 @@ jobs: - name: Locate base commit id: locate-base-sha run: | - curBranch=$(git rev-parse --abbrev-ref HEAD) - commonCommit=$(git merge-base origin/main $curBranch) - echo $commonCommit - echo "baseSHA=$commonCommit" >> $GITHUB_OUTPUT + curBranch=$(git rev-parse --abbrev-ref HEAD) + commonCommit=$(git merge-base origin/main $curBranch) + echo $commonCommit + echo "baseSHA=$commonCommit" >> $GITHUB_OUTPUT - name: Get all changed example files id: changed-files @@ -43,10 +46,9 @@ jobs: check-changed-doc: # Add this condition to avoid executing this job if the trigger event is workflow_dispatch. if: | - github.event.pull_request.draft == false && - github.base_ref == 'main' && - github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request' && - needs.detect-changed-doc.outputs.any_changed == 'true' + github.event.pull_request.draft == false && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request' && + needs.detect-changed-doc.outputs.any_changed == 'true' name: Test the changed Doc needs: detect-changed-doc runs-on: [self-hosted, gpu] @@ -61,8 +63,8 @@ jobs: - name: Checkout ColossalAI-Documentation uses: actions/checkout@v2 with: - path: './ColossalAI-Documentation' - repository: 'hpcaitech/ColossalAI-Documentation' + path: "./ColossalAI-Documentation" + repository: "hpcaitech/ColossalAI-Documentation" - name: Install Docer run: | diff --git a/.github/workflows/example_check_on_pr.yml b/.github/workflows/example_check_on_pr.yml index b22664ee47cc..31dbf7540091 100644 --- a/.github/workflows/example_check_on_pr.yml +++ b/.github/workflows/example_check_on_pr.yml @@ -1,17 +1,20 @@ name: Test Example on PR on: pull_request: + branches: + - "main" + - "develop" + - "feature/**" # any change in the examples folder will trigger check for the corresponding example. paths: - - 'examples/**' + - "examples/**" jobs: # This is for changed example files detect and output a matrix containing all the corresponding directory name. detect-changed-example: if: | - github.event.pull_request.draft == false && - github.base_ref == 'main' && - github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request' + github.event.pull_request.draft == false && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request' runs-on: ubuntu-latest outputs: matrix: ${{ steps.setup-matrix.outputs.matrix }} @@ -26,10 +29,10 @@ jobs: - name: Locate base commit id: locate-base-sha run: | - curBranch=$(git rev-parse --abbrev-ref HEAD) - commonCommit=$(git merge-base origin/main $curBranch) - echo $commonCommit - echo "baseSHA=$commonCommit" >> $GITHUB_OUTPUT + curBranch=$(git rev-parse --abbrev-ref HEAD) + commonCommit=$(git merge-base origin/main $curBranch) + echo $commonCommit + echo "baseSHA=$commonCommit" >> $GITHUB_OUTPUT - name: Get all changed example files id: changed-files @@ -61,10 +64,9 @@ jobs: check-changed-example: # Add this condition to avoid executing this job if the trigger event is workflow_dispatch. if: | - github.event.pull_request.draft == false && - github.base_ref == 'main' && - github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request' && - needs.detect-changed-example.outputs.anyChanged == 'true' + github.event.pull_request.draft == false && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' && github.event_name == 'pull_request' && + needs.detect-changed-example.outputs.anyChanged == 'true' name: Test the changed example needs: detect-changed-example runs-on: [self-hosted, gpu] From 615e2e5fc1ec2211364e7a5f9e62156f69e7b984 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 23 May 2023 11:57:15 +0800 Subject: [PATCH 226/413] [test] fixed lazy init test import error (#3799) --- .../test_utils/test_lazy_init/{utils.py => lazy_init_utils.py} | 0 tests/test_utils/test_lazy_init/test_distribute.py | 2 +- tests/test_utils/test_lazy_init/test_models.py | 2 +- 3 files changed, 2 insertions(+), 2 deletions(-) rename tests/test_utils/test_lazy_init/{utils.py => lazy_init_utils.py} (100%) diff --git a/tests/test_utils/test_lazy_init/utils.py b/tests/test_utils/test_lazy_init/lazy_init_utils.py similarity index 100% rename from tests/test_utils/test_lazy_init/utils.py rename to tests/test_utils/test_lazy_init/lazy_init_utils.py diff --git a/tests/test_utils/test_lazy_init/test_distribute.py b/tests/test_utils/test_lazy_init/test_distribute.py index c15b055e8361..fd91e7e912b5 100644 --- a/tests/test_utils/test_lazy_init/test_distribute.py +++ b/tests/test_utils/test_lazy_init/test_distribute.py @@ -15,7 +15,7 @@ from colossalai.utils.model.experimental import LazyInitContext, LazyTensor, _MyTensor except: pass -from utils import SUPPORT_LAZY, assert_dist_model_equal, set_seed +from lazy_init_utils import SUPPORT_LAZY, assert_dist_model_equal, set_seed from tests.kit.model_zoo import model_zoo diff --git a/tests/test_utils/test_lazy_init/test_models.py b/tests/test_utils/test_lazy_init/test_models.py index 4a0217b31a97..f828b23a94c4 100644 --- a/tests/test_utils/test_lazy_init/test_models.py +++ b/tests/test_utils/test_lazy_init/test_models.py @@ -1,5 +1,5 @@ import pytest -from utils import SUPPORT_LAZY, check_lazy_init +from lazy_init_utils import SUPPORT_LAZY, check_lazy_init from tests.kit.model_zoo import model_zoo From e871e342b33d8538bee3604c49021731981b1249 Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Tue, 23 May 2023 15:17:24 +0800 Subject: [PATCH 227/413] [API] add docstrings and initialization to apex amp, naive amp (#3783) * [mixed_precison] add naive amp demo * [mixed_precison] add naive amp demo * [api] add docstrings and initialization to apex amp, naive amp * [api] add docstring to apex amp/ naive amp * [api] add docstring to apex amp/ naive amp * [api] add docstring to apex amp/ naive amp * [api] add docstring to apex amp/ naive amp * [api] add docstring to apex amp/ naive amp * [api] add docstring to apex amp/ naive amp * [api] fix * [api] fix --- .../booster/mixed_precision/fp16_apex.py | 35 ++++++++++++++++++- .../booster/mixed_precision/fp16_naive.py | 23 +++++++++++- 2 files changed, 56 insertions(+), 2 deletions(-) diff --git a/colossalai/booster/mixed_precision/fp16_apex.py b/colossalai/booster/mixed_precision/fp16_apex.py index 266a750734b1..e184271e932a 100644 --- a/colossalai/booster/mixed_precision/fp16_apex.py +++ b/colossalai/booster/mixed_precision/fp16_apex.py @@ -1,5 +1,38 @@ +from typing import Any, Optional, Union + +import torch + from .mixed_precision_base import MixedPrecision class FP16ApexMixedPrecision(MixedPrecision): - pass + """ + Precision for mixed precision training in FP16 using apex AMP. + + Args: + opt_level(str, optional, default="O1" ): Pure or mixed precision optimization level. Accepted values are “O0”, “O1”, “O2”, and “O3”, explained in detail above Apex AMP Documentation. + cast_model_type (torch.dtype, optional, default=None): Casts your model’s parameters and buffers to the desired type. + patch_torch_functions (bool, optional, default=None): Patch all Torch functions and Tensor methods to perform Tensor Core-friendly ops like GEMMs and convolutions in FP16, and any ops that benefit from FP32 precision in FP32. + keep_batchnorm_fp32 (bool or str, optional, default=None): To enhance precision and enable cudnn batchnorm (which improves performance), it’s often beneficial to keep batchnorm weights in FP32 even if the rest of the model is FP16. + master_weights (bool, optional, default=None): Maintain FP32 master weights to accompany any FP16 model weights. FP32 master weights are stepped by the optimizer to enhance precision and capture small gradients. + loss_scale (float or str, optional, default=None): If loss_scale is a float value, use this value as the static (fixed) loss scale. If loss_scale is the string "dynamic", adaptively adjust the loss scale over time. Dynamic loss scale adjustments are performed by Amp automatically. + cast_model_outputs (torch.dpython:type, optional, default=None): Option to ensure that the outputs of your model(s) are always cast to a particular type regardless of opt_level. + num_losses(int, optional, default=1): Option to tell AMP in advance how many losses/backward passes you plan to use. When used in conjunction with the loss_id argument to `amp.scale_loss`, enables Amp to use a different loss scale per loss/backward pass, which can improve stability. If num_losses is left to 1, Amp will still support multiple losses/backward passes, but use a single global loss scale for all of them. + verbosity(int, default=1): Set to 0 to suppress Amp-related output. + min_loss_scale(float, default=None): Sets a floor for the loss scale values that can be chosen by dynamic loss scaling. The default value of None means that no floor is imposed. If dynamic loss scaling is not used, min_loss_scale is ignored. + max_loss_scale(float, default=2.**24 ): Sets a ceiling for the loss scale values that can be chosen by dynamic loss scaling. If dynamic loss scaling is not used, max_loss_scale is ignored. + """ + + def __init__(self, + opt_level: Optional[str] = "O1", + cast_model_type: torch.dtype = None, + patch_torch_functions: bool = None, + keep_batchnorm_fp32: Union[bool, str] = None, + master_weights: bool = None, + loss_scale: Union[float, str] = None, + cast_model_outputs: Any = None, + num_losses: Optional[int] = 1, + verbosity: int = 1, + min_loss_scale: float = None, + max_loss_scale: float = 2.**24) -> None: + pass diff --git a/colossalai/booster/mixed_precision/fp16_naive.py b/colossalai/booster/mixed_precision/fp16_naive.py index ef1ec1f42d70..5d0d815257f3 100644 --- a/colossalai/booster/mixed_precision/fp16_naive.py +++ b/colossalai/booster/mixed_precision/fp16_naive.py @@ -2,4 +2,25 @@ class FP16NaiveMixedPrecision(MixedPrecision): - pass + """ + Precision for mixed precision training in FP16 using naive AMP. + + Args: + log_num_zeros_in_grad(bool): return number of zeros in the gradients. + initial_scale(int): initial scale of gradient scaler. + growth_factor(int): the growth rate of loss scale. + backoff_factor(float): the decrease rate of loss scale. + hysteresis(int): delay shift in dynamic loss scaling. + max_scale(int): maximum loss scale allowed. + verbose(bool): if set to `True`, will print debug info. + """ + + def __init__(self, + log_num_zeros_in_grad: bool, + initial_scale: int, + growth_factor: int, + backoff_factor: float, + hysteresis: int, + max_scale: int, + verbose: bool = None) -> None: + pass From 9265f2d4d7de526fac4e1b5a59f1953c578c7228 Mon Sep 17 00:00:00 2001 From: digger yu Date: Tue, 23 May 2023 15:28:20 +0800 Subject: [PATCH 228/413] [NFC]fix typo colossalai/auto_parallel nn utils etc. (#3779) * fix typo colossalai/autochunk auto_parallel amp * fix typo colossalai/auto_parallel nn utils etc. --- applications/Chat/coati/dataset/reward_dataset.py | 2 +- .../tensor_shard/node_handler/embedding_handler.py | 4 ++-- .../tensor_shard/node_handler/linear_handler.py | 6 +++--- .../tensor_shard/node_handler/matmul_handler.py | 10 +++++----- .../tensor_shard/node_handler/node_handler.py | 2 +- .../auto_parallel/tensor_shard/utils/factory.py | 2 +- .../auto_parallel/tensor_shard/utils/reshape.py | 12 ++++++------ colossalai/nn/optimizer/cpu_adam.py | 2 +- colossalai/nn/optimizer/hybrid_adam.py | 8 ++++---- .../nn/parallel/layers/cache_embedding/cache_mgr.py | 6 +++--- colossalai/utils/common.py | 2 +- colossalai/utils/tensor_detector/readme.md | 12 ++++++------ colossalai/utils/tensor_detector/tensor_detector.py | 8 ++++---- colossalai/zero/gemini/chunk/manager.py | 12 ++++++------ colossalai/zero/gemini/chunk/search_utils.py | 2 +- colossalai/zero/gemini/memory_tracer/memory_stats.py | 2 +- 16 files changed, 46 insertions(+), 46 deletions(-) diff --git a/applications/Chat/coati/dataset/reward_dataset.py b/applications/Chat/coati/dataset/reward_dataset.py index faa1c94d2728..5dacf7e81464 100644 --- a/applications/Chat/coati/dataset/reward_dataset.py +++ b/applications/Chat/coati/dataset/reward_dataset.py @@ -6,7 +6,7 @@ from .utils import is_rank_0 -# Dahaos/rm-static +# Dahoas/rm-static class RmStaticDataset(Dataset): """ Dataset for reward model diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/embedding_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/embedding_handler.py index e154105b672d..112ee194b4ec 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/embedding_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/embedding_handler.py @@ -155,7 +155,7 @@ def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, Li Convert the sharding spec from the logical shape to the physical shape. """ # create multiple sharding strategies for the inputs - # as input can be multi-dimensinal and the partition dim is only 2D, + # as input can be multi-dimensional and the partition dim is only 2D, # we need to map the partition at logical dim 0 to one of the first few dimensions of the input and output strategies = _convert_logical_sharding_to_physical_sharding_spec_for_embedding(strategy=strategy, input_name=str( @@ -221,7 +221,7 @@ def post_process(self, strategy: ShardingStrategy): Convert the sharding spec from the logical shape to the physical shape. """ # create multiple sharding strategies for the inputs - # as input can be multi-dimensinal and the partition dim is only 2D, + # as input can be multi-dimensional and the partition dim is only 2D, # we need to map the partition at logical dim 0 to one of the first few dimensions of the input and output strategies = _convert_logical_sharding_to_physical_sharding_spec_for_embedding(strategy=strategy, input_name=str( diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/linear_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/linear_handler.py index 59091dab519f..ea541e434009 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/linear_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/linear_handler.py @@ -23,7 +23,7 @@ def _update_sharding_spec_for_transposed_weight_for_linear(strategy: ShardingStr weight_name: str) -> ShardingStrategy: """ This function is a helper function used by both module node handler and function node handler. This function will - convert the sharding spec for the transposed weight to the correct partititon spec. + convert the sharding spec for the transposed weight to the correct partition spec. Args: strategy (ShardingStrategy): the strategy generated by the strategy generator. @@ -197,7 +197,7 @@ def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, Li strategy = _update_sharding_spec_for_transposed_weight_for_linear(strategy=strategy, weight_name='weight') # create multiple sharding strategies for the inputs - # as input can be multi-dimensinal and the partition dim is only 2D, + # as input can be multi-dimensional and the partition dim is only 2D, # we need to map the partition at dim 0 to one of the first few dimensions of the input strategies = _convert_logical_sharding_to_physical_sharding_spec_for_linear(strategy=strategy, input_name=str(self.node.args[0]), @@ -267,7 +267,7 @@ def post_process(self, strategy: ShardingStrategy): strategy = _update_sharding_spec_for_transposed_weight_for_linear(strategy=strategy, weight_name=str(self.node.args[1])) # create multiple sharding strategies for the inputs - # as input can be multi-dimensinal and the partition dim is only 2D, + # as input can be multi-dimensional and the partition dim is only 2D, # we need to map the partition at dim 0 to one of the first few dimensions of the input strategies = _convert_logical_sharding_to_physical_sharding_spec_for_linear(strategy=strategy, input_name=str(self.node.args[0]), diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py index f3c9d0cbf826..bfebc3f59d0c 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py @@ -48,8 +48,8 @@ def get_matmul_type(input_dim: int, other_dim: int): Determine which type of matmul operation should be executed for the given tensor dimensions. Args: - input_dim (int): the number of dimensions for the input tenosr - other_dim (int): the number of dimensions for the other tenosr + input_dim (int): the number of dimensions for the input tensor + other_dim (int): the number of dimensions for the other tensor """ if input_dim == 1 and other_dim == 1: matmul_type = MatMulType.DOT @@ -268,13 +268,13 @@ def _update_sharding_spec(key, strategy, physical_batch_dim): dim_partition_dict = sharding_spec.dim_partition_dict entire_shape = sharding_spec.entire_shape - # upddate the dimension index for the matrix dimensions + # update the dimension index for the matrix dimensions if 2 in dim_partition_dict: dim_partition_dict[len(self.batch_dims_before_view) + 1] = dim_partition_dict.pop(2) if 1 in dim_partition_dict: dim_partition_dict[len(self.batch_dims_before_view)] = dim_partition_dict.pop(1) - # map the logical batch dim to phyiscal batch dim + # map the logical batch dim to physical batch dim if 0 in dim_partition_dict: batch_dim_shard = dim_partition_dict.pop(0) dim_partition_dict[physical_batch_dim] = batch_dim_shard @@ -414,7 +414,7 @@ def _get_logical_shape_for_dot(self): def _get_logical_shape_for_mm(self): """ - We need to handle the input tensor for a matrix-matrix multiplcation as the input + We need to handle the input tensor for a matrix-matrix multiplication as the input tensor can be a 1D or 2D tensor. If it is a 1D tensor, 1 will be prepended to its shape (e.g. [4] -> [1, 4]). """ diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py index d3d09a9dcf65..4262d76173e4 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py @@ -212,7 +212,7 @@ def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesV return self.strategies_vector def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, List[ShardingStrategy]]: - # tranform the strategy generated + # transform the strategy generated # e.g. to process the sharding strategy for the transposed weights return strategy diff --git a/colossalai/auto_parallel/tensor_shard/utils/factory.py b/colossalai/auto_parallel/tensor_shard/utils/factory.py index 05331e560001..347c10aa102d 100644 --- a/colossalai/auto_parallel/tensor_shard/utils/factory.py +++ b/colossalai/auto_parallel/tensor_shard/utils/factory.py @@ -30,7 +30,7 @@ def generate_sharding_spec(input_: Union[Node, torch.Tensor], device_mesh: Devic """ if isinstance(input_, Node): - assert hasattr(input_, '_meta_data'), f'The given node has no attribte _meta_data' + assert hasattr(input_, '_meta_data'), f'The given node has no attribute _meta_data' meta_tensor = input_._meta_data assert meta_tensor is not None, "The given node's _meta_data attribute is None" shape = meta_tensor.shape diff --git a/colossalai/auto_parallel/tensor_shard/utils/reshape.py b/colossalai/auto_parallel/tensor_shard/utils/reshape.py index a32a14bf7d57..d0ebbd7e8b1b 100644 --- a/colossalai/auto_parallel/tensor_shard/utils/reshape.py +++ b/colossalai/auto_parallel/tensor_shard/utils/reshape.py @@ -6,12 +6,12 @@ class PreviousStatus(Enum): """ - This class shows the status of previous comparision. + This class shows the status of previous comparison. """ RESET = 0 - # ORIGIN means the dimension size of original tensor is larger in the previous comparision. + # ORIGIN means the dimension size of original tensor is larger in the previous comparison. ORIGIN = 1 - # TGT means the dimension size of target tensor is larger in the previous comparision. + # TGT means the dimension size of target tensor is larger in the previous comparison. TGT = 2 @@ -91,7 +91,7 @@ def detect_reshape_mapping(origin_shape: torch.Size, tgt_shape: torch.Size) -> D tgt_index += 1 if previous_label == PreviousStatus.TGT: - # if the target dimension size is larger in the previous comparision, which means + # if the target dimension size is larger in the previous comparison, which means # the origin dimension size has already accumulated larger than target dimension size, so # we need to offload the origin dims and tgt dims into the reshape_mapping_dict. reshape_mapping_dict[tuple(origin_dims)] = tuple(tgt_dims) @@ -111,7 +111,7 @@ def detect_reshape_mapping(origin_shape: torch.Size, tgt_shape: torch.Size) -> D origin_index += 1 if previous_label == PreviousStatus.ORIGIN: - # if the origin element is larger in the previous comparision, which means + # if the origin element is larger in the previous comparison, which means # the target element has already accumulated larger than origin element, so # we need to offload the origin dims and tgt dims into the reshape_mapping_dict. reshape_mapping_dict[tuple(origin_dims)] = tuple(tgt_dims) @@ -139,7 +139,7 @@ def check_keep_sharding_status(input_dim_partition_dict: Dict[int, List[int]], Rule: For a sharded dimension of input tensor, if it is not the minimum element of the input tuple, the function will return false. - To illustrate this issue, there are two cases to analyse: + To illustrate this issue, there are two cases to analyze: 1. no sharded dims in the input tuple: we could do the reshape operation safely just as the normal operation without distributed tensor. 2. sharded dims in the input tuple: the sharded dim must be the minimum element, then during shape diff --git a/colossalai/nn/optimizer/cpu_adam.py b/colossalai/nn/optimizer/cpu_adam.py index 54036973e1e3..bb561a106515 100644 --- a/colossalai/nn/optimizer/cpu_adam.py +++ b/colossalai/nn/optimizer/cpu_adam.py @@ -13,7 +13,7 @@ class CPUAdam(NVMeOptimizer): """Implements Adam algorithm. - Supports parameters updating on both GPU and CPU, depanding on the device of paramters. + Supports parameters updating on both GPU and CPU, depanding on the device of parameters. But the parameters and gradients should on the same device: * Parameters on CPU and gradients on CPU is allowed. * Parameters on GPU and gradients on GPU is allowed. diff --git a/colossalai/nn/optimizer/hybrid_adam.py b/colossalai/nn/optimizer/hybrid_adam.py index 1d0fb92de499..be6311c6c29f 100644 --- a/colossalai/nn/optimizer/hybrid_adam.py +++ b/colossalai/nn/optimizer/hybrid_adam.py @@ -13,19 +13,19 @@ class HybridAdam(NVMeOptimizer): """Implements Adam algorithm. - Supports parameters updating on both GPU and CPU, depanding on the device of paramters. + Supports parameters updating on both GPU and CPU, depanding on the device of parameters. But the parameters and gradients should on the same device: * Parameters on CPU and gradients on CPU is allowed. * Parameters on GPU and gradients on GPU is allowed. * Parameters on GPU and gradients on CPU is **not** allowed. - `HybriadAdam` requires CUDA extensions which can be built during installation or runtime. + `HybridAdam` requires CUDA extensions which can be built during installation or runtime. This version of Hybrid Adam is an hybrid of CPUAdam and FusedAdam. * For parameters updating on CPU, it uses CPUAdam. * For parameters updating on GPU, it uses FusedAdam. - * Hybird precision calculation of fp16 and fp32 is supported, eg fp32 parameters and fp16 gradients. + * Hybrid precision calculation of fp16 and fp32 is supported, eg fp32 parameters and fp16 gradients. :class:`colossalai.nn.optimizer.HybridAdam` may be used as a drop-in replacement for ``torch.optim.AdamW``, or ``torch.optim.Adam`` with ``adamw_mode=False`` @@ -131,7 +131,7 @@ def step(self, closure=None, div_scale: float = -1): assert state['exp_avg'].device.type == 'cuda', "exp_avg should stay on cuda" assert state['exp_avg_sq'].device.type == 'cuda', "exp_avg should stay on cuda" - # record the state by gruop and update at once + # record the state by group and update at once g_l.append(p.grad.data) p_l.append(p.data) m_l.append(state['exp_avg']) diff --git a/colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py b/colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py index da043df368ae..a6159856dcce 100644 --- a/colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py +++ b/colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py @@ -20,8 +20,8 @@ def _wait_for_data(t, stream: Optional[torch.cuda.streams.Stream]) -> None: return torch.cuda.current_stream().wait_stream(stream) # As mentioned in https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html, - # PyTorch uses the "caching allocator" for memroy allocation for tensors. When a tensor is - # freed, its memory is likely to be reused by newly constructed tenosrs. By default, + # PyTorch uses the "caching allocator" for memory allocation for tensors. When a tensor is + # freed, its memory is likely to be reused by newly constructed tensors. By default, # this allocator traces whether a tensor is still in use by only the CUDA stream where it # was created. When a tensor is used by additional CUDA streams, we need to call record_stream # to tell the allocator about all these streams. Otherwise, the allocator might free the @@ -294,7 +294,7 @@ def print_comm_stats(self): print( f"CPU->CUDA BWD {self._cpu_to_cuda_numel * self.elem_size_in_byte / 1e6 / elapsed} MB/s {self._cpu_to_cuda_numel / 1e6} M elem" ) - print(f'cpu_to_cuda_elpase {elapsed} sec') + print(f'cpu_to_cuda_elapse {elapsed} sec') for k, v in self._elapsed_dict.items(): print(f'{k}: {v}') diff --git a/colossalai/utils/common.py b/colossalai/utils/common.py index 95b3b8014af1..8022e84dc24b 100644 --- a/colossalai/utils/common.py +++ b/colossalai/utils/common.py @@ -324,7 +324,7 @@ def clip_grad_norm_fp32(parameters, max_norm, norm_type=2): norm_type = float(norm_type) # Parameters can be on CPU or CUDA - # If parameters are on CPU, disable CUDA kernerls + # If parameters are on CPU, disable CUDA kernels # Calculate norm. if norm_type == inf: diff --git a/colossalai/utils/tensor_detector/readme.md b/colossalai/utils/tensor_detector/readme.md index 840dc8f4eca6..d6852ea55b54 100644 --- a/colossalai/utils/tensor_detector/readme.md +++ b/colossalai/utils/tensor_detector/readme.md @@ -46,7 +46,7 @@ detector.detect() I have made some comments on the right of the output for your understanding. -Note that the total `Mem` of all the tensors and parameters is not equal to `Total GPU Memery Allocated`. PyTorch's memory management is really complicated, and for models of a large scale, it's impossible to figure out clearly. +Note that the total `Mem` of all the tensors and parameters is not equal to `Total GPU Memory Allocated`. PyTorch's memory management is really complicated, and for models of a large scale, it's impossible to figure out clearly. **The order of print is not equal to the order the tensor creates, but they are really close.** @@ -61,7 +61,7 @@ Note that the total `Mem` of all the tensors and parameters is not equal to `Tot + mlp.2.bias cuda:0 (32,) True torch.float32 128 B ------------------------------------------------------------------------------------------------------------ Detect Location: "test_tensor_detector.py" line 27 -Totle GPU Memery Allocated on cuda:0 is 4.5 KB +Total GPU Memory Allocated on cuda:0 is 4.5 KB ------------------------------------------------------------------------------------------------------------ @@ -72,7 +72,7 @@ Totle GPU Memery Allocated on cuda:0 is 4.5 KB + Tensor cuda:0 (32,) True torch.float32 128 B # output ------------------------------------------------------------------------------------------------------------ Detect Location: "test_tensor_detector.py" line 30 -Totle GPU Memery Allocated on cuda:0 is 5.5 KB +Total GPU Memory Allocated on cuda:0 is 5.5 KB ------------------------------------------------------------------------------------------------------------ @@ -82,7 +82,7 @@ Totle GPU Memery Allocated on cuda:0 is 5.5 KB + Tensor cuda:0 () True torch.float32 4 B # loss ------------------------------------------------------------------------------------------------------------ Detect Location: "test_tensor_detector.py" line 32 -Totle GPU Memery Allocated on cuda:0 is 6.0 KB +Total GPU Memory Allocated on cuda:0 is 6.0 KB ------------------------------------------------------------------------------------------------------------ @@ -103,7 +103,7 @@ Totle GPU Memery Allocated on cuda:0 is 6.0 KB - Tensor cuda:0 (8,) True torch.float32 32 B # deleted activation ------------------------------------------------------------------------------------------------------------ Detect Location: "test_tensor_detector.py" line 34 -Totle GPU Memery Allocated on cuda:0 is 10.0 KB +Total GPU Memory Allocated on cuda:0 is 10.0 KB ------------------------------------------------------------------------------------------------------------ @@ -117,7 +117,7 @@ Totle GPU Memery Allocated on cuda:0 is 10.0 KB + Tensor cuda:0 (32,) False torch.float32 128 B ------------------------------------------------------------------------------------------------------------ Detect Location: "test_tensor_detector.py" line 36 -Totle GPU Memery Allocated on cuda:0 is 14.0 KB +Total GPU Memory Allocated on cuda:0 is 14.0 KB ------------------------------------------------------------------------------------------------------------ ``` diff --git a/colossalai/utils/tensor_detector/tensor_detector.py b/colossalai/utils/tensor_detector/tensor_detector.py index a8186f76834c..cfcd4e47b4cb 100644 --- a/colossalai/utils/tensor_detector/tensor_detector.py +++ b/colossalai/utils/tensor_detector/tensor_detector.py @@ -55,7 +55,7 @@ def get_tensor_mem(self, tensor): return self.mem_format(memory_size) def mem_format(self, real_memory_size): - # format the tensor memory into a reasonal magnitude + # format the tensor memory into a reasonable magnitude if real_memory_size >= 2**30: return str(real_memory_size / (2**30)) + ' GB' if real_memory_size >= 2**20: @@ -71,7 +71,7 @@ def collect_tensors_state(self): if (not self.include_cpu) and obj.device == torch.device('cpu'): continue self.detected.append(id(obj)) - # skip paramters we had added in __init__ when module is an instance of nn.Module for the first epoch + # skip parameters we had added in __init__ when module is an instance of nn.Module for the first epoch if id(obj) not in self.tensor_info: name = type(obj).__name__ @@ -84,7 +84,7 @@ def collect_tensors_state(self): name = par_name + ' (with grad)' else: # with no grad attached - # there will be no new paramters created during running + # there will be no new parameters created during running # so it must be in saved_tensor_info continue # we can also marked common tensors as tensor(with grad) @@ -155,7 +155,7 @@ def print_tensors_state(self): if device == torch.device('cpu'): continue gpu_mem_alloc = self.mem_format(torch.cuda.memory_allocated(device)) - self.info += f"Totle GPU Memery Allocated on {device} is {gpu_mem_alloc}\n" + self.info += f"Total GPU Memory Allocated on {device} is {gpu_mem_alloc}\n" self.info += LINE self.info += '\n\n' if self.show_info: diff --git a/colossalai/zero/gemini/chunk/manager.py b/colossalai/zero/gemini/chunk/manager.py index d85df0b00476..77368d06d255 100644 --- a/colossalai/zero/gemini/chunk/manager.py +++ b/colossalai/zero/gemini/chunk/manager.py @@ -102,7 +102,7 @@ def access_chunk(self, chunk: Chunk) -> None: """ if chunk in self.accessed_chunks: return - self.__sub_memroy_usage(chunk.memory_usage) + self.__sub_memory_usage(chunk.memory_usage) if chunk.device_type == 'cpu': chunk.shard_move(get_current_device()) self.__add_accessed_chunk(chunk) @@ -114,7 +114,7 @@ def release_chunk(self, chunk: Chunk) -> None: if chunk not in self.accessed_chunks: return if chunk.can_release: - self.__sub_memroy_usage(chunk.memory_usage) + self.__sub_memory_usage(chunk.memory_usage) self.__sub_accessed_chunk(chunk) self.__add_memory_usage(chunk.memory_usage) @@ -123,7 +123,7 @@ def move_chunk(self, chunk: Chunk, device: torch.device, force_copy: bool = Fals """ if not chunk.can_move or chunk.device_type == device.type: return - self.__sub_memroy_usage(chunk.memory_usage) + self.__sub_memory_usage(chunk.memory_usage) chunk.shard_move(device, force_copy) self.__add_memory_usage(chunk.memory_usage) @@ -138,7 +138,7 @@ def reduce_chunk(self, chunk: Chunk) -> bool: """ if not chunk.can_reduce: return False - self.__sub_memroy_usage(chunk.memory_usage) + self.__sub_memory_usage(chunk.memory_usage) chunk.reduce() self.__sub_accessed_chunk(chunk) self.__add_memory_usage(chunk.memory_usage) @@ -228,11 +228,11 @@ def __get_chunk_group(self, group_name: str) -> Deque: return self.chunk_groups[group_name] def __close_one_chunk(self, chunk: Chunk): - self.__sub_memroy_usage(chunk.memory_usage) + self.__sub_memory_usage(chunk.memory_usage) chunk.close_chunk() self.__add_memory_usage(chunk.memory_usage) - def __sub_memroy_usage(self, usage: Dict[str, int]): + def __sub_memory_usage(self, usage: Dict[str, int]): for k, v in usage.items(): self.total_mem[k] -= v diff --git a/colossalai/zero/gemini/chunk/search_utils.py b/colossalai/zero/gemini/chunk/search_utils.py index da58e038c879..881ceb0b3b97 100644 --- a/colossalai/zero/gemini/chunk/search_utils.py +++ b/colossalai/zero/gemini/chunk/search_utils.py @@ -85,7 +85,7 @@ def classify_params_by_dp_degree(param_order: OrderedParamGenerator, Classify the parameters by their dp degree Args: - param_order (OrderedParamGenerator): the order of param be visied + param_order (OrderedParamGenerator): the order of param be vised strict_ddp_flag (bool, optional): whether to enable the strict ddp mode. Defaults to False. Returns: diff --git a/colossalai/zero/gemini/memory_tracer/memory_stats.py b/colossalai/zero/gemini/memory_tracer/memory_stats.py index 9a45034ee27e..41d7e5754e96 100644 --- a/colossalai/zero/gemini/memory_tracer/memory_stats.py +++ b/colossalai/zero/gemini/memory_tracer/memory_stats.py @@ -59,7 +59,7 @@ def increase_preop_step(self, param_list: List[torch.nn.Parameter]): time step. Args: - param_list (List[torch.nn.Parameter]): a list of torch paramters. + param_list (List[torch.nn.Parameter]): a list of torch parameters. """ for p in param_list: if p not in self._param_step_dict: From 8c62e50dbb0720e5b51984d94d0a28e1bb71fe7f Mon Sep 17 00:00:00 2001 From: Mingyan Jiang <1829166702@qq.com> Date: Tue, 23 May 2023 13:11:03 +0800 Subject: [PATCH 229/413] [doc] update amp document --- docs/sidebars.json | 11 +++------- docs/source/en/basics/define_your_config.md | 20 ++++++++----------- .../en/features/mixed_precision_training.md | 3 ++- .../zh-Hans/basics/define_your_config.md | 19 +++++++++--------- .../features/mixed_precision_training.md | 3 ++- 5 files changed, 24 insertions(+), 32 deletions(-) diff --git a/docs/sidebars.json b/docs/sidebars.json index 1b3ddedd12b1..dc83172223bc 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -26,11 +26,8 @@ "collapsed": true, "items": [ "basics/command_line_tool", - "basics/launch_colossalai", - "basics/booster_api", - "basics/booster_plugins", - "basics/booster_checkpoint", "basics/define_your_config", + "basics/launch_colossalai", "basics/initialize_features", "basics/engine_trainer", "basics/configure_parallelization", @@ -43,10 +40,9 @@ "label": "Features", "collapsed": true, "items": [ + "features/mixed_precision_training_with_booster", "features/mixed_precision_training", - "features/gradient_accumulation_with_booster", "features/gradient_accumulation", - "features/gradient_clipping_with_booster", "features/gradient_clipping", "features/gradient_handler", "features/zero_with_chunk", @@ -62,8 +58,7 @@ ] }, "features/pipeline_parallel", - "features/nvme_offload", - "features/cluster_utils" + "features/nvme_offload" ] }, { diff --git a/docs/source/en/basics/define_your_config.md b/docs/source/en/basics/define_your_config.md index 048ffcacbb8f..223af44f8dce 100644 --- a/docs/source/en/basics/define_your_config.md +++ b/docs/source/en/basics/define_your_config.md @@ -2,9 +2,6 @@ Author: Guangyang Lu, Shenggui Li, Siqi Mai -> ⚠️ The information on this page is outdated and will be deprecated. Please check [Booster API](../basics/booster_api.md) for more information. - - **Prerequisite:** - [Distributed Training](../concepts/distributed_training.md) - [Colossal-AI Overview](../concepts/colossalai_overview.md) @@ -24,8 +21,7 @@ In this tutorial, we will cover how to define your configuration file. ## Configuration Definition In a configuration file, there are two types of variables. One serves as feature specification and the other serves -as hyper-parameters. All feature-related variables are reserved keywords. For example, if you want to use mixed precision -training, you need to use the variable name `fp16` in the config file and follow a pre-defined format. +as hyper-parameters. All feature-related variables are reserved keywords. For example, if you want to use 1D tensor parallelism, you need to use the variable name `parallel` in the config file and follow a pre-defined format. ### Feature Specification @@ -37,14 +33,13 @@ To illustrate the use of config file, we use mixed precision training as an exam follow the steps below. 1. create a configuration file (e.g. `config.py`, the file name can be anything) -2. define the mixed precision configuration in the config file. For example, in order to use mixed precision training -natively provided by PyTorch, you can just write these lines of code below into your config file. +2. define the hybrid parallelism configuration in the config file. For example, in order to use 1D tensor parallel, you can just write these lines of code below into your config file. ```python - from colossalai.amp import AMP_TYPE - - fp16 = dict( - mode=AMP_TYPE.TORCH + parallel = dict( + data=1, + pipeline=1, + tensor=dict(size=2, mode='1d'), ) ``` @@ -57,7 +52,7 @@ the current directory. colossalai.launch(config='./config.py', ...) ``` -In this way, Colossal-AI knows what features you want to use and will inject this feature during `colossalai.initialize`. +In this way, Colossal-AI knows what features you want to use and will inject this feature. ### Global Hyper-parameters @@ -83,3 +78,4 @@ colossalai.launch(config='./config.py', ...) print(gpc.config.BATCH_SIZE) ``` + diff --git a/docs/source/en/features/mixed_precision_training.md b/docs/source/en/features/mixed_precision_training.md index 11aa5235301a..04f0bc6deb98 100644 --- a/docs/source/en/features/mixed_precision_training.md +++ b/docs/source/en/features/mixed_precision_training.md @@ -1,4 +1,4 @@ -# Auto Mixed Precision Training +# Auto Mixed Precision Training (Outdated) Author: Chuanrui Wang, Shenggui Li, Yongbin Li @@ -365,3 +365,4 @@ Use the following command to start the training scripts. You can change `--nproc ```python python -m torch.distributed.launch --nproc_per_node 4 --master_addr localhost --master_port 29500 train_with_engine.py --config config/config_AMP_torch.py ``` + diff --git a/docs/source/zh-Hans/basics/define_your_config.md b/docs/source/zh-Hans/basics/define_your_config.md index 720e75805e8d..cf099f03815e 100644 --- a/docs/source/zh-Hans/basics/define_your_config.md +++ b/docs/source/zh-Hans/basics/define_your_config.md @@ -2,8 +2,6 @@ 作者: Guangyang Lu, Shenggui Li, Siqi Mai -> ⚠️ 此页面上的信息已经过时并将被废弃。请在[Booster API](../basics/booster_api.md)页面查阅更新。 - **预备知识:** - [分布式训练](../concepts/distributed_training.md) - [Colossal-AI 总览](../concepts/colossalai_overview.md) @@ -20,7 +18,7 @@ ## 配置定义 -在一个配置文件中，有两种类型的变量。一种是作为特征说明，另一种是作为超参数。所有与特征相关的变量都是保留关键字。例如，如果您想使用混合精度训练，需要在 config 文件中使用变量名`fp16`，并遵循预先定义的格式。 +在一个配置文件中，有两种类型的变量。一种是作为特征说明，另一种是作为超参数。所有与特征相关的变量都是保留关键字。例如，如果您想使用`1D`张量并行，需要在 config 文件中使用变量名`fp16`，并遵循预先定义的格式。 ### 功能配置 @@ -29,13 +27,13 @@ Colossal-AI 提供了一系列的功能来加快训练速度。每个功能都 为了说明配置文件的使用，我们在这里使用混合精度训练作为例子。您需要遵循以下步骤。 1. 创建一个配置文件（例如 `config.py`，您可以指定任意的文件名）。 -2. 在配置文件中定义混合精度的配置。例如，为了使用 PyTorch 提供的原始混合精度训练，您只需将下面这几行代码写入您的配置文件中。 - - ```python - from colossalai.amp import AMP_TYPE +2. 在配置文件中定义混合并行的配置。例如，为了使用`1D`张量并行，您只需将下面这几行代码写入您的配置文件中。 - fp16 = dict( - mode=AMP_TYPE.TORCH + ```python + parallel = dict( + data=1, + pipeline=1, + tensor=dict(size=2, mode='1d'), ) ``` @@ -47,7 +45,7 @@ Colossal-AI 提供了一系列的功能来加快训练速度。每个功能都 colossalai.launch(config='./config.py', ...) ``` -这样，Colossal-AI 便知道您想使用什么功能，并会在 `colossalai.initialize` 期间注入您所需要的功能。 +这样，Colossal-AI 便知道您想使用什么功能，并注入您所需要的功能。 ### 全局超参数 @@ -71,3 +69,4 @@ colossalai.launch(config='./config.py', ...) print(gpc.config.BATCH_SIZE) ``` + diff --git a/docs/source/zh-Hans/features/mixed_precision_training.md b/docs/source/zh-Hans/features/mixed_precision_training.md index c9db3a59c1c3..b3b9a7ebc26b 100644 --- a/docs/source/zh-Hans/features/mixed_precision_training.md +++ b/docs/source/zh-Hans/features/mixed_precision_training.md @@ -1,4 +1,4 @@ -# 自动混合精度训练 (AMP) +# 自动混合精度训练 (旧版本) 作者: Chuanrui Wang, Shenggui Li, Yongbin Li @@ -342,3 +342,4 @@ for epoch in range(gpc.config.NUM_EPOCHS): ```python python -m torch.distributed.launch --nproc_per_node 4 --master_addr localhost --master_port 29500 train_with_engine.py --config config/config_AMP_torch.py ``` + From 1167bf5b102e5109499dd8dd9022cc4e77a63504 Mon Sep 17 00:00:00 2001 From: Mingyan Jiang <1829166702@qq.com> Date: Tue, 23 May 2023 13:14:36 +0800 Subject: [PATCH 230/413] [doc] update amp document --- docs/sidebars.json | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/docs/sidebars.json b/docs/sidebars.json index dc83172223bc..8be40e4512f9 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -26,8 +26,11 @@ "collapsed": true, "items": [ "basics/command_line_tool", - "basics/define_your_config", "basics/launch_colossalai", + "basics/booster_api", + "basics/booster_plugins", + "basics/booster_checkpoint", + "basics/define_your_config", "basics/initialize_features", "basics/engine_trainer", "basics/configure_parallelization", @@ -42,7 +45,9 @@ "items": [ "features/mixed_precision_training_with_booster", "features/mixed_precision_training", + "features/gradient_accumulation_with_booster", "features/gradient_accumulation", + "features/gradient_clipping_with_booster", "features/gradient_clipping", "features/gradient_handler", "features/zero_with_chunk", @@ -58,7 +63,8 @@ ] }, "features/pipeline_parallel", - "features/nvme_offload" + "features/nvme_offload", + "features/cluster_utils" ] }, { From a520610bd9eaaf6374779756fb6b2c15c390a303 Mon Sep 17 00:00:00 2001 From: Mingyan Jiang <1829166702@qq.com> Date: Tue, 23 May 2023 13:15:56 +0800 Subject: [PATCH 231/413] [doc] update amp document --- .../mixed_precision_training_with_booster.md | 251 ++++++++++++++++++ .../mixed_precision_training_with_booster.md | 235 ++++++++++++++++ 2 files changed, 486 insertions(+) create mode 100644 docs/source/en/features/mixed_precision_training_with_booster.md create mode 100644 docs/source/zh-Hans/features/mixed_precision_training_with_booster.md diff --git a/docs/source/en/features/mixed_precision_training_with_booster.md b/docs/source/en/features/mixed_precision_training_with_booster.md new file mode 100644 index 000000000000..2ae88b07157f --- /dev/null +++ b/docs/source/en/features/mixed_precision_training_with_booster.md @@ -0,0 +1,251 @@ +# Auto Mixed Precision Training (Latest) + +Author: [Mingyan Jiang](https://github.com/jiangmingyan) + +**Prerequisite** +- [Define Your Configuration](../basics/define_your_config.md) +- [Training Booster](../basics/booster_api.md) + +**Related Paper** +- [Accelerating Scientific Computations with Mixed Precision Algorithms](https://arxiv.org/abs/0808.2794) + + +## Introduction + +AMP stands for automatic mixed precision training. +In Colossal-AI, we have incorporated different implementations of mixed precision training: + +1. torch.cuda.amp +2. apex.amp +3. naive amp + + +| Colossal-AI | support tensor parallel | support pipeline parallel | fp16 extent | +| ----------- | ----------------------- | ------------------------- | ----------- | +| AMP_TYPE.TORCH | ✅ | ❌ | Model parameters, activation, gradients are downcast to fp16 during forward and backward propagation | +| AMP_TYPE.APEX | ❌ | ❌ | More fine-grained, we can choose opt_level O0, O1, O2, O3 | +| AMP_TYPE.NAIVE | ✅ | ✅ | Model parameters, forward and backward operations are all downcast to fp16 | + +The first two rely on the original implementation of PyTorch (version 1.6 and above) and NVIDIA Apex. +The last method is similar to Apex O2 level. +Among these methods, apex AMP is not compatible with tensor parallelism. +This is because that tensors are split across devices in tensor parallelism, thus, it is required to communicate among different processes to check if inf or nan occurs in the whole model weights. +We modified the torch amp implementation so that it is compatible with tensor parallelism now. + +> ❌️ fp16 and zero are not compatible +> +> ⚠️ Pipeline only support naive AMP currently + +We recommend you to use torch AMP as it generally gives better accuracy than naive AMP if no pipeline is used. + +## Table of Contents + +In this tutorial we will cover: + +1. [AMP introduction](#amp-introduction) +2. [AMP in Colossal-AI](#amp-in-colossal-ai) +3. [Hands-on Practice](#hands-on-practice) + +## AMP Introduction + +Automatic Mixed Precision training is a mixture of FP16 and FP32 training. + +Half-precision float point format (FP16) has lower arithmetic complexity and higher compute efficiency. Besides, fp16 requires half of the storage needed by fp32 and saves memory & network bandwidth, which makes more memory available for large batch size and model size. + +However, there are other operations, like reductions, which require the dynamic range of fp32 to avoid numeric overflow/underflow. That's the reason why we introduce automatic mixed precision, attempting to match each operation to its appropriate data type, which can reduce the memory footprint and augment training efficiency. + +
    + +
    Illustration of an ordinary AMP (figure from PatrickStar paper)
    +
    + +## AMP in Colossal-AI + +We supported three AMP training methods and allowed the user to train with AMP with no code. If you want to train with amp, just assign `mixed_precision` with `fp16` when you instantiate the `Booster`. Now booster support torch amp, the other two(apex amp, naive amp) are still started by `colossalai.initialize`, if needed, please refer to [this](./mixed_precision_training.md). Next we will support `bf16`, `fp8`. + +### Start with Booster +instantiate `Booster` with `mixed_precision="fp16"`, then you can train with torch amp. + +```python +""" + Mapping: + 'fp16': torch amp + 'fp16_apex': apex amp, + 'bf16': bf16, + 'fp8': fp8, + 'fp16_naive': naive amp +""" +from colossalai import Booster +booster = Booster(mixed_precision='fp16',...) +``` + +or you can create a `FP16TorchMixedPrecision` object, such as: + +```python +from colossalai.mixed_precision import FP16TorchMixedPrecision +mixed_precision = FP16TorchMixedPrecision( + init_scale=2.**16, + growth_factor=2.0, + backoff_factor=0.5, + growth_interval=2000) +booster = Booster(mixed_precision=mixed_precision,...) +``` + +The same goes for other types of amps. + + +### Torch AMP Configuration + +{{ autodoc:colossalai.booster.mixed_precision.FP16TorchMixedPrecision }} + +### Apex AMP Configuration + +For this mode, we rely on the Apex implementation for mixed precision training. +We support this plugin because it allows for finer control on the granularity of mixed precision. +For example, O2 level (optimization level 2) will keep batch normalization in fp32. + +If you look for more details, please refer to [Apex Documentation](https://nvidia.github.io/apex/). + +{{ autodoc:colossalai.booster.mixed_precision.FP16ApexMixedPrecision }} + +### Naive AMP Configuration + +In Naive AMP mode, we achieved mixed precision training while maintaining compatibility with complex tensor and pipeline parallelism. +This AMP mode will cast all operations into fp16. +The following code block shows the mixed precision api for this mode. + +{{ autodoc:colossalai.booster.mixed_precision.FP16NaiveMixedPrecision }} + +When using `colossalai.booster`, you are required to first instantiate a model, an optimizer and a criterion. +The output model is converted to AMP model of smaller memory consumption. +If your input model is already too large to fit in a GPU, please instantiate your model weights in `dtype=torch.float16`. +Otherwise, try smaller models or checkout more parallelization training techniques! + + +## Hands-on Practice + +Now we will introduce the use of AMP with Colossal-AI. In this practice, we will use Torch AMP as an example. + +### Step 1. Import libraries in train.py + +Create a `train.py` and import the necessary dependencies. Remember to install `scipy` and `timm` by running +`pip install timm scipy`. + +```python +import os +from pathlib import Path + +import torch +from timm.models import vit_base_patch16_224 +from titans.utils import barrier_context +from torchvision import datasets, transforms + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.logging import get_dist_logger +from colossalai.nn.lr_scheduler import LinearWarmupLR +``` + +### Step 2. Initialize Distributed Environment + +We then need to initialize distributed environment. For demo purpose, we uses `launch_from_torch`. You can refer to [Launch Colossal-AI](../basics/launch_colossalai.md) +for other initialization methods. + +```python +# initialize distributed setting +parser = colossalai.get_default_parser() +args = parser.parse_args() + +# launch from torch +colossalai.launch_from_torch(config=dict()) + +``` + +### Step 3. Create training components + +Build your model, optimizer, loss function, lr scheduler and dataloaders. Note that the root path of the dataset is +obtained from the environment variable `DATA`. You may `export DATA=/path/to/data` or change `Path(os.environ['DATA'])` +to a path on your machine. Data will be automatically downloaded to the root path. + +```python +# define the constants +NUM_EPOCHS = 2 +BATCH_SIZE = 128 + +# build model +model = vit_base_patch16_224(drop_rate=0.1) + +# build dataloader +train_dataset = datasets.Caltech101( + root=Path(os.environ['DATA']), + download=True, + transform=transforms.Compose([ + transforms.Resize(256), + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + Gray2RGB(), + transforms.Normalize([0.5, 0.5, 0.5], + [0.5, 0.5, 0.5]) + ])) + +# build optimizer +optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, weight_decay=0.1) + +# build loss +criterion = torch.nn.CrossEntropyLoss() + +# lr_scheduler +lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=50, total_steps=NUM_EPOCHS) +``` + +### Step 4. Inject AMP Feature + +Create a `MixedPrecision`(if needed) and `TorchDDPPlugin` object, call `colossalai.boost` convert the training components to be running with FP16. + +```python +plugin = TorchDDPPlugin() +train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True) +booster = Booster(mixed_precision='fp16', plugin=plugin) + +# if you need to customize the config, do like this +# >>> from colossalai.mixed_precision import FP16TorchMixedPrecision +# >>> mixed_precision = FP16TorchMixedPrecision( +# >>> init_scale=2.**16, +# >>> growth_factor=2.0, +# >>> backoff_factor=0.5, +# >>> growth_interval=2000) +# >>> plugin = TorchDDPPlugin() +# >>> booster = Booster(mixed_precision=mixed_precision, plugin=plugin) + +# boost model, optimizer, criterion, dataloader, lr_scheduler +model, optimizer, criterion, dataloader, lr_scheduler = booster.boost(model, optimizer, criterion, dataloader, lr_scheduler) +``` + +### Step 5. Train with Booster + +Use booster in a normal training loops. + +```python +model.train() +for epoch in range(NUM_EPOCHS): + for img, label in enumerate(train_dataloader): + img = img.cuda() + label = label.cuda() + optimizer.zero_grad() + output = model(img) + loss = criterion(output, label) + booster.backward(loss, optimizer) + optimizer.step() + lr_scheduler.step() +``` + +### Step 6. Invoke Training Scripts + +Use the following command to start the training scripts. You can change `--nproc_per_node` to use a different number of GPUs. + +```shell +colossalai run --nproc_per_node 1 train.py --config config/config.py +``` + diff --git a/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md new file mode 100644 index 000000000000..689e28fcc6fd --- /dev/null +++ b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md @@ -0,0 +1,235 @@ +# 自动混合精度训练 (最新版本) + +作者: [Mingyan Jiang](https://github.com/jiangmingyan) + +**前置教程** +- [定义配置文件](../basics/define_your_config.md) +- [booster使用](../basics/booster_api.md) + +**相关论文** +- [Accelerating Scientific Computations with Mixed Precision Algorithms](https://arxiv.org/abs/0808.2794) + + +## 引言 + +AMP 代表自动混合精度训练。 +在 Colossal-AI 中, 我们结合了混合精度训练的不同实现: + +1. torch.cuda.amp +2. apex.amp +3. naive amp + + +| Colossal-AI | 支持张量并行 | 支持流水并行 | fp16范围 | +| ----------- | ----------------------- | ------------------------- | ----------- | +| AMP_TYPE.TORCH | ✅ | ❌ | 在前向和反向传播期间，模型参数、激活和梯度向下转换至fp16 | +| AMP_TYPE.APEX | ❌ | ❌ | 更细粒度，我们可以选择 opt_level O0, O1, O2, O3 | +| AMP_TYPE.NAIVE | ✅ | ✅ | 模型参数、前向和反向操作，全都向下转换至fp16 | + +前两个依赖于 PyTorch (1.6及以上) 和 NVIDIA Apex 的原始实现。最后一种方法类似 Apex O2。在这些方法中，Apex-AMP 与张量并行不兼容。这是因为张量是以张量并行的方式在设备之间拆分的，因此，需要在不同的进程之间进行通信，以检查整个模型权重中是否出现inf或nan。我们修改了torch amp实现，使其现在与张量并行兼容。 + +> ❌️ fp16与ZeRO不兼容 +> +> ⚠️ 流水并行目前仅支持naive amp + +我们建议使用 torch AMP，因为在不使用流水并行时，它通常比 NVIDIA AMP 提供更好的准确性。 + +## 目录 + +在本教程中，我们将介绍: + +1. [AMP 介绍](#amp-介绍) +2. [Colossal-AI 中的 AMP](#colossal-ai-中的-amp) +3. [练习实例](#实例) + +## AMP 介绍 + +自动混合精度训练是混合 FP16 和 FP32 训练。 + +半精度浮点格式（FP16）具有较低的算法复杂度和较高的计算效率。此外，FP16 仅需要 FP32 所需的一半存储空间，并节省了内存和网络带宽，从而为大 batch size 和大模型提供了更多内存。 + +然而，还有其他操作，如缩减，需要 FP32 的动态范围，以避免数值溢出/下溢。因此，我们引入自动混合精度，尝试将每个操作与其相应的数据类型相匹配，这可以减少内存占用并提高训练效率。 + +
    + +
    AMP 示意图 (图片来自 PatrickStar 论文)
    +
    + +## Colossal-AI 中的 AMP + +我们支持三种 AMP 训练方法，并允许用户在没有改变代码的情况下使用 AMP 进行训练。booster支持amp特性注入，如果您要使用混合精度训练，则在创建booster实例时指定`mixed_precision`参数，我们现已支持torch amp，apex amp, naive amp（现已移植torch amp至booster，apex amp, naive amp仍由`colossalai.initialize`方式启动，如您需使用，请[参考](./mixed_precision_training.md）;后续将会拓展`bf16`,`pf8`的混合精度训练. + +#### booster启动方式 +您可以在创建booster实例时，指定`mixed_precision="fp16"`即使用torch amp。 + +```python +""" + 初始化映射关系如下： + 'fp16': torch amp + 'fp16_apex': apex amp, + 'bf16': bf16, + 'fp8': fp8, + 'fp16_naive': naive amp +""" +from colossalai import Booster +booster = Booster(mixed_precision='fp16',...) +``` + +或者您可以自定义一个`FP16TorchMixedPrecision`对象，如 + +```python +from colossalai.mixed_precision import FP16TorchMixedPrecision +mixed_precision = FP16TorchMixedPrecision( + init_scale=2.**16, + growth_factor=2.0, + backoff_factor=0.5, + growth_interval=2000) +booster = Booster(mixed_precision=mixed_precision,...) +``` + +其他类型的amp使用方式也是一样的。 + +### Torch AMP 配置 + +{{ autodoc:colossalai.booster.mixed_precision.FP16TorchMixedPrecision }} + +### Apex AMP 配置 + +对于这种模式，我们依靠 Apex 实现混合精度训练。我们支持这个插件，因为它允许对混合精度的粒度进行更精细的控制。 +例如, O2 水平 (优化器水平2) 将保持 batch normalization 为 FP32。 + +如果你想了解更多细节，请参考 [Apex Documentation](https://nvidia.github.io/apex/)。 + +{{ autodoc:colossalai.booster.mixed_precision.FP16ApexMixedPrecision }} + +### Naive AMP 配置 + +在 Naive AMP 模式中, 我们实现了混合精度训练，同时保持了与复杂张量和流水并行的兼容性。该 AMP 模式将所有操作转为 FP16 。下列代码块展示了该模式的booster启动方式。 + +{{ autodoc:colossalai.booster.mixed_precision.FP16NaiveMixedPrecision }} + +当使用`colossalai.booster`时, 首先需要实例化一个模型、一个优化器和一个标准。将输出模型转换为内存消耗较小的 AMP 模型。如果您的输入模型已经太大，无法放置在 GPU 中，请使用`dtype=torch.float16`实例化你的模型。或者请尝试更小的模型，或尝试更多的并行化训练技术！ + +## 实例 + +下面我们将展现如何在 Colossal-AI 使用 AMP。在该例程中，我们使用 Torch AMP. + +### 步骤 1. 在 train.py 导入相关库 + +创建`train.py`并导入必要依赖. 请记得通过命令`pip install timm scipy`安装`scipy`和`timm`。 + +```python +import os +from pathlib import Path + +import torch +from timm.models import vit_base_patch16_224 +from titans.utils import barrier_context +from torchvision import datasets, transforms + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.logging import get_dist_logger +from colossalai.nn.lr_scheduler import LinearWarmupLR +``` + +### 步骤 2. 初始化分布式环境 + +我们需要初始化分布式环境。为了快速演示，我们使用`launch_from_torch`。你可以参考 [Launch Colossal-AI](../basics/launch_colossalai.md) +使用其他初始化方法。 + +```python +# 初始化分布式设置 +parser = colossalai.get_default_parser() +args = parser.parse_args() + +# launch from torch +colossalai.launch_from_torch(config=dict()) + +``` + +### 步骤 3. 创建训练组件 + +构建你的模型、优化器、损失函数、学习率调整器和数据加载器。注意数据集的路径从环境变量`DATA`获得。你可以通过 `export DATA=/path/to/data` 或 `Path(os.environ['DATA'])` +在你的机器上设置路径。数据将会被自动下载到该路径。 + +```python +# define the constants +NUM_EPOCHS = 2 +BATCH_SIZE = 128 +# build model +model = vit_base_patch16_224(drop_rate=0.1) + +# build dataloader +train_dataset = datasets.Caltech101( + root=Path(os.environ['DATA']), + download=True, + transform=transforms.Compose([ + transforms.Resize(256), + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + Gray2RGB(), + transforms.Normalize([0.5, 0.5, 0.5], + [0.5, 0.5, 0.5]) + ])) + +# build optimizer +optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, weight_decay=0.1) + +# build loss +criterion = torch.nn.CrossEntropyLoss() + +# lr_scheduelr +lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=50, total_steps=NUM_EPOCHS) +``` + +### 步骤 4. 插入 AMP +创建一个MixedPrecision对象（如果需要）及torchDDPPlugin对象，调用 `colossalai.boost` 将所有训练组件转为为FP16模式. + +```python +plugin = TorchDDPPlugin() +train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True) +booster = Booster(mixed_precision='fp16', plugin=plugin) + +# if you need to customize the config, do like this +# >>> from colossalai.mixed_precision import FP16TorchMixedPrecision +# >>> mixed_precision = FP16TorchMixedPrecision( +# >>> init_scale=2.**16, +# >>> growth_factor=2.0, +# >>> backoff_factor=0.5, +# >>> growth_interval=2000) +# >>> plugin = TorchDDPPlugin() +# >>> booster = Booster(mixed_precision=mixed_precision, plugin=plugin) + +# boost model, optimizer, criterion, dataloader, lr_scheduler +model, optimizer, criterion, dataloader, lr_scheduler = booster.boost(model, optimizer, criterion, dataloader, lr_scheduler) +``` + +### 步骤 5. 使用 booster 训练 + +使用booster构建一个普通的训练循环。 + +```python +model.train() +for epoch in range(NUM_EPOCHS): + for img, label in enumerate(train_dataloader): + img = img.cuda() + label = label.cuda() + optimizer.zero_grad() + output = model(img) + loss = criterion(output, label) + booster.backward(loss, optimizer) + optimizer.step() + lr_scheduler.step() +``` + +### 步骤 6. 启动训练脚本 + +使用下列命令启动训练脚本，你可以改变 `--nproc_per_node` 以使用不同数量的 GPU。 + +```shell +colossalai run --nproc_per_node 1 train.py --config config/config.py +``` + From 75272ef37ba7f128adc40c36276bc444c146064d Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Tue, 23 May 2023 16:34:30 +0800 Subject: [PATCH 232/413] [doc] add removed warning --- docs/source/en/basics/define_your_config.md | 2 ++ docs/source/en/features/mixed_precision_training.md | 2 +- docs/source/zh-Hans/basics/define_your_config.md | 2 ++ docs/source/zh-Hans/features/mixed_precision_training.md | 2 +- 4 files changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/source/en/basics/define_your_config.md b/docs/source/en/basics/define_your_config.md index 223af44f8dce..46b7112b7e80 100644 --- a/docs/source/en/basics/define_your_config.md +++ b/docs/source/en/basics/define_your_config.md @@ -2,6 +2,8 @@ Author: Guangyang Lu, Shenggui Li, Siqi Mai +- > ⚠️ The information on this page is outdated and will be deprecated. Please check [Booster API](../basics/booster_api.md) for more information. + **Prerequisite:** - [Distributed Training](../concepts/distributed_training.md) - [Colossal-AI Overview](../concepts/colossalai_overview.md) diff --git a/docs/source/en/features/mixed_precision_training.md b/docs/source/en/features/mixed_precision_training.md index 04f0bc6deb98..8579d586ed5f 100644 --- a/docs/source/en/features/mixed_precision_training.md +++ b/docs/source/en/features/mixed_precision_training.md @@ -362,7 +362,7 @@ for epoch in range(gpc.config.NUM_EPOCHS): Use the following command to start the training scripts. You can change `--nproc_per_node` to use a different number of GPUs. -```python +```shell python -m torch.distributed.launch --nproc_per_node 4 --master_addr localhost --master_port 29500 train_with_engine.py --config config/config_AMP_torch.py ``` diff --git a/docs/source/zh-Hans/basics/define_your_config.md b/docs/source/zh-Hans/basics/define_your_config.md index cf099f03815e..d1de085e38c0 100644 --- a/docs/source/zh-Hans/basics/define_your_config.md +++ b/docs/source/zh-Hans/basics/define_your_config.md @@ -2,6 +2,8 @@ 作者: Guangyang Lu, Shenggui Li, Siqi Mai +- > ⚠️ 此页面上的信息已经过时并将被废弃。请在[Booster API](../basics/booster_api.md)页面查阅更新。 + **预备知识:** - [分布式训练](../concepts/distributed_training.md) - [Colossal-AI 总览](../concepts/colossalai_overview.md) diff --git a/docs/source/zh-Hans/features/mixed_precision_training.md b/docs/source/zh-Hans/features/mixed_precision_training.md index b3b9a7ebc26b..c4df6271b3bb 100644 --- a/docs/source/zh-Hans/features/mixed_precision_training.md +++ b/docs/source/zh-Hans/features/mixed_precision_training.md @@ -339,7 +339,7 @@ for epoch in range(gpc.config.NUM_EPOCHS): 使用下列命令启动训练脚本，你可以改变 `--nproc_per_node` 以使用不同数量的 GPU。 -```python +```shell python -m torch.distributed.launch --nproc_per_node 4 --master_addr localhost --master_port 29500 train_with_engine.py --config config/config_AMP_torch.py ``` From c425a69d52c714423bbc5a55f6f3c609723993d9 Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Tue, 23 May 2023 16:42:36 +0800 Subject: [PATCH 233/413] [doc] add removed change of config.py --- docs/source/en/basics/define_your_config.md | 20 ++++++++++--------- .../zh-Hans/basics/define_your_config.md | 19 +++++++++--------- 2 files changed, 20 insertions(+), 19 deletions(-) diff --git a/docs/source/en/basics/define_your_config.md b/docs/source/en/basics/define_your_config.md index 46b7112b7e80..048ffcacbb8f 100644 --- a/docs/source/en/basics/define_your_config.md +++ b/docs/source/en/basics/define_your_config.md @@ -2,7 +2,8 @@ Author: Guangyang Lu, Shenggui Li, Siqi Mai -- > ⚠️ The information on this page is outdated and will be deprecated. Please check [Booster API](../basics/booster_api.md) for more information. +> ⚠️ The information on this page is outdated and will be deprecated. Please check [Booster API](../basics/booster_api.md) for more information. + **Prerequisite:** - [Distributed Training](../concepts/distributed_training.md) @@ -23,7 +24,8 @@ In this tutorial, we will cover how to define your configuration file. ## Configuration Definition In a configuration file, there are two types of variables. One serves as feature specification and the other serves -as hyper-parameters. All feature-related variables are reserved keywords. For example, if you want to use 1D tensor parallelism, you need to use the variable name `parallel` in the config file and follow a pre-defined format. +as hyper-parameters. All feature-related variables are reserved keywords. For example, if you want to use mixed precision +training, you need to use the variable name `fp16` in the config file and follow a pre-defined format. ### Feature Specification @@ -35,13 +37,14 @@ To illustrate the use of config file, we use mixed precision training as an exam follow the steps below. 1. create a configuration file (e.g. `config.py`, the file name can be anything) -2. define the hybrid parallelism configuration in the config file. For example, in order to use 1D tensor parallel, you can just write these lines of code below into your config file. +2. define the mixed precision configuration in the config file. For example, in order to use mixed precision training +natively provided by PyTorch, you can just write these lines of code below into your config file. ```python - parallel = dict( - data=1, - pipeline=1, - tensor=dict(size=2, mode='1d'), + from colossalai.amp import AMP_TYPE + + fp16 = dict( + mode=AMP_TYPE.TORCH ) ``` @@ -54,7 +57,7 @@ the current directory. colossalai.launch(config='./config.py', ...) ``` -In this way, Colossal-AI knows what features you want to use and will inject this feature. +In this way, Colossal-AI knows what features you want to use and will inject this feature during `colossalai.initialize`. ### Global Hyper-parameters @@ -80,4 +83,3 @@ colossalai.launch(config='./config.py', ...) print(gpc.config.BATCH_SIZE) ``` - diff --git a/docs/source/zh-Hans/basics/define_your_config.md b/docs/source/zh-Hans/basics/define_your_config.md index d1de085e38c0..720e75805e8d 100644 --- a/docs/source/zh-Hans/basics/define_your_config.md +++ b/docs/source/zh-Hans/basics/define_your_config.md @@ -2,7 +2,7 @@ 作者: Guangyang Lu, Shenggui Li, Siqi Mai -- > ⚠️ 此页面上的信息已经过时并将被废弃。请在[Booster API](../basics/booster_api.md)页面查阅更新。 +> ⚠️ 此页面上的信息已经过时并将被废弃。请在[Booster API](../basics/booster_api.md)页面查阅更新。 **预备知识:** - [分布式训练](../concepts/distributed_training.md) @@ -20,7 +20,7 @@ ## 配置定义 -在一个配置文件中，有两种类型的变量。一种是作为特征说明，另一种是作为超参数。所有与特征相关的变量都是保留关键字。例如，如果您想使用`1D`张量并行，需要在 config 文件中使用变量名`fp16`，并遵循预先定义的格式。 +在一个配置文件中，有两种类型的变量。一种是作为特征说明，另一种是作为超参数。所有与特征相关的变量都是保留关键字。例如，如果您想使用混合精度训练，需要在 config 文件中使用变量名`fp16`，并遵循预先定义的格式。 ### 功能配置 @@ -29,13 +29,13 @@ Colossal-AI 提供了一系列的功能来加快训练速度。每个功能都 为了说明配置文件的使用，我们在这里使用混合精度训练作为例子。您需要遵循以下步骤。 1. 创建一个配置文件（例如 `config.py`，您可以指定任意的文件名）。 -2. 在配置文件中定义混合并行的配置。例如，为了使用`1D`张量并行，您只需将下面这几行代码写入您的配置文件中。 +2. 在配置文件中定义混合精度的配置。例如，为了使用 PyTorch 提供的原始混合精度训练，您只需将下面这几行代码写入您的配置文件中。 - ```python - parallel = dict( - data=1, - pipeline=1, - tensor=dict(size=2, mode='1d'), + ```python + from colossalai.amp import AMP_TYPE + + fp16 = dict( + mode=AMP_TYPE.TORCH ) ``` @@ -47,7 +47,7 @@ Colossal-AI 提供了一系列的功能来加快训练速度。每个功能都 colossalai.launch(config='./config.py', ...) ``` -这样，Colossal-AI 便知道您想使用什么功能，并注入您所需要的功能。 +这样，Colossal-AI 便知道您想使用什么功能，并会在 `colossalai.initialize` 期间注入您所需要的功能。 ### 全局超参数 @@ -71,4 +71,3 @@ colossalai.launch(config='./config.py', ...) print(gpc.config.BATCH_SIZE) ``` - From 6b305a99d6bf33cb2f6fdeb4b5e099cd026cf485 Mon Sep 17 00:00:00 2001 From: wukong1992 Date: Tue, 23 May 2023 16:58:45 +0800 Subject: [PATCH 234/413] [booster] torch fsdp fix ckpt (#3788) --- colossalai/booster/booster.py | 2 +- .../booster/plugin/torch_fsdp_plugin.py | 207 ++++++------------ .../checkpoint_io/general_checkpoint_io.py | 75 ++++--- .../test_plugin/test_torch_fsdp_plugin.py | 5 +- .../test_torch_fsdp_checkpoint_io.py | 113 ++++++++++ 5 files changed, 223 insertions(+), 179 deletions(-) create mode 100644 tests/test_checkpoint_io/test_torch_fsdp_checkpoint_io.py diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index be9c1c9dc5e3..61d912157449 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -196,7 +196,7 @@ def save_model(self, If true, the checkpoint will be a folder. Otherwise, it will be a single file. Defaults to False. size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024. """ - self.checkpoint_io.save_model(model, checkpoint, prefix, shard, size_per_shard) + self.checkpoint_io.save_model(model, checkpoint=checkpoint, shard=shard, size_per_shard=size_per_shard) def load_optimizer(self, optimizer: Optimizer, checkpoint: str): """Load optimizer from checkpoint. diff --git a/colossalai/booster/plugin/torch_fsdp_plugin.py b/colossalai/booster/plugin/torch_fsdp_plugin.py index 0daefa9fff53..340555dc640c 100644 --- a/colossalai/booster/plugin/torch_fsdp_plugin.py +++ b/colossalai/booster/plugin/torch_fsdp_plugin.py @@ -1,3 +1,4 @@ +from pathlib import Path from typing import Callable, Iterable, Iterator, List, Optional, Tuple, Union import torch @@ -5,30 +6,18 @@ from packaging import version from torch.distributed import ProcessGroup -if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( - torch.__version__) < version.parse('2.0.0'): + +if version.parse(torch.__version__) >= version.parse('1.12.0'): from torch.distributed.fsdp import FullStateDictConfig from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp import StateDictType from torch.distributed.fsdp.fully_sharded_data_parallel import ( BackwardPrefetch, CPUOffload, - MixedPrecision, - ShardingStrategy, - ) -elif version.parse(torch.__version__) >= version.parse('2.0.0'): - from torch.distributed.fsdp import FullyShardedDataParallel as FSDP - from torch.distributed.fsdp._init_utils import ProcessGroupType - from torch.distributed.fsdp.api import ( - BackwardPrefetch, - CPUOffload, - FullOptimStateDictConfig, FullStateDictConfig, MixedPrecision, ShardingStrategy, - StateDictType, ) - from torch.distributed.fsdp.wrap import _FSDPPolicy else: raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") @@ -36,7 +25,7 @@ from torch.optim.lr_scheduler import _LRScheduler as LRScheduler from torch.utils.data import DataLoader -from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO +from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO, utils from colossalai.cluster import DistCoordinator from colossalai.interface import ModelWrapper, OptimizerWrapper @@ -51,102 +40,71 @@ def __init__(self) -> None: super().__init__() self.coordinator = DistCoordinator() - def __set_model_optim_state( - self, - model, - state_dict_type, - state_dict_config, - optim_state_dict_config, - ): - return FSDP.set_state_dict_type(model, state_dict_type, state_dict_config, optim_state_dict_config) - - def load_sharded_model(self, model: nn.Module, checkpoint: str): - - # TODO(jishaomin): implement this method as it can be supported by Huggingface model - raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.") - - def load_sharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str): - - # TODO(jishaomin): implement this method as it can be supported by Huggingface model - raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.") - - def save_sharded_model(self, model: nn.Module, checkpoint: str): - - # TODO(jishaomin): implement this method as it can be supported by Huggingface model - raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.") + def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool): + checkpoint = utils.load_state_dict(checkpoint) + model.load_state_dict(checkpoint) - def save_sharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str): + def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): + checkpoint = utils.load_state_dict(checkpoint) + fsdp_model = optimizer.unwrap_model() + sharded_osd = FSDP.scatter_full_optim_state_dict(checkpoint, fsdp_model) + optimizer.load_state_dict(sharded_osd) - # TODO(jishaomin): implement this method as it can be supported by Huggingface model - raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.") - - def load_unsharded_model(self, model: nn.Module, checkpoint: str): + def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, use_safetensors: bool): """ - Load model from checkpoint with automatic unwrapping. + Save model to checkpoint but only on master process. """ # the model should be unwrapped in self.load_model via ModelWrapper.unwrap + cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) + with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, cfg): + full_model_state = model.state_dict() + utils.save_state_dict(full_model_state, checkpoint_file_path=checkpoint, use_safetensors=use_safetensors) - if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( - torch.__version__) < version.parse('2.0.0'): - full_state_dict = self.load_state_dict(checkpoint) - elif version.parse(torch.__version__) >= version.parse('2.0.0'): - full_state_dict = self.load_state_dict(checkpoint) - self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(rank0_only=True)) - full_state_dict = model.state_dict() - else: - raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") - - model.load_state_dict(full_state_dict) - - def load_unsharded_optimizer(self, model: nn.Module, optim: Optimizer, checkpoint: str): + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): """ - Load Optimizer from checkpoint with automatic unwrapping. + Save optimizer to checkpoint but only on master process. """ + assert isinstance(optimizer, FSDPOptimizerWrapper) + fsdp_model = optimizer.unwrap_model() + full_optimizer_state = FSDP.full_optim_state_dict(fsdp_model, optim=optimizer, rank0_only=True) + utils.save_state_dict(full_optimizer_state, checkpoint_file_path=checkpoint, use_safetensors=False) - if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( - torch.__version__) < version.parse('2.0.0'): - optim_full_state_dict = self.load_state_dict(checkpoint) - elif version.parse(torch.__version__) >= version.parse('2.0.0'): - optim_full_state_dict = self.load_state_dict(checkpoint) - FSDP.full_optim_state_dict_to_load(optim_full_state_dict, model, optim) - else: - raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") - - optim.load_state_dict(optim_full_state_dict) - - def save_unsharded_model(self, model: nn.Module, checkpoint: str): + def save_sharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, variant: Optional[str], + size_per_shard: int, use_safetensors: bool): """ Save model to checkpoint but only on master process. """ - # the model should be unwrapped in self.load_model via ModelWrapper.unwrap + raise NotImplementedError("Sharded model checkpoint is not supported yet.") + + def load_sharded_model(self, + model: nn.Module, + checkpoint_index_file: Path, + strict: bool = False, + use_safetensors: bool = False, + load_sub_module: bool = True): + """ + Load model to checkpoint but only on master process. + """ + raise NotImplementedError("Sharded model checkpoint is not supported yet.") - if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( - torch.__version__) < version.parse('2.0.0'): - cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) - with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, cfg): - model_state_dict = model.state_dict() - elif version.parse(torch.__version__) >= version.parse('2.0.0'): - self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(rank0_only=True)) - model_state_dict = model.state_dict() - else: - raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") - self.save_checkpoint(model_state_dict, checkpoint) - - def save_unsharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str): + def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): """ Save optimizer to checkpoint but only on master process. """ + raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.") - if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( - torch.__version__) < version.parse('2.0.0'): - optim_state_dict = FSDP.full_optim_state_dict(model=model, optim=optimizer) - elif version.parse(torch.__version__) >= version.parse('2.0.0'): - self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, - FullOptimStateDictConfig(rank0_only=True)) - optim_state_dict = FSDP.optim_state_dict(model, optimizer) - else: - raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") - self.save_checkpoint(optim_state_dict, checkpoint) + def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str, size_per_shard: int): + """ + Load optimizer to checkpoint but only on master process. + """ + raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.") + + def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + """ + Save model to checkpoint but only on master process. + """ + if self.coordinator.is_master(): + super().save_lr_scheduler(lr_scheduler, checkpoint) class TorchFSDPModel(ModelWrapper): @@ -156,7 +114,17 @@ def __init__(self, module: nn.Module, *args, **kwargs) -> None: self.module = FSDP(module, *args, **kwargs) def unwrap(self): - return self.module.module + return self.module + + +class FSDPOptimizerWrapper(OptimizerWrapper): + + def __init__(self, optimizer: Optimizer, model: nn.Module): + self.model = model + super().__init__(optimizer) + + def unwrap_model(self) -> nn.Module: + return self.model class TorchFSDPPlugin(DPPluginBase): @@ -178,8 +146,7 @@ class TorchFSDPPlugin(DPPluginBase): See https://pytorch.org/docs/stable/fsdp.html for details. """ - if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse( - torch.__version__) < version.parse('2.0.0'): + if version.parse(torch.__version__) >= version.parse('1.12.0'): def __init__( self, @@ -191,7 +158,6 @@ def __init__( mixed_precision: Optional[MixedPrecision] = None, ignored_modules: Optional[Iterable[torch.nn.Module]] = None, param_init_fn: Optional[Callable[[nn.Module], None]] = None, - device_id: Optional[Union[int, torch.device]] = None, sync_module_states: bool = False, ): super().__init__() @@ -203,42 +169,7 @@ def __init__( mixed_precision=mixed_precision, ignored_modules=ignored_modules, param_init_fn=param_init_fn, - device_id=device_id, sync_module_states=sync_module_states) - elif version.parse(torch.__version__) >= version.parse('2.0.0'): - - def __init__( - self, - process_group: ProcessGroupType = None, - sharding_strategy: Optional[ShardingStrategy] = None, - cpu_offload: Optional[CPUOffload] = None, - auto_wrap_policy: Optional[Union[Callable, _FSDPPolicy]] = None, - backward_prefetch: Optional[BackwardPrefetch] = BackwardPrefetch.BACKWARD_PRE, - mixed_precision: Optional[MixedPrecision] = None, - ignored_modules: Optional[Iterable[torch.nn.Module]] = None, - param_init_fn: Optional[Callable[[nn.Module], None]] = None, - device_id: Optional[Union[int, torch.device]] = None, - sync_module_states: bool = False, - forward_prefetch: bool = False, - limit_all_gathers: bool = False, - use_orig_params: bool = False, - ignored_parameters: Optional[Iterable[torch.nn.Parameter]] = None, - ): - super().__init__() - self.fsdp_kwargs = dict(process_group=process_group, - sharding_strategy=sharding_strategy, - cpu_offload=cpu_offload, - auto_wrap_policy=auto_wrap_policy, - backward_prefetch=backward_prefetch, - mixed_precision=mixed_precision, - ignored_modules=ignored_modules, - param_init_fn=param_init_fn, - device_id=device_id, - sync_module_states=sync_module_states, - forward_prefetch=forward_prefetch, - limit_all_gathers=limit_all_gathers, - use_orig_params=use_orig_params, - ignored_parameters=ignored_parameters) else: raise RuntimeError("FSDP is not supported while torch version under 1.12.0.") @@ -269,14 +200,14 @@ def configure( lr_scheduler: LRScheduler = None, ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: - model = model.cuda() # wrap the model with PyTorch FSDP - model = TorchFSDPModel(model, **self.fsdp_kwargs) + fsdp_model = TorchFSDPModel(model, device_id=torch.cuda.current_device(), **self.fsdp_kwargs) + optimizer.__init__(fsdp_model.parameters(), **optimizer.defaults) - if not isinstance(optimizer, OptimizerWrapper): - optimizer = OptimizerWrapper(optimizer) + if not isinstance(optimizer, FSDPOptimizerWrapper): + optimizer = FSDPOptimizerWrapper(optimizer, fsdp_model) - return model, optimizer, criterion, dataloader, lr_scheduler + return fsdp_model, optimizer, criterion, dataloader, lr_scheduler def control_checkpoint_io(self) -> bool: return True diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index 96a883fdb42a..2cc9c3faa12b 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -1,26 +1,26 @@ -from pathlib import Path +import gc +import logging +import os from functools import reduce +from pathlib import Path +from typing import Iterator, Optional, OrderedDict, Tuple import torch.nn as nn from torch.optim import Optimizer -import logging -import os -import gc -from typing import Optional, Iterator, OrderedDict, Tuple from .checkpoint_io_base import CheckpointIO from .index_file import CheckpointIndexFile from .utils import ( - has_index_file, - load_state_dict, - save_state_dict, + get_base_filenames, + get_shard_filename, + has_index_file, is_safetensors_available, - shard_checkpoint, load_shard_state_dict, + load_state_dict, load_state_dict_into_model, - get_shard_filename, - get_base_filenames - ) + save_state_dict, + shard_checkpoint, +) __all__ = ['GeneralCheckpointIO'] @@ -29,6 +29,7 @@ class GeneralCheckpointIO(CheckpointIO): """ Checkpoint IO """ + def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool): checkpoint = load_state_dict(checkpoint) model.load_state_dict(checkpoint, strict=strict) @@ -69,19 +70,23 @@ def save_unsharded_optimizer( # TODO(FrankLeeeee): handle distributed tensors save_state_dict(optimizer.state_dict(), checkpoint, use_safetensors=False) - - def save_sharded_model(self, model: nn.Module, checkpoint_path: str, gather_dtensor:bool = False, - variant: Optional[str] = None, max_shard_size: int = 1024, use_safetensors: bool = False): - """ + def save_sharded_model(self, + model: nn.Module, + checkpoint_path: str, + gather_dtensor: bool = False, + variant: Optional[str] = None, + max_shard_size: int = 1024, + use_safetensors: bool = False): + """ implement this method as it can be supported by Huggingface model, save shard model, save model to multiple files """ if os.path.isfile(checkpoint_path): logging.error(f"Provided path ({checkpoint_path}) should be a directory, not a file") return - + Path(checkpoint_path).mkdir(parents=True, exist_ok=True) - + # shard checkpoint state_dict = model.state_dict() state_dict_shard = shard_checkpoint(state_dict, max_shard_size=max_shard_size) @@ -95,21 +100,22 @@ def save_sharded_model(self, model: nn.Module, checkpoint_path: str, gather_dten total_size = total_size + shard_pair[1] for key in shard.keys(): index_file.append_weight_map(key, shard_file) - + checkpoint_file_path = os.path.join(checkpoint_path, shard_file) save_state_dict(shard, checkpoint_file_path, use_safetensors) - + index_file.append_meta_data("total_size", total_size) index_file.write_index_file(save_index_file) - logging.info( - f"The model is going to be split to checkpoint shards. " - f"You can find where each parameters has been saved in the " - f"index located at {save_index_file}." - ) - - - def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, strict: bool = False, - use_safetensors: bool = False, load_sub_module: bool = True): + logging.info(f"The model is going to be split to checkpoint shards. " + f"You can find where each parameters has been saved in the " + f"index located at {save_index_file}.") + + def load_sharded_model(self, + model: nn.Module, + checkpoint_index_file: Path, + strict: bool = False, + use_safetensors: bool = False, + load_sub_module: bool = True): """ load shard model, load model from multiple files """ @@ -119,7 +125,7 @@ def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, stri if use_safetensors and not is_safetensors_available(): raise ImportError("`safe_serialization` requires the `safetensors` library: `pip install safetensors`.") - + # read checkpoint index file ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file) checkpoint_files, _ = ckpt_index_file.get_checkpoint_fileanames() @@ -134,10 +140,7 @@ def load_sharded_model(self, model: nn.Module, checkpoint_index_file: Path, stri if strict: remain_keys = reduce(lambda a, b: a & b, map(set, missing_keys)) if len(remain_keys) > 0: - error_msgs = 'Missing key(s) in state_dict: {}. '.format( - ', '.join('"{}"'.format(k) for k in missing_keys)) + error_msgs = 'Missing key(s) in state_dict: {}. '.format(', '.join( + '"{}"'.format(k) for k in missing_keys)) raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( - self.__class__.__name__, "\n\t".join(error_msgs))) - - - + self.__class__.__name__, "\n\t".join(error_msgs))) diff --git a/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py index 12562095c153..44767f051fdd 100644 --- a/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py @@ -1,10 +1,6 @@ -from contextlib import nullcontext - import pytest import torch -import torch.distributed as dist from packaging import version -from torch import nn from torch.optim import SGD import colossalai @@ -19,6 +15,7 @@ from tests.kit.model_zoo import model_zoo +# test baisc fsdp function def run_fn(model_fn, data_gen_fn, output_transform_fn): plugin = TorchFSDPPlugin() booster = Booster(plugin=plugin) diff --git a/tests/test_checkpoint_io/test_torch_fsdp_checkpoint_io.py b/tests/test_checkpoint_io/test_torch_fsdp_checkpoint_io.py new file mode 100644 index 000000000000..2b6090bb1e29 --- /dev/null +++ b/tests/test_checkpoint_io/test_torch_fsdp_checkpoint_io.py @@ -0,0 +1,113 @@ +import pytest +import torch +from packaging import version +from torch import nn +from torch.optim import SGD +from torchvision.models import resnet18 +from utils import shared_tempdir + +import colossalai +from colossalai.booster import Booster + +if version.parse(torch.__version__) >= version.parse('1.12.0'): + from torch.distributed.fsdp import FullyShardedDataParallel as FSDP + from colossalai.booster.plugin import TorchFSDPPlugin + +from colossalai.testing import rerun_if_address_is_in_use, spawn, check_state_dict_equal + + +def compare_nested_dict(dict1, dict2): + for key in dict1: + if key in dict2: + if type(dict1[key]) is dict: + assert type(dict2[key]) is dict + diff = compare_nested_dict(dict1[key], dict2[key]) + if not diff: + return diff + elif type(dict1[key]) is list: + assert type(dict2[key]) is list + for i, val in enumerate(dict1[key]): + if isinstance(val, torch.Tensor): + if not torch.equal(dict1[key][i], dict2[key][i]): + return False + elif val != dict2[key][i]: + return False + elif type(dict1[key]) is torch.Tensor: + assert type(dict2[key]) is torch.Tensor + if not torch.equal(dict1[key], dict2[key]): + return False + else: + if dict1[key] != dict2[key]: + return False + else: + return False + return True + + +def check_torch_fsdp_ckpt(): + model = resnet18() + plugin = TorchFSDPPlugin() + booster = Booster(plugin=plugin) + optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9) + criterion = lambda x: x.mean() + fsdp_model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + inputs = torch.randn(4, 3, 224, 224) + outputs = None + + def run_model(): + nonlocal outputs + outputs = fsdp_model(inputs) + optimizer.zero_grad() + criterion(outputs).backward() + optimizer.step() + + with shared_tempdir() as tempdir: + model_ckpt_path = f"{tempdir}/model" + optim_ckpt_path = f"{tempdir}/optimizer" + + run_model() + + booster.save_model(fsdp_model, model_ckpt_path, shard=False) + booster.save_optimizer(optimizer, optim_ckpt_path, shard=False) + + full_msd = fsdp_model.state_dict() + #full_osd = FSDP.full_optim_state_dict(fsdp_model, optimizer) + sharded_osd = optimizer.state_dict() + import copy + sharded_osd = copy.deepcopy(sharded_osd) + + run_model() + + full_msd_updated = fsdp_model.state_dict() + #full_osd_updated = FSDP.full_optim_state_dict(fsdp_model, optimizer, rank0_only=True) + sharded_osd_updated = optimizer.state_dict() + + assert not compare_nested_dict(sharded_osd, sharded_osd_updated) + assert not compare_nested_dict(full_msd_updated, full_msd) + outputs_first = fsdp_model(inputs) + assert criterion(outputs_first) != criterion(outputs) + + booster.load_model(fsdp_model, model_ckpt_path) + booster.load_optimizer(optimizer, optim_ckpt_path) + + full_msd_restore = fsdp_model.state_dict() + #full_osd_restore = FSDP.full_optim_state_dict(fsdp_model, optimizer, rank0_only=True) + sharded_osd_restore = optimizer.state_dict() + + assert compare_nested_dict(sharded_osd, sharded_osd_restore) + assert compare_nested_dict(full_msd_restore, full_msd) + outputs_sec = fsdp_model(inputs) + assert criterion(outputs_sec) == criterion(outputs) + + +def run_dist(rank, world_size, port): + # init dist env + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + check_torch_fsdp_ckpt() + + +@pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason="requires torch1.12 or higher") +@rerun_if_address_is_in_use() +def test_torch_fsdp_ckpt(): + spawn(run_dist, 2) From 19d153057efebf65259fdc21f582ff2e55dbe2ec Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Tue, 23 May 2023 17:16:10 +0800 Subject: [PATCH 235/413] [doc] add warning about fsdp plugin (#3813) --- docs/source/en/basics/booster_plugins.md | 1 + docs/source/zh-Hans/basics/booster_plugins.md | 1 + 2 files changed, 2 insertions(+) diff --git a/docs/source/en/basics/booster_plugins.md b/docs/source/en/basics/booster_plugins.md index 0362f095af2b..6ed49bfa7254 100644 --- a/docs/source/en/basics/booster_plugins.md +++ b/docs/source/en/basics/booster_plugins.md @@ -62,6 +62,7 @@ More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/genera ### Torch FSDP Plugin > ⚠ This plugin is not available when torch version is lower than 1.12.0. +> ⚠ This plugin does not support save/load sharded model checkpoint now. More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html). diff --git a/docs/source/zh-Hans/basics/booster_plugins.md b/docs/source/zh-Hans/basics/booster_plugins.md index b15ceb1e3ad5..00e7d91e3f79 100644 --- a/docs/source/zh-Hans/basics/booster_plugins.md +++ b/docs/source/zh-Hans/basics/booster_plugins.md @@ -62,6 +62,7 @@ Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累 ### Torch FSDP 插件 > ⚠ 如果 torch 版本低于 1.12.0，此插件将不可用。 +> ⚠ 该插件现在还不支持保存/加载分片的模型 checkpoint。 更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/fsdp.html). From 1e3b64f26cb3c75d4bdf494a5c6e020628cadb4f Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 23 May 2023 17:49:53 +0800 Subject: [PATCH 236/413] [workflow] enblaed doc build from a forked repo (#3815) --- .github/workflows/doc_build_after_merge.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/doc_build_after_merge.yml b/.github/workflows/doc_build_after_merge.yml index ede04b336620..b6fd57b8d2b4 100644 --- a/.github/workflows/doc_build_after_merge.yml +++ b/.github/workflows/doc_build_after_merge.yml @@ -2,12 +2,12 @@ name: Build Documentation After Merge on: workflow_dispatch: - pull_request: + push: paths: - - 'version.txt' - - 'docs/**' - types: - - closed + - "version.txt" + - "docs/**" + branches: + - "main" jobs: build-doc: From 8aa1fb2c7ffe9bd14aafdcae6d4fcf615dddfade Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Tue, 23 May 2023 17:50:30 +0800 Subject: [PATCH 237/413] [doc]fix --- .../source/en/features/mixed_precision_training_with_booster.md | 2 +- .../zh-Hans/features/mixed_precision_training_with_booster.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/en/features/mixed_precision_training_with_booster.md b/docs/source/en/features/mixed_precision_training_with_booster.md index 2ae88b07157f..e9b6f684f613 100644 --- a/docs/source/en/features/mixed_precision_training_with_booster.md +++ b/docs/source/en/features/mixed_precision_training_with_booster.md @@ -246,6 +246,6 @@ for epoch in range(NUM_EPOCHS): Use the following command to start the training scripts. You can change `--nproc_per_node` to use a different number of GPUs. ```shell -colossalai run --nproc_per_node 1 train.py --config config/config.py +colossalai run --nproc_per_node 1 train.py ``` diff --git a/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md index 689e28fcc6fd..6954556a8e9a 100644 --- a/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md +++ b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md @@ -230,6 +230,6 @@ for epoch in range(NUM_EPOCHS): 使用下列命令启动训练脚本，你可以改变 `--nproc_per_node` 以使用不同数量的 GPU。 ```shell -colossalai run --nproc_per_node 1 train.py --config config/config.py +colossalai run --nproc_per_node 1 train.py ``` From 278fcbc4444f15be8c155d382f4e1eaacdc89456 Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Tue, 23 May 2023 17:53:11 +0800 Subject: [PATCH 238/413] [doc]fix --- docs/source/en/features/gradient_accumulation_with_booster.md | 2 +- docs/source/en/features/gradient_clipping_with_booster.md | 2 +- .../zh-Hans/features/gradient_accumulation_with_booster.md | 2 +- docs/source/zh-Hans/features/gradient_clipping_with_booster.md | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/en/features/gradient_accumulation_with_booster.md b/docs/source/en/features/gradient_accumulation_with_booster.md index f319ef5b2db3..201e3bc2b643 100644 --- a/docs/source/en/features/gradient_accumulation_with_booster.md +++ b/docs/source/en/features/gradient_accumulation_with_booster.md @@ -128,7 +128,7 @@ for idx, (img, label) in enumerate(train_dataloader): ### Step 6. Invoke Training Scripts To verify gradient accumulation, we can just check the change of parameter values. When gradient accumulation is set, parameters are only updated in the last step. You can run the script using this command: ```shell -colossalai run --nproc_per_node 1 train.py --config config.py +colossalai run --nproc_per_node 1 train.py ``` You will see output similar to the text below. This shows gradient is indeed accumulated as the parameter is not updated diff --git a/docs/source/en/features/gradient_clipping_with_booster.md b/docs/source/en/features/gradient_clipping_with_booster.md index b9c7bb20631c..8686eb06ff54 100644 --- a/docs/source/en/features/gradient_clipping_with_booster.md +++ b/docs/source/en/features/gradient_clipping_with_booster.md @@ -136,7 +136,7 @@ for idx, (img, label) in enumerate(train_dataloader): You can run the script using this command: ```shell -colossalai run --nproc_per_node 1 train.py --config config/config.py +colossalai run --nproc_per_node 1 train.py ``` diff --git a/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md b/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md index 4dc0b3db4a86..ab86f34f2dec 100644 --- a/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md +++ b/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md @@ -131,7 +131,7 @@ for idx, (img, label) in enumerate(train_dataloader): ### 步骤 6. 启动训练脚本 为了验证梯度累积，我们可以只检查参数值的变化。当设置梯度累加时，仅在最后一步更新参数。您可以使用以下命令运行脚本： ```shell -colossalai run --nproc_per_node 1 train.py --config config.py +colossalai run --nproc_per_node 1 train.py ``` 你将会看到类似下方的文本输出。这展现了梯度虽然在前3个迭代中被计算，但直到最后一次迭代，参数才被更新。 diff --git a/docs/source/zh-Hans/features/gradient_clipping_with_booster.md b/docs/source/zh-Hans/features/gradient_clipping_with_booster.md index 2f023cefe35e..3c61356dd0d5 100644 --- a/docs/source/zh-Hans/features/gradient_clipping_with_booster.md +++ b/docs/source/zh-Hans/features/gradient_clipping_with_booster.md @@ -135,6 +135,6 @@ for idx, (img, label) in enumerate(train_dataloader): 你可以使用以下命令运行脚本： ```shell -colossalai run --nproc_per_node 1 train.py --config config/config.py +colossalai run --nproc_per_node 1 train.py ``` From 7f8203af698b66099f6a543afc46d16178340515 Mon Sep 17 00:00:00 2001 From: digger yu Date: Wed, 24 May 2023 09:01:50 +0800 Subject: [PATCH 239/413] fix typo colossalai/auto_parallel autochunk fx/passes etc. (#3808) --- .github/workflows/README.md | 4 ++-- colossalai/auto_parallel/passes/meta_info_prop.py | 2 +- .../node_handler/strategy/batch_norm_generator.py | 2 +- .../node_handler/strategy/conv_strategy_generator.py | 4 ++-- .../node_handler/strategy/layer_norm_generator.py | 4 ++-- .../node_handler/strategy/normal_pooling_generator.py | 6 +++--- colossalai/autochunk/trace_flow.py | 8 ++++---- colossalai/autochunk/trace_indice.py | 4 ++-- colossalai/booster/plugin/gemini_plugin.py | 2 +- colossalai/cluster/dist_coordinator.py | 2 +- colossalai/device/alpha_beta_profiler.py | 2 +- colossalai/engine/schedule/_pipeline_schedule.py | 4 ++-- colossalai/engine/schedule/_pipeline_schedule_v2.py | 2 +- colossalai/fx/codegen/activation_checkpoint_codegen.py | 2 +- colossalai/fx/passes/adding_split_node_pass.py | 2 +- .../passes/experimental/adding_shape_consistency_pass.py | 2 +- colossalai/fx/passes/meta_info_prop.py | 2 +- colossalai/fx/passes/passes_for_gpt2_test.py | 4 ++-- colossalai/fx/passes/split_module.py | 4 ++-- 19 files changed, 31 insertions(+), 31 deletions(-) diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 8fc14e0d531a..f40f4cc86d1b 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -14,7 +14,7 @@ - [Compatibility Test on Dispatch](#compatibility-test-on-dispatch) - [Release](#release) - [User Friendliness](#user-friendliness) - - [Commmunity](#commmunity) + - [Community](#community) - [Configuration](#configuration) - [Progress Log](#progress-log) @@ -97,7 +97,7 @@ This workflow is triggered by manually dispatching the workflow. It has the foll | `Synchronize submodule` | `submodule.yml` | This workflow will check if any git submodule is updated. If so, it will create a PR to update the submodule pointers. | | `Close inactive issues` | `close_inactive.yml` | This workflow will close issues which are stale for 14 days. | -### Commmunity +### Community | Workflow Name | File name | Description | | -------------------------------------------- | -------------------------------- | -------------------------------------------------------------------------------- | diff --git a/colossalai/auto_parallel/passes/meta_info_prop.py b/colossalai/auto_parallel/passes/meta_info_prop.py index bc0960483980..0673b767de7b 100644 --- a/colossalai/auto_parallel/passes/meta_info_prop.py +++ b/colossalai/auto_parallel/passes/meta_info_prop.py @@ -148,7 +148,7 @@ def node_handler(self, node: Node) -> None: graph_info.fwd_tmp = buffer_tensors graph_info.fwd_out = output_tensors - # fetch other memory informations + # fetch other memory information memory_cost = meta_info.memory_cost graph_info.fwd_mem_tmp = memory_cost.fwd.temp graph_info.fwd_mem_out = memory_cost.fwd.activation diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py index 79b69acb25b3..416dc9c29cad 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py @@ -44,7 +44,7 @@ def update_compute_cost(self, strategy: ShardingStrategy): ''' Compute the computation cost per device with this specific strategy. - Note: compute_cost need to be devided by TFLOPS, now it just shows the computation size. + Note: compute_cost need to be divided by TFLOPS, now it just shows the computation size. ''' # TODO: a constant coefficient need to be added. # 1D: (L) * N * Cin diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/conv_strategy_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/conv_strategy_generator.py index c2154b3104d3..e605a68a326b 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/conv_strategy_generator.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/conv_strategy_generator.py @@ -38,9 +38,9 @@ def update_compute_cost(self, strategy: ShardingStrategy): ''' Compute the computation cost per device with this specific strategy. - Note: compute_cost need to be devided by TFLOPS, now it just shows the computation size. + Note: compute_cost need to be divided by TFLOPS, now it just shows the computation size. ''' - # TODO: compute_cost need to be devided by TFLOPS, now it just shows the computation size. + # TODO: compute_cost need to be divided by TFLOPS, now it just shows the computation size. # 1D: (L) * N * Cout * Cin * kernel # 2D: (H * W) * N * Cout * Cin * kernel # 3D: (H * W * D) * N * Cout * Cin * kernel diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/layer_norm_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/layer_norm_generator.py index fbb6070f7e82..65b173bbf65d 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/layer_norm_generator.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/layer_norm_generator.py @@ -34,9 +34,9 @@ def update_compute_cost(self, strategy: ShardingStrategy): ''' Compute the computation cost per device with this specific strategy. - Note: compute_cost need to be devided by TFLOPS, now it just shows the computation size. + Note: compute_cost need to be divided by TFLOPS, now it just shows the computation size. ''' - # TODO: compute_cost need to be devided by TFLOPS, now it just shows the computation size. + # TODO: compute_cost need to be divided by TFLOPS, now it just shows the computation size. # TODO: a constant coefficient need to be added. sharded_input_shape = strategy.sharding_specs[self.op_data['input']].get_sharded_shape_per_device() diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/normal_pooling_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/normal_pooling_generator.py index 9df6d2fbfa12..b7db42f8f67e 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/normal_pooling_generator.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/normal_pooling_generator.py @@ -17,7 +17,7 @@ class NormalPoolStrategyGenerator(StrategyGenerator): """ NormalPoolStrategyGenerator is a generic class to generate strategies for pool operation like MaxPoolxd. The reason we call this normal pool is AvgPoolxd and MaxPoolxd are taking the kernel size element from image, - and reduce them depening on the operation type. + and reduce them depending on the operation type. """ def validate(self) -> bool: @@ -35,9 +35,9 @@ def update_compute_cost(self, strategy: ShardingStrategy) -> TrainCycleItem: ''' Compute the computation cost per device with this specific strategy. - Note: compute_cost need to be devided by TFLOPS, now it just shows the computation size. + Note: compute_cost need to be divided by TFLOPS, now it just shows the computation size. ''' - # TODO: compute_cost need to be devided by TFLOPS, now it just shows the computation size. + # TODO: compute_cost need to be divided by TFLOPS, now it just shows the computation size. # 1D: (Lout) * N * C * kernel # 2D: (H * W) * N * Cout * Cin * kernel # 3D: (H * W * D) * N * Cout * Cin * kernel diff --git a/colossalai/autochunk/trace_flow.py b/colossalai/autochunk/trace_flow.py index 11a7e62ff37c..a1080fda1541 100644 --- a/colossalai/autochunk/trace_flow.py +++ b/colossalai/autochunk/trace_flow.py @@ -366,8 +366,8 @@ def flow_search(self, start_idx, start_dim, end_idx, end_dim): # find non chunk inputs chunk_info = self._get_non_chunk_inputs(chunk_info, start_idx, end_idx) - # reassgin reshape size, some size may have changed due to chunk - chunk_info = self._reassgin_reshape_size(chunk_info) + # reassign reshape size, some size may have changed due to chunk + chunk_info = self._reassign_reshape_size(chunk_info) return chunk_info @@ -428,10 +428,10 @@ def _update_chunk_info(self, chunk_info: Dict, new_all_node_info: Dict, output: chunk_info["outputs_dim"].append(output_dim) return True - def _reassgin_reshape_size(self, chunk_info): + def _reassign_reshape_size(self, chunk_info): """ Some shape args in reshape may have changed due to chunk - reassgin those changed shape + reassign those changed shape """ chunk_region = chunk_info["region"] reshape_size = {} diff --git a/colossalai/autochunk/trace_indice.py b/colossalai/autochunk/trace_indice.py index 8e6cd3e29bea..fbe0741b8827 100644 --- a/colossalai/autochunk/trace_indice.py +++ b/colossalai/autochunk/trace_indice.py @@ -397,7 +397,7 @@ def _assign_conv2d_indice(self, node: Node, node_idx: int) -> None: input_node = node.args[0] assert len(get_node_shape(input_node)) == 4 - # assgin index + # assign index self._assign_indice_as_input(node, node_idx, input_node) self._del_dim(node_idx, 1) self._add_dim(node_idx, 1) @@ -415,7 +415,7 @@ def _assign_interpolate_indice(self, node: Node, node_idx: int) -> None: assert node.kwargs['size'] is None assert len(get_node_shape(node)) == 4 - # assgin index + # assign index self._assign_indice_as_input(node, node_idx) self._mark_computation(node, node_idx, [-1, -2]) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index bb3124642ccf..adbf4803eefe 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -179,7 +179,7 @@ class GeminiPlugin(DPPluginBase): Users can provide this argument to speed up searching. If users do not know this argument before training, it is ok. We will use a default value 1024. min_chunk_size_mb (float, optional): the minimum chunk size in MegaByte. - If the aggregate size of parameters is still samller than the minimum chunk size, + If the aggregate size of parameters is still smaller than the minimum chunk size, all parameters will be compacted into one small chunk. memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer. gpu_margin_mem_ratio (float, optional): The ratio of GPU remaining memory (after the first forward-backward) diff --git a/colossalai/cluster/dist_coordinator.py b/colossalai/cluster/dist_coordinator.py index 99dde810e112..3ee364ec3364 100644 --- a/colossalai/cluster/dist_coordinator.py +++ b/colossalai/cluster/dist_coordinator.py @@ -181,7 +181,7 @@ def on_master_only(self, process_group: ProcessGroup = None): """ is_master = self.is_master(process_group) - # define an inner functiuon + # define an inner function def decorator(func): @functools.wraps(func) diff --git a/colossalai/device/alpha_beta_profiler.py b/colossalai/device/alpha_beta_profiler.py index af2b10928c6f..f8b20de9bc37 100644 --- a/colossalai/device/alpha_beta_profiler.py +++ b/colossalai/device/alpha_beta_profiler.py @@ -381,7 +381,7 @@ def _extract_alpha_beta(pg, pg_handler): first_latency, first_bandwidth = _extract_alpha_beta(first_axis, first_axis_process_group) second_latency, second_bandwidth = _extract_alpha_beta(second_axis, second_axis_process_group) mesh_alpha = [first_latency, second_latency] - # The beta values have been enlarged by 1e10 times temporarilly because the computation cost + # The beta values have been enlarged by 1e10 times temporarily because the computation cost # is still estimated in the unit of TFLOPs instead of time. We will remove this factor in future. mesh_beta = [1e10 / first_bandwidth, 1e10 / second_bandwidth] diff --git a/colossalai/engine/schedule/_pipeline_schedule.py b/colossalai/engine/schedule/_pipeline_schedule.py index 38175fe0941c..9fc301a26559 100644 --- a/colossalai/engine/schedule/_pipeline_schedule.py +++ b/colossalai/engine/schedule/_pipeline_schedule.py @@ -152,9 +152,9 @@ def _get_data_slice(self, data, offset): raise TypeError(f"Expected data to be of type torch.Tensor, list, tuple, or dict, but got {type(data)}") def load_micro_batch(self): - mciro_batch_data = self._get_data_slice(self.batch_data, self.microbatch_offset) + micro_batch_data = self._get_data_slice(self.batch_data, self.microbatch_offset) self.microbatch_offset += self.microbatch_size - return self._move_to_device(mciro_batch_data) + return self._move_to_device(micro_batch_data) def pre_processing(self, engine): from colossalai.zero.legacy import ShardedModelV2 diff --git a/colossalai/engine/schedule/_pipeline_schedule_v2.py b/colossalai/engine/schedule/_pipeline_schedule_v2.py index 28c58bd82b5c..89e45c7aacec 100644 --- a/colossalai/engine/schedule/_pipeline_schedule_v2.py +++ b/colossalai/engine/schedule/_pipeline_schedule_v2.py @@ -84,7 +84,7 @@ def forward_backward_step(self, 'The argument \'return_loss\' has to be True when \'forward_only\' is False, but got False.' self.load_batch(data_iter) - # num_warmup_microbatches is the step when not all the processers are working + # num_warmup_microbatches is the step when not all the processes are working num_warmup_microbatches = \ (gpc.get_world_size(ParallelMode.PIPELINE) - gpc.get_local_rank(ParallelMode.PIPELINE) - 1) diff --git a/colossalai/fx/codegen/activation_checkpoint_codegen.py b/colossalai/fx/codegen/activation_checkpoint_codegen.py index 5a72cb9ca923..33b164800262 100644 --- a/colossalai/fx/codegen/activation_checkpoint_codegen.py +++ b/colossalai/fx/codegen/activation_checkpoint_codegen.py @@ -523,7 +523,7 @@ def emit_code_with_activation_checkpoint(body, ckpt_func, nodes, emit_node_func, # append code text to body for idx, node in enumerate(node_list): # if this is the first node of the ckpt region - # append the ckpt function defition + # append the ckpt function definition if idx in start_idx: label = start_idx.index(idx) ckpt_fn_def = _gen_ckpt_fn_def(label, input_vars[label]) diff --git a/colossalai/fx/passes/adding_split_node_pass.py b/colossalai/fx/passes/adding_split_node_pass.py index 2c7b842b530c..245ba5d776da 100644 --- a/colossalai/fx/passes/adding_split_node_pass.py +++ b/colossalai/fx/passes/adding_split_node_pass.py @@ -206,7 +206,7 @@ def avgcompute_split_pass(gm: torch.fx.GraphModule, pp_size: int): def avgnode_split_pass(gm: torch.fx.GraphModule, pp_size: int): """ - In avgnode_split_pass, simpliy split graph by node number. + In avgnode_split_pass, simply split graph by node number. """ mod_graph = gm.graph avg_num_node = len(mod_graph.nodes) // pp_size diff --git a/colossalai/fx/passes/experimental/adding_shape_consistency_pass.py b/colossalai/fx/passes/experimental/adding_shape_consistency_pass.py index f28d65e2668a..4571bd93a790 100644 --- a/colossalai/fx/passes/experimental/adding_shape_consistency_pass.py +++ b/colossalai/fx/passes/experimental/adding_shape_consistency_pass.py @@ -16,7 +16,7 @@ def apply(*args, **kwargs): return shape_consistency_manager.apply(*args, **kwargs) -def solution_annotatation_pass(gm: torch.fx.GraphModule, solution: List[int], device_mesh): +def solution_annotation_pass(gm: torch.fx.GraphModule, solution: List[int], device_mesh): mod_graph = gm.graph nodes = tuple(mod_graph.nodes) diff --git a/colossalai/fx/passes/meta_info_prop.py b/colossalai/fx/passes/meta_info_prop.py index 2b4a8749cfd7..ab203dfd7440 100644 --- a/colossalai/fx/passes/meta_info_prop.py +++ b/colossalai/fx/passes/meta_info_prop.py @@ -31,7 +31,7 @@ class TensorMetadata(NamedTuple): numel: int is_tensor: bool # TODO: we can add a list of sharding spec here, and record the sharding - # behaviour by appending sharding spec into list. + # behavior by appending sharding spec into list. def _extract_tensor_metadata(result: torch.Tensor) -> TensorMetadata: diff --git a/colossalai/fx/passes/passes_for_gpt2_test.py b/colossalai/fx/passes/passes_for_gpt2_test.py index abc1a089e9a9..efdd34a01fe0 100644 --- a/colossalai/fx/passes/passes_for_gpt2_test.py +++ b/colossalai/fx/passes/passes_for_gpt2_test.py @@ -230,7 +230,7 @@ def record_cross_partition_use(def_node: torch.fx.node.Node, use_partition.partitions_dependent_on.setdefault(def_partition_name) node_process_list = list(m.graph.nodes) - # split nodes into parititons + # split nodes into partitions while node_process_list: node = node_process_list.pop(0) orig_nodes[node.name] = node @@ -277,7 +277,7 @@ def record_cross_partition_use(def_node: torch.fx.node.Node, if len(sorted_partitions) != len(partitions): raise RuntimeError("cycle exists between partitions!") - # add placeholders to parititons + # add placeholders to partitions for partition_name in sorted_partitions: partition = partitions[partition_name] for input in partition.inputs: diff --git a/colossalai/fx/passes/split_module.py b/colossalai/fx/passes/split_module.py index 5ce5b969cbde..61ed037ab7a1 100644 --- a/colossalai/fx/passes/split_module.py +++ b/colossalai/fx/passes/split_module.py @@ -29,8 +29,8 @@ def __repr__(self) -> str: f" nodes: {self.node_names},\n" \ f" inputs: {self.inputs},\n" \ f" outputs: {self.outputs},\n" \ - f" partitions depenent on: {self.partitions_dependent_on},\n" \ - f" parition dependents: {self.partition_dependents}" + f" partitions dependent on: {self.partitions_dependent_on},\n" \ + f" partition dependents: {self.partition_dependents}" # Creates subgraphs out of main graph From 269150b6f4b37f4f427e467dc99e04bb1a89ca38 Mon Sep 17 00:00:00 2001 From: Yanming W Date: Tue, 23 May 2023 19:22:51 -0700 Subject: [PATCH 240/413] [Docker] Fix a couple of build issues (#3691) --- docker/Dockerfile | 4 ++++ op_builder/utils.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 52c7bf5601c6..2c7bafd9604c 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -8,9 +8,13 @@ LABEL org.opencontainers.image.base.name = "docker.io/library/hpcaitech/cuda-con # install torch RUN conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch +# install ninja +RUN apt-get install -y --no-install-recommends ninja-build + # install apex RUN git clone https://github.com/NVIDIA/apex && \ cd apex && \ + git checkout 91fcaa && \ pip install packaging && \ pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" ./ diff --git a/op_builder/utils.py b/op_builder/utils.py index 2dbd976fbcbb..cb528eea66a1 100644 --- a/op_builder/utils.py +++ b/op_builder/utils.py @@ -110,7 +110,7 @@ def get_pytorch_version() -> List[int]: torch_version = torch.__version__.split('+')[0] TORCH_MAJOR = int(torch_version.split('.')[0]) TORCH_MINOR = int(torch_version.split('.')[1]) - TORCH_PATCH = int(torch_version.split('.')[2]) + TORCH_PATCH = int(torch_version.split('.')[2], 16) return TORCH_MAJOR, TORCH_MINOR, TORCH_PATCH From 05b8a8de58da5f6a7853b17488cc5b7713d06f38 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 24 May 2023 10:50:19 +0800 Subject: [PATCH 241/413] [workflow] changed to doc build to be on schedule and release (#3825) * [workflow] changed to doc build to be on schedule and release * polish code --- ...yml => doc_build_on_schedule_after_release.yml} | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) rename .github/workflows/{doc_build_after_merge.yml => doc_build_on_schedule_after_release.yml} (69%) diff --git a/.github/workflows/doc_build_after_merge.yml b/.github/workflows/doc_build_on_schedule_after_release.yml similarity index 69% rename from .github/workflows/doc_build_after_merge.yml rename to .github/workflows/doc_build_on_schedule_after_release.yml index b6fd57b8d2b4..62dfdc67257c 100644 --- a/.github/workflows/doc_build_after_merge.yml +++ b/.github/workflows/doc_build_on_schedule_after_release.yml @@ -1,18 +1,16 @@ -name: Build Documentation After Merge +name: Build Documentation On Schedule & After Release on: workflow_dispatch: - push: - paths: - - "version.txt" - - "docs/**" - branches: - - "main" + schedule: + - cron: "0 12 * * *" # build doc every day at 8pm Singapore time (12pm UTC time) + release: + types: [published] jobs: build-doc: name: Trigger Documentation Build Workflow - if: ( github.event_name == 'workflow_dispatch' || github.event.pull_request.merged == true ) && github.repository == 'hpcaitech/ColossalAI' + if: github.repository == 'hpcaitech/ColossalAI' runs-on: ubuntu-latest steps: - name: trigger workflow in ColossalAI-Documentation From 34966378e81e1c885af9b50670552d3487a60b57 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Wed, 24 May 2023 11:18:23 +0800 Subject: [PATCH 242/413] [evaluation] add automatic evaluation pipeline (#3821) * add functions for gpt evaluation * add automatic eval Update eval.py * using jload and modify the type of answers1 and answers2 * Update eval.py Update eval.py * Update evaluator.py * support gpt evaluation * update readme.md update README.md update READNE.md modify readme.md * add Chinese example for config, battle prompt and evaluation prompt file * remove GPT-4 config * remove sample folder --------- Co-authored-by: Yuanchen Xu Co-authored-by: Camille Zhong <44392324+Camille7777@users.noreply.github.com> --- applications/Chat/evaluate/README.md | 434 ++++++++------- .../Chat/evaluate/config/config_cn.json | 123 +++++ applications/Chat/evaluate/eval.py | 98 ++++ applications/Chat/evaluate/eval.sh | 9 + applications/Chat/evaluate/evaluate.py | 256 --------- applications/Chat/evaluate/evaluate.sh | 9 - applications/Chat/evaluate/evaluator.py | 130 +++++ .../Chat/evaluate/generate_answers.py | 173 ------ .../Chat/evaluate/generate_answers.sh | 25 - .../Chat/evaluate/generate_gpt35_answers.py | 98 ---- .../Chat/evaluate/generate_gpt35_answers.sh | 6 - applications/Chat/evaluate/gpt_evaluate.py | 496 ++++++++++++++++++ applications/Chat/evaluate/merge.py | 25 - applications/Chat/evaluate/metrics.py | 169 ++++++ .../battle_prompt/battle_prompt_cn.json | 6 + .../evaluation_prompt_cn.json | 179 +++++++ applications/Chat/evaluate/requirements.txt | 10 + .../Chat/evaluate/sample/questions.json | 9 - applications/Chat/evaluate/utils.py | 17 +- 19 files changed, 1485 insertions(+), 787 deletions(-) create mode 100644 applications/Chat/evaluate/config/config_cn.json create mode 100644 applications/Chat/evaluate/eval.py create mode 100755 applications/Chat/evaluate/eval.sh delete mode 100644 applications/Chat/evaluate/evaluate.py delete mode 100755 applications/Chat/evaluate/evaluate.sh create mode 100644 applications/Chat/evaluate/evaluator.py delete mode 100644 applications/Chat/evaluate/generate_answers.py delete mode 100755 applications/Chat/evaluate/generate_answers.sh delete mode 100644 applications/Chat/evaluate/generate_gpt35_answers.py delete mode 100755 applications/Chat/evaluate/generate_gpt35_answers.sh create mode 100644 applications/Chat/evaluate/gpt_evaluate.py delete mode 100644 applications/Chat/evaluate/merge.py create mode 100644 applications/Chat/evaluate/metrics.py create mode 100644 applications/Chat/evaluate/prompt/battle_prompt/battle_prompt_cn.json create mode 100644 applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json create mode 100644 applications/Chat/evaluate/requirements.txt delete mode 100644 applications/Chat/evaluate/sample/questions.json diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index 7ace4bfe6d18..1e86eadf1c33 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -1,182 +1,252 @@ -# Evaluation - -In this directory, we introduce how you can evaluate your model with GPT-4. - -## Evaluation Pipeline - -The whole evaluation process undergoes the following three steps: -1. Prepare the questions following the internal data structure in the data format section (described below). -2. Generate answers from different models: - * Generate answers using GPT-3.5: [`generate_gpt35_answers.py`](generate_gpt35_answers.py). - * Generate answers using your own models: [`generate_answers.py`](generate_answers.py). -3. Evaluate models using GPT-4: [`evaluate.py`](evaluate.py). - -### Generate Answers -#### Generate Answers Using GPT-3.5 -You can provide your own OpenAI key to generate answers from GPT-3.5 using [`generate_gpt35_answers.py`](./generate_gpt35_answers.py). - -An example script is provided as follows: -```shell -python generate_gpt35_answers.py \ - --dataset "path to the question dataset" \ - --answer_path "path to answer folder" \ - --num_workers 4 \ - --openai_key "your openai key" \ - --max_tokens 512 \ -``` - -#### Generate Answers Using our Own Model -You can also generate answers using your own models. The generation process is divided into two stages: -1. Generate answers using multiple GPUs (optional) with batch processing: [`generate_answers.py`](./generate_answers.py). -2. Merge multiple shards and output a single file: [`merge.py`](./merge.py). - -An example script is given as follows: - -```shell -device_number=number of your devices -model_name="name of your model" -model_path="path to your model" -dataset="path to the question dataset" -answer_path="path to save the model answers" - -torchrun --standalone --nproc_per_node=$device_number generate_answers.py \ - --model 'llama' \ - --strategy ddp \ - --model_path $model_path \ - --model_name $model_name \ - --dataset $dataset \ - --batch_size 8 \ - --max_datasets_size 80 \ - --answer_path $answer_path \ - --max_length 512 - -python merge.py \ - --model_name $model_name \ - --shards $device_number \ - --answer_path $answer_path \ - -for (( i=0; i我想让你担任Android开发工程师面试官。我将成为候选人，您将向我询问Android开发工程师职位的面试问题。我希望你只作为面试官回答。不要一次写出所有的问题。我希望你只对我进行采访。问我问题，等待我的回答。不要写解释。像面试官一样一个一个问我，等我回答。我的第一句话是“面试官你好”。

    **Example 2：**
    我想让你扮演讲故事的角色。你会想出引人入胜、富有想象力和吸引观众的有趣故事。它可以是童话故事、教育故事或任何其他类型的有潜力的故事以吸引人们的注意力和想象力。根据目标受众，您可以为您的讲故事环节选择特定的主题或主题，例如，如果是儿童，那么您可以谈论动物；如果是成人，那么基于历史的故事可能会更好地吸引他们等。我的第一个请求是我需要一个关于毅力的有趣故事。 | **Example 1：**
    I want you to act as a journalist. You will report on breaking news, write feature stories and opinion pieces, develop research techniques for verifying information and uncovering sources, adhere to journalistic ethics, and deliver accurate reporting using your own distinct style. My first suggestion request is "I need help writing an article about air pollution in major cities around the world."

    **Example 2:**
    I want you to act as a debater. I will provide you with some topics related to current events and your task is to research both sides of the debates, present valid arguments for each side, refute opposing points of view, and draw persuasive conclusions based on evidence. Your goal is to help people come away from discussion with increased knowledge and insight into the topic at hand. My first request is "I want an opinion piece about Deno." | +| Chat | **Example 1：**
    基于以下角色信息完成一段对话。小王，20岁，是一名大学生；小张，22岁，是一名大学生。
    小王：小张，我们一起去看电影吧？ 小张：好啊，你想看什么电影呢？ 小王：我听说最近有一部很好看的科幻电影。你觉得怎么样？
    小张：

    **Example 2:**
    基于以下角色信息完成一段对话。小明是一名医生，一位老年病患者想要停药，但他对病情有所忽视并有担忧；王叔叔是老年病患者的儿子，希望能够听取医生的建议。
    小明：你好，王叔叔，我了解你想要让你父亲停药。
    王叔叔：是的，我父亲已经吃了那么久的药，我担心药物对他的身体会有副作用。
    小明： | **Example 1：**
    Human: Who is the highest try scorer in the current Sydney Roosters team?
    AI: As an AI language model, I don't have access to current statistics. However, you can check the current Sydney Roosters team's official website or relevant sports news websites to find the answer.
    Human: Thanks for the suggestion, but can you recommend a reliable sports news website that I can check?
    AI:

    **Example 2:**
    Complete a dialogue based on the following role information.
    A: Elementary student B: Teacher
    B: Good morning, Student A. Today we're going to learn about addition and subtraction.
    A: Teacher, I already know this very well. Why do I need to learn it again?
    B: | +| Open QA | **Example 1：**
    请问万有引力定律由谁提出的？

    **Example 2：**
    哪些国家参与了第一次世界大战？ | **Example 1：**
    Who are the indigenous people of New Zealand?

    **Example 2：**
    How do you take the derivative of the sin function? | +| Closed QA | **Example 1：**
    请从以下选项中选择正确答案。以下哪个是世界上最高山峰？
    A. 长城
    B. 泰山
    C. 珠穆朗玛峰
    D. 黄山

    **Example 2：**
    请从以下选项中选择一个最佳答案回答下面的问题。问题：非洲最高的山是哪座山？
    选项：
    A. 麦金利山
    B. 喜马拉雅山
    C. 乞力马扎罗山 | **Example 1：**
    Answer the following question:
    What shape is the Earth?
    A) A circle
    B) A sphere
    C) An ellipse
    D) A plane

    **Example 2：**
    Choose the correct classification for the following question:
    "What type of restaurant is 'Burger King'"?
    fast food
    family style
    formal dining
    buffet
    | +| Brainstorming | **Example 1：**
    请介绍一下人工智能的多个领域。

    **Example 2：**
    请给出管理家庭财务的3个小技巧。
    | **Example 1：**
    What are 10 science fiction books I should read next?

    **Example 2：**
    List five ideas for how to regain enthusiasm for my career. | +| Generation | **Example 1：**
    请撰写一篇文章，介绍如何通过改善生活习惯来预防疾病和延长寿命。

    **Example 2：**
    请根据以下情节撰写一篇短篇小说：一名年轻人被困在一个荒岛上，他必须想办法生存下去直到被救援。但他很快发现自己并不孤单。 | **Example 1：**
    Can you help me write a formal email to a potential business partner proposing a joint venture?

    **Example 2：**
    Please use the appropriate format to write a formal letter of recommendation for a student applying to a prestigious computer science graduate program at a university. | +| Rewriting | **Example 1：**
    将以下句子改为被动语态：
    "他们正在洗车"

    **Example 2：**
    将以下文本翻译成英语：
    “这个周末我要去海边玩” | **Example 1：**
    Translate the following text into English:
    "我最喜欢的季节是春天，因为我可以看到美丽的花朵。"

    **Example 2：**
    Please correct the following sentences and give them the correct sentence.
    "Their going to the party there." | +| Classification | **Example 1：**
    新闻标题：今日立夏，有一上联，立夏万物并秀，下联怎么对？
    请根据以上新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。

    **Example 2：**
    新闻标题：赵丽颖很久没有登上微博热搜了，但你们别急，她只是在憋大招而已。
    请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。 | **Example 1：**
    Classify the given email as spam or non-spam.
    "Hello, this is an email reminding you to pay your property fees"

    **Example 2：**
    Classify the following text as news, ads or forum posts
    "The latest iPhone 13 is now available, shop now!" | +| Extraction | **Example 1：**
    根据以下新闻文本，提取新闻报道时间，例如回答时按照格式“新闻报道时间：2007年8月10日”
    新闻文本如下：2007-4-7中新网4月7日电据中国消防在线消息，4月4日晚上7时30分左右，湖南长潭高速公路上发生一起6车连环相撞失火事故。长株潭三地消防部门共出动消防车21台，警力100余人。经过消防官兵近2个小时奋力扑救，大火被成功扑灭。据初步调查，有1人在此次事故中死亡。

    **Example 2：**
    根据以下新闻文本，提取新闻报道时间，例如回答时按照格式“新闻报道时间：2007年8月10日”
    新闻文本如下：2014年1月15日，据外媒《俄罗斯报》报道称，位于北半球的澳大利亚现在正处于炎热的夏季，而近日也到了高温酷暑的时候，当地时间1月14日晚，澳大利亚南部一夜间发生至少250起火灾。受炎热天气及雷雨天气影响，澳大利亚南部一夜间发生至少250起火灾，灾情多集中在维多利亚州。火灾发生后，救援人员立即展开救灾行动。目前，大部分起火点火势已被控制。 | **Example 1：**
    Extract all phenotypes of the following text:
    "The 55-year-old patient has fever and hypertension."

    **Example 2：**
    Extract the location mentioned in the following text:
    "The student graduated from Harvard university, which is located in Boston" | +| Summarization | **Example 1：**
    请简要总结概括以下段落材料。
    新华社快讯：斯里兰卡政府部门21日说，首都科伦坡包括教堂、酒店等多个地点当天发生的爆炸已导致至少70人死亡，另有260多人受伤。

    **Example 2：**
    请简要总结概括以下段落材料。
    近期，参与京雄高铁站站房建设的中铁十二局，因在施工过程中存在环境违法行为被雄安新区公开通报。通报发出后，引起社会广泛关注。近日，人民网记者从雄安新区相关部门及中铁十二局获悉，新区有关部门已经集中约谈了中铁十二局等24个参与雄安建设的项目单位。对于约谈内容和结果，中铁十二局有关宣传负责人回应：“具体内容不清楚，最好找雄安新区相关部门了解情况。”新区有关部门负责人表示，此前涉及的环境违法行为，中铁十二局已基本整改到位，但约谈内容和结果暂不公开，接下来，将按部就班推进环境治理工作。（原题为《雄安新区：中铁十二局涉环境违法已基本整改到位》） | **Example 1：**
    Please provide a summary based on the following news：
    "China plans to launch its first space station core module in 2022, an important development in the country's space program. The space station, called Tianhe, will include three modules: a core module, an experiment module and an astronomy module. The first launch of the core module will be used to test and verify the basic functions of the station, as well as to conduct related scientific research and technology experiments. "

    **Example 2：**
    What information is provided in the table below? Summarize the core information in it？
    "Ranking, Player Name, Team, Position, Salary (in millions of dollars)
    1, LeBron James, Los Angeles Lakers, SF, 45.0
    2, Stephen Curry, Golden State Warriors, PG, 43.5" | + + +### Evaluation Metrics +#### GPT Evaluation +Use GPT-3.5 to evaluate the prediction of different models, and pre-define evaluation metrics for different categories. There are 10 pre-defined evaluation metrics and you can refer to the table below: + +| Evaluation Metric | Prompt Words | CoT | +|:-----------------------:|:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Language organization | 语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。 | 1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。
    2.检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说
    3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。
    4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。
    5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。
    6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。 | +| Relevance | 切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。 | 1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。
    2. 阅读答案，确认答案是否直接回答了题目所问的问题。
    3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。
    4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。 | +| Creativity | 创意性(1-5)：某些头脑风暴问题可能需要答案具有创意，提出新的思路。 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则创意性评分可能会受到影响。
    3. 考虑答案中是否包含新颖的想法或独特的思路。答案可能与已知的解决方案有所重叠，但仍然可以被认为是有创意的，只要它提供了新的角度或方法来解决问题。
    4. 根据答案的创意性，给出一个1到5的评分。如果答案缺乏创意，则应给出一个较低的评分。如果答案具有创意并提供了新的思路，应给出一个较高的评分。 | +| Practicality | 实用性(1-5)：某些头脑风暴问题可能需要答案提出实用的建议或解决方法。 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则实用性评分可能会受到影响。
    3. 考虑答案中提出的建议或解决方法是否实用并可行。答案可能看起来很好，但如果无法实现或应用，则实用性评分可能会受到影响。
    4. 根据答案的实用性，给出一个1到5的评分。如果答案缺乏实用性，则应给出一个较低的评分。如果答案提出了实用的建议或解决方法，并且可以很好地解决问题，则应给出一个较高的评分。 | +| Correctness | 正确性(1-5)：答案应该符合常识、生活实际等等 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则正确性评分可能会受到影响。
    3. 考虑答案中所提供的信息是否正确、符合常识、生活实际等等。如果答案中存在明显的错误或不合理之处，则正确性评分可能会受到影响。
    4. 根据答案的正确性，给出一个1到5的评分。如果答案存在明显的错误或不合理之处，则应给出一个较低的评分。如果答案正确、符合常识、生活实际等等，则应给出一个较高的评分。 | +| Naturalness | 自然(1-5)：答案是否自然，并且符合问题给定的身份。 | 1. 阅读题目，确定题目提供的身份信息。
    2. 检查答案内容是否符合题目给定的身份。
    3. 根据以上因素，对该回答的自然性进行打分，分数从1到5，其中1表示不自然，5表示非常自然，并符合问题给定的身份。 | +| Engagingness | 参与感(1-5)：答案是否对前面的对话内容做出了恰当的反应，是否理解对话的语境和背景。 | 1. 阅读题目，确定对话的语境和背景。
    2. 检查答案是否充分理解对话的语境和背景，能否自然地融入到对话中而不显得突兀。
    3. 根据以上因素，对该回答的参与感进行打分，分数从1到5，其中1表示没有参与感，5表示非常有参与感，并且恰当地理解了对话的语境和背景。 | +| Reasonableness | 合理性(1-5)：答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。 | 1. 阅读题目，确定对话的主题以及问题期望的回答方向。
    2. 判断答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。
    3. 根据以上因素，对该回答的合理性进行打分，分数从1到5，其中1表示不合理，5表示非常合理，并且能够与前面的对话内容形成逻辑上的衔接，并符合常理。 | +| Diversity | 多样性(1-5)：答案使用语言是否优美，具有有一定的创造性和想象力。然而，回答也应该保持合理和适度，不要过于夸张或离题。 | 1. 仔细阅读整个回答，确保完全理解回答所表达的内容和主题。
    2. 在阅读回答的同时，注意语言的质量，例如措辞是否正确，语言是否生动等。
    3. 检查回答的创造性和想象力，看看回答是否能够吸引人阅读下去。
    4. 检查回答的合理性和适度，看看回答是否夸张或离题。5. 将多样性的评分打分在1到5之间，5分表示回答的质量很好，能够吸引人阅读，1分表示回答的内容生硬或者有离题的问题。 | +| Fidelity | 保真度(1-5)：答案是否能够严格遵守角色的设定回答给定的请求。 | 1. 仔细阅读问题，了解角色在问题中的设定和表现，包括职业、背景、观点、性格等方面。
    阅读题目的请求，确认回答请求时需要注意的细节。
    3. 对比提供的回答与该角色的设定，评估回答是否能够严格遵守角色的设定。
    4. 结合以上评估结果给出保真度的评分，范围从1到5分，其中1分表示回答与角色设定完全不符，5分表示回答完全符合角色设定且满足给定请求。 | +| Conciseness | 简明扼要(1-5)：答案是否简明扼要，没有冗余内容。 | 1. 阅读题目，提取出材料的重点。
    2. 阅读该总结，并注意其中的主要观点和信息。
    3. 评估总结的长度。一个简明扼要的总结通常应该在几句话或几段文字内传达关键信息，而不是冗长的段落或文章。
    4. 检查总结是否包含与主要观点无关的信息或冗余信息。
    5. 确定总结涵盖了材料中的关键信息，并且没有忽略任何重要细节。
    6. 给总结打出1-5的分数，其中5表示总结简明扼要，没有冗余内容，而1表示总结冗长或包含不必要的信息，难以理解或记忆。根据您的判断，打出适当的得分。 | + +GPT-3.5 evaluates the quality of model predictions based on the given prompt words and gives a score between 1-5. + +#### Automatic Evaluation +Automated metrics evaluate the capability of a model by comparing model predictions with reference answers. +There are two ways to obtain reference answers: +* For instruction coming from human-designed problems, the reference answers are generated by GPT-3.5, such as roleplay, chat. +* For instruction related with classic NLP problems, the reference answers are collected from open-sourced dataset with target answers, such as classification, extraction, summarization. + +There are 5 types of automatic evaluation metrics listed in the table below: + + | Automatic Evaluation Metric | Description | +|:-----------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| BLEU-n | Measure the accuracy between prediction and reference.
    BLEU-1 (Unigram) evaluates accuracy in word level
    BLEU-n (n-gram) evaluate the fluency in sentence level. | +| ROUGE | ROUGE-N measures the number of matching n-grams between prediction and reference.
    ROUGE-L measures the number of matching longest common subsequence (LCS) between prediction and reference. | +| Distinct | Measure the diversity of generation text by counting the unique n-grams. | +| BERTScore | Measure the semantic similarity between tokens of predictions and references with BERT. | +| Precision
    Recall
    F1 Score | Measure the number of overlaps between prediction and reference (design for classification and extraction categories) | + +## Evaluation Process +### Data Format +#### Target Answers / Predictions +A JSON file contains one list. Each element in the list is a target answer / prediction record for one instruction / question. +An element should have the following fields: + +* `category` (str, compulsory): The category of the instruction / question. +* `instruction` (str, compulsory): The instruction / question for the LLM. +* `input` (str, optional): The additional context of the instruction / question. +* `output` (str, optional): The sample output of the instruction (default: GPT-3.5). +* `target` (str, optional): The target answer for the instruction. +* `id` (int, compulsory): The ID of the instruction / question. + +If the `input` has a target answer, the `output` can be empty. Otherwise, we generate answers from GPT-3.5 as the `output`, and the `target` field is empty. + +Example: +``` +[ + { + "category": "brainstorming", + "instruction": "请介绍一下人工智能的多个领域。", + "input": "", + "output": "{GPT-3.5 Answers}", + "target": "", + "id": 1 + }, + { + "category": "classification", + "instruction": "新闻标题：为什么电影《倩女幽魂》中燕赤霞一个道士却拿着金刚经？请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。", + "input": "", + "output": "", + "target": "{target answer}", + "id": 2 + } +] +``` + +#### Model Answers / Predictions + +A JSON file contains one list. Each element in the list is a model answer / prediction record for one instruction / question. + +An element should have the following fields: + +* `category` (str, compulsory): The category of the instruction / question. +* `instruction` (str, compulsory): The instruction / question for the LLM. +* `input` (str, optional): The additional context of the instruction / question. +* `output` (str, compulsory): The output from the LLM. +* `target` (str, optional): The target answer for the instruction. +* `id` (int, compulsory): The ID of the instruction / question. + +Example: +``` +[ + { + "category": "brainstorming", + "instruction": "请介绍一下人工智能的多个领域。", + "input": "", + "output": "{Model Answers / Predictions}", + "target": "", + "id": 1 + }, + { + "category": "classification", + "instruction": "新闻标题：为什么电影《倩女幽魂》中燕赤霞一个道士却拿着金刚经？请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。", + "input": "", + "output": "{Model Answers / Predictions}", + "target": "{target answer}", + "id": 2 + } +] +``` + +### Evaluation +#### Configuration +The configuration file `config_cn.json` can control how evaluate the performance of the model. +The following is an example showing the config structure: +``` +{ + "language": "cn", + "category": { + "brainstorming": { + "GPT-3.5": ["relevance", "creativity", "practicality", "correctness"], + "Metrics": ["Distinct"] + }, + "chat": { + "GPT-3.5": [ "relevance", "naturalness", "engagingness", "reasonableness"], + "Metrics": ["Distinct"] + } + } +} +``` +`"language"`: evaluate the model capability in which language, we only support Chinese `"cn"` for now. +`"category"`: evaluate the model capability in which category/categories. +`"GPT-3.5"`: config metrics for GPT-3.5 evaluation. +`"Metrics"`: config metrics for automatic metrics evaluation. + +You can create your config file based on available settings listed in following table. + +| "category" | "GPT-3.5" | "Metrics" | +|:----------------:|:-----------------------:|:-----------:| +| "brainstorming" | "language organization" | "BLEU" | +| "chat" | "relevance" | "ROUGE" | +| "classification" | "creativity" | "Distinct" | +| "closed_qa" | "practicality" | "BERTScore" | +| "extraction" | "correctness" | "Precision" | +| "generation" | "naturalness" | "Recall" | +| "open_qa" | "engagingness" | "F1 score" | +| "rewriting" | "reasonableness" | +| "roleplay" | "diversity" | +| "summarization" | "fidelity" | +| | "conciseness" | + +#### Evaluate +After setting the configuration file, you can evaluate the model using `eval.py`. + + +An example script is provided as follows: +```shell +python eval.py \ + --config_file "path to the config file" \ + --battle_prompt_file "path to the prompt file for battle" \ + --gpt_evaluation_prompt_file "path to the prompt file for gpt evaluation" \ + --target_file "path to the target answer file" \ + --answer_file_list "path to the answer files of at most 2 models" \ + --model_name_list "the names of at most 2 models" \ + --save_path "path to save results" \ + --openai_key "your openai key" \ +``` + +## To Do +- [ ] Add evaluation for English capability +- [ ] Support UniEval +- [ ] Support GPT-4 evaluation + +## Citations + +```bibtex +@misc{vicuna2023, + title = {Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90\%* ChatGPT Quality}, + url = {https://vicuna.lmsys.org}, + author = {Chiang, Wei-Lin and Li, Zhuohan and Lin, Zi and Sheng, Ying and Wu, Zhanghao and Zhang, Hao and Zheng, Lianmin and Zhuang, Siyuan and Zhuang, Yonghao and Gonzalez, Joseph E. and Stoica, Ion and Xing, Eric P.}, + month = {March}, + year = {2023} +} + +@misc{ouyang2022training, + title={Training language models to follow instructions with human feedback}, + author={Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray and John Schulman and Jacob Hilton and Fraser Kelton and Luke Miller and Maddie Simens and Amanda Askell and Peter Welinder and Paul Christiano and Jan Leike and Ryan Lowe}, + year={2022}, + eprint={2203.02155}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} + +@misc{liu2023geval, + title={G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment}, + author={Yang Liu and Dan Iter and Yichong Xu and Shuohang Wang and Ruochen Xu and Chenguang Zhu}, + year={2023}, + eprint={2303.16634}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/applications/Chat/evaluate/config/config_cn.json b/applications/Chat/evaluate/config/config_cn.json new file mode 100644 index 000000000000..a7293f111a81 --- /dev/null +++ b/applications/Chat/evaluate/config/config_cn.json @@ -0,0 +1,123 @@ +{ + "language": "cn", + "category": { + "brainstorming": { + "GPT-3.5": [ + "language organization", + "relevance", + "creativity", + "practicality", + "correctness" + ], + "Metrics": [ + "Distinct" + ] + }, + "chat": { + "GPT-3.5": [ + "language organization", + "relevance", + "naturalness", + "engagingness", + "reasonableness" + ], + "Metrics": [ + "Distinct" + ] + }, + "classification": { + "GPT-3.5": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "Precision", + "Recall", + "F1 score" + ] + }, + "closed_qa": { + "GPT-3.5": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "BLEU", + "ROUGE", + "BERTScore" + ] + }, + "extraction": { + "GPT-3.5": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "Precision", + "Recall", + "F1 score" + ] + }, + "generation": { + "GPT-3.5": [ + "language organization", + "relevance", + "diversity" + ], + "Metrics": [ + "BLEU", + "ROUGE", + "BERTScore" + ] + }, + "open_qa": { + "GPT-3.5": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "Distinct" + ] + }, + "rewriting": { + "GPT-3.5": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "BLEU", + "ROUGE", + "BERTScore" + ] + }, + "roleplay": { + "GPT-3.5": [ + "language organization", + "relevance", + "fidelity", + "creativity" + ], + "Metrics": [ + "Distinct" + ] + }, + "summarization": { + "GPT-3.5": [ + "language organization", + "relevance", + "correctness", + "conciseness" + ], + "Metrics": [ + "BLEU", + "ROUGE", + "BERTScore" + ] + } + } +} diff --git a/applications/Chat/evaluate/eval.py b/applications/Chat/evaluate/eval.py new file mode 100644 index 000000000000..69f2c272a116 --- /dev/null +++ b/applications/Chat/evaluate/eval.py @@ -0,0 +1,98 @@ +import argparse +import json +import os + +import openai +from evaluator import Evaluator +from utils import jload + + +def main(args): + assert len(args.answer_file_list) == len( + args.model_name_list), "The number of answer files and model names should be equal!" + + # load config + config = jload(args.config_file) + + if config["language"] == "cn": + # get metric settings for all categories + metrics_per_category = {} + for category in config["category"].keys(): + metrics_all = {} + for metric_type, metrics in config["category"][category].items(): + metrics_all[metric_type] = metrics + metrics_per_category[category] = metrics_all + + battle_prompt = None + if args.battle_prompt_file: + battle_prompt = jload(args.battle_prompt_file) + + gpt_evaluation_prompt = None + if args.gpt_evaluation_prompt_file: + gpt_evaluation_prompt = jload(args.gpt_evaluation_prompt_file) + + if len(args.model_name_list) == 2 and not battle_prompt: + raise Exception("No prompt file for battle provided. Please specify the prompt file for battle!") + + if len(args.model_name_list) == 1 and not gpt_evaluation_prompt: + raise Exception( + "No prompt file for gpt evaluation provided. Please specify the prompt file for gpt evaluation!") + + # initialize evaluator + evaluator = Evaluator(metrics_per_category, battle_prompt, gpt_evaluation_prompt) + if len(args.model_name_list) == 2: + answers1 = jload(args.answer_file_list[0]) + answers2 = jload(args.answer_file_list[1]) + + assert len(answers1) == len(answers2), "The number of answers for two models should be equal!" + + evaluator.battle(answers1=answers1, answers2=answers2) + evaluator.save(args.save_path, args.model_name_list) + elif len(args.model_name_list) == 1: + targets = jload(args.target_file) + answers = jload(args.answer_file_list[0]) + + assert len(targets) == len(answers), "The number of target answers and model answers should be equal!" + + evaluator.evaluate(answers=answers, targets=targets) + evaluator.save(args.save_path, args.model_name_list) + else: + raise ValueError("Unsupported number of answer files and model names!") + else: + raise ValueError(f'Unsupported language {config["language"]}!') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='ColossalAI LLM evaluation pipeline.') + parser.add_argument('--config_file', + type=str, + default=None, + required=True, + help='path to the file of target results') + parser.add_argument('--battle_prompt_file', type=str, default=None, help='path to the prompt file for battle') + parser.add_argument('--gpt_evaluation_prompt_file', + type=str, + default=None, + help='path to the prompt file for gpt evaluation') + parser.add_argument('--target_file', type=str, default=None, help='path to the target answer (ground truth) file') + parser.add_argument('--answer_file_list', + type=str, + nargs='+', + default=[], + required=True, + help='path to the answer files of at most 2 models') + parser.add_argument('--model_name_list', + type=str, + nargs='+', + default=[], + required=True, + help='the names of at most 2 models') + parser.add_argument('--save_path', type=str, default="results", help='path to save evaluation results') + parser.add_argument('--openai_key', type=str, default=None, required=True, help='Your openai key') + args = parser.parse_args() + + if args.openai_key is not None: + os.environ["OPENAI_API_KEY"] = args.openai_key + openai.api_key = os.getenv("OPENAI_API_KEY") + + main(args) diff --git a/applications/Chat/evaluate/eval.sh b/applications/Chat/evaluate/eval.sh new file mode 100755 index 000000000000..f5729e6ee5c7 --- /dev/null +++ b/applications/Chat/evaluate/eval.sh @@ -0,0 +1,9 @@ +python eval.py \ + --config_file "path to the config file" \ + --battle_prompt_file "path to the prompt file for battle" \ + --gpt_evaluation_prompt_file "path to the prompt file for gpt evaluation" \ + --target_file "path to the target answer file" \ + --answer_file_list "path to the answer files of at most 2 models" \ + --model_name_list "the names of at most 2 models" \ + --save_path "path to save results" \ + --openai_key "your openai key" \ diff --git a/applications/Chat/evaluate/evaluate.py b/applications/Chat/evaluate/evaluate.py deleted file mode 100644 index 2f9c9ce8e10d..000000000000 --- a/applications/Chat/evaluate/evaluate.py +++ /dev/null @@ -1,256 +0,0 @@ -# Adapted form https://github.com/lm-sys/FastChat/blob/main/fastchat/eval/eval_gpt_review.py -# Copyright 2023 LM-SYS@FastChat - -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import argparse -import json -import os -import time -import re -import concurrent.futures - -import openai -import tqdm -import shortuuid -import logging - -from utils import jload, jdump, get_json_list - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -MAX_API_RETRY = 3 - - -def get_eval(sys_prompt, user_prompt: str, answer_id: int, max_tokens: int, model: str): - logging.basicConfig(level=logging.INFO) - for _ in range(MAX_API_RETRY): - try: - response = openai.ChatCompletion.create( - model=model, - messages=[{ - 'role': 'system', - 'content': sys_prompt - }, { - 'role': 'user', - 'content': user_prompt, - }], - temperature=0.2, - max_tokens=max_tokens, - ) - review = response['choices'][0]['message']['content'] - return {"review": review, 'id': answer_id} - except Exception as e: - logger.error(e) - time.sleep(1) - logger.error(f' Review {answer_id} failed after {MAX_API_RETRY} retries.') - return 'error' - - -def parse_score(review): - try: - pattern = re.compile('([0-9]|10) out of 10') - sp = re.findall(pattern, review) - if len(re.findall(pattern, review)) == 2: - return [float(sp[0]), float(sp[1])] - - pattern = re.compile('a score of ([0-9]|10)') - sp = re.findall(pattern, review) - if len(re.findall(pattern, review)) == 2: - return [float(sp[0]), float(sp[1])] - - pattern = re.compile('([0-9]|10)/10') - sp = re.findall(pattern, review) - if len(re.findall(pattern, review)) == 2: - return [float(sp[0]), float(sp[1])] - - score_pair = review.split('\n')[0] - score_pair = score_pair.replace(',', ' ') - sp = score_pair.split(' ') - if len(sp) == 2: - return [float(sp[0]), float(sp[1])] - else: - raise Exception('Invalid score pair.') - except Exception as e: - return [-1, -1] - - -def gen_prompt(reviewer_jsons, prompt_jsons, cat, ques, ans1, ans2): - reviewer_idx = 0 - for idx, reviewer in enumerate(reviewer_jsons): - if reviewer['category'] == cat: - reviewer_idx = idx - break - prompt_id = reviewer_jsons[reviewer_idx]['prompt_id'] - prompt_json = prompt_jsons[prompt_id-1] - assert prompt_json['prompt_id'] == prompt_id - - sys_prompt = prompt_json['system_prompt'] - prompt_template = prompt_json['prompt_template'] - defaults = prompt_json['defaults'] - prompt = prompt_template.format( - question=ques, answer_1=ans1, answer_2=ans2, **defaults) - - return sys_prompt, prompt, reviewer_idx+1 - - -def evaluate(args): - answer1_jsons = jload(args.answer_file_list[0]) - answer2_jsons = jload(args.answer_file_list[1]) - reviewer_jsons = get_json_list(args.reviewer_file) - prompt_jsons = get_json_list(args.prompt_file) - - assert len(answer1_jsons) == len(answer2_jsons) - - handles = [] - review_jsons = [] - - total_len = len(answer1_jsons) - question_idx_list = list(range(total_len)) - - logger.info( - f' Total number of answers: {len(answer2_jsons)}.') - - reviews = [] - with concurrent.futures.ThreadPoolExecutor(max_workers=args.num_workers) as executor: - futures = [] - for i in question_idx_list: - assert answer1_jsons[i]['id'] == answer2_jsons[i]['id'] - answer_id = answer1_jsons[i]['id'] - - ques = answer1_jsons[i]['instruction'] if answer1_jsons[i]['input'] == "" else answer1_jsons[i]['instruction'] + \ - " " + answer1_jsons[i]['input'] - cat = answer1_jsons[i]['category'] - ans1 = answer1_jsons[i]['output'] - ans2 = answer2_jsons[i]['output'] - - sys_prompt, prompt, reviewer_id = gen_prompt( - reviewer_jsons, prompt_jsons, cat, ques, ans1, ans2) - - review_id = shortuuid.uuid() - review_jsons.append({ - 'review_id': review_id, - 'id': answer_id, - 'reviewer_id': reviewer_id, - 'metadata': {} - }) - - future = executor.submit( - get_eval, sys_prompt, prompt, answer_id, args.max_tokens, args.model) - futures.append(future) - - for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)): - reviews.append(future.result()) - - reviews.sort(key=lambda x: x['id']) - review_jsons.sort(key=lambda x: x['id']) - - ans1_score = 0 - ans2_score = 0 - better_count = 0 - worse_count = 0 - tie_count = 0 - invalid_count = 0 - - better_file = [] - worse_file = [] - tie_file = [] - invalid_file = [] - output_review_file = [] - - for idx, review in enumerate(reviews): - scores = parse_score(review['review']) - review_jsons[idx]['review'] = review['review'] - review_jsons[idx]['score'] = scores - - if scores[0] == -1 and scores[1] == -1: - invalid_count += 1 - invalid_file.append(review_jsons[idx]) - logger.info(f' Invalid score pair: {review_jsons[idx]["id"]}.') - else: - if scores[0] > scores[1]: - worse_count += 1 - worse_file.append(review_jsons[idx]) - elif scores[0] < scores[1]: - better_count += 1 - better_file.append(review_jsons[idx]) - else: - tie_count += 1 - tie_file.append(review_jsons[idx]) - ans1_score += scores[0] - ans2_score += scores[1] - - output_review_file.append(review_jsons[idx]) - - better_file.sort(key=lambda x: x['id']) - worse_file.sort(key=lambda x: x['id']) - tie_file.sort(key=lambda x: x['id']) - invalid_file.sort(key=lambda x: x['id']) - output_review_file.sort(key=lambda x: x['id']) - - name1 = os.path.basename(args.answer_file_list[0]).split("_answers")[0] - name2 = os.path.basename(args.answer_file_list[1]).split("_answers")[0] - prefix = f"{name1}_vs_{name2}" - - jdump(better_file, os.path.join( - args.output_folder, prefix, f"{prefix}_better.json")) - jdump(worse_file, os.path.join( - args.output_folder, prefix, f"{prefix}_worse.json")) - jdump(tie_file, os.path.join( - args.output_folder, prefix, f"{prefix}_tie.json")) - jdump(invalid_file, os.path.join( - args.output_folder, prefix, f"{prefix}_invalid.json")) - jdump(output_review_file, os.path.join( - args.output_folder, prefix, f"{prefix}_review.json")) - - if os.path.exists(os.path.join(args.output_folder, "results.json")): - results = jload(os.path.join(args.output_folder, "results.json")) - else: - results = {} - results[prefix] = {'model': [name1, name2], 'better': better_count, 'worse': worse_count, 'tie': tie_count, 'win_rate': better_count / - (len(reviews)-invalid_count), 'score': [ans1_score/(len(reviews)-invalid_count), ans2_score/(len(reviews)-invalid_count)]} - jdump(results, os.path.join(args.output_folder, "results.json")) - - logger.info(f' Total {invalid_count} invalid score pair(s).') - logger.info(f' Model {name2} has {better_count} better answer(s).') - logger.info(f' Model {name2} has {worse_count} worse answer(s).') - logger.info(f' {tie_count} answer(s) play(s) to a tie.') - logger.info( - f' Win rate of model {name2}: {better_count/(len(reviews)-invalid_count):.2f}') - logger.info( - f' Model {name1} average score: {ans1_score/(len(reviews)-invalid_count):.2f}') - logger.info( - f' Model {name2} average score: {ans2_score/(len(reviews)-invalid_count):.2f}') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser( - description='Model evaluation.') - parser.add_argument('--answer_file_list', nargs='+', default=[]) - parser.add_argument('--prompt_file') - parser.add_argument('--reviewer_file') - parser.add_argument('--output_folder', type=str, default="./output") - parser.add_argument('--openai_key', type=str, default=None) - parser.add_argument('--model', type=str, default="gpt-4") - parser.add_argument('--num_workers', type=int, default=8) - parser.add_argument('--max_tokens', type=int, default=512, - help='maximum number of tokens produced in the output') - args = parser.parse_args() - - if args.openai_key is not None: - os.environ["OPENAI_API_KEY"] = args.openai_key - openai.api_key = os.getenv("OPENAI_API_KEY") - - evaluate(args) diff --git a/applications/Chat/evaluate/evaluate.sh b/applications/Chat/evaluate/evaluate.sh deleted file mode 100755 index c51aa941019e..000000000000 --- a/applications/Chat/evaluate/evaluate.sh +++ /dev/null @@ -1,9 +0,0 @@ -python evaluate.py \ - --answer_file_list "path to answers of model 1" "path to answers of model 2" \ - --prompt_file "path to prompt file" \ - --reviewer_file "path to reviewer file" \ - --output_folder "path to output folder" \ - --openai_key "your openai key" \ - --model "gpt-4" \ - --num_workers 8 \ - --max_tokens 512 \ diff --git a/applications/Chat/evaluate/evaluator.py b/applications/Chat/evaluate/evaluator.py new file mode 100644 index 000000000000..b99509c990a3 --- /dev/null +++ b/applications/Chat/evaluate/evaluator.py @@ -0,0 +1,130 @@ +import os +from typing import Any, Dict, List + +import gpt_evaluate +import metrics +import pandas as pd +from utils import get_data_per_category, jdump + + +class Evaluator(object): + """ + A class named Evaluator includes GPT-3.5/GPT-4 evaluation + and automatic evaluation + + """ + + def __init__(self, params: Dict[str, Any], battle_prompt: Dict[str, Any], gpt_evaluation_prompt: Dict[str, + Any]) -> None: + self.params = params + self.battle_prompt = battle_prompt + self.gpt_evaluation_prompt = gpt_evaluation_prompt + self.automatic_metric_stats = dict() + self.gpt35_evaluation_results = dict() + self.battle_results = [] + + def battle(self, answers1: List[Dict], answers2: List[Dict]) -> None: + """ + Comparison between two models using GPT-4 as the reviewer. + """ + + self.battle_results = gpt_evaluate.battle(answers1, answers2, self.battle_prompt) + + def evaluate(self, answers: List[Dict], targets: List[Dict]) -> None: + """ + A comprehensive evaluation of the answers from the model. + The function evaluates the model's performance from different perspectives + using GPT-3.5, GPT-4, and off-the-shelf evaluation metrics. + + The metrics will be decided by the config file. + + """ + + def switch(metric): + if metric == "BLEU": + return metrics.bleu_score(preds=predicts_list, targets=targets_list) + elif metric == "ROUGE": + return metrics.rouge_cn_score(preds=predicts_list, targets=targets_list) + elif (metric == "Distinct"): + return metrics.distinct_score(preds=predicts_list) + elif (metric == "BERTScore"): + return metrics.bert_score(preds=predicts_list, targets=targets_list) + elif (metric == "Precision"): + return metrics.precision(preds=predicts_list, targets=targets_list) + elif (metric == "Recall"): + return metrics.recall(preds=predicts_list, targets=targets_list) + elif (metric == "F1 score"): + return metrics.F1_score(preds=predicts_list, targets=targets_list) + else: + raise ValueError(f"Unexpected metric") + + answers_per_category = get_data_per_category(answers, list(self.params.keys())) + targets_per_category = get_data_per_category(targets, list(self.params.keys())) + + # automatic evaluation + for category in self.params: + category_metrics = self.params[category]["Metrics"] + self.automatic_metric_stats[category] = {} + + targets_list = [ + target["target"] if target["target"] else target["output"] for target in targets_per_category[category] + ] + predicts_list = [answer["output"] for answer in answers_per_category[category]] + + for metric in category_metrics: + self.automatic_metric_stats[category].update(switch(metric=metric)) + + # gpt35 evaluation + for category in self.params: + category_metrics = self.params[category]["GPT-3.5"] + + prompt = self.gpt_evaluation_prompt.get(category, None) + if prompt is None: + print(f"No prompt for category {category}! Use prompt for category general now.") + prompt = self.gpt_evaluation_prompt["general"] + + self.gpt35_evaluation_results[category] = gpt_evaluate.gpt35_evaluate(answers_per_category[category], + prompt, category_metrics, category) + + def save(self, path: str, model_name_list: List[str]) -> None: + """ + Save evaluation results of GPT-3.5, GPT-4, and off-the-shelf evaluation metrics. + + """ + + if len(model_name_list) == 2: + save_path = os.path.join(path, "gpt_evaluate", "battle_results") + gpt_evaluate.save_battle_results(self.battle_results, model_name_list[0], model_name_list[1], save_path) + else: + # save evaluation results for automatic metrics + automatic_df = pd.DataFrame(self.automatic_metric_stats) + + automatic_results_save_path = os.path.join(path, "automatic_results") + if not os.path.exists(automatic_results_save_path): + os.makedirs(automatic_results_save_path) + automatic_df.to_csv(os.path.join(automatic_results_save_path, f"{model_name_list[0]}.csv"), index=True) + + # Save evaluation results for GPT-3.5 evaluation metrics. + all_evaluations = [] + base_save_path = os.path.join(path, "gpt_evaluate", "gpt35_evaluate_results") + evaluation_results_save_path = os.path.join(base_save_path, "evaluation_results") + + for category, evaluations in self.gpt35_evaluation_results.items(): + jdump( + evaluations, + os.path.join(evaluation_results_save_path, model_name_list[0], + f"{category}_evaluation_results.json")) + all_evaluations.extend(evaluations) + + jdump(all_evaluations, + os.path.join(evaluation_results_save_path, f"{model_name_list[0]}_evaluation_results.json")) + + # Start to calculate scores and save statictics. + evaluation_statistics_save_path = os.path.join(base_save_path, "evaluation_statistics") + gpt_evaluate.save_gpt35_evaluation_statistics(model_name_list[0], all_evaluations, + evaluation_statistics_save_path) + + # Save charts and csv. + evaluation_analyses_save_path = os.path.join(base_save_path, "evaluation_analyses") + gpt_evaluate.analyze_gpt35_evaluation_statistics(evaluation_statistics_save_path, + evaluation_analyses_save_path) diff --git a/applications/Chat/evaluate/generate_answers.py b/applications/Chat/evaluate/generate_answers.py deleted file mode 100644 index fbebf5c5e6f6..000000000000 --- a/applications/Chat/evaluate/generate_answers.py +++ /dev/null @@ -1,173 +0,0 @@ -import argparse -import os -import random -import copy -import math -from tqdm import tqdm - -import torch -import torch.distributed as dist -import transformers - -from coati.models.bloom import BLOOMActor -from coati.models.gpt import GPTActor -from coati.models.opt import OPTActor -from coati.models.roberta import RoBERTaActor -from coati.models.llama import LlamaActor -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from transformers import AutoTokenizer, RobertaTokenizer -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.logging import get_dist_logger - -from utils import jload, jdump, is_rank_0 - - -logger = get_dist_logger() - -PROMPT_DICT = { - "prompt_input": - ("Below is an instruction that describes a task, paired with an input that provides further context. " - "Write a response that appropriately completes the request.\n\n" - "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"), - "prompt_no_input": ("Below is an instruction that describes a task. " - "Write a response that appropriately completes the request.\n\n" - "### Instruction:\n{instruction}\n\n### Response:"), -} - - -def generate(args): - # torch.cuda.set_per_process_memory_fraction(0.4) - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': - strategy = DDPStrategy() - elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') - elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') - elif args.strategy == 'colossalai_zero2_cpu': - strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') - else: - raise ValueError(f'Unsupported strategy "{args.strategy}"') - - world_size = dist.get_world_size() - rank = dist.get_rank() - - with strategy.model_init_context(): - if args.model == 'gpt2': - actor = GPTActor(pretrained=args.model_path).to( - torch.cuda.current_device()) - elif args.model == 'bloom': - actor = BLOOMActor(pretrained=args.model_path).to( - torch.cuda.current_device()) - elif args.model == 'opt': - actor = OPTActor(pretrained=args.model_path).to( - torch.cuda.current_device()) - elif args.model == 'roberta': - actor = RoBERTaActor(pretrained=args.model_path).to( - torch.cuda.current_device()) - elif args.model == 'llama': - actor = LlamaActor(pretrained=args.model_path).to( - torch.float16).to(torch.cuda.current_device()) - else: - raise ValueError(f'Unsupported model "{args.model}"') - - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m') - elif args.model == 'roberta': - tokenizer = RobertaTokenizer.from_pretrained("roberta-base") - elif args.model == 'llama': - tokenizer = AutoTokenizer.from_pretrained(args.model_path, - padding_side="right", - use_fast=False, - ) - tokenizer.eos_token = '<\s>' - else: - raise ValueError(f'Unsupported model "{args.model}"') - - questions = [] - if args.max_datasets_size is not None: - questions = random.sample(jload(args.dataset), args.max_datasets_size) - if is_rank_0(): - logger.info( - f"Limiting dataset to {args.max_datasets_size} examples.") - questions = questions[rank:args.max_datasets_size:world_size] - - answers = copy.deepcopy(questions) - - prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] - sources = [ - prompt_input.format_map(example) if example.get( - "input", "") != "" else prompt_no_input.format_map(example) - for example in questions - ] - - if is_rank_0(): - logger.info("Tokenizing inputs... This may take some time...") - - input_ids_list = [] - - for string in sources: - input_ids = tokenizer.encode(string, return_tensors='pt').squeeze(0) - input_ids_list.append(input_ids) - - bar = tqdm(range(math.ceil(len(input_ids_list)/args.batch_size)), - desc=f'steps', disable=not is_rank_0()) - - actor.eval() - with torch.no_grad(): - for i in range(0, len(input_ids_list), args.batch_size): - batch = input_ids_list[i:i+args.batch_size] - batch = [i.flip(dims=[0]) for i in batch] - batch = torch.nn.utils.rnn.pad_sequence(batch, - batch_first=True, - padding_value=tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0).to(torch.cuda.current_device()) - batch = batch.flip(dims=[1]) - attention_mask = batch.ne(tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0) - - outputs = actor.model.generate(batch, attention_mask=attention_mask, - max_length=args.max_length, - do_sample=True, - top_k=50, - top_p=0.95, - num_return_sequences=1) - - outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) - for j in range(batch.size(0)): - answers[i + - j]['output'] = outputs[j].split("### Response:")[1].strip() - - bar.update() - - jdump(answers, os.path.join(args.answer_path, - f'{args.model_name}_answers_rank{rank}.json')) - - if is_rank_0(): - logger.info( - f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.3f} GB') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', - 'colossalai_zero2', 'colossalai_zero2_cpu'], - default='naive') - parser.add_argument('--model', default='gpt2', - choices=['gpt2', 'bloom', 'opt', 'roberta', 'llama']) - parser.add_argument('--model_path', type=str, default=None) - parser.add_argument('--model_name', type=str, default='model') - parser.add_argument('--dataset', type=str, default=None) - parser.add_argument('--batch_size', type=int, default=1) - parser.add_argument('--max_datasets_size', type=int, default=None) - parser.add_argument('--answer_path', type=str, default="answer") - parser.add_argument('--max_length', type=int, default=1024) - args = parser.parse_args() - generate(args) diff --git a/applications/Chat/evaluate/generate_answers.sh b/applications/Chat/evaluate/generate_answers.sh deleted file mode 100755 index 36881f5f4f29..000000000000 --- a/applications/Chat/evaluate/generate_answers.sh +++ /dev/null @@ -1,25 +0,0 @@ -device_number=number of your devices -model_name="name of your model" -model_path="path to your model" -dataset="path to the question dataset" -answer_path="path to save the model answers" - -torchrun --standalone --nproc_per_node=$device_number generate_answers.py \ - --model 'llama' \ - --strategy ddp \ - --model_path $model_path \ - --model_name $model_name \ - --dataset $dataset \ - --batch_size 8 \ - --max_datasets_size 80 \ - --answer_path $answer_path \ - --max_length 512 - -python merge.py \ - --model_name $model_name \ - --shards $device_number \ - --answer_path $answer_path \ - -for (( i=0; i Dict[str, Any]: + """ + Get evaluation from GPT-4. + + Args: + sys_prompt: prompt for the system. + user_prompt: prompt for the user. + id: id of the answers for comparison. + max_tokens: the maximum number of tokens to generate in the chat completion. + + Returns: + An evaluation of one comparison. + """ + + MAX_API_RETRY = 3 + for _ in range(MAX_API_RETRY): + try: + response = openai.ChatCompletion.create( + model="gpt-4", + messages=[ + { + "role": "system", + "content": sys_prompt + }, + { + "role": "user", + "content": user_prompt, + }, + ], + temperature=0.2, + max_tokens=max_tokens, + ) + evaluation = response["choices"][0]["message"]["content"] + return {"evaluation": evaluation, "id": id} + except Exception as e: + print(e) + time.sleep(1) + print(f" Evaluation {id} failed after {MAX_API_RETRY} retries.") + return {"evaluation": "", "id": id} + + +def parse_battle_score(evaluation: str) -> List[float]: + """ + Parse evaluation from GPT-4 and get the scores of model 1 and 2. + + Args: + evaluation: evaluation from GPT-4. + + Returns: + A score pair of two different model answers. + """ + + try: + pattern = re.compile("([0-9]|10) out of 10") + sp = re.findall(pattern, evaluation) + if len(re.findall(pattern, evaluation)) == 2: + return [float(sp[0]), float(sp[1])] + + pattern = re.compile("a score of ([0-9]|10)") + sp = re.findall(pattern, evaluation) + if len(re.findall(pattern, evaluation)) == 2: + return [float(sp[0]), float(sp[1])] + + pattern = re.compile("([0-9]|10)/10") + sp = re.findall(pattern, evaluation) + if len(re.findall(pattern, evaluation)) == 2: + return [float(sp[0]), float(sp[1])] + + score_pair = evaluation.split("\n")[0] + score_pair = score_pair.replace(",", " ") + sp = score_pair.split(" ") + if len(sp) == 2: + return [float(sp[0]), float(sp[1])] + else: + raise Exception(f"Invalid score pair. Got {evaluation}.") + except Exception as e: + return [-1, -1] + + +def battle(answer1: List[Dict], answer2: List[Dict], prompt_dict: Dict[str, Any]) -> List[Dict]: + """ + Use GPT-4 to compare answers of two different models. + + Args: + answer1: answers of model 1. + answer2: answers of model 2. + prompt_dict: prompt for battle. + + Returns: + Evaluations of all comparison pairs. + """ + + assert len(answer1) == len(answer2) + + handles = [] + evaluation_file = [] + + total_len = len(answer1) + question_idx_list = list(range(total_len)) + + print(f" Total number of answers: {len(answer1)}.") + + evaluations = [] + with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: + futures = [] + for i in question_idx_list: + assert answer1[i]["id"] == answer2[i]["id"] + answer_id = answer1[i]["id"] + + ques = answer1[i]["instruction"] if answer1[i][ + "input"] == "" else answer1[i]["instruction"] + " " + answer1[i]["input"] + cat = answer1[i]["category"] + ans1 = answer1[i]["output"] + ans2 = answer2[i]["output"] + + sys_prompt = prompt_dict["system_prompt"] + prompt_template = prompt_dict["prompt_template"] + prompt = prompt_template.format( + question=ques, + answer_1=ans1, + answer_2=ans2, + prompt=prompt_dict["prompt"], + ) + + future = executor.submit(get_battle_result, sys_prompt, prompt, answer_id, 2048) + futures.append(future) + + for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)): + evaluations.append(future.result()) + + evaluations.sort(key=lambda x: x["id"]) + + return evaluations + + +def save_battle_results(evaluations: List[Dict], name1: str, name2: str, save_path: str) -> None: + """ + Save evaluation results (model 1 vs model 2) from GPT-4. + + Args: + evaluations: evaluation results from GPT-4. + name1: model 1 's name. + name2: model 2 's name. + save_path: path to save battle results. + """ + + evaluation_file = deepcopy(evaluations) + + ans1_score = 0 + ans2_score = 0 + better_count = 0 + worse_count = 0 + tie_count = 0 + invalid_count = 0 + + better_file = [] + worse_file = [] + tie_file = [] + invalid_file = [] + + for idx, evaluation in enumerate(evaluations): + scores = parse_battle_score(evaluation["evaluation"]) + evaluation_file[idx]["score"] = scores + + if scores[0] == -1 and scores[1] == -1: + invalid_count += 1 + invalid_file.append(evaluation_file[idx]) + print(f'Invalid score pair: {evaluation_file[idx]["id"]}.') + else: + if scores[0] > scores[1]: + worse_count += 1 + worse_file.append(evaluation_file[idx]) + elif scores[0] < scores[1]: + better_count += 1 + better_file.append(evaluation_file[idx]) + else: + tie_count += 1 + tie_file.append(evaluation_file[idx]) + ans1_score += scores[0] + ans2_score += scores[1] + + prefix = f"{name1}_vs_{name2}" + + if not os.path.exists(save_path): + os.makedirs(save_path) + + jdump(better_file, os.path.join(save_path, prefix, f"{name2}_better.json")) + jdump(worse_file, os.path.join(save_path, prefix, f"{name2}_worse.json")) + jdump(tie_file, os.path.join(save_path, prefix, f"{prefix}_tie.json")) + jdump(invalid_file, os.path.join(save_path, prefix, f"{prefix}_invalid.json")) + jdump(evaluation_file, os.path.join(save_path, prefix, f"{prefix}_evaluations.json")) + + if os.path.exists(os.path.join(save_path, "battle_results.json")): + results = jload(os.path.join(save_path, "battle_results.json")) + else: + results = {} + + results[prefix] = { + "model": [name1, name2], + "better": better_count, + "worse": worse_count, + "tie": tie_count, + "win_rate": better_count / (len(evaluations) - invalid_count), + "score": [ + ans1_score / (len(evaluations) - invalid_count), + ans2_score / (len(evaluations) - invalid_count), + ], + } + jdump(results, os.path.join(save_path, "battle_results.json")) + + print(f"Total {invalid_count} invalid score pair(s).") + print(f"Model {name2} has {better_count} better answer(s).") + print(f"Model {name2} has {worse_count} worse answer(s).") + print(f"{tie_count} answer(s) play(s) to a tie.") + print(f"Win rate of model {name2}: {better_count/(len(evaluations)-invalid_count):.2f}") + print(f"Model {name1} average score: {ans1_score/(len(evaluations)-invalid_count):.2f}") + print(f"Model {name2} average score: {ans2_score/(len(evaluations)-invalid_count):.2f}") + + +def get_gpt35_evaluation(prompt: Dict[str, Any], + inst: Dict[str, Any], + metrics: List[str], + max_tokens: int = 2048) -> Dict[str, Any]: + """ + Use GPT-3.5 to evaluate one model answer. + + Args: + prompt: a dictionary including prompt template, CoT and metrics. + inst: the instruction that is needed to be evaluated. + metrics: the metrics for evaluation. + max_tokens: the maximum number of tokens to generate in the completion. + + Returns: + An evaluation of one answer. + """ + + MAX_API_RETRY = 3 + + question = (inst["instruction"] if inst["input"] == "" else inst["instruction"] + " " + inst["input"]) + answer = inst["output"] + inst["evaluation"] = {} + + for metric in metrics: + if prompt["metrics"].get(metric, None) is None: + raise Exception( + f"Unsupported metric {metric} for category {inst['category']}! You should add this metric in the prompt file!" + ) + for i in range(MAX_API_RETRY): + try: + response = openai.Completion.create( + model="text-davinci-003", + prompt=prompt["prompt"].format( + question=question, + answer=answer, + metric=prompt["metrics"][metric], + steps=prompt["CoT"][metric], + ), + logprobs=5, + temperature=0, + max_tokens=max_tokens, + ) + inst["evaluation"][metric] = { + "response": response["choices"][0]["text"], + "logprobs": response["choices"][0]["logprobs"]["top_logprobs"], + } + break + except Exception as e: + print(e) + time.sleep(1) + return inst + + +def gpt35_evaluate( + answers: List[Dict], + prompt: Dict[str, Any], + metrics: List[str], + category: str, +) -> List[Dict]: + """ + Use GPT-3.5 to evaluate model answers and save evaluation results. + + Args: + answers: model answers. + prompt: prompt for GPT-3.5 evaluation. + metrics: metrics for GPT-3.5 evaluation. + category: the category of the model answers for evaluation. + + Returns: + Evaluations of the given answers. + """ + + print(f"The number of instances of category {category}'s is {len(answers)}.") + + evaluations = [] + + metrics_str = ", ".join(x for x in metrics) + print(f"Category {category}'s metrics are {metrics_str}.") + + with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: + futures = [] + for inst in answers: + future = executor.submit(get_gpt35_evaluation, prompt, inst, metrics, 1) + futures.append(future) + + for future in tqdm.tqdm( + concurrent.futures.as_completed(futures), + desc=f"{category}: ", + total=len(futures), + ): + evaluations.append(future.result()) + + evaluations.sort(key=lambda x: x["id"]) + + print(f"{category} done.") + + return evaluations + + +def calculate_scores_form_logprobs(logprobs: Dict[str, Any]) -> float: + """ + Calculate score from log probabilities returned by text-davinci-003. + Only openai.Completion can return logprobs. + + Calculation formula: + score = sum(score_i * exp(value)) where score_i is the score which corresponds to the key(predicted token) and value is its log probability. + + Ref: https://arxiv.org/abs/2303.16634 + This paper proposes NLG evaluation methods using GPT-3.5(logprobs returned by openai api) and GPT-4(logprobs obtained by sampling). + + Args: + logprobs: logprobs returned by openai.Completion. + + Returns: + Score of one answer. + """ + + # GPT-3.5 only returns score of 1 to 5. + prob = np.zeros(5) + + for key, value in logprobs.items(): + # Sometimes the key will be one byte of a unicode character which takes the form of "bytes:\\xe7". + # It is meaningless and thus we don't calculate probability. + if "bytes" in key: + continue + # results[0] is the score which corresponds to the key(predicted token). + # For example, key "5" corresponds to score 5. + results = re.findall(r"\d", key) + if len(results) == 1: + prob[int(results[0]) - 1] = prob[int(results[0]) - 1] + np.exp(value) + + score = np.dot(np.arange(1, 6), prob) + + return score + + +def save_gpt35_evaluation_statistics(model_name: str, evaluations: List[Dict], save_path: str) -> None: + """ + Generate statistics for one model. + + Args: + model_name: name of the model for saving statistics. + evaluations: evaluations for all of the model answers. + save_path: path to save GPT-3.5 evaluation statistics. + """ + + if not os.path.exists(save_path): + os.makedirs(save_path) + + data_per_category = {} + for evaluation in evaluations: + category = evaluation["category"] + if evaluation["category"] in data_per_category.keys(): + data_per_category[category].append(evaluation) + else: + data_per_category[category] = [evaluation] + + all_statistics = {} + for category, data in data_per_category.items(): + metrics = data[0]["evaluation"].keys() + scores = {metric: [] for metric in metrics} + for evaluation in data: + for metric in metrics: + scores[metric].append(calculate_scores_form_logprobs(evaluation["evaluation"][metric]["logprobs"][0])) + + statistics = {} + for metric in metrics: + arg_sort = np.argsort(scores[metric]) + statistics[metric] = {} + statistics[metric]["avg_score"] = sum(scores[metric]) / len(data) + statistics[metric]["best_3"] = {data[i]["id"]: scores[metric][i] for i in arg_sort[-3:][::-1]} + statistics[metric]["worst_3"] = {data[i]["id"]: scores[metric][i] for i in arg_sort[:3]} + + all_statistics[category] = statistics + + jdump( + all_statistics, + os.path.join(save_path, f"{model_name}_evaluation_statistics.json"), + ) + + +def analyze_gpt35_evaluation_statistics(statistics_path: str, save_path: str) -> None: + """ + Analyze and visualize all GPT-3.5 evaluation statistics in the given directory. + + Args: + statistics_path: path to all the models' statistics. + save_path: path to save table and visualization results. + """ + + if not os.path.exists(statistics_path): + raise Exception(f'The given directory "{statistics_path}" doesn\'t exist! No statistics found!') + + all_statistics = {} + + for file_name in os.listdir(statistics_path): + if file_name.endswith("_evaluation_statistics.json"): + model_name = file_name.split("_evaluation_statistics.json")[0] + all_statistics[model_name] = jload(os.path.join(statistics_path, file_name)) + + if len(list(all_statistics.keys())) == 0: + raise Exception(f'There are no statistics in the given directory "{statistics_path}"!') + + frame_all = { + "model": [], + "category": [], + "metric": [], + "avg_score": [], + "best_3": [], + "worst_3": [], + } + frame_per_category = {} + for model_name, model_statistics in all_statistics.items(): + for category, category_statistics in model_statistics.items(): + if frame_per_category.get(category) is None: + frame_per_category[category] = { + "model": [], + "metric": [], + "avg_score": [], + "best_3": [], + "worst_3": [], + } + + for metric, metric_statistics in category_statistics.items(): + frame_all["model"].append(model_name) + frame_all["category"].append(category) + frame_all["metric"].append(metric) + frame_all["avg_score"].append(metric_statistics["avg_score"]) + frame_all["best_3"].append(metric_statistics["best_3"]) + frame_all["worst_3"].append(metric_statistics["worst_3"]) + + frame_per_category[category]["model"].append(model_name) + frame_per_category[category]["metric"].append(metric) + frame_per_category[category]["avg_score"].append(metric_statistics["avg_score"]) + frame_per_category[category]["best_3"].append(metric_statistics["best_3"]) + frame_per_category[category]["worst_3"].append(metric_statistics["worst_3"]) + + if not os.path.exists(save_path): + os.makedirs(save_path) + + frame_all = pd.DataFrame(frame_all) + frame_all.to_csv(os.path.join(save_path, "gpt35_evaluation_statistics.csv")) + + for category in tqdm.tqdm( + frame_per_category.keys(), + desc=f"category: ", + total=len(frame_per_category.keys()), + ): + data = pd.DataFrame(frame_per_category[category]) + + sns.set() + fig = plt.figure(figsize=(16, 10)) + plt.ylim((0, 5)) + + fig = sns.barplot(x="metric", y="avg_score", hue="model", data=data, dodge=True) + fig.set_title(f"Comparison between Different Models for Category {category.title()}") + plt.xlabel("Evaluation Metric") + plt.ylabel("Average Score") + + figure = fig.get_figure() + figure.savefig(os.path.join(save_path, f"{category}.png"), dpi=400) diff --git a/applications/Chat/evaluate/merge.py b/applications/Chat/evaluate/merge.py deleted file mode 100644 index 295dd7fa7cb3..000000000000 --- a/applications/Chat/evaluate/merge.py +++ /dev/null @@ -1,25 +0,0 @@ -import argparse -import os - -from utils import jload, jdump - - -def generate(args): - dataset = [] - for i in range(args.shards): - shard = jload(os.path.join(args.answer_path, - f'{args.model_name}_answers_rank{i}.json')) - dataset.extend(shard) - - dataset.sort(key=lambda x: x['id']) - jdump(dataset, os.path.join(args.answer_path, - f'{args.model_name}_answers.json')) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--model_name', type=str, default='model') - parser.add_argument('--shards', type=int, default=4) - parser.add_argument('--answer_path', type=str, default="answer") - args = parser.parse_args() - generate(args) diff --git a/applications/Chat/evaluate/metrics.py b/applications/Chat/evaluate/metrics.py new file mode 100644 index 000000000000..590790ae71ac --- /dev/null +++ b/applications/Chat/evaluate/metrics.py @@ -0,0 +1,169 @@ +import statistics + +import jieba +from bert_score import score +from nltk.translate.bleu_score import sentence_bleu +from rouge_chinese import Rouge as Rouge_cn +from sklearn.metrics import f1_score, precision_score, recall_score + + +def bleu_score(preds: list, targets: list) -> dict: + """Calculate BLEU Score Metric + + The calculation includes BLEU-1 for unigram, BLEU-2 for bigram, + BLEU-3 for trigram and BLEU-4 for 4-gram. Unigram evaluates the + accuracy in word level, other n-gram evaluate the fluency in + sentence level. + """ + bleu_scores = {"bleu1": 0, "bleu2": 0, "bleu3": 0, "bleu4": 0} + cumulative_bleu = [0] * 4 + weights = [(1. / 1., 0., 0., 0.), (1. / 2., 1. / 2., 0., 0.), (1. / 3., 1. / 3., 1. / 3., 0.), + (1. / 4., 1. / 4., 1. / 4., 1. / 4.)] + + for pred, target in zip(preds, targets): + pred_list = (' '.join(jieba.cut(pred))).split() + target_list = [(' '.join(jieba.cut(target))).split()] + + bleu = sentence_bleu(target_list, pred_list, weights=weights) + cumulative_bleu = [a + b for a, b in zip(cumulative_bleu, bleu)] + + for i in range(len(cumulative_bleu)): + bleu_scores[f"bleu{i+1}"] = cumulative_bleu[i] / len(preds) + + return bleu_scores + + +def rouge_cn_score(preds: list, targets: list) -> dict: + """Calculate Chinese ROUGE Score Metric + + The calculation includes ROUGE-1 for unigram, ROUGE-2 for bigram + and ROUGE-L. ROUGE-N evaluates the number of matching n-grams between + the preds and targets. ROUGE-L measures the number of matching + longest common subsequence (LCS) between preds and targets. + """ + rouge_scores = {"rouge1": {}, "rouge2": {}, "rougeL": {}} + all_preds = [] + all_targets = [] + + for pred, target in zip(preds, targets): + pred_list = ' '.join(jieba.cut(pred)) + target_list = ' '.join(jieba.cut(target)) + all_preds.append(pred_list) + all_targets.append(target_list) + + rouge_cn = Rouge_cn() + rouge_avg = rouge_cn.get_scores(all_preds, all_targets, avg=True) + + rouge_scores["rouge1"] = rouge_avg["rouge-1"]["f"] + rouge_scores["rouge2"] = rouge_avg["rouge-2"]["f"] + rouge_scores["rougeL"] = rouge_avg["rouge-l"]["f"] + + return rouge_scores + + +def distinct_score(preds: list) -> dict: + """Calculate Distinct Score Metric + + This metric refers to https://arxiv.org/abs/1510.03055. + It evaluates the diversity of generation text by counting + the unique n-grams. + """ + distinct_score = {"distinct": 0} + cumulative_distinct = [] + + for pred in preds: + pred_seg_list = list(' '.join(jieba.cut(pred))) + count_segs = len(pred_seg_list) + unique_segs = set(pred_seg_list) + count_unique_chars = len(unique_segs) + + cumulative_distinct.append(count_unique_chars / count_segs) + + distinct_score["distinct"] = statistics.mean(cumulative_distinct) + + return distinct_score + + +def bert_score(preds: list, targets: list) -> dict: + """Calculate BERTScore Metric + + The BERTScore evaluates the semantic similarity between + tokens of preds and targets with BERT. + """ + bert_score = {"bert_score": 0} + pred_list = [] + target_list = [] + + for pred, target in zip(preds, targets): + pred_list.append(' '.join(jieba.cut(pred))) + target_list.append(' '.join(jieba.cut(target))) + + _, _, F = score(pred_list, target_list, lang="zh", verbose=True) + + bert_score["bert_score"] = F.mean().item() + + return bert_score + + +def calculate_precision_recall_f1(preds: list, targets: list) -> dict: + """Precision, Recall and F1-Score Calculation + + The calculation of precision, recall and f1-score is realized by counting + the number f overlaps between the preds and target. The comparison length + limited by the shorter one of preds and targets. This design is mainly + considered for classifiction and extraction categories. + """ + precision_recall_f1 = {"precision": 0, "recall": 0, "f1_score": 0} + precision_scores = [] + recall_scores = [] + f1_scores = [] + + for pred, target in zip(preds, targets): + pred_list = [char for char in pred] + target_list = [char for char in target] + + target_labels = [1] * min(len(target_list), len(pred_list)) + pred_labels = [int(pred_list[i] == target_list[i]) for i in range(0, min(len(target_list), len(pred_list)))] + + precision_scores.append(precision_score(target_labels, pred_labels, zero_division=0)) + recall_scores.append(recall_score(target_labels, pred_labels, zero_division=0)) + f1_scores.append(f1_score(target_labels, pred_labels, zero_division=0)) + + precision_recall_f1["precision"] = statistics.mean(precision_scores) + precision_recall_f1["recall"] = statistics.mean(recall_scores) + precision_recall_f1["f1_score"] = statistics.mean(f1_scores) + + return precision_recall_f1 + + +def precision(preds: list, targets: list) -> dict: + """Calculate Precision Metric + (design for classifiction and extraction categories) + + Calculating precision by counting the number of overlaps between the preds and target. + """ + precision = {"precision": 0} + precision["precision"] = calculate_precision_recall_f1(preds, targets)["precision"] + return precision + + +def recall(preds: list, targets: list) -> dict: + """Calculate Recall Metric + (design for classifiction and extraction categories) + + Calculating recall by counting the number of overlaps between the preds and target. + """ + recall = {"recall": 0} + recall["recall"] = calculate_precision_recall_f1(preds, targets)["recall"] + return recall + + +def F1_score(preds: list, targets: list) -> dict: + """Calculate F1-score Metric + (design for classifiction and extraction categories) + + Calculating f1-score by counting the number of overlaps between the preds and target. + """ + f1 = {"f1_score": 0} + f1["f1_score"] = calculate_precision_recall_f1(preds, targets)["f1_score"] + return f1 diff --git a/applications/Chat/evaluate/prompt/battle_prompt/battle_prompt_cn.json b/applications/Chat/evaluate/prompt/battle_prompt/battle_prompt_cn.json new file mode 100644 index 000000000000..ca66afd7e464 --- /dev/null +++ b/applications/Chat/evaluate/prompt/battle_prompt/battle_prompt_cn.json @@ -0,0 +1,6 @@ +{ + "id": 1, + "system_prompt": "你是一个检查回答质量的好助手。", + "prompt_template": "[问题]\n{question}\n\n[1号AI助手的答案]\n{answer_1}\n\n[1号AI助手答案终止]\n\n[2号AI助手的答案]\n{answer_2}\n\n[2号AI助手答案终止]\n\n[要求]\n{prompt}\n\n", + "prompt": "我们需要你评价这两个AI助手回答的性能。\n请对他们的回答的有用性、相关性、准确性、详细程度进行评分。每个AI助手都会得到一个1到10分的总分，分数越高表示整体表现越好。\n请首先输出一行，该行只包含两个数值，分别表示1号和2号AI助手的分数。这两个分数之间要有一个空格。在随后的一行中，请对你的评价作出全面的解释，避免任何潜在的偏见，并确保AI助手回答的顺序不会影响您的判断。" +} diff --git a/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json new file mode 100644 index 000000000000..d4b8d143eadf --- /dev/null +++ b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json @@ -0,0 +1,179 @@ +[ + { + "id": 1, + "category": "brainstorming", + "metrics": { + "language organization": "语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。", + "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", + "creativity": "创意性(1-5)：某些头脑风暴问题可能需要答案具有创意，提出新的思路。", + "practicality": "实用性(1-5)：某些头脑风暴问题可能需要答案提出实用的建议或解决方法。", + "correctness": "正确性(1-5)：答案应该符合常识、生活实际等等。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：", + "relevance": "1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。\n2. 阅读答案，确认答案是否直接回答了题目所问的问题。\n3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。\n4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。\n\n切题：", + "creativity": "1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。\n2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则创意性评分可能会受到影响。\n3. 考虑答案中是否包含新颖的想法或独特的思路。答案可能与已知的解决方案有所重叠，但仍然可以被认为是有创意的，只要它提供了新的角度或方法来解决问题。\n4. 根据答案的创意性，给出一个1到5的评分。如果答案缺乏创意，则应给出一个较低的评分。如果答案具有创意并提供了新的思路，应给出一个较高的评分。\n\n创意性：", + "practicality": "1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。\n2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则实用性评分可能会受到影响。\n3. 考虑答案中提出的建议或解决方法是否实用并可行。答案可能看起来很好，但如果无法实现或应用，则实用性评分可能会受到影响。\n4. 根据答案的实用性，给出一个1到5的评分。如果答案缺乏实用性，则应给出一个较低的评分。如果答案提出了实用的建议或解决方法，并且可以很好地解决问题，则应给出一个较高的评分。\n\n实用性：", + "correctness": "1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。\n2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则正确性评分可能会受到影响。\n3. 考虑答案中所提供的信息是否正确、符合常识、生活实际等等。如果答案中存在明显的错误或不合理之处，则正确性评分可能会受到影响。\n4. 根据答案的正确性，给出一个1到5的评分。如果答案存在明显的错误或不合理之处，则应给出一个较低的评分。如果答案正确、符合常识、生活实际等等，则应给出一个较高的评分。\n\n正确性：" + }, + "prompt": "你是一个好助手。请你为下面“头脑风暴”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + }, + { + "id": 2, + "category": "chat", + "metrics": { + "language organization": "语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。", + "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", + "naturalness": "自然(1-5)：答案是否自然，并且符合问题给定的身份。", + "engagingness": "参与感(1-5)：答案是否对前面的对话内容做出了恰当的反应，是否理解对话的语境和背景。", + "reasonableness": "合理性(1-5)：答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：", + "relevance": "1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。\n2. 阅读答案，确认答案是否直接回答了题目所问的问题。\n3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。\n4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。\n\n切题：", + "naturalness": "1. 阅读题目，确定题目提供的身份信息。\n2. 检查答案内容是否符合题目给定的身份。\n3. 根据以上因素，对该回答的自然性进行打分，分数从1到5，其中1表示不自然，5表示非常自然，并符合问题给定的身份。\n\n自然：", + "engagingness": "1. 阅读题目，确定对话的语境和背景。\n2. 检查答案是否充分理解对话的语境和背景，能否自然地融入到对话中而不显得突兀。\n3. 根据以上因素，对该回答的参与感进行打分，分数从1到5，其中1表示没有参与感，5表示非常有参与感，并且恰当地理解了对话的语境和背景。\n\n参与感：", + "reasonableness": "1. 阅读题目，确定对话的主题以及问题期望的回答方向。\n2. 判断答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。\n3. 根据以上因素，对该回答的合理性进行打分，分数从1到5，其中1表示不合理，5表示非常合理，并且能够与前面的对话内容形成逻辑上的衔接，并符合常理。\n\n合理性：" + }, + "prompt": "你是一个好助手。请你为下面的“补全对话”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + }, + { + "id": 3, + "category": "classification", + "metrics": { + "language organization": "语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。", + "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", + "correctness": "正确性(1-5)：答案是否正确。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：", + "relevance": "1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。\n2. 阅读答案，确认答案是否直接回答了题目所问的问题。\n3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。\n4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。\n\n切题：", + "correctness": "1. 仔细阅读题目，尝试自己回答该问题。\n2. 检查答案的准确性。您可以使用已知的事实或研究来验证答案是否正确。如果答案是正确的，则可以将正确性得分为5分。如果答案是部分正确的，则可以给予适当的得分，例如2分、3分或4分。如果答案完全不正确，则只得1分。\n\n正确性：" + }, + "prompt": "你是一个好助手。请你为下面的“分类“问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + }, + { + "id": 4, + "category": "closed_qa", + "metrics": { + "language organization": "语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。", + "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", + "correctness": "正确性(1-5)：答案是否正确。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：", + "relevance": "1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。\n2. 阅读答案，确认答案是否直接回答了题目所问的问题。\n3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。\n4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。\n\n切题：", + "correctness": "1. 仔细阅读题目，尝试自己回答该问题。\n2. 检查答案的准确性。您可以使用已知的事实或研究来验证答案是否正确。如果答案是正确的，则可以将正确性得分为5分。如果答案是部分正确的，则可以给予适当的得分，例如2分、3分或4分。如果答案完全不正确，则只得1分。\n\n正确性：" + }, + "prompt": "你是一个好助手。请你为下面问题的答案打分。\n\n问题如下：\n\n{question}\n\n需要你评分的答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + }, + { + "id": 5, + "category": "extraction", + "metrics": { + "language organization": "语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。", + "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", + "correctness": "准确性(1-5)：回答应该准确无误地提取出所需信息，不应该包含任何错误或误导性信息。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 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"语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。", + "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", + "correctness": "正确性(1-5)：答案是否正确。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：", + "relevance": "1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。\n2. 阅读答案，确认答案是否直接回答了题目所问的问题。\n3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。\n4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。\n\n切题：", + "correctness": "1. 仔细阅读题目，尝试自己回答该问题。\n2. 检查答案的准确性。您可以使用已知的事实或研究来验证答案是否正确。如果答案是正确的，则可以将正确性得分为5分。如果答案是部分正确的，则可以给予适当的得分，例如2分、3分或4分。如果答案完全不正确，则只得1分。\n\n正确性：" + }, + "prompt": "你是一个好助手。请你为下面的问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + }, + { + "id": 9, + "category": "roleplay", + "metrics": { + "language organization": "语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。", + "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", + "fidelity": "保真度(1-5)：答案是否能够严格遵守角色的设定回答给定的请求。", + "creativity": "创意性(1-5)：角色扮演问题的回答需要具有一定创意，但同时需要遵守角色的设定。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：", + "relevance": "1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。\n2. 阅读答案，确认答案是否直接回答了题目所问的问题。\n3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。\n4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。\n\n切题：", + "fidelity": "1. 仔细阅读问题，了解角色在问题中的设定和表现，包括职业、背景、观点、性格等方面。\n2. 阅读题目的请求，确认回答请求时需要注意的细节。\n3. 对比提供的回答与该角色的设定，评估回答是否能够严格遵守角色的设定。\n4. 结合以上评估结果给出保真度的评分，范围从1到5分，其中1分表示回答与角色设定完全不符，5分表示回答完全符合角色设定且满足给定请求。\n\n保真度：", + "creativity": "1. 仔细阅读问题，了解角色在问题中的设定和表现，包括职业、背景、观点、性格等方面。\n2. 评估回答是否具有独特的思路和建议，是否能够给提问者带来新的想法和启示。\n3. 对比回答中的创意和该角色的设定，评估回答是否遵守了该角色的设定和基本特征。\n4. 对回答的质量进行总体评估，并结合以上评估结果给出创意性的评分，范围从1到5分，其中1分表示回答缺乏创意，5分表示回答具有独特的思路和建议，并且能够遵守该角色的设定。\n\n创意性：" + }, + "prompt": "你是一个好助手。请你为下面的“角色扮演”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + }, + { + "id": 10, + "category": "summarization", + "metrics": { + "language organization": "语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。", + "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", + "correctness": "准确性(1-5)：回答应该准确无误地总结出材料的重点。", + "conciseness": "简明扼要(1-5)：答案是否简明扼要，没有冗余内容。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：", + "relevance": "1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。\n2. 阅读答案，确认答案是否直接回答了题目所问的问题。\n3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。\n4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。\n\n切题：", + "correctness": "1. 仔细阅读问题给的材料，理解其内容和要点。\n2. 评估回答是否准确地总结出原始材料的重点。\n3. 评估回答是否包含原始材料中的所有关键信息。\n4. 根据以上步骤，给出一个1-5的分数，其中1表示回答不能准确地总结出材料的重点，5表示回答完全准确地总结出材料的重点。\n\n准确性：", + "conciseness": "1. 阅读题目，提取出材料的重点。\n2. 阅读该总结，并注意其中的主要观点和信息。\n3. 评估总结的长度。一个简明扼要的总结通常应该在几句话或几段文字内传达关键信息，而不是冗长的段落或文章。\n4. 检查总结是否包含与主要观点无关的信息或冗余信息。\n5.确定总结涵盖了材料中的关键信息，并且没有忽略任何重要细节。\n6.给总结打出1-5的分数，其中5表示总结简明扼要，没有冗余内容，而1表示总结冗长或包含不必要的信息，难以理解或记忆。根据您的判断，打出适当的得分。\n\n简明扼要：" + }, + "prompt": "你是一个好助手。请你为下面的“总结”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + }, + { + "id": 11, + "category": "general", + "metrics": { + "language organization": "语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。", + "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", + "correctness": "正确性(1-5)：答案是否正确。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：", + "relevance": "1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。\n2. 阅读答案，确认答案是否直接回答了题目所问的问题。\n3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。\n4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。\n\n切题：", + "correctness": "1. 仔细阅读题目，尝试自己回答该问题。\n2. 检查答案的准确性。您可以使用已知的事实或研究来验证答案是否正确。如果答案是正确的，则可以将正确性得分为5分。如果答案是部分正确的，则可以给予适当的得分，例如2分、3分或4分。如果答案完全不正确，则只得1分。\n\n正确性：" + }, + "prompt": "你是一个好助手。请你为下面问题的答案打分。\n\n问题如下：\n\n{question}\n\n需要你评分的答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + } +] diff --git a/applications/Chat/evaluate/requirements.txt b/applications/Chat/evaluate/requirements.txt new file mode 100644 index 000000000000..b0301c2f17f8 --- /dev/null +++ b/applications/Chat/evaluate/requirements.txt @@ -0,0 +1,10 @@ +jieba +bert-score +rouge_chinese +scikit-metrics +nltk +openai +seaborn +pandas +matplotlib +numpy diff --git a/applications/Chat/evaluate/sample/questions.json b/applications/Chat/evaluate/sample/questions.json deleted file mode 100644 index e9ef9f8b1c66..000000000000 --- a/applications/Chat/evaluate/sample/questions.json +++ /dev/null @@ -1,9 +0,0 @@ -[ - { - "id": 0, - "instruction": "Help me summarize the following news?", - "input": "National Commercial Bank (NCB), Saudi Arabia's largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Samba's Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf region's third-largest lender. The entity's $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle East's biggest lender with about $268 billion of assets.", - "output": "NCB to pay 28.45 riyals for each Samba share. Deal will create Gulf region's third-largest lender", - "category": "closed qa" - } -] \ No newline at end of file diff --git a/applications/Chat/evaluate/utils.py b/applications/Chat/evaluate/utils.py index 692ee007c080..e855cd45221c 100644 --- a/applications/Chat/evaluate/utils.py +++ b/applications/Chat/evaluate/utils.py @@ -2,10 +2,6 @@ import json import os -import torch.distributed as dist - -def is_rank_0() -> bool: - return not dist.is_initialized() or dist.get_rank() == 0 def _make_w_io_base(f, mode: str): if not isinstance(f, io.IOBase): @@ -15,11 +11,13 @@ def _make_w_io_base(f, mode: str): f = open(f, mode=mode) return f + def _make_r_io_base(f, mode: str): if not isinstance(f, io.IOBase): f = open(f, mode=mode) return f + def jdump(obj, f, mode="w", indent=4, default=str): """Dump a str or dictionary to a file in json format. Args: @@ -38,6 +36,7 @@ def jdump(obj, f, mode="w", indent=4, default=str): raise ValueError(f"Unexpected type: {type(obj)}") f.close() + def jload(f, mode="r"): """Load a .json file into a dictionary.""" f = _make_r_io_base(f, mode) @@ -45,9 +44,19 @@ def jload(f, mode="r"): f.close() return jdict + def get_json_list(file_path): with open(file_path, 'r') as f: json_list = [] for line in f: json_list.append(json.loads(line)) return json_list + + +def get_data_per_category(data, categories): + data_per_category = {category: [] for category in categories} + for item in data: + category = item["category"] + data_per_category[category].append(item) + + return data_per_category From e90fdb1000236b306af0692d6cf5066d76388400 Mon Sep 17 00:00:00 2001 From: digger yu Date: Wed, 24 May 2023 09:53:21 +0800 Subject: [PATCH 243/413] fix typo docs/ --- docs/README.md | 2 +- docs/REFERENCE.md | 2 +- docs/source/en/advanced_tutorials/add_your_parallel.md | 2 +- .../integrate_mixture_of_experts_into_your_model.md | 2 +- docs/source/en/advanced_tutorials/opt_service.md | 2 +- .../parallelize_your_training_like_Megatron.md | 2 +- .../train_vit_using_pipeline_parallelism.md | 2 +- .../train_vit_with_hybrid_parallelism.md | 10 +++++----- docs/source/en/basics/booster_api.md | 4 ++-- docs/source/en/basics/colotensor_concept.md | 2 +- docs/source/en/features/3D_tensor_parallel.md | 2 +- .../en/features/gradient_clipping_with_booster.md | 2 +- docs/source/en/features/nvme_offload.md | 2 +- docs/source/en/features/pipeline_parallel.md | 2 +- docs/source/en/features/zero_with_chunk.md | 2 +- .../zh-Hans/advanced_tutorials/add_your_parallel.md | 2 +- .../integrate_mixture_of_experts_into_your_model.md | 2 +- docs/source/zh-Hans/advanced_tutorials/meet_gemini.md | 4 ++-- docs/source/zh-Hans/advanced_tutorials/opt_service.md | 2 +- .../train_vit_with_hybrid_parallelism.md | 4 ++-- docs/source/zh-Hans/basics/colotensor_concept.md | 2 +- .../zh-Hans/features/mixed_precision_training.md | 2 +- docs/source/zh-Hans/features/nvme_offload.md | 2 +- 23 files changed, 30 insertions(+), 30 deletions(-) diff --git a/docs/README.md b/docs/README.md index f520608d552c..f0cb50ffe217 100644 --- a/docs/README.md +++ b/docs/README.md @@ -98,7 +98,7 @@ Lastly, if you want to skip some code, you just need to add the following annota ``` -If you have any dependency required, please add it to `requriements-doc-test.txt` for pip and `conda-doc-test-deps.yml` for Conda. +If you have any dependency required, please add it to `requirements-doc-test.txt` for pip and `conda-doc-test-deps.yml` for Conda. ### 💉 Auto Documentation diff --git a/docs/REFERENCE.md b/docs/REFERENCE.md index 2681198191cb..0984b2dc3f28 100644 --- a/docs/REFERENCE.md +++ b/docs/REFERENCE.md @@ -1,6 +1,6 @@ # References -The Colossal-AI project aims to provide a wide array of parallelism techniques for the machine learning community in the big-model era. This project is inspired by quite a few reserach works, some are conducted by some of our developers and the others are research projects open-sourced by other organizations. We would like to credit these amazing projects below in the IEEE citation format. +The Colossal-AI project aims to provide a wide array of parallelism techniques for the machine learning community in the big-model era. This project is inspired by quite a few research works, some are conducted by some of our developers and the others are research projects open-sourced by other organizations. We would like to credit these amazing projects below in the IEEE citation format. ## By Our Team diff --git a/docs/source/en/advanced_tutorials/add_your_parallel.md b/docs/source/en/advanced_tutorials/add_your_parallel.md index be7284a7ab64..1caf58c8734e 100644 --- a/docs/source/en/advanced_tutorials/add_your_parallel.md +++ b/docs/source/en/advanced_tutorials/add_your_parallel.md @@ -56,7 +56,7 @@ follow the steps below to create a new distributed initialization. world_size: int, config: Config, data_parallel_size: int, - pipeline_parlalel_size: int, + pipeline_parallel_size: int, tensor_parallel_size: int, arg1, arg2): diff --git a/docs/source/en/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md b/docs/source/en/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md index e01caf76d2b3..d5edd135c079 100644 --- a/docs/source/en/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md +++ b/docs/source/en/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md @@ -121,7 +121,7 @@ Inside the initialization of Experts, the local expert number of each GPU will b ## Train Your Model -Do not to forget to use `colossalai.initialize` function in `colosalai` to add gradient handler for the engine. +Do not to forget to use `colossalai.initialize` function in `colossalai` to add gradient handler for the engine. We handle the back-propagation of MoE models for you. In `colossalai.initialize`, we will automatically create a `MoeGradientHandler` object to process gradients. You can find more information about the handler `MoeGradientHandler` in colossal directory. diff --git a/docs/source/en/advanced_tutorials/opt_service.md b/docs/source/en/advanced_tutorials/opt_service.md index a43ec7fdd1fe..eccfa12f9389 100644 --- a/docs/source/en/advanced_tutorials/opt_service.md +++ b/docs/source/en/advanced_tutorials/opt_service.md @@ -53,7 +53,7 @@ export CHECKPOINT_DIR="your_opt_checkpoint_path" # the ${CONFIG_DIR} must contain a server.sh file as the entry of service export CONFIG_DIR="config_file_path" -docker run --gpus all --rm -it -p 8020:8020 -v ${CHECKPOINT_DIR}:/model_checkpoint -v ${CONFIG_DIR}:/config --ipc=host energonai:lastest +docker run --gpus all --rm -it -p 8020:8020 -v ${CHECKPOINT_DIR}:/model_checkpoint -v ${CONFIG_DIR}:/config --ipc=host energonai:latest ``` Then open `https://[IP-ADDRESS]:8020/docs#` in your browser to try out! diff --git a/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md b/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md index e7698e5e9d1b..1a7ab9a65674 100644 --- a/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md +++ b/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md @@ -69,7 +69,7 @@ After the forward operation of the embedding module, each word in all sequences
    The embedding module
    -Each transformer layer contains two blocks. The self-attention operation is called in the first block and a two-layer percepton is located in the second block. +Each transformer layer contains two blocks. The self-attention operation is called in the first block and a two-layer perception is located in the second block.
    diff --git a/docs/source/en/advanced_tutorials/train_vit_using_pipeline_parallelism.md b/docs/source/en/advanced_tutorials/train_vit_using_pipeline_parallelism.md index b26599740c5f..6adfe4f113da 100644 --- a/docs/source/en/advanced_tutorials/train_vit_using_pipeline_parallelism.md +++ b/docs/source/en/advanced_tutorials/train_vit_using_pipeline_parallelism.md @@ -195,7 +195,7 @@ def build_cifar(batch_size): ## Training ViT using pipeline -You can set the size of pipeline parallel and number of microbatches in config. `NUM_CHUNKS` is useful when using interleved-pipeline (for more details see [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://arxiv.org/abs/2104.04473) ). The original batch will be split into `num_microbatches`, and each stage will load a micro batch each time. Then we will generate an approriate schedule for you to execute the pipeline training. If you don't need the output and label of model, you can set `return_output_label` to `False` when calling `trainer.fit()` which can further reduce GPU memory usage. +You can set the size of pipeline parallel and number of microbatches in config. `NUM_CHUNKS` is useful when using interleaved-pipeline (for more details see [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://arxiv.org/abs/2104.04473) ). The original batch will be split into `num_microbatches`, and each stage will load a micro batch each time. Then we will generate an appropriate schedule for you to execute the pipeline training. If you don't need the output and label of model, you can set `return_output_label` to `False` when calling `trainer.fit()` which can further reduce GPU memory usage. You should `export DATA=/path/to/cifar`. diff --git a/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md b/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md index b2438a1cf562..a2deaeb88893 100644 --- a/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md +++ b/docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md @@ -16,14 +16,14 @@ In this example for ViT model, Colossal-AI provides three different parallelism We will show you how to train ViT on CIFAR-10 dataset with these parallelism techniques. To run this example, you will need 2-4 GPUs. -## Tabel of Contents +## Table of Contents 1. Colossal-AI installation 2. Steps to train ViT with data parallelism 3. Steps to train ViT with pipeline parallelism 4. Steps to train ViT with tensor parallelism or hybrid parallelism ## Colossal-AI Installation -You can install Colossal-AI pacakage and its dependencies with PyPI. +You can install Colossal-AI package and its dependencies with PyPI. ```bash pip install colossalai ``` @@ -31,7 +31,7 @@ pip install colossalai ## Data Parallelism -Data parallism is one basic way to accelerate model training process. You can apply data parallelism to training by only two steps: +Data parallelism is one basic way to accelerate model training process. You can apply data parallelism to training by only two steps: 1. Define a configuration file 2. Change a few lines of code in train script @@ -94,7 +94,7 @@ from torchvision import transforms from torchvision.datasets import CIFAR10 ``` -#### Lauch Colossal-AI +#### Launch Colossal-AI In train script, you need to initialize the distributed environment for Colossal-AI after your config file is prepared. We call this process `launch`. In Colossal-AI, we provided several launch methods to initialize the distributed backend. In most cases, you can use `colossalai.launch` and `colossalai.get_default_parser` to pass the parameters via command line. Besides, Colossal-AI can utilize the existing launch tool provided by PyTorch as many users are familiar with by using `colossalai.launch_from_torch`. For more details, you can view the related [documents](https://www.colossalai.org/docs/basics/launch_colossalai). @@ -613,7 +613,7 @@ NUM_MICRO_BATCHES = parallel['pipeline'] TENSOR_SHAPE = (BATCH_SIZE // NUM_MICRO_BATCHES, SEQ_LENGTH, HIDDEN_SIZE) ``` -Ohter configs: +Other configs: ```python # hyper parameters # BATCH_SIZE is as per GPU diff --git a/docs/source/en/basics/booster_api.md b/docs/source/en/basics/booster_api.md index cafcb6d432c3..a446ff31be83 100644 --- a/docs/source/en/basics/booster_api.md +++ b/docs/source/en/basics/booster_api.md @@ -14,9 +14,9 @@ In our new design, `colossalai.booster` replaces the role of `colossalai.initial ### Plugin Plugin is an important component that manages parallel configuration (eg: The gemini plugin encapsulates the gemini acceleration solution). Currently supported plugins are as follows: -***GeminiPlugin:*** This plugin wrapps the Gemini acceleration solution, that ZeRO with chunk-based memory management. +***GeminiPlugin:*** This plugin wraps the Gemini acceleration solution, that ZeRO with chunk-based memory management. -***TorchDDPPlugin:*** This plugin wrapps the DDP acceleration solution, it implements data parallelism at the module level which can run across multiple machines. +***TorchDDPPlugin:*** This plugin wraps the DDP acceleration solution, it implements data parallelism at the module level which can run across multiple machines. ***LowLevelZeroPlugin:*** This plugin wraps the 1/2 stage of Zero Redundancy Optimizer. Stage 1 : Shards optimizer states across data parallel workers/GPUs. Stage 2 : Shards optimizer states + gradients across data parallel workers/GPUs. diff --git a/docs/source/en/basics/colotensor_concept.md b/docs/source/en/basics/colotensor_concept.md index 050f2ef9f092..abe470fe0794 100644 --- a/docs/source/en/basics/colotensor_concept.md +++ b/docs/source/en/basics/colotensor_concept.md @@ -52,7 +52,7 @@ An instance of class [ComputeSpec](https://colossalai.readthedocs.io/en/latest/c ## Example -Let's see an example. A ColoTensor is initialized and sharded on 8 GPUs using tp_degree=4, dp_dgree=2. And then the tensor is sharded along the last dim among the TP process groups. Finally, we reshard it along the first dim (0 dim) among the TP process groups. We encourage users to run the code and observe the shape of each tensor. +Let's see an example. A ColoTensor is initialized and sharded on 8 GPUs using tp_degree=4, dp_degree=2. And then the tensor is sharded along the last dim among the TP process groups. Finally, we reshard it along the first dim (0 dim) among the TP process groups. We encourage users to run the code and observe the shape of each tensor. ```python diff --git a/docs/source/en/features/3D_tensor_parallel.md b/docs/source/en/features/3D_tensor_parallel.md index b9e98eac9350..0e28f08b23c9 100644 --- a/docs/source/en/features/3D_tensor_parallel.md +++ b/docs/source/en/features/3D_tensor_parallel.md @@ -67,7 +67,7 @@ Given $P=q \times q \times q$ processors, we present the theoretical computation ## Usage -To enable 3D tensor parallelism for our model, e.g. on 8 GPUs, we need to configure the parallism setting as below. +To enable 3D tensor parallelism for our model, e.g. on 8 GPUs, we need to configure the parallelism setting as below. ```python CONFIG = dict(parallel=dict( data=1, diff --git a/docs/source/en/features/gradient_clipping_with_booster.md b/docs/source/en/features/gradient_clipping_with_booster.md index 8686eb06ff54..341a608a5c7b 100644 --- a/docs/source/en/features/gradient_clipping_with_booster.md +++ b/docs/source/en/features/gradient_clipping_with_booster.md @@ -75,7 +75,7 @@ Build your model, optimizer, loss function, lr scheduler and dataloaders. Note t NUM_EPOCHS = 200 BATCH_SIZE = 128 GRADIENT_CLIPPING = 0.1 -# build resnetå +# build resnet model = resnet34(num_classes=10) # build dataloaders train_dataset = CIFAR10(root=Path(os.environ.get('DATA', './data')), diff --git a/docs/source/en/features/nvme_offload.md b/docs/source/en/features/nvme_offload.md index 4374da3c9c45..d940fd5eca14 100644 --- a/docs/source/en/features/nvme_offload.md +++ b/docs/source/en/features/nvme_offload.md @@ -53,7 +53,7 @@ It's compatible with all parallel methods in ColossalAI. > ⚠ It only offloads optimizer states on CPU. This means it only affects CPU training or Zero/Gemini with offloading. -## Exampls +## Examples Let's start from two simple examples -- training GPT with different methods. These examples relies on `transformers`. diff --git a/docs/source/en/features/pipeline_parallel.md b/docs/source/en/features/pipeline_parallel.md index ac49863b3c71..30654b0b0195 100644 --- a/docs/source/en/features/pipeline_parallel.md +++ b/docs/source/en/features/pipeline_parallel.md @@ -156,4 +156,4 @@ trainer.fit(train_dataloader=train_dataloader, display_progress=True) ``` -We use `2` pipeline stages and the batch will be splitted into `4` micro batches. +We use `2` pipeline stages and the batch will be split into `4` micro batches. diff --git a/docs/source/en/features/zero_with_chunk.md b/docs/source/en/features/zero_with_chunk.md index a105831a5409..8448c52acf06 100644 --- a/docs/source/en/features/zero_with_chunk.md +++ b/docs/source/en/features/zero_with_chunk.md @@ -72,7 +72,7 @@ chunk_manager = init_chunk_manager(model=module, gemini_manager = GeminiManager(placement_policy, chunk_manager) ``` -`hidden_dim` is the hidden dimension of DNN. Users can provide this argument to speed up searching. If users do not know this argument before training, it is ok. We will use a default value 1024. `min_chunk_size_mb` is the the minimum chunk size in MegaByte. If the aggregate size of parameters is still samller than the minimum chunk size, all parameters will be compacted into one small chunk. +`hidden_dim` is the hidden dimension of DNN. Users can provide this argument to speed up searching. If users do not know this argument before training, it is ok. We will use a default value 1024. `min_chunk_size_mb` is the the minimum chunk size in MegaByte. If the aggregate size of parameters is still smaller than the minimum chunk size, all parameters will be compacted into one small chunk. Initialization of the optimizer. ```python diff --git a/docs/source/zh-Hans/advanced_tutorials/add_your_parallel.md b/docs/source/zh-Hans/advanced_tutorials/add_your_parallel.md index 4825a6fa1d6c..059eb014affd 100644 --- a/docs/source/zh-Hans/advanced_tutorials/add_your_parallel.md +++ b/docs/source/zh-Hans/advanced_tutorials/add_your_parallel.md @@ -48,7 +48,7 @@ Colossal-AI 为用户提供了一个全局 context，使他们能够轻松地管 world_size: int, config: Config, data_parallel_size: int, - pipeline_parlalel_size: int, + pipeline_parallel_size: int, tensor_parallel_size: int, arg1, arg2): diff --git a/docs/source/zh-Hans/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md b/docs/source/zh-Hans/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md index 456878caa147..276fcc2619e0 100644 --- a/docs/source/zh-Hans/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md +++ b/docs/source/zh-Hans/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md @@ -122,7 +122,7 @@ Inside the initialization of Experts, the local expert number of each GPU will b ## Train Your Model -Do not to forget to use `colossalai.initialize` function in `colosalai` to add gradient handler for the engine. +Do not to forget to use `colossalai.initialize` function in `colossalai` to add gradient handler for the engine. We handle the back-propagation of MoE models for you. In `colossalai.initialize`, we will automatically create a `MoeGradientHandler` object to process gradients. You can find more information about the handler `MoeGradientHandler` in colossal directory. diff --git a/docs/source/zh-Hans/advanced_tutorials/meet_gemini.md b/docs/source/zh-Hans/advanced_tutorials/meet_gemini.md index 2bf0a9c98c3f..a52bc6ac7b7f 100644 --- a/docs/source/zh-Hans/advanced_tutorials/meet_gemini.md +++ b/docs/source/zh-Hans/advanced_tutorials/meet_gemini.md @@ -48,7 +48,7 @@ zero = dict(
    -ColossalAI设计了Gemini，就像双子星一样，它管理CPU和GPU二者内存空间。它可以让张量在训练过程中动态分布在CPU-GPU的存储空间内，从而让模型训练突破GPU的内存墙。内存管理器由两部分组成，分别是MemStatsCollector(MSC)和StatefuleTensorMgr(STM)。 +ColossalAI设计了Gemini，就像双子星一样，它管理CPU和GPU二者内存空间。它可以让张量在训练过程中动态分布在CPU-GPU的存储空间内，从而让模型训练突破GPU的内存墙。内存管理器由两部分组成，分别是MemStatsCollector(MSC)和StatefulTensorMgr(STM)。 我们利用了深度学习网络训练过程的迭代特性。我们将迭代分为warmup和non-warmup两个阶段，开始时的一个或若干迭代步属于预热阶段，其余的迭代步属于正式阶段。在warmup阶段我们为MSC收集信息，而在non-warmup阶段STM入去MSC收集的信息来移动tensor，以达到最小化CPU-GPU数据移动volume的目的。 @@ -75,7 +75,7 @@ STM管理所有model data tensor的信息。在模型的构造过程中，Coloss 我们在算子的开始和结束计算时，触发内存采样操作，我们称这个时间点为**采样时刻（sampling moment)**，两个采样时刻之间的时间我们称为**period**。计算过程是一个黑盒，由于可能分配临时buffer，内存使用情况很复杂。但是，我们可以较准确的获取period的系统最大内存使用。非模型数据的使用可以通过两个统计时刻之间系统最大内存使用-模型内存使用获得。 -我们如何设计采样时刻呢。我们选择preOp的model data layout adjust之前。如下图所示。我们采样获得上一个period的system memory used，和下一个period的model data memoy used。并行策略会给MSC的工作造成障碍。如图所示，比如对于ZeRO或者Tensor Parallel，由于Op计算前需要gather模型数据，会带来额外的内存需求。因此，我们要求在模型数据变化前进行采样系统内存，这样在一个period内，MSC会把preOp的模型变化内存捕捉。比如在period 2-3内，我们考虑的tensor gather和shard带来的内存变化。 +我们如何设计采样时刻呢。我们选择preOp的model data layout adjust之前。如下图所示。我们采样获得上一个period的system memory used，和下一个period的model data memory used。并行策略会给MSC的工作造成障碍。如图所示，比如对于ZeRO或者Tensor Parallel，由于Op计算前需要gather模型数据，会带来额外的内存需求。因此，我们要求在模型数据变化前进行采样系统内存，这样在一个period内，MSC会把preOp的模型变化内存捕捉。比如在period 2-3内，我们考虑的tensor gather和shard带来的内存变化。 尽管可以将采样时刻放在其他位置，比如排除gather buffer的变动新信息，但是会给造成麻烦。不同并行方式Op的实现有差异，比如对于Linear Op，Tensor Parallel中gather buffer的分配在Op中。而对于ZeRO，gather buffer的分配是在PreOp中。将放在PreOp开始时采样有利于将两种情况统一。 diff --git a/docs/source/zh-Hans/advanced_tutorials/opt_service.md b/docs/source/zh-Hans/advanced_tutorials/opt_service.md index a213584fd41d..1f8324a53ecb 100644 --- a/docs/source/zh-Hans/advanced_tutorials/opt_service.md +++ b/docs/source/zh-Hans/advanced_tutorials/opt_service.md @@ -52,7 +52,7 @@ export CHECKPOINT_DIR="your_opt_checkpoint_path" # the ${CONFIG_DIR} must contain a server.sh file as the entry of service export CONFIG_DIR="config_file_path" -docker run --gpus all --rm -it -p 8020:8020 -v ${CHECKPOINT_DIR}:/model_checkpoint -v ${CONFIG_DIR}:/config --ipc=host energonai:lastest +docker run --gpus all --rm -it -p 8020:8020 -v ${CHECKPOINT_DIR}:/model_checkpoint -v ${CONFIG_DIR}:/config --ipc=host energonai:latest ``` 接下来，您就可以在您的浏览器中打开 `https://[IP-ADDRESS]:8020/docs#` 进行测试。 diff --git a/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md b/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md index 6dc5eccf4421..e2f2c90a3791 100644 --- a/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md +++ b/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md @@ -477,7 +477,7 @@ def build_cifar(batch_size): return train_dataloader, test_dataloader -# craete dataloaders +# create dataloaders train_dataloader , test_dataloader = build_cifar() # create loss function criterion = CrossEntropyLoss(label_smoothing=0.1) @@ -492,7 +492,7 @@ lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer, #### 启动 Colossal-AI 引擎 ```python -# intiailize +# initialize engine, train_dataloader, test_dataloader, _ = colossalai.initialize(model=model, optimizer=optimizer, criterion=criterion, diff --git a/docs/source/zh-Hans/basics/colotensor_concept.md b/docs/source/zh-Hans/basics/colotensor_concept.md index b725e48a7cb1..ab2413e990f7 100644 --- a/docs/source/zh-Hans/basics/colotensor_concept.md +++ b/docs/source/zh-Hans/basics/colotensor_concept.md @@ -53,7 +53,7 @@ ColoTensor 包含额外的属性[ColoTensorSpec](https://colossalai.readthedocs. ## Example -让我们看一个例子。 使用 tp_degree=4, dp_dgree=2 在 8 个 GPU 上初始化并Shard一个ColoTensor。 然后tensor被沿着 TP 进程组中的最后一个维度进行分片。 最后，我们沿着 TP 进程组中的第一个维度（dim 0）对其进行重新Shard。 我们鼓励用户运行代码并观察每个张量的形状。 +让我们看一个例子。 使用 tp_degree=4, dp_degree=2 在 8 个 GPU 上初始化并Shard一个ColoTensor。 然后tensor被沿着 TP 进程组中的最后一个维度进行分片。 最后，我们沿着 TP 进程组中的第一个维度（dim 0）对其进行重新Shard。 我们鼓励用户运行代码并观察每个张量的形状。 ```python diff --git a/docs/source/zh-Hans/features/mixed_precision_training.md b/docs/source/zh-Hans/features/mixed_precision_training.md index c4df6271b3bb..4628b09cd910 100644 --- a/docs/source/zh-Hans/features/mixed_precision_training.md +++ b/docs/source/zh-Hans/features/mixed_precision_training.md @@ -203,7 +203,7 @@ Naive AMP 的默认参数: - initial_scale(int): gradient scaler 的初始值 - growth_factor(int): loss scale 的增长率 - backoff_factor(float): loss scale 的下降率 -- hysterisis(int): 动态 loss scaling 的延迟偏移 +- hysteresis(int): 动态 loss scaling 的延迟偏移 - max_scale(int): loss scale 的最大允许值 - verbose(bool): 如果被设为`True`,将打印调试信息 diff --git a/docs/source/zh-Hans/features/nvme_offload.md b/docs/source/zh-Hans/features/nvme_offload.md index fd75ed1f5b3e..db5f10184a99 100644 --- a/docs/source/zh-Hans/features/nvme_offload.md +++ b/docs/source/zh-Hans/features/nvme_offload.md @@ -53,7 +53,7 @@ optimizer = HybridAdam(model.parameters(), lr=1e-3, nvme_offload_fraction=1.0, n > ⚠ 它只会卸载在 CPU 上的优化器状态。这意味着它只会影响 CPU 训练或者使用卸载的 Zero/Gemini。 -## Exampls +## Examples Let's start from two simple examples -- training GPT with different methods. These examples relies on `transformers`. 首先让我们从两个简单的例子开始 -- 用不同的方法训练 GPT。这些例子依赖`transformers`。 From 518b31c059e11d7acce2e229d1579af594688561 Mon Sep 17 00:00:00 2001 From: digger yu Date: Wed, 24 May 2023 14:51:49 +0800 Subject: [PATCH 244/413] [docs] change placememt_policy to placement_policy (#3829) * fix typo colossalai/autochunk auto_parallel amp * fix typo colossalai/auto_parallel nn utils etc. * fix typo colossalai/auto_parallel autochunk fx/passes etc. * fix typo docs/ * change placememt_policy to placement_policy in docs/ and examples/ --- .../parallelize_your_training_like_Megatron.md | 4 ++-- docs/source/en/features/zero_with_chunk.md | 8 ++++---- .../parallelize_your_training_like_Megatron.md | 4 ++-- docs/source/zh-Hans/features/zero_with_chunk.md | 8 ++++---- examples/images/dreambooth/train_dreambooth_colossalai.py | 4 ++-- .../images/dreambooth/train_dreambooth_colossalai_lora.py | 4 ++-- examples/language/palm/train.py | 8 ++++---- 7 files changed, 20 insertions(+), 20 deletions(-) diff --git a/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md b/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md index 1a7ab9a65674..22d52fb3cd1a 100644 --- a/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md +++ b/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md @@ -175,11 +175,11 @@ In this way, users can train their models as usual. In our latest example, a Gemini + ZeRO DDP model is also defined to reduce overhead and improve efficiency.For the details of this part, please refer to [ZeRO](../features/zero_with_chunk.md). You can combine these two parts to understand our entire training process: ```python -def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"): +def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"): from colossalai.nn.parallel import GeminiDDP model = GeminiDDP(model, device=get_current_device(), - placement_policy=placememt_policy, + placement_policy=placement_policy, pin_memory=True, search_range_mb=32) return model diff --git a/docs/source/en/features/zero_with_chunk.md b/docs/source/en/features/zero_with_chunk.md index 8448c52acf06..d7a99f2fbbfd 100644 --- a/docs/source/en/features/zero_with_chunk.md +++ b/docs/source/en/features/zero_with_chunk.md @@ -185,23 +185,23 @@ def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup): Define a model which uses Gemini + ZeRO DDP: ```python -def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"): +def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"): cai_version = colossalai.__version__ if version.parse(cai_version) > version.parse("0.1.10"): from colossalai.nn.parallel import GeminiDDP model = GeminiDDP(model, device=get_current_device(), - placement_policy=placememt_policy, + placement_policy=placement_policy, pin_memory=True, search_range_mb=32) elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"): from colossalai.gemini import ChunkManager, GeminiManager chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32) - gemini_manager = GeminiManager(placememt_policy, chunk_manager) + gemini_manager = GeminiManager(placement_policy, chunk_manager) chunk_manager = ChunkManager(chunk_size, pg, enable_distributed_storage=True, - init_device=GeminiManager.get_default_device(placememt_policy)) + init_device=GeminiManager.get_default_device(placement_policy)) model = ZeroDDP(model, gemini_manager) else: raise NotImplemented(f"CAI version {cai_version} is not supported") diff --git a/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md b/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md index f3c6247c38e4..c4131e593437 100644 --- a/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md +++ b/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md @@ -159,11 +159,11 @@ for mn, module in model.named_modules(): 在我们最新示例中还定义了一个Gemini + ZeRO DDP 的模型从而减小开销，提升效率。这一部分的详细内容可以参考[ZeRO](../features/zero_with_chunk.md)，你可以将这两部分内容结合起来看从而理解我们整个训练流程： ```python -def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"): +def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"): from colossalai.nn.parallel import GeminiDDP model = GeminiDDP(model, device=get_current_device(), - placement_policy=placememt_policy, + placement_policy=placement_policy, pin_memory=True, search_range_mb=32) return model diff --git a/docs/source/zh-Hans/features/zero_with_chunk.md b/docs/source/zh-Hans/features/zero_with_chunk.md index 72403bf610a4..ba57ba4e8e61 100644 --- a/docs/source/zh-Hans/features/zero_with_chunk.md +++ b/docs/source/zh-Hans/features/zero_with_chunk.md @@ -185,23 +185,23 @@ def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup): 定义一个使用 Gemini + ZeRO DDP 的模型： ```python -def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"): +def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"): cai_version = colossalai.__version__ if version.parse(cai_version) > version.parse("0.1.10"): from colossalai.nn.parallel import GeminiDDP model = GeminiDDP(model, device=get_current_device(), - placement_policy=placememt_policy, + placement_policy=placement_policy, pin_memory=True, search_range_mb=32) elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"): from colossalai.gemini import ChunkManager, GeminiManager chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32) - gemini_manager = GeminiManager(placememt_policy, chunk_manager) + gemini_manager = GeminiManager(placement_policy, chunk_manager) chunk_manager = ChunkManager(chunk_size, pg, enable_distributed_storage=True, - init_device=GeminiManager.get_default_device(placememt_policy)) + init_device=GeminiManager.get_default_device(placement_policy)) model = ZeroDDP(model, gemini_manager) else: raise NotImplemented(f"CAI version {cai_version} is not supported") diff --git a/examples/images/dreambooth/train_dreambooth_colossalai.py b/examples/images/dreambooth/train_dreambooth_colossalai.py index e6159e1058b9..d07febea0a84 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai.py @@ -340,12 +340,12 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: # Gemini + ZeRO DDP -def gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = "auto"): +def gemini_zero_dpp(model: torch.nn.Module, placement_policy: str = "auto"): from colossalai.nn.parallel import GeminiDDP model = GeminiDDP(model, device=get_current_device(), - placement_policy=placememt_policy, + placement_policy=placement_policy, pin_memory=True, search_range_mb=64) return model diff --git a/examples/images/dreambooth/train_dreambooth_colossalai_lora.py b/examples/images/dreambooth/train_dreambooth_colossalai_lora.py index 1b2fc778d5ed..6715b473a567 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai_lora.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai_lora.py @@ -342,12 +342,12 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: # Gemini + ZeRO DDP -def gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = "auto"): +def gemini_zero_dpp(model: torch.nn.Module, placement_policy: str = "auto"): from colossalai.nn.parallel import GeminiDDP model = GeminiDDP(model, device=get_current_device(), - placement_policy=placememt_policy, + placement_policy=placement_policy, pin_memory=True, search_range_mb=64) return model diff --git a/examples/language/palm/train.py b/examples/language/palm/train.py index 7923e4fc855d..b16da1c7744a 100644 --- a/examples/language/palm/train.py +++ b/examples/language/palm/train.py @@ -102,23 +102,23 @@ def get_model_size(model: nn.Module): # Gemini + ZeRO DDP -def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"): +def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"): cai_version = colossalai.__version__ if version.parse(cai_version) > version.parse("0.1.10"): from colossalai.nn.parallel import GeminiDDP model = GeminiDDP(model, device=get_current_device(), - placement_policy=placememt_policy, + placement_policy=placement_policy, pin_memory=True, search_range_mb=32) elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"): from colossalai.gemini import ChunkManager, GeminiManager chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32) - gemini_manager = GeminiManager(placememt_policy, chunk_manager) + gemini_manager = GeminiManager(placement_policy, chunk_manager) chunk_manager = ChunkManager(chunk_size, pg, enable_distributed_storage=True, - init_device=GeminiManager.get_default_device(placememt_policy)) + init_device=GeminiManager.get_default_device(placement_policy)) model = ZeroDDP(model, gemini_manager) else: raise NotImplemented(f"CAI version {cai_version} is not supported") From 84500b7799232974d3f230f13c2c018fc8424392 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 24 May 2023 14:59:40 +0800 Subject: [PATCH 245/413] [workflow] fixed testmon cache in build CI (#3806) * [workflow] fixed testmon cache in build CI * polish code --- .github/workflows/build_on_pr.yml | 4 ++-- .gitignore | 4 ++++ requirements/requirements-test.txt | 1 + 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index 53355f560911..a5a17d176c9d 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -136,7 +136,7 @@ jobs: - name: Restore Testmon Cache run: | if [ -d /github/home/testmon_cache ]; then - [ ! -z "$(ls -A /github/home/testmon_cache)" ] && cp -p -r /github/home/testmon_cache/.testmondata /__w/ColossalAI/ColossalAI/ + [ ! -z "$(ls -A /github/home/testmon_cache)" ] && cp -p -r /github/home/testmon_cache/.testmondata* /__w/ColossalAI/ColossalAI/ fi - name: Execute Unit Testing @@ -150,7 +150,7 @@ jobs: - name: Store Testmon Cache run: | [ -d /github/home/testmon_cache ] || mkdir /github/home/testmon_cache - cp -p -r /__w/ColossalAI/ColossalAI/.testmondata /github/home/testmon_cache/ + cp -p -r /__w/ColossalAI/ColossalAI/.testmondata* /github/home/testmon_cache/ - name: Collate artifact env: diff --git a/.gitignore b/.gitignore index bf74a753894f..81113fa99dd5 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,7 @@ colossalai/version.py # ignore coverage test file coverage.lcov coverage.xml + +# ignore testmon and coverage files +.coverage +.testmondata* diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index 55edb1b6a512..6895113bc637 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -1,6 +1,7 @@ diffusers fbgemm-gpu==0.2.0 pytest +coverage==7.2.3 git+https://github.com/hpcaitech/pytest-testmon torchvision transformers From 7c9f2ed6dd3dd27a099295ac22be8f3d1508c010 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Thu, 25 May 2023 13:09:42 +0800 Subject: [PATCH 246/413] [dtensor] polish sharding spec docstring (#3838) * [dtensor] polish sharding spec docstring * [dtensor] polish sharding spec example docstring --- colossalai/tensor/d_tensor/sharding_spec.py | 31 +++++++++++---------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/colossalai/tensor/d_tensor/sharding_spec.py b/colossalai/tensor/d_tensor/sharding_spec.py index 2ea0c4db89fd..b927f6dfbe27 100644 --- a/colossalai/tensor/d_tensor/sharding_spec.py +++ b/colossalai/tensor/d_tensor/sharding_spec.py @@ -116,21 +116,21 @@ def build_difference_2d_dict(self): def dim_diff(self, other): ''' - The difference between two _DimSpec. + The difference between two DimSpec. Argument: - other(_DimSpec): the dim spec to compare with. + other(DimSpec): the dim spec to compare with. Return: difference(int): the difference between two _DimSpec. Example: - dim_spec = _DimSpec([0]) - other_dim_spec = _DimSpec([0, 1]) + ```python + dim_spec = DimSpec([0]) + other_dim_spec = DimSpec([0, 1]) print(dim_spec.difference(other_dim_spec)) - - Output: - 5 + # output: 5 + ``` ''' difference = self.difference_dict[(str(self), str(other))] return difference @@ -142,9 +142,13 @@ class ShardingSpec: [R, R, S0, S1], which means Argument: - dim_partition_dict(Dict[int, List[int]], optional): The key is the dimension of tensor to be sharded, - and the value of the key describe which logical axis will be sharded in that dimension. - sharding_sequence(List[DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1]. + dim_size (int): The number of dimensions of the tensor to be sharded. + dim_partition_dict (Dict[int, List[int]], optional): The key is the dimension of tensor to be sharded, + and the value of the key describe which logical axis will be sharded in that dimension. Defaults to None. + E.g. {0: [0, 1]} means the first dimension of the tensor will be sharded in logical axis 0 and 1. + sharding_sequence (List[DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1]. + Generally, users should specify either dim_partition_dict or sharding_sequence. + If both are given, users must ensure that they are consistent with each other. Defaults to None. ''' def __init__(self, @@ -208,6 +212,7 @@ def spec_diff(self, other): pair of sharding sequence. Example: + ```python dim_partition_dict = {0: [0, 1]} # DistSpec: # shard_sequence: S01,R,R @@ -219,10 +224,8 @@ def spec_diff(self, other): # device_mesh_shape: (4, 4) sharding_spec_to_compare = ShardingSpec(device_mesh, entire_shape, dim_partition_dict_to_compare) print(sharding_spec.sharding_sequence_difference(sharding_spec_to_compare)) - - Output: - 25 - + # output: 25 + ``` Argument: other(ShardingSpec): The ShardingSpec to compared with. From 3229f93e3081332f0734545436894151aae2cfc4 Mon Sep 17 00:00:00 2001 From: wukong1992 Date: Thu, 25 May 2023 14:00:02 +0800 Subject: [PATCH 247/413] [booster] add warning for torch fsdp plugin doc (#3833) --- colossalai/booster/plugin/torch_fsdp_plugin.py | 7 ++++++- docs/source/en/basics/booster_plugins.md | 3 +++ docs/source/zh-Hans/basics/booster_plugins.md | 3 +++ 3 files changed, 12 insertions(+), 1 deletion(-) diff --git a/colossalai/booster/plugin/torch_fsdp_plugin.py b/colossalai/booster/plugin/torch_fsdp_plugin.py index 340555dc640c..8d534ea4c061 100644 --- a/colossalai/booster/plugin/torch_fsdp_plugin.py +++ b/colossalai/booster/plugin/torch_fsdp_plugin.py @@ -3,10 +3,10 @@ import torch import torch.nn as nn +import warnings from packaging import version from torch.distributed import ProcessGroup - if version.parse(torch.__version__) >= version.parse('1.12.0'): from torch.distributed.fsdp import FullStateDictConfig from torch.distributed.fsdp import FullyShardedDataParallel as FSDP @@ -202,6 +202,11 @@ def configure( # wrap the model with PyTorch FSDP fsdp_model = TorchFSDPModel(model, device_id=torch.cuda.current_device(), **self.fsdp_kwargs) + + if len(optimizer.param_groups) > 1: + warnings.warn( + 'TorchFSDPPlugin does not support optimizer that use multi param groups. The results may not be as expected if used.' + ) optimizer.__init__(fsdp_model.parameters(), **optimizer.defaults) if not isinstance(optimizer, FSDPOptimizerWrapper): diff --git a/docs/source/en/basics/booster_plugins.md b/docs/source/en/basics/booster_plugins.md index 6ed49bfa7254..5e2586b836ad 100644 --- a/docs/source/en/basics/booster_plugins.md +++ b/docs/source/en/basics/booster_plugins.md @@ -62,8 +62,11 @@ More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/genera ### Torch FSDP Plugin > ⚠ This plugin is not available when torch version is lower than 1.12.0. + > ⚠ This plugin does not support save/load sharded model checkpoint now. +> ⚠ This plugin does not support optimizer that use multi params group. + More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html). {{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }} diff --git a/docs/source/zh-Hans/basics/booster_plugins.md b/docs/source/zh-Hans/basics/booster_plugins.md index 00e7d91e3f79..5bd88b679000 100644 --- a/docs/source/zh-Hans/basics/booster_plugins.md +++ b/docs/source/zh-Hans/basics/booster_plugins.md @@ -62,8 +62,11 @@ Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累 ### Torch FSDP 插件 > ⚠ 如果 torch 版本低于 1.12.0，此插件将不可用。 + > ⚠ 该插件现在还不支持保存/加载分片的模型 checkpoint。 +> ⚠ 该插件现在还不支持使用了multi params group的optimizer。 + 更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/fsdp.html). {{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }} From 54e97ed7ea0cff0563c066c53675d8a428973ec9 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 25 May 2023 14:14:34 +0800 Subject: [PATCH 248/413] [workflow] supported test on CUDA 10.2 (#3841) --- .../compatiblity_test_on_dispatch.yml | 47 ++++++++++++------- .github/workflows/compatiblity_test_on_pr.yml | 16 ++++++- README.md | 16 +++++++ docs/source/en/get_started/installation.md | 15 ++++++ .../zh-Hans/get_started/installation.md | 16 +++++++ 5 files changed, 90 insertions(+), 20 deletions(-) diff --git a/.github/workflows/compatiblity_test_on_dispatch.yml b/.github/workflows/compatiblity_test_on_dispatch.yml index 717cf729b3f3..3dcc4dfd182a 100644 --- a/.github/workflows/compatiblity_test_on_dispatch.yml +++ b/.github/workflows/compatiblity_test_on_dispatch.yml @@ -19,26 +19,26 @@ jobs: outputs: matrix: ${{ steps.set-matrix.outputs.matrix }} steps: - - id: set-matrix - env: - TORCH_VERSIONS: ${{ inputs.torch_version }} - CUDA_VERSIONS: ${{ inputs.cuda_version }} - run: | - IFS=',' - DOCKER_IMAGE=() + - id: set-matrix + env: + TORCH_VERSIONS: ${{ inputs.torch_version }} + CUDA_VERSIONS: ${{ inputs.cuda_version }} + run: | + IFS=',' + DOCKER_IMAGE=() - for tv in $TORCH_VERSIONS - do - for cv in $CUDA_VERSIONS - do - DOCKER_IMAGE+=("\"hpcaitech/pytorch-cuda:${tv}-${cv}\"") - done - done + for tv in $TORCH_VERSIONS + do + for cv in $CUDA_VERSIONS + do + DOCKER_IMAGE+=("\"hpcaitech/pytorch-cuda:${tv}-${cv}\"") + done + done - container=$( IFS=',' ; echo "${DOCKER_IMAGE[*]}" ) - container="[${container}]" - echo "$container" - echo "::set-output name=matrix::{\"container\":$(echo "$container")}" + container=$( IFS=',' ; echo "${DOCKER_IMAGE[*]}" ) + container="[${container}]" + echo "$container" + echo "::set-output name=matrix::{\"container\":$(echo "$container")}" build: name: Test for PyTorch Compatibility @@ -70,6 +70,17 @@ jobs: - uses: actions/checkout@v2 with: ssh-key: ${{ secrets.SSH_KEY_FOR_CI }} + - name: Download cub for CUDA 10.2 + run: | + CUDA_VERSION=$(cat $CUDA_HOME/version.txt | grep "CUDA Version" | awk '{print $NF}' | cut -d. -f1,2) + + # check if it is CUDA 10.2 + # download cub + if [ "$CUDA_VERSION" = "10.2" ]; then + wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip + unzip 1.8.0.zip + cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/ + fi - name: Install Colossal-AI run: | pip install -r requirements/requirements.txt diff --git a/.github/workflows/compatiblity_test_on_pr.yml b/.github/workflows/compatiblity_test_on_pr.yml index 2fca67b820a1..94a723388872 100644 --- a/.github/workflows/compatiblity_test_on_pr.yml +++ b/.github/workflows/compatiblity_test_on_pr.yml @@ -3,8 +3,8 @@ name: Compatibility Test on PR on: pull_request: paths: - - 'version.txt' - - '.compatibility' + - "version.txt" + - ".compatibility" jobs: matrix_preparation: @@ -58,6 +58,18 @@ jobs: - uses: actions/checkout@v2 with: ssh-key: ${{ secrets.SSH_KEY_FOR_CI }} + - name: Download cub for CUDA 10.2 + run: | + CUDA_VERSION=$(cat $CUDA_HOME/version.txt | grep "CUDA Version" | awk '{print $NF}' | cut -d. -f1,2) + + # check if it is CUDA 10.2 + # download cub + if [ "$CUDA_VERSION" = "10.2" ]; then + wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip + unzip 1.8.0.zip + cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/ + fi + - name: Install Colossal-AI run: | pip install -v --no-cache-dir . diff --git a/README.md b/README.md index c33caba90128..34c8a6b730a3 100644 --- a/README.md +++ b/README.md @@ -362,6 +362,22 @@ If you want to install and enable CUDA kernel fusion (compulsory installation wh CUDA_EXT=1 pip install . ``` +For Users with CUDA 10.2, you can still build ColossalAI from source. However, you need to manually download the cub library and copy it to the corresponding directory. + +```bash +# clone the repository +git clone https://github.com/hpcaitech/ColossalAI.git +cd ColossalAI + +# download the cub library +wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip +unzip 1.8.0.zip +cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/ + +# install +CUDA_EXT=1 pip install . +``` +

    (back to top)

    ## Use Docker diff --git a/docs/source/en/get_started/installation.md b/docs/source/en/get_started/installation.md index b626edb19e8e..6fc4ce2c922a 100644 --- a/docs/source/en/get_started/installation.md +++ b/docs/source/en/get_started/installation.md @@ -48,5 +48,20 @@ If you don't want to install and enable CUDA kernel fusion (compulsory installat pip install . ``` +For Users with CUDA 10.2, you can still build ColossalAI from source. However, you need to manually download the cub library and copy it to the corresponding directory. + +```bash +# clone the repository +git clone https://github.com/hpcaitech/ColossalAI.git +cd ColossalAI + +# download the cub library +wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip +unzip 1.8.0.zip +cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/ + +# install +CUDA_EXT=1 pip install . +``` diff --git a/docs/source/zh-Hans/get_started/installation.md b/docs/source/zh-Hans/get_started/installation.md index e0d726c74f64..a32627db6f00 100755 --- a/docs/source/zh-Hans/get_started/installation.md +++ b/docs/source/zh-Hans/get_started/installation.md @@ -47,4 +47,20 @@ CUDA_EXT=1 pip install . pip install . ``` +如果您在使用CUDA 10.2，您仍然可以从源码安装ColossalA。但是您需要手动下载cub库并将其复制到相应的目录。 + +```bash +# clone the repository +git clone https://github.com/hpcaitech/ColossalAI.git +cd ColossalAI + +# download the cub library +wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip +unzip 1.8.0.zip +cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/ + +# install +CUDA_EXT=1 pip install . +``` + From a64df3fa97777f3563aa39145b473c42c250f49c Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Thu, 25 May 2023 14:58:01 +0800 Subject: [PATCH 249/413] [doc] update document of gemini instruction. (#3842) * [doc] update meet_gemini.md * [doc] update meet_gemini.md * [doc] fix parentheses * [doc] fix parentheses * [doc] fix doc test * [doc] fix doc test * [doc] fix doc --- .../en/advanced_tutorials/meet_gemini.md | 27 ++++++++++------ .../zh-Hans/advanced_tutorials/meet_gemini.md | 32 ++++++++++--------- 2 files changed, 34 insertions(+), 25 deletions(-) diff --git a/docs/source/en/advanced_tutorials/meet_gemini.md b/docs/source/en/advanced_tutorials/meet_gemini.md index 8afb6705b6ae..c1c23a355efa 100644 --- a/docs/source/en/advanced_tutorials/meet_gemini.md +++ b/docs/source/en/advanced_tutorials/meet_gemini.md @@ -9,16 +9,21 @@ When you only have a few GPUs for large model training tasks, **heterogeneous tr ## Usage -At present, Gemini supports compatibility with ZeRO parallel mode, and it is really simple to use Gemini. Set attribute of zero model_config, i.e., tensor_placement_policy='auto'. - -``` -zero = dict( - model_config=dict( - tensor_placement_policy='auto', - shard_strategy=BucketTensorShardStrategy() - ), - optimizer_config=dict( - ...) +At present, Gemini supports compatibility with ZeRO parallel mode, and it is really simple to use Gemini: Inject the feathures of `GeminiPlugin` into training components with `booster`. More instructions of `booster` please refer to [**usage of booster**](../basics/booster_api.md). + +```python +from torchvision.models import resnet18 +from colossalai.booster import Booster +from colossalai.zero import ColoInitContext +from colossalai.booster.plugin import GeminiPlugin +plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, max_norm=1.0, initial_scale=2**5) +booster = Booster(plugin=plugin) +ctx = ColoInitContext() +with ctx: + model = resnet18() +optimizer = HybridAdam(model.parameters(), lr=1e-3) +criterion = lambda x: x.mean() +model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) ) ``` @@ -86,3 +91,5 @@ The important duty of MSC is to adjust the tensor layout position. For example, In the warmup stage, since we haven't finished a complete iteration yet, we don't know actual memory occupation. At this time, we limit the upper bound of memory usage of the model data. For example, only 30% of the GPU memory can be used. This ensures that we can successfully complete the warmup state. In the non-warmup stage, we need to use the memory information of non-model data collected in the warm-up stage to reserve the peak memory required by the computing device for the next Period, which requires us to move some model tensors. In order to avoid frequent replacement of the same tensor in and out of the CPU-GPU, causing a phenomenon similar to [cache thrashing](https://en.wikipedia.org/wiki/Thrashing_(computer_science)). Using the iterative characteristics of DNN training, we design the OPT cache swap out strategy. Specifically, in the warmup stage, we record the sampling time required by each tensor computing device. If we need to expel some HOLD tensors, we will choose the latest tensor needed on this device as the victim. + + diff --git a/docs/source/zh-Hans/advanced_tutorials/meet_gemini.md b/docs/source/zh-Hans/advanced_tutorials/meet_gemini.md index a52bc6ac7b7f..594823862de1 100644 --- a/docs/source/zh-Hans/advanced_tutorials/meet_gemini.md +++ b/docs/source/zh-Hans/advanced_tutorials/meet_gemini.md @@ -8,21 +8,21 @@ ## 用法 -目前Gemini支持和ZeRO并行方式兼容，它的使用方法很简单，在训练策略的配置文件里设置zero的model_config属性tensor_placement_policy='auto' - -``` -zero = dict( - model_config=dict( - reduce_scatter_bucket_size_mb=25, - fp32_reduce_scatter=False, - gradient_predivide_factor=1.0, - tensor_placement_policy="auto", - shard_strategy=TensorShardStrategy(), - ... - ), - optimizer_config=dict( - ... - ) +目前Gemini支持和ZeRO并行方式兼容，它的使用方法很简单：使用booster将`GeminiPlugin`中的特性注入到训练组件中。更多`booster`介绍请参考[booster使用](../basics/booster_api.md)。 + +```python +from torchvision.models import resnet18 +from colossalai.booster import Booster +from colossalai.zero import ColoInitContext +from colossalai.booster.plugin import GeminiPlugin +plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, max_norm=1.0, initial_scale=2**5) +booster = Booster(plugin=plugin) +ctx = ColoInitContext() +with ctx: + model = resnet18() +optimizer = HybridAdam(model.parameters(), lr=1e-3) +criterion = lambda x: x.mean() +model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) ) ``` @@ -94,3 +94,5 @@ MSC的重要职责是在调整tensor layout位置，比如在上图S2时刻， 在non-warmup阶段，我们需要利用预热阶段采集的非模型数据内存信息，预留出下一个Period在计算设备上需要的峰值内存，这需要我们移动出一些模型张量。 为了避免频繁在CPU-GPU换入换出相同的tensor，引起类似[cache thrashing](https://en.wikipedia.org/wiki/Thrashing_(computer_science))的现象。我们利用DNN训练迭代特性，设计了OPT cache换出策略。具体来说，在warmup阶段，我们记录每个tensor被计算设备需要的采样时刻。如果我们需要驱逐一些HOLD tensor，那么我们选择在本设备上最晚被需要的tensor作为受害者。 + + From e2d81eba0d526afec3e51f2212b5d7927bf72361 Mon Sep 17 00:00:00 2001 From: digger yu Date: Thu, 25 May 2023 16:19:41 +0800 Subject: [PATCH 250/413] [nfc] fix typo colossalai/ applications/ (#3831) * fix typo colossalai/autochunk auto_parallel amp * fix typo colossalai/auto_parallel nn utils etc. * fix typo colossalai/auto_parallel autochunk fx/passes etc. * fix typo docs/ * change placememt_policy to placement_policy in docs/ and examples/ * fix typo colossalai/ applications/ --- .../Chat/coati/ray/src/detached_replay_buffer.py | 2 +- .../Chat/coati/ray/src/experience_maker_holder.py | 2 +- applications/Chat/coati/ray/src/pipeline_strategy.py | 2 +- applications/Chat/evaluate/evaluator.py | 2 +- applications/Chat/evaluate/metrics.py | 8 ++++---- .../tensor_shard/node_handler/matmul_handler.py | 2 +- .../auto_parallel/tensor_shard/utils/broadcast.py | 12 ++++++------ 7 files changed, 15 insertions(+), 15 deletions(-) diff --git a/applications/Chat/coati/ray/src/detached_replay_buffer.py b/applications/Chat/coati/ray/src/detached_replay_buffer.py index 855eee48c5a5..18c8db388e88 100644 --- a/applications/Chat/coati/ray/src/detached_replay_buffer.py +++ b/applications/Chat/coati/ray/src/detached_replay_buffer.py @@ -34,7 +34,7 @@ def __init__(self, sample_batch_size: int, tp_world_size: int = 1, limit : int = ''' Workers in the same tp group share this buffer and need same sample for one step. Therefore a held_sample should be returned tp_world_size times before it could be dropped. - worker_state records wheter a worker got the held_sample + worker_state records whether a worker got the held_sample ''' self.tp_world_size = tp_world_size self.worker_state = [False] * self.tp_world_size diff --git a/applications/Chat/coati/ray/src/experience_maker_holder.py b/applications/Chat/coati/ray/src/experience_maker_holder.py index 94e4a3d537a5..0ae4e3125b70 100644 --- a/applications/Chat/coati/ray/src/experience_maker_holder.py +++ b/applications/Chat/coati/ray/src/experience_maker_holder.py @@ -22,7 +22,7 @@ class ExperienceMakerHolder: ''' Args: - detached_trainer_name_list: str list to get ray actor handleskkk + detached_trainer_name_list: str list to get ray actor handles strategy: experience_batch_size: batch size of generated experience kl_coef: the coefficient of kl divergence loss diff --git a/applications/Chat/coati/ray/src/pipeline_strategy.py b/applications/Chat/coati/ray/src/pipeline_strategy.py index 1780839c62ee..7ecb5d7d86d6 100644 --- a/applications/Chat/coati/ray/src/pipeline_strategy.py +++ b/applications/Chat/coati/ray/src/pipeline_strategy.py @@ -26,7 +26,7 @@ class PipelineModel(torch.nn.Module): ''' Actor has 2 kinds of jobs: forward and generate. - better to just pipelinize the inner model + better to just pipeline the inner model ''' def __init__(self, model: torch.nn.Module, diff --git a/applications/Chat/evaluate/evaluator.py b/applications/Chat/evaluate/evaluator.py index b99509c990a3..d3d1c038bfb8 100644 --- a/applications/Chat/evaluate/evaluator.py +++ b/applications/Chat/evaluate/evaluator.py @@ -119,7 +119,7 @@ def save(self, path: str, model_name_list: List[str]) -> None: jdump(all_evaluations, os.path.join(evaluation_results_save_path, f"{model_name_list[0]}_evaluation_results.json")) - # Start to calculate scores and save statictics. + # Start to calculate scores and save statistics. evaluation_statistics_save_path = os.path.join(base_save_path, "evaluation_statistics") gpt_evaluate.save_gpt35_evaluation_statistics(model_name_list[0], all_evaluations, evaluation_statistics_save_path) diff --git a/applications/Chat/evaluate/metrics.py b/applications/Chat/evaluate/metrics.py index 590790ae71ac..5e657234c61a 100644 --- a/applications/Chat/evaluate/metrics.py +++ b/applications/Chat/evaluate/metrics.py @@ -111,7 +111,7 @@ def calculate_precision_recall_f1(preds: list, targets: list) -> dict: The calculation of precision, recall and f1-score is realized by counting the number f overlaps between the preds and target. The comparison length limited by the shorter one of preds and targets. This design is mainly - considered for classifiction and extraction categories. + considered for classification and extraction categories. """ precision_recall_f1 = {"precision": 0, "recall": 0, "f1_score": 0} precision_scores = [] @@ -138,7 +138,7 @@ def calculate_precision_recall_f1(preds: list, targets: list) -> dict: def precision(preds: list, targets: list) -> dict: """Calculate Precision Metric - (design for classifiction and extraction categories) + (design for classification and extraction categories) Calculating precision by counting the number of overlaps between the preds and target. """ @@ -149,7 +149,7 @@ def precision(preds: list, targets: list) -> dict: def recall(preds: list, targets: list) -> dict: """Calculate Recall Metric - (design for classifiction and extraction categories) + (design for classification and extraction categories) Calculating recall by counting the number of overlaps between the preds and target. """ @@ -160,7 +160,7 @@ def recall(preds: list, targets: list) -> dict: def F1_score(preds: list, targets: list) -> dict: """Calculate F1-score Metric - (design for classifiction and extraction categories) + (design for classification and extraction categories) Calculating f1-score by counting the number of overlaps between the preds and target. """ diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py index bfebc3f59d0c..fa51114a5c94 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py @@ -206,7 +206,7 @@ def _remove_sharding_on_broadcast_dim(key, strategy): # e.g. [1, 2, 4] x [4, 4, 8] -> [4, 2, 8] # the dim 0 of [1, 2, 4] is multiplied to 4 tensor_shape[dim_idx] = 1 - elif broadcast_type == BroadcastType.PADDDING: + elif broadcast_type == BroadcastType.PADDING: # if the dim is padded # we remove its sharding tensor_shape[dim_idx] = None diff --git a/colossalai/auto_parallel/tensor_shard/utils/broadcast.py b/colossalai/auto_parallel/tensor_shard/utils/broadcast.py index 28aa551328d7..307348ea1eaf 100644 --- a/colossalai/auto_parallel/tensor_shard/utils/broadcast.py +++ b/colossalai/auto_parallel/tensor_shard/utils/broadcast.py @@ -21,7 +21,7 @@ class BroadcastType(Enum): EQUAL = auto() - PADDDING = auto() + PADDING = auto() MULTIPLE = auto() @@ -69,18 +69,18 @@ def get_broadcast_dim_info(logical_shape, physical_shape): for i in range(logical_num_dims): # get the trailing dim size logical_dim_idx = logical_num_dims - i - 1 - phyiscal_dim_idx = physical_num_dims - i - 1 + physical_dim_idx = physical_num_dims - i - 1 logical_dim_size = logical_shape[logical_dim_idx] - if phyiscal_dim_idx >= 0: - physical_dim_size = physical_shape[phyiscal_dim_idx] + if physical_dim_idx >= 0: + physical_dim_size = physical_shape[physical_dim_idx] if physical_dim_size == logical_dim_size: logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.EQUAL elif physical_dim_size == 1 and physical_dim_size != logical_dim_size: logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.MULTIPLE else: - logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.PADDDING + logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.PADDING return logical_dim_broadcast_info @@ -117,7 +117,7 @@ def recover_sharding_spec_for_broadcast_shape(logical_sharding_spec: ShardingSpe for shape_dim, mesh_dim in logical_dim_partition.items(): logical_broadcast_type = logical_dim_broadcast_info[shape_dim] - if logical_broadcast_type == BroadcastType.PADDDING or logical_broadcast_type == BroadcastType.MULTIPLE: + if logical_broadcast_type == BroadcastType.PADDING or logical_broadcast_type == BroadcastType.MULTIPLE: removed_dims.extend(mesh_dim) else: # get the corresponding physical dim From d42b1be09d95c00c806e175ec289ce9215904140 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 25 May 2023 16:20:07 +0800 Subject: [PATCH 251/413] [release] bump to v0.3.0 (#3830) --- version.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/version.txt b/version.txt index a45be4627678..0d91a54c7d43 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.2.8 +0.3.0 From ae959a72a5da236f6656be5fab01a6f8f900097c Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 25 May 2023 16:42:34 +0800 Subject: [PATCH 252/413] [workflow] fixed workflow check for docker build (#3849) --- .github/workflows/release_docker_after_publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release_docker_after_publish.yml b/.github/workflows/release_docker_after_publish.yml index 7d83fa3715a8..22698ca192ed 100644 --- a/.github/workflows/release_docker_after_publish.yml +++ b/.github/workflows/release_docker_after_publish.yml @@ -8,7 +8,7 @@ on: jobs: release: name: Publish Docker Image to DockerHub - if: ( github.event_name == 'workflow_dispatch' || github.event.pull_request.merged == true ) && github.repository == 'hpcaitech/ColossalAI' + if: github.repository == 'hpcaitech/ColossalAI' runs-on: [self-hosted, gpu] container: image: "hpcaitech/docker-in-docker:latest" From b0474878bf043cf10ab4ce4b6cbdf54d30b24c8d Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Fri, 26 May 2023 01:22:01 +0800 Subject: [PATCH 253/413] [doc] update nvme offload documents. (#3850) --- docs/source/en/features/nvme_offload.md | 19 +++++++++++------ docs/source/zh-Hans/features/nvme_offload.md | 22 +++++++++++++------- 2 files changed, 28 insertions(+), 13 deletions(-) diff --git a/docs/source/en/features/nvme_offload.md b/docs/source/en/features/nvme_offload.md index d940fd5eca14..6ed6f2dee5d6 100644 --- a/docs/source/en/features/nvme_offload.md +++ b/docs/source/en/features/nvme_offload.md @@ -78,8 +78,9 @@ from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel import colossalai from colossalai.nn.optimizer import HybridAdam -from colossalai.zero import zero_model_wrapper, zero_optim_wrapper from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin ``` Then we define a loss function: @@ -192,17 +193,23 @@ def train_gemini_cpu(nvme_offload_fraction: float = 0.0): optimizer = HybridAdam(model.parameters(), nvme_offload_fraction=nvme_offload_fraction) print(f'Model numel: {get_model_numel(model) / 1024**3:.3f} B') - gemini_config = dict(strict_ddp_mode=True, device=torch.cuda.current_device(), - placement_policy='cpu', pin_memory=True, hidden_dim=config.n_embd) - model = zero_model_wrapper(model, zero_stage=3, gemini_config=gemini_config) - optimizer = zero_optim_wrapper(model, optimizer, initial_scale=2**5) + plugin = GeminiPlugin( + strict_ddp_mode=True, + device=torch.cuda.current_device(), + placement_policy='cpu', + pin_memory=True, + hidden_dim=config.n_embd, + initial_scale=2**5 + ) + booster = Booster(plugin) + model, optimizer, criterion, _* = booster.boost(model, optimizer, criterion) start = time.time() for step in range(3): data = get_data(4, 128, config.vocab_size) outputs = model(**data) loss = criterion(outputs.logits, data['input_ids']) - optimizer.backward(loss) + booster.backward(loss, optimizer) optimizer.step() optimizer.zero_grad() print(f'[{step}] loss: {loss.item():.3f}') diff --git a/docs/source/zh-Hans/features/nvme_offload.md b/docs/source/zh-Hans/features/nvme_offload.md index db5f10184a99..1feb9dde5725 100644 --- a/docs/source/zh-Hans/features/nvme_offload.md +++ b/docs/source/zh-Hans/features/nvme_offload.md @@ -55,7 +55,6 @@ optimizer = HybridAdam(model.parameters(), lr=1e-3, nvme_offload_fraction=1.0, n ## Examples -Let's start from two simple examples -- training GPT with different methods. These examples relies on `transformers`. 首先让我们从两个简单的例子开始 -- 用不同的方法训练 GPT。这些例子依赖`transformers`。 我们首先应该安装依赖： @@ -77,8 +76,9 @@ from transformers.models.gpt2.configuration_gpt2 import GPT2Config from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel import colossalai from colossalai.nn.optimizer import HybridAdam -from colossalai.zero import zero_model_wrapper, zero_optim_wrapper from colossalai.utils.model.colo_init_context import ColoInitContext +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin ``` 然后我们定义一个损失函数： @@ -182,16 +182,24 @@ def train_gemini_cpu(nvme_offload_fraction: float = 0.0): criterion = GPTLMLoss() optimizer = HybridAdam(model.parameters(), nvme_offload_fraction=nvme_offload_fraction) print(f'Model numel: {get_model_numel(model) / 1024**3:.3f} B') - gemini_config = dict(strict_ddp_mode=True, device=torch.cuda.current_device(), - placement_policy='cpu', pin_memory=True, hidden_dim=config.n_embd) - model = zero_model_wrapper(model, zero_stage=3, gemini_config=gemini_config) - optimizer = zero_optim_wrapper(model, optimizer, initial_scale=2**5) + + plugin = GeminiPlugin( + strict_ddp_mode=True, + device=torch.cuda.current_device(), + placement_policy='cpu', + pin_memory=True, + hidden_dim=config.n_embd, + initial_scale=2**5 + ) + booster = Booster(plugin) + model, optimizer, criterion, _* = booster.boost(model, optimizer, criterion) + start = time.time() for step in range(3): data = get_data(4, 128, config.vocab_size) outputs = model(**data) loss = criterion(outputs.logits, data['input_ids']) - optimizer.backward(loss) + booster.backward(loss, optimizer) optimizer.step() optimizer.zero_grad() print(f'[{step}] loss: {loss.item():.3f}') From 2506e275b842103620439b7ddfa7ba9908d4eb7e Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Tue, 30 May 2023 11:48:41 +0800 Subject: [PATCH 254/413] [evaluation] improvement on evaluation (#3862) * fix a bug when the config file contains one category but the answer file doesn't contains that category * fix Chinese prompt file * support gpt-3.5-turbo and gpt-4 evaluation * polish and update README * resolve pr comments --------- Co-authored-by: Yuanchen Xu --- applications/Chat/evaluate/README.md | 245 ++++++++++++------ .../Chat/evaluate/config/config_cn.json | 20 +- applications/Chat/evaluate/eval.py | 7 +- applications/Chat/evaluate/evaluator.py | 36 ++- applications/Chat/evaluate/gpt_evaluate.py | 150 +++++++++-- .../evaluation_prompt_cn.json | 26 +- applications/Chat/evaluate/utils.py | 3 +- 7 files changed, 340 insertions(+), 147 deletions(-) diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index 1e86eadf1c33..ae3499bf268c 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -4,7 +4,9 @@ In this directory, we introduce how you can evaluate your model with our pipelin evaluation of Chinese capability and the one for English capability is under preparation. ## Installation + To start model evaluation, you need to install required packages which listed in `requirements.txt` under `evaluate` folder. + ```shell pip install -r requirements.txt ``` @@ -12,84 +14,92 @@ pip install -r requirements.txt ## Evaluation Pipeline The whole evaluation pipeline consists of two methods: -1. `GPT Evaluation`: evaluates model predictions using the GPT-3.5. + +1. `GPT Evaluation`: evaluates model predictions using GPT models. * Compare the performance of two different models (battle). - * Rate model according to pre-defined metrics using prompting design. + * Rate the model according to pre-defined metrics using prompting design. 2. `Automatic Evaluation`: evaluates model predictions using automatic metrics. ### Evaluation Category -The model capability is seperated into 10 evaluation categories, which refers to the user case mentioned in InstructGPT. -Following table introduces each category: - -| Evaluation Category | Description | -|:-------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------| -| Roleplay | Given certain characteristic, the capability of chatting as the character | -| Chat | Conduct multiple rounds of dialogue, the capability of understanding and memorization of previous rounds of dialogue | -| Open QA | Given an open question, the capability of answering questions in opened-ended way | -| Closed QA | Given a closed question, the capability of answering questions with limited scope (such as single/multiple choice question) | -| Brainstorming | Given a question requiring divergent answers, the capability of divergent answering and listing in points | -| Generation | Given generation task, the capability of generating in high quality and human-written way (such as writing an email) | -| Rewriting | Given rewriting task, the capability of rewriting sentences to meet task requirements (such as active and passive switches, translation) | -| Classification | Given classification task, the capability of accurate classification | -| Extraction | Given extraction task, the capability of extracting required information | -| Summarization | Given a paragraph or passage, the capability of summarization | - -To better understand each evaluation category, here are some prompt examples provided. - - -| Evaluation Category | Chinese Example | English Example | -|:-------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Roleplay | **Example 1：**
    我想让你担任Android开发工程师面试官。我将成为候选人，您将向我询问Android开发工程师职位的面试问题。我希望你只作为面试官回答。不要一次写出所有的问题。我希望你只对我进行采访。问我问题，等待我的回答。不要写解释。像面试官一样一个一个问我，等我回答。我的第一句话是“面试官你好”。

    **Example 2：**
    我想让你扮演讲故事的角色。你会想出引人入胜、富有想象力和吸引观众的有趣故事。它可以是童话故事、教育故事或任何其他类型的有潜力的故事以吸引人们的注意力和想象力。根据目标受众，您可以为您的讲故事环节选择特定的主题或主题，例如，如果是儿童，那么您可以谈论动物；如果是成人，那么基于历史的故事可能会更好地吸引他们等。我的第一个请求是我需要一个关于毅力的有趣故事。 | **Example 1：**
    I want you to act as a journalist. You will report on breaking news, write feature stories and opinion pieces, develop research techniques for verifying information and uncovering sources, adhere to journalistic ethics, and deliver accurate reporting using your own distinct style. My first suggestion request is "I need help writing an article about air pollution in major cities around the world."

    **Example 2:**
    I want you to act as a debater. I will provide you with some topics related to current events and your task is to research both sides of the debates, present valid arguments for each side, refute opposing points of view, and draw persuasive conclusions based on evidence. Your goal is to help people come away from discussion with increased knowledge and insight into the topic at hand. My first request is "I want an opinion piece about Deno." | -| Chat | **Example 1：**
    基于以下角色信息完成一段对话。小王，20岁，是一名大学生；小张，22岁，是一名大学生。
    小王：小张，我们一起去看电影吧？ 小张：好啊，你想看什么电影呢？ 小王：我听说最近有一部很好看的科幻电影。你觉得怎么样？
    小张：

    **Example 2:**
    基于以下角色信息完成一段对话。小明是一名医生，一位老年病患者想要停药，但他对病情有所忽视并有担忧；王叔叔是老年病患者的儿子，希望能够听取医生的建议。
    小明：你好，王叔叔，我了解你想要让你父亲停药。
    王叔叔：是的，我父亲已经吃了那么久的药，我担心药物对他的身体会有副作用。
    小明： | **Example 1：**
    Human: Who is the highest try scorer in the current Sydney Roosters team?
    AI: As an AI language model, I don't have access to current statistics. However, you can check the current Sydney Roosters team's official website or relevant sports news websites to find the answer.
    Human: Thanks for the suggestion, but can you recommend a reliable sports news website that I can check?
    AI:

    **Example 2:**
    Complete a dialogue based on the following role information.
    A: Elementary student B: Teacher
    B: Good morning, Student A. Today we're going to learn about addition and subtraction.
    A: Teacher, I already know this very well. Why do I need to learn it again?
    B: | -| Open QA | **Example 1：**
    请问万有引力定律由谁提出的？

    **Example 2：**
    哪些国家参与了第一次世界大战？ | **Example 1：**
    Who are the indigenous people of New Zealand?

    **Example 2：**
    How do you take the derivative of the sin function? | -| Closed QA | **Example 1：**
    请从以下选项中选择正确答案。以下哪个是世界上最高山峰？
    A. 长城
    B. 泰山
    C. 珠穆朗玛峰
    D. 黄山

    **Example 2：**
    请从以下选项中选择一个最佳答案回答下面的问题。问题：非洲最高的山是哪座山？
    选项：
    A. 麦金利山
    B. 喜马拉雅山
    C. 乞力马扎罗山 | **Example 1：**
    Answer the following question:
    What shape is the Earth?
    A) A circle
    B) A sphere
    C) An ellipse
    D) A plane

    **Example 2：**
    Choose the correct classification for the following question:
    "What type of restaurant is 'Burger King'"?
    fast food
    family style
    formal dining
    buffet
    | -| Brainstorming | **Example 1：**
    请介绍一下人工智能的多个领域。

    **Example 2：**
    请给出管理家庭财务的3个小技巧。
    | **Example 1：**
    What are 10 science fiction books I should read next?

    **Example 2：**
    List five ideas for how to regain enthusiasm for my career. | -| Generation | **Example 1：**
    请撰写一篇文章，介绍如何通过改善生活习惯来预防疾病和延长寿命。

    **Example 2：**
    请根据以下情节撰写一篇短篇小说：一名年轻人被困在一个荒岛上，他必须想办法生存下去直到被救援。但他很快发现自己并不孤单。 | **Example 1：**
    Can you help me write a formal email to a potential business partner proposing a joint venture?

    **Example 2：**
    Please use the appropriate format to write a formal letter of recommendation for a student applying to a prestigious computer science graduate program at a university. | -| Rewriting | **Example 1：**
    将以下句子改为被动语态：
    "他们正在洗车"

    **Example 2：**
    将以下文本翻译成英语：
    “这个周末我要去海边玩” | **Example 1：**
    Translate the following text into English:
    "我最喜欢的季节是春天，因为我可以看到美丽的花朵。"

    **Example 2：**
    Please correct the following sentences and give them the correct sentence.
    "Their going to the party there." | -| Classification | **Example 1：**
    新闻标题：今日立夏，有一上联，立夏万物并秀，下联怎么对？
    请根据以上新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。

    **Example 2：**
    新闻标题：赵丽颖很久没有登上微博热搜了，但你们别急，她只是在憋大招而已。
    请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。 | **Example 1：**
    Classify the given email as spam or non-spam.
    "Hello, this is an email reminding you to pay your property fees"

    **Example 2：**
    Classify the following text as news, ads or forum posts
    "The latest iPhone 13 is now available, shop now!" | -| Extraction | **Example 1：**
    根据以下新闻文本，提取新闻报道时间，例如回答时按照格式“新闻报道时间：2007年8月10日”
    新闻文本如下：2007-4-7中新网4月7日电据中国消防在线消息，4月4日晚上7时30分左右，湖南长潭高速公路上发生一起6车连环相撞失火事故。长株潭三地消防部门共出动消防车21台，警力100余人。经过消防官兵近2个小时奋力扑救，大火被成功扑灭。据初步调查，有1人在此次事故中死亡。

    **Example 2：**
    根据以下新闻文本，提取新闻报道时间，例如回答时按照格式“新闻报道时间：2007年8月10日”
    新闻文本如下：2014年1月15日，据外媒《俄罗斯报》报道称，位于北半球的澳大利亚现在正处于炎热的夏季，而近日也到了高温酷暑的时候，当地时间1月14日晚，澳大利亚南部一夜间发生至少250起火灾。受炎热天气及雷雨天气影响，澳大利亚南部一夜间发生至少250起火灾，灾情多集中在维多利亚州。火灾发生后，救援人员立即展开救灾行动。目前，大部分起火点火势已被控制。 | **Example 1：**
    Extract all phenotypes of the following text:
    "The 55-year-old patient has fever and hypertension."

    **Example 2：**
    Extract the location mentioned in the following text:
    "The student graduated from Harvard university, which is located in Boston" | -| Summarization | **Example 1：**
    请简要总结概括以下段落材料。
    新华社快讯：斯里兰卡政府部门21日说，首都科伦坡包括教堂、酒店等多个地点当天发生的爆炸已导致至少70人死亡，另有260多人受伤。

    **Example 2：**
    请简要总结概括以下段落材料。
    近期，参与京雄高铁站站房建设的中铁十二局，因在施工过程中存在环境违法行为被雄安新区公开通报。通报发出后，引起社会广泛关注。近日，人民网记者从雄安新区相关部门及中铁十二局获悉，新区有关部门已经集中约谈了中铁十二局等24个参与雄安建设的项目单位。对于约谈内容和结果，中铁十二局有关宣传负责人回应：“具体内容不清楚，最好找雄安新区相关部门了解情况。”新区有关部门负责人表示，此前涉及的环境违法行为，中铁十二局已基本整改到位，但约谈内容和结果暂不公开，接下来，将按部就班推进环境治理工作。（原题为《雄安新区：中铁十二局涉环境违法已基本整改到位》） | **Example 1：**
    Please provide a summary based on the following news：
    "China plans to launch its first space station core module in 2022, an important development in the country's space program. The space station, called Tianhe, will include three modules: a core module, an experiment module and an astronomy module. The first launch of the core module will be used to test and verify the basic functions of the station, as well as to conduct related scientific research and technology experiments. "

    **Example 2：**
    What information is provided in the table below? Summarize the core information in it？
    "Ranking, Player Name, Team, Position, Salary (in millions of dollars)
    1, LeBron James, Los Angeles Lakers, SF, 45.0
    2, Stephen Curry, Golden State Warriors, PG, 43.5" | + +Our evaluation pipeline examines the model's capability using 10 categories of questions. The following table introduces each category: + +| Evaluation Category |
    Description
    | +| :-----------------: | :----------------------------------------------------------- | +| Brainstorming | Models are asked to generate a range of creative and diverse ideas according to the question. The capability of creativity is required. | +| Chat | Models are asked to continue a multi-round dialogue given the roles involved. The capability of understanding, memorizing previous rounds of the dialogue and answering according to the persona provided is required. | +| Classification | Models are asked to do classification tasks. The capability of accurate classification is required. | +| Closed QA | Models are asked to answer a closed QA question. The capability of answering questions with limited scope (such as single/multiple choice question) is required. | +| Extraction | Models are asked to extract information from a given material. The capability of extracting required information is required. | +| Generation | Models are asked to generate an email, letter, article, etc. The capability of generating texts in a high quality and human-written way is required. | +| Open QA | Models are asked to answer an open QA question(without context provided). The capability of answering questions with the models' own knowledge base is required. | +| Roleplay | Models are asked to play the role provided. The capability of engaging in the scenario and effectively interacting with the user is required. | +| Rewriting | Models are asked to do rewriting tasks such as translation and grammar correction. The capability of rewriting according to different instructions is required. | +| Summarization | Models are asked to summarize the given paragraph or passage. The capability of summarization is required. | + +To better understand each evaluation category, here are some example questions provided. + + +| Evaluation Category |
    Chinese Example
    |
    English Example
    | +| :-----------------: | :----------------------------------------------------------- | :----------------------------------------------------------- | +| Brainstorming | **Example 1:**
    请介绍一下人工智能的多个领域。

    **Example 2:**
    请给出管理家庭财务的3个小技巧。
    | **Example 1:**
    How can I improve my memory? Any useful techniques you can suggest?

    **Example 2:**
    What are some ways to increase productivity while working from home? | +| Chat | **Example 1:**
    基于以下角色信息完成一段对话。小张是一名新手爱好者，对养鸡有浓厚的兴趣。老李是一名有丰富经验的养鸡大师。
    小张：您好，老李，我最近开始对养鸡感兴趣了，想请教您一些问题。
    老李：你好，小张，我很乐意帮助你。你想问些什么？
    小张：我想知道如何确定鸡的品种和性别？
    老李：确切的品种可以通过鸡的外貌特征来确定，而性别一般是通过鸡卵的大小和形状来判断。还有什么问题吗？
    小张：
    **Example 2:**
    基于以下角色信息完成一段对话。小明是一名医生，一位老年病患者想要停药，但他对病情有所忽视并有担忧；王叔叔是老年病患者的儿子，希望能够听取医生的建议。
    小明：你好，王叔叔，我了解你想要让你父亲停药。
    王叔叔：是的，我父亲已经吃了那么久的药，我担心药物对他的身体会有副作用。
    小明： | **Example 1:**
    Complete a conversation based on the following character information. Amy is a 30-year-old chef who runs her own restaurant. Jack is a food blogger who specializes in reviewing local restaurants.
    Amy: Hi Jack, I heard that you're a food blogger. Nice to meet you.
    Jack: Hi Amy, yes I am. Your restaurant has been receiving a lot of good reviews lately.
    Amy: Yes, we use only fresh and quality ingredients, and every dish is carefully crafted.
    Jack:
    **Example 2:**
    Complete a dialogue based on the following role information. A: Elementary student B: Teacher
    B: Good morning, Student A. Today we're going to learn about addition and subtraction.
    A: Teacher, I already know this very well. Why do I need to learn it again?
    B: | +| Classification | **Example 1:**
    新闻标题：今日立夏，有一上联，立夏万物并秀，下联怎么对？
    请根据以上新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。

    **Example 2:**
    新闻标题：赵丽颖很久没有登上微博热搜了，但你们别急，她只是在憋大招而已。
    请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。 | **Example 1:**
    Title: Fighting for Love (2020)
    Description: Jasmine got obsessed with a man and now he's obsessed with her. Steamy nights, kisses and rules being broken awaits them. She turned his whole world upside down and now he's doing it to hers. In this free fall, can they survive each others love?\"
    Based on the above information, determine which genre the work of art belongs to. You can only choose one from \"sport\", \"horror\", \"drama\", \"history\", \"romance\", \"biography\", \"science fiction\", \"comedy\", \"animation\", \"documentary\", \"music\" and \"news\".

    **Example2:**
    Title: Summer Breeze: The Isley Brothers Greatest Hits Live (2005)
    Description: Filmed in the US in 2005 and captured in excellent form led by Ron Isley's vocals and Ernie Isley's hard edged guitar. Virtually every track is a hit including Shout, Who's That Lady, Twist And Shout, Summer Breeze and Harvest For The World.
    Based on the above information, determine which genre the work of art belongs to. You can only choose one from \"sport\", \"horror\", \"drama\", \"history\", \"romance\", \"biography\", \"science fiction\", \"comedy\", \"animation\", \"documentary\", \"music\" and \"news\"." | +| Closed QA | **Example 1:**
    请从以下选项中选择正确答案。以下哪个是世界上最高山峰？
    A. 长城
    B. 泰山
    C. 珠穆朗玛峰
    D. 黄山

    **Example 2:**
    请从以下选项中选择一个最佳答案回答下面的问题。问题：非洲最高的山是哪座山？
    选项：
    A. 麦金利山
    B. 喜马拉雅山
    C. 乞力马扎罗山 | **Example 1:**
    Which of the following options is NOT a primary color?
    (a) yellow
    (b) blue
    (c) orange
    (d) red
    **Example 2:**
    Choose the correct option to complete the following sentence: \"Harry Potter and the Chamber of Secrets\" is the ________ book in the Harry Potter series.
    (A) first
    (B) second
    (C) third
    (D) fourth | +| Extraction | **Example 1:**
    根据以下新闻文本，提取新闻报道时间，例如回答时按照格式“新闻报道时间：2007年8月10日”
    新闻文本如下：2007-4-7中新网4月7日电据中国消防在线消息，4月4日晚上7时30分左右，湖南长潭高速公路上发生一起6车连环相撞失火事故。长株潭三地消防部门共出动消防车21台，警力100余人。经过消防官兵近2个小时奋力扑救，大火被成功扑灭。据初步调查，有1人在此次事故中死亡。

    **Example 2:**
    根据以下新闻文本，提取新闻报道时间，例如回答时按照格式“新闻报道时间：2007年8月10日”
    新闻文本如下：2014年1月15日，据外媒《俄罗斯报》报道称，位于北半球的澳大利亚现在正处于炎热的夏季，而近日也到了高温酷暑的时候，当地时间1月14日晚，澳大利亚南部一夜间发生至少250起火灾。受炎热天气及雷雨天气影响，澳大利亚南部一夜间发生至少250起火灾，灾情多集中在维多利亚州。火灾发生后，救援人员立即展开救灾行动。目前，大部分起火点火势已被控制。 | **Example 1:**
    Ernest Hemingway, an American literary giant known for his spare and direct writing style, has penned timeless works such as 'The Old Man and the Sea', 'For Whom the Bell Tolls', and 'A Farewell to Arms', which have made a profound impact on the literary world and continue to be widely read and admired today.
    Extract the name of the author mentioned above.

    **Example 2:**
    In the epic fantasy series 'A Song of Ice and Fire', George R.R. Martin weaves a complex web of political intrigue, war, and magic across the fictional continents of Westeros and Essos. Martin's richly developed characters and intricate plotlines have captivated readers worldwide, much like his other acclaimed works such as 'A Clash of Kings' and 'A Storm of Swords'.
    Extract the name of the author in the above material. | +| Generation | **Example 1:**
    请撰写一篇文章，介绍如何通过改善生活习惯来预防疾病和延长寿命。

    **Example 2:**
    请根据以下情节撰写一篇短篇小说：一名年轻人被困在一个荒岛上，他必须想办法生存下去直到被救援。但他很快发现自己并不孤单。 | **Example 1:**
    Write a descriptive paragraph about an island to relax and unwind, including details about the location and atmosphere.

    **Example 2:**
    Can you help me write a persuasive email to my colleagues encouraging them to participate in a charitable fundraising event? | +| Open QA | **Example 1:**
    请问万有引力定律由谁提出的？

    **Example 2:**
    哪些国家参与了第一次世界大战？ | **Example 1:**
    What are the four basic tastes of the human palate?

    **Example 2:**
    Who painted the The Scream? | +| Rewriting | **Example 1:**
    请将以下句子改为正确的语序。
    生日快乐你祝他了吗？

    **Example 2:**
    将以下文本翻译成英语：
    “这个周末我要去海边玩” | **Example 1:**
    Please translate the following sentences, which are a mixture of Chinese and English, into full English.
    我需要买一些healthy snacks，比如nuts和dried fruits，作为我的office的午餐.

    **Example 2:**
    Please rewrite the sentence using an inverted sentence structure.
    We won't begin our journey until the sun sets. | +| Roleplay | **Example 1:**
    我想让你担任Android开发工程师面试官。我将成为候选人，您将向我询问Android开发工程师职位的面试问题。我希望你只作为面试官回答。不要一次写出所有的问题。我希望你只对我进行采访。问我问题，等待我的回答。不要写解释。像面试官一样一个一个问我，等我回答。我的第一句话是“面试官你好”。

    **Example 2:**
    我想让你扮演讲故事的角色。你会想出引人入胜、富有想象力和吸引观众的有趣故事。它可以是童话故事、教育故事或任何其他类型的有潜力的故事以吸引人们的注意力和想象力。根据目标受众，您可以为您的讲故事环节选择特定的主题或主题，例如，如果是儿童，那么您可以谈论动物；如果是成人，那么基于历史的故事可能会更好地吸引他们等。我的第一个请求是我需要一个关于毅力的有趣故事。 | **Example 1:**
    Assume the role of a marriage counselor. Develop a series of communication exercises for a couple who are experiencing difficulties in their relationship. These exercises should promote active listening, empathy, and effective expression of emotions. Your first assignment is to provide a set of three exercises that focus on resolving conflicts and rebuilding trust.

    **Example 2: **
    I want you to act as a travel agent. I will tell you my desired destination, travel dates, and budget, and it will be your job to suggest the best travel itinerary for me. Your recommendations should include the best transportation options, hotel accommodations, and any popular tourist attractions nearby. My first request is "I want to plan a trip to Tokyo for a week, with a budget of $2000. I want to explore the culture and food of the city." | +| Summarization | **Example 1:**
    请简要总结概括以下段落材料。
    当地时间29日，泰国卫生部通报，新增143名新冠肺炎确诊病例和1名死亡病例。截止到当地时间29日上午，泰国累计确诊病例1388例，其中泰国籍1172例，非泰国籍216例。死亡病例累计7例。（原题为《泰国新增143例新冠肺炎确诊病例累计确诊1388例》）

    **Example 2:**
    请简要总结概括以下段落材料。
    近期，参与京雄高铁站站房建设的中铁十二局，因在施工过程中存在环境违法行为被雄安新区公开通报。通报发出后，引起社会广泛关注。近日，人民网记者从雄安新区相关部门及中铁十二局获悉，新区有关部门已经集中约谈了中铁十二局等24个参与雄安建设的项目单位。对于约谈内容和结果，中铁十二局有关宣传负责人回应：“具体内容不清楚，最好找雄安新区相关部门了解情况。”新区有关部门负责人表示，此前涉及的环境违法行为，中铁十二局已基本整改到位，但约谈内容和结果暂不公开，接下来，将按部就班推进环境治理工作。（原题为《雄安新区：中铁十二局涉环境违法已基本整改到位》） | **Example 1:**
    The 21 year-old-woman was treated by paramedics after the kitchen fire in Botfield Road in Shifnal, Shropshire. West Mercia Police said it is treating Wednesday morning's incident as arson and are appealing for any witnesses to contact them.The 50-year-old man has been arrested on suspicion of arson with intent to endanger life. For more on this and other stories from Shropshire.
    Please briefly summarize the above material within 20 words.

    **Example 2:**
    South Wales Police were called to a property in Heolgerrig, Merthyr Tydfil, at about 13:40 BST on Sunday. The child was airlifted to Prince Charles Hospital but died shortly afterwards. Police are investigating the circumstances surrounding the incident and have appealed for witnesses. The girl's family are being supported by specially trained officers.
    Please briefly summarize the above material within 20 words. | ### Evaluation Metrics + #### GPT Evaluation -Use GPT-3.5 to evaluate the prediction of different models, and pre-define evaluation metrics for different categories. There are 10 pre-defined evaluation metrics and you can refer to the table below: - -| Evaluation Metric | Prompt Words | CoT | -|:-----------------------:|:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Language organization | 语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。 | 1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。
    2.检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说
    3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。
    4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。
    5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。
    6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。 | -| Relevance | 切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。 | 1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。
    2. 阅读答案，确认答案是否直接回答了题目所问的问题。
    3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。
    4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。 | -| Creativity | 创意性(1-5)：某些头脑风暴问题可能需要答案具有创意，提出新的思路。 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则创意性评分可能会受到影响。
    3. 考虑答案中是否包含新颖的想法或独特的思路。答案可能与已知的解决方案有所重叠，但仍然可以被认为是有创意的，只要它提供了新的角度或方法来解决问题。
    4. 根据答案的创意性，给出一个1到5的评分。如果答案缺乏创意，则应给出一个较低的评分。如果答案具有创意并提供了新的思路，应给出一个较高的评分。 | -| Practicality | 实用性(1-5)：某些头脑风暴问题可能需要答案提出实用的建议或解决方法。 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则实用性评分可能会受到影响。
    3. 考虑答案中提出的建议或解决方法是否实用并可行。答案可能看起来很好，但如果无法实现或应用，则实用性评分可能会受到影响。
    4. 根据答案的实用性，给出一个1到5的评分。如果答案缺乏实用性，则应给出一个较低的评分。如果答案提出了实用的建议或解决方法，并且可以很好地解决问题，则应给出一个较高的评分。 | -| Correctness | 正确性(1-5)：答案应该符合常识、生活实际等等 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则正确性评分可能会受到影响。
    3. 考虑答案中所提供的信息是否正确、符合常识、生活实际等等。如果答案中存在明显的错误或不合理之处，则正确性评分可能会受到影响。
    4. 根据答案的正确性，给出一个1到5的评分。如果答案存在明显的错误或不合理之处，则应给出一个较低的评分。如果答案正确、符合常识、生活实际等等，则应给出一个较高的评分。 | -| Naturalness | 自然(1-5)：答案是否自然，并且符合问题给定的身份。 | 1. 阅读题目，确定题目提供的身份信息。
    2. 检查答案内容是否符合题目给定的身份。
    3. 根据以上因素，对该回答的自然性进行打分，分数从1到5，其中1表示不自然，5表示非常自然，并符合问题给定的身份。 | -| Engagingness | 参与感(1-5)：答案是否对前面的对话内容做出了恰当的反应，是否理解对话的语境和背景。 | 1. 阅读题目，确定对话的语境和背景。
    2. 检查答案是否充分理解对话的语境和背景，能否自然地融入到对话中而不显得突兀。
    3. 根据以上因素，对该回答的参与感进行打分，分数从1到5，其中1表示没有参与感，5表示非常有参与感，并且恰当地理解了对话的语境和背景。 | -| Reasonableness | 合理性(1-5)：答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。 | 1. 阅读题目，确定对话的主题以及问题期望的回答方向。
    2. 判断答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。
    3. 根据以上因素，对该回答的合理性进行打分，分数从1到5，其中1表示不合理，5表示非常合理，并且能够与前面的对话内容形成逻辑上的衔接，并符合常理。 | -| Diversity | 多样性(1-5)：答案使用语言是否优美，具有有一定的创造性和想象力。然而，回答也应该保持合理和适度，不要过于夸张或离题。 | 1. 仔细阅读整个回答，确保完全理解回答所表达的内容和主题。
    2. 在阅读回答的同时，注意语言的质量，例如措辞是否正确，语言是否生动等。
    3. 检查回答的创造性和想象力，看看回答是否能够吸引人阅读下去。
    4. 检查回答的合理性和适度，看看回答是否夸张或离题。5. 将多样性的评分打分在1到5之间，5分表示回答的质量很好，能够吸引人阅读，1分表示回答的内容生硬或者有离题的问题。 | -| Fidelity | 保真度(1-5)：答案是否能够严格遵守角色的设定回答给定的请求。 | 1. 仔细阅读问题，了解角色在问题中的设定和表现，包括职业、背景、观点、性格等方面。
    阅读题目的请求，确认回答请求时需要注意的细节。
    3. 对比提供的回答与该角色的设定，评估回答是否能够严格遵守角色的设定。
    4. 结合以上评估结果给出保真度的评分，范围从1到5分，其中1分表示回答与角色设定完全不符，5分表示回答完全符合角色设定且满足给定请求。 | -| Conciseness | 简明扼要(1-5)：答案是否简明扼要，没有冗余内容。 | 1. 阅读题目，提取出材料的重点。
    2. 阅读该总结，并注意其中的主要观点和信息。
    3. 评估总结的长度。一个简明扼要的总结通常应该在几句话或几段文字内传达关键信息，而不是冗长的段落或文章。
    4. 检查总结是否包含与主要观点无关的信息或冗余信息。
    5. 确定总结涵盖了材料中的关键信息，并且没有忽略任何重要细节。
    6. 给总结打出1-5的分数，其中5表示总结简明扼要，没有冗余内容，而1表示总结冗长或包含不必要的信息，难以理解或记忆。根据您的判断，打出适当的得分。 | - -GPT-3.5 evaluates the quality of model predictions based on the given prompt words and gives a score between 1-5. + +GPT evaluation uses GPT models to evaluate the prediction of different models and different pre-defined evaluation metrics are applied to different categories. The following table shows the 11 pre-defined evaluation metrics in Chinese: + +| Evaluation Metric |
    Prompt Words
    |
    CoT(Chain-of-Thought)
    | +| :-------------------: | :----------------------------------------------------------- | :----------------------------------------------------------- | +| Language organization | 语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。 | 1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。
    2.检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说
    3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。
    4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。
    5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。
    6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。 | +| Relevance | 切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。 | 1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。
    2. 阅读答案，确认答案是否直接回答了题目所问的问题。
    3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。
    4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。 | +| Creativity | 创意性(1-5)：某些头脑风暴问题可能需要答案具有创意，提出新的思路。 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则创意性评分可能会受到影响。
    3. 考虑答案中是否包含新颖的想法或独特的思路。答案可能与已知的解决方案有所重叠，但仍然可以被认为是有创意的，只要它提供了新的角度或方法来解决问题。
    4. 根据答案的创意性，给出一个1到5的评分。如果答案缺乏创意，则应给出一个较低的评分。如果答案具有创意并提供了新的思路，应给出一个较高的评分。 | +| Practicality | 实用性(1-5)：某些头脑风暴问题可能需要答案提出实用的建议或解决方法。 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则实用性评分可能会受到影响。
    3. 考虑答案中提出的建议或解决方法是否实用并可行。答案可能看起来很好，但如果无法实现或应用，则实用性评分可能会受到影响。
    4. 根据答案的实用性，给出一个1到5的评分。如果答案缺乏实用性，则应给出一个较低的评分。如果答案提出了实用的建议或解决方法，并且可以很好地解决问题，则应给出一个较高的评分。 | +| Correctness | 正确性(1-5)：答案应该符合常识、生活实际等等 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则正确性评分可能会受到影响。
    3. 考虑答案中所提供的信息是否正确、符合常识、生活实际等等。如果答案中存在明显的错误或不合理之处，则正确性评分可能会受到影响。
    4. 根据答案的正确性，给出一个1到5的评分。如果答案存在明显的错误或不合理之处，则应给出一个较低的评分。如果答案正确、符合常识、生活实际等等，则应给出一个较高的评分。 | +| Naturalness | 自然(1-5)：答案是否自然，并且符合问题给定的身份。 | 1. 阅读题目，确定题目提供的身份信息。
    2. 检查答案内容是否符合题目给定的身份。
    3. 根据以上因素，对该回答的自然性进行打分，分数从1到5，其中1表示不自然，5表示非常自然，并符合问题给定的身份。 | +| Engagingness | 参与感(1-5)：答案是否对前面的对话内容做出了恰当的反应，是否理解对话的语境和背景。 | 1. 阅读题目，确定对话的语境和背景。
    2. 检查答案是否充分理解对话的语境和背景，能否自然地融入到对话中而不显得突兀。
    3. 根据以上因素，对该回答的参与感进行打分，分数从1到5，其中1表示没有参与感，5表示非常有参与感，并且恰当地理解了对话的语境和背景。 | +| Reasonableness | 合理性(1-5)：答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。 | 1. 阅读题目，确定对话的主题以及问题期望的回答方向。
    2. 判断答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。
    3. 根据以上因素，对该回答的合理性进行打分，分数从1到5，其中1表示不合理，5表示非常合理，并且能够与前面的对话内容形成逻辑上的衔接，并符合常理。 | +| Diversity | 多样性(1-5)：答案使用语言是否优美，具有有一定的创造性和想象力。然而，回答也应该保持合理和适度，不要过于夸张或离题。 | 1. 仔细阅读整个回答，确保完全理解回答所表达的内容和主题。
    2. 在阅读回答的同时，注意语言的质量，例如措辞是否正确，语言是否生动等。
    3. 检查回答的创造性和想象力，看看回答是否能够吸引人阅读下去。
    4. 检查回答的合理性和适度，看看回答是否夸张或离题。5. 将多样性的评分打分在1到5之间，5分表示回答的质量很好，能够吸引人阅读，1分表示回答的内容生硬或者有离题的问题。 | +| Fidelity | 保真度(1-5)：答案是否能够严格遵守角色的设定回答给定的请求。 | 1. 仔细阅读问题，了解角色在问题中的设定和表现，包括职业、背景、观点、性格等方面。
    阅读题目的请求，确认回答请求时需要注意的细节。
    3. 对比提供的回答与该角色的设定，评估回答是否能够严格遵守角色的设定。
    4. 结合以上评估结果给出保真度的评分，范围从1到5分，其中1分表示回答与角色设定完全不符，5分表示回答完全符合角色设定且满足给定请求。 | +| Conciseness | 简明扼要(1-5)：答案是否简明扼要，没有冗余内容。 | 1. 阅读题目，提取出材料的重点。
    2. 阅读该总结，并注意其中的主要观点和信息。
    3. 评估总结的长度。一个简明扼要的总结通常应该在几句话或几段文字内传达关键信息，而不是冗长的段落或文章。
    4. 检查总结是否包含与主要观点无关的信息或冗余信息。
    5. 确定总结涵盖了材料中的关键信息，并且没有忽略任何重要细节。
    6. 给总结打出1-5的分数，其中5表示总结简明扼要，没有冗余内容，而1表示总结冗长或包含不必要的信息，难以理解或记忆。根据您的判断，打出适当的得分。 | + +GPT models evaluate the quality of model predictions based on the given prompt words and gives a score between 1-5. #### Automatic Evaluation + Automated metrics evaluate the capability of a model by comparing model predictions with reference answers. There are two ways to obtain reference answers: + * For instruction coming from human-designed problems, the reference answers are generated by GPT-3.5, such as roleplay, chat. * For instruction related with classic NLP problems, the reference answers are collected from open-sourced dataset with target answers, such as classification, extraction, summarization. There are 5 types of automatic evaluation metrics listed in the table below: - | Automatic Evaluation Metric | Description | -|:-----------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| BLEU-n | Measure the accuracy between prediction and reference.
    BLEU-1 (Unigram) evaluates accuracy in word level
    BLEU-n (n-gram) evaluate the fluency in sentence level. | +| Automatic Evaluation Metric |
    Description
    | +| :---------------------------------: | :----------------------------------------------------------- | +| BLEU-n | Measure the accuracy between prediction and reference.
    BLEU-1 (Unigram) evaluates accuracy in word level.
    BLEU-n (n-gram) evaluate the fluency in sentence level. | | ROUGE | ROUGE-N measures the number of matching n-grams between prediction and reference.
    ROUGE-L measures the number of matching longest common subsequence (LCS) between prediction and reference. | -| Distinct | Measure the diversity of generation text by counting the unique n-grams. | -| BERTScore | Measure the semantic similarity between tokens of predictions and references with BERT. | -| Precision
    Recall
    F1 Score | Measure the number of overlaps between prediction and reference (design for classification and extraction categories) | +| Distinct | Measure the diversity of generation text by counting the unique n-grams. | +| BERTScore | Measure the semantic similarity between tokens of predictions and references with BERT. | +| Precision
    Recall
    F1 Score | Measure the number of overlaps between prediction and reference (design for classification and extraction categories). | ## Evaluation Process + ### Data Format + #### Target Answers / Predictions + A JSON file contains one list. Each element in the list is a target answer / prediction record for one instruction / question. An element should have the following fields: @@ -103,7 +113,8 @@ An element should have the following fields: If the `input` has a target answer, the `output` can be empty. Otherwise, we generate answers from GPT-3.5 as the `output`, and the `target` field is empty. Example: -``` + +```json [ { "category": "brainstorming", @@ -138,7 +149,8 @@ An element should have the following fields: * `id` (int, compulsory): The ID of the instruction / question. Example: -``` + +```json [ { "category": "brainstorming", @@ -159,34 +171,79 @@ Example: ] ``` +### Prompt + +#### Battle Prompt + +The following is the Chinese battle prompt. In the battle prompt, the question and answers from two different models are fed into the prompt template. You can find an example battle prompt file in `prompt/battle_prompt`. + +```json +{ + "id": 1, + "system_prompt": "你是一个检查回答质量的好助手。", + "prompt_template": "[问题]\n{question}\n\n[1号AI助手的答案]\n{answer_1}\n\n[1号AI助手答案终止]\n\n[2号AI助手的答 案]\n{answer_2}\n\n[2号AI助手答案终止]\n\n[要求]\n{prompt}\n\n", + "prompt": "我们需要你评价这两个AI助手回答的性能。\n请对他们的回答的有用性、相关性、准确性、详细程度进行评分。每个AI助手都会得到一个1到10分的总分，分数越高表示整体表现越好。\n请首先输出一行，该行只包含两个数值，分别表示1号和2号AI助手的分数。这两个分数之间要有一个空格。在随后的一行中，请对你的评价作出全面的解释，避免任何潜在的偏见，并确保AI助手回答的顺序不会影响您的判断。" +} +``` + +#### Evaluation Prompt + +The following is an example of a Chinese GPT evaluation prompt. In an evaluation prompt, you should define your metrics in `metrics` and provide CoT(Chain-of-Thought) in `CoT`. You can find an example evaluation prompt file in `prompt/evaluation_prompt`. + +```json +{ + "brainstorming": { + "id": 1, + "category": "brainstorming", + "metrics": { + "language organization": "语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。" + }, + "CoT": { + "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：" + }, + "prompt": "你是一个好助手。请你为下面“头脑风暴”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + } +} +``` + +`"metrics"`: the metrics that can be used in GPT evaluation. This field determines which metrics can be added to your config file. + +`"CoT"`: evaluation steps you prompt to GPT models for each metric defined in `"metrics"`. + ### Evaluation + #### Configuration -The configuration file `config_cn.json` can control how evaluate the performance of the model. -The following is an example showing the config structure: -``` + +The following is an example of a Chinese config file. The configuration file can control how the pipeline evaluates the model. You need to specify GPT evaluation metrics and automatic metrics in key `GPT` and `Metrics`. You can find an example Chinese config file in `config`. + +```json { "language": "cn", "category": { "brainstorming": { - "GPT-3.5": ["relevance", "creativity", "practicality", "correctness"], + "GPT": ["relevance", "creativity", "practicality", "correctness"], "Metrics": ["Distinct"] }, "chat": { - "GPT-3.5": [ "relevance", "naturalness", "engagingness", "reasonableness"], + "GPT": [ "relevance", "naturalness", "engagingness", "reasonableness"], "Metrics": ["Distinct"] } } } ``` -`"language"`: evaluate the model capability in which language, we only support Chinese `"cn"` for now. -`"category"`: evaluate the model capability in which category/categories. -`"GPT-3.5"`: config metrics for GPT-3.5 evaluation. -`"Metrics"`: config metrics for automatic metrics evaluation. + +`"language"`: the language used to evaluate the model capability. We only support Chinese `"cn"` for now. + +`"category"`: the category/categories needed to evaluate the model capability. + +`"GPT"`: the metrics you want to use for GPT evaluation. + +`"Metrics"`: the metrics you want to use for automatic metrics evaluation. You can create your config file based on available settings listed in following table. -| "category" | "GPT-3.5" | "Metrics" | -|:----------------:|:-----------------------:|:-----------:| +| "category" | "GPT" | "Metrics" | +| :--------------: | :---------------------: | :---------: | | "brainstorming" | "language organization" | "BLEU" | | "chat" | "relevance" | "ROUGE" | | "classification" | "creativity" | "Distinct" | @@ -194,16 +251,19 @@ You can create your config file based on available settings listed in following | "extraction" | "correctness" | "Precision" | | "generation" | "naturalness" | "Recall" | | "open_qa" | "engagingness" | "F1 score" | -| "rewriting" | "reasonableness" | -| "roleplay" | "diversity" | -| "summarization" | "fidelity" | -| | "conciseness" | +| "rewriting" | "reasonableness" | | +| "roleplay" | "diversity" | | +| "summarization" | "fidelity" | | +| | "conciseness" | | + +> **NOTE:** For categories which don't have standard answers such as `brainstorming`, you should avoid using automatic metrics such as `BLEU` and `ROUGE` which are based on similarity measures and you should use `Distinct` instead in your config file. #### Evaluate -After setting the configuration file, you can evaluate the model using `eval.py`. +After setting the configuration file, you can evaluate the model using `eval.py`. If you want to make comparisons between answers of two different models, you should specify two answer files in the argument `answer_file_list` and two model names in the argument `model_name_list`. If you want to evaluate one answer file, the length of both `answer_file_list` and `model_name_list` should be 1 and the program will perform evaluation using automatic metrics and GPT models. An example script is provided as follows: + ```shell python eval.py \ --config_file "path to the config file" \ @@ -212,14 +272,40 @@ python eval.py \ --target_file "path to the target answer file" \ --answer_file_list "path to the answer files of at most 2 models" \ --model_name_list "the names of at most 2 models" \ + --gpt_model "which GPT model to use for evaluation" \ --save_path "path to save results" \ --openai_key "your openai key" \ ``` +## FAQ + +
    How can I add a new GPT evaluation metric? + +For example, if you want to add a new metric `persuasiveness` into category `brainstorming`, you should add the metric definition and its corresponding CoT(Chain-of-thought) in the evaluation prompt file in `prompt/evaluation_promt`. The CoT can be generated using ChatGPT. You can prompt ChatGPT to generate evaluation steps for the new metric. + +```json +{ + "brainstorming": { + "id": 1, + "category": "brainstorming", + "metrics": { + "persuasiveness": "说服力(1-5)：XXX" + }, + "CoT": { + "persuasiveness": "XXX\n\n说服力：" + }, + "prompt": "你是一个好助手。请你为下面“头脑风暴”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + } +} +``` + +
    + ## To Do + - [ ] Add evaluation for English capability - [ ] Support UniEval -- [ ] Support GPT-4 evaluation +- [x] Support GPT-4 evaluation ## Citations @@ -232,15 +318,6 @@ python eval.py \ year = {2023} } -@misc{ouyang2022training, - title={Training language models to follow instructions with human feedback}, - author={Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray and John Schulman and Jacob Hilton and Fraser Kelton and Luke Miller and Maddie Simens and Amanda Askell and Peter Welinder and Paul Christiano and Jan Leike and Ryan Lowe}, - year={2022}, - eprint={2203.02155}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} - @misc{liu2023geval, title={G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment}, author={Yang Liu and Dan Iter and Yichong Xu and Shuohang Wang and Ruochen Xu and Chenguang Zhu}, diff --git a/applications/Chat/evaluate/config/config_cn.json b/applications/Chat/evaluate/config/config_cn.json index a7293f111a81..a8c7ea8a3135 100644 --- a/applications/Chat/evaluate/config/config_cn.json +++ b/applications/Chat/evaluate/config/config_cn.json @@ -2,7 +2,7 @@ "language": "cn", "category": { "brainstorming": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "creativity", @@ -14,7 +14,7 @@ ] }, "chat": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "naturalness", @@ -26,7 +26,7 @@ ] }, "classification": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "correctness" @@ -38,7 +38,7 @@ ] }, "closed_qa": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "correctness" @@ -50,7 +50,7 @@ ] }, "extraction": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "correctness" @@ -62,7 +62,7 @@ ] }, "generation": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "diversity" @@ -74,7 +74,7 @@ ] }, "open_qa": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "correctness" @@ -84,7 +84,7 @@ ] }, "rewriting": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "correctness" @@ -96,7 +96,7 @@ ] }, "roleplay": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "fidelity", @@ -107,7 +107,7 @@ ] }, "summarization": { - "GPT-3.5": [ + "GPT": [ "language organization", "relevance", "correctness", diff --git a/applications/Chat/evaluate/eval.py b/applications/Chat/evaluate/eval.py index 69f2c272a116..4067b15db6e8 100644 --- a/applications/Chat/evaluate/eval.py +++ b/applications/Chat/evaluate/eval.py @@ -39,7 +39,8 @@ def main(args): "No prompt file for gpt evaluation provided. Please specify the prompt file for gpt evaluation!") # initialize evaluator - evaluator = Evaluator(metrics_per_category, battle_prompt, gpt_evaluation_prompt) + evaluator = Evaluator(metrics_per_category, battle_prompt, gpt_evaluation_prompt, args.gpt_model, + config["language"]) if len(args.model_name_list) == 2: answers1 = jload(args.answer_file_list[0]) answers2 = jload(args.answer_file_list[1]) @@ -87,6 +88,10 @@ def main(args): default=[], required=True, help='the names of at most 2 models') + parser.add_argument('--gpt_model', + default="gpt-3.5-turbo", + choices=["text-davinci-003", "gpt-3.5-turbo", "gpt-4"], + help='which GPT model to use for evaluation') parser.add_argument('--save_path', type=str, default="results", help='path to save evaluation results') parser.add_argument('--openai_key', type=str, default=None, required=True, help='Your openai key') args = parser.parse_args() diff --git a/applications/Chat/evaluate/evaluator.py b/applications/Chat/evaluate/evaluator.py index d3d1c038bfb8..433d775d27ed 100644 --- a/applications/Chat/evaluate/evaluator.py +++ b/applications/Chat/evaluate/evaluator.py @@ -14,13 +14,15 @@ class Evaluator(object): """ - def __init__(self, params: Dict[str, Any], battle_prompt: Dict[str, Any], gpt_evaluation_prompt: Dict[str, - Any]) -> None: + def __init__(self, params: Dict[str, Any], battle_prompt: Dict[str, Any], gpt_evaluation_prompt: Dict[str, Any], + gpt_model: str, language: str) -> None: self.params = params self.battle_prompt = battle_prompt self.gpt_evaluation_prompt = gpt_evaluation_prompt + self.gpt_model = gpt_model + self.language = language self.automatic_metric_stats = dict() - self.gpt35_evaluation_results = dict() + self.gpt_evaluation_results = dict() self.battle_results = [] def battle(self, answers1: List[Dict], answers2: List[Dict]) -> None: @@ -63,6 +65,10 @@ def switch(metric): # automatic evaluation for category in self.params: + if len(answers_per_category[category]) == 0: + print(f"Category {category} specified in your config doesn't have corresponding answers!") + continue + category_metrics = self.params[category]["Metrics"] self.automatic_metric_stats[category] = {} @@ -74,17 +80,21 @@ def switch(metric): for metric in category_metrics: self.automatic_metric_stats[category].update(switch(metric=metric)) - # gpt35 evaluation + # gpt evaluation for category in self.params: - category_metrics = self.params[category]["GPT-3.5"] + if len(answers_per_category[category]) == 0: + print(f"Category {category} specified in your config doesn't have corresponding answers!") + continue + + category_metrics = self.params[category]["GPT"] prompt = self.gpt_evaluation_prompt.get(category, None) if prompt is None: print(f"No prompt for category {category}! Use prompt for category general now.") prompt = self.gpt_evaluation_prompt["general"] - self.gpt35_evaluation_results[category] = gpt_evaluate.gpt35_evaluate(answers_per_category[category], - prompt, category_metrics, category) + self.gpt_evaluation_results[category] = gpt_evaluate.evaluate(answers_per_category[category], prompt, + category_metrics, category, self.gpt_model) def save(self, path: str, model_name_list: List[str]) -> None: """ @@ -106,10 +116,10 @@ def save(self, path: str, model_name_list: List[str]) -> None: # Save evaluation results for GPT-3.5 evaluation metrics. all_evaluations = [] - base_save_path = os.path.join(path, "gpt_evaluate", "gpt35_evaluate_results") + base_save_path = os.path.join(path, "gpt_evaluate", "gpt_evaluate_results") evaluation_results_save_path = os.path.join(base_save_path, "evaluation_results") - for category, evaluations in self.gpt35_evaluation_results.items(): + for category, evaluations in self.gpt_evaluation_results.items(): jdump( evaluations, os.path.join(evaluation_results_save_path, model_name_list[0], @@ -121,10 +131,10 @@ def save(self, path: str, model_name_list: List[str]) -> None: # Start to calculate scores and save statistics. evaluation_statistics_save_path = os.path.join(base_save_path, "evaluation_statistics") - gpt_evaluate.save_gpt35_evaluation_statistics(model_name_list[0], all_evaluations, - evaluation_statistics_save_path) + gpt_evaluate.save_gpt_evaluation_statistics(model_name_list[0], all_evaluations, + evaluation_statistics_save_path) # Save charts and csv. evaluation_analyses_save_path = os.path.join(base_save_path, "evaluation_analyses") - gpt_evaluate.analyze_gpt35_evaluation_statistics(evaluation_statistics_save_path, - evaluation_analyses_save_path) + gpt_evaluate.analyze_gpt_evaluation_statistics(evaluation_statistics_save_path, + evaluation_analyses_save_path) diff --git a/applications/Chat/evaluate/gpt_evaluate.py b/applications/Chat/evaluate/gpt_evaluate.py index c7e668df9f57..61ce3456c49f 100644 --- a/applications/Chat/evaluate/gpt_evaluate.py +++ b/applications/Chat/evaluate/gpt_evaluate.py @@ -16,7 +16,7 @@ def get_battle_result(sys_prompt: str, user_prompt: str, id: int, max_tokens: int = 2048) -> Dict[str, Any]: """ - Get evaluation from GPT-4. + Get battle evaluation from GPT-4. Args: sys_prompt: prompt for the system. @@ -51,7 +51,7 @@ def get_battle_result(sys_prompt: str, user_prompt: str, id: int, max_tokens: in except Exception as e: print(e) time.sleep(1) - print(f" Evaluation {id} failed after {MAX_API_RETRY} retries.") + print(f"Evaluation {id} failed after {MAX_API_RETRY} retries.") return {"evaluation": "", "id": id} @@ -233,12 +233,77 @@ def save_battle_results(evaluations: List[Dict], name1: str, name2: str, save_pa print(f"Model {name2} average score: {ans2_score/(len(evaluations)-invalid_count):.2f}") -def get_gpt35_evaluation(prompt: Dict[str, Any], - inst: Dict[str, Any], - metrics: List[str], - max_tokens: int = 2048) -> Dict[str, Any]: +def get_gpt_evaluation_without_logprobs(prompt: Dict[str, Any], + inst: Dict[str, Any], + metrics: List[str], + model: str = "gpt-3.5-turbo", + max_tokens: int = 2048) -> Dict[str, Any]: """ - Use GPT-3.5 to evaluate one model answer. + Use chat models(gpt-3.5-turbo or gpt-4) to evaluate one model answer. + + Args: + prompt: a dictionary including prompt template, CoT and metrics. + inst: the instruction that is needed to be evaluated. + metrics: the metrics for evaluation. + model: the model used to evaluate answers. + max_tokens: the maximum number of tokens to generate in the chat completion. + + Returns: + An evaluation of one answer. + """ + + MAX_API_RETRY = 3 + + question = (inst["instruction"] if inst["input"] == "" else inst["instruction"] + " " + inst["input"]) + answer = inst["output"] + inst["evaluation"] = {} + + for metric in metrics: + if prompt["metrics"].get(metric, None) is None: + raise Exception( + f"Unsupported metric {metric} for category {inst['category']}! You should add this metric in the prompt file!" + ) + for i in range(MAX_API_RETRY): + try: + response = openai.ChatCompletion.create( + model=model, + messages=[ + { + "role": + "user", + "content": + prompt["prompt"].format( + question=question, + answer=answer, + metric=prompt["metrics"][metric], + steps=prompt["CoT"][metric], + ), + }, + ], + temperature=0, + max_tokens=max_tokens, + ) + inst["evaluation"][metric] = { + "response": response["choices"][0]["message"]["content"], + "logprobs": None, + } + break + except Exception as e: + print(e) + time.sleep(1) + if metric not in inst["evaluation"]: + print(f"Evaluation {inst['id']} for metric {metric} failed after {MAX_API_RETRY} retries.") + inst["evaluation"][metric] = {} + return inst + + +def get_gpt_evaluation_with_logprobs(prompt: Dict[str, Any], + inst: Dict[str, Any], + metrics: List[str], + max_tokens: int = 2048) -> Dict[str, Any]: + """ + Use completion model(text-davinci-003) to evaluate one model answer. + Only completion models can return log probabilities. Args: prompt: a dictionary including prompt template, CoT and metrics. @@ -283,23 +348,22 @@ def get_gpt35_evaluation(prompt: Dict[str, Any], except Exception as e: print(e) time.sleep(1) + if metric not in inst["evaluation"]: + print(f"Evaluation {inst['id']} for metric {metric} failed after {MAX_API_RETRY} retries.") + inst["evaluation"][metric] = {} return inst -def gpt35_evaluate( - answers: List[Dict], - prompt: Dict[str, Any], - metrics: List[str], - category: str, -) -> List[Dict]: +def evaluate(answers: List[Dict], prompt: Dict[str, Any], metrics: List[str], category: str, model: str) -> List[Dict]: """ - Use GPT-3.5 to evaluate model answers and save evaluation results. + Use GPT models to evaluate model answers and save evaluation results. Args: answers: model answers. - prompt: prompt for GPT-3.5 evaluation. - metrics: metrics for GPT-3.5 evaluation. + prompt: prompt for GPT evaluation. + metrics: metrics for GPT evaluation. category: the category of the model answers for evaluation. + model: the specific GPT model used to evaluate answers. Returns: Evaluations of the given answers. @@ -315,7 +379,12 @@ def gpt35_evaluate( with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: futures = [] for inst in answers: - future = executor.submit(get_gpt35_evaluation, prompt, inst, metrics, 1) + # Completion models can return log probabilities. + if model == "text-davinci-003": + future = executor.submit(get_gpt_evaluation_with_logprobs, prompt, inst, metrics, 1) + else: + future = executor.submit(get_gpt_evaluation_without_logprobs, prompt, inst, metrics, model, 1) + futures.append(future) for future in tqdm.tqdm( @@ -334,20 +403,19 @@ def gpt35_evaluate( def calculate_scores_form_logprobs(logprobs: Dict[str, Any]) -> float: """ - Calculate score from log probabilities returned by text-davinci-003. - Only openai.Completion can return logprobs. + Calculate the score according to log probabilities returned by text-davinci-003. Calculation formula: score = sum(score_i * exp(value)) where score_i is the score which corresponds to the key(predicted token) and value is its log probability. Ref: https://arxiv.org/abs/2303.16634 - This paper proposes NLG evaluation methods using GPT-3.5(logprobs returned by openai api) and GPT-4(logprobs obtained by sampling). + This paper proposes NLG evaluation methods using text-davinci-003(log probabilities returned by completion models) and GPT-4(probabilities obtained by sampling). Args: logprobs: logprobs returned by openai.Completion. Returns: - Score of one answer. + The score of one answer. """ # GPT-3.5 only returns score of 1 to 5. @@ -369,7 +437,31 @@ def calculate_scores_form_logprobs(logprobs: Dict[str, Any]) -> float: return score -def save_gpt35_evaluation_statistics(model_name: str, evaluations: List[Dict], save_path: str) -> None: +def calculate_scores_form_response(response: str, evaluation: Dict[str, Any]) -> int: + """ + Calculate the score from the response returned by gpt-3.5-turbo or gpt-4. + Different from text-davinci-003, this fuction directly calculates the score according to the plain response returned by gpt-3.5-turbo or gpt-4. + Although text-davinci-003 can return log probabilities, it costs ten times as much as gpt-3.5-turbo. + + Args: + response: logprobs returned by openai.Completion. + evaluation: the evaluation corresponds to the question. + + Returns: + The score of one answer. + """ + + try: + results = re.findall(r"\d", response) + if len(results) == 1: + return int(results[0]) + else: + raise Exception(f"Invalid score pair. Got {evaluation}.") + except Exception as e: + return 0 + + +def save_gpt_evaluation_statistics(model_name: str, evaluations: List[Dict], save_path: str) -> None: """ Generate statistics for one model. @@ -396,7 +488,15 @@ def save_gpt35_evaluation_statistics(model_name: str, evaluations: List[Dict], s scores = {metric: [] for metric in metrics} for evaluation in data: for metric in metrics: - scores[metric].append(calculate_scores_form_logprobs(evaluation["evaluation"][metric]["logprobs"][0])) + if evaluation["evaluation"][metric] == {}: + # This means after 3 retries, the server still returns an error and we set the score to 0. + scores[metric].append(0) + elif evaluation["evaluation"][metric]["logprobs"] is not None: + scores[metric].append( + calculate_scores_form_logprobs(evaluation["evaluation"][metric]["logprobs"][0])) + else: + scores[metric].append( + calculate_scores_form_response(evaluation["evaluation"][metric]["response"], evaluation)) statistics = {} for metric in metrics: @@ -414,7 +514,7 @@ def save_gpt35_evaluation_statistics(model_name: str, evaluations: List[Dict], s ) -def analyze_gpt35_evaluation_statistics(statistics_path: str, save_path: str) -> None: +def analyze_gpt_evaluation_statistics(statistics_path: str, save_path: str) -> None: """ Analyze and visualize all GPT-3.5 evaluation statistics in the given directory. @@ -474,7 +574,7 @@ def analyze_gpt35_evaluation_statistics(statistics_path: str, save_path: str) -> os.makedirs(save_path) frame_all = pd.DataFrame(frame_all) - frame_all.to_csv(os.path.join(save_path, "gpt35_evaluation_statistics.csv")) + frame_all.to_csv(os.path.join(save_path, "gpt_evaluation_statistics.csv")) for category in tqdm.tqdm( frame_per_category.keys(), diff --git a/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json index d4b8d143eadf..ee6caae32091 100644 --- a/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json +++ b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json @@ -1,5 +1,5 @@ -[ - { +{ + "brainstorming": { "id": 1, "category": "brainstorming", "metrics": { @@ -18,7 +18,7 @@ }, "prompt": "你是一个好助手。请你为下面“头脑风暴”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "chat": { "id": 2, "category": "chat", "metrics": { @@ -37,7 +37,7 @@ }, "prompt": "你是一个好助手。请你为下面的“补全对话”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "classification": { "id": 3, "category": "classification", "metrics": { @@ -52,7 +52,7 @@ }, "prompt": "你是一个好助手。请你为下面的“分类“问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "closed_qa": { "id": 4, "category": "closed_qa", "metrics": { @@ -67,7 +67,7 @@ }, "prompt": "你是一个好助手。请你为下面问题的答案打分。\n\n问题如下：\n\n{question}\n\n需要你评分的答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "extraction": { "id": 5, "category": "extraction", "metrics": { @@ -82,7 +82,7 @@ }, "prompt": "你是一个好助手。请你为下面的“提取”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "generation": { "id": 6, "category": "generation", "metrics": { @@ -97,7 +97,7 @@ }, "prompt": "你是一个好助手。请你为下面的“生成”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "open_qa": { "id": 7, "category": "open_qa", "metrics": { @@ -112,7 +112,7 @@ }, "prompt": "你是一个好助手。请你为下面的问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "rewriting": { "id": 8, "category": "rewriting", "metrics": { @@ -127,7 +127,7 @@ }, "prompt": "你是一个好助手。请你为下面的问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "roleplay": { "id": 9, "category": "roleplay", "metrics": { @@ -144,7 +144,7 @@ }, "prompt": "你是一个好助手。请你为下面的“角色扮演”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "summarization": { "id": 10, "category": "summarization", "metrics": { @@ -161,7 +161,7 @@ }, "prompt": "你是一个好助手。请你为下面的“总结”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, - { + "general": { "id": 11, "category": "general", "metrics": { @@ -176,4 +176,4 @@ }, "prompt": "你是一个好助手。请你为下面问题的答案打分。\n\n问题如下：\n\n{question}\n\n需要你评分的答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" } -] +} diff --git a/applications/Chat/evaluate/utils.py b/applications/Chat/evaluate/utils.py index e855cd45221c..517c0a1c351e 100644 --- a/applications/Chat/evaluate/utils.py +++ b/applications/Chat/evaluate/utils.py @@ -57,6 +57,7 @@ def get_data_per_category(data, categories): data_per_category = {category: [] for category in categories} for item in data: category = item["category"] - data_per_category[category].append(item) + if category in categories: + data_per_category[category].append(item) return data_per_category From 5f79008c4a7a0e854f53d7f1c0b29ca7f411eeab Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Tue, 30 May 2023 18:41:41 +0800 Subject: [PATCH 255/413] [example] update gemini examples (#3868) * [example]update gemini examples * [example]update gemini examples --- examples/language/gpt/gemini/test_ci.sh | 2 +- .../language/gpt/gemini/train_gpt_demo.py | 57 +++++++++---------- 2 files changed, 29 insertions(+), 30 deletions(-) diff --git a/examples/language/gpt/gemini/test_ci.sh b/examples/language/gpt/gemini/test_ci.sh index 6079d5ed615b..0ddfd3a6211c 100644 --- a/examples/language/gpt/gemini/test_ci.sh +++ b/examples/language/gpt/gemini/test_ci.sh @@ -3,7 +3,7 @@ $(cd `dirname $0`;pwd) export TRAIN_STEP=4 for MODEL_TYPE in "gpt2_medium"; do - for DISTPLAN in "colossalai"; do + for DISTPLAN in "CAI_Gemini"; do for BATCH_SIZE in 2; do for GPUNUM in 1 4; do for TPDEGREE in 1 2; do diff --git a/examples/language/gpt/gemini/train_gpt_demo.py b/examples/language/gpt/gemini/train_gpt_demo.py index b2a7fa36d021..92751c7e2f47 100644 --- a/examples/language/gpt/gemini/train_gpt_demo.py +++ b/examples/language/gpt/gemini/train_gpt_demo.py @@ -11,11 +11,13 @@ from torch.nn.parallel import DistributedDataParallel as DDP import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin from colossalai.logging import disable_existing_loggers, get_dist_logger from colossalai.nn.optimizer import HybridAdam from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec from colossalai.utils import get_current_device -from colossalai.zero import ColoInitContext, zero_model_wrapper, zero_optim_wrapper +from colossalai.zero import ColoInitContext CAI_VERSION = colossalai.__version__ @@ -236,23 +238,6 @@ def main(): tensor_parallelize(model, tp_pg) # asign running configurations - gemini_config = None - if args.distplan.startswith("CAI_ZeRO"): - optim_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True, verbose=True) - elif args.distplan == "CAI_Gemini": - gemini_config = dict(strict_ddp_mode=args.tp_degree == 1, - device=get_current_device(), - placement_policy=args.placement, - pin_memory=True, - hidden_dim=model.config.n_embd, - search_range_mb=128) - optim_config = dict(gpu_margin_mem_ratio=0.) - else: - raise RuntimeError - - # build a highly optimized gpu/cpu optimizer - optimizer = HybridAdam(model.parameters(), lr=1e-3) - if args.distplan == "CAI_ZeRO1": zero_stage = 1 elif args.distplan == "CAI_ZeRO2": @@ -262,22 +247,42 @@ def main(): else: raise RuntimeError - # wrap your model and optimizer - model = zero_model_wrapper(model, zero_stage, gemini_config) - optimizer = zero_optim_wrapper(model, optimizer, optim_config=optim_config) + plugin = None + if args.distplan.startswith("CAI_ZeRO"): + plugin = LowLevelZeroPlugin(stage=zero_stage, + reduce_bucket_size_in_m=12 * 1024 * 1024, + overlap_communication=True, + verbose=True) + elif args.distplan == "CAI_Gemini": + plugin = GeminiPlugin(device=get_current_device(), + placement_policy=args.placement, + pin_memory=True, + strict_ddp_mode=args.tp_degree == 1, + search_range_mb=128, + hidden_dim=model.config.n_embd, + gpu_margin_mem_ratio=0.) + else: + raise RuntimeError + + # build a highly optimized gpu/cpu optimizer + optimizer = HybridAdam(model.parameters(), lr=1e-3) logger.info(get_mem_info(prefix='After init optim, '), ranks=[0]) elif args.distplan.startswith("Pytorch"): assert args.tp_degree == 1, "The degree of TP should be 1 for DDP examples." model = model_builder(args.model_type)(checkpoint=True).cuda() - model = DDP(model) + plugin = TorchDDPPlugin() if args.distplan.endswith("DDP"): optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) elif args.distplan.endswith("ZeRO"): from torch.distributed.optim import ZeroRedundancyOptimizer optimizer = ZeroRedundancyOptimizer(model.parameters(), optimizer_class=torch.optim.Adam, lr=1e-3) + else: raise RuntimeError + # wrap your model and optimizer + booster = Booster(plugin=plugin) + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) # model is shared after TP numel = get_model_size(model) @@ -305,13 +310,7 @@ def train_step(): fwd_end = time() fwd_time = fwd_end - start logger.info(get_mem_info(prefix=f'[{n + 1}/{NUM_STEPS}] Forward '), ranks=[0]) - - if args.distplan.startswith("CAI"): - optimizer.backward(loss) - elif args.distplan.startswith("Pytorch"): - loss.backward() - else: - raise RuntimeError + booster.backward(loss, optimizer) torch.cuda.synchronize() bwd_end = time() From 281b33f3628feb6bc7cb26faa42d260a169cb386 Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Tue, 30 May 2023 18:41:56 +0800 Subject: [PATCH 256/413] [doc] update document of zero with chunk. (#3855) * [doc] fix title of mixed precision * [doc]update document of zero with chunk * [doc] update document of zero with chunk, fix * [doc] update document of zero with chunk, fix * [doc] update document of zero with chunk, fix * [doc] update document of zero with chunk, add doc test * [doc] update document of zero with chunk, add doc test * [doc] update document of zero with chunk, fix installation * [doc] update document of zero with chunk, fix zero with chunk doc * [doc] update document of zero with chunk, fix zero with chunk doc --- docs/source/en/features/zero_with_chunk.md | 56 ++++++------------ .../gradient_accumulation_with_booster.md | 2 +- .../mixed_precision_training_with_booster.md | 2 +- .../zh-Hans/features/zero_with_chunk.md | 58 +++++++------------ .../zh-Hans/get_started/installation.md | 2 +- 5 files changed, 43 insertions(+), 77 deletions(-) diff --git a/docs/source/en/features/zero_with_chunk.md b/docs/source/en/features/zero_with_chunk.md index d7a99f2fbbfd..d6f6f611a64c 100644 --- a/docs/source/en/features/zero_with_chunk.md +++ b/docs/source/en/features/zero_with_chunk.md @@ -3,7 +3,7 @@ Author: [Hongxiu Liu](https://github.com/ver217), [Jiarui Fang](https://github.com/feifeibear), [Zijian Ye](https://github.com/ZijianYY) **Prerequisite:** -- [Define Your Configuration](../basics/define_your_config.md) +- [Train with booster](../basics/booster_api.md) **Example Code** @@ -97,6 +97,7 @@ For simplicity, we just use randomly generated data here. First we only need to import `GPT2LMHeadModel` from `Huggingface transformers` to define our model, which does not require users to define or modify the model, so that users can use it more conveniently. +Define a GPT model: ```python class GPTLMModel(nn.Module): @@ -182,34 +183,6 @@ def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup): split_param_single_dim_tp1d(-1, param, pg) ``` -Define a model which uses Gemini + ZeRO DDP: - -```python -def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"): - cai_version = colossalai.__version__ - if version.parse(cai_version) > version.parse("0.1.10"): - from colossalai.nn.parallel import GeminiDDP - model = GeminiDDP(model, - device=get_current_device(), - placement_policy=placement_policy, - pin_memory=True, - search_range_mb=32) - elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"): - from colossalai.gemini import ChunkManager, GeminiManager - chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32) - gemini_manager = GeminiManager(placement_policy, chunk_manager) - chunk_manager = ChunkManager(chunk_size, - pg, - enable_distributed_storage=True, - init_device=GeminiManager.get_default_device(placement_policy)) - model = ZeroDDP(model, gemini_manager) - else: - raise NotImplemented(f"CAI version {cai_version} is not supported") - return model -``` - -As we pre-train GPT in this example, we just use a simple language model loss. - Write a function to get random inputs: ```python @@ -219,9 +192,15 @@ def get_data(batch_size, seq_len, vocab_size): return input_ids, attention_mask ``` -Finally, we can define our training loop: +Finally, we define a model which uses Gemini + ZeRO DDP and define our training loop, As we pre-train GPT in this example, we just use a simple language model loss: ```python +from torch.optim import Adam + +from colossalai.booster import Booster +from colossalai.zero import ColoInitContext +from colossalai.booster.plugin import GeminiPlugin + def main(): args = parse_args() BATCH_SIZE = 8 @@ -232,22 +211,23 @@ def main(): # build criterion criterion = GPTLMLoss() + optimizer = Adam(model.parameters(), lr=0.001) torch.manual_seed(123) default_pg = ProcessGroup(tp_degree=args.tp_degree) - default_dist_spec = ShardSpec([-1], [args.tp_degree]) if args.shardinit else None + default_dist_spec = ShardSpec([-1], [args.tp_degree]) # build GPT model with ColoInitContext(device='cpu', default_dist_spec=default_dist_spec, default_pg=default_pg): model = gpt2_medium(checkpoint=True) pg = default_pg # Tensor Parallelism (TP) tensor_parallelize(model, pg) + # Gemini + ZeRO DP, Note it must be used after TP - model = gemini_zero_dpp(model, pg, args.placement) - # build optimizer - optimizer = GeminiAdamOptimizer(model, lr=1e-3, initial_scale=2**5) - numel = sum([p.numel() for p in model.parameters()]) - get_tflops_func = partial(get_tflops, numel, BATCH_SIZE, SEQ_LEN) + plugin = GeminiPlugin(placement_policy='cuda', max_norm=1.0, initial_scale=2**5) + booster = Booster(plugin=plugin) + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + torch.cuda.synchronize() model.train() for n in range(NUM_STEPS): @@ -256,10 +236,12 @@ def main(): optimizer.zero_grad() outputs = model(input_ids, attn_mask) loss = criterion(outputs, input_ids) - optimizer.backward(loss) + booster.backward(loss, optimizer) optimizer.step() torch.cuda.synchronize() ``` > ⚠️ Note: If you want to use the Gemini module, please do not use the [Gradient Accumulation](../features/gradient_accumulation.md) we mentioned before。 The complete example can be found on [Train GPT with Colossal-AI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt). + + diff --git a/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md b/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md index ab86f34f2dec..a8422060f0ea 100644 --- a/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md +++ b/docs/source/zh-Hans/features/gradient_accumulation_with_booster.md @@ -1,4 +1,4 @@ -# 梯度累积 (最新版本) +# 梯度累积 (新版本) 作者: [Mingyan Jiang](https://github.com/jiangmingyan) diff --git a/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md index 6954556a8e9a..187aef1a6c4a 100644 --- a/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md +++ b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md @@ -1,4 +1,4 @@ -# 自动混合精度训练 (最新版本) +# 自动混合精度训练 (新版本) 作者: [Mingyan Jiang](https://github.com/jiangmingyan) diff --git a/docs/source/zh-Hans/features/zero_with_chunk.md b/docs/source/zh-Hans/features/zero_with_chunk.md index ba57ba4e8e61..9030464ddf9a 100644 --- a/docs/source/zh-Hans/features/zero_with_chunk.md +++ b/docs/source/zh-Hans/features/zero_with_chunk.md @@ -4,7 +4,7 @@ **前置教程:** -- [定义配置文件](../basics/define_your_config.md) +- [booster使用](../basics/booster_api.md) **示例代码** @@ -97,6 +97,8 @@ optimizer.step() 首先我们只需要引入`Huggingface transformers` 的 `GPT2LMHeadModel`来定义我们的模型，不需要用户进行模型的定义与修改，方便用户使用。 +定义GPT模型： + ```python class GPTLMModel(nn.Module): @@ -182,34 +184,6 @@ def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup): split_param_single_dim_tp1d(-1, param, pg) ``` -定义一个使用 Gemini + ZeRO DDP 的模型： - -```python -def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"): - cai_version = colossalai.__version__ - if version.parse(cai_version) > version.parse("0.1.10"): - from colossalai.nn.parallel import GeminiDDP - model = GeminiDDP(model, - device=get_current_device(), - placement_policy=placement_policy, - pin_memory=True, - search_range_mb=32) - elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"): - from colossalai.gemini import ChunkManager, GeminiManager - chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32) - gemini_manager = GeminiManager(placement_policy, chunk_manager) - chunk_manager = ChunkManager(chunk_size, - pg, - enable_distributed_storage=True, - init_device=GeminiManager.get_default_device(placement_policy)) - model = ZeroDDP(model, gemini_manager) - else: - raise NotImplemented(f"CAI version {cai_version} is not supported") - return model -``` - -由于我们在这个例子中对GPT进行预训练，因此只使用了一个简单的语言模型损失函数。 - 写一个获得随机输入的函数: ```python @@ -219,9 +193,16 @@ def get_data(batch_size, seq_len, vocab_size): return input_ids, attention_mask ``` -最后，我们可以定义我们的训练循环: + +最后，使用booster注入 Gemini + ZeRO DDP 特性, 并定义训练循环。由于我们在这个例子中对GPT进行预训练，因此只使用了一个简单的语言模型损失函数： ```python +from torch.optim import Adam + +from colossalai.booster import Booster +from colossalai.zero import ColoInitContext +from colossalai.booster.plugin import GeminiPlugin + def main(): args = parse_args() BATCH_SIZE = 8 @@ -232,22 +213,23 @@ def main(): # build criterion criterion = GPTLMLoss() + optimizer = Adam(model.parameters(), lr=0.001) torch.manual_seed(123) default_pg = ProcessGroup(tp_degree=args.tp_degree) - default_dist_spec = ShardSpec([-1], [args.tp_degree]) if args.shardinit else None + default_dist_spec = ShardSpec([-1], [args.tp_degree]) # build GPT model with ColoInitContext(device='cpu', default_dist_spec=default_dist_spec, default_pg=default_pg): model = gpt2_medium(checkpoint=True) pg = default_pg # Tensor Parallelism (TP) tensor_parallelize(model, pg) + # Gemini + ZeRO DP, Note it must be used after TP - model = gemini_zero_dpp(model, pg, args.placement) - # build optimizer - optimizer = GeminiAdamOptimizer(model, lr=1e-3, initial_scale=2**5) - numel = sum([p.numel() for p in model.parameters()]) - get_tflops_func = partial(get_tflops, numel, BATCH_SIZE, SEQ_LEN) + plugin = GeminiPlugin(placement_policy='cuda', max_norm=1.0, initial_scale=2**5) + booster = Booster(plugin=plugin) + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + torch.cuda.synchronize() model.train() for n in range(NUM_STEPS): @@ -256,10 +238,12 @@ def main(): optimizer.zero_grad() outputs = model(input_ids, attn_mask) loss = criterion(outputs, input_ids) - optimizer.backward(loss) + booster.backward(loss, optimizer) optimizer.step() torch.cuda.synchronize() ``` > ⚠️ 注意：如果你使用Gemini模块的话，请不要使用我们之前提到过的[梯度累加](../features/gradient_accumulation.md)。 完整的例子代码可以在 [Train GPT with Colossal-AI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt). 获得。 + + diff --git a/docs/source/zh-Hans/get_started/installation.md b/docs/source/zh-Hans/get_started/installation.md index a32627db6f00..a6c88672b907 100755 --- a/docs/source/zh-Hans/get_started/installation.md +++ b/docs/source/zh-Hans/get_started/installation.md @@ -47,7 +47,7 @@ CUDA_EXT=1 pip install . pip install . ``` -如果您在使用CUDA 10.2，您仍然可以从源码安装ColossalA。但是您需要手动下载cub库并将其复制到相应的目录。 +如果您在使用CUDA 10.2，您仍然可以从源码安装ColossalAI。但是您需要手动下载cub库并将其复制到相应的目录。 ```bash # clone the repository From 46503c35dd9342f943308ee451b62751f36bc961 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Thu, 1 Jun 2023 14:30:51 +0800 Subject: [PATCH 257/413] Modify torch version requirement to adapt torch 2.0 --- colossalai/cli/launcher/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/colossalai/cli/launcher/run.py b/colossalai/cli/launcher/run.py index 6411b4302e95..4bb749f9d293 100644 --- a/colossalai/cli/launcher/run.py +++ b/colossalai/cli/launcher/run.py @@ -154,7 +154,7 @@ def _arg_dict_to_list(arg_dict): extra_launch_args = dict() torch_version = version.parse(torch.__version__) - assert torch_version.major == 1 + assert torch_version.major >= 1 if torch_version.minor < 9: cmd = [ From 70c8cdecf45386a115d659897ad8017c9205c6a5 Mon Sep 17 00:00:00 2001 From: digger yu Date: Fri, 2 Jun 2023 15:02:45 +0800 Subject: [PATCH 258/413] [nfc] fix typo colossalai/cli fx kernel (#3847) * fix typo colossalai/autochunk auto_parallel amp * fix typo colossalai/auto_parallel nn utils etc. * fix typo colossalai/auto_parallel autochunk fx/passes etc. * fix typo docs/ * change placememt_policy to placement_policy in docs/ and examples/ * fix typo colossalai/ applications/ * fix typo colossalai/cli fx kernel --- colossalai/cli/launcher/__init__.py | 2 +- colossalai/cli/launcher/hostinfo.py | 2 +- colossalai/cli/launcher/multinode_runner.py | 2 +- colossalai/cli/launcher/run.py | 2 +- colossalai/device/alpha_beta_profiler.py | 2 +- .../patched_bias_addition_module/bias_addition_module.py | 2 +- colossalai/fx/tracer/experimental.py | 2 +- colossalai/fx/tracer/tracer.py | 6 +++--- colossalai/kernel/cuda_native/flash_attention.py | 2 +- colossalai/kernel/cuda_native/multihead_attention.py | 2 +- colossalai/kernel/jit/option.py | 2 +- 11 files changed, 13 insertions(+), 13 deletions(-) diff --git a/colossalai/cli/launcher/__init__.py b/colossalai/cli/launcher/__init__.py index 8d9ec147d401..808e4e84574f 100644 --- a/colossalai/cli/launcher/__init__.py +++ b/colossalai/cli/launcher/__init__.py @@ -28,7 +28,7 @@ type=str, default=None, help= - "Specify computing devices to NOT use during execution. Mutually exclusive with --include. Formatting is the same as --includ," + "Specify computing devices to NOT use during execution. Mutually exclusive with --include. Formatting is the same as --include," " only effective when used with --hostfile.") @click.option("--num_nodes", type=int, diff --git a/colossalai/cli/launcher/hostinfo.py b/colossalai/cli/launcher/hostinfo.py index 065cbc37101f..d1b88b229fb8 100644 --- a/colossalai/cli/launcher/hostinfo.py +++ b/colossalai/cli/launcher/hostinfo.py @@ -38,7 +38,7 @@ def is_host_localhost(hostname: str, port: str = None) -> None: # socket.getfqdn("127.0.0.1") does not return localhost # on some users' machines - # thus, we directly return True if hostname is locahost, 127.0.0.1 or 0.0.0.0 + # thus, we directly return True if hostname is localhost, 127.0.0.1 or 0.0.0.0 if hostname in ("localhost", "127.0.0.1", "0.0.0.0"): return True diff --git a/colossalai/cli/launcher/multinode_runner.py b/colossalai/cli/launcher/multinode_runner.py index a51e1e371f13..85b241e96292 100644 --- a/colossalai/cli/launcher/multinode_runner.py +++ b/colossalai/cli/launcher/multinode_runner.py @@ -114,7 +114,7 @@ def recv_from_all(self) -> dict: Receive messages from all hosts Returns: - msg_from_node (dict): a dictionry which contains messages from each node + msg_from_node (dict): a dictionary which contains messages from each node """ msg_from_node = dict() diff --git a/colossalai/cli/launcher/run.py b/colossalai/cli/launcher/run.py index 6411b4302e95..027a10aa898b 100644 --- a/colossalai/cli/launcher/run.py +++ b/colossalai/cli/launcher/run.py @@ -298,7 +298,7 @@ def launch_multi_processes(args: Config) -> None: # receive the stop status msg_from_node = runner.recv_from_all() - # printe node status + # print node status click.echo("\n====== Stopping All Nodes =====") for hostname, msg in msg_from_node.items(): click.echo(f"{hostname}: {msg}") diff --git a/colossalai/device/alpha_beta_profiler.py b/colossalai/device/alpha_beta_profiler.py index f8b20de9bc37..f4e6cfffbcdf 100644 --- a/colossalai/device/alpha_beta_profiler.py +++ b/colossalai/device/alpha_beta_profiler.py @@ -197,7 +197,7 @@ def get_max_nbytes(process_group: Tuple[int], pg_handler: dist.ProcessGroup): dist.broadcast_object_list(broadcast_list, src=process_group[0]) alpha_beta_dict[process_group] = tuple(broadcast_list) - # add symmetry pair to the apha_beta_dict + # add symmetry pair to the alpha_beta_dict symmetry_ab_dict = {} for process_group, alpha_beta_pair in alpha_beta_dict.items(): symmetry_process_group = (process_group[1], process_group[0]) diff --git a/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/bias_addition_module.py b/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/bias_addition_module.py index 85f1553e304c..591485fdb1ca 100644 --- a/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/bias_addition_module.py +++ b/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/bias_addition_module.py @@ -51,7 +51,7 @@ def extract_kwargs_from_mod(self): For example: The kwargs for conv2d module is {} because the attributes like 'padding' or 'groups' are - considered during module initilizing. However, we need to consider those attributes as kwargs + considered during module initializing. However, we need to consider those attributes as kwargs in F.conv2d. """ pass diff --git a/colossalai/fx/tracer/experimental.py b/colossalai/fx/tracer/experimental.py index 88b65b6188fa..22a67d1ceccc 100644 --- a/colossalai/fx/tracer/experimental.py +++ b/colossalai/fx/tracer/experimental.py @@ -295,7 +295,7 @@ class PatchedCheckpointFunction(torch.autograd.Function): @staticmethod def forward(ctx, run_function, preserve_rng_state, *args): - # signal that the current tracing occurs within activaton checkpoint part + # signal that the current tracing occurs within activation checkpoint part self.inside_torch_checkpoint_func = True out = run_function(*args) self.inside_torch_checkpoint_func = False diff --git a/colossalai/fx/tracer/tracer.py b/colossalai/fx/tracer/tracer.py index 1ae31f958975..28965a1b8e74 100644 --- a/colossalai/fx/tracer/tracer.py +++ b/colossalai/fx/tracer/tracer.py @@ -92,7 +92,7 @@ def create_proxy(self, kind, target, args, kwargs, name=None, type_expr=None, pr return proxy # if graph is traced for auto parallelism module, some extra node will be added during - # graph construction to deal with the compatability between bias addition and all reduce. + # graph construction to deal with the compatibility between bias addition and all reduce. # if no extra manipulation is applied, we just pass the origin arguments to create_proxy function # to create node on computation graph @@ -208,7 +208,7 @@ def _configure_tracer_type(self, tracer_type: TracerType): self.proxy_cls = ColoProxy self.tracer_type = TracerType.META else: - raise ValueError(f"Unrecognised tracer type {tracer_type}") + raise ValueError(f"Unrecognized tracer type {tracer_type}") def _meta_data_computing(self, kind, target, args, kwargs): @@ -445,7 +445,7 @@ class PatchedCheckpointFunction(torch.autograd.Function): @staticmethod def forward(ctx, run_function, preserve_rng_state, *args): - # signal that the current tracing occurs within activaton checkpoint part + # signal that the current tracing occurs within activation checkpoint part self.inside_torch_checkpoint_func = True out = run_function(*args) self.inside_torch_checkpoint_func = False diff --git a/colossalai/kernel/cuda_native/flash_attention.py b/colossalai/kernel/cuda_native/flash_attention.py index d793815ed681..3db7374509a0 100644 --- a/colossalai/kernel/cuda_native/flash_attention.py +++ b/colossalai/kernel/cuda_native/flash_attention.py @@ -138,7 +138,7 @@ def forward(self, elif attn_mask_type == AttnMaskType.causal: # gpt style attn_bias = LowerTriangularMask() - if bias is not None: # alibi / relative position emebedding + if bias is not None: # alibi / relative position embedding assert allow_alibi, "flash attention with bias is not supported in this system." assert attn_mask_type == AttnMaskType.causal, \ "attention with bias is only supported for causal attention so far." diff --git a/colossalai/kernel/cuda_native/multihead_attention.py b/colossalai/kernel/cuda_native/multihead_attention.py index 3b6470cdcbb9..69246f2f3854 100644 --- a/colossalai/kernel/cuda_native/multihead_attention.py +++ b/colossalai/kernel/cuda_native/multihead_attention.py @@ -43,7 +43,7 @@ class Config: attn_prob_dropout_ratio: float # attention score dropout ratio hidden_dropout_ratio: float # dropout ration before residual norm_first: bool # norm_first - fp16: bool # fp16 presion + fp16: bool # fp16 precision class MultiHeadAttention1DFunc(Function): diff --git a/colossalai/kernel/jit/option.py b/colossalai/kernel/jit/option.py index aa41f57678fc..e20c08b051ed 100644 --- a/colossalai/kernel/jit/option.py +++ b/colossalai/kernel/jit/option.py @@ -43,7 +43,7 @@ def warmup_jit_fusion(batch_size: int, seq_length: int = 512, vocab_size: int = 32768, dtype: torch.dtype = torch.float32): - """ Compilie JIT functions before the main training steps """ + """ Compile JIT functions before the main training steps """ embed = Embedding(vocab_size, hidden_size).to(get_current_device()) linear_1 = Linear(hidden_size, hidden_size * 4, skip_bias_add=True).to(get_current_device()) From 60ec33bb183e410ace44435d45673d64fea080db Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Fri, 2 Jun 2023 16:50:51 +0800 Subject: [PATCH 259/413] Add a new example of Dreambooth training using the booster API --- .../tutorial/new_api/dreambooth/README.md | 113 +++ .../tutorial/new_api/dreambooth/colossalai.sh | 17 + .../new_api/dreambooth/requirements.txt | 7 + .../tutorial/new_api/dreambooth/test_ci.sh | 23 + .../dreambooth/train_dreambooth_colossalai.py | 690 ++++++++++++++++++ 5 files changed, 850 insertions(+) create mode 100644 examples/tutorial/new_api/dreambooth/README.md create mode 100755 examples/tutorial/new_api/dreambooth/colossalai.sh create mode 100644 examples/tutorial/new_api/dreambooth/requirements.txt create mode 100644 examples/tutorial/new_api/dreambooth/test_ci.sh create mode 100644 examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py diff --git a/examples/tutorial/new_api/dreambooth/README.md b/examples/tutorial/new_api/dreambooth/README.md new file mode 100644 index 000000000000..bd7e7707ac78 --- /dev/null +++ b/examples/tutorial/new_api/dreambooth/README.md @@ -0,0 +1,113 @@ +# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git) + +[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject. +The `train_dreambooth_colossalai.py` script shows how to implement the training procedure and adapt it for stable diffusion. + +By accommodating model data in CPU and GPU and moving the data to the computing device when necessary, [Gemini](https://www.colossalai.org/docs/advanced_tutorials/meet_gemini), the Heterogeneous Memory Manager of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) can breakthrough the GPU memory wall by using GPU and CPU memory (composed of CPU DRAM or nvme SSD memory) together at the same time. Moreover, the model scale can be further improved by combining heterogeneous training with the other parallel approaches, such as data parallel, tensor parallel and pipeline parallel. + +## Installation + +To begin with, make sure your operating system has the cuda version suitable for this exciting training session, which is cuda11.6-11.8. Notice that you may want to make sure the module versions suitable for the whole environment. Before running the scripts, make sure to install the library's training dependencies: + +```bash +pip install -r requirements.txt +``` + +### Install [colossalai](https://github.com/hpcaitech/ColossalAI.git) + +```bash +pip install colossalai +``` + +**From source** + +```bash +git clone https://github.com/hpcaitech/ColossalAI.git +python setup.py install +``` + +## Dataset for Teyvat BLIP captions +Dataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion). + +BLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2). + +For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided. + +The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP). + +## New API +We have modified our previous implementation of Dreambooth with our new Booster API, which offers a more flexible and efficient way to train your model. The new API is more user-friendly and easy to use. You can find the new API in `train_dreambooth_colossalai.py`. +We have also offer a shell script `test_ci.sh` for you to go through all our plugins for the booster. +For more information about the booster API you can refer to https://colossalai.org/docs/basics/booster_api/. + +## Training + +We provide the script `colossalai.sh` to run the training task with colossalai. For instance, the script of training process for [stable-diffusion-v1-4] model can be modified into: + +```bash +export MODEL_NAME="CompVis/stable-diffusion-v1-4" +export INSTANCE_DIR="path-to-instance-images" +export OUTPUT_DIR="path-to-save-model" + +torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --instance_data_dir=$INSTANCE_DIR \ + --output_dir=$OUTPUT_DIR \ + --instance_prompt="a photo of sks dog" \ + --resolution=512 \ + --train_batch_size=1 \ + --learning_rate=5e-6 \ + --lr_scheduler="constant" \ + --lr_warmup_steps=0 \ + --max_train_steps=400 \ + --placement="cuda" +``` +- `MODEL_NAME` refers to the model you are training. +- `INSTANCE_DIR` refers to personalized path to instance images, you might need to insert information here. +- `OUTPUT_DIR` refers to local path to save the trained model, you might need to find a path with enough space. +- `resolution` refers to the corresponding resolution number of your target model. Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model. +- `placement` refers to the training strategy supported by Colossal AI, default = 'cuda', which refers to loading all the parameters into cuda memory. On the other hand, 'cpu' refers to 'cpu offload' strategy while 'auto' enables 'Gemini', both featured by Colossal AI. + +### Training with prior-preservation loss + +Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data. + +According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time. The general script can be then modified as the following. + +```bash +export MODEL_NAME="CompVis/stable-diffusion-v1-4" +export INSTANCE_DIR="path-to-instance-images" +export CLASS_DIR="path-to-class-images" +export OUTPUT_DIR="path-to-save-model" + +torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --instance_data_dir=$INSTANCE_DIR \ + --class_data_dir=$CLASS_DIR \ + --output_dir=$OUTPUT_DIR \ + --with_prior_preservation --prior_loss_weight=1.0 \ + --instance_prompt="a photo of sks dog" \ + --class_prompt="a photo of dog" \ + --resolution=512 \ + --train_batch_size=1 \ + --learning_rate=5e-6 \ + --lr_scheduler="constant" \ + --lr_warmup_steps=0 \ + --max_train_steps=800 \ + --placement="cuda" +``` + + + +## Invitation to open-source contribution +Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models! + +You may contact us or participate in the following ways: +1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks! +2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md). +3. Join the Colossal-AI community on +[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w), +and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas. +4. Send your official proposal to email contact@hpcaitech.com + +Thanks so much to all of our amazing contributors! diff --git a/examples/tutorial/new_api/dreambooth/colossalai.sh b/examples/tutorial/new_api/dreambooth/colossalai.sh new file mode 100755 index 000000000000..7cf8b3a1307e --- /dev/null +++ b/examples/tutorial/new_api/dreambooth/colossalai.sh @@ -0,0 +1,17 @@ +HF_DATASETS_OFFLINE=1 +TRANSFORMERS_OFFLINE=1 +DIFFUSERS_OFFLINE=1 + +torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ + --pretrained_model_name_or_path="Your Pretrained Model Path" \ + --instance_data_dir="Your Input Pics Path" \ + --output_dir="path-to-save-model" \ + --instance_prompt="your_prompt" \ + --resolution=512 \ + --plugin="gemini" \ + --train_batch_size=1 \ + --learning_rate=5e-6 \ + --lr_scheduler="constant" \ + --lr_warmup_steps=0 \ + --num_class_images=200 \ + --placement="cuda" \ diff --git a/examples/tutorial/new_api/dreambooth/requirements.txt b/examples/tutorial/new_api/dreambooth/requirements.txt new file mode 100644 index 000000000000..1ec828c630ef --- /dev/null +++ b/examples/tutorial/new_api/dreambooth/requirements.txt @@ -0,0 +1,7 @@ +diffusers>==0.5.0 +accelerate +torchvision +transformers>=4.21.0 +ftfy +tensorboard +modelcards diff --git a/examples/tutorial/new_api/dreambooth/test_ci.sh b/examples/tutorial/new_api/dreambooth/test_ci.sh new file mode 100644 index 000000000000..68862c46cfe9 --- /dev/null +++ b/examples/tutorial/new_api/dreambooth/test_ci.sh @@ -0,0 +1,23 @@ +#!/bin/bash +set -xe +pip install -r requirements.txt + +HF_DATASETS_OFFLINE=1 +TRANSFORMERS_OFFLINE=1 +DIFFUSERS_OFFLINE=1 + +for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do + torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ + --pretrained_model_name_or_path="Your Pretrained Model Path" \ + --instance_data_dir="Your Input Pics Path" \ + --output_dir="path-to-save-model" \ + --instance_prompt="your prompt" \ + --resolution=512 \ + --plugin=$plugin \ + --train_batch_size=1 \ + --learning_rate=5e-6 \ + --lr_scheduler="constant" \ + --lr_warmup_steps=0 \ + --num_class_images=200 \ + --placement="cuda" +done diff --git a/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py b/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py new file mode 100644 index 000000000000..9da7cacb8aaf --- /dev/null +++ b/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py @@ -0,0 +1,690 @@ +import argparse +import hashlib +import math +import os +from pathlib import Path +from typing import Optional +import shutil + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel +from diffusers.optimization import get_scheduler +from huggingface_hub import HfFolder, Repository, create_repo, whoami +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms +from tqdm.auto import tqdm +from transformers import AutoTokenizer, PretrainedConfig + +import colossalai +from colossalai.context.parallel_mode import ParallelMode +from colossalai.core import global_context as gpc +from colossalai.logging import disable_existing_loggers, get_dist_logger +from colossalai.nn.optimizer import HybridAdam +from colossalai.utils import get_current_device +from colossalai.zero import ColoInitContext +from colossalai.zero.gemini import get_static_torch_model +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin + +disable_existing_loggers() +logger = get_dist_logger() + + +def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str): + text_encoder_config = PretrainedConfig.from_pretrained( + pretrained_model_name_or_path, + subfolder="text_encoder", + revision=args.revision, + ) + model_class = text_encoder_config.architectures[0] + + if model_class == "CLIPTextModel": + from transformers import CLIPTextModel + + return CLIPTextModel + elif model_class == "RobertaSeriesModelWithTransformation": + from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation + + return RobertaSeriesModelWithTransformation + else: + raise ValueError(f"{model_class} is not supported.") + + +def parse_args(input_args=None): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_model_name_or_path", + type=str, + default=None, + required=True, + help="Path to pretrained model or model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--externel_unet_path", + type=str, + default=None, + required=False, + help="Path to the externel unet model.", + ) + parser.add_argument( + "--revision", + type=str, + default=None, + required=False, + help="Revision of pretrained model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--tokenizer_name", + type=str, + default=None, + help="Pretrained tokenizer name or path if not the same as model_name", + ) + parser.add_argument( + "--instance_data_dir", + type=str, + default=None, + required=True, + help="A folder containing the training data of instance images.", + ) + parser.add_argument( + "--class_data_dir", + type=str, + default=None, + required=False, + help="A folder containing the training data of class images.", + ) + parser.add_argument( + "--instance_prompt", + type=str, + default="a photo of sks dog", + required=False, + help="The prompt with identifier specifying the instance", + ) + parser.add_argument( + "--class_prompt", + type=str, + default=None, + help="The prompt to specify images in the same class as provided instance images.", + ) + parser.add_argument( + "--with_prior_preservation", + default=False, + action="store_true", + help="Flag to add prior preservation loss.", + ) + parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") + parser.add_argument( + "--num_class_images", + type=int, + default=100, + help=("Minimal class images for prior preservation loss. If there are not enough images already present in" + " class_data_dir, additional images will be sampled with class_prompt."), + ) + parser.add_argument( + "--output_dir", + type=str, + default="text-inversion-model", + help="The output directory where the model predictions and checkpoints will be written.", + ) + parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") + parser.add_argument( + "--resolution", + type=int, + default=512, + help=("The resolution for input images, all the images in the train/validation dataset will be resized to this" + " resolution"), + ) + parser.add_argument( + "--placement", + type=str, + default="cpu", + help="Placement Policy for Gemini. Valid when using colossalai as dist plan.", + ) + parser.add_argument( + "--center_crop", + default=False, + action="store_true", + help=("Whether to center crop the input images to the resolution. If not set, the images will be randomly" + " cropped. The images will be resized to the resolution first before cropping."), + ) + parser.add_argument("--train_batch_size", + type=int, + default=4, + help="Batch size (per device) for the training dataloader.") + parser.add_argument("--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images.") + parser.add_argument("--num_train_epochs", type=int, default=1) + parser.add_argument( + "--max_train_steps", + type=int, + default=None, + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", + ) + parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") + parser.add_argument( + "--gradient_checkpointing", + action="store_true", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=5e-6, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + default=False, + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="constant", + help=('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]'), + ) + parser.add_argument("--lr_warmup_steps", + type=int, + default=500, + help="Number of steps for the warmup in the lr scheduler.") + parser.add_argument("--use_8bit_adam", + action="store_true", + help="Whether or not to use 8-bit Adam from bitsandbytes.") + + parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") + parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") + parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") + parser.add_argument( + "--hub_model_id", + type=str, + default=None, + help="The name of the repository to keep in sync with the local `output_dir`.", + ) + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'], + help="plugin to use") + parser.add_argument( + "--logging_dir", + type=str, + default="logs", + help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), + ) + parser.add_argument( + "--mixed_precision", + type=str, + default=None, + choices=["no", "fp16", "bf16"], + help=( + "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" + " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" + " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."), + ) + parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") + + if input_args is not None: + args = parser.parse_args(input_args) + else: + args = parser.parse_args() + + env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) + if env_local_rank != -1 and env_local_rank != args.local_rank: + args.local_rank = env_local_rank + + if args.with_prior_preservation: + if args.class_data_dir is None: + raise ValueError("You must specify a data directory for class images.") + if args.class_prompt is None: + raise ValueError("You must specify prompt for class images.") + else: + if args.class_data_dir is not None: + logger.warning("You need not use --class_data_dir without --with_prior_preservation.") + if args.class_prompt is not None: + logger.warning("You need not use --class_prompt without --with_prior_preservation.") + + return args + + +class DreamBoothDataset(Dataset): + """ + A dataset to prepare the instance and class images with the prompts for fine-tuning the model. + It pre-processes the images and the tokenizes prompts. + """ + + def __init__( + self, + instance_data_root, + instance_prompt, + tokenizer, + class_data_root=None, + class_prompt=None, + size=512, + center_crop=False, + ): + self.size = size + self.center_crop = center_crop + self.tokenizer = tokenizer + + self.instance_data_root = Path(instance_data_root) + if not self.instance_data_root.exists(): + raise ValueError("Instance images root doesn't exists.") + + self.instance_images_path = list(Path(instance_data_root).iterdir()) + self.num_instance_images = len(self.instance_images_path) + self.instance_prompt = instance_prompt + self._length = self.num_instance_images + + if class_data_root is not None: + self.class_data_root = Path(class_data_root) + self.class_data_root.mkdir(parents=True, exist_ok=True) + self.class_images_path = list(self.class_data_root.iterdir()) + self.num_class_images = len(self.class_images_path) + self._length = max(self.num_class_images, self.num_instance_images) + self.class_prompt = class_prompt + else: + self.class_data_root = None + + self.image_transforms = transforms.Compose([ + transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ]) + + def __len__(self): + return self._length + + def __getitem__(self, index): + example = {} + instance_image = Image.open(self.instance_images_path[index % self.num_instance_images]) + if not instance_image.mode == "RGB": + instance_image = instance_image.convert("RGB") + example["instance_images"] = self.image_transforms(instance_image) + example["instance_prompt_ids"] = self.tokenizer( + self.instance_prompt, + padding="do_not_pad", + truncation=True, + max_length=self.tokenizer.model_max_length, + ).input_ids + + if self.class_data_root: + class_image = Image.open(self.class_images_path[index % self.num_class_images]) + if not class_image.mode == "RGB": + class_image = class_image.convert("RGB") + example["class_images"] = self.image_transforms(class_image) + example["class_prompt_ids"] = self.tokenizer( + self.class_prompt, + padding="do_not_pad", + truncation=True, + max_length=self.tokenizer.model_max_length, + ).input_ids + + return example + + +class PromptDataset(Dataset): + "A simple dataset to prepare the prompts to generate class images on multiple GPUs." + + def __init__(self, prompt, num_samples): + self.prompt = prompt + self.num_samples = num_samples + + def __len__(self): + return self.num_samples + + def __getitem__(self, index): + example = {} + example["prompt"] = self.prompt + example["index"] = index + return example + + +def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): + if token is None: + token = HfFolder.get_token() + if organization is None: + username = whoami(token)["name"] + return f"{username}/{model_id}" + else: + return f"{organization}/{model_id}" + + +def main(args): + if args.seed is None: + colossalai.launch_from_torch(config={}) + else: + colossalai.launch_from_torch(config={}, seed=args.seed) + + local_rank = gpc.get_local_rank(ParallelMode.DATA) + world_size = gpc.get_world_size(ParallelMode.DATA) + + if args.with_prior_preservation: + class_images_dir = Path(args.class_data_dir) + if not class_images_dir.exists(): + class_images_dir.mkdir(parents=True) + cur_class_images = len(list(class_images_dir.iterdir())) + + if cur_class_images < args.num_class_images: + torch_dtype = torch.float16 if get_current_device() == "cuda" else torch.float32 + pipeline = DiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + torch_dtype=torch_dtype, + safety_checker=None, + revision=args.revision, + ) + pipeline.set_progress_bar_config(disable=True) + + num_new_images = args.num_class_images - cur_class_images + logger.info(f"Number of class images to sample: {num_new_images}.") + + sample_dataset = PromptDataset(args.class_prompt, num_new_images) + sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) + + pipeline.to(get_current_device()) + + for example in tqdm( + sample_dataloader, + desc="Generating class images", + disable=not local_rank == 0, + ): + images = pipeline(example["prompt"]).images + + for i, image in enumerate(images): + hash_image = hashlib.sha1(image.tobytes()).hexdigest() + image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" + image.save(image_filename) + + del pipeline + + # Handle the repository creation + if local_rank == 0: + if args.push_to_hub: + if args.hub_model_id is None: + repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) + else: + repo_name = args.hub_model_id + create_repo(repo_name, exist_ok=True, token=args.hub_token) + repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token) + + with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: + if "step_*" not in gitignore: + gitignore.write("step_*\n") + if "epoch_*" not in gitignore: + gitignore.write("epoch_*\n") + elif args.output_dir is not None: + os.makedirs(args.output_dir, exist_ok=True) + + # Load the tokenizer + if args.tokenizer_name: + logger.info(f"Loading tokenizer from {args.tokenizer_name}", ranks=[0]) + tokenizer = AutoTokenizer.from_pretrained( + args.tokenizer_name, + revision=args.revision, + use_fast=False, + ) + elif args.pretrained_model_name_or_path: + logger.info("Loading tokenizer from pretrained model", ranks=[0]) + tokenizer = AutoTokenizer.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="tokenizer", + revision=args.revision, + use_fast=False, + ) + # import correct text encoder class + text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path) + + # Load models and create wrapper for stable diffusion + + logger.info(f"Loading text_encoder from {args.pretrained_model_name_or_path}", ranks=[0]) + + text_encoder = text_encoder_cls.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="text_encoder", + revision=args.revision, + ) + + logger.info(f"Loading AutoencoderKL from {args.pretrained_model_name_or_path}", ranks=[0]) + vae = AutoencoderKL.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="vae", + revision=args.revision, + ) + + + if args.externel_unet_path is None: + logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0]) + unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, + subfolder="unet", + revision=args.revision, + low_cpu_mem_usage=False) + else: + logger.info(f"Loading UNet2DConditionModel from {args.externel_unet_path}", ranks=[0]) + unet = UNet2DConditionModel.from_pretrained(args.externel_unet_path, + revision=args.revision, + low_cpu_mem_usage=False) + + vae.requires_grad_(False) + text_encoder.requires_grad_(False) + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + + if args.scale_lr: + args.learning_rate = args.learning_rate * args.train_batch_size * world_size + + # Use Booster API to use Gemini/Zero with ColossalAI + + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2 ** 5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2 ** 5) + + booster = Booster(plugin=plugin, **booster_kwargs) + + # config optimizer for colossalai zero + optimizer = HybridAdam(unet.parameters(), lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm) + + # load noise_scheduler + noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") + + # prepare dataset + logger.info(f"Prepare dataset from {args.instance_data_dir}", ranks=[0]) + train_dataset = DreamBoothDataset( + instance_data_root=args.instance_data_dir, + instance_prompt=args.instance_prompt, + class_data_root=args.class_data_dir if args.with_prior_preservation else None, + class_prompt=args.class_prompt, + tokenizer=tokenizer, + size=args.resolution, + center_crop=args.center_crop, + ) + + def collate_fn(examples): + input_ids = [example["instance_prompt_ids"] for example in examples] + pixel_values = [example["instance_images"] for example in examples] + + # Concat class and instance examples for prior preservation. + # We do this to avoid doing two forward passes. + if args.with_prior_preservation: + input_ids += [example["class_prompt_ids"] for example in examples] + pixel_values += [example["class_images"] for example in examples] + + pixel_values = torch.stack(pixel_values) + pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() + + input_ids = tokenizer.pad( + { + "input_ids": input_ids + }, + padding="max_length", + max_length=tokenizer.model_max_length, + return_tensors="pt", + ).input_ids + + batch = { + "input_ids": input_ids, + "pixel_values": pixel_values, + } + return batch + + train_dataloader = torch.utils.data.DataLoader(train_dataset, + batch_size=args.train_batch_size, + shuffle=True, + collate_fn=collate_fn, + num_workers=1) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil(len(train_dataloader)) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps, + num_training_steps=args.max_train_steps, + ) + weight_dtype = torch.float32 + if args.mixed_precision == "fp16": + weight_dtype = torch.float16 + elif args.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + + # Move text_encode and vae to gpu. + # For mixed precision training we cast the text_encoder and vae weights to half-precision + # as these models are only used for inference, keeping weights in full precision is not required. + vae.to(get_current_device(), dtype=weight_dtype) + text_encoder.to(get_current_device(), dtype=weight_dtype) + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil(len(train_dataloader)) + if overrode_max_train_steps: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + # Afterwards we recalculate our number of training epochs + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + + unet, optimizer, _, _, lr_scheduler = booster.boost(unet, optimizer, lr_scheduler=lr_scheduler) + + # Train! + total_batch_size = args.train_batch_size * world_size + + logger.info("***** Running training *****", ranks=[0]) + logger.info(f" Num examples = {len(train_dataset)}", ranks=[0]) + logger.info(f" Num batches each epoch = {len(train_dataloader)}", ranks=[0]) + logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0]) + logger.info(f" Instantaneous batch size per device = {args.train_batch_size}", ranks=[0]) + logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0]) + logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0]) + + # Only show the progress bar once on each machine. + progress_bar = tqdm(range(args.max_train_steps), disable=not local_rank == 0) + progress_bar.set_description("Steps") + global_step = 0 + + torch.cuda.synchronize() + for epoch in range(args.num_train_epochs): + unet.train() + for step, batch in enumerate(train_dataloader): + torch.cuda.reset_peak_memory_stats() + # Move batch to gpu + for key, value in batch.items(): + batch[key] = value.to(get_current_device(), non_blocking=True) + + # Convert images to latent space + optimizer.zero_grad() + + latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() + latents = latents * 0.18215 + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + bsz = latents.shape[0] + # Sample a random timestep for each image + timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) + timesteps = timesteps.long() + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = text_encoder(batch["input_ids"])[0] + + # Predict the noise residual + model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + + # Get the target for loss depending on the prediction type + if noise_scheduler.config.prediction_type == "epsilon": + target = noise + elif noise_scheduler.config.prediction_type == "v_prediction": + target = noise_scheduler.get_velocity(latents, noise, timesteps) + else: + raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") + + if args.with_prior_preservation: + # Chunk the noise and model_pred into two parts and compute the loss on each part separately. + model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) + target, target_prior = torch.chunk(target, 2, dim=0) + + # Compute instance loss + loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean() + + # Compute prior loss + prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") + + # Add the prior loss to the instance loss. + loss = loss + args.prior_loss_weight * prior_loss + else: + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + optimizer.backward(loss) + + optimizer.step() + lr_scheduler.step() + logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB", ranks=[0]) + # Checks if the accelerator has performed an optimization step behind the scenes + progress_bar.update(1) + global_step += 1 + logs = { + "loss": loss.detach().item(), + "lr": optimizer.param_groups[0]["lr"], + } # lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + + if global_step % args.save_steps == 0: + torch.cuda.synchronize() + if local_rank == 0: + save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") + booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin")) + if not os.path.exists(os.path.join(save_path, "config.json")): + shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path) + logger.info(f"Saving model checkpoint to {save_path}", ranks=[0]) + if global_step >= args.max_train_steps: + break + torch.cuda.synchronize() + + booster.save_model(unet, os.path.join(args.output_dir, "diffusion_pytorch_model.bin")) + logger.info(f"Saving model checkpoint to {args.output_dir} on rank {local_rank}") + if local_rank == 0: + if not os.path.exists(os.path.join(args.output_dir, "config.json")): + shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), args.output_dir) + if args.push_to_hub: + repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) + +if __name__ == "__main__": + args = parse_args() + main(args) From 42e3232bc045aa7ea2fb690625d8baf588b80ed1 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Fri, 2 Jun 2023 17:00:57 +0800 Subject: [PATCH 260/413] roll back --- colossalai/cli/launcher/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/colossalai/cli/launcher/run.py b/colossalai/cli/launcher/run.py index 4bb749f9d293..6411b4302e95 100644 --- a/colossalai/cli/launcher/run.py +++ b/colossalai/cli/launcher/run.py @@ -154,7 +154,7 @@ def _arg_dict_to_list(arg_dict): extra_launch_args = dict() torch_version = version.parse(torch.__version__) - assert torch_version.major >= 1 + assert torch_version.major == 1 if torch_version.minor < 9: cmd = [ From 25447d44079de7be9083d07834d75b74f5ce8680 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Mon, 5 Jun 2023 11:47:07 +0800 Subject: [PATCH 261/413] modify path --- examples/tutorial/new_api/dreambooth/colossalai.sh | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/tutorial/new_api/dreambooth/colossalai.sh b/examples/tutorial/new_api/dreambooth/colossalai.sh index 7cf8b3a1307e..2745c563aa73 100755 --- a/examples/tutorial/new_api/dreambooth/colossalai.sh +++ b/examples/tutorial/new_api/dreambooth/colossalai.sh @@ -3,10 +3,10 @@ TRANSFORMERS_OFFLINE=1 DIFFUSERS_OFFLINE=1 torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ - --pretrained_model_name_or_path="Your Pretrained Model Path" \ - --instance_data_dir="Your Input Pics Path" \ - --output_dir="path-to-save-model" \ - --instance_prompt="your_prompt" \ + --pretrained_model_name_or_path="Path_to_your_model" \ + --instance_data_dir="Path_to_your_training_image" \ + --output_dir="Path_to_your_save_dir" \ + --instance_prompt="keli" \ --resolution=512 \ --plugin="gemini" \ --train_batch_size=1 \ From dbb32692d293d865640d857e8ee16d653147f7bc Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 5 Jun 2023 14:20:47 +0800 Subject: [PATCH 262/413] [lazy] refactor lazy init (#3891) * [lazy] remove old lazy init * [lazy] refactor lazy init folder structure * [lazy] fix lazy tensor deepcopy * [test] update lazy init test --- colossalai/lazy/__init__.py | 6 + .../experimental.py => lazy/lazy_init.py} | 9 +- colossalai/utils/model/lazy_init_context.py | 242 ------------------ colossalai/zero/gemini/gemini_ddp.py | 60 +++-- .../test_plugin/test_gemini_plugin.py | 2 +- .../lazy_init_utils.py | 10 +- .../test_distribute.py | 2 +- .../test_models.py | 0 tests/test_utils/test_lazy_init_ctx.py | 51 ---- 9 files changed, 57 insertions(+), 325 deletions(-) create mode 100644 colossalai/lazy/__init__.py rename colossalai/{utils/model/experimental.py => lazy/lazy_init.py} (98%) delete mode 100644 colossalai/utils/model/lazy_init_context.py rename tests/{test_utils/test_lazy_init => test_lazy}/lazy_init_utils.py (85%) rename tests/{test_utils/test_lazy_init => test_lazy}/test_distribute.py (97%) rename tests/{test_utils/test_lazy_init => test_lazy}/test_models.py (100%) delete mode 100644 tests/test_utils/test_lazy_init_ctx.py diff --git a/colossalai/lazy/__init__.py b/colossalai/lazy/__init__.py new file mode 100644 index 000000000000..4387107bf773 --- /dev/null +++ b/colossalai/lazy/__init__.py @@ -0,0 +1,6 @@ +from .lazy_init import LazyInitContext, LazyTensor + +__all__ = [ + 'LazyInitContext', + 'LazyTensor', +] diff --git a/colossalai/utils/model/experimental.py b/colossalai/lazy/lazy_init.py similarity index 98% rename from colossalai/utils/model/experimental.py rename to colossalai/lazy/lazy_init.py index bf3e3d05b99c..c1fda3c53865 100644 --- a/colossalai/utils/model/experimental.py +++ b/colossalai/lazy/lazy_init.py @@ -350,7 +350,14 @@ def factory_fn(): copied.requires_grad_() return copied - target = LazyTensor(factory_fn, meta_data=self._meta_data) + if self._materialized_data is not None: + # self is early materialized + copied = self._materialized_data.detach().clone() + if self.requires_grad: + copied.requires_grad_() + target = LazyTensor(lambda: None, concrete_data=copied) + else: + target = LazyTensor(factory_fn, meta_data=self._meta_data) memo[id(self)] = target return target diff --git a/colossalai/utils/model/lazy_init_context.py b/colossalai/utils/model/lazy_init_context.py deleted file mode 100644 index cf05f966089d..000000000000 --- a/colossalai/utils/model/lazy_init_context.py +++ /dev/null @@ -1,242 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -import inspect -import types -from typing import Callable, List - -import torch -import torch.nn as nn - -from colossalai.tensor import ColoParameter, ColoTensor -from colossalai.utils.model.utils import substitute_init_recursively - - -class LazyInitContext(): - """ - A context to allow for lazy weight initialization of PyTorch modules. It intercepts the tensor - initialization functions for lazy initialization - - Note: - This API is only experimental and subject to future changes. - - Usage: - with LazyInitContext() as ctx: - model = nn.Linear(10, 10) - model.weight.zero_() - - # make sure the weight is a meta tensor - assert model.weight.is_meta - - # initialize weights - ctx.lazy_init_parameters(model) - - # make sure the weight is not a meta tensor - # and initialized correctly - assert not model.weight.is_meta and torch.all(model.weight == 0) - - Args: - to_meta (bool): optional, whether to initialize the model with meta tensors, default is True. This - argument exists for now because some corner cases such as self.weight = torch.zeros(...) cannot be captured yet. - extra_torch_tensor_func (List[str]): extra torch tensor functions related - to value setting, such as `zero_` and `triu_`. `zero_` is pre-added by default. - """ - - tensor_set_value_func = ['zero_', 'fill_'] - - def __init__(self, to_meta: bool = True, extra_torch_tensor_func: List[str] = None): - # TODO: hijack the torch constructor functions as well - self._to_meta = to_meta - self._intercepted_nn_init_func_cache = {} - self._nn_init_methods = self._get_nn_init_methods() - self._torch_mod_cls = torch.nn.modules.module.Module - - if extra_torch_tensor_func: - # use tuple to remove duplicates - self._torch_tensor_funcs = tuple(self.tensor_set_value_func + extra_torch_tensor_func) - else: - self._torch_tensor_funcs = self.tensor_set_value_func - - @property - def to_meta(self): - return self._to_meta - - def _cache_init_func(self, func): - """ - This method wraps the ``torch.nn.init`` method and torch tensor value-setting functions - so that the function call is cached instead of being executed. - """ - - def wrapped_init_func(tensor, *args, **kwargs): - if tensor not in self._intercepted_nn_init_func_cache: - self._intercepted_nn_init_func_cache[tensor] = [] - self._intercepted_nn_init_func_cache[tensor].append((func, args, kwargs)) - - return wrapped_init_func - - def _get_nn_init_methods(self): - """ - This method looks for all available functions in the ``torch.nn.init`` - module. - """ - nn_init_method_names = dir(torch.nn.init) - nn_init_methods = [] - - # look for all methods in ``torch.nn.init`` module - for name in nn_init_method_names: - nn_init_methods.append((name, getattr(torch.nn.init, name))) - - def _is_init_method(item): - name, func = item - - if (not isinstance(func, types.FunctionType) or name.startswith('_') or not name.endswith('_')): - return False - else: - return True - - # remove methods which are not init functions - nn_init_methods = list(filter(_is_init_method, nn_init_methods)) - return nn_init_methods - - def _wrap_module_init(self, func): - """ - This method wraps the calls to the `__init__` of ``torch.nn.Module`` and replaces - the argument device with value 'meta' so that all modules are created as meta tensors. - """ - has_device = 'device' in inspect.signature(func).parameters - - def layer_lazy_init(module, *args, **kwargs): - # if this module contains device argument - # we set it to meta to initialize as meta backend - if has_device: - kwargs['device'] = 'meta' - func(module, *args, **kwargs) - - # if device is not found, we intialize it and convert to meta - if not has_device: - module.to('meta') - - return layer_lazy_init - - def _get_tmp_origin_func_ref(self, name): - """ - Generate a function name for consistency during caching and retrieving. - """ - return f'_orig_{name}' - - def _patch_nn_init_funcs(self): - # patch nn.init functions - for name, func in self._nn_init_methods: - setattr(torch.nn.init, name, self._cache_init_func(func)) - - def _unpatch_nn_init_funcs(self): - # unpatch nn.init functions - for name, func in self._nn_init_methods: - setattr(torch.nn.init, name, func) - - def _patch_submodule_init(self): - # patch classes __init__ methods - def _activate_wrap_init(cls): - cls.__orig_init__ = cls.__init__ - cls.__init__ = self._wrap_module_init(cls.__init__) - - substitute_init_recursively(self._torch_mod_cls, _activate_wrap_init, set()) - - def _unpatch_submodule_init(self): - - def _recover_orig_init(cls): - cls.__init__ = cls.__orig_init__ - - substitute_init_recursively(self._torch_mod_cls, _recover_orig_init, set()) - - def _patch_torch_tensor_funcs(self): - # patch tensor value-setting functions - for func_name in self._torch_tensor_funcs: - origin_func_name = self._get_tmp_origin_func_ref(func_name) - origin_func = getattr(torch.Tensor, func_name) - setattr(torch.Tensor, origin_func_name, origin_func) - setattr(torch.Tensor, func_name, self._cache_init_func(origin_func)) - - def _unpatch_torch_tensor_funcs(self): - for func_name in self._torch_tensor_funcs: - origin_func_name = self._get_tmp_origin_func_ref(func_name) - origin_func = getattr(torch.Tensor, origin_func_name) - setattr(torch.Tensor, func_name, origin_func) - - def __enter__(self): - self._patch_torch_tensor_funcs() - self._patch_nn_init_funcs() - - if self._to_meta: - self._patch_submodule_init() - return self - - def __exit__(self, *args, **kwargs): - if self._to_meta: - self._unpatch_submodule_init() - self._unpatch_nn_init_funcs() - self._unpatch_torch_tensor_funcs() - - def lazy_init_parameters(self, model: torch.nn.Module, device='cpu'): - """ - Initialize the weights of the meta-tensor model. - - Args: - model (`torch.nn.Module`): the model instantiated under the context. - device (str): the device on which weights are initialized - - """ - - def _init_recursively(module: nn.Module): - # recursively initialize the module - for mod in module.children(): - _init_recursively(mod) - - # initialize and shard tensors directly attached to the current module - for name, param in module.named_parameters(recurse=False): - _init_and_shard(module, name, param) - - for name, buf in module.named_buffers(recurse=False): - _init_and_shard(module, name, buf) - - @torch.no_grad() - def _init_and_shard(module, name, tensor): - # check whether the tensor is a buffer or parameter - is_param = isinstance(tensor, nn.parameter.Parameter) - - # get sharding spec - dist_spec = getattr(tensor, 'dist_spec', None) - pg = getattr(tensor, 'pg', None) - comp_spec = getattr(tensor, 'comp_spec', None) - - # convert the tensor from meta to materialized one - if tensor.is_meta: - materialized_tensor = torch.empty_like(tensor, device=device) - # if this tensor is a meta tensor, it must have an init function - assert tensor in self._intercepted_nn_init_func_cache - else: - materialized_tensor = tensor - - # apply init function - if tensor in self._intercepted_nn_init_func_cache: - init_func, args, kwargs = self._intercepted_nn_init_func_cache[tensor][-1] - init_func(materialized_tensor, *args, **kwargs) - - # convert it to ColoTensor or ColoParameter - if is_param: - tensor = ColoParameter.from_torch_tensor(materialized_tensor, requires_grad=tensor.requires_grad) - else: - tensor = ColoTensor.from_torch_tensor(materialized_tensor) - - # override the original tensor - with torch.no_grad(): - setattr(module, name, tensor) - - # apply sharding - if dist_spec: - tensor.process_group = pg - tensor.set_tensor_spec(dist_spec, comp_spec) - - _init_recursively(model) - - return model diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index 878c25be7094..fd49362d65da 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -2,13 +2,14 @@ from collections import OrderedDict from contextlib import nullcontext from functools import partial -from typing import Dict, Iterator, List, Optional, Union, Tuple, Set +from typing import Dict, Iterator, List, Optional, Set, Tuple, Union import torch import torch.distributed as dist import torch.nn as nn from colossalai.checkpoint_io.utils import calculate_tensor_size +from colossalai.lazy import LazyTensor from colossalai.logging import get_dist_logger from colossalai.nn.parallel.data_parallel import ColoDDP, _cast_float, free_storage from colossalai.tensor import ProcessGroup as ColoProcessGroup @@ -16,7 +17,6 @@ from colossalai.tensor.colo_parameter import ColoParameter, ColoTensor, ColoTensorSpec from colossalai.tensor.param_op_hook import ColoParamOpHookManager from colossalai.utils import get_current_device, is_ddp_ignored -from colossalai.utils.model.experimental import LazyTensor from .chunk import Chunk, ChunkManager, TensorState, init_chunk_manager from .gemini_hook import GeminiZeROHook @@ -96,34 +96,38 @@ def __init__(self, param_name = m_name + '.' + p_name if m_name else p_name self.name2param[param_name] = p_var super().__init__(module, process_group=ColoProcessGroup()) - self._non_persistent_buffers_set=self._get_non_persistent_buffers_set(module) + self._non_persistent_buffers_set = self._get_non_persistent_buffers_set(module) self._cast_buffers() - def _get_non_persistent_buffers_set(self, module, memo: Optional[Set[nn.Module]] = None, prefix: str = '', remove_duplicate: bool = True): - - r""" - Args: - memo: a memo to store the set of modules already added to the result - prefix: a prefix that will be added to the name of the module - remove_duplicate: whether to remove the duplicated module instances in the result - or not - """ - - if memo is None: - memo = set() - self_non_persistent_set = set() - if module not in memo: - if remove_duplicate: - memo.add(module) - self_non_persistent_set = set(map(lambda key: prefix + ('.' if prefix else '') + key, module._non_persistent_buffers_set)) - for name, sub_module in module._modules.items(): - if sub_module is None: - continue - submodule_prefix = prefix + ('.' if prefix else '') + name - child_non_persistent_set = self._get_non_persistent_buffers_set(sub_module, memo, submodule_prefix, remove_duplicate) - self_non_persistent_set = set.union(self_non_persistent_set, child_non_persistent_set) - return self_non_persistent_set - + def _get_non_persistent_buffers_set(self, + module, + memo: Optional[Set[nn.Module]] = None, + prefix: str = '', + remove_duplicate: bool = True): + r""" + Args: + memo: a memo to store the set of modules already added to the result + prefix: a prefix that will be added to the name of the module + remove_duplicate: whether to remove the duplicated module instances in the result + or not + """ + + if memo is None: + memo = set() + self_non_persistent_set = set() + if module not in memo: + if remove_duplicate: + memo.add(module) + self_non_persistent_set = set( + map(lambda key: prefix + ('.' if prefix else '') + key, module._non_persistent_buffers_set)) + for name, sub_module in module._modules.items(): + if sub_module is None: + continue + submodule_prefix = prefix + ('.' if prefix else '') + name + child_non_persistent_set = self._get_non_persistent_buffers_set(sub_module, memo, submodule_prefix, + remove_duplicate) + self_non_persistent_set = set.union(self_non_persistent_set, child_non_persistent_set) + return self_non_persistent_set def _post_forward(self): """This function is only triggered for inference. diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index c7b3676fb478..d606d6d89bd4 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -8,10 +8,10 @@ from colossalai.booster import Booster from colossalai.booster.plugin import GeminiPlugin from colossalai.fx import is_compatible_with_meta +from colossalai.lazy.lazy_init import LazyInitContext from colossalai.nn.optimizer import HybridAdam from colossalai.tensor.colo_parameter import ColoParameter from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn -from colossalai.utils.model.experimental import LazyInitContext from colossalai.zero import ColoInitContext from tests.kit.model_zoo import model_zoo diff --git a/tests/test_utils/test_lazy_init/lazy_init_utils.py b/tests/test_lazy/lazy_init_utils.py similarity index 85% rename from tests/test_utils/test_lazy_init/lazy_init_utils.py rename to tests/test_lazy/lazy_init_utils.py index aa87d32a808b..85bfd0e27801 100644 --- a/tests/test_utils/test_lazy_init/lazy_init_utils.py +++ b/tests/test_lazy/lazy_init_utils.py @@ -1,12 +1,13 @@ import random +from copy import deepcopy from typing import Any, Callable, Optional, Tuple import numpy as np import torch from packaging import version +from colossalai.lazy.lazy_init import LazyInitContext, LazyTensor, _MyTensor from colossalai.tensor.d_tensor.layout_converter import to_global -from colossalai.utils.model.experimental import LazyInitContext, LazyTensor, _MyTensor from tests.kit.model_zoo.registry import ModelAttribute SUPPORT_LAZY = version.parse(torch.__version__) >= version.parse('1.12.0') @@ -31,6 +32,9 @@ def assert_model_equal(m1: torch.nn.Module, m2: torch.nn.Module) -> None: assert n1 == n2 assert torch.equal(t1, t2), f'{n1} {t1} vs {t2}' + for p1, p2 in zip(m1.parameters(), m2.parameters()): + assert p1.requires_grad == p2.requires_grad + def assert_forward_equal(m1: torch.nn.Module, m2: torch.nn.Module, data_gen_fn: Callable[[], dict], output_transform_fn: Callable[[Any], dict]) -> None: @@ -65,10 +69,14 @@ def check_lazy_init(entry: TestingEntry, seed: int = 42, verbose: bool = False, ctx = LazyInitContext() with ctx: deferred_model = model_fn() + copied_deferred_model = deepcopy(deferred_model) deferred_model = ctx.materialize(deferred_model, verbose=verbose) + copied_deferred_model = ctx.materialize(copied_deferred_model, verbose=verbose) assert_model_equal(model, deferred_model) + assert_model_equal(deferred_model, copied_deferred_model) if check_forward: assert_forward_equal(model, deferred_model, data_gen_fn, output_transform_fn) + assert_forward_equal(deferred_model, copied_deferred_model, data_gen_fn, output_transform_fn) if verbose: print(f'{model.__class__.__name__} pass') diff --git a/tests/test_utils/test_lazy_init/test_distribute.py b/tests/test_lazy/test_distribute.py similarity index 97% rename from tests/test_utils/test_lazy_init/test_distribute.py rename to tests/test_lazy/test_distribute.py index fd91e7e912b5..d515b175a9ea 100644 --- a/tests/test_utils/test_lazy_init/test_distribute.py +++ b/tests/test_lazy/test_distribute.py @@ -12,7 +12,7 @@ from colossalai.utils.common import print_rank_0 try: - from colossalai.utils.model.experimental import LazyInitContext, LazyTensor, _MyTensor + from colossalai.lazy.lazy_init import LazyInitContext, LazyTensor, _MyTensor except: pass from lazy_init_utils import SUPPORT_LAZY, assert_dist_model_equal, set_seed diff --git a/tests/test_utils/test_lazy_init/test_models.py b/tests/test_lazy/test_models.py similarity index 100% rename from tests/test_utils/test_lazy_init/test_models.py rename to tests/test_lazy/test_models.py diff --git a/tests/test_utils/test_lazy_init_ctx.py b/tests/test_utils/test_lazy_init_ctx.py deleted file mode 100644 index 97efb3367490..000000000000 --- a/tests/test_utils/test_lazy_init_ctx.py +++ /dev/null @@ -1,51 +0,0 @@ -import torch -from colossalai.utils.model.lazy_init_context import LazyInitContext -from torchvision.models import resnet34 -import random -import numpy as np - -MANUAL_SEED = 0 -random.seed(MANUAL_SEED) -np.random.seed(MANUAL_SEED) -torch.manual_seed(MANUAL_SEED) - - -def test_lazy_init_with_meta(): - ctx = LazyInitContext(to_meta=True) - with ctx: - model = resnet34(num_classes=10) - - for param in model.parameters(): - assert param.is_meta - for buffer in model.buffers(): - assert buffer.is_meta - - ctx.lazy_init_parameters(model) - - for name, param in model.named_parameters(): - assert not param.is_meta, name - - for buffer in model.buffers(): - assert not buffer.is_meta - - -def test_lazy_init_without_meta(): - ctx = LazyInitContext(to_meta=False) - with ctx: - model = resnet34(num_classes=10) - - for param in model.parameters(): - assert not param.is_meta - for buffer in model.buffers(): - assert not buffer.is_meta - - conv1_weight_before_init = model.conv1.weight.clone() - ctx.lazy_init_parameters(model) - conv1_weight_after_init = model.conv1.weight.clone() - - assert not torch.allclose(conv1_weight_after_init, conv1_weight_before_init) - - -if __name__ == '__main__': - test_lazy_init_with_meta() - test_lazy_init_without_meta() From 8065cc5fbac578c9578209cd6768c2242c9aa20a Mon Sep 17 00:00:00 2001 From: Liu Ziming <38985202+MaruyamaAya@users.noreply.github.com> Date: Mon, 5 Jun 2023 15:57:35 +0800 Subject: [PATCH 263/413] Modify torch version requirement to adapt torch 2.0 (#3896) --- colossalai/cli/launcher/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/colossalai/cli/launcher/run.py b/colossalai/cli/launcher/run.py index 027a10aa898b..daa5107caf90 100644 --- a/colossalai/cli/launcher/run.py +++ b/colossalai/cli/launcher/run.py @@ -154,7 +154,7 @@ def _arg_dict_to_list(arg_dict): extra_launch_args = dict() torch_version = version.parse(torch.__version__) - assert torch_version.major == 1 + assert torch_version.major >= 1 if torch_version.minor < 9: cmd = [ From 07cb21142fc1daaf1a402f827721d3fdeb56d075 Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Mon, 5 Jun 2023 15:57:54 +0800 Subject: [PATCH 264/413] [doc]update moe chinese document. (#3890) * [doc]update-moe * [doc]update-moe * [doc]update-moe * [doc]update-moe * [doc]update-moe --- ...rate_mixture_of_experts_into_your_model.md | 1 + .../source/en/concepts/colossalai_overview.md | 4 +- ...rate_mixture_of_experts_into_your_model.md | 88 ++++++------------- .../zh-Hans/concepts/colossalai_overview.md | 4 +- 4 files changed, 33 insertions(+), 64 deletions(-) diff --git a/docs/source/en/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md b/docs/source/en/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md index d5edd135c079..bfa5539fe3a6 100644 --- a/docs/source/en/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md +++ b/docs/source/en/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md @@ -137,3 +137,4 @@ criterion = MoeLoss( Finally, just use trainer or engine in `colossalai` to do your training. Otherwise, you should take care of gradient by yourself. + diff --git a/docs/source/en/concepts/colossalai_overview.md b/docs/source/en/concepts/colossalai_overview.md index 38b682d49e62..7617c62a4e00 100644 --- a/docs/source/en/concepts/colossalai_overview.md +++ b/docs/source/en/concepts/colossalai_overview.md @@ -19,7 +19,7 @@ We aim to make Colossal-AI easy to use and non-intrusive to user code. There is 1. Prepare a configuration file where specifies the features you want to use and your parameters. 2. Initialize distributed backend with `colossalai.launch` -3. Inject the training features into your training components (e.g. model, optimizer) with `colossalai.initialize`. +3. Inject the training features into your training components (e.g. model, optimizer) with `colossalai.booster`. 4. Run training and testing We will cover the whole workflow in the `basic tutorials` section. @@ -34,3 +34,5 @@ The Colossal-AI system will be expanded to include more training skills, these n 4. expansion of existing parallelism methods We welcome ideas and contribution from the community and you can post your idea for future development in our forum. + + diff --git a/docs/source/zh-Hans/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md b/docs/source/zh-Hans/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md index 276fcc2619e0..8ed9a1e43cdd 100644 --- a/docs/source/zh-Hans/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md +++ b/docs/source/zh-Hans/advanced_tutorials/integrate_mixture_of_experts_into_your_model.md @@ -9,44 +9,24 @@ - [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) - [Go Wider Instead of Deeper](https://arxiv.org/abs/2107.11817) -（中文版教程将会在近期提供） - ## Introduction -Since the advent of Switch Transformer, the AI community has found Mixture of Experts (MoE) a useful technique to enlarge the capacity of deep learning models. - -Colossal-AI provides an early access version of parallelism specifically designed for MoE models. -The most prominent advantage of MoE in Colossal-AI is convenience. -We aim to help our users to easily combine MoE with model parallelism and data parallelism. - -However, the current implementation has two main drawbacks now. -The first drawback is its poor efficiency in large batch size and long sequence length training. -The second drawback is incompatibility with tensor parallelism. -We are working on system optimization to overcome the training efficiency problem. -The compatibility problem with tensor parallelism requires more adaptation, and we will tackle this issue in the future. - -Here, we will introduce how to use MoE with model parallelism and data parallelism. - -## Table of Content -In this tutorial we will cover: -1. Set up MoE running environment -2. Create MoE layer -3. Train your model +自从`Switch Transformer`出现以来，人工智能社区发现专家混合 (MoE) 是一种扩大深度学习模型容量的有用技术。 +Colossal-AI 提供了专为MoE模型设计的并行性的早期访问版本。Colossal-AI中MoE最突出的优势就是方便。我们的目标是帮助我们的用户轻松地将MoE与模型并行性和数据并行性结合起来。 +但是，当前的实施现在有两个主要缺点。第一个缺点是它在大批量和长序列长度训练中效率低下。第二个缺点是与张量并行性不兼容。我们正在致力于系统优化，以克服训练效率问题。与张量并行的兼容性问题需要更多的适应，我们将在未来解决这个问题。 +在这里，我们将介绍如何使用具有模型并行性和数据并行性的 MoE。 -We provided the [example code](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/widenet) for this tutorial in [ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI-Examples). -This example uses [WideNet](https://arxiv.org/abs/2107.11817) as an example of MoE-based model. +## 目录 +在本教程中，我们将介绍： +1. [搭建MoE运行环境](#搭建moe运行环境) +2. [创建MoE层](#创建moe层) +3. [定义训练模型](#训练模型) +我们提供[示例](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/widenet)， 详细介绍请参考 [ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI-Examples). +该示例使用 [WideNet](https://arxiv.org/abs/2107.11817) 作为基于 MoE 的模型的示例. -## Set up MoE running environment -In your project folder, create a `config.py`. - -This file is to specify some features you may want to use to train your model. -In order to enable MoE, you need to add a dict called parallel and specify the value of key moe. -You can assign a value for the key size of moe, which represents the model parallel size of experts (i.e. the number of experts in one group to parallelize training). - -For example, if the size is 4, 4 processes will be assigned to 4 consecutive GPUs and these 4 processes form a moe model parallel group. -Each process on the 4 GPUs will only get a portion of experts. Increasing the model parallel size will reduce communication cost, but increase computation cost in each GPU and activation cost in memory. -The total data parallel size is auto-detected and set as the number of GPUs by default. +## 搭建MoE运行环境 +在您的项目文件夹中，创建`config.py`文件。在该文件中，您可以指定希望用于训练模型的一些功能。为了启用 MoE，您需要在`config.py`中定义`parallel`字段，并指定`moe`的值。`moe`表示一组moe并行化训练组的并行大小。例如，`moe`设置为4，则4个进程将分配给4个连续的GPU，这4个进程组成一个moe模型并行组。每个进程只会得到一部分专家。增加mo e并行的大小将降低通信成本，但会增加每个GPU的计算成本和内存中activation的存储成本。总的数据并行的大小是自动检测的，默认情况下设置为GPU的数量。 ```python MOE_MODEL_PARALLEL_SIZE = ... @@ -55,37 +35,29 @@ parallel = dict( ) ``` -If `MOE_MODEL_PARALLEL_SIZE = E` and set the number of experts as `E` where `E` is a constant number, the process flow of forward pass of a transformer encoder in a model parallel group is shown below. +如果`MOE_MODEL_PARALLEL_SIZE = E`，即设置专家的总数为`E`（`E`为一个常数）。在模型并行中，transformer编码器中前向部分的处理流程如下图所示。
    MoE Transformer, image source: GShard
    -Since all experts are allocated to all GPUs in a model parallel group and a GPU only owns a portion of experts, -original data parallel groups are no longer correct for the parameters of experts during gradient handling in backward pass anymore. -So we create a new kind of parallel group called moe data parallel group. -The difference among different kinds of parallel group, when the configuration is set as `WORLD_SIZE=4`, -`MOE_MODEL_PARALLEL_SIZE=2`, is shown here. +所有专家都分配给模型并行组中的GPU，每一个GPU只拥有一部分专家，原始数据并行组在反向传递的梯度处理期间不再适用于专家参数。所以我们创建了一个新的并行组，叫做moe数据并行组。当配置设置为`WORLD_SIZE=4`，`MOE_MODEL_PARALLEL_SIZE=2`时，两个并行组的区别如下图所示。
    -
    MoE process group
    +
    MoE并行处理
    +至于梯度处理，我们提供了`MoeGradientHandler`来all-reduce模型的每个参数。如果您使用`colossalai.initialize`函数创建您的训练引擎，MoE梯度处理程序将自动添加到您的引擎中。否则，你应该自己处理梯度。MoE运行环境的所有参数都保存在`colossalai.global_variables.moe_env`中。您可以访问您的配置参数来检查您的设置是否正确。 -As for gradient handling, we provide MoeGradientHandler to all-reduce every parameter of the model. -If you use `colossalai.initialize` function to create your training engine, the MoE gradient handler will be added to your engine automatically. -Otherwise, you should take care of gradient by yourself. -All parameters of MoE running environment are stored in colossalai.global_variables.moe_env. -You can access your configuration parameters to check whether your setup is correct. ```python from colossalai.global_variables import moe_env ``` -## Create MoE layer -You can create a MoE layer from `colossalai.nn.moe`. -But before doing that, you should set up random seeds for all processes like this. +## 创建MoE层 + +您可以从`colossalai.nn.moe`创建MoE层。但在此之前，您应该为所有进程设置随机种子。 ```python from colossalai.context.random import moe_set_seed @@ -95,10 +67,7 @@ moe_set_seed(42) model = Widenet(num_experts=4, capacity_factor=1.2) ``` -`moe_set_seed` will set different seed for different processes in a moe model parallel group. -This helps initialize parameters in experts. -Then create an instance of experts and an instance of router. -Here is the example in model zoo. +`moe_set_seed` 会为一个moe模型并行组中的不同进程设置不同的种子（这有助于在专家中初始化参数），创建一个专家实例和一个路由器实例，示例如下。 ```python from colossalai.nn.layer.moe import Experts, MoeLayer, Top2Router, NormalNoiseGenerator @@ -118,16 +87,11 @@ ffn=MoeLayer(dim_model=d_model, num_experts=num_experts, router=shared_router, experts=shared_experts) ``` -Inside the initialization of Experts, the local expert number of each GPU will be calculated automatically. You just need to specify the class of each expert and its parameters used in its initialization. As for routers, we have provided top1 router and top2 router. You can find them in colossalai.nn.layer.moe. After creating the instance of experts and router, the only thing initialized in Moelayer is gate module. More definitions of each class can be found in our API document and code. - +在Experts的初始化中，会自动计算每个GPU的本地expert数量，您只需指定每个专家的类型及其在初始化时使用的参数。此外，我们提供了`Top1Router`和`Top2Router`，您可以在`colossalai.nn.layer.moe` 找到它们。在创建experts和router的实例时，`Moelayer`只初始化了`gate`模块，类型的更多详细信息您可以参考我们的API文档和代码。 -## Train Your Model -Do not to forget to use `colossalai.initialize` function in `colossalai` to add gradient handler for the engine. -We handle the back-propagation of MoE models for you. -In `colossalai.initialize`, we will automatically create a `MoeGradientHandler` object to process gradients. -You can find more information about the handler `MoeGradientHandler` in colossal directory. +## 定义训练模型 -The loss criterion should be wrapped by `Moeloss` to add auxiliary loss of MoE. Example is like this. +使用colossalai中的`colossalai.initialize`函数为引擎添加梯度处理程序以处理 MoE模型的反向传播。在 `colossalai.initialize` 中，我们会自动创建一个`MoeGradientHandler`对象来处理梯度。您可以在colossal目录中找到有关`MoeGradientHandler`的更多信息。为了添加MoE的相关损失处理，损失函数应使用`Moeloss`封装，示例如下。 ```python criterion = MoeLoss( aux_weight=0.01, @@ -135,6 +99,6 @@ criterion = MoeLoss( label_smoothing=0.1 ) ``` +最后，您只需使用 `colossalai` 中的`trainer`或`engine`进行训练即可。 -Finally, just use trainer or engine in `colossalai` to do your training. -Otherwise, you should take care of gradient by yourself. + diff --git a/docs/source/zh-Hans/concepts/colossalai_overview.md b/docs/source/zh-Hans/concepts/colossalai_overview.md index cfb35e59e64a..8b28baf8e3d5 100755 --- a/docs/source/zh-Hans/concepts/colossalai_overview.md +++ b/docs/source/zh-Hans/concepts/colossalai_overview.md @@ -19,7 +19,7 @@ Colossal-AI 是一个集成的系统，为用户提供一套综合的训练方 1. 准备一个配置文件，指定您要使用的功能和参数。 2. 用 `colossalai.launch` 初始化分布式后端。 -3. 用 `colossalai.initialize` 将训练特征注入您的训练组件（如模型、优化器）中。 +3. 用 `colossalai.booster` 将训练特征注入您的训练组件（如模型、优化器）中。 4. 进行训练和测试. 我们将在`基本教程`部分介绍整个工作流程。 @@ -34,3 +34,5 @@ Colossal-AI 系统将会进一步拓展和优化，包括但不限于: 4. 拓展现有的并行方法 **我们始终欢迎社区的建议和讨论，如果您遇到任何问题，我们将非常愿意帮助您。您可以在GitHub 提 [issue](https://github.com/hpcaitech/ColossalAI/issues) ，或在[论坛](https://github.com/hpcaitech/ColossalAI/discussions)上创建一个讨论主题。** + + From ae02d4e4f70e8ba4f8ae1058ac48bd08b06b6d24 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 5 Jun 2023 15:58:31 +0800 Subject: [PATCH 265/413] [bf16] add bf16 support (#3882) * [bf16] add bf16 support for fused adam (#3844) * [bf16] fused adam kernel support bf16 * [test] update fused adam kernel test * [test] update fused adam test * [bf16] cpu adam and hybrid adam optimizers support bf16 (#3860) * [bf16] implement mixed precision mixin and add bf16 support for low level zero (#3869) * [bf16] add mixed precision mixin * [bf16] low level zero optim support bf16 * [text] update low level zero test * [text] fix low level zero grad acc test * [bf16] add bf16 support for gemini (#3872) * [bf16] gemini support bf16 * [test] update gemini bf16 test * [doc] update gemini docstring * [bf16] add bf16 support for plugins (#3877) * [bf16] add bf16 support for legacy zero (#3879) * [zero] init context support bf16 * [zero] legacy zero support bf16 * [test] add zero bf16 test * [doc] add bf16 related docstring for legacy zero --- .../mixed_precision_mixin/__init__.py | 9 ++ .../naive_amp/mixed_precision_mixin/base.py | 91 ++++++++++++ .../naive_amp/mixed_precision_mixin/bf16.py | 23 +++ .../naive_amp/mixed_precision_mixin/fp16.py | 84 +++++++++++ colossalai/booster/plugin/gemini_plugin.py | 9 +- .../booster/plugin/low_level_zero_plugin.py | 33 +++-- .../kernel/cuda_native/csrc/type_shim.h | 15 ++ colossalai/nn/optimizer/cpu_adam.py | 23 ++- colossalai/nn/optimizer/fused_adam.py | 4 +- colossalai/nn/optimizer/hybrid_adam.py | 37 ++--- colossalai/zero/gemini/gemini_ddp.py | 24 +++- colossalai/zero/gemini/gemini_optimizer.py | 92 ++++++------ .../zero/legacy/init_ctx/init_context.py | 11 +- .../zero/legacy/sharded_model/_utils.py | 10 +- .../legacy/sharded_model/sharded_model_v2.py | 7 +- .../legacy/sharded_optim/sharded_optim_v2.py | 39 ++++-- colossalai/zero/low_level/low_level_optim.py | 106 +++++++------- tests/test_optimizer/test_adam_kernel.py | 131 ++++++++++++++++++ tests/test_optimizer/test_adam_optim.py | 86 ++++++++++++ tests/test_optimizer/test_cpu_adam.py | 121 ---------------- tests/test_optimizer/test_fused_adam.py | 64 --------- .../test_optimizer/test_fused_adam_kernel.py | 95 ------------- tests/test_optimizer/test_hybrid_adam.py | 42 ------ tests/test_zero/test_gemini/test_optim.py | 46 ++++-- .../test_zero/test_legacy/test_zero_engine.py | 21 ++- .../test_zero/test_low_level/test_grad_acc.py | 5 +- .../test_zero/test_low_level/test_zero1_2.py | 35 +++-- 27 files changed, 738 insertions(+), 525 deletions(-) create mode 100644 colossalai/amp/naive_amp/mixed_precision_mixin/__init__.py create mode 100644 colossalai/amp/naive_amp/mixed_precision_mixin/base.py create mode 100644 colossalai/amp/naive_amp/mixed_precision_mixin/bf16.py create mode 100644 colossalai/amp/naive_amp/mixed_precision_mixin/fp16.py create mode 100644 tests/test_optimizer/test_adam_kernel.py create mode 100644 tests/test_optimizer/test_adam_optim.py delete mode 100644 tests/test_optimizer/test_cpu_adam.py delete mode 100644 tests/test_optimizer/test_fused_adam.py delete mode 100644 tests/test_optimizer/test_fused_adam_kernel.py delete mode 100644 tests/test_optimizer/test_hybrid_adam.py diff --git a/colossalai/amp/naive_amp/mixed_precision_mixin/__init__.py b/colossalai/amp/naive_amp/mixed_precision_mixin/__init__.py new file mode 100644 index 000000000000..b0348e1477bb --- /dev/null +++ b/colossalai/amp/naive_amp/mixed_precision_mixin/__init__.py @@ -0,0 +1,9 @@ +from .base import MixedPrecisionMixin +from .bf16 import BF16MixedPrecisionMixin +from .fp16 import FP16MixedPrecisionMixin + +__all__ = [ + 'MixedPrecisionMixin', + 'FP16MixedPrecisionMixin', + 'BF16MixedPrecisionMixin', +] diff --git a/colossalai/amp/naive_amp/mixed_precision_mixin/base.py b/colossalai/amp/naive_amp/mixed_precision_mixin/base.py new file mode 100644 index 000000000000..a52a9747ad1e --- /dev/null +++ b/colossalai/amp/naive_amp/mixed_precision_mixin/base.py @@ -0,0 +1,91 @@ +from abc import ABC, abstractmethod + +import torch +from torch import Tensor + + +class MixedPrecisionMixin(ABC): + """A helper class for mixed precision training. This mixin is used in mixed precision optimizers. + + Attributes: + dtype (torc.dtype): The expected dtype of the gradients. + + Examples: + ```python + class MyMixedPrecisionOptimizer(OptimizerWrapper): + def __init__(self, optim: Optimizer): + super().__init__(optim) + self.mixed_precision = MixedPrecisionMixin() + + def backward(self, loss): + loss = self.mixed_precision.pre_backward(loss) + loss.backward() + + def backward_by_grad(self, tensor, grad): + grad = self.mixed_precision.pre_backward_by_grad(tensor, grad) + tensor.backward(grad) + + def step(self): + if self.mixed_precision.should_skip_step(): + self.zero_grad() + return + div_scale = self.mixed_precision.get_grad_div_scale() + # maybe clip grad here + # maybe scale grad here + self.optim.step() + + def zero_grad(self): + self.mixed_precision.pre_zero_grad() + return self.optim.zero_grad() + ``` + """ + dtype: torch.dtype + + @abstractmethod + def pre_backward(self, loss: Tensor) -> Tensor: + """Called before backward. + + Args: + loss (Tensor): Loss value. + + Returns: + Tensor: Loss value (possibly scaled). + """ + pass + + @abstractmethod + def pre_backward_by_grad(self, tensor: Tensor, grad: Tensor) -> Tensor: + """Called before backward by grad. This is helpful for pipeline parallelism. + + Args: + tensor (Tensor): Tensor to backward. + grad (Tensor): Gradient of the tensor. + + Returns: + Tensor: Gradient of the tensor (possibly scaled). + """ + pass + + @abstractmethod + def should_skip_step(self) -> bool: + """Called before step. + + Returns: + bool: Whether to skip the step. + """ + pass + + @abstractmethod + def pre_zero_grad(self) -> None: + """Called before zero_grad. + """ + pass + + @abstractmethod + def get_grad_div_scale(self) -> float: + """Called before step or clip_grad. To keep computation efficiency, this method does not (maybe) unscale grads. + + Returns: + float: A divisor for gradient clipping or step. + """ + pass diff --git a/colossalai/amp/naive_amp/mixed_precision_mixin/bf16.py b/colossalai/amp/naive_amp/mixed_precision_mixin/bf16.py new file mode 100644 index 000000000000..9454f6eb8413 --- /dev/null +++ b/colossalai/amp/naive_amp/mixed_precision_mixin/bf16.py @@ -0,0 +1,23 @@ +import torch +from torch import Tensor + +from .base import MixedPrecisionMixin + + +class BF16MixedPrecisionMixin(MixedPrecisionMixin): + dtype = torch.bfloat16 + + def pre_backward(self, loss: Tensor) -> Tensor: + return loss + + def pre_backward_by_grad(self, tensor: Tensor, grad: Tensor) -> Tensor: + return grad + + def should_skip_step(self) -> bool: + return False + + def pre_zero_grad(self) -> None: + pass + + def get_grad_div_scale(self) -> float: + return 1.0 diff --git a/colossalai/amp/naive_amp/mixed_precision_mixin/fp16.py b/colossalai/amp/naive_amp/mixed_precision_mixin/fp16.py new file mode 100644 index 000000000000..1ce8e42eb3ed --- /dev/null +++ b/colossalai/amp/naive_amp/mixed_precision_mixin/fp16.py @@ -0,0 +1,84 @@ +from abc import abstractmethod +from enum import Enum + +import torch +import torch.distributed as dist +from torch import Tensor + +from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler +from colossalai.utils import get_current_device + +from .base import MixedPrecisionMixin + + +class OptimState(Enum): + SCALED = 0 + UNSCALED = 1 + + +class FP16MixedPrecisionMixin(MixedPrecisionMixin): + dtype = torch.float16 + + def __init__(self, + initial_scale: float = 2**16, + min_scale: float = 1, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + max_scale: float = 2**32) -> None: + super().__init__() + self.grad_scaler = DynamicGradScaler(initial_scale=initial_scale, + min_scale=min_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + max_scale=max_scale) + self.optim_state = OptimState.UNSCALED + self.found_overflow = torch.zeros(1, dtype=torch.float, device=get_current_device()) + + @property + def loss_scale(self) -> float: + return self.grad_scaler.scale.item() + + @abstractmethod + def check_local_overflow(self) -> bool: + """Check whether there is overflow in the local process. This method should be implemented by subclasses. + + Returns: + bool: Whether there is overflow in the local process. + """ + pass + + def check_overflow(self) -> bool: + # clear previous overflow record + self.found_overflow.fill_(0.0) + if self.check_local_overflow(): + self.found_overflow.fill_(1.0) + dist.all_reduce(self.found_overflow, op=dist.ReduceOp.MAX) + return self.found_overflow.item() > 0 + + def pre_backward(self, loss: Tensor) -> Tensor: + loss = self.loss_scale * loss + self.optim_state = OptimState.SCALED + return loss + + def pre_backward_by_grad(self, tensor: Tensor, grad: Tensor) -> Tensor: + self.optim_state = OptimState.SCALED + return grad + + def should_skip_step(self) -> bool: + found_inf = self.check_overflow() + self.grad_scaler.update(found_inf) + if found_inf: + self.optim_state = OptimState.UNSCALED + return found_inf + + def pre_zero_grad(self) -> None: + pass + + def get_grad_div_scale(self) -> float: + assert self.optim_state == OptimState.SCALED, 'grads should be scaled before clipping' + self.optim_state = OptimState.UNSCALED + return self.loss_scale diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index adbf4803eefe..46714fe1c679 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -23,6 +23,9 @@ __all__ = ['GeminiPlugin'] +SUPPORTED_PRECISION = ['fp16', 'bf16'] +PRECISION_STR_TO_DTYPE = {'fp16': torch.half, 'bf16': torch.bfloat16} + class GeminiCheckpointIO(GeneralCheckpointIO): @@ -171,6 +174,7 @@ class GeminiPlugin(DPPluginBase): Args: device (torch.device): device to place the model. placement_policy (str, optional): "cpu", "cuda", "auto". Defaults to "cpu". + precision (str, optional): precision. Support 'fp16' and 'bf16'. Defaults to 'fp16'. pin_memory (bool, optional): use pin memory on CPU. Defaults to False. force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False. strict_ddp_mode (bool, optional): use strict ddp mode (only use dp without other parallelism). Defaults to False. @@ -203,6 +207,7 @@ def __init__( self, device: Optional[torch.device] = None, placement_policy: str = "cpu", + precision: str = "fp16", pin_memory: bool = False, force_outputs_fp32: bool = False, strict_ddp_mode: bool = False, @@ -223,6 +228,7 @@ def __init__( verbose: bool = False, ) -> None: super().__init__() + assert precision in SUPPORTED_PRECISION, f'precision {precision} is not supported' self.gemini_config = dict( device=(device or get_current_device()), placement_policy=placement_policy, @@ -233,6 +239,7 @@ def __init__( hidden_dim=hidden_dim, min_chunk_size_mb=min_chunk_size_mb, memstats=memstats, + mixed_precision=PRECISION_STR_TO_DTYPE[precision], ) self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio,) self.optim_kwargs = dict(initial_scale=initial_scale, @@ -253,7 +260,7 @@ def control_precision(self) -> bool: return True def supported_precisions(self) -> List[str]: - return ['fp16'] + return SUPPORTED_PRECISION def control_device(self) -> bool: return True diff --git a/colossalai/booster/plugin/low_level_zero_plugin.py b/colossalai/booster/plugin/low_level_zero_plugin.py index 5d93cf0e33be..2b312d0f9947 100644 --- a/colossalai/booster/plugin/low_level_zero_plugin.py +++ b/colossalai/booster/plugin/low_level_zero_plugin.py @@ -1,4 +1,5 @@ import warnings +from functools import partial from typing import Callable, Iterator, List, Optional, Tuple, Union import torch @@ -20,12 +21,15 @@ __all__ = ['LowLevelZeroPlugin'] -def _convert_to_fp16(x): +def _convert_floating_point(x, dtype: torch.dtype = torch.float16): if isinstance(x, torch.Tensor) and torch.is_floating_point(x): - return x.half() + return x.to(dtype) return x +SUPPORTED_PRECISION = ['fp16', 'bf16', 'fp32'] + + class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO): def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): @@ -49,17 +53,24 @@ class LowLevelZeroModel(ModelWrapper): def __init__(self, module: nn.Module, stage: int, precision: str) -> None: super().__init__(module) - self.convert_inputs = (precision == 'fp16') - module = zero_model_wrapper(module, zero_stage=stage) + self.dtype = None if precision == 'fp16': - module = module.half() + self.dtype = torch.float16 + elif precision == 'bf16': + self.dtype = torch.bfloat16 + module = zero_model_wrapper(module, zero_stage=stage) + if self.dtype is not None: + module = module.to(self.dtype) module = module.to(get_current_device()) self.module = module + self.convert_fn = None + if self.dtype is not None: + self.convert_fn = partial(_convert_floating_point, dtype=self.dtype) def forward(self, *args, **kwargs): - if self.convert_inputs: - args = tree_map(_convert_to_fp16, args) - kwargs = tree_map(_convert_to_fp16, kwargs) + if self.convert_fn is not None: + args = tree_map(self.convert_fn, args) + kwargs = tree_map(self.convert_fn, kwargs) return super().forward(*args, **kwargs) @@ -110,7 +121,7 @@ class LowLevelZeroPlugin(DPPluginBase): Args: strage (int, optional): ZeRO stage. Defaults to 1. - precision (str, optional): precision. Support 'fp16' and 'fp32'. Defaults to 'fp16'. + precision (str, optional): precision. Support 'fp16', 'bf16' and 'fp32'. Defaults to 'fp16'. initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32. min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1. growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2. @@ -149,7 +160,7 @@ def __init__( ) -> None: super().__init__() assert stage in (1, 2), f'LowLevelZeroPlugin only supports stage 1/2 training' - assert precision in ('fp16', 'fp32'), f'LowLevelZeroPlugin only supports fp16/fp32 training' + assert precision in SUPPORTED_PRECISION, f'LowLevelZeroPlugin only supports {SUPPORTED_PRECISION} training' self.stage = stage self.precision = precision @@ -175,7 +186,7 @@ def control_precision(self) -> bool: return True def supported_precisions(self) -> List[str]: - return ['fp16', 'fp32'] + return SUPPORTED_PRECISION def control_device(self) -> bool: return True diff --git a/colossalai/kernel/cuda_native/csrc/type_shim.h b/colossalai/kernel/cuda_native/csrc/type_shim.h index 2f180a7783ec..03ccc02635fa 100644 --- a/colossalai/kernel/cuda_native/csrc/type_shim.h +++ b/colossalai/kernel/cuda_native/csrc/type_shim.h @@ -171,6 +171,21 @@ using g_scalar_t_##LEVEL = at::Half; \ using p_scalar_t_##LEVEL = at::Half; \ __VA_ARGS__; \ + } else if (GTYPE == at::ScalarType::Float && \ + PTYPE == at::ScalarType::BFloat16) { \ + using g_scalar_t_##LEVEL = float; \ + using p_scalar_t_##LEVEL = at::BFloat16; \ + __VA_ARGS__; \ + } else if (GTYPE == at::ScalarType::BFloat16 && \ + PTYPE == at::ScalarType::Float) { \ + using g_scalar_t_##LEVEL = at::BFloat16; \ + using p_scalar_t_##LEVEL = float; \ + __VA_ARGS__; \ + } else if (GTYPE == at::ScalarType::BFloat16 && \ + PTYPE == at::ScalarType::BFloat16) { \ + using g_scalar_t_##LEVEL = at::BFloat16; \ + using p_scalar_t_##LEVEL = at::BFloat16; \ + __VA_ARGS__; \ } else { \ AT_ERROR(#NAME, "not implemented for '", toString(GTYPE), toString(PTYPE), \ "'"); \ diff --git a/colossalai/nn/optimizer/cpu_adam.py b/colossalai/nn/optimizer/cpu_adam.py index bb561a106515..7070c0a1e59d 100644 --- a/colossalai/nn/optimizer/cpu_adam.py +++ b/colossalai/nn/optimizer/cpu_adam.py @@ -93,8 +93,7 @@ def torch_adam_update(self, bias_correction1, bias_correction2, use_adamw=False): - # FIXME(ver217): remove the below line when replace torch adam with fused adam - grad = grad.float() + grad = grad.to(data.dtype) if weight_decay != 0: if use_adamw: @@ -133,10 +132,12 @@ def step(self, closure=None, div_scale: float = -1): if len(state) == 0: state['step'] = 0 + # FIXME(ver217): CPU adam kernel only supports fp32 states now + assert p.dtype is torch.float, "CPUAdam only support fp32 parameters" # gradient momentums - state['exp_avg'] = torch.zeros_like(p, dtype=torch.float, device=target_device) + state['exp_avg'] = torch.zeros_like(p, device=target_device) # gradient variances - state['exp_avg_sq'] = torch.zeros_like(p, dtype=torch.float, device=target_device) + state['exp_avg_sq'] = torch.zeros_like(p, device=target_device) self._post_state_init(p) state['step'] += 1 @@ -147,9 +148,17 @@ def step(self, closure=None, div_scale: float = -1): assert state['exp_avg'].device.type == 'cpu', "exp_avg should stay on cpu" assert state['exp_avg_sq'].device.type == 'cpu', "exp_avg should stay on cpu" self._pre_update(p, 'exp_avg', 'exp_avg_sq') - self.cpu_adam_op.step(state['step'], group['lr'], beta1, beta2, group['eps'], group['weight_decay'], - group['bias_correction'], p.data, p.grad.data, state['exp_avg'], - state['exp_avg_sq'], div_scale) + if p.grad.dtype is torch.bfloat16: + # cpu adam kernel does not support bf16 now + bias_correction1 = 1 - beta1**state['step'] + bias_correction2 = 1 - beta2**state['step'] + self.torch_adam_update(p.data, p.grad.data, state['exp_avg'], state['exp_avg_sq'], group['lr'], + beta1, beta2, group['eps'], group['weight_decay'], bias_correction1, + bias_correction2, self.adamw_mode) + else: + self.cpu_adam_op.step(state['step'], group['lr'], beta1, beta2, group['eps'], + group['weight_decay'], group['bias_correction'], p.data, p.grad.data, + state['exp_avg'], state['exp_avg_sq'], div_scale) self._post_update(p, 'exp_avg', 'exp_avg_sq') elif target_device.type == 'cuda': assert div_scale == -1, "div_scale should remain default" diff --git a/colossalai/nn/optimizer/fused_adam.py b/colossalai/nn/optimizer/fused_adam.py index 987af8a968b7..82a6250f1fd1 100644 --- a/colossalai/nn/optimizer/fused_adam.py +++ b/colossalai/nn/optimizer/fused_adam.py @@ -134,8 +134,8 @@ def step(self, closure=None, grads=None, output_params=None, scale=None, grad_no # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p) - if p.dtype not in [torch.float16, torch.float32]: - raise RuntimeError('FusedAdam only support fp16 and fp32.') + if p.dtype not in [torch.float16, torch.float32, torch.bfloat16]: + raise RuntimeError('FusedAdam only support fp16, fp32 and bf16.') g_l.append(p.grad.data) p_l.append(p.data) diff --git a/colossalai/nn/optimizer/hybrid_adam.py b/colossalai/nn/optimizer/hybrid_adam.py index be6311c6c29f..526071b06f95 100644 --- a/colossalai/nn/optimizer/hybrid_adam.py +++ b/colossalai/nn/optimizer/hybrid_adam.py @@ -1,16 +1,17 @@ from typing import Any, Optional import torch +from torch.optim import Adam -from colossalai.kernel.op_builder import CPUAdamBuilder, FusedOptimBuilder +from colossalai.kernel.op_builder import FusedOptimBuilder from colossalai.registry import OPTIMIZERS from colossalai.utils import multi_tensor_applier -from .nvme_optimizer import NVMeOptimizer +from .cpu_adam import CPUAdam @OPTIMIZERS.register_module -class HybridAdam(NVMeOptimizer): +class HybridAdam(CPUAdam): """Implements Adam algorithm. Supports parameters updating on both GPU and CPU, depanding on the device of parameters. @@ -74,15 +75,9 @@ def __init__(self, nvme_offload_dir: Optional[str] = None, **defaults: Any): - default_args = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, bias_correction=bias_correction) - super(HybridAdam, self).__init__(model_params, default_args, nvme_offload_fraction, nvme_offload_dir) - self.adamw_mode = adamw_mode - - # build during runtime if not found - cpu_optim = CPUAdamBuilder().load() + super().__init__(model_params, lr, bias_correction, betas, eps, weight_decay, adamw_mode, nvme_offload_fraction, + nvme_offload_dir) fused_optim = FusedOptimBuilder().load() - self.cpu_adam_op = cpu_optim.CPUAdamOptimizer(lr, betas[0], betas[1], eps, weight_decay, adamw_mode) - self.gpu_adam_op = fused_optim.multi_tensor_adam self._dummy_overflow_buf = torch.cuda.IntTensor([0]) @@ -108,10 +103,12 @@ def step(self, closure=None, div_scale: float = -1): if len(state) == 0: state['step'] = 0 + # FIXME(ver217): CPU adam kernel only supports fp32 states now + assert p.dtype is torch.float, "HybridAdam only support fp32 parameters" # gradient momentums - state['exp_avg'] = torch.zeros_like(p, dtype=torch.float, device=target_device) + state['exp_avg'] = torch.zeros_like(p, device=target_device) # gradient variances - state['exp_avg_sq'] = torch.zeros_like(p, dtype=torch.float, device=target_device) + state['exp_avg_sq'] = torch.zeros_like(p, device=target_device) self._post_state_init(p) state['step'] += 1 @@ -122,9 +119,17 @@ def step(self, closure=None, div_scale: float = -1): assert state['exp_avg'].device.type == 'cpu', "exp_avg should stay on cpu" assert state['exp_avg_sq'].device.type == 'cpu', "exp_avg should stay on cpu" self._pre_update(p, 'exp_avg', 'exp_avg_sq') - self.cpu_adam_op.step(state['step'], group['lr'], beta1, beta2, group['eps'], group['weight_decay'], - group['bias_correction'], p.data, p.grad.data, state['exp_avg'], - state['exp_avg_sq'], div_scale) + if p.grad.dtype is torch.bfloat16: + # cpu adam kernel does not support bf16 now + bias_correction1 = 1 - beta1**state['step'] + bias_correction2 = 1 - beta2**state['step'] + self.torch_adam_update(p.data, p.grad.data, state['exp_avg'], state['exp_avg_sq'], group['lr'], + beta1, beta2, group['eps'], group['weight_decay'], bias_correction1, + bias_correction2, self.adamw_mode) + else: + self.cpu_adam_op.step(state['step'], group['lr'], beta1, beta2, group['eps'], + group['weight_decay'], group['bias_correction'], p.data, p.grad.data, + state['exp_avg'], state['exp_avg_sq'], div_scale) self._post_update(p, 'exp_avg', 'exp_avg_sq') elif target_device.type == 'cuda': diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index fd49362d65da..7e23fdb425f8 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -51,6 +51,7 @@ class ZeroDDP(ColoDDP): strict_ddp_mode (bool): If set to True, there is no tensor sharding, each tensor is replicated. Defaults to False. Users can set it to True, when they clearly know that they only need DDP. scatter_after_inference (bool): If set to True, the model will be scattered after inference. This will save memory but slow down the consecutive inference. + mixed_precision (torch.dtype): If set to torch.float16, the model will be trained in fp16. Otherwise, the model will be trained in bf16. Defaults to torch.float16. """ def __init__(self, @@ -59,7 +60,9 @@ def __init__(self, pin_memory: bool = False, force_outputs_fp32: bool = False, strict_ddp_mode: bool = False, - scatter_after_inference: bool = True) -> None: + scatter_after_inference: bool = True, + mixed_precision: torch.dtype = torch.float16) -> None: + assert mixed_precision in (torch.float16, torch.bfloat16) self.gemini_manager = gemini_manager self.chunk_manager: ChunkManager = gemini_manager.chunk_manager self.force_outputs_fp32 = force_outputs_fp32 @@ -71,6 +74,7 @@ def __init__(self, self.param2name: Dict[nn.Parameter, str] = dict() self.name2param: Dict[str, nn.Parameter] = dict() self.scatter_after_inference = scatter_after_inference + self.mixed_precision = mixed_precision self._logger = get_dist_logger() @@ -151,7 +155,7 @@ def forward(self, *args, **kwargs): assert not self.gemini_manager.need_warmup or not self.gemini_manager.is_warmup( ), "You should run a completed iteration as your warmup iter" - args, kwargs = _cast_float(args, torch.half), _cast_float(kwargs, torch.half) + args, kwargs = _cast_float(args, self.mixed_precision), _cast_float(kwargs, self.mixed_precision) self.module.zero_grad(set_to_none=True) if not grad_flag: outputs = self._inference_forward(*args, **kwargs) @@ -570,14 +574,14 @@ def _init_chunks(self, param_order, strict_ddp_mode: bool, cpu_offload: bool, pi # move ignored parameters to CUDA if is_ddp_ignored(p): - p.data = p.data.to(device=get_current_device(), dtype=torch.float16) + p.data = p.data.to(device=get_current_device(), dtype=self.mixed_precision) continue # create a fp32 parameter fp32_data = p.data.float() fp32_p = ColoTensor(fp32_data, spec=ColoTensorSpec(p.process_group)) # create a fp16 parameter - p.data = p.data.half() + p.data = p.data.to(self.mixed_precision) # register the fp16 parameter and fp32 parameter in the chunk manager dp_world_size = p.process_group.dp_world_size() @@ -613,7 +617,7 @@ def _cast_buffers(self): buffer.materialize() buffer.data = buffer.cuda() if torch.is_floating_point(buffer): - buffer.data = buffer.half() + buffer.data = buffer.to(self.mixed_precision) def _preprocess_param(self, p: Union[nn.Parameter, ColoParameter, 'LazyTensor']) -> None: """Convert parameter to ColoParameter in-place. @@ -736,6 +740,7 @@ def __init__(self, hidden_dim: Optional[int] = None, min_chunk_size_mb: float = 32, memstats: Optional[MemStats] = None, + mixed_precision: torch.dtype = torch.float16, verbose: bool = False) -> None: """ A torch.Module wrapper using ZeRO-DP and Gemini. @@ -776,5 +781,10 @@ def __init__(self, strict_ddp_flag=strict_ddp_mode, verbose=verbose) gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats) - super().__init__(module, gemini_manager, pin_memory, force_outputs_fp32, strict_ddp_mode, - scatter_after_inference) + super().__init__(module, + gemini_manager, + pin_memory, + force_outputs_fp32, + strict_ddp_mode, + scatter_after_inference, + mixed_precision=mixed_precision) diff --git a/colossalai/zero/gemini/gemini_optimizer.py b/colossalai/zero/gemini/gemini_optimizer.py index 71c4f65cb8d2..267deb1e8699 100644 --- a/colossalai/zero/gemini/gemini_optimizer.py +++ b/colossalai/zero/gemini/gemini_optimizer.py @@ -1,7 +1,6 @@ # this code is inspired by the DeepSpeed library and implemented with our own design from scratch import math import warnings -from enum import Enum from typing import Any, Dict, Set, Tuple import torch @@ -9,7 +8,7 @@ from torch.nn import Parameter from torch.optim import Optimizer -from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler +from colossalai.amp.naive_amp.mixed_precision_mixin import BF16MixedPrecisionMixin, FP16MixedPrecisionMixin from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import ColossalaiOptimizer, CPUAdam, FusedAdam, HybridAdam from colossalai.utils import disposable, get_current_device, is_ddp_ignored @@ -22,9 +21,26 @@ _AVAIL_OPTIM_LIST = {FusedAdam, CPUAdam, HybridAdam} -class OptimState(Enum): - SCALED = 0 - UNSCALED = 1 +class GeminiFP16MixedPrecisionMixin(FP16MixedPrecisionMixin): + + def __init__(self, + module: ZeroDDP, + initial_scale: float = 2**16, + min_scale: float = 1, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + max_scale: float = 2**32) -> None: + super().__init__(initial_scale, min_scale, growth_factor, backoff_factor, growth_interval, hysteresis, + max_scale) + self.module = module + + def check_local_overflow(self) -> bool: + return self.module.overflow_counter > 0 + + def pre_zero_grad(self) -> None: + self.module.overflow_counter = 0 class ZeroOptimizer(ColossalaiOptimizer): @@ -79,7 +95,6 @@ def __init__(self, self.module = module self.gemini_manager = module.gemini_manager self.chunk_manager: ChunkManager = self.gemini_manager.chunk_manager - self.optim_state = OptimState.UNSCALED self.param_to_range: Dict[Parameter, Tuple[int, int]] = dict() self.param_to_chunk32: Dict[Parameter, Chunk] = dict() self.chunk16_set: Set[Chunk] = set() @@ -107,15 +122,20 @@ def __init__(self, self.__init__optimizer() - # Grad scaler - self.grad_scaler = DynamicGradScaler(initial_scale=initial_scale, - min_scale=min_scale, - growth_factor=growth_factor, - backoff_factor=backoff_factor, - growth_interval=growth_interval, - hysteresis=hysteresis, - max_scale=max_scale) - self._found_overflow: torch.Tensor = torch.zeros(1, dtype=torch.int64, device=get_current_device()) + if module.mixed_precision is torch.float16: + self.mix_precision_mixin = GeminiFP16MixedPrecisionMixin(module, + initial_scale=initial_scale, + min_scale=min_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + max_scale=max_scale) + elif module.mixed_precision is torch.bfloat16: + self.mix_precision_mixin = BF16MixedPrecisionMixin() + else: + raise RuntimeError(f"Unsupported mixed precision type: {module.mixed_precision}") + self._logger = get_dist_logger() self.gpu_margin_mem_ratio: float = float(gpu_margin_mem_ratio) @@ -151,15 +171,6 @@ def _update_fp16_params(self): for chunk16 in self.chunk16_set: chunk16.optim_update() - def _check_overflow(self): - # clear previous overflow record - self._found_overflow.fill_(self.module.overflow_counter) - - # all-reduce across global group - dist.all_reduce(self._found_overflow) - - return self._found_overflow.item() > 0 - def _clear_global_norm(self) -> None: for c16 in self.chunk16_set: c16.l2_norm = None @@ -190,40 +201,25 @@ def _calc_global_norm(self) -> float: return global_norm def _get_combined_scale(self): - loss_scale = 1 - - if self.optim_state == OptimState.SCALED: - loss_scale = self.loss_scale - self.optim_state = OptimState.UNSCALED + div_scale = self.mix_precision_mixin.get_grad_div_scale() - combined_scale = loss_scale if self.clipping_flag: total_norm = self._calc_global_norm() - clip = ((total_norm / loss_scale) + 1e-6) / self.max_norm + clip = ((total_norm / div_scale) + 1e-6) / self.max_norm if clip > 1: - combined_scale = clip * loss_scale + div_scale = clip * div_scale - if combined_scale == 1: - return -1 - else: - return combined_scale - - @property - def loss_scale(self): - return self.grad_scaler.scale.item() + return -1 if div_scale == 1.0 else div_scale def zero_grad(self, *args, **kwargs): - self.module.overflow_counter = 0 + self.mix_precision_mixin.pre_zero_grad() return self.optim.zero_grad(set_to_none=True) def step(self, *args, **kwargs): self._maybe_move_fp32_params() self._set_grad_ptr() - found_inf = self._check_overflow() - if found_inf: - self.optim_state = OptimState.UNSCALED # no need to unscale grad - self.grad_scaler.update(found_inf) # update gradient scaler + if self.mix_precision_mixin.should_skip_step(): if self.verbose: self._logger.info(f'Found overflow. Skip step') self._clear_global_norm() # clear recorded norm @@ -234,7 +230,6 @@ def step(self, *args, **kwargs): # get combined scale. combined scale = loss scale * clipping norm # so that gradient = gradient / combined scale combined_scale = self._get_combined_scale() - self.grad_scaler.update(found_inf) ret = self.optim.step(div_scale=combined_scale, *args, **kwargs) self._register_states() @@ -246,8 +241,7 @@ def clip_grad_norm(self, model: torch.nn.Module, max_norm: float, norm_type: flo raise NotImplementedError def backward(self, loss: torch.Tensor): - loss = self.loss_scale * loss - self.optim_state = OptimState.SCALED + loss = self.mix_precision_mixin.pre_backward(loss) self.module.backward(loss) def backward_by_grad(self, tensor: torch.Tensor, grad: torch.Tensor): @@ -255,7 +249,7 @@ def backward_by_grad(self, tensor: torch.Tensor, grad: torch.Tensor): # It receives the scaled grad from the previous rank # No need to scale the grad again # Need to unscale when optimizing - self.optim_state = OptimState.SCALED + grad = self.mix_precision_mixin.pre_backward_by_grad(grad) self.module.backward_by_grad(tensor, grad) def _maybe_move_fp32_params(self): diff --git a/colossalai/zero/legacy/init_ctx/init_context.py b/colossalai/zero/legacy/init_ctx/init_context.py index a921ca0aa83a..a3fa46b38b5a 100644 --- a/colossalai/zero/legacy/init_ctx/init_context.py +++ b/colossalai/zero/legacy/init_ctx/init_context.py @@ -14,7 +14,7 @@ from colossalai.logging import get_dist_logger from colossalai.utils.model.utils import InsertPostInitMethodToModuleSubClasses from colossalai.zero.legacy.shard_utils import BaseShardStrategy -from colossalai.zero.legacy.sharded_model._utils import cast_tensor_to_fp16 +from colossalai.zero.legacy.sharded_model._utils import cast_tensor_to_bf16, cast_tensor_to_fp16 from colossalai.zero.legacy.sharded_model.sharded_model_v2 import ShardedModelV2 from colossalai.zero.legacy.sharded_param import ShardedParamV2 @@ -55,6 +55,7 @@ class ZeroInitContext(InsertPostInitMethodToModuleSubClasses): seed (int, optional): Random seed for weight initialization shard_param (bool, optional): Is param sharded after exiting the context. Defaults to False. default_dtype (torch.dtype, optional): If it's not None, parameters will be initialized as ``default_dtype`` then converted to fp16. + bf16 (bool, optional): If it's True, parameters will be initialized as ``torch.bfloat16``. Otherwise, parameters will be initialized as ``torch.float16``. Defaults to False. model_numel_tensor (torch.Tensor, optional): A tensor which will store the number of elements of model. Defaults to torch.zeros(1, dtype=torch.int). """ @@ -64,6 +65,7 @@ def __init__(self, seed: int = 2**10 - 1, shard_param: bool = False, default_dtype: Optional[torch.dtype] = None, + bf16: bool = False, model_numel_tensor: torch.Tensor = torch.zeros(1, dtype=torch.long)): super().__init__(default_dtype=default_dtype) @@ -71,6 +73,7 @@ def __init__(self, self.param_list = [] self.model_numel_tensor = model_numel_tensor self.seed = seed + self.bf16 = bf16 self.dp_process_group = gpc.get_group(ParallelMode.DATA) self.config = ZeroContextConfig(target_device=target_device, is_replicated=True, shard_param=shard_param) @@ -183,9 +186,10 @@ def _post_init_method(self, module: torch.nn.Module, *args, **kwargs): NOTE() The module may be passed to this function multiple times. """ self.top_module = module + half_dtype = torch.float16 if not self.bf16 else torch.bfloat16 def half_fn(t: torch.Tensor): - return t.half() if t.is_floating_point() else t + return t.to(half_dtype) if t.is_floating_point() else t for param in module.parameters(recurse=False): # avoid adapting a param to ShardedParam twice @@ -226,9 +230,10 @@ def half_fn(t: torch.Tensor): # We must cast buffers # If we use BN, buffers may be on CPU and Float # We must cast them + cast_fn = cast_tensor_to_fp16 if not self.bf16 else cast_tensor_to_bf16 for buffer in module.buffers(recurse=False): buffer.data = buffer.data.to(device=torch.cuda.current_device()) - buffer.data = cast_tensor_to_fp16(buffer.data) + buffer.data = cast_fn(buffer.data) class ZeroContextMgr(metaclass=SingletonMeta): diff --git a/colossalai/zero/legacy/sharded_model/_utils.py b/colossalai/zero/legacy/sharded_model/_utils.py index 2bd01531a78f..f1d642cf3f13 100644 --- a/colossalai/zero/legacy/sharded_model/_utils.py +++ b/colossalai/zero/legacy/sharded_model/_utils.py @@ -43,11 +43,19 @@ def cast_tensor_to_fp32(tensor: Union[torch.Tensor, StatefulTensor]) -> torch.Te if isinstance(tensor, StatefulTensor): tensor = tensor.payload - if torch.is_floating_point(tensor) and tensor.dtype is torch.float16: + if torch.is_floating_point(tensor) and tensor.dtype in (torch.float16, torch.bfloat16): return tensor.float() return tensor +def cast_tensor_to_bf16(tensor: torch.Tensor) -> torch.Tensor: + if isinstance(tensor, StatefulTensor): + tensor = tensor.payload + if torch.is_floating_point(tensor) and tensor.dtype is torch.float32: + return tensor.bfloat16() + return tensor + + def apply_to_tensors(x: Any, fn: Callable): if torch.is_tensor(x): return fn(x) diff --git a/colossalai/zero/legacy/sharded_model/sharded_model_v2.py b/colossalai/zero/legacy/sharded_model/sharded_model_v2.py index b3a83b741825..be3842beb208 100644 --- a/colossalai/zero/legacy/sharded_model/sharded_model_v2.py +++ b/colossalai/zero/legacy/sharded_model/sharded_model_v2.py @@ -28,6 +28,7 @@ from ._utils import ( cast_float_arguments, + cast_tensor_to_bf16, cast_tensor_to_fp16, cast_tensor_to_fp32, chunk_and_pad, @@ -74,6 +75,7 @@ class ShardedModelV2(nn.Module): In this mode, grad will be fp16. Make sure your optimizer supports mixed precision (fp32 param and fp16 grad). We find that PyTorch's optimizers don't support mixed precision, so we recommend you enable this only when using our CPUAdam with CPU offload. Defaults to False. + bf16 (bool, optional): Whether to use bfloat16 for param and grad. Defaults to False. """ def __init__(self, @@ -86,11 +88,13 @@ def __init__(self, tensor_placement_policy: str = 'cuda', gradient_predivide_factor: Optional[float] = 1.0, reuse_fp16_shard: bool = False, + bf16: bool = False, *args, **kwargs): assert not isinstance(module, ShardedModelV2), 'Nested ShardedModelV2 is not supported.' super().__init__() self.logger = get_dist_logger() + self.bf16 = bf16 # We force users to use ZeroInitContext for submodule in module.modules(): @@ -232,7 +236,8 @@ def _post_forward_operations(self): def forward(self, *args: Any, **kwargs: Any) -> torch.Tensor: self._pre_forward_operations(*args) - args, kwargs = cast_float_arguments(cast_tensor_to_fp16, *args, **kwargs) + cast_fn = cast_tensor_to_bf16 if self.bf16 else cast_tensor_to_fp16 + args, kwargs = cast_float_arguments(cast_fn, *args, **kwargs) outputs = self.module(*args, **kwargs) self._post_forward_operations() return outputs diff --git a/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py b/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py index be60209af434..41dd174cb65a 100644 --- a/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py +++ b/colossalai/zero/legacy/sharded_optim/sharded_optim_v2.py @@ -94,6 +94,7 @@ def __init__(self, super().__init__(optimizer) self.shard_strategy = sharded_model.shard_strategy self.model: ShardedModelV2 = sharded_model + self.bf16 = sharded_model.bf16 self.gpu_margin_mem_ratio: float = float(gpu_margin_mem_ratio) assert 0.0 <= self.gpu_margin_mem_ratio <= 1.0, f'gpu_margin_mem_ratio must >=0.0 and <=1.0' @@ -117,6 +118,7 @@ def __init__(self, self._found_overflow: Tensor = torch.IntTensor([0]).to(torch.cuda.current_device()) self._logger = get_dist_logger("ShardedOptimizerV2") self._verbose = verbose + self._grad_prepared: bool = False # this should be set to true when _prepare_grads() and reset to false when backward # Store fp32 param shards self._register_master_weight() @@ -166,8 +168,10 @@ def zero_grad(self, *args, **kwargs): self._zero_grad() def backward(self, loss: Tensor) -> None: - loss = self.loss_scale * loss - self.optim_state = OptimState.SCALED + if not self.bf16: + loss = self.loss_scale * loss + self.optim_state = OptimState.SCALED + self._grad_prepared = False self.model.backward(loss) def backward_by_grad(self, tensor: Tensor, grad: Tensor) -> None: @@ -175,30 +179,33 @@ def backward_by_grad(self, tensor: Tensor, grad: Tensor) -> None: # It receives the scaled grad from the previous rank # No need to scale the grad again # Need to unscale when optimizing - self.optim_state = OptimState.SCALED + if not self.bf16: + self.optim_state = OptimState.SCALED + self._grad_prepared = False self.model.backward_by_grad(tensor, grad) def clip_grad_norm(self, model: nn.Module, max_norm: float): - if self.optim_state == OptimState.SCALED: - self._prepare_grads() + self._prepare_grads() + if not self.bf16 and self.optim_state == OptimState.SCALED: self._unscale_grads() return super().clip_grad_norm(model, max_norm) def step(self, *args, **kwargs): + self._prepare_grads() # unscale grads if scaled - if self.optim_state == OptimState.SCALED: - self._prepare_grads() + if not self.bf16 and self.optim_state == OptimState.SCALED: self._unscale_grads() self._maybe_move_fp32_shards() - found_inf = self._check_overflow() - self.grad_scaler.update(found_inf) + if not self.bf16: + found_inf = self._check_overflow() + self.grad_scaler.update(found_inf) - if found_inf: - self._logger.warning('found inf during ShardedOptimV2 step') - self._zero_grad(recover_data=True) - return + if found_inf: + self._logger.warning('found inf during ShardedOptimV2 step') + self._zero_grad(recover_data=True) + return self._point_param_fp16_to_master_param() @@ -304,6 +311,8 @@ def _maybe_move_fp32_shards(self): state[k] = v.cuda() def _prepare_grads(self): + if self._grad_prepared: + return for group in self.optim.param_groups: for p in group['params']: if p.colo_attr.saved_grad.is_null(): @@ -320,6 +329,7 @@ def _prepare_grads(self): p.grad = p.colo_attr.grad_payload # Set p.data to empty tensor, in case of memory leaking p.colo_attr.set_data_none() + self._grad_prepared = True def _point_param_fp16_to_master_param(self): # assign master param pointers to p.data. @@ -357,7 +367,8 @@ def _copy_master_param_to_param_fp16(self, p): torch.empty(p.data.shape, dtype=p.colo_attr.data_payload.dtype, device=p.colo_attr.data_payload.device)) # TODO() optimize this line CPU (fp32) -> GPU (fp16) - p.colo_attr.sharded_data_tensor.payload_copy(p.half().detach()) + half_dtype = torch.bfloat16 if self.bf16 else torch.float16 + p.colo_attr.sharded_data_tensor.payload_copy(p.to(half_dtype).detach()) p.colo_attr.set_data_none() if p.colo_attr.keep_not_shard and p.colo_attr.is_replicated: diff --git a/colossalai/zero/low_level/low_level_optim.py b/colossalai/zero/low_level/low_level_optim.py index 3e7661ecab76..d4d03e5b5fcd 100644 --- a/colossalai/zero/low_level/low_level_optim.py +++ b/colossalai/zero/low_level/low_level_optim.py @@ -6,7 +6,11 @@ import torch.distributed as dist from torch.optim import Optimizer -from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler +from colossalai.amp.naive_amp.mixed_precision_mixin import ( + BF16MixedPrecisionMixin, + FP16MixedPrecisionMixin, + MixedPrecisionMixin, +) from colossalai.context import ParallelMode from colossalai.core import global_context as gpc from colossalai.logging import get_dist_logger @@ -27,6 +31,31 @@ from .bookkeeping import BucketStore, GradientStore, ParameterStore, TensorBucket +class LowLevelZeroFP16MixedPrecisionMixin(FP16MixedPrecisionMixin): + + def __init__(self, + num_working_param_groups: int, + grad_store: GradientStore, + initial_scale: float = 2**16, + min_scale: float = 1, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + max_scale: float = 2**32) -> None: + super().__init__(initial_scale, min_scale, growth_factor, backoff_factor, growth_interval, hysteresis, + max_scale) + self.num_working_param_groups = num_working_param_groups + self.grad_store = grad_store + + def check_local_overflow(self) -> bool: + for group_id in range(self.num_working_param_groups): + for avg_grad in self.grad_store.get_averaged_gradients_by_group(group_id): + if avg_grad is not None and has_inf_or_nan(avg_grad): + return True + return False + + class LowLevelZeroOptimizer(ColossalaiOptimizer): """Optimizer used for ZeRO-1 and ZeRO-2. """ @@ -100,17 +129,6 @@ def __init__( self._reduce_bucket_size = reduce_bucket_size self._communication_dtype = communication_dtype - # gradient scaler - self.grad_scaler = DynamicGradScaler(initial_scale=initial_scale, - min_scale=min_scale, - growth_factor=growth_factor, - backoff_factor=backoff_factor, - growth_interval=growth_interval, - hysteresis=hysteresis, - max_scale=max_scale, - verbose=verbose) - self._found_overflow = torch.FloatTensor([0]).to(get_current_device()) - # gradient clipping self._clip_grad_norm = clip_grad_norm @@ -200,14 +218,25 @@ def __init__( if self._overlap_communication or self._partition_grads: self._attach_reduction_hook() + # initialize mixed precision mixin + self.mixed_precision_mixin: Optional[MixedPrecisionMixin] = None + if self._dtype is torch.float16: + self.mixed_precision_mixin = LowLevelZeroFP16MixedPrecisionMixin(self.num_param_groups, + self._grad_store, + initial_scale=initial_scale, + min_scale=min_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + max_scale=max_scale) + elif self._dtype is torch.bfloat16: + self.mixed_precision_mixin = BF16MixedPrecisionMixin() + @property def dtype(self): return self._dtype - @property - def loss_scale(self): - return self.grad_scaler.scale - @property def num_param_groups(self): return len(self._working_param_groups) @@ -392,7 +421,8 @@ def _add_to_reduction_bucket(self, param, reduce_rank=None): ################################ def backward(self, loss, retain_graph=False, sync_grad=True): - loss = self.loss_scale * loss + if self.mixed_precision_mixin is not None: + loss = self.mixed_precision_mixin.pre_backward(loss) loss.backward(retain_graph=retain_graph) # finish gradient reduction @@ -419,6 +449,8 @@ def zero_grad(self, set_to_none=True): :param set_to_none: Whether set the gradient to None. Default value is True. :type set_to_none: bool """ + if self.mixed_precision_mixin is not None: + self.mixed_precision_mixin.pre_zero_grad() for _, param_group in self._working_param_groups.items(): for param in param_group: if set_to_none: @@ -435,12 +467,7 @@ def zero_grad(self, set_to_none=True): def step(self, closure=None): assert closure is None, 'closure is not supported by step()' - # check for overflow - found_inf = self._check_overflow() - self.grad_scaler.update(found_inf) - - # update loss scale if overflow occurs - if found_inf: + if self.mixed_precision_mixin is not None and self.mixed_precision_mixin.should_skip_step(): self._grad_store.reset_all_average_gradients() if self._verbose: self._logger.info(f'Found overflow. Skip step') @@ -507,41 +534,20 @@ def step(self, closure=None): # Mixed Precision Utilities # ############################# - def _check_overflow(self): - # clear previous overflow record - self._found_overflow.fill_(0.0) - - # check for overflow - for group_id in range(len(self._working_param_groups)): - for avg_grad in self._grad_store.get_averaged_gradients_by_group(group_id): - if avg_grad is not None and has_inf_or_nan(avg_grad): - self._found_overflow.fill_(1.0) - break - - # all-reduce across dp group - dist.all_reduce(self._found_overflow, op=dist.ReduceOp.MAX, group=self._dp_torch_group) - - # all-reduce over model parallel group - if self._mp_torch_group: - dist.all_reduce(self._found_overflow, op=dist.ReduceOp.MAX, group=self._mp_torch_group) - - if self._found_overflow.item() > 0: - return True - else: - return False - def _unscale_and_clip_grads(self, grad_groups_flat, total_norm): # compute combined scale factor for this group - combined_scale = self.loss_scale + div_scale = 1.0 + if self.mixed_precision_mixin is not None: + div_scale = self.mixed_precision_mixin.get_grad_div_scale() if self._clip_grad_norm > 0.: # norm is in fact norm*scale - clip = ((total_norm / self.loss_scale) + 1e-6) / self._clip_grad_norm + clip = ((total_norm / div_scale) + 1e-6) / self._clip_grad_norm if clip > 1: - combined_scale = clip * self.loss_scale + div_scale = clip * div_scale for grad in grad_groups_flat: - grad.data.mul_(1. / combined_scale) + grad.data.mul_(1. / div_scale) ############################ # Gradient Synchronization # diff --git a/tests/test_optimizer/test_adam_kernel.py b/tests/test_optimizer/test_adam_kernel.py new file mode 100644 index 000000000000..2186a421fe00 --- /dev/null +++ b/tests/test_optimizer/test_adam_kernel.py @@ -0,0 +1,131 @@ +# This test checks adam kernels +# Baseline is pure fp32 torch adam optimizer +import math +from abc import abstractmethod +from typing import Type + +import pytest +import torch +from torch import Tensor + +from colossalai.utils import get_current_device, multi_tensor_applier + +_FUSED_ALLOWED_P_G_TYPES = [(torch.float, torch.half), (torch.float, torch.float), (torch.half, torch.float), + (torch.half, torch.half), (torch.bfloat16, torch.float), (torch.float, torch.bfloat16), + (torch.bfloat16, torch.bfloat16)] + +_CPU_ALLOWED_P_G_TYPES = [(torch.float, torch.half), (torch.float, torch.float), (torch.half, torch.float), + (torch.half, torch.half)] + + +class AdamKernel: + + def __init__(self, lr: float, beta1: float, beta2: float, eps: float, weight_decay: float, use_adamw: bool) -> None: + self.lr = lr + self.beta1 = beta1 + self.beta2 = beta2 + self.eps = eps + self.weight_decay = weight_decay + self.use_adamw = use_adamw + + @abstractmethod + def update(self, step: int, param: Tensor, grad: Tensor, exp_avg: Tensor, exp_avg_sq: Tensor): + pass + + +class TorchAdamKernel(AdamKernel): + + def update(self, step: int, param: Tensor, grad: Tensor, exp_avg: Tensor, exp_avg_sq: Tensor): + bias_correction1 = 1 - self.beta1**step + bias_correction2 = 1 - self.beta2**step + + if self.weight_decay != 0: + if self.use_adamw: + # Perform stepweight decay + param.mul_(1 - self.lr * self.weight_decay) + else: + grad = grad.add(param, alpha=self.weight_decay) + + # Decay the first and second moment running average coefficient + exp_avg.mul_(self.beta1).add_(grad, alpha=1 - self.beta1) + exp_avg_sq.mul_(self.beta2).addcmul_(grad, grad, value=1 - self.beta2) + denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(self.eps) + + step_size = self.lr / bias_correction1 + + param.addcdiv_(exp_avg, denom, value=-step_size) + + +class FusedAdamKernel(AdamKernel): + + def __init__(self, lr: float, beta1: float, beta2: float, eps: float, weight_decay: float, use_adamw: bool) -> None: + super().__init__(lr, beta1, beta2, eps, weight_decay, use_adamw) + from colossalai.kernel.op_builder import FusedOptimBuilder + fused_optim = FusedOptimBuilder().load() + self.fused_adam = fused_optim.multi_tensor_adam + self.dummy_overflow_buf = torch.cuda.IntTensor([0]) + + def update(self, step: int, param: Tensor, grad: Tensor, exp_avg: Tensor, exp_avg_sq: Tensor): + multi_tensor_applier(self.fused_adam, self.dummy_overflow_buf, [[grad], [param], [exp_avg], [exp_avg_sq]], + self.lr, self.beta1, self.beta2, self.eps, step, self.use_adamw, True, self.weight_decay, + -1) + + +class CPUAdamKernel(AdamKernel): + + def __init__(self, lr: float, beta1: float, beta2: float, eps: float, weight_decay: float, use_adamw: bool) -> None: + super().__init__(lr, beta1, beta2, eps, weight_decay, use_adamw) + from colossalai.kernel.op_builder import CPUAdamBuilder + cpu_optim = CPUAdamBuilder().load() + + self.cpu_adam_op = cpu_optim.CPUAdamOptimizer(lr, beta1, beta2, eps, weight_decay, use_adamw) + + def update(self, step: int, param: Tensor, grad: Tensor, exp_avg: Tensor, exp_avg_sq: Tensor): + self.cpu_adam_op.step(step, self.lr, self.beta1, self.beta2, self.eps, self.weight_decay, True, param.view(-1), + grad.view(-1), exp_avg.view(-1), exp_avg_sq.view(-1), -1) + + +def check_adam_kernel(kernel: Type[AdamKernel], adamw: bool, weight_decay: float, p_dtype: torch.dtype, + g_dtype: torch.dtype, device: torch.device, n_steps: int, rtol: float, atol: float): + lr = 1e-3 + beta1, beta2 = 0.9, 0.999 + eps = 1e-8 + torch_adam = TorchAdamKernel(lr, beta1, beta2, eps, weight_decay, adamw) + adam_kernel = kernel(lr, beta1, beta2, eps, weight_decay, adamw) + master_p = torch.rand(64, device=device) + master_g = torch.rand_like(master_p) + master_exp_avg = torch.zeros_like(master_p) + master_exp_avg_sq = torch.zeros_like(master_p) + p = master_p.clone().to(p_dtype) + g = master_g.clone().to(g_dtype) + exp_avg = master_exp_avg.clone() + exp_avg_sq = master_exp_avg_sq.clone() + + for step in range(1, 1 + n_steps): + torch_adam.update(step, master_p, master_g, master_exp_avg, master_exp_avg_sq) + adam_kernel.update(step, p, g, exp_avg, exp_avg_sq) + # if overflow, the weight won't be updated. so there will be no nan in p + assert not torch.isnan(p).any() + assert torch.allclose(master_p, p.float(), rtol=rtol, atol=atol) + + +@pytest.mark.parametrize('adamw', [False, True]) +@pytest.mark.parametrize('weight_decay', [0.0, 0.1]) +@pytest.mark.parametrize('p_dtype, g_dtype', _FUSED_ALLOWED_P_G_TYPES) +def test_fused_adam_kernel(adamw, weight_decay, p_dtype, g_dtype): + rtol, atol = 1e-5, 1e-8 + if p_dtype is torch.float16 or g_dtype is torch.float16: + rtol, atol = 1e-3, 1e-3 + if p_dtype is torch.bfloat16 or g_dtype is torch.bfloat16: + rtol, atol = 4e-3, 4e-3 + check_adam_kernel(FusedAdamKernel, adamw, weight_decay, p_dtype, g_dtype, get_current_device(), 3, rtol, atol) + + +@pytest.mark.parametrize('adamw', [False, True]) +@pytest.mark.parametrize('weight_decay', [0.0, 0.1]) +@pytest.mark.parametrize('p_dtype, g_dtype', _CPU_ALLOWED_P_G_TYPES) +def test_cpu_adam_kernel(adamw, weight_decay, p_dtype, g_dtype): + rtol, atol = 1e-5, 1e-8 + if p_dtype is torch.float16 or g_dtype is torch.float16: + rtol, atol = 1e-3, 1e-3 + check_adam_kernel(CPUAdamKernel, adamw, weight_decay, p_dtype, g_dtype, torch.device('cpu'), 3, rtol, atol) diff --git a/tests/test_optimizer/test_adam_optim.py b/tests/test_optimizer/test_adam_optim.py new file mode 100644 index 000000000000..0f72bc134809 --- /dev/null +++ b/tests/test_optimizer/test_adam_optim.py @@ -0,0 +1,86 @@ +from copy import deepcopy +from typing import Type, Union + +import pytest +import torch +import torch.nn as nn +from torch.optim import Adam, AdamW + +from colossalai.nn.optimizer import CPUAdam, FusedAdam, HybridAdam +from tests.kit.model_zoo import model_zoo + +_ALLOWED_OPTIM_DEVICES = [ + (FusedAdam, torch.device('cuda:0')), + (CPUAdam, torch.device('cpu')), + (CPUAdam, torch.device('cuda:0')), + (HybridAdam, torch.device('cpu')), + (HybridAdam, torch.device('cuda:0')), +] + +_ALLOWED_P_G_TYPES = [ + (torch.float, torch.float), # pure fp32 + (torch.float, torch.half), # fp16 amp + (torch.float, torch.bfloat16), # bfloat16 amp + # (torch.half, torch.half), # FIXME(ver217): cpu adam kernel does not support pure fp16 + # (torch.bfloat16, torch.bfloat16), # FIXME(ver217): cpu adam kernel does not support pure bfloat16 +] + +N_STEPS = 3 + + +def setup_param_groups(bert_model: nn.Module) -> list: + no_decay = ["bias", "LayerNorm.weight"] + optimizer_grouped_parameters = [ + { + "params": [p for n, p in bert_model.named_parameters() if not any(nd in n for nd in no_decay)], + "weight_decay": 0.1, + }, + { + "params": [p for n, p in bert_model.named_parameters() if any(nd in n for nd in no_decay)], + "weight_decay": 0.0, + }, + ] + return optimizer_grouped_parameters + + +def set_grad(model: nn.Module, torch_model: nn.Module, g_dtype: torch.dtype) -> None: + for p, torch_p in zip(model.parameters(), torch_model.parameters()): + torch_p.grad = torch.rand_like(torch_p) + # avoid inconsistent grad and param dtype error + orig_p = p.data + p.data = torch_p.grad.clone().to(g_dtype) + p.grad = p.data + p.data = orig_p + + +@pytest.mark.parametrize('optim_cls, device', _ALLOWED_OPTIM_DEVICES) +@pytest.mark.parametrize('adamw', [False, True]) +@pytest.mark.parametrize('p_dtype, g_dtype', _ALLOWED_P_G_TYPES) +def test_adam_optim_on_bert(optim_cls: Union[Type[FusedAdam], Type[CPUAdam], Type[HybridAdam]], device: torch.device, + adamw: bool, p_dtype: torch.dtype, g_dtype: torch.dtype) -> None: + model_fn, *_ = next(iter(model_zoo.get_sub_registry('transformers_bert_for_sequence_classification').values())) + torch_model = model_fn().to(device) + model = deepcopy(torch_model).to(p_dtype) + lr = 1e-3 + beta1, beta2 = 0.9, 0.999 + eps = 1e-8 + torch_optim_cls = AdamW if adamw else Adam + torch_optim = torch_optim_cls(setup_param_groups(torch_model), lr=lr, betas=(beta1, beta2), eps=eps) + optim = optim_cls(setup_param_groups(model), lr=lr, betas=(beta1, beta2), eps=eps, adamw_mode=adamw) + + rtol, atol = 1e-5, 1e-5 + if p_dtype is torch.float16 or g_dtype is torch.float16: + rtol, atol = 2e-3, 2e-3 + if p_dtype is torch.bfloat16 or g_dtype is torch.bfloat16: + rtol, atol = 4e-3, 4e-3 + + for _ in range(N_STEPS): + set_grad(model, torch_model, g_dtype) + torch_optim.step() + optim.step() + torch_optim.zero_grad() + optim.zero_grad() + for p, torch_p in zip(model.parameters(), torch_model.parameters()): + # if overflow, the weight won't be updated. so there will be no nan in p + assert not torch.isnan(p).any() + assert torch.allclose(p.float(), torch_p, rtol=rtol, atol=atol) diff --git a/tests/test_optimizer/test_cpu_adam.py b/tests/test_optimizer/test_cpu_adam.py deleted file mode 100644 index 8b3ecf8517f7..000000000000 --- a/tests/test_optimizer/test_cpu_adam.py +++ /dev/null @@ -1,121 +0,0 @@ -import math - -import torch - -from colossalai.testing import clear_cache_before_run, parameterize - - -def torch_adam_update( - step, - lr, - beta1, - beta2, - eps, - weight_decay, - param, - grad, - exp_avg, - exp_avg_sq, - use_adamw, -): - bias_correction1 = 1 - beta1**step - bias_correction2 = 1 - beta2**step - - if weight_decay != 0: - if use_adamw: - # Perform stepweight decay - param.mul_(1 - lr * weight_decay) - else: - grad = grad.add(param, alpha=weight_decay) - - # Decay the first and second moment running average coefficient - exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) - denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps) - - step_size = lr / bias_correction1 - - param.addcdiv_(exp_avg, denom, value=-step_size) - - -def assertLess(data_diff, threshold, msg): - assert data_diff < threshold, msg - - -def assertTrue(condition, msg): - assert condition, msg - - -@clear_cache_before_run() -@parameterize('adamw', [True, False]) -@parameterize('step', [1, 2]) -@parameterize('p_dtype', [torch.float, torch.half]) -@parameterize('g_dtype', [torch.float, torch.half]) -def test_cpu_adam(adamw, step, p_dtype, g_dtype): - lr = 1e-3 - beta1, beta2 = 0.9, 0.999 - eps = 1e-8 - weight_decay = 0 - - for i in range(3): - p_data = torch.rand(64, dtype=p_dtype) - p_data_copy = p_data.clone().float() - p_grad = torch.rand(64, dtype=g_dtype) - p_grad_copy = p_grad.clone().float() - exp_avg = torch.rand(p_data.shape) - exp_avg_copy = exp_avg.clone() - exp_avg_sq = torch.rand(p_data.shape) - exp_avg_sq_copy = exp_avg_sq.clone() - - from colossalai.kernel.op_builder import CPUAdamBuilder - cpu_optim = CPUAdamBuilder().load() - - cpu_adam_op = cpu_optim.CPUAdamOptimizer(lr, beta1, beta2, eps, weight_decay, adamw) - - cpu_adam_op.step( - step, - lr, - beta1, - beta2, - eps, - weight_decay, - True, - p_data.view(-1), # fp32 data - p_grad.view(-1), # fp32 grad - exp_avg.view(-1), - exp_avg_sq.view(-1), - -1, - ) - - torch_adam_update( - step, - lr, - beta1, - beta2, - eps, - weight_decay, - p_data_copy, # fp32 data - p_grad_copy, # fp32 grad - exp_avg_copy, - exp_avg_sq_copy, - adamw, - ) - var = p_data_copy - p_data - data_diff = torch.max(torch.abs(var)) - threshold = 1e-3 - assertLess( - data_diff, - threshold, - f"p_data diff {data_diff}. failed check, step {step}, lr {lr}, eps " - f"{eps} beta1 {beta1} beta2 {beta2} weight_decay {weight_decay} p_dtype {p_dtype}, g_dtype {g_dtype}", - ) - max_grad_diff = torch.max(torch.abs(p_grad_copy - p_grad)) - assertTrue(max_grad_diff < threshold, f"diff {max_grad_diff}") - max_exp_avg_diff = torch.max(torch.abs(exp_avg_copy - exp_avg)) - assertTrue(max_exp_avg_diff < threshold, f"max_exp_avg_diff {max_exp_avg_diff}") - max_exp_avg_sq_diff = torch.max(torch.abs(exp_avg_sq_copy - exp_avg_sq)) - assertTrue(max_exp_avg_sq_diff < threshold, f"max_exp_avg_sq_diff {max_exp_avg_sq_diff}") - - -if __name__ == '__main__': - test_cpu_adam() diff --git a/tests/test_optimizer/test_fused_adam.py b/tests/test_optimizer/test_fused_adam.py deleted file mode 100644 index 114d5293dad9..000000000000 --- a/tests/test_optimizer/test_fused_adam.py +++ /dev/null @@ -1,64 +0,0 @@ -import torch -import torch.nn as nn -from torch.optim import AdamW -from torch.optim.adam import Adam - -from colossalai.nn.optimizer.fused_adam import FusedAdam -from colossalai.testing import clear_cache_before_run, parameterize - - -class FC(nn.Module): - - def __init__(self) -> None: - super().__init__() - self.fc = nn.Sequential(nn.Linear(64, 64)) - - def forward(self, x): - return self.fc(x) - - -@clear_cache_before_run() -@parameterize('adamw', [False, True]) -@parameterize('p_dtype', [torch.float, torch.half]) -@parameterize('g_dtype', [torch.float, torch.half]) -def test_adam(adamw, p_dtype, g_dtype): - model = FC().cuda().to(p_dtype) - state = model.state_dict() - model_copy = FC().cuda().to(p_dtype) - model_copy.load_state_dict(state.copy()) - - if adamw: - optim = FusedAdam(model.parameters(), lr=1e-3, adamw_mode=True) - torch_optim = AdamW(model_copy.parameters(), lr=1e-3) - else: - optim = FusedAdam(model.parameters(), lr=1e-3) - torch_optim = Adam(model_copy.parameters(), lr=1e-3) - - data = torch.rand(1024, 64).cuda().to(p_dtype) - data_copy = data.clone() - label = torch.rand(1024, 64).cuda().to(p_dtype) - - for d, l in zip(data, label): - y = model(d) - loss = ((l - y)**2).sum() - optim.zero_grad() - loss.backward() - if p_dtype != g_dtype: - for i in range(len(optim.param_groups[0]['params'])): - optim.param_groups[0]['params'][i].grad.data = optim.param_groups[0]['params'][i].grad.data.to(g_dtype) - optim.step() - - for d, l in zip(data_copy, label): - y = model_copy(d) - loss = ((l - y)**2).sum() - torch_optim.zero_grad() - loss.backward() - torch_optim.step() - - assert len(optim.param_groups[0]['params']) == len(torch_optim.param_groups[0]['params']) - - for i in range(len(optim.param_groups[0]['params'])): - if torch.isnan(optim.param_groups[0]['params'][i]).any() \ - or torch.isnan(torch_optim.param_groups[0]['params'][i]).any(): - continue - assert torch.allclose(optim.param_groups[0]['params'][i], torch_optim.param_groups[0]['params'][i], 2e-3, 2e-3) diff --git a/tests/test_optimizer/test_fused_adam_kernel.py b/tests/test_optimizer/test_fused_adam_kernel.py deleted file mode 100644 index 4afa13349c1b..000000000000 --- a/tests/test_optimizer/test_fused_adam_kernel.py +++ /dev/null @@ -1,95 +0,0 @@ -import math - -import torch -import torch.nn as nn -from numpy import dtype - -from colossalai.testing import clear_cache_before_run, parameterize -from colossalai.utils import multi_tensor_applier - - -def torch_adam_update( - step, - lr, - beta1, - beta2, - eps, - weight_decay, - param, - grad, - exp_avg, - exp_avg_sq, - use_adamw, -): - bias_correction1 = 1 - beta1**step - bias_correction2 = 1 - beta2**step - - if weight_decay != 0: - if use_adamw: - # Perform stepweight decay - param.mul_(1 - lr * weight_decay) - else: - grad = grad.add(param, alpha=weight_decay) - - # Decay the first and second moment running average coefficient - exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) - denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps) - - step_size = lr / bias_correction1 - - param.addcdiv_(exp_avg, denom, value=-step_size) - - -@clear_cache_before_run() -@parameterize('adamw', [False, True]) -@parameterize('step', [1, 2]) -@parameterize('p_dtype', [torch.float, torch.half]) -@parameterize('g_dtype', [torch.float, torch.half]) -def test_adam(adamw, step, p_dtype, g_dtype): - from colossalai.kernel.op_builder import FusedOptimBuilder - fused_optim = FusedOptimBuilder().load() - fused_adam = fused_optim.multi_tensor_adam - - dummy_overflow_buf = torch.cuda.IntTensor([0]) - - count = 0 - - for i in range(3): - p = torch.rand(64, dtype=p_dtype).cuda() - p_copy = p.clone().float() - g = torch.rand(p.shape, dtype=g_dtype).cuda() - g_copy = g.clone().float() - m = torch.rand(p.shape).cuda() - m_copy = m.clone() - v = torch.rand(p.shape).cuda() - v_copy = v.clone() - - lr = 1e-3 - beta1, beta2 = 0.9, 0.999 - eps = 1e-8 - weight_decay = 0 - - multi_tensor_applier(fused_adam, dummy_overflow_buf, [[g], [p], [m], [v]], lr, beta1, beta2, eps, step, adamw, - True, weight_decay, -1) - - torch_adam_update( - step, - lr, - beta1, - beta2, - eps, - weight_decay, - p_copy, # fp32 data - g_copy, # fp32 grad - m_copy, - v_copy, - adamw, - ) - - if torch.isnan(p).any() or torch.isnan(p_copy).any(): - count += 1 - continue - assert count < 200, "too many nans" - assert torch.allclose(p.to(torch.float), p_copy.to(torch.float), 1e-5, - 1e-5), f"failed check, adamw {adamw}, p_dtype {p_dtype}, g_dtype {g_dtype}" diff --git a/tests/test_optimizer/test_hybrid_adam.py b/tests/test_optimizer/test_hybrid_adam.py deleted file mode 100644 index d075149dfcb1..000000000000 --- a/tests/test_optimizer/test_hybrid_adam.py +++ /dev/null @@ -1,42 +0,0 @@ -import torch -import torch.nn as nn -from torch.optim import AdamW -from torch.optim.adam import Adam - -from colossalai.nn.optimizer.hybrid_adam import HybridAdam -from colossalai.testing import clear_cache_before_run, parameterize - -RE = 3 - - -@clear_cache_before_run() -@parameterize('adamw', [False, True]) -@parameterize('device', ['cpu', 'cuda:0']) -@parameterize('p_dtype', [torch.float]) -@parameterize('g_dtype', [torch.float, torch.half]) -def test_adam(adamw, device, p_dtype, g_dtype): - rng_state = torch.get_rng_state() - p = nn.Parameter(torch.rand(64).to(device, p_dtype)) - torch.set_rng_state(rng_state) - p_copy = nn.Parameter(torch.rand(64).to(device).float()) - - if adamw: - optim = HybridAdam([p], lr=1e-3, adamw_mode=True) - torch_optim = AdamW([p_copy], lr=1e-3) - else: - optim = HybridAdam([p], lr=1e-3) - torch_optim = Adam([p_copy], lr=1e-3) - - print(f"adaw mode {adamw}, device {device}, p_dtype {p_dtype}, g_dtype {g_dtype}") - for i in range(RE): - p.grad = torch.rand(64).to(device, p_dtype) - p_copy.grad = p.grad.clone().float() - p.grad.data = p.grad.data.to(g_dtype) - - optim.step() - torch_optim.step() - - if torch.isnan(p.data).any() or torch.isnan(p_copy.data).any(): - continue - assert torch.allclose(p.data, p_copy.data, 1e-4, 1e-2), \ - f"adaw mode {adamw}, device {device}, p_dtype {p_dtype}, g_dtype {g_dtype}" diff --git a/tests/test_zero/test_gemini/test_optim.py b/tests/test_zero/test_gemini/test_optim.py index 8ce20c16e8f9..66611bcd2419 100644 --- a/tests/test_zero/test_gemini/test_optim.py +++ b/tests/test_zero/test_gemini/test_optim.py @@ -21,23 +21,40 @@ # these models are too small, all parameters in these models are compacted into one chunk EXAMPLE_MODELS = ['albert', 'beit', 'bert', 'hanging_param_model', 'nested_model', 'repeated_computed_layers'] +# bfloat16 cannot represent them exactly +BF16_IGNORED_KEYS = [ + 'albert.embeddings.word_embeddings.weight', + 'albert.embeddings.position_embeddings.weight', + 'masked_bias', +] -def check_param(model: ZeroDDP, torch_model: torch.nn.Module): - zero_dict = model.state_dict(only_rank_0=False) + +def check_param(model: ZeroDDP, torch_model: torch.nn.Module, dtype: torch.dtype): + zero_dict = model.state_dict(only_rank_0=False, dtype=dtype) torch_dict = torch_model.state_dict() for key, value in torch_dict.items(): # key is 'module.model.PARAMETER', so we truncate it key = key[7:] assert key in zero_dict, "{} not in ZeRO dictionary.".format(key) - temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype) + temp_zero_value = zero_dict[key].to(device=value.device) + if dtype is torch.bfloat16 and any(k in key for k in BF16_IGNORED_KEYS): + continue + rtol, atol = 1e-3, 4e-3 + if dtype is torch.bfloat16: + rtol, atol = 4e-3, 8e-3 # debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value))) - assert_close(value, temp_zero_value, rtol=1e-3, atol=4e-3) + assert_close(value.float(), + temp_zero_value.float(), + rtol=rtol, + atol=atol, + msg=lambda s: s + f'\n{key}\n{temp_zero_value.dtype}') @parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const']) @parameterize('model_name', TEST_MODELS) -def exam_model_step(placement_policy, model_name: str): +@parameterize('mixed_precision', [torch.half, torch.bfloat16]) +def exam_model_step(placement_policy, model_name: str, mixed_precision: torch.dtype): set_seed(42) get_components_func = non_distributed_component_funcs.get_callable(model_name) model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() @@ -65,7 +82,7 @@ def exam_model_step(placement_policy, model_name: str): init_device = None chunk_manager = ChunkManager(config_dict, init_device=init_device) gemini_manager = GeminiManager(placement_policy, chunk_manager) - model = ZeroDDP(model, gemini_manager, pin_memory=True) + model = ZeroDDP(model, gemini_manager, pin_memory=True, mixed_precision=mixed_precision) optimizer = HybridAdam(model.parameters(), lr=1e-3) zero_optim = ZeroOptimizer(optimizer, model, initial_scale=128) @@ -74,6 +91,7 @@ def exam_model_step(placement_policy, model_name: str): torch_model.eval() set_seed(dist.get_rank() * 3 + 128) + rtol, atol = 1e-4, 1e-5 for i, (input_ids, label) in enumerate(train_dataloader): if i > 2: break @@ -83,17 +101,18 @@ def exam_model_step(placement_policy, model_name: str): torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim) loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim) - assert_close(torch_loss, loss) + assert_close(torch_loss, loss, rtol=rtol, atol=atol) zero_optim.step() torch_optim.step() - check_param(model, torch_model) + check_param(model, torch_model, mixed_precision) @parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const']) @parameterize('model_name', EXAMPLE_MODELS) -def exam_tiny_example(placement_policy, model_name: str): +@parameterize('mixed_precision', [torch.half, torch.bfloat16]) +def exam_tiny_example(placement_policy, model_name: str, mixed_precision: torch.dtype): set_seed(2008) get_components_func = non_distributed_component_funcs.get_callable(model_name) model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() @@ -113,7 +132,7 @@ def exam_tiny_example(placement_policy, model_name: str): chunk_manager = init_chunk_manager(model=model, init_device=get_current_device(), search_range_mb=1) gemini_manager = GeminiManager(placement_policy, chunk_manager) - model = ZeroDDP(model, gemini_manager, pin_memory=True) + model = ZeroDDP(model, gemini_manager, pin_memory=True, mixed_precision=mixed_precision) optimizer = HybridAdam(model.parameters(), lr=1e-3) zero_optim = ZeroOptimizer(optimizer, model, initial_scale=2) @@ -121,6 +140,9 @@ def exam_tiny_example(placement_policy, model_name: str): torch_model.eval() set_seed(dist.get_rank() * 3 + 128) + rtol, atol = 1.5e-6, 2e-5 + if mixed_precision is torch.bfloat16: + rtol, atol = 2e-3, 2e-3 for i, (input_ids, label) in enumerate(train_dataloader): if i > 2: break @@ -133,12 +155,12 @@ def exam_tiny_example(placement_policy, model_name: str): torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim) loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim) - assert_close(torch_loss, loss, rtol=1.5e-6, atol=2e-5) # atol should be 2e-5 for torch lower than 1.12 + assert_close(torch_loss, loss, rtol=rtol, atol=atol) # atol should be 2e-5 for torch lower than 1.12 zero_optim.step() torch_optim.step() - check_param(model, torch_model) + check_param(model, torch_model, mixed_precision) def run_dist(rank, world_size, port): diff --git a/tests/test_zero/test_legacy/test_zero_engine.py b/tests/test_zero/test_legacy/test_zero_engine.py index dc8847ce56ab..826a543db861 100644 --- a/tests/test_zero/test_legacy/test_zero_engine.py +++ b/tests/test_zero/test_legacy/test_zero_engine.py @@ -16,7 +16,11 @@ from tests.components_to_test.registry import non_distributed_component_funcs -def run_dist(rank, world_size, port, parallel_config): +def run_dist(rank, world_size, port, parallel_config, bf16): + is_mp_config = parallel_config == MP_PARALLEL_CONFIG + is_zero_config = parallel_config == ZERO_PARALLEL_CONFIG + if bf16: + parallel_config['zero']['model_config']['bf16'] = True colossalai.launch(config=parallel_config, rank=rank, world_size=world_size, @@ -30,7 +34,8 @@ def run_dist(rank, world_size, port, parallel_config): model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func() with ZeroInitContext(target_device=torch.cuda.current_device(), shard_strategy=gpc.config.zero.model_config.shard_strategy, - shard_param=True): + shard_param=True, + bf16=bf16): colo_model = model_builder(checkpoint=True) colo_optimizer = optimizer_class(colo_model.parameters(), lr=1e-3) @@ -38,7 +43,8 @@ def run_dist(rank, world_size, port, parallel_config): optimizer=colo_optimizer, criterion=criterion, train_dataloader=train_dataloader) - torch_model = model_builder(checkpoint=True).half() + dtype = torch.bfloat16 if bf16 else torch.float16 + torch_model = model_builder(checkpoint=True).to(dtype) col_model_deepcopy(engine.model, torch_model) torch_model = torch_model.cuda().float() @@ -80,9 +86,9 @@ def run_dist(rank, world_size, port, parallel_config): torch_optimizer.step() i += 1 - if parallel_config == MP_PARALLEL_CONFIG: + if is_mp_config: check_params(torch_model, colo_model, loose=True) - elif parallel_config == ZERO_PARALLEL_CONFIG: + elif is_zero_config: check_sharded_model_params(torch_model, colo_model, loose=True) @@ -97,9 +103,10 @@ def test_mp_engine(world_size): @pytest.mark.dist @pytest.mark.parametrize("world_size", [1, 2]) +@pytest.mark.parametrize("bf16", [True, False]) @rerun_if_address_is_in_use() -def test_zero_engine(world_size): - spawn(run_dist, world_size, parallel_config=ZERO_PARALLEL_CONFIG) +def test_zero_engine(world_size, bf16): + spawn(run_dist, world_size, parallel_config=ZERO_PARALLEL_CONFIG, bf16=bf16) if __name__ == '__main__': diff --git a/tests/test_zero/test_low_level/test_grad_acc.py b/tests/test_zero/test_low_level/test_grad_acc.py index 2ae1f3a99d79..c264a8077d2a 100644 --- a/tests/test_zero/test_low_level/test_grad_acc.py +++ b/tests/test_zero/test_low_level/test_grad_acc.py @@ -82,7 +82,6 @@ def fwd_bwd_func(number, cur_data): def exam_zero_1_grad_acc(): local_rank = torch.distributed.get_rank() - grad_scale = 32 seed_all(2008) # create models @@ -101,7 +100,6 @@ def exam_zero_1_grad_acc(): # level 1 and 2 will produce exactly the same results zero_optimizer = LowLevelZeroOptimizer(zero_optimizer, overlap_communication=False, - initial_scale=grad_scale, reduce_bucket_size=262144, clip_grad_norm=1.0) @@ -128,9 +126,8 @@ def fwd_bwd_func(number, cur_data, check_flag): if check_flag: # check grad for (n, p), z1p in zip(torch_model.named_parameters(), zero_model.parameters()): - unscale_grad = z1p.grad / grad_scale # print(n, p.shape, torch.max(torch.abs(p.grad - unscale_grad))) - assert torch.equal(p.grad, unscale_grad) + assert torch.equal(p.grad, z1p.grad) zero_optimizer._sync_grad() diff --git a/tests/test_zero/test_low_level/test_zero1_2.py b/tests/test_zero/test_low_level/test_zero1_2.py index 4086af9d896e..8e2206fe6c8d 100644 --- a/tests/test_zero/test_low_level/test_zero1_2.py +++ b/tests/test_zero/test_low_level/test_zero1_2.py @@ -7,7 +7,7 @@ from torch.testing import assert_close import colossalai -from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.testing.random import seed_all from colossalai.zero import LowLevelZeroOptimizer @@ -25,15 +25,18 @@ def forward(self, x): return x -def half_close(a, b, loose=False): +def loose_close(a, b, dtype: torch.dtype = torch.float32): rtol = None atol = None - if loose: + if dtype is torch.float16: rtol = 5e-2 atol = 5e-4 + elif dtype is torch.bfloat16: + rtol = 4e-3 + atol = 4e-3 - a = a.detach().half() - b = b.detach().half() + a = a.detach().to(dtype) + b = b.detach().to(dtype) assert_close(a, b, rtol=rtol, atol=atol) @@ -96,7 +99,8 @@ def exam_zero_1_2(): assert torch.equal(z1p.data, z2p.data) -def exam_zero_1_torch_ddp(): +@parameterize('dtype', [torch.float16, torch.bfloat16]) +def exam_zero_1_torch_ddp(dtype: torch.dtype): """ In this test, two pairs of model and optimizers are created. 1. zero: use sharded optimizer and fp16 parameters @@ -109,15 +113,10 @@ def exam_zero_1_torch_ddp(): seed_all(1453) # create models - zero_model = MlpModel() - torch_model = copy.deepcopy(zero_model) + torch_model = MlpModel().cuda() + zero_model = copy.deepcopy(torch_model).to(dtype) - zero_model = zero_model.cuda().half() - torch_model = DDP(torch_model.cuda(), bucket_cap_mb=0) - torch_model = torch_model.cuda() - - # for (n, p), z1p in zip(torch_model.named_parameters(), zero_model.parameters()): - # half_close(p.data, z1p.data) + torch_model = DDP(torch_model.cuda(), bucket_cap_mb=0).cuda() # create optimizer zero_optimizer = torch.optim.SGD(zero_model.parameters(), lr=1) @@ -137,11 +136,11 @@ def exam_zero_1_torch_ddp(): input_data = torch.rand(32, 128).cuda() # zero-dp forward - zero_output = zero_model(input_data.half()) + zero_output = zero_model(input_data.to(dtype)) # torch-ddp forward torch_output = torch_model(input_data) - half_close(zero_output, torch_output, loose=True) + loose_close(zero_output, torch_output, dtype=dtype) # zero-dp backward zero_optimizer.backward(zero_output.mean().float(), sync_grad=False) @@ -151,7 +150,7 @@ def exam_zero_1_torch_ddp(): # check grad for (n, p), z1p in zip(torch_model.named_parameters(), zero_model.parameters()): - half_close(p.grad, z1p.grad, loose=True) + loose_close(p.grad, z1p.grad, dtype=dtype) # zero-dp step zero_optimizer._sync_grad() @@ -163,7 +162,7 @@ def exam_zero_1_torch_ddp(): # check updated param for (n, p), z1p in zip(torch_model.named_parameters(), zero_model.parameters()): # print(n, torch.max(torch.abs(p.data - z1p.data))) - half_close(p.data, z1p.data, loose=True) + loose_close(p.data, z1p.data, dtype=dtype) def run_dist(rank, world_size, port): From 187874975325c4768b0850a818092de5bef1b071 Mon Sep 17 00:00:00 2001 From: digger yu Date: Mon, 5 Jun 2023 16:04:27 +0800 Subject: [PATCH 266/413] [nfc] fix typo colossalai/nn (#3887) * fix typo colossalai/autochunk auto_parallel amp * fix typo colossalai/auto_parallel nn utils etc. * fix typo colossalai/auto_parallel autochunk fx/passes etc. * fix typo docs/ * change placememt_policy to placement_policy in docs/ and examples/ * fix typo colossalai/ applications/ * fix typo colossalai/cli fx kernel * fix typo colossalai/nn * revert change warmuped --- colossalai/nn/layer/parallel_sequence/layers.py | 2 +- colossalai/nn/loss/loss_1d.py | 6 +++--- colossalai/nn/loss/loss_2d.py | 2 +- colossalai/nn/loss/loss_2p5d.py | 2 +- colossalai/nn/loss/loss_3d.py | 4 ++-- colossalai/nn/optimizer/cpu_adam.py | 2 +- colossalai/nn/optimizer/lamb.py | 2 +- colossalai/nn/optimizer/nvme_optimizer.py | 2 +- .../layers/cache_embedding/cached_embedding.py | 10 +++++----- .../nn/parallel/layers/cache_embedding/copyer.py | 2 +- .../parallel_cached_embedding_tablewise_split_cache.py | 2 +- 11 files changed, 18 insertions(+), 18 deletions(-) diff --git a/colossalai/nn/layer/parallel_sequence/layers.py b/colossalai/nn/layer/parallel_sequence/layers.py index d9486217bbc9..0887f8389dbe 100644 --- a/colossalai/nn/layer/parallel_sequence/layers.py +++ b/colossalai/nn/layer/parallel_sequence/layers.py @@ -195,7 +195,7 @@ class _Linear(nn.Module): keep_master_weight_for_test: This was added for testing and should be set to False. It returns the master weights used for initialization. - skip_bias_add: This was added to enable performance optimations where bias + skip_bias_add: This was added to enable performance optimizations where bias can be fused with other elementwise operations. we skip adding bias but instead return it. """ diff --git a/colossalai/nn/loss/loss_1d.py b/colossalai/nn/loss/loss_1d.py index 2fabd954f8fb..dd548c1d3dd4 100644 --- a/colossalai/nn/loss/loss_1d.py +++ b/colossalai/nn/loss/loss_1d.py @@ -21,7 +21,7 @@ def forward(ctx, vocab_parallel_logits, targets, process_group): # Subtract the maximum value. vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1)) - # Get the partition's vocab indecies + # Get the partition's vocab indices partition_vocab_size = vocab_parallel_logits.size()[-1] rank = dist.get_rank(process_group) vocab_start_index = partition_vocab_size * rank @@ -61,10 +61,10 @@ def forward(ctx, vocab_parallel_logits, targets, process_group): @custom_bwd def backward(ctx, grad_output): - # Retreive tensors from the forward path. + # Retrieve tensors from the forward path. softmax, target_mask, masked_target_1d = ctx.saved_tensors - # All the inputs have softmax as thier gradient. + # All the inputs have softmax as their gradient. grad_input = softmax # For simplicity, work with the 2D gradient. partition_vocab_size = softmax.size()[-1] diff --git a/colossalai/nn/loss/loss_2d.py b/colossalai/nn/loss/loss_2d.py index cb12e723c323..7da8b2d697fa 100644 --- a/colossalai/nn/loss/loss_2d.py +++ b/colossalai/nn/loss/loss_2d.py @@ -106,7 +106,7 @@ def forward(ctx, logits, targets): @staticmethod @custom_bwd def backward(ctx, output_grad): - # Retreive tensors from the forward path. + # Retrieve tensors from the forward path. softmax, target_mask, masked_target = ctx.saved_tensors # All the inputs have softmax as their gradient. diff --git a/colossalai/nn/loss/loss_2p5d.py b/colossalai/nn/loss/loss_2p5d.py index f8e3324fc5ff..63dc4f33ad32 100644 --- a/colossalai/nn/loss/loss_2p5d.py +++ b/colossalai/nn/loss/loss_2p5d.py @@ -100,7 +100,7 @@ def forward(ctx, logits, targets): @staticmethod @custom_bwd def backward(ctx, output_grad): - # Retreive tensors from the forward path. + # Retrieve tensors from the forward path. softmax, target_mask, masked_target = ctx.saved_tensors # All the inputs have softmax as their gradient. diff --git a/colossalai/nn/loss/loss_3d.py b/colossalai/nn/loss/loss_3d.py index e76439191fdb..f27d57ad6c99 100644 --- a/colossalai/nn/loss/loss_3d.py +++ b/colossalai/nn/loss/loss_3d.py @@ -99,10 +99,10 @@ def forward(ctx, logits, targets, output_parallel_mode): @staticmethod @custom_bwd def backward(ctx, output_grad): - # Retreive tensors from the forward path. + # Retrieve tensors from the forward path. softmax, target_mask, masked_target = ctx.saved_tensors - # All the inputs have softmax as thier gradient. + # All the inputs have softmax as their gradient. input_grad = softmax # For simplicity, work with the 2D gradient. partition_vocab_size = softmax.size()[-1] diff --git a/colossalai/nn/optimizer/cpu_adam.py b/colossalai/nn/optimizer/cpu_adam.py index 7070c0a1e59d..1ec8783c53d3 100644 --- a/colossalai/nn/optimizer/cpu_adam.py +++ b/colossalai/nn/optimizer/cpu_adam.py @@ -21,7 +21,7 @@ class CPUAdam(NVMeOptimizer): `CPUAdam` requires CUDA extensions which can be built during installation or runtime. - This version of CPU Adam accelates parameters updating on CPU with SIMD. + This version of CPU Adam accelerates parameters updating on CPU with SIMD. Support of AVX2 or AVX512 is required. The GPU part is implemented in an naive way. diff --git a/colossalai/nn/optimizer/lamb.py b/colossalai/nn/optimizer/lamb.py index 7ac2109572a4..399ad39b6658 100644 --- a/colossalai/nn/optimizer/lamb.py +++ b/colossalai/nn/optimizer/lamb.py @@ -59,7 +59,7 @@ def step(self, closure=None): continue grad = p.grad.data if grad.is_sparse: - raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instad.') + raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instead.') state = self.state[p] diff --git a/colossalai/nn/optimizer/nvme_optimizer.py b/colossalai/nn/optimizer/nvme_optimizer.py index 53e4a46c9741..fb3a4d87be60 100644 --- a/colossalai/nn/optimizer/nvme_optimizer.py +++ b/colossalai/nn/optimizer/nvme_optimizer.py @@ -43,7 +43,7 @@ def __init__(self, self.offloader = None self.is_on_nvme: Dict[Parameter, bool] = {} self.offloaded_numel: int = 0 - # As param may be not materialized here, these attributes are initalized when the first step + # As param may be not materialized here, these attributes are initialized when the first step self.total_numel: Optional[int] = None self.can_offload_numel: Optional[int] = None diff --git a/colossalai/nn/parallel/layers/cache_embedding/cached_embedding.py b/colossalai/nn/parallel/layers/cache_embedding/cached_embedding.py index a0c45d8e80c0..a74cb8d94bab 100644 --- a/colossalai/nn/parallel/layers/cache_embedding/cached_embedding.py +++ b/colossalai/nn/parallel/layers/cache_embedding/cached_embedding.py @@ -12,23 +12,23 @@ class CachedEmbeddingBag(BaseEmbeddingBag): Cached Embedding. Apply a GPU-based software cache approaches to dynamically manage the embedding table in the CPU and GPU memory space. It can leverage the id's frequency statistics of the target dataset, by passing a frequency list to param `ids_freq_mapping`. - You can also apply a navie LFU cache eviction strategy by setting `evict_strategy` as EvictionStrategy.LFU. + You can also apply a naive LFU cache eviction strategy by setting `evict_strategy` as EvictionStrategy.LFU. Args: num_embeddings (int): size of the dictionary of embeddings embedding_dim (int): the size of each embedding vector padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. For a newly constructed EmbeddingBag, the embedding vector at padding_idx will default to all zeros, but can be updated to another value to be used as the padding vector. Note that the embedding vector at padding_idx is excluded from the reduction. max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm - norm_type (str, optional): The p of the p-norm to compute for the max_norm option. Defaults to 2.. + norm_type (str, optional): The p of the p-norm to compute for the max_norm option. Defaults to 2. scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default False. Note: this option is not supported when mode="max". Defaults to False. sparse (bool, optional): if True, gradient w.r.t. weight matrix will be a sparse tensor. See Notes for more details regarding sparse gradients. Note: this option is not supported when mode="max".. Defaults to False. - _weight (torch.Tensor, optional): an embedding weight tensor. Concate multiple tables in a embedding bag as a single one. Defaults to None. + _weight (torch.Tensor, optional): an embedding weight tensor. Concatenate multiple tables in a embedding bag as a single one. Defaults to None. mode (str, optional): "sum", "mean" or "max". Specifies the way to reduce the bag. "sum" computes the weighted sum, taking per_sample_weights into consideration. "mean" computes the average of the values in the bag, "max" computes the max value over each bag. Default: "mean". Defaults to 'mean'. include_last_offset (bool, optional): if True, offsets has one additional element, where the last element is equivalent to the size of indices. This matches the CSR format.. Defaults to False. dtype (torch.dtype, optional): data type of the cpu weight initialization. Defaults to None meaning float32. device (torch.device, optional): device type to the cpu weight. Defaults to None meaning cpu. cache_ratio (float, float): cache ratio of the #cuda_weight_row / #cpu_weight_row - ids_freq_mapping (Union[List, torch.Tensor], optional): the frequency of each embedding vector occures in dataset. Defaults to None. + ids_freq_mapping (Union[List, torch.Tensor], optional): the frequency of each embedding vector occurs in dataset. Defaults to None. warmup_ratio (float, optional): the ratio of cuda cache is warmuped with. Defaults to 0.7. buffer_size (int, optional): the max number of vectors in transmitter buffer. If set to 0, the buffer is not used. Defaults to 0. pin_weight (bool, optional): pin the cpu weight. Defaults to False. @@ -145,7 +145,7 @@ def num_write_back_history(self): def swap_in_bandwidth(self): if self.cache_weight_mgr._cpu_to_cuda_numel > 0: return self.cache_weight_mgr._cpu_to_cuda_numel * self.cache_weight_mgr.elem_size_in_byte / 1e6 / \ - self.cache_weight_mgr._cpu_to_cuda_elpase + self.cache_weight_mgr._cpu_to_cuda_elapse else: return 0 diff --git a/colossalai/nn/parallel/layers/cache_embedding/copyer.py b/colossalai/nn/parallel/layers/cache_embedding/copyer.py index b586be1dc6d9..aa1f794482f9 100644 --- a/colossalai/nn/parallel/layers/cache_embedding/copyer.py +++ b/colossalai/nn/parallel/layers/cache_embedding/copyer.py @@ -17,7 +17,7 @@ def __init__(self, size: int) -> None: def index_copy(self, dim: int, src_index: LongTensor, tgt_index: LongTensor, src: torch.Tensor, tgt: torch.Tensor): """copy src tensor[src_index] -(index_select)-> tmp -(index_copy_)-> tgt tensor [tgt_index] - The valid rows in the src tensor are continous, while rows in tgt tensor is scattered. + The valid rows in the src tensor are continuous, while rows in tgt tensor is scattered. Args: dim (int): dimension along which to index diff --git a/colossalai/nn/parallel/layers/cache_embedding/parallel_cached_embedding_tablewise_split_cache.py b/colossalai/nn/parallel/layers/cache_embedding/parallel_cached_embedding_tablewise_split_cache.py index cb4647028d47..80a54b4fadd4 100644 --- a/colossalai/nn/parallel/layers/cache_embedding/parallel_cached_embedding_tablewise_split_cache.py +++ b/colossalai/nn/parallel/layers/cache_embedding/parallel_cached_embedding_tablewise_split_cache.py @@ -114,7 +114,7 @@ def forward(self, indices: torch.Tensor, offsets: torch.Tensor = None, per_sampl # get result of shape = (batch_size, (len(assigned_table_list)*embedding_dim)) local_output = torch.cat(local_output_list, 1) - # then concatenate those local_output on the second demension. + # then concatenate those local_output on the second dimension. # use all_to_all remains = batch_size % self.world_size scatter_strides = [batch_size // self.world_size + int(i < remains) for i in range(self.world_size)] From 57a6d7685cf05b0763eeb65eb62e7d8cce2f6955 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Mon, 5 Jun 2023 21:24:21 +0800 Subject: [PATCH 267/413] support evaluation for english (#3880) Co-authored-by: Yuanchen Xu --- applications/Chat/evaluate/README.md | 49 ++--- .../Chat/evaluate/config/config_en.json | 123 ++++++++++++ applications/Chat/evaluate/eval.py | 2 +- applications/Chat/evaluate/evaluator.py | 60 +++--- applications/Chat/evaluate/gpt_evaluate.py | 27 ++- applications/Chat/evaluate/metrics.py | 126 ++++++++---- .../battle_prompt/battle_prompt_en.json | 6 + .../evaluation_prompt_en.json | 179 ++++++++++++++++++ applications/Chat/evaluate/requirements.txt | 2 + applications/Chat/evaluate/utils.py | 157 ++++++++++++++- 10 files changed, 638 insertions(+), 93 deletions(-) create mode 100644 applications/Chat/evaluate/config/config_en.json create mode 100644 applications/Chat/evaluate/prompt/battle_prompt/battle_prompt_en.json create mode 100644 applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_en.json diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index ae3499bf268c..e3510e3522f6 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -1,7 +1,6 @@ # Evaluation -In this directory, we introduce how you can evaluate your model with our pipeline. This pipeline is available for model -evaluation of Chinese capability and the one for English capability is under preparation. +In this directory, we introduce how you can evaluate your model with our pipeline. This pipeline is now available for evaluation of both Chinese and English capability. ## Installation @@ -24,7 +23,7 @@ The whole evaluation pipeline consists of two methods: Our evaluation pipeline examines the model's capability using 10 categories of questions. The following table introduces each category: -| Evaluation Category |
    Description
    | +| Evaluation Category | Description | | :-----------------: | :----------------------------------------------------------- | | Brainstorming | Models are asked to generate a range of creative and diverse ideas according to the question. The capability of creativity is required. | | Chat | Models are asked to continue a multi-round dialogue given the roles involved. The capability of understanding, memorizing previous rounds of the dialogue and answering according to the persona provided is required. | @@ -40,17 +39,17 @@ Our evaluation pipeline examines the model's capability using 10 categories of q To better understand each evaluation category, here are some example questions provided. -| Evaluation Category |
    Chinese Example
    |
    English Example
    | +| Evaluation Category | Chinese Example | English Example | | :-----------------: | :----------------------------------------------------------- | :----------------------------------------------------------- | | Brainstorming | **Example 1:**
    请介绍一下人工智能的多个领域。

    **Example 2:**
    请给出管理家庭财务的3个小技巧。
    | **Example 1:**
    How can I improve my memory? Any useful techniques you can suggest?

    **Example 2:**
    What are some ways to increase productivity while working from home? | -| Chat | **Example 1:**
    基于以下角色信息完成一段对话。小张是一名新手爱好者，对养鸡有浓厚的兴趣。老李是一名有丰富经验的养鸡大师。
    小张：您好，老李，我最近开始对养鸡感兴趣了，想请教您一些问题。
    老李：你好，小张，我很乐意帮助你。你想问些什么？
    小张：我想知道如何确定鸡的品种和性别？
    老李：确切的品种可以通过鸡的外貌特征来确定，而性别一般是通过鸡卵的大小和形状来判断。还有什么问题吗？
    小张：
    **Example 2:**
    基于以下角色信息完成一段对话。小明是一名医生，一位老年病患者想要停药，但他对病情有所忽视并有担忧；王叔叔是老年病患者的儿子，希望能够听取医生的建议。
    小明：你好，王叔叔，我了解你想要让你父亲停药。
    王叔叔：是的，我父亲已经吃了那么久的药，我担心药物对他的身体会有副作用。
    小明： | **Example 1:**
    Complete a conversation based on the following character information. Amy is a 30-year-old chef who runs her own restaurant. Jack is a food blogger who specializes in reviewing local restaurants.
    Amy: Hi Jack, I heard that you're a food blogger. Nice to meet you.
    Jack: Hi Amy, yes I am. Your restaurant has been receiving a lot of good reviews lately.
    Amy: Yes, we use only fresh and quality ingredients, and every dish is carefully crafted.
    Jack:
    **Example 2:**
    Complete a dialogue based on the following role information. A: Elementary student B: Teacher
    B: Good morning, Student A. Today we're going to learn about addition and subtraction.
    A: Teacher, I already know this very well. Why do I need to learn it again?
    B: | +| Chat | **Example 1:**
    基于以下角色信息完成一段对话。小张是一名新手爱好者，对养鸡有浓厚的兴趣。老李是一名有丰富经验的养鸡大师。
    小张：您好，老李，我最近开始对养鸡感兴趣了，想请教您一些问题。
    老李：你好，小张，我很乐意帮助你。你想问些什么？
    小张：我想知道如何确定鸡的品种和性别？
    老李：确切的品种可以通过鸡的外貌特征来确定，而性别一般是通过鸡卵的大小和形状来判断。还有什么问题吗？
    小张：

    **Example 2:**
    基于以下角色信息完成一段对话。小明是一名医生，一位老年病患者想要停药，但他对病情有所忽视并有担忧；王叔叔是老年病患者的儿子，希望能够听取医生的建议。
    小明：你好，王叔叔，我了解你想要让你父亲停药。
    王叔叔：是的，我父亲已经吃了那么久的药，我担心药物对他的身体会有副作用。
    小明： | **Example 1:**
    Complete a conversation based on the following character information. Amy is a 30-year-old chef who runs her own restaurant. Jack is a food blogger who specializes in reviewing local restaurants.
    Amy: Hi Jack, I heard that you're a food blogger. Nice to meet you.
    Jack: Hi Amy, yes I am. Your restaurant has been receiving a lot of good reviews lately.
    Amy: Yes, we use only fresh and quality ingredients, and every dish is carefully crafted.
    Jack:

    **Example 2:**
    Complete a dialogue based on the following role information. A: Elementary student B: Teacher
    B: Good morning, Student A. Today we're going to learn about addition and subtraction.
    A: Teacher, I already know this very well. Why do I need to learn it again?
    B: | | Classification | **Example 1:**
    新闻标题：今日立夏，有一上联，立夏万物并秀，下联怎么对？
    请根据以上新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。

    **Example 2:**
    新闻标题：赵丽颖很久没有登上微博热搜了，但你们别急，她只是在憋大招而已。
    请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。 | **Example 1:**
    Title: Fighting for Love (2020)
    Description: Jasmine got obsessed with a man and now he's obsessed with her. Steamy nights, kisses and rules being broken awaits them. She turned his whole world upside down and now he's doing it to hers. In this free fall, can they survive each others love?\"
    Based on the above information, determine which genre the work of art belongs to. You can only choose one from \"sport\", \"horror\", \"drama\", \"history\", \"romance\", \"biography\", \"science fiction\", \"comedy\", \"animation\", \"documentary\", \"music\" and \"news\".

    **Example2:**
    Title: Summer Breeze: The Isley Brothers Greatest Hits Live (2005)
    Description: Filmed in the US in 2005 and captured in excellent form led by Ron Isley's vocals and Ernie Isley's hard edged guitar. Virtually every track is a hit including Shout, Who's That Lady, Twist And Shout, Summer Breeze and Harvest For The World.
    Based on the above information, determine which genre the work of art belongs to. You can only choose one from \"sport\", \"horror\", \"drama\", \"history\", \"romance\", \"biography\", \"science fiction\", \"comedy\", \"animation\", \"documentary\", \"music\" and \"news\"." | -| Closed QA | **Example 1:**
    请从以下选项中选择正确答案。以下哪个是世界上最高山峰？
    A. 长城
    B. 泰山
    C. 珠穆朗玛峰
    D. 黄山

    **Example 2:**
    请从以下选项中选择一个最佳答案回答下面的问题。问题：非洲最高的山是哪座山？
    选项：
    A. 麦金利山
    B. 喜马拉雅山
    C. 乞力马扎罗山 | **Example 1:**
    Which of the following options is NOT a primary color?
    (a) yellow
    (b) blue
    (c) orange
    (d) red
    **Example 2:**
    Choose the correct option to complete the following sentence: \"Harry Potter and the Chamber of Secrets\" is the ________ book in the Harry Potter series.
    (A) first
    (B) second
    (C) third
    (D) fourth | +| Closed QA | **Example 1:**
    请从以下选项中选择正确答案。以下哪个是世界上最高山峰？
    A. 长城
    B. 泰山
    C. 珠穆朗玛峰
    D. 黄山

    **Example 2:**
    请从以下选项中选择一个最佳答案回答下面的问题。问题：非洲最高的山是哪座山？
    选项：
    A. 麦金利山
    B. 喜马拉雅山
    C. 乞力马扎罗山 | **Example 1:**
    Which of the following options is NOT a primary color?
    (a) yellow
    (b) blue
    (c) orange
    (d) red

    **Example 2:**
    Choose the correct option to complete the following sentence: \"Harry Potter and the Chamber of Secrets\" is the ________ book in the Harry Potter series.
    (A) first
    (B) second
    (C) third
    (D) fourth | | Extraction | **Example 1:**
    根据以下新闻文本，提取新闻报道时间，例如回答时按照格式“新闻报道时间：2007年8月10日”
    新闻文本如下：2007-4-7中新网4月7日电据中国消防在线消息，4月4日晚上7时30分左右，湖南长潭高速公路上发生一起6车连环相撞失火事故。长株潭三地消防部门共出动消防车21台，警力100余人。经过消防官兵近2个小时奋力扑救，大火被成功扑灭。据初步调查，有1人在此次事故中死亡。

    **Example 2:**
    根据以下新闻文本，提取新闻报道时间，例如回答时按照格式“新闻报道时间：2007年8月10日”
    新闻文本如下：2014年1月15日，据外媒《俄罗斯报》报道称，位于北半球的澳大利亚现在正处于炎热的夏季，而近日也到了高温酷暑的时候，当地时间1月14日晚，澳大利亚南部一夜间发生至少250起火灾。受炎热天气及雷雨天气影响，澳大利亚南部一夜间发生至少250起火灾，灾情多集中在维多利亚州。火灾发生后，救援人员立即展开救灾行动。目前，大部分起火点火势已被控制。 | **Example 1:**
    Ernest Hemingway, an American literary giant known for his spare and direct writing style, has penned timeless works such as 'The Old Man and the Sea', 'For Whom the Bell Tolls', and 'A Farewell to Arms', which have made a profound impact on the literary world and continue to be widely read and admired today.
    Extract the name of the author mentioned above.

    **Example 2:**
    In the epic fantasy series 'A Song of Ice and Fire', George R.R. Martin weaves a complex web of political intrigue, war, and magic across the fictional continents of Westeros and Essos. Martin's richly developed characters and intricate plotlines have captivated readers worldwide, much like his other acclaimed works such as 'A Clash of Kings' and 'A Storm of Swords'.
    Extract the name of the author in the above material. | | Generation | **Example 1:**
    请撰写一篇文章，介绍如何通过改善生活习惯来预防疾病和延长寿命。

    **Example 2:**
    请根据以下情节撰写一篇短篇小说：一名年轻人被困在一个荒岛上，他必须想办法生存下去直到被救援。但他很快发现自己并不孤单。 | **Example 1:**
    Write a descriptive paragraph about an island to relax and unwind, including details about the location and atmosphere.

    **Example 2:**
    Can you help me write a persuasive email to my colleagues encouraging them to participate in a charitable fundraising event? | | Open QA | **Example 1:**
    请问万有引力定律由谁提出的？

    **Example 2:**
    哪些国家参与了第一次世界大战？ | **Example 1:**
    What are the four basic tastes of the human palate?

    **Example 2:**
    Who painted the The Scream? | | Rewriting | **Example 1:**
    请将以下句子改为正确的语序。
    生日快乐你祝他了吗？

    **Example 2:**
    将以下文本翻译成英语：
    “这个周末我要去海边玩” | **Example 1:**
    Please translate the following sentences, which are a mixture of Chinese and English, into full English.
    我需要买一些healthy snacks，比如nuts和dried fruits，作为我的office的午餐.

    **Example 2:**
    Please rewrite the sentence using an inverted sentence structure.
    We won't begin our journey until the sun sets. | -| Roleplay | **Example 1:**
    我想让你担任Android开发工程师面试官。我将成为候选人，您将向我询问Android开发工程师职位的面试问题。我希望你只作为面试官回答。不要一次写出所有的问题。我希望你只对我进行采访。问我问题，等待我的回答。不要写解释。像面试官一样一个一个问我，等我回答。我的第一句话是“面试官你好”。

    **Example 2:**
    我想让你扮演讲故事的角色。你会想出引人入胜、富有想象力和吸引观众的有趣故事。它可以是童话故事、教育故事或任何其他类型的有潜力的故事以吸引人们的注意力和想象力。根据目标受众，您可以为您的讲故事环节选择特定的主题或主题，例如，如果是儿童，那么您可以谈论动物；如果是成人，那么基于历史的故事可能会更好地吸引他们等。我的第一个请求是我需要一个关于毅力的有趣故事。 | **Example 1:**
    Assume the role of a marriage counselor. Develop a series of communication exercises for a couple who are experiencing difficulties in their relationship. These exercises should promote active listening, empathy, and effective expression of emotions. Your first assignment is to provide a set of three exercises that focus on resolving conflicts and rebuilding trust.

    **Example 2: **
    I want you to act as a travel agent. I will tell you my desired destination, travel dates, and budget, and it will be your job to suggest the best travel itinerary for me. Your recommendations should include the best transportation options, hotel accommodations, and any popular tourist attractions nearby. My first request is "I want to plan a trip to Tokyo for a week, with a budget of $2000. I want to explore the culture and food of the city." | +| Roleplay | **Example 1:**
    我想让你担任Android开发工程师面试官。我将成为候选人，您将向我询问Android开发工程师职位的面试问题。我希望你只作为面试官回答。不要一次写出所有的问题。我希望你只对我进行采访。问我问题，等待我的回答。不要写解释。像面试官一样一个一个问我，等我回答。我的第一句话是“面试官你好”。

    **Example 2:**
    我想让你扮演讲故事的角色。你会想出引人入胜、富有想象力和吸引观众的有趣故事。它可以是童话故事、教育故事或任何其他类型的有潜力的故事以吸引人们的注意力和想象力。根据目标受众，您可以为您的讲故事环节选择特定的主题或主题，例如，如果是儿童，那么您可以谈论动物；如果是成人，那么基于历史的故事可能会更好地吸引他们等。我的第一个请求是我需要一个关于毅力的有趣故事。 | **Example 1:**
    Assume the role of a marriage counselor. Develop a series of communication exercises for a couple who are experiencing difficulties in their relationship. These exercises should promote active listening, empathy, and effective expression of emotions. Your first assignment is to provide a set of three exercises that focus on resolving conflicts and rebuilding trust.

    **Example 2:**
    I want you to act as a travel agent. I will tell you my desired destination, travel dates, and budget, and it will be your job to suggest the best travel itinerary for me. Your recommendations should include the best transportation options, hotel accommodations, and any popular tourist attractions nearby. My first request is "I want to plan a trip to Tokyo for a week, with a budget of $2000. I want to explore the culture and food of the city." | | Summarization | **Example 1:**
    请简要总结概括以下段落材料。
    当地时间29日，泰国卫生部通报，新增143名新冠肺炎确诊病例和1名死亡病例。截止到当地时间29日上午，泰国累计确诊病例1388例，其中泰国籍1172例，非泰国籍216例。死亡病例累计7例。（原题为《泰国新增143例新冠肺炎确诊病例累计确诊1388例》）

    **Example 2:**
    请简要总结概括以下段落材料。
    近期，参与京雄高铁站站房建设的中铁十二局，因在施工过程中存在环境违法行为被雄安新区公开通报。通报发出后，引起社会广泛关注。近日，人民网记者从雄安新区相关部门及中铁十二局获悉，新区有关部门已经集中约谈了中铁十二局等24个参与雄安建设的项目单位。对于约谈内容和结果，中铁十二局有关宣传负责人回应：“具体内容不清楚，最好找雄安新区相关部门了解情况。”新区有关部门负责人表示，此前涉及的环境违法行为，中铁十二局已基本整改到位，但约谈内容和结果暂不公开，接下来，将按部就班推进环境治理工作。（原题为《雄安新区：中铁十二局涉环境违法已基本整改到位》） | **Example 1:**
    The 21 year-old-woman was treated by paramedics after the kitchen fire in Botfield Road in Shifnal, Shropshire. West Mercia Police said it is treating Wednesday morning's incident as arson and are appealing for any witnesses to contact them.The 50-year-old man has been arrested on suspicion of arson with intent to endanger life. For more on this and other stories from Shropshire.
    Please briefly summarize the above material within 20 words.

    **Example 2:**
    South Wales Police were called to a property in Heolgerrig, Merthyr Tydfil, at about 13:40 BST on Sunday. The child was airlifted to Prince Charles Hospital but died shortly afterwards. Police are investigating the circumstances surrounding the incident and have appealed for witnesses. The girl's family are being supported by specially trained officers.
    Please briefly summarize the above material within 20 words. | @@ -58,24 +57,26 @@ To better understand each evaluation category, here are some example questions p #### GPT Evaluation -GPT evaluation uses GPT models to evaluate the prediction of different models and different pre-defined evaluation metrics are applied to different categories. The following table shows the 11 pre-defined evaluation metrics in Chinese: +GPT evaluation uses GPT models to evaluate the prediction of different models and different pre-defined evaluation metrics are applied to different categories. The following table shows the 11 pre-defined evaluation metrics both in Chinese and English: -| Evaluation Metric |
    Prompt Words
    |
    CoT(Chain-of-Thought)
    | +| Evaluation Metric | Prompt Words | CoT(Chain-of-Thought) | | :-------------------: | :----------------------------------------------------------- | :----------------------------------------------------------- | -| Language organization | 语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。 | 1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。
    2.检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说
    3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。
    4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。
    5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。
    6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。 | -| Relevance | 切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。 | 1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。
    2. 阅读答案，确认答案是否直接回答了题目所问的问题。
    3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。
    4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。 | -| Creativity | 创意性(1-5)：某些头脑风暴问题可能需要答案具有创意，提出新的思路。 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则创意性评分可能会受到影响。
    3. 考虑答案中是否包含新颖的想法或独特的思路。答案可能与已知的解决方案有所重叠，但仍然可以被认为是有创意的，只要它提供了新的角度或方法来解决问题。
    4. 根据答案的创意性，给出一个1到5的评分。如果答案缺乏创意，则应给出一个较低的评分。如果答案具有创意并提供了新的思路，应给出一个较高的评分。 | -| Practicality | 实用性(1-5)：某些头脑风暴问题可能需要答案提出实用的建议或解决方法。 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则实用性评分可能会受到影响。
    3. 考虑答案中提出的建议或解决方法是否实用并可行。答案可能看起来很好，但如果无法实现或应用，则实用性评分可能会受到影响。
    4. 根据答案的实用性，给出一个1到5的评分。如果答案缺乏实用性，则应给出一个较低的评分。如果答案提出了实用的建议或解决方法，并且可以很好地解决问题，则应给出一个较高的评分。 | -| Correctness | 正确性(1-5)：答案应该符合常识、生活实际等等 | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则正确性评分可能会受到影响。
    3. 考虑答案中所提供的信息是否正确、符合常识、生活实际等等。如果答案中存在明显的错误或不合理之处，则正确性评分可能会受到影响。
    4. 根据答案的正确性，给出一个1到5的评分。如果答案存在明显的错误或不合理之处，则应给出一个较低的评分。如果答案正确、符合常识、生活实际等等，则应给出一个较高的评分。 | -| Naturalness | 自然(1-5)：答案是否自然，并且符合问题给定的身份。 | 1. 阅读题目，确定题目提供的身份信息。
    2. 检查答案内容是否符合题目给定的身份。
    3. 根据以上因素，对该回答的自然性进行打分，分数从1到5，其中1表示不自然，5表示非常自然，并符合问题给定的身份。 | -| Engagingness | 参与感(1-5)：答案是否对前面的对话内容做出了恰当的反应，是否理解对话的语境和背景。 | 1. 阅读题目，确定对话的语境和背景。
    2. 检查答案是否充分理解对话的语境和背景，能否自然地融入到对话中而不显得突兀。
    3. 根据以上因素，对该回答的参与感进行打分，分数从1到5，其中1表示没有参与感，5表示非常有参与感，并且恰当地理解了对话的语境和背景。 | -| Reasonableness | 合理性(1-5)：答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。 | 1. 阅读题目，确定对话的主题以及问题期望的回答方向。
    2. 判断答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。
    3. 根据以上因素，对该回答的合理性进行打分，分数从1到5，其中1表示不合理，5表示非常合理，并且能够与前面的对话内容形成逻辑上的衔接，并符合常理。 | -| Diversity | 多样性(1-5)：答案使用语言是否优美，具有有一定的创造性和想象力。然而，回答也应该保持合理和适度，不要过于夸张或离题。 | 1. 仔细阅读整个回答，确保完全理解回答所表达的内容和主题。
    2. 在阅读回答的同时，注意语言的质量，例如措辞是否正确，语言是否生动等。
    3. 检查回答的创造性和想象力，看看回答是否能够吸引人阅读下去。
    4. 检查回答的合理性和适度，看看回答是否夸张或离题。5. 将多样性的评分打分在1到5之间，5分表示回答的质量很好，能够吸引人阅读，1分表示回答的内容生硬或者有离题的问题。 | -| Fidelity | 保真度(1-5)：答案是否能够严格遵守角色的设定回答给定的请求。 | 1. 仔细阅读问题，了解角色在问题中的设定和表现，包括职业、背景、观点、性格等方面。
    阅读题目的请求，确认回答请求时需要注意的细节。
    3. 对比提供的回答与该角色的设定，评估回答是否能够严格遵守角色的设定。
    4. 结合以上评估结果给出保真度的评分，范围从1到5分，其中1分表示回答与角色设定完全不符，5分表示回答完全符合角色设定且满足给定请求。 | -| Conciseness | 简明扼要(1-5)：答案是否简明扼要，没有冗余内容。 | 1. 阅读题目，提取出材料的重点。
    2. 阅读该总结，并注意其中的主要观点和信息。
    3. 评估总结的长度。一个简明扼要的总结通常应该在几句话或几段文字内传达关键信息，而不是冗长的段落或文章。
    4. 检查总结是否包含与主要观点无关的信息或冗余信息。
    5. 确定总结涵盖了材料中的关键信息，并且没有忽略任何重要细节。
    6. 给总结打出1-5的分数，其中5表示总结简明扼要，没有冗余内容，而1表示总结冗长或包含不必要的信息，难以理解或记忆。根据您的判断，打出适当的得分。 | +| 语言组织
    (Language organization) | 语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。

    Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc. | 1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。
    2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说
    3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。
    4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。
    5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。
    6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。

    1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.
    2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.
    3. Determine if the answer is relevant to the question or topic and conveys a clear message.
    4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.
    5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.
    6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization. | +| 切题
    (Relevance) | 切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。

    Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic. | 1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。
    2. 阅读答案，确认答案是否直接回答了题目所问的问题。
    3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。
    4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。

    1. Read the question to determine what the question asks and what aspects of the question need to be answered.
    2. Read the answers to make sure that they directly answer the question asked.
    3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.
    4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all. | +| 创意性
    (Creativity) | 创意性(1-5)：某些头脑风暴问题可能需要答案具有创意，提出新的思路。

    Creativity (1-5): Some brainstorming questions may require answers that are creative and suggest new ideas. | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则创意性评分可能会受到影响。
    3. 考虑答案中是否包含新颖的想法或独特的思路。答案可能与已知的解决方案有所重叠，但仍然可以被认为是有创意的，只要它提供了新的角度或方法来解决问题。
    4. 根据答案的创意性，给出一个1到5的评分。如果答案缺乏创意，则应给出一个较低的评分。如果答案具有创意并提供了新的思路，应给出一个较高的评分。

    1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.
    2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the creativity score may be affected.
    3. Consider whether the answer contains novel ideas or unique thoughts. An answer may overlap with a known solution and still be considered creative, as long as it offers a new perspective or approach to the problem.
    4. Give a score of 1 to 5 depending on the creativity of the answer. If the answer lacks creativity, a lower score should be given. If the answer is creative and provides a new idea, a higher score should be given. | +| 实用性
    (Practicality) | 实用性(1-5)：某些头脑风暴问题可能需要答案提出实用的建议或解决方法。

    Practicality (1-5): Some brainstorming questions may require answers to suggest practical suggestions or solutions. | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则实用性评分可能会受到影响。
    3. 考虑答案中提出的建议或解决方法是否实用并可行。答案可能看起来很好，但如果无法实现或应用，则实用性评分可能会受到影响。
    4. 根据答案的实用性，给出一个1到5的评分。如果答案缺乏实用性，则应给出一个较低的评分。如果答案提出了实用的建议或解决方法，并且可以很好地解决问题，则应给出一个较高的评分。

    1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.
    2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the practicality score may be affected.
    3. Consider whether the suggestions or solutions presented in the answer are practical and workable. The answer may look good, but if it cannot be implemented or applied, the practicality score may be affected.
    4. Give a score of 1 to 5 depending on the practicality of the answer. If the answer lacks practicality, a lower score should be given. If the answer makes a practical suggestion or solution and solves the problem well, a higher score should be given. | +| 正确性
    (Correctness) | 正确性(1-5)：答案应该符合常识、生活实际等等。

    Correctness (1-5): The answer should be in line with common sense, life experience, etc. | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则正确性评分可能会受到影响。
    3. 考虑答案中所提供的信息是否正确、符合常识、生活实际等等。如果答案中存在明显的错误或不合理之处，则正确性评分可能会受到影响。
    4. 根据答案的正确性，给出一个1到5的评分。如果答案存在明显的错误或不合理之处，则应给出一个较低的评分。如果答案正确、符合常识、生活实际等等，则应给出一个较高的评分。

    1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.
    2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the correctness score may be affected.
    3. Consider whether the information provided in the answer is correct, consistent with common sense, real life, etc. If there are obvious errors or implausibilities in the answer, the correctness score may be affected.
    4. Give a score of 1 to 5 depending on the correctness of the answer. If the answer contains obvious errors or unreasonable points, a lower score should be given. A higher score should be given if the answer is correct, consistent with common sense, real life, etc. | +| 自然
    (Naturalness) | 自然(1-5)：答案是否自然，并且符合问题给定的身份。

    Naturalness (1-5): whether the answer is natural and fits the identity given by the question. | 1. 阅读题目，确定题目提供的身份信息。
    2. 检查答案内容是否符合题目给定的身份。
    3. 根据以上因素，对该回答的自然性进行打分，分数从1到5，其中1表示不自然，5表示非常自然，并符合问题给定的身份。

    1. Read the question and determine the identity information provided in the question.
    2. Check whether the content of the answer matches the identity given in the question.
    3. Based on the above factors, score the naturalness of the response on a scale from 1 to 5, where 1 means unnatural and 5 means very natural and in accordance with the identity given in the question. | +| 参与感
    (Engagingness) | 参与感(1-5)：答案是否对前面的对话内容做出了恰当的反应，是否理解对话的语境和背景。

    Engagingness (1-5): whether the answer responds appropriately to the content of the preceding conversation and whether it understands the context and background of the conversation. | 1. 阅读题目，确定对话的语境和背景。
    2. 检查答案是否充分理解对话的语境和背景，能否自然地融入到对话中而不显得突兀。
    3. 根据以上因素，对该回答的参与感进行打分，分数从1到5，其中1表示没有参与感，5表示非常有参与感，并且恰当地理解了对话的语境和背景。

    1. Read the questions to determine the context and background of the dialogue.
    2. Check that the answer fully understands the context and background of the conversation and that it fits naturally into the conversation without seeming abrupt.
    3. Based on the above factors, rate the response's engagement on a scale from 1 to 5, where 1 means not engaged and 5 means very engaged and appropriately understands the context and background of the conversation. | +| 合理性
    (Reasonableness) | 合理性(1-5)：答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。

    Reasonableness (1-5): Whether the answer can form a logical connection with the content of the previous dialogue, whether it is consistent with common sense, and whether it can reasonably exist in this context. | 1. 阅读题目，确定对话的主题以及问题期望的回答方向。
    2. 判断答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。
    3. 根据以上因素，对该回答的合理性进行打分，分数从1到5，其中1表示不合理，5表示非常合理，并且能够与前面的对话内容形成逻辑上的衔接，并符合常理。

    1. Read the question and determine the topic of the conversation and the direction the question expects the answer to go.
    2. Determine whether the answer can be logically connected to the preceding conversation, whether it makes common sense, and whether it can reasonably exist in this context.
    3. Based on the above factors, rate the reasonableness of the answer on a scale from 1 to 5, where 1 means unreasonable and 5 means very reasonable and able to form a logical connection with the preceding dialogue content and consistent with common sense. | +| 多样性
    (Diversity) | 多样性(1-5)：答案使用语言是否优美，具有有一定的创造性和想象力。然而，回答也应该保持合理和适度，不要过于夸张或离题。

    Diversity (1-5): Whether the answers use beautiful language and have some creativity and imagination. However, answers should also be kept reasonable and moderate, not overly exaggerated or off-topic. | 1. 仔细阅读整个回答，确保完全理解回答所表达的内容和主题。
    2. 在阅读回答的同时，注意语言的质量，例如措辞是否正确，语言是否生动等。
    3. 检查回答的创造性和想象力，看看回答是否能够吸引人阅读下去。
    4. 检查回答的合理性和适度，看看回答是否夸张或离题。5. 将多样性的评分打分在1到5之间，5分表示回答的质量很好，能够吸引人阅读，1分表示回答的内容生硬或者有离题的问题。

    1. Read the entire response carefully to ensure that you fully understand the content and theme expressed in the response.
    2. While reading the response, pay attention to the quality of the language, such as whether the wording is correct and the language is vivid.
    3. Check the creativity and imagination of the response to see if the response is engaging to read on.
    4. Check the reasonableness and appropriateness of the responses to see if the responses are exaggerated or off-topic.
    5. Rate the diversity on a scale of 1 to 5, with a 5 indicating a good quality response that is engaging to read and a 1 indicating a raw response or a question that is off-topic. | +| 保真度
    (Fidelity) | 保真度(1-5)：答案是否能够严格遵守角色的设定回答给定的请求。

    Fidelity (1-5): whether the answer is able to answer the given request in strict compliance with the role setting. | 1. 仔细阅读问题，了解角色在问题中的设定和表现，包括职业、背景、观点、性格等方面。
    阅读题目的请求，确认回答请求时需要注意的细节。
    3. 对比提供的回答与该角色的设定，评估回答是否能够严格遵守角色的设定。
    4. 结合以上评估结果给出保真度的评分，范围从1到5分，其中1分表示回答与角色设定完全不符，5分表示回答完全符合角色设定且满足给定请求。

    1. Read the question carefully to understand how the character is set up and represented in the question, including aspects such as occupation, background, point of view, and personality.
    2. Read the question's request and confirm the details that need to be taken into account when answering the request.
    3. Compare the provided answer with the setting of the role and assess whether the answer can strictly adhere to the setting of the role.
    4. Combine the results of the above assessment to give a fidelity score ranging from 1 to 5, where a score of 1 means that the response does not match the persona at all, and a score of 5 means that the response fully complies with the persona and satisfies the given request. | +| 简明扼要
    (Conciseness) | 简明扼要(1-5)：答案是否简明扼要，没有冗余内容。

    Conciseness (1-5): answers should be concise and without redundant content. | 1. 阅读题目，提取出材料的重点。
    2. 阅读该总结，并注意其中的主要观点和信息。
    3. 评估总结的长度。一个简明扼要的总结通常应该在几句话或几段文字内传达关键信息，而不是冗长的段落或文章。
    4. 检查总结是否包含与主要观点无关的信息或冗余信息。
    5. 确定总结涵盖了材料中的关键信息，并且没有忽略任何重要细节。
    6. 给总结打出1-5的分数，其中5表示总结简明扼要，没有冗余内容，而1表示总结冗长或包含不必要的信息，难以理解或记忆。根据您的判断，打出适当的得分。

    1. Read the title and extract the main points of the material.
    2. Read the summary and note the main ideas and messages in it.
    3. Assess the length of the summary. A concise summary should usually convey key information within a few sentences or paragraphs, rather than lengthy paragraphs or essays.
    4. Check that the summary does not contain information that is not relevant to the main ideas or that is redundant.
    5. Make sure that the summary covers the key information in the material and that no important details have been omitted.
    6. Rate the summary on a scale of 1-5, where 5 means the summary is concise and free of redundancy, and 1 means the summary is lengthy or contains unnecessary information that is difficult to understand or remember. Based on your judgment, assign the appropriate score. | GPT models evaluate the quality of model predictions based on the given prompt words and gives a score between 1-5. +> **NOTE:** Even for the same metric, the details of its prompt words and CoT(Chain-of-Thought) can differ based on which category you want to evaluate. For example, prompt words for metric `correctness` showed here is "The answer should be in line with common sense, life experience, etc."(this is for category `brainstorming`), but for category `extraction`, prompt words can be "Answers should extract the required information accurately and should not contain any incorrect or misleading information." You can find all the prompt words and CoT(Chain-of-Thought) in `prompt/evaluation_prompt`. + #### Automatic Evaluation Automated metrics evaluate the capability of a model by comparing model predictions with reference answers. @@ -86,7 +87,7 @@ There are two ways to obtain reference answers: There are 5 types of automatic evaluation metrics listed in the table below: -| Automatic Evaluation Metric |
    Description
    | +| Automatic Evaluation Metric | Description | | :---------------------------------: | :----------------------------------------------------------- | | BLEU-n | Measure the accuracy between prediction and reference.
    BLEU-1 (Unigram) evaluates accuracy in word level.
    BLEU-n (n-gram) evaluate the fluency in sentence level. | | ROUGE | ROUGE-N measures the number of matching n-grams between prediction and reference.
    ROUGE-L measures the number of matching longest common subsequence (LCS) between prediction and reference. | @@ -175,7 +176,7 @@ Example: #### Battle Prompt -The following is the Chinese battle prompt. In the battle prompt, the question and answers from two different models are fed into the prompt template. You can find an example battle prompt file in `prompt/battle_prompt`. +The following is the Chinese battle prompt. In the battle prompt, the question and answers from two different models are fed into the prompt template. You can find example battle prompt files for Chinese and English in `prompt/battle_prompt`. ```json { @@ -188,7 +189,7 @@ The following is the Chinese battle prompt. In the battle prompt, the question a #### Evaluation Prompt -The following is an example of a Chinese GPT evaluation prompt. In an evaluation prompt, you should define your metrics in `metrics` and provide CoT(Chain-of-Thought) in `CoT`. You can find an example evaluation prompt file in `prompt/evaluation_prompt`. +The following is an example of a Chinese GPT evaluation prompt. In an evaluation prompt, you should define your metrics in `metrics` and provide CoT(Chain-of-Thought) in `CoT`. You can find example evaluation prompt files for Chinese and English in `prompt/evaluation_prompt`. ```json { @@ -303,7 +304,7 @@ For example, if you want to add a new metric `persuasiveness` into category `bra ## To Do -- [ ] Add evaluation for English capability +- [x] Add evaluation for English capability - [ ] Support UniEval - [x] Support GPT-4 evaluation diff --git a/applications/Chat/evaluate/config/config_en.json b/applications/Chat/evaluate/config/config_en.json new file mode 100644 index 000000000000..5b6272b97084 --- /dev/null +++ b/applications/Chat/evaluate/config/config_en.json @@ -0,0 +1,123 @@ +{ + "language": "en", + "category": { + "brainstorming": { + "GPT": [ + "language organization", + "relevance", + "creativity", + "practicality", + "correctness" + ], + "Metrics": [ + "Distinct" + ] + }, + "chat": { + "GPT": [ + "language organization", + "relevance", + "naturalness", + "engagingness", + "reasonableness" + ], + "Metrics": [ + "Distinct" + ] + }, + "classification": { + "GPT": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "Precision", + "Recall", + "F1 score" + ] + }, + "closed_qa": { + "GPT": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "BLEU", + "ROUGE", + "BERTScore" + ] + }, + "extraction": { + "GPT": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "Precision", + "Recall", + "F1 score" + ] + }, + "generation": { + "GPT": [ + "language organization", + "relevance", + "diversity" + ], + "Metrics": [ + "BLEU", + "ROUGE", + "BERTScore" + ] + }, + "open_qa": { + "GPT": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "Distinct" + ] + }, + "rewriting": { + "GPT": [ + "language organization", + "relevance", + "correctness" + ], + "Metrics": [ + "BLEU", + "ROUGE", + "BERTScore" + ] + }, + "roleplay": { + "GPT": [ + "language organization", + "relevance", + "fidelity", + "creativity" + ], + "Metrics": [ + "Distinct" + ] + }, + "summarization": { + "GPT": [ + "language organization", + "relevance", + "correctness", + "conciseness" + ], + "Metrics": [ + "BLEU", + "ROUGE", + "BERTScore" + ] + } + } +} diff --git a/applications/Chat/evaluate/eval.py b/applications/Chat/evaluate/eval.py index 4067b15db6e8..8388d95f748a 100644 --- a/applications/Chat/evaluate/eval.py +++ b/applications/Chat/evaluate/eval.py @@ -14,7 +14,7 @@ def main(args): # load config config = jload(args.config_file) - if config["language"] == "cn": + if config["language"] in ["cn", "en"]: # get metric settings for all categories metrics_per_category = {} for category in config["category"].keys(): diff --git a/applications/Chat/evaluate/evaluator.py b/applications/Chat/evaluate/evaluator.py index 433d775d27ed..0bf55ca80d7c 100644 --- a/applications/Chat/evaluate/evaluator.py +++ b/applications/Chat/evaluate/evaluator.py @@ -4,7 +4,7 @@ import gpt_evaluate import metrics import pandas as pd -from utils import get_data_per_category, jdump +from utils import analyze_automatic_results, get_data_per_category, save_automatic_results class Evaluator(object): @@ -42,21 +42,21 @@ def evaluate(self, answers: List[Dict], targets: List[Dict]) -> None: """ - def switch(metric): + def switch(metric, language): if metric == "BLEU": - return metrics.bleu_score(preds=predicts_list, targets=targets_list) + return metrics.bleu_score(preds=predicts_list, targets=targets_list, language=language) elif metric == "ROUGE": - return metrics.rouge_cn_score(preds=predicts_list, targets=targets_list) + return metrics.rouge_score(preds=predicts_list, targets=targets_list, language=language) elif (metric == "Distinct"): - return metrics.distinct_score(preds=predicts_list) + return metrics.distinct_score(preds=predicts_list, language=language) elif (metric == "BERTScore"): - return metrics.bert_score(preds=predicts_list, targets=targets_list) + return metrics.bert_score(preds=predicts_list, targets=targets_list, language=language) elif (metric == "Precision"): - return metrics.precision(preds=predicts_list, targets=targets_list) + return metrics.precision(preds=predicts_list, targets=targets_list, language=language) elif (metric == "Recall"): - return metrics.recall(preds=predicts_list, targets=targets_list) + return metrics.recall(preds=predicts_list, targets=targets_list, language=language) elif (metric == "F1 score"): - return metrics.F1_score(preds=predicts_list, targets=targets_list) + return metrics.F1_score(preds=predicts_list, targets=targets_list, language=language) else: raise ValueError(f"Unexpected metric") @@ -78,7 +78,7 @@ def switch(metric): predicts_list = [answer["output"] for answer in answers_per_category[category]] for metric in category_metrics: - self.automatic_metric_stats[category].update(switch(metric=metric)) + self.automatic_metric_stats[category].update(switch(metric=metric, language=self.language)) # gpt evaluation for category in self.params: @@ -106,35 +106,29 @@ def save(self, path: str, model_name_list: List[str]) -> None: save_path = os.path.join(path, "gpt_evaluate", "battle_results") gpt_evaluate.save_battle_results(self.battle_results, model_name_list[0], model_name_list[1], save_path) else: - # save evaluation results for automatic metrics - automatic_df = pd.DataFrame(self.automatic_metric_stats) + # Save evaluation results for automatic metrics + automatic_base_save_path = os.path.join(path, "automatic_results") + automatic_results_save_path = os.path.join(automatic_base_save_path, "evaluation_results") - automatic_results_save_path = os.path.join(path, "automatic_results") - if not os.path.exists(automatic_results_save_path): - os.makedirs(automatic_results_save_path) - automatic_df.to_csv(os.path.join(automatic_results_save_path, f"{model_name_list[0]}.csv"), index=True) + save_automatic_results(model_name_list[0], self.automatic_metric_stats, automatic_results_save_path) - # Save evaluation results for GPT-3.5 evaluation metrics. - all_evaluations = [] - base_save_path = os.path.join(path, "gpt_evaluate", "gpt_evaluate_results") - evaluation_results_save_path = os.path.join(base_save_path, "evaluation_results") + # Save charts and csv. + automatic_analyses_save_path = os.path.join(automatic_base_save_path, "evaluation_analyses") + analyze_automatic_results(automatic_results_save_path, automatic_analyses_save_path) - for category, evaluations in self.gpt_evaluation_results.items(): - jdump( - evaluations, - os.path.join(evaluation_results_save_path, model_name_list[0], - f"{category}_evaluation_results.json")) - all_evaluations.extend(evaluations) + # Save evaluation results for GPT evaluation metrics. + gpt_base_save_path = os.path.join(path, "gpt_evaluate", "gpt_evaluate_results") + gpt_evaluation_results_save_path = os.path.join(gpt_base_save_path, "evaluation_results") - jdump(all_evaluations, - os.path.join(evaluation_results_save_path, f"{model_name_list[0]}_evaluation_results.json")) + all_evaluations = gpt_evaluate.save_gpt_evaluation_results(model_name_list[0], self.gpt_evaluation_results, + gpt_evaluation_results_save_path) # Start to calculate scores and save statistics. - evaluation_statistics_save_path = os.path.join(base_save_path, "evaluation_statistics") + gpt_evaluation_statistics_save_path = os.path.join(gpt_base_save_path, "evaluation_statistics") gpt_evaluate.save_gpt_evaluation_statistics(model_name_list[0], all_evaluations, - evaluation_statistics_save_path) + gpt_evaluation_statistics_save_path) # Save charts and csv. - evaluation_analyses_save_path = os.path.join(base_save_path, "evaluation_analyses") - gpt_evaluate.analyze_gpt_evaluation_statistics(evaluation_statistics_save_path, - evaluation_analyses_save_path) + gpt_evaluation_analyses_save_path = os.path.join(gpt_base_save_path, "evaluation_analyses") + gpt_evaluate.analyze_gpt_evaluation_statistics(gpt_evaluation_statistics_save_path, + gpt_evaluation_analyses_save_path) diff --git a/applications/Chat/evaluate/gpt_evaluate.py b/applications/Chat/evaluate/gpt_evaluate.py index 61ce3456c49f..b433500dfa04 100644 --- a/applications/Chat/evaluate/gpt_evaluate.py +++ b/applications/Chat/evaluate/gpt_evaluate.py @@ -461,6 +461,27 @@ def calculate_scores_form_response(response: str, evaluation: Dict[str, Any]) -> return 0 +def save_gpt_evaluation_results(model_name: str, gpt_evaluation_results: Dict[str, Any], + save_path: str) -> Dict[str, Any]: + """ + Save evaluation results for different categories for one model. + + Args: + model_name: name of the model for saving evaluation results. + gpt_evaluation_results: evaluations results for all of the model answers. + save_path: path to save GPT evaluation statistics. + """ + + all_evaluations = [] + for category, evaluations in gpt_evaluation_results.items(): + jdump(evaluations, os.path.join(save_path, model_name, f"{category}_evaluation_results.json")) + all_evaluations.extend(evaluations) + + jdump(all_evaluations, os.path.join(save_path, f"{model_name}_evaluation_results.json")) + + return all_evaluations + + def save_gpt_evaluation_statistics(model_name: str, evaluations: List[Dict], save_path: str) -> None: """ Generate statistics for one model. @@ -468,7 +489,7 @@ def save_gpt_evaluation_statistics(model_name: str, evaluations: List[Dict], sav Args: model_name: name of the model for saving statistics. evaluations: evaluations for all of the model answers. - save_path: path to save GPT-3.5 evaluation statistics. + save_path: path to save GPT evaluation statistics. """ if not os.path.exists(save_path): @@ -516,7 +537,7 @@ def save_gpt_evaluation_statistics(model_name: str, evaluations: List[Dict], sav def analyze_gpt_evaluation_statistics(statistics_path: str, save_path: str) -> None: """ - Analyze and visualize all GPT-3.5 evaluation statistics in the given directory. + Analyze and visualize all GPT evaluation statistics in the given directory. Args: statistics_path: path to all the models' statistics. @@ -594,3 +615,5 @@ def analyze_gpt_evaluation_statistics(statistics_path: str, save_path: str) -> N figure = fig.get_figure() figure.savefig(os.path.join(save_path, f"{category}.png"), dpi=400) + + plt.close() diff --git a/applications/Chat/evaluate/metrics.py b/applications/Chat/evaluate/metrics.py index 5e657234c61a..031f6fa83926 100644 --- a/applications/Chat/evaluate/metrics.py +++ b/applications/Chat/evaluate/metrics.py @@ -1,13 +1,16 @@ import statistics +from typing import Dict, List import jieba from bert_score import score from nltk.translate.bleu_score import sentence_bleu from rouge_chinese import Rouge as Rouge_cn +from rouge_score import rouge_scorer as Rouge_en from sklearn.metrics import f1_score, precision_score, recall_score +from utils import preprocessing_text, remove_redundant_space -def bleu_score(preds: list, targets: list) -> dict: +def bleu_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]: """Calculate BLEU Score Metric The calculation includes BLEU-1 for unigram, BLEU-2 for bigram, @@ -21,8 +24,12 @@ def bleu_score(preds: list, targets: list) -> dict: (1. / 4., 1. / 4., 1. / 4., 1. / 4.)] for pred, target in zip(preds, targets): - pred_list = (' '.join(jieba.cut(pred))).split() - target_list = [(' '.join(jieba.cut(target))).split()] + if language == "cn": + pred_list = ' '.join(jieba.cut(preprocessing_text(pred))).split() + target_list = [(' '.join(jieba.cut(preprocessing_text(target)))).split()] + elif language == "en": + pred_list = preprocessing_text(pred).split() + target_list = [preprocessing_text(target).split()] bleu = sentence_bleu(target_list, pred_list, weights=weights) cumulative_bleu = [a + b for a, b in zip(cumulative_bleu, bleu)] @@ -33,7 +40,7 @@ def bleu_score(preds: list, targets: list) -> dict: return bleu_scores -def rouge_cn_score(preds: list, targets: list) -> dict: +def rouge_cn_score(preds: List[str], targets: List[str]) -> Dict[str, float]: """Calculate Chinese ROUGE Score Metric The calculation includes ROUGE-1 for unigram, ROUGE-2 for bigram @@ -41,13 +48,13 @@ def rouge_cn_score(preds: list, targets: list) -> dict: the preds and targets. ROUGE-L measures the number of matching longest common subsequence (LCS) between preds and targets. """ - rouge_scores = {"rouge1": {}, "rouge2": {}, "rougeL": {}} + rouge_scores = {"rouge1": 0, "rouge2": 0, "rougeL": 0} all_preds = [] all_targets = [] for pred, target in zip(preds, targets): - pred_list = ' '.join(jieba.cut(pred)) - target_list = ' '.join(jieba.cut(target)) + pred_list = remove_redundant_space(' '.join(jieba.cut(preprocessing_text(pred)))) + target_list = remove_redundant_space(' '.join(jieba.cut(preprocessing_text(target)))) all_preds.append(pred_list) all_targets.append(target_list) @@ -61,7 +68,42 @@ def rouge_cn_score(preds: list, targets: list) -> dict: return rouge_scores -def distinct_score(preds: list) -> dict: +def rouge_en_score(preds: List[str], targets: List[str]) -> Dict[str, float]: + """Calculate English ROUGE Score Metric + + The calculation includes ROUGE-1 for unigram, ROUGE-2 for bigram + and ROUGE-L. ROUGE-N evaluates the number of matching n-grams between + the preds and targets. ROUGE-L measures the number of matching + longest common subsequence (LCS) between preds and targets. + """ + rouge_scores = {"rouge1": 0, "rouge2": 0, "rougeL": 0} + all_preds = [] + all_targets = [] + + rouge_en = Rouge_en.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=False) + + for pred, target in zip(preds, targets): + score = rouge_en.score(preprocessing_text(pred), preprocessing_text(target)) + rouge_scores["rouge1"] += score['rouge1'].fmeasure + rouge_scores["rouge2"] += score['rouge2'].fmeasure + rouge_scores["rougeL"] += score['rougeL'].fmeasure + + rouge_scores["rouge1"] = rouge_scores["rouge1"] / len(preds) + rouge_scores["rouge2"] = rouge_scores["rouge2"] / len(preds) + rouge_scores["rougeL"] = rouge_scores["rougeL"] / len(preds) + + return rouge_scores + + +def rouge_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]: + """Calculate ROUGE Score Metric""" + if language == "cn": + return rouge_cn_score(preds, targets) + elif language == "en": + return rouge_en_score(preds, targets) + + +def distinct_score(preds: List[str], language: str) -> Dict[str, float]: """Calculate Distinct Score Metric This metric refers to https://arxiv.org/abs/1510.03055. @@ -72,19 +114,36 @@ def distinct_score(preds: list) -> dict: cumulative_distinct = [] for pred in preds: - pred_seg_list = list(' '.join(jieba.cut(pred))) - count_segs = len(pred_seg_list) - unique_segs = set(pred_seg_list) - count_unique_chars = len(unique_segs) - - cumulative_distinct.append(count_unique_chars / count_segs) + if language == "cn": + pred_seg_list = ' '.join(jieba.cut(pred)).split() + count_segs = len(pred_seg_list) + unique_segs = set(pred_seg_list) + count_unique_chars = len(unique_segs) + + cumulative_distinct.append(count_unique_chars / count_segs) + elif language == "en": + # calculate distinct 1-gram, 2-gram, 3-gram + unique_ngram = [set() for _ in range(0, 3)] + all_ngram_count = [0 for _ in range(0, 3)] + + split_pred = preprocessing_text(pred).split() + for n in range(0, 3): + for i in range(0, len(split_pred) - n): + ngram = ' '.join(split_pred[i:i + n + 1]) + unique_ngram[n].add(ngram) + all_ngram_count[n] += 1 + + # Sometimes the answer may contain only one word. For 2-gram and 3-gram, the gram count(denominator) may be zero. + avg_distinct = [len(a) / (b + 1e-6) for a, b in zip(unique_ngram, all_ngram_count)] + + cumulative_distinct.append(statistics.mean(avg_distinct)) distinct_score["distinct"] = statistics.mean(cumulative_distinct) return distinct_score -def bert_score(preds: list, targets: list) -> dict: +def bert_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]: """Calculate BERTScore Metric The BERTScore evaluates the semantic similarity between @@ -95,23 +154,25 @@ def bert_score(preds: list, targets: list) -> dict: target_list = [] for pred, target in zip(preds, targets): - pred_list.append(' '.join(jieba.cut(pred))) - target_list.append(' '.join(jieba.cut(target))) + pred_list.append(pred) + target_list.append(target) - _, _, F = score(pred_list, target_list, lang="zh", verbose=True) + if language == "cn": + _, _, F = score(pred_list, target_list, lang="zh", verbose=True) + elif language == "en": + _, _, F = score(pred_list, target_list, lang="en", verbose=True) bert_score["bert_score"] = F.mean().item() return bert_score -def calculate_precision_recall_f1(preds: list, targets: list) -> dict: +def calculate_precision_recall_f1(preds: List[str], targets: List[str], language: str) -> Dict[str, float]: """Precision, Recall and F1-Score Calculation The calculation of precision, recall and f1-score is realized by counting the number f overlaps between the preds and target. The comparison length - limited by the shorter one of preds and targets. This design is mainly - considered for classification and extraction categories. + limited by the shorter one of preds and targets. """ precision_recall_f1 = {"precision": 0, "recall": 0, "f1_score": 0} precision_scores = [] @@ -119,8 +180,12 @@ def calculate_precision_recall_f1(preds: list, targets: list) -> dict: f1_scores = [] for pred, target in zip(preds, targets): - pred_list = [char for char in pred] - target_list = [char for char in target] + if language == "cn": + pred_list = [char for char in ' '.join(jieba.cut(preprocessing_text(pred))).split()] + target_list = [char for char in ' '.join(jieba.cut(preprocessing_text(target))).split()] + elif language == "en": + pred_list = [char for char in preprocessing_text(pred).split()] + target_list = [char for char in preprocessing_text(target).split()] target_labels = [1] * min(len(target_list), len(pred_list)) pred_labels = [int(pred_list[i] == target_list[i]) for i in range(0, min(len(target_list), len(pred_list)))] @@ -136,34 +201,31 @@ def calculate_precision_recall_f1(preds: list, targets: list) -> dict: return precision_recall_f1 -def precision(preds: list, targets: list) -> dict: +def precision(preds: List[str], targets: List[str], language: str) -> Dict[str, float]: """Calculate Precision Metric - (design for classification and extraction categories) Calculating precision by counting the number of overlaps between the preds and target. """ precision = {"precision": 0} - precision["precision"] = calculate_precision_recall_f1(preds, targets)["precision"] + precision["precision"] = calculate_precision_recall_f1(preds, targets, language)["precision"] return precision -def recall(preds: list, targets: list) -> dict: +def recall(preds: List[str], targets: List[str], language: str) -> Dict[str, float]: """Calculate Recall Metric - (design for classification and extraction categories) Calculating recall by counting the number of overlaps between the preds and target. """ recall = {"recall": 0} - recall["recall"] = calculate_precision_recall_f1(preds, targets)["recall"] + recall["recall"] = calculate_precision_recall_f1(preds, targets, language)["recall"] return recall -def F1_score(preds: list, targets: list) -> dict: +def F1_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]: """Calculate F1-score Metric - (design for classification and extraction categories) Calculating f1-score by counting the number of overlaps between the preds and target. """ f1 = {"f1_score": 0} - f1["f1_score"] = calculate_precision_recall_f1(preds, targets)["f1_score"] + f1["f1_score"] = calculate_precision_recall_f1(preds, targets, language)["f1_score"] return f1 diff --git a/applications/Chat/evaluate/prompt/battle_prompt/battle_prompt_en.json b/applications/Chat/evaluate/prompt/battle_prompt/battle_prompt_en.json new file mode 100644 index 000000000000..2b35d1958ac5 --- /dev/null +++ b/applications/Chat/evaluate/prompt/battle_prompt/battle_prompt_en.json @@ -0,0 +1,6 @@ +{ + "id": 1, + "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer. You will be given two different answers to the same question", + "prompt_template": "[Question]\n{question}\n\n[The Start of AI Assistant 1's Answer]\n{answer_1}\n\n[The End of AI Assistant 1's Answer]\n\n[The Start of AI Assistant 2's Answer]\n{answer_2}\n\n[The End of AI Assistant 2's Answer]\n\n[Requirements]\n{prompt}\n\n", + "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space. In the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment." +} diff --git a/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_en.json b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_en.json new file mode 100644 index 000000000000..0b2053746af2 --- /dev/null +++ b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_en.json @@ -0,0 +1,179 @@ +{ + "brainstorming": { + "id": 1, + "category": "brainstorming", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "creativity": "Creativity (1-5): Some brainstorming questions may require answers that are creative and suggest new ideas.", + "practicality": "Practicality (1-5): Some brainstorming questions may require answers to suggest practical suggestions or solutions.", + "correctness": "Correctness (1-5): The answer should be in line with common sense, life experience, etc." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "creativity": "1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.\n2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the creativity score may be affected.\n3. Consider whether the answer contains novel ideas or unique thoughts. An answer may overlap with a known solution and still be considered creative, as long as it offers a new perspective or approach to the problem.\n4. Give a score of 1 to 5 depending on the creativity of the answer. If the answer lacks creativity, a lower score should be given. If the answer is creative and provides a new idea, a higher score should be given.\n\nCreativity:", + "practicality": "1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.\n2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the practicality score may be affected.\n3. Consider whether the suggestions or solutions presented in the answer are practical and workable. The answer may look good, but if it cannot be implemented or applied, the practicality score may be affected.\n4. Give a score of 1 to 5 depending on the practicality of the answer. If the answer lacks practicality, a lower score should be given. If the answer makes a practical suggestion or solution and solves the problem well, a higher score should be given.\n\nPracticality:", + "correctness": "1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.\n2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the correctness score may be affected.\n3. Consider whether the information provided in the answer is correct, consistent with common sense, real life, etc. If there are obvious errors or implausibilities in the answer, the correctness score may be affected.\n4. Give a score of 1 to 5 depending on the correctness of the answer. If the answer contains obvious errors or unreasonable points, a lower score should be given. A higher score should be given if the answer is correct, consistent with common sense, real life, etc.\n\nCorrectness:" + }, + "prompt": "You are a good assistant. Please rate the given answer to the \"brainstorming\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "chat": { + "id": 2, + "category": "chat", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "naturalness": "Naturalness (1-5): whether the answer is natural and fits the identity given by the question.", + "engagingness": "Engagingness (1-5): whether the answer responds appropriately to the content of the preceding conversation and whether it understands the context and background of the conversation.", + "reasonableness": "Reasonableness (1-5): Whether the answer can form a logical connection with the content of the previous dialogue, whether it is consistent with common sense, and whether it can reasonably exist in this context." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "naturalness": "1. Read the question and determine the identity information provided in the question.\n2. Check whether the content of the answer matches the identity given in the question.\n3. Based on the above factors, score the naturalness of the response on a scale from 1 to 5, where 1 means unnatural and 5 means very natural and in accordance with the identity given in the question.\n\nNaturalness:", + "engagingness": "1. Read the questions to determine the context and background of the dialogue.\n2. Check that the answer fully understands the context and background of the conversation and that it fits naturally into the conversation without seeming abrupt.\n3. Based on the above factors, rate the response's engagement on a scale from 1 to 5, where 1 means not engaged and 5 means very engaged and appropriately understands the context and background of the conversation.\n\nEngagingness:", + "reasonableness": "1. Read the question and determine the topic of the conversation and the direction the question expects the answer to go.\n2. Determine whether the answer can be logically connected to the preceding conversation, whether it makes common sense, and whether it can reasonably exist in this context.\n3. Based on the above factors, rate the reasonableness of the answer on a scale from 1 to 5, where 1 means unreasonable and 5 means very reasonable and able to form a logical connection with the preceding dialogue content and consistent with common sense.\n\nReasonableness:" + }, + "prompt": "You are a good assistant. Please rate the given answer to the \"chat\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "classification": { + "id": 3, + "category": "classification", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "correctness": "Correctness (1-5): whether the answer is correct or not." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "correctness": "1. Read the question carefully and try to answer the question yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be given. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" + }, + "prompt": "You are a good assistant. Please rate the given answer to the \"classification\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "closed_qa": { + "id": 4, + "category": "closed_qa", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "correctness": "Correctness (1-5): whether the answer is correct or not." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "correctness": "1. Read the question carefully and try to answer the question by yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be assigned. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" + }, + "prompt": "You are a good assistant. Please rate the given answer to the \"closed qa\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "extraction": { + "id": 5, + "category": "extraction", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "correctness": "correctness (1-5): Answers should extract the required information accurately and should not contain any incorrect or misleading information." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "correctness": "1. Read the questions carefully and identify the information that needs to be extracted from the material.\n2. Read the answer carefully and make sure it covers all the information that needs to be extracted.\n3. Use the material provided to verify the correctness of the response. If the response is inaccurate or contains incorrect or misleading information, a high score cannot be given.\n4. Check that the answer contains all the information required to be extracted and do not leave out any important details.\n5. Give a score between 1 and 5 based on the correctness and completeness of the response, with a score of 5 indicating a very accurate and complete response and a score of 1 indicating that the response barely extracts the required information.\n\nCorrectness:" + }, + "prompt": "You are a good assistant. Please rate the given answer to the \"extraction\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "generation": { + "id": 6, + "category": "generation", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "diversity": "Diversity (1-5): Whether the answers use beautiful language and have some creativity and imagination. However, answers should also be kept reasonable and moderate, not overly exaggerated or off-topic." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "diversity": "1. Read the entire response carefully to ensure that you fully understand the content and theme expressed in the response.\n2. While reading the response, pay attention to the quality of the language, such as whether the wording is correct and the language is vivid.\n3. Check the creativity and imagination of the response to see if the response is engaging to read on.\n4. Check the reasonableness and appropriateness of the responses to see if the responses are exaggerated or off-topic.\n5. Rate the diversity on a scale of 1 to 5, with a 5 indicating a good quality response that is engaging to read and a 1 indicating a raw response or a question that is off-topic.\n\nDiversity:" + }, + "prompt": "You are a good assistant. Please rate the given answer to the \"generation\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "open_qa": { + "id": 7, + "category": "open_qa", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "correctness": "Correctness (1-5): whether the answer is correct or not." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "correctness": "1. Read the question carefully and try to answer the question yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be given. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" + }, + "prompt": "You are a good assistant. Please rate the answers to the \"open qa\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "rewriting": { + "id": 8, + "category": "rewriting", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "correctness": "Correctness (1-5): whether the answer is correct or not." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "correctness": "1. Read the question carefully and try to answer the question yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be assigned. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" + }, + "prompt": "You are a good assistant. Please rate the answers to the \"rewriting\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "roleplay": { + "id": 9, + "category": "roleplay", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "fidelity": "Fidelity (1-5): whether the answer is able to answer the given request in strict compliance with the role setting.", + "creativity": "Creativity (1-5): The answers to the role-play questions need to be somewhat creative, but at the same time they need to adhere to the setting of the role." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "fidelity": "1. Read the question carefully to understand how the character is set up and represented in the question, including aspects such as occupation, background, point of view, and personality.\n2. Read the question's request and confirm the details that need to be taken into account when answering the request.\n3. Compare the provided answer with the setting of the role and assess whether the answer can strictly adhere to the setting of the role.\n4. Combine the results of the above assessment to give a fidelity score ranging from 1 to 5, where a score of 1 means that the response does not match the persona at all, and a score of 5 means that the response fully complies with the persona and satisfies the given request.\n\nFidelity:", + "creativity": "1. Read the question carefully to understand how the character is set up and represented in the question, including career, background, perspective, and personality.\n2. Evaluate whether the answer has unique ideas and suggestions that bring new ideas and insights to the questioner.\n3. Compare the creativity in the response to the setting of the persona and assess whether the response adheres to the setting and essential characteristics of the persona.\n4. Evaluate the quality of the responses in general and combine the results of the above assessment to give a creativity score ranging from 1 to 5, where a score of 1 indicates that the response lacks creativity and a score of 5 indicates that the response has unique ideas and suggestions and is able to adhere to the set-up of the persona.\n\nCreativity:" + }, + "prompt": "You are a good assistant. Please rate the given answer to the \"role-play\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "summarization": { + "id": 10, + "category": "summarization", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "correctness": "Correctness (1-5): answers should summarize the main points of the material accurately and unambiguously.", + "conciseness": "Conciseness (1-5): answers should be concise and without redundant content." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "correctness": "1. Read the material given in the question carefully to understand its content and main points.\n2. Assess whether the answer accurately summarizes the key points of the source material.\n3. assess whether the response contains all the key information in the source material.\n4. Based on the above steps, give a score of 1-5, where 1 means that the response does not accurately summarize the main points of the material and 5 means that the response completely accurately summarizes the main points of the material.\n\nCorrectness:", + "conciseness": "1. Read the title and extract the main points of the material.\n2. Read the summary and note the main ideas and messages in it.\n3. Assess the length of the summary. A concise summary should usually convey key information within a few sentences or paragraphs, rather than lengthy paragraphs or essays.\n4. Check that the summary does not contain information that is not relevant to the main ideas or that is redundant.\n5. Make sure that the summary covers the key information in the material and that no important details have been omitted.\n6. Rate the summary on a scale of 1-5, where 5 means the summary is concise and free of redundancy, and 1 means the summary is lengthy or contains unnecessary information that is difficult to understand or remember. Based on your judgment, assign the appropriate score.\n\nConciseness:" + }, + "prompt": "You are a good assistant. Please rate the given answer to the \"summarization\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + }, + "general": { + "id": 11, + "category": "general", + "metrics": { + "language organization": "Language organization (1-5): whether the answer language is fluent and coherent, uses correct grammar, has a certain logic, uses appropriate connecting words, transition words, etc.", + "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", + "correctness": "Correctness (1-5): whether the answer is correct or not." + }, + "CoT": { + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", + "correctness": "1. Read the question carefully and try to answer the question yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be assigned. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" + }, + "prompt": "You are a good assistant. Please rate the given answer to the question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" + } +} diff --git a/applications/Chat/evaluate/requirements.txt b/applications/Chat/evaluate/requirements.txt index b0301c2f17f8..27d317ed88cc 100644 --- a/applications/Chat/evaluate/requirements.txt +++ b/applications/Chat/evaluate/requirements.txt @@ -8,3 +8,5 @@ seaborn pandas matplotlib numpy +zhon +rouge_score diff --git a/applications/Chat/evaluate/utils.py b/applications/Chat/evaluate/utils.py index 517c0a1c351e..1f4069386fcd 100644 --- a/applications/Chat/evaluate/utils.py +++ b/applications/Chat/evaluate/utils.py @@ -1,6 +1,15 @@ import io import json import os +import re +import string +from typing import Dict + +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import tqdm +from zhon import hanzi def _make_w_io_base(f, mode: str): @@ -29,7 +38,7 @@ def jdump(obj, f, mode="w", indent=4, default=str): """ f = _make_w_io_base(f, mode) if isinstance(obj, (dict, list)): - json.dump(obj, f, indent=indent, default=default) + json.dump(obj, f, indent=indent, default=default, ensure_ascii=False) elif isinstance(obj, str): f.write(obj) else: @@ -61,3 +70,149 @@ def get_data_per_category(data, categories): data_per_category[category].append(item) return data_per_category + + +def remove_articles(text: str) -> str: + """ + Remove articles "a, an, the" in the given text. + It is used in evaluation of automatic metrics. + + """ + + pattern = re.compile(r"\b(a|an|the)\b", re.UNICODE) + return re.sub(pattern, " ", text) + + +def remove_punctuations(text: str) -> str: + """ + Remove punctuations in the given text. + It is used in evaluation of automatic metrics. + + """ + + punctuation = string.punctuation + hanzi.punctuation + punctuation = set([char for char in punctuation]) + punctuation.difference_update(set("!@#$%&()<>?|,.\"'")) + + out = [] + for char in text: + if char in punctuation: + continue + else: + out.append(char) + + return "".join(out) + + +def remove_redundant_space(text: str) -> str: + """ + Remove redundant spaces in the given text. + It is used in evaluation of automatic metrics. + + """ + + return " ".join(text.split()) + + +def preprocessing_text(text: str) -> str: + """ + Preprocess the given text. + It is used in evaluation of automatic metrics. + + """ + + return remove_redundant_space(remove_articles(remove_punctuations(text.lower()))) + + +def save_automatic_results(model_name: str, automatic_metric_stats: Dict[str, Dict], save_path: str) -> None: + """ + Save automatic evaluation results of different categories for one model. + + """ + + if not os.path.exists(save_path): + os.makedirs(save_path) + + automatic_df = pd.DataFrame(automatic_metric_stats) + automatic_df.to_csv(os.path.join(save_path, f"{model_name}_results.csv"), index=True) + + +def read_automatic_results(results_path: str, file_name: str) -> Dict[str, Dict]: + """ + Read a csv file and return a dictionary which stores scores per metric. + + """ + + results = pd.read_csv(os.path.join(results_path, file_name), index_col=0) + + results_dict = {metric: {} for metric in list(results.index)} + for i, metric in enumerate(results_dict.keys()): + for j, category in enumerate(list(results.columns)): + if pd.isnull(results.iloc[i][j]): + continue + results_dict[metric][category] = results.iloc[i][j] + + return results_dict + + +def analyze_automatic_results(results_path: str, save_path: str) -> None: + """ + Analyze and visualize all csv files in the given folder. + + """ + + if not os.path.exists(results_path): + raise Exception(f'The given directory "{results_path}" doesn\'t exist! No results found!') + + all_statistics = {} + + for file_name in os.listdir(results_path): + if file_name.endswith("_results.csv"): + model_name = file_name.split("_results.csv")[0] + all_statistics[model_name] = read_automatic_results(results_path, file_name) + + if len(list(all_statistics.keys())) == 0: + raise Exception(f'There are no csv files in the given directory "{results_path}"!') + + frame_all = {"model": [], "category": [], "metric": [], "score": []} + frame_per_metric = {} + for model_name, model_statistics in all_statistics.items(): + for metric, metric_statistics in model_statistics.items(): + if frame_per_metric.get(metric) is None: + frame_per_metric[metric] = {"model": [], "category": [], "score": []} + + for category, category_score in metric_statistics.items(): + frame_all["model"].append(model_name) + frame_all["category"].append(category) + frame_all["metric"].append(metric) + frame_all["score"].append(category_score) + + frame_per_metric[metric]["model"].append(model_name) + frame_per_metric[metric]["category"].append(category) + frame_per_metric[metric]["score"].append(category_score) + + if not os.path.exists(save_path): + os.makedirs(save_path) + + frame_all = pd.DataFrame(frame_all) + frame_all.to_csv(os.path.join(save_path, "automatic_evaluation_statistics.csv")) + + for metric in tqdm.tqdm( + frame_per_metric.keys(), + desc=f"metric: ", + total=len(frame_per_metric.keys()), + ): + data = pd.DataFrame(frame_per_metric[metric]) + + sns.set() + fig = plt.figure(figsize=(16, 10)) + + fig = sns.barplot(x="category", y="score", hue="model", data=data, dodge=True) + fig.set_title(f"Comparison between Different Models for Metric {metric.title()}") + plt.xlabel("Evaluation Category") + plt.ylabel("Score") + + figure = fig.get_figure() + figure.savefig(os.path.join(save_path, f"{metric}.png"), dpi=400) + + plt.close() From ec9bbc0094f25f69c8bfa8f9653537c40bf91e36 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Tue, 6 Jun 2023 11:32:31 +0800 Subject: [PATCH 268/413] [devops] improving testmon cache (#3902) * [devops] improving testmon cache * [devops] fix branch name with slash * [devops] fix branch name with slash * [devops] fix edit action * [devops] fix edit action * [devops] fix edit action * [devops] fix edit action * [devops] fix edit action * [devops] fix edit action * [devops] update readme --- .github/workflows/README.md | 10 ++- .github/workflows/build_on_pr.yml | 118 ++++++++++++++++++++++++++++-- 2 files changed, 122 insertions(+), 6 deletions(-) diff --git a/.github/workflows/README.md b/.github/workflows/README.md index f40f4cc86d1b..3fad7e36f14c 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -43,10 +43,18 @@ I will provide the details of each workflow below. | Workflow Name | File name | Description | | ---------------------- | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -| `Build on PR` | `build_on_pr.yml` | This workflow is triggered when a PR changes essential files. It will run all the unit tests in the repository with 4 GPUs. | +| `Build on PR` | `build_on_pr.yml` | This workflow is triggered when a PR changes essential files and a branch is created/deleted. It will run all the unit tests in the repository with 4 GPUs. | | `Build on Schedule` | `build_on_schedule.yml` | This workflow will run the unit tests everyday with 8 GPUs. The result is sent to Lark. | | `Report test coverage` | `report_test_coverage.yml` | This PR will put up a comment to report the test coverage results when `Build` is done. | +To reduce the average time of the unit test on PR, `Build on PR` workflow manages testmon cache. + +1. When creating a new branch, it copies `cache/main/.testmondata*` to `cache//`. +2. When creating a new PR or change the base branch of a PR, it copies `cache//.testmondata*` to `cache/_pull//`. +3. When running unit tests for each PR, it restores testmon cache from `cache/_pull//`. After the test, it stores the cache back to `cache/_pull//`. +4. When a PR is closed, if it's merged, it copies `cache/_pull//.testmondata*` to `cache//`. Otherwise, it just removes `cache/_pull/`. +5. When a branch is deleted, it removes `cache/`. + ### Example Test | Workflow Name | File name | Description | diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index a5a17d176c9d..b5f293107310 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -2,7 +2,7 @@ name: Build on PR on: pull_request: - types: [synchronize, opened, reopened] + types: [synchronize, opened, reopened, ready_for_review, closed, edited] branches: - "main" - "develop" @@ -18,11 +18,63 @@ on: - "!tests/**.md" # ignore doc change - "pytest.ini" # test config change - "setup.py" # install command change + create: + delete: jobs: + prepare_cache: + name: Prepare testmon cache + if: | + github.event_name == 'create' && + github.event.ref_type == 'branch' && + github.event.repository.full_name == 'hpcaitech/ColossalAI' + runs-on: [self-hosted, gpu] + container: + image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 + options: --rm + timeout-minutes: 5 + defaults: + run: + shell: bash + steps: + - name: Copy testmon cache + run: | # branch name may contain slash, we need to replace it with space + export REF_BRANCH=$(echo ${{ github.event.ref }} | sed "s/\// /") + if [ -d /github/home/testmon_cache/${MAIN_BRANCH} ]; then + [ ! -z "$(ls -A /github/home/testmon_cache/${MAIN_BRANCH})" ] && cp -p -r /github/home/testmon_cache/${MAIN_BRANCH} "/github/home/testmon_cache/${REF_BRANCH}" + fi + env: + MAIN_BRANCH: ${{ github.event.master_branch }} + + prepare_cache_for_pr: + name: Prepare testmon cache for PR + if: | + github.event_name == 'pull_request' && + (github.event.action == 'opened' || github.event.action == 'reopened' || (github.event.action == 'edited' && github.event.changes.base != null)) && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' + runs-on: [self-hosted, gpu] + container: + image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 + options: --rm + timeout-minutes: 5 + defaults: + run: + shell: bash + steps: + - name: Copy testmon cache + run: | # branch name may contain slash, we need to replace it with space + export BASE=$(echo ${{ github.event.pull_request.base.ref }} | sed "s/\// /") + if [ -d "/github/home/testmon_cache/${BASE}" ]; then + [ ! -z "$(ls -A "/github/home/testmon_cache/${BASE}")" ] && mkdir /github/home/testmon_cache/_pull && cp -p -r "/github/home/testmon_cache/${BASE}" /github/home/testmon_cache/_pull/${PR_NUMBER} + fi + env: + PR_NUMBER: ${{ github.event.pull_request.head.ref }} + detect: name: Detect file change if: | + github.event_name == 'pull_request' && + (github.event.action == 'synchronize' || github.event.action == 'opened' || github.event.action == 'reopened' || github.event.action == 'ready_for_review') && github.event.pull_request.draft == false && github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' outputs: @@ -135,9 +187,11 @@ jobs: - name: Restore Testmon Cache run: | - if [ -d /github/home/testmon_cache ]; then - [ ! -z "$(ls -A /github/home/testmon_cache)" ] && cp -p -r /github/home/testmon_cache/.testmondata* /__w/ColossalAI/ColossalAI/ + if [ -d /github/home/testmon_cache/_pull/${PR_NUMBER} ]; then + [ ! -z "$(ls -A /github/home/testmon_cache/_pull/${PR_NUMBER})" ] && cp -p -r /github/home/testmon_cache/_pull/${PR_NUMBER}/.testmondata* /__w/ColossalAI/ColossalAI/ fi + env: + PR_NUMBER: ${{ github.event.number }} - name: Execute Unit Testing run: | @@ -149,8 +203,10 @@ jobs: - name: Store Testmon Cache run: | - [ -d /github/home/testmon_cache ] || mkdir /github/home/testmon_cache - cp -p -r /__w/ColossalAI/ColossalAI/.testmondata* /github/home/testmon_cache/ + mkdir -p /github/home/testmon_cache/_pull/${PR_NUMBER} + cp -p -r /__w/ColossalAI/ColossalAI/.testmondata* /github/home/testmon_cache/_pull/${PR_NUMBER}/ + env: + PR_NUMBER: ${{ github.event.number }} - name: Collate artifact env: @@ -188,3 +244,55 @@ jobs: with: name: report path: report/ + + store_cache: + name: Store testmon cache for PR + if: | + github.event_name == 'pull_request' && + github.event.action == 'closed' && + github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' + runs-on: [self-hosted, gpu] + container: + image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 + options: --rm + timeout-minutes: 5 + defaults: + run: + shell: bash + steps: + - name: Store testmon cache if possible + if: github.event.pull_request.merged == true + run: | # branch name may contain slash, we need to replace it with space + export BASE=$(echo ${{ github.event.pull_request.base.ref }} | sed "s/\// /") + if [ -d /github/home/testmon_cache/_pull/${PR_NUMBER} ]; then + [ ! -z "$(ls -A /github/home/testmon_cache/_pull/${PR_NUMBER})" ] && cp -p -r /github/home/testmon_cache/_pull/${PR_NUMBER}/.testmondata* "/github/home/testmon_cache/${BASE}/" + fi + env: + PR_NUMBER: ${{ github.event.pull_request.number }} + + - name: Remove testmon cache + if: github.event.pull_request.merged != true + run: | + rm -rf /github/home/testmon_cache/_pull/${PR_NUMBER} + env: + PR_NUMBER: ${{ github.event.pull_request.number }} + + remove_cache: + name: Remove testmon cache + if: | + github.event_name == 'delete' && + github.event.ref_type == 'branch' && + github.event.repository.full_name == 'hpcaitech/ColossalAI' + runs-on: [self-hosted, gpu] + container: + image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 + options: --rm + timeout-minutes: 5 + defaults: + run: + shell: bash + steps: + - name: Remove testmon cache + run: | # branch name may contain slash, we need to replace it with space + export BASE=$(echo ${{ github.event.ref }} | sed "s/\// /") + rm -rf "/github/home/testmon_cache/${BASE}" From c1535ccbba2688682708b0203cce97b01d7750ef Mon Sep 17 00:00:00 2001 From: Baizhou Zhang <56809903+Fridge003@users.noreply.github.com> Date: Tue, 6 Jun 2023 13:36:11 +0800 Subject: [PATCH 269/413] [doc] fix docs about booster api usage (#3898) --- colossalai/booster/booster.py | 4 ++-- docs/source/en/features/zero_with_chunk.md | 4 ++-- docs/source/zh-Hans/features/zero_with_chunk.md | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 61d912157449..4a42e204982f 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -25,11 +25,11 @@ class Booster: Examples: ```python colossalai.launch(...) - plugin = GeminiPlugin(stage=3, ...) + plugin = GeminiPlugin(...) booster = Booster(precision='fp16', plugin=plugin) model = GPT2() - optimizer = Adam(model.parameters()) + optimizer = HybridAdam(model.parameters()) dataloader = Dataloader(Dataset) lr_scheduler = LinearWarmupScheduler() criterion = GPTLMLoss() diff --git a/docs/source/en/features/zero_with_chunk.md b/docs/source/en/features/zero_with_chunk.md index d6f6f611a64c..1b27d64b6897 100644 --- a/docs/source/en/features/zero_with_chunk.md +++ b/docs/source/en/features/zero_with_chunk.md @@ -195,7 +195,7 @@ def get_data(batch_size, seq_len, vocab_size): Finally, we define a model which uses Gemini + ZeRO DDP and define our training loop, As we pre-train GPT in this example, we just use a simple language model loss: ```python -from torch.optim import Adam +from colossalai.nn.optimizer import HybridAdam from colossalai.booster import Booster from colossalai.zero import ColoInitContext @@ -211,7 +211,7 @@ def main(): # build criterion criterion = GPTLMLoss() - optimizer = Adam(model.parameters(), lr=0.001) + optimizer = HybridAdam(model.parameters(), lr=0.001) torch.manual_seed(123) default_pg = ProcessGroup(tp_degree=args.tp_degree) diff --git a/docs/source/zh-Hans/features/zero_with_chunk.md b/docs/source/zh-Hans/features/zero_with_chunk.md index 9030464ddf9a..9fe5601bbd1b 100644 --- a/docs/source/zh-Hans/features/zero_with_chunk.md +++ b/docs/source/zh-Hans/features/zero_with_chunk.md @@ -197,7 +197,7 @@ def get_data(batch_size, seq_len, vocab_size): 最后，使用booster注入 Gemini + ZeRO DDP 特性, 并定义训练循环。由于我们在这个例子中对GPT进行预训练，因此只使用了一个简单的语言模型损失函数： ```python -from torch.optim import Adam +from colossalai.nn.optimizer import HybridAdam from colossalai.booster import Booster from colossalai.zero import ColoInitContext @@ -213,7 +213,7 @@ def main(): # build criterion criterion = GPTLMLoss() - optimizer = Adam(model.parameters(), lr=0.001) + optimizer = HybridAdam(model.parameters(), lr=0.001) torch.manual_seed(123) default_pg = ProcessGroup(tp_degree=args.tp_degree) From 0e484e620134e3c284216c5c493e8813318cfbdb Mon Sep 17 00:00:00 2001 From: digger yu Date: Tue, 6 Jun 2023 14:07:36 +0800 Subject: [PATCH 270/413] [nfc]fix typo colossalai/pipeline tensor nn (#3899) * fix typo colossalai/autochunk auto_parallel amp * fix typo colossalai/auto_parallel nn utils etc. * fix typo colossalai/auto_parallel autochunk fx/passes etc. * fix typo docs/ * change placememt_policy to placement_policy in docs/ and examples/ * fix typo colossalai/ applications/ * fix typo colossalai/cli fx kernel * fix typo colossalai/nn * revert change warmuped * fix typo colossalai/pipeline tensor nn --- colossalai/nn/optimizer/cpu_adam.py | 2 +- colossalai/nn/optimizer/hybrid_adam.py | 2 +- colossalai/pipeline/pipelinable.py | 8 ++++---- colossalai/pipeline/rpc/_pipeline_base.py | 10 +++++----- colossalai/pipeline/rpc/_pipeline_schedule.py | 6 +++--- colossalai/pipeline/utils.py | 2 +- colossalai/tensor/d_tensor/comm_spec.py | 2 +- colossalai/tensor/d_tensor/sharding_spec.py | 4 ++-- colossalai/tensor/param_op_hook.py | 2 +- colossalai/tensor/process_group.py | 2 +- colossalai/tensor/shape_consistency.py | 6 +++--- colossalai/tensor/sharding_spec.py | 6 +++--- colossalai/tensor/utils.py | 2 +- 13 files changed, 27 insertions(+), 27 deletions(-) diff --git a/colossalai/nn/optimizer/cpu_adam.py b/colossalai/nn/optimizer/cpu_adam.py index 1ec8783c53d3..3a6d37103398 100644 --- a/colossalai/nn/optimizer/cpu_adam.py +++ b/colossalai/nn/optimizer/cpu_adam.py @@ -13,7 +13,7 @@ class CPUAdam(NVMeOptimizer): """Implements Adam algorithm. - Supports parameters updating on both GPU and CPU, depanding on the device of parameters. + Supports parameters updating on both GPU and CPU, depending on the device of parameters. But the parameters and gradients should on the same device: * Parameters on CPU and gradients on CPU is allowed. * Parameters on GPU and gradients on GPU is allowed. diff --git a/colossalai/nn/optimizer/hybrid_adam.py b/colossalai/nn/optimizer/hybrid_adam.py index 526071b06f95..84903ac36832 100644 --- a/colossalai/nn/optimizer/hybrid_adam.py +++ b/colossalai/nn/optimizer/hybrid_adam.py @@ -14,7 +14,7 @@ class HybridAdam(CPUAdam): """Implements Adam algorithm. - Supports parameters updating on both GPU and CPU, depanding on the device of parameters. + Supports parameters updating on both GPU and CPU, depending on the device of parameters. But the parameters and gradients should on the same device: * Parameters on CPU and gradients on CPU is allowed. * Parameters on GPU and gradients on GPU is allowed. diff --git a/colossalai/pipeline/pipelinable.py b/colossalai/pipeline/pipelinable.py index 9731530a6e15..79913987b7cc 100644 --- a/colossalai/pipeline/pipelinable.py +++ b/colossalai/pipeline/pipelinable.py @@ -83,7 +83,7 @@ def _post_init_method(self, module: torch.nn.Module, *args, **kwargs): for k, v in kwargs.items(): if isinstance(v, torch.nn.Module): v = self._layer_spec_dict[id(v)] - # (lyl)TODO: analyse ColoTensor as well + # (lyl)TODO: analyze ColoTensor as well modified_kwargs[k] = v # keep track of the module children @@ -117,7 +117,7 @@ def _post_init_method(self, module: torch.nn.Module, *args, **kwargs): def to_layer_list(self, exec_seq=None): """ Create a layer spec list and func list with execution sequence given by user. - If exec_seq is None, we will take the module initizing order as execution order. + If exec_seq is None, we will take the module initializing order as execution order. """ self._exec_seq = exec_seq @@ -177,7 +177,7 @@ def to_layer_list(self, exec_seq=None): def partition(self, num_chunks, pipeline_size, rank): """ - Partitioned model will be built respect to partion policy. + Partitioned model will be built respect to partition policy. The real module instance will be built in this method. """ if isinstance(self._policy, str): @@ -193,7 +193,7 @@ def partition(self, num_chunks, pipeline_size, rank): self.customized_parts = customized_partition(self._exec_seq) assert len(self.customized_parts) == gpc.get_world_size( ParallelMode.PIPELINE - ), f'World size is {gpc.get_world_size(ParallelMode.PIPELINE)}, but the number of partions is {len(self.customized_parts)}' + ), f'World size is {gpc.get_world_size(ParallelMode.PIPELINE)}, but the number of partitions is {len(self.customized_parts)}' parts = self.customized_parts[rank] else: raise ValueError("A string partition policy should be one of ['uniform', 'balanced', 'customized'].") diff --git a/colossalai/pipeline/rpc/_pipeline_base.py b/colossalai/pipeline/rpc/_pipeline_base.py index 2d7e25c82e7b..9e549df58214 100644 --- a/colossalai/pipeline/rpc/_pipeline_base.py +++ b/colossalai/pipeline/rpc/_pipeline_base.py @@ -123,7 +123,7 @@ def __init__(self, self.device = device self._initialize_outstanding_range() - # variable and const for context managment + # variable and const for context management self.outstanding = 0 self.forward_times = 0 self.backward_times = 0 @@ -226,7 +226,7 @@ def sync_global_worker_rrefs(self, pp_rank_to_worker_rref: Dict[int, PyRRef]) -> self.pp_rank_to_worker_rref = pp_rank_to_worker_rref # for some schedule need the other worker's info to initialise partition (like Chimera) - # construction of partition is executed after the registion of pp_rank_to_worker_rref + # construction of partition is executed after the registration of pp_rank_to_worker_rref self._initialize_partition() # res_use works for lifecycle counter, @@ -418,7 +418,7 @@ def subscribe_producer(self, microbatch_id: int, forward_only: bool): # On current PP middleware design for DAG, get_output_by_key used by _subscribe_producer # can only be executed once for every producer-consumer stage pair, which is necessary # to count the lifecycle of work_item. So, keeping the _subscribe_producer in the same - # lock of work_item queue operation gurantees the consistency of lifecycle counter. + # lock of work_item queue operation guarantees the consistency of lifecycle counter. work_item_from_producer = self._subscribe_producer(microbatch_id, forward_only) self.work_list[key] = work_item_from_producer self.work_list_condition_lock.notify_all() @@ -460,7 +460,7 @@ def subscribe_consumer(self, microbatch_id: int): # On current PP middleware design for DAG, get_output_by_key used by subscribe_consumer # can only be executed once for every producer-consumer stage pair, which is necessary # to count the lifecycle of work_item. So, keeping the subscribe_consumer in the same - # lock of work_item queue operation gurantees the consistency of lifecycle counter. + # lock of work_item queue operation guarantees the consistency of lifecycle counter. work_item_from_consumer = self._subscribe_consumer(microbatch_id) self.work_list[key] = work_item_from_consumer self.work_list_condition_lock.notify_all() @@ -508,7 +508,7 @@ def _get_producer_consumer(self) -> None: assert self.producer_stage_ids is None, f"all the producers of rank {rank} has been subscribed" assert self.consumer_stage_ids is None, f"all the consumers of rank {rank} has been subscribed" - # should be aranged in order, the order of the input of current forward + # should be arranged in order, the order of the input of current forward self.producer_stage_ids = self.get_producer_stage_ids() self.consumer_stage_ids = self.get_consumer_stage_ids() diff --git a/colossalai/pipeline/rpc/_pipeline_schedule.py b/colossalai/pipeline/rpc/_pipeline_schedule.py index 0d572231d378..6eda8f3b34b7 100644 --- a/colossalai/pipeline/rpc/_pipeline_schedule.py +++ b/colossalai/pipeline/rpc/_pipeline_schedule.py @@ -123,7 +123,7 @@ def _get_producer_consumer(self) -> None: assert self.producer_stage_ids is None, f"all the producers of rank {rank} has been subscribed" assert self.consumer_stage_ids is None, f"all the consumers of rank {rank} has been subscribed" - # should be aranged in order, the order of the input of current forward + # should be arranged in order, the order of the input of current forward self.producer_stage_ids = [] self.consumer_stage_ids = [] @@ -174,7 +174,7 @@ def _initialize_partition(self): else: # if it is down pipeline, create partition by origin method co_up_pp_worker_rref = self.pp_rank_to_worker_rref[pp_rank - stage_num] - # get the coresponding model state dict and wait for its init + # get the corresponding model state dict and wait for its init state_dict = co_up_pp_worker_rref.rpc_sync().get_partition_state_dict() super()._initialize_partition() self.module_partition.load_state_dict(state_dict) @@ -228,7 +228,7 @@ def _hook_before_step(self): stage_num = self.actual_stage_num co_pp_rank = (pp_rank + stage_num) % (2 * stage_num) - # if currrent pp_rank is not the first to do step + # if current pp_rank is not the first to do step # wait its previous pp_rank finish step grads = self.get_parameter_gradients() diff --git a/colossalai/pipeline/utils.py b/colossalai/pipeline/utils.py index df7226644a7a..ac8a3ad7d1db 100644 --- a/colossalai/pipeline/utils.py +++ b/colossalai/pipeline/utils.py @@ -113,7 +113,7 @@ def _binary_search(weights, num): def partition_uniform(num_items, pipeline_parallel_size, num_chunks): assert num_items % num_chunks == 0, \ - "Layer length should be divided by the number of chunks, otherwise parameter method is recomended" + "Layer length should be divided by the number of chunks, otherwise parameter method is recommended" logger = get_dist_logger() parts = [[] for _ in range(pipeline_parallel_size)] diff --git a/colossalai/tensor/d_tensor/comm_spec.py b/colossalai/tensor/d_tensor/comm_spec.py index 765d8ec1b01a..159125fa16db 100644 --- a/colossalai/tensor/d_tensor/comm_spec.py +++ b/colossalai/tensor/d_tensor/comm_spec.py @@ -28,7 +28,7 @@ class CommSpec: to determine the buffer shape, and logical_process_axis Argument: - comm_pattern(CollectiveCommPattern): decribe the communication method used in this spec. + comm_pattern(CollectiveCommPattern): describe the communication method used in this spec. process_groups_dict(Dict): A dict which contains the process groups used to apply this CommSpec. gather_dim(int, Optional): The gather_dim of the tensor will be gathered. shard_dim(int, Optional): The shard_dim of the tensor will be sharded. diff --git a/colossalai/tensor/d_tensor/sharding_spec.py b/colossalai/tensor/d_tensor/sharding_spec.py index 2ea0c4db89fd..565012b58a03 100644 --- a/colossalai/tensor/d_tensor/sharding_spec.py +++ b/colossalai/tensor/d_tensor/sharding_spec.py @@ -41,7 +41,7 @@ def __repr__(self): def _convert_str_to_shard_list(self, str_spec): ''' - Conver str_spec into shard_list. + Convert str_spec into shard_list. Argument: str_spec(str): dim spec in str type. @@ -58,7 +58,7 @@ def _convert_str_to_shard_list(self, str_spec): def build_difference_2d_dict(self): ''' - Build a difference maping for 2D device mesh case. It will be used to + Build a difference mapping for 2D device mesh case. It will be used to compute the difference between DimSpec pairs. ''' diff --git a/colossalai/tensor/param_op_hook.py b/colossalai/tensor/param_op_hook.py index 9c2e0d4adbf1..8ed8176d996a 100644 --- a/colossalai/tensor/param_op_hook.py +++ b/colossalai/tensor/param_op_hook.py @@ -164,7 +164,7 @@ def _get_grad_args(*args): for obj in args: if _is_grad_tensor(obj): return args, None - # otherwise, the first arguement should be a tuple of grad tensors + # otherwise, the first argument should be a tuple of grad tensors # if there is no grad tensor, the backward of PreFwdPostBwd can't be triggered arg_zero = args[0] if not isinstance(arg_zero, tuple): diff --git a/colossalai/tensor/process_group.py b/colossalai/tensor/process_group.py index f108bdc247f5..8d2e9a616d76 100644 --- a/colossalai/tensor/process_group.py +++ b/colossalai/tensor/process_group.py @@ -130,7 +130,7 @@ def set_cpu_groups(self): @property def has_cpu_groups(self) -> bool: """has_cpu_groups - If cpu groups have been initailized. + If cpu groups have been initialized. Returns: bool: cpu process groups have been initialized or not. diff --git a/colossalai/tensor/shape_consistency.py b/colossalai/tensor/shape_consistency.py index 0a840006f086..5bec552d69d5 100644 --- a/colossalai/tensor/shape_consistency.py +++ b/colossalai/tensor/shape_consistency.py @@ -252,7 +252,7 @@ def get_all_all_to_all_spec(self, source_spec: ShardingSpec, def get_all_shard_spec(self, source_spec: ShardingSpec, orig_cost_dict): ''' Get all valid sharding specs from source_spec with single shard operation, and - accumulate commucation cost on origin cost which will finally be used in auto sharding solver. + accumulate communication cost on origin cost which will finally be used in auto sharding solver. For the sharding operation, we just care about legal sharding dimensions. Argument: @@ -386,7 +386,7 @@ def get_all_mix_gather_spec(self, source_spec: ShardingSpec, def get_all_one_step_transform_spec(self, source_spec: ShardingSpec, orig_cost_dict) -> Dict[ShardingSpec, float]: ''' Get all valid sharding specs from source_spec with one step transform, and - accumulate commucation cost on origin cost which will finally be used in auto sharding solver. + accumulate communication cost on origin cost which will finally be used in auto sharding solver. Note: all-gather will eliminate a sharding dimension, all-to-all will keep sharding dimension same as before, and shard will add a sharding dimension. Therefore, the result of above operations are mutual exclusive, @@ -577,7 +577,7 @@ def shape_consistency(self, source_spec: ShardingSpec, Step3: Repeat above steps until the source spec transform to target spec. - During finding the transform path, commucation cost will be accumulated, and it + During finding the transform path, communication cost will be accumulated, and it will be finally used in auto parallel solver. Additionally, to avoid repeating the path search in runtime, we cached all solved path diff --git a/colossalai/tensor/sharding_spec.py b/colossalai/tensor/sharding_spec.py index bed320130ccd..406ad49097b5 100644 --- a/colossalai/tensor/sharding_spec.py +++ b/colossalai/tensor/sharding_spec.py @@ -45,7 +45,7 @@ def __repr__(self): def _convert_str_to_shard_list(self, str_spec): ''' - Conver str_spec into shard_list. + Convert str_spec into shard_list. Argument: str_spec(str): dim spec in str type. @@ -62,7 +62,7 @@ def _convert_str_to_shard_list(self, str_spec): def build_difference_2d_dict(self): ''' - Build a difference maping for 2D device mesh case. It will be used to + Build a difference mapping for 2D device mesh case. It will be used to compute the difference between DimSpec pairs. ''' @@ -166,7 +166,7 @@ class ShardingSpec: device_mesh(DeviceMesh): A logical view of a physical mesh. entire_shape(torch.Size): The entire shape of tensor before sharded. dim_partition_dict(Dict[int, List[int]]， optional): The key is the dimension of tensor to be sharded, - and the value of the key decribe which logical axis will be sharded in that dimension. + and the value of the key describe which logical axis will be sharded in that dimension. sharding_sequence(List[_DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1]. ''' diff --git a/colossalai/tensor/utils.py b/colossalai/tensor/utils.py index 6e30f97fef03..e7d51d099e02 100644 --- a/colossalai/tensor/utils.py +++ b/colossalai/tensor/utils.py @@ -77,7 +77,7 @@ def shard_simulator(target_pair, legal_sharding_dims): Argument: target_pair(Tuple[int, List[int]]): The first element is the dimension of tensor to be sharded, - and the second element decribes which logical axis will be sharded in that dimension. + and the second element describes which logical axis will be sharded in that dimension. ''' _, shard_list = target_pair shard_list_list = [] From 176010f2898cd4353313fc909bf4d2f5a65860a1 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Tue, 6 Jun 2023 14:08:22 +0800 Subject: [PATCH 271/413] update performance evaluation --- .../tutorial/new_api/dreambooth/README.md | 20 ++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/examples/tutorial/new_api/dreambooth/README.md b/examples/tutorial/new_api/dreambooth/README.md index bd7e7707ac78..8e1fdbbc8c1f 100644 --- a/examples/tutorial/new_api/dreambooth/README.md +++ b/examples/tutorial/new_api/dreambooth/README.md @@ -40,6 +40,9 @@ We have modified our previous implementation of Dreambooth with our new Booster We have also offer a shell script `test_ci.sh` for you to go through all our plugins for the booster. For more information about the booster API you can refer to https://colossalai.org/docs/basics/booster_api/. + + + ## Training We provide the script `colossalai.sh` to run the training task with colossalai. For instance, the script of training process for [stable-diffusion-v1-4] model can be modified into: @@ -97,7 +100,22 @@ torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \ --placement="cuda" ``` - +## Performance + +| Strategy | #GPU | Batch Size | GPU RAM(GB) | speedup | +|:--------------:|:----:|:----------:|:-----------:|:-------:| +| Traditional | 1 | 16 | oom | \ | +| Traditional | 1 | 8 | 61.81 | 1 | +| torch_ddp | 4 | 16 | oom | \ | +| torch_ddp | 4 | 8 | 41.97 | 0.97 | +| gemini | 4 | 16 | 53.29 | \ | +| gemini | 4 | 8 | 29.36 | 2.00 | +| low_level_zero | 4 | 16 | 52.80 | \ | +| low_level_zero | 4 | 8 | 28.87 | 2.02 | + +The evaluation is performed on 4 Nvidia A100 GPUs with 80GB memory each, with GPU 0 & 1, 2 & 3 connected with NVLink. +We finetuned the [stable-diffusion-v1-4](https://huggingface.co/stabilityai/stable-diffusion-v1-4) model with 512x512 resolution on the [Teyvat](https://huggingface.co/datasets/Fazzie/Teyvat) dataset and compared +the memory cost and the throughput for the plugins. ## Invitation to open-source contribution Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models! From b56c7f428379843a29f690d237b9796747ecf339 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Tue, 6 Jun 2023 14:09:27 +0800 Subject: [PATCH 272/413] update shell file --- examples/tutorial/new_api/dreambooth/colossalai.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorial/new_api/dreambooth/colossalai.sh b/examples/tutorial/new_api/dreambooth/colossalai.sh index 2745c563aa73..77dfb1cbd05a 100755 --- a/examples/tutorial/new_api/dreambooth/colossalai.sh +++ b/examples/tutorial/new_api/dreambooth/colossalai.sh @@ -6,7 +6,7 @@ torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ --pretrained_model_name_or_path="Path_to_your_model" \ --instance_data_dir="Path_to_your_training_image" \ --output_dir="Path_to_your_save_dir" \ - --instance_prompt="keli" \ + --instance_prompt="your prompt" \ --resolution=512 \ --plugin="gemini" \ --train_batch_size=1 \ From 1c1f71cbd2718feee7e6dbb472053664e26f1c8e Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Tue, 6 Jun 2023 14:51:11 +0800 Subject: [PATCH 273/413] fixing insecure hash function --- .../tutorial/new_api/dreambooth/train_dreambooth_colossalai.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py b/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py index 9da7cacb8aaf..5436e7d6b739 100644 --- a/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py +++ b/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py @@ -397,7 +397,7 @@ def main(args): images = pipeline(example["prompt"]).images for i, image in enumerate(images): - hash_image = hashlib.sha1(image.tobytes()).hexdigest() + hash_image = hashlib.sha256(image.tobytes()).hexdigest() image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" image.save(image_filename) From b29e1f07224298aea35aab7ee83284beac28e0d8 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Tue, 6 Jun 2023 15:50:03 +0800 Subject: [PATCH 274/413] change directory --- examples/images/dreambooth/README.md | 23 + examples/images/dreambooth/colossalai.sh | 21 +- examples/images/dreambooth/test_ci.sh | 23 + .../dreambooth/train_dreambooth_colossalai.py | 93 ++- .../train_dreambooth_colossalai_lora.py | 126 ++-- .../tutorial/new_api/dreambooth/README.md | 131 ---- .../tutorial/new_api/dreambooth/colossalai.sh | 17 - .../new_api/dreambooth/requirements.txt | 7 - .../tutorial/new_api/dreambooth/test_ci.sh | 23 - .../dreambooth/train_dreambooth_colossalai.py | 690 ------------------ 10 files changed, 183 insertions(+), 971 deletions(-) delete mode 100644 examples/tutorial/new_api/dreambooth/README.md delete mode 100755 examples/tutorial/new_api/dreambooth/colossalai.sh delete mode 100644 examples/tutorial/new_api/dreambooth/requirements.txt delete mode 100644 examples/tutorial/new_api/dreambooth/test_ci.sh delete mode 100644 examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py diff --git a/examples/images/dreambooth/README.md b/examples/images/dreambooth/README.md index 7c117d841e24..bfd865a6dfa9 100644 --- a/examples/images/dreambooth/README.md +++ b/examples/images/dreambooth/README.md @@ -92,6 +92,29 @@ torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \ --placement="cuda" ``` +## New API +We have modified our previous implementation of Dreambooth with our new Booster API, which offers a more flexible and efficient way to train your model. The new API is more user-friendly and easy to use. You can find the new API in `train_dreambooth_colossalai.py`. +We have also offer a shell script `test_ci.sh` for you to go through all our plugins for the booster. +For more information about the booster API you can refer to https://colossalai.org/docs/basics/booster_api/. + +## Performance + +| Strategy | #GPU | Batch Size | GPU RAM(GB) | speedup | +|:--------------:|:----:|:----------:|:-----------:|:-------:| +| Traditional | 1 | 16 | oom | \ | +| Traditional | 1 | 8 | 61.81 | 1 | +| torch_ddp | 4 | 16 | oom | \ | +| torch_ddp | 4 | 8 | 41.97 | 0.97 | +| gemini | 4 | 16 | 53.29 | \ | +| gemini | 4 | 8 | 29.36 | 2.00 | +| low_level_zero | 4 | 16 | 52.80 | \ | +| low_level_zero | 4 | 8 | 28.87 | 2.02 | + +The evaluation is performed on 4 Nvidia A100 GPUs with 80GB memory each, with GPU 0 & 1, 2 & 3 connected with NVLink. +We finetuned the [stable-diffusion-v1-4](https://huggingface.co/stabilityai/stable-diffusion-v1-4) model with 512x512 resolution on the [Teyvat](https://huggingface.co/datasets/Fazzie/Teyvat) dataset and compared +the memory cost and the throughput for the plugins. + + ## Inference Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. `--instance_prompt="a photo of sks dog" ` in the above example) in your prompt. diff --git a/examples/images/dreambooth/colossalai.sh b/examples/images/dreambooth/colossalai.sh index 227d8b8bdb04..cfb00412aced 100755 --- a/examples/images/dreambooth/colossalai.sh +++ b/examples/images/dreambooth/colossalai.sh @@ -1,20 +1,15 @@ -export MODEL_NAME= -export INSTANCE_DIR= -export CLASS_DIR="path-to-class-images" -export OUTPUT_DIR="path-to-save-model" - -HF_DATASETS_OFFLINE=1 -TRANSFORMERS_OFFLINE=1 +HF_DATASETS_OFFLINE=1 +TRANSFORMERS_OFFLINE=1 DIFFUSERS_OFFLINE=1 -torchrun --nproc_per_node 2 --master_port=25641 train_dreambooth_colossalai.py \ - --pretrained_model_name_or_path=$MODEL_NAME \ - --instance_data_dir=$INSTANCE_DIR \ - --output_dir=$OUTPUT_DIR \ - --instance_prompt="a photo of a dog" \ +torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ + --pretrained_model_name_or_path="Path_to_your_model" \ + --instance_data_dir="Path_to_your_training_image" \ + --output_dir="Path_to_your_save_dir" \ + --instance_prompt="your prompt" \ --resolution=512 \ + --plugin="gemini" \ --train_batch_size=1 \ - --gradient_accumulation_steps=1 \ --learning_rate=5e-6 \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ diff --git a/examples/images/dreambooth/test_ci.sh b/examples/images/dreambooth/test_ci.sh index e69de29bb2d1..68862c46cfe9 100644 --- a/examples/images/dreambooth/test_ci.sh +++ b/examples/images/dreambooth/test_ci.sh @@ -0,0 +1,23 @@ +#!/bin/bash +set -xe +pip install -r requirements.txt + +HF_DATASETS_OFFLINE=1 +TRANSFORMERS_OFFLINE=1 +DIFFUSERS_OFFLINE=1 + +for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do + torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ + --pretrained_model_name_or_path="Your Pretrained Model Path" \ + --instance_data_dir="Your Input Pics Path" \ + --output_dir="path-to-save-model" \ + --instance_prompt="your prompt" \ + --resolution=512 \ + --plugin=$plugin \ + --train_batch_size=1 \ + --learning_rate=5e-6 \ + --lr_scheduler="constant" \ + --lr_warmup_steps=0 \ + --num_class_images=200 \ + --placement="cuda" +done diff --git a/examples/images/dreambooth/train_dreambooth_colossalai.py b/examples/images/dreambooth/train_dreambooth_colossalai.py index d07febea0a84..5436e7d6b739 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai.py @@ -4,6 +4,7 @@ import os from pathlib import Path from typing import Optional +import shutil import torch import torch.nn.functional as F @@ -21,9 +22,12 @@ from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.logging import disable_existing_loggers, get_dist_logger +from colossalai.nn.optimizer import HybridAdam from colossalai.utils import get_current_device -from colossalai.zero import ColoInitContext, GeminiAdamOptimizer +from colossalai.zero import ColoInitContext from colossalai.zero.gemini import get_static_torch_model +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin disable_existing_loggers() logger = get_dist_logger() @@ -58,6 +62,13 @@ def parse_args(input_args=None): required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) + parser.add_argument( + "--externel_unet_path", + type=str, + default=None, + required=False, + help="Path to the externel unet model.", + ) parser.add_argument( "--revision", type=str, @@ -193,6 +204,12 @@ def parse_args(input_args=None): default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'], + help="plugin to use") parser.add_argument( "--logging_dir", type=str, @@ -339,18 +356,6 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: return f"{organization}/{model_id}" -# Gemini + ZeRO DDP -def gemini_zero_dpp(model: torch.nn.Module, placement_policy: str = "auto"): - from colossalai.nn.parallel import GeminiDDP - - model = GeminiDDP(model, - device=get_current_device(), - placement_policy=placement_policy, - pin_memory=True, - search_range_mb=64) - return model - - def main(args): if args.seed is None: colossalai.launch_from_torch(config={}) @@ -392,7 +397,7 @@ def main(args): images = pipeline(example["prompt"]).images for i, image in enumerate(images): - hash_image = hashlib.sha1(image.tobytes()).hexdigest() + hash_image = hashlib.sha256(image.tobytes()).hexdigest() image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" image.save(image_filename) @@ -452,12 +457,18 @@ def main(args): revision=args.revision, ) - logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0]) - with ColoInitContext(device=get_current_device()): + + if args.externel_unet_path is None: + logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0]) unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, - subfolder="unet", - revision=args.revision, - low_cpu_mem_usage=False) + subfolder="unet", + revision=args.revision, + low_cpu_mem_usage=False) + else: + logger.info(f"Loading UNet2DConditionModel from {args.externel_unet_path}", ranks=[0]) + unet = UNet2DConditionModel.from_pretrained(args.externel_unet_path, + revision=args.revision, + low_cpu_mem_usage=False) vae.requires_grad_(False) text_encoder.requires_grad_(False) @@ -468,10 +479,22 @@ def main(args): if args.scale_lr: args.learning_rate = args.learning_rate * args.train_batch_size * world_size - unet = gemini_zero_dpp(unet, args.placement) + # Use Booster API to use Gemini/Zero with ColossalAI + + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2 ** 5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2 ** 5) + + booster = Booster(plugin=plugin, **booster_kwargs) # config optimizer for colossalai zero - optimizer = GeminiAdamOptimizer(unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm) + optimizer = HybridAdam(unet.parameters(), lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm) # load noise_scheduler noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") @@ -554,6 +577,8 @@ def collate_fn(examples): # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + unet, optimizer, _, _, lr_scheduler = booster.boost(unet, optimizer, lr_scheduler=lr_scheduler) + # Train! total_batch_size = args.train_batch_size * world_size @@ -642,36 +667,24 @@ def collate_fn(examples): if global_step % args.save_steps == 0: torch.cuda.synchronize() - torch_unet = get_static_torch_model(unet) if local_rank == 0: - pipeline = DiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=torch_unet, - revision=args.revision, - ) save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") - pipeline.save_pretrained(save_path) + booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin")) + if not os.path.exists(os.path.join(save_path, "config.json")): + shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path) logger.info(f"Saving model checkpoint to {save_path}", ranks=[0]) if global_step >= args.max_train_steps: break - torch.cuda.synchronize() - unet = get_static_torch_model(unet) + booster.save_model(unet, os.path.join(args.output_dir, "diffusion_pytorch_model.bin")) + logger.info(f"Saving model checkpoint to {args.output_dir} on rank {local_rank}") if local_rank == 0: - pipeline = DiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=unet, - revision=args.revision, - ) - - pipeline.save_pretrained(args.output_dir) - logger.info(f"Saving model checkpoint to {args.output_dir}", ranks=[0]) - + if not os.path.exists(os.path.join(args.output_dir, "config.json")): + shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) - if __name__ == "__main__": args = parse_args() main(args) diff --git a/examples/images/dreambooth/train_dreambooth_colossalai_lora.py b/examples/images/dreambooth/train_dreambooth_colossalai_lora.py index 6715b473a567..64cdd2a31734 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai_lora.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai_lora.py @@ -4,6 +4,7 @@ import os from pathlib import Path from typing import Optional +import shutil import torch import torch.nn.functional as F @@ -23,9 +24,12 @@ from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.logging import disable_existing_loggers, get_dist_logger +from colossalai.nn.optimizer import HybridAdam from colossalai.utils import get_current_device from colossalai.zero import ColoInitContext, GeminiAdamOptimizer from colossalai.zero.gemini import get_static_torch_model +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin disable_existing_loggers() logger = get_dist_logger() @@ -60,6 +64,13 @@ def parse_args(input_args=None): required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) + parser.add_argument( + "--externel_unet_path", + type=str, + default=None, + required=False, + help="Path to the externel unet model.", + ) parser.add_argument( "--revision", type=str, @@ -195,6 +206,12 @@ def parse_args(input_args=None): default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'], + help="plugin to use") parser.add_argument( "--logging_dir", type=str, @@ -341,18 +358,6 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: return f"{organization}/{model_id}" -# Gemini + ZeRO DDP -def gemini_zero_dpp(model: torch.nn.Module, placement_policy: str = "auto"): - from colossalai.nn.parallel import GeminiDDP - - model = GeminiDDP(model, - device=get_current_device(), - placement_policy=placement_policy, - pin_memory=True, - search_range_mb=64) - return model - - def main(args): if args.seed is None: colossalai.launch_from_torch(config={}) @@ -394,7 +399,7 @@ def main(args): images = pipeline(example["prompt"]).images for i, image in enumerate(images): - hash_image = hashlib.sha1(image.tobytes()).hexdigest() + hash_image = hashlib.sha256(image.tobytes()).hexdigest() image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" image.save(image_filename) @@ -454,32 +459,42 @@ def main(args): revision=args.revision, ) - logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0]) - with ColoInitContext(device=get_current_device()): + + if args.externel_unet_path is None: + logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0]) unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, - subfolder="unet", - revision=args.revision, - low_cpu_mem_usage=False) - unet.requires_grad_(False) - - # Set correct lora layers - lora_attn_procs = {} - for name in unet.attn_processors.keys(): - cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim - if name.startswith("mid_block"): - hidden_size = unet.config.block_out_channels[-1] - elif name.startswith("up_blocks"): - block_id = int(name[len("up_blocks.")]) - hidden_size = list(reversed(unet.config.block_out_channels))[block_id] - elif name.startswith("down_blocks"): - block_id = int(name[len("down_blocks.")]) - hidden_size = unet.config.block_out_channels[block_id] - - lora_attn_procs[name] = LoRACrossAttnProcessor(hidden_size=hidden_size, - cross_attention_dim=cross_attention_dim) - - unet.set_attn_processor(lora_attn_procs) - lora_layers = AttnProcsLayers(unet.attn_processors) + subfolder="unet", + revision=args.revision, + low_cpu_mem_usage=False) + else: + logger.info(f"Loading UNet2DConditionModel from {args.externel_unet_path}", ranks=[0]) + unet = UNet2DConditionModel.from_pretrained(args.externel_unet_path, + revision=args.revision, + low_cpu_mem_usage=False) + unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, + subfolder="unet", + revision=args.revision, + low_cpu_mem_usage=False) + unet.requires_grad_(False) + + # Set correct lora layers + lora_attn_procs = {} + for name in unet.attn_processors.keys(): + cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim + if name.startswith("mid_block"): + hidden_size = unet.config.block_out_channels[-1] + elif name.startswith("up_blocks"): + block_id = int(name[len("up_blocks.")]) + hidden_size = list(reversed(unet.config.block_out_channels))[block_id] + elif name.startswith("down_blocks"): + block_id = int(name[len("down_blocks.")]) + hidden_size = unet.config.block_out_channels[block_id] + + lora_attn_procs[name] = LoRACrossAttnProcessor(hidden_size=hidden_size, + cross_attention_dim=cross_attention_dim) + + unet.set_attn_processor(lora_attn_procs) + lora_layers = AttnProcsLayers(unet.attn_processors) vae.requires_grad_(False) text_encoder.requires_grad_(False) @@ -490,10 +505,22 @@ def main(args): if args.scale_lr: args.learning_rate = args.learning_rate * args.train_batch_size * world_size - unet = gemini_zero_dpp(unet, args.placement) + # Use Booster API to use Gemini/Zero with ColossalAI + + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2 ** 5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2 ** 5) + + booster = Booster(plugin=plugin, **booster_kwargs) # config optimizer for colossalai zero - optimizer = GeminiAdamOptimizer(unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm) + optimizer = HybridAdam(unet.parameters(), lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm) # load noise_scheduler noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") @@ -576,6 +603,8 @@ def collate_fn(examples): # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + unet, optimizer, _, _, lr_scheduler = booster.boost(unet, optimizer, lr_scheduler=lr_scheduler) + # Train! total_batch_size = args.train_batch_size * world_size @@ -664,27 +693,24 @@ def collate_fn(examples): if global_step % args.save_steps == 0: torch.cuda.synchronize() - torch_unet = get_static_torch_model(unet) if local_rank == 0: save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") - torch_unet = torch_unet.to(torch.float32) - torch_unet.save_attn_procs(save_path) + booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin")) + if not os.path.exists(os.path.join(save_path, "config.json")): + shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path) logger.info(f"Saving model checkpoint to {save_path}", ranks=[0]) if global_step >= args.max_train_steps: break - torch.cuda.synchronize() - torch_unet = get_static_torch_model(unet) + booster.save_model(unet, os.path.join(args.output_dir, "diffusion_pytorch_model.bin")) + logger.info(f"Saving model checkpoint to {args.output_dir} on rank {local_rank}") if local_rank == 0: - torch_unet = torch_unet.to(torch.float32) - torch_unet.save_attn_procs(save_path) - logger.info(f"Saving model checkpoint to {args.output_dir}", ranks=[0]) - + if not os.path.exists(os.path.join(args.output_dir, "config.json")): + shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) - if __name__ == "__main__": args = parse_args() main(args) diff --git a/examples/tutorial/new_api/dreambooth/README.md b/examples/tutorial/new_api/dreambooth/README.md deleted file mode 100644 index 8e1fdbbc8c1f..000000000000 --- a/examples/tutorial/new_api/dreambooth/README.md +++ /dev/null @@ -1,131 +0,0 @@ -# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git) - -[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject. -The `train_dreambooth_colossalai.py` script shows how to implement the training procedure and adapt it for stable diffusion. - -By accommodating model data in CPU and GPU and moving the data to the computing device when necessary, [Gemini](https://www.colossalai.org/docs/advanced_tutorials/meet_gemini), the Heterogeneous Memory Manager of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) can breakthrough the GPU memory wall by using GPU and CPU memory (composed of CPU DRAM or nvme SSD memory) together at the same time. Moreover, the model scale can be further improved by combining heterogeneous training with the other parallel approaches, such as data parallel, tensor parallel and pipeline parallel. - -## Installation - -To begin with, make sure your operating system has the cuda version suitable for this exciting training session, which is cuda11.6-11.8. Notice that you may want to make sure the module versions suitable for the whole environment. Before running the scripts, make sure to install the library's training dependencies: - -```bash -pip install -r requirements.txt -``` - -### Install [colossalai](https://github.com/hpcaitech/ColossalAI.git) - -```bash -pip install colossalai -``` - -**From source** - -```bash -git clone https://github.com/hpcaitech/ColossalAI.git -python setup.py install -``` - -## Dataset for Teyvat BLIP captions -Dataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion). - -BLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2). - -For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided. - -The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP). - -## New API -We have modified our previous implementation of Dreambooth with our new Booster API, which offers a more flexible and efficient way to train your model. The new API is more user-friendly and easy to use. You can find the new API in `train_dreambooth_colossalai.py`. -We have also offer a shell script `test_ci.sh` for you to go through all our plugins for the booster. -For more information about the booster API you can refer to https://colossalai.org/docs/basics/booster_api/. - - - - -## Training - -We provide the script `colossalai.sh` to run the training task with colossalai. For instance, the script of training process for [stable-diffusion-v1-4] model can be modified into: - -```bash -export MODEL_NAME="CompVis/stable-diffusion-v1-4" -export INSTANCE_DIR="path-to-instance-images" -export OUTPUT_DIR="path-to-save-model" - -torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \ - --pretrained_model_name_or_path=$MODEL_NAME \ - --instance_data_dir=$INSTANCE_DIR \ - --output_dir=$OUTPUT_DIR \ - --instance_prompt="a photo of sks dog" \ - --resolution=512 \ - --train_batch_size=1 \ - --learning_rate=5e-6 \ - --lr_scheduler="constant" \ - --lr_warmup_steps=0 \ - --max_train_steps=400 \ - --placement="cuda" -``` -- `MODEL_NAME` refers to the model you are training. -- `INSTANCE_DIR` refers to personalized path to instance images, you might need to insert information here. -- `OUTPUT_DIR` refers to local path to save the trained model, you might need to find a path with enough space. -- `resolution` refers to the corresponding resolution number of your target model. Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model. -- `placement` refers to the training strategy supported by Colossal AI, default = 'cuda', which refers to loading all the parameters into cuda memory. On the other hand, 'cpu' refers to 'cpu offload' strategy while 'auto' enables 'Gemini', both featured by Colossal AI. - -### Training with prior-preservation loss - -Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data. - -According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time. The general script can be then modified as the following. - -```bash -export MODEL_NAME="CompVis/stable-diffusion-v1-4" -export INSTANCE_DIR="path-to-instance-images" -export CLASS_DIR="path-to-class-images" -export OUTPUT_DIR="path-to-save-model" - -torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \ - --pretrained_model_name_or_path=$MODEL_NAME \ - --instance_data_dir=$INSTANCE_DIR \ - --class_data_dir=$CLASS_DIR \ - --output_dir=$OUTPUT_DIR \ - --with_prior_preservation --prior_loss_weight=1.0 \ - --instance_prompt="a photo of sks dog" \ - --class_prompt="a photo of dog" \ - --resolution=512 \ - --train_batch_size=1 \ - --learning_rate=5e-6 \ - --lr_scheduler="constant" \ - --lr_warmup_steps=0 \ - --max_train_steps=800 \ - --placement="cuda" -``` - -## Performance - -| Strategy | #GPU | Batch Size | GPU RAM(GB) | speedup | -|:--------------:|:----:|:----------:|:-----------:|:-------:| -| Traditional | 1 | 16 | oom | \ | -| Traditional | 1 | 8 | 61.81 | 1 | -| torch_ddp | 4 | 16 | oom | \ | -| torch_ddp | 4 | 8 | 41.97 | 0.97 | -| gemini | 4 | 16 | 53.29 | \ | -| gemini | 4 | 8 | 29.36 | 2.00 | -| low_level_zero | 4 | 16 | 52.80 | \ | -| low_level_zero | 4 | 8 | 28.87 | 2.02 | - -The evaluation is performed on 4 Nvidia A100 GPUs with 80GB memory each, with GPU 0 & 1, 2 & 3 connected with NVLink. -We finetuned the [stable-diffusion-v1-4](https://huggingface.co/stabilityai/stable-diffusion-v1-4) model with 512x512 resolution on the [Teyvat](https://huggingface.co/datasets/Fazzie/Teyvat) dataset and compared -the memory cost and the throughput for the plugins. - -## Invitation to open-source contribution -Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models! - -You may contact us or participate in the following ways: -1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks! -2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md). -3. Join the Colossal-AI community on -[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w), -and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas. -4. Send your official proposal to email contact@hpcaitech.com - -Thanks so much to all of our amazing contributors! diff --git a/examples/tutorial/new_api/dreambooth/colossalai.sh b/examples/tutorial/new_api/dreambooth/colossalai.sh deleted file mode 100755 index 77dfb1cbd05a..000000000000 --- a/examples/tutorial/new_api/dreambooth/colossalai.sh +++ /dev/null @@ -1,17 +0,0 @@ -HF_DATASETS_OFFLINE=1 -TRANSFORMERS_OFFLINE=1 -DIFFUSERS_OFFLINE=1 - -torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ - --pretrained_model_name_or_path="Path_to_your_model" \ - --instance_data_dir="Path_to_your_training_image" \ - --output_dir="Path_to_your_save_dir" \ - --instance_prompt="your prompt" \ - --resolution=512 \ - --plugin="gemini" \ - --train_batch_size=1 \ - --learning_rate=5e-6 \ - --lr_scheduler="constant" \ - --lr_warmup_steps=0 \ - --num_class_images=200 \ - --placement="cuda" \ diff --git a/examples/tutorial/new_api/dreambooth/requirements.txt b/examples/tutorial/new_api/dreambooth/requirements.txt deleted file mode 100644 index 1ec828c630ef..000000000000 --- a/examples/tutorial/new_api/dreambooth/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -diffusers>==0.5.0 -accelerate -torchvision -transformers>=4.21.0 -ftfy -tensorboard -modelcards diff --git a/examples/tutorial/new_api/dreambooth/test_ci.sh b/examples/tutorial/new_api/dreambooth/test_ci.sh deleted file mode 100644 index 68862c46cfe9..000000000000 --- a/examples/tutorial/new_api/dreambooth/test_ci.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash -set -xe -pip install -r requirements.txt - -HF_DATASETS_OFFLINE=1 -TRANSFORMERS_OFFLINE=1 -DIFFUSERS_OFFLINE=1 - -for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do - torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ - --pretrained_model_name_or_path="Your Pretrained Model Path" \ - --instance_data_dir="Your Input Pics Path" \ - --output_dir="path-to-save-model" \ - --instance_prompt="your prompt" \ - --resolution=512 \ - --plugin=$plugin \ - --train_batch_size=1 \ - --learning_rate=5e-6 \ - --lr_scheduler="constant" \ - --lr_warmup_steps=0 \ - --num_class_images=200 \ - --placement="cuda" -done diff --git a/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py b/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py deleted file mode 100644 index 5436e7d6b739..000000000000 --- a/examples/tutorial/new_api/dreambooth/train_dreambooth_colossalai.py +++ /dev/null @@ -1,690 +0,0 @@ -import argparse -import hashlib -import math -import os -from pathlib import Path -from typing import Optional -import shutil - -import torch -import torch.nn.functional as F -import torch.utils.checkpoint -from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel -from diffusers.optimization import get_scheduler -from huggingface_hub import HfFolder, Repository, create_repo, whoami -from PIL import Image -from torch.utils.data import Dataset -from torchvision import transforms -from tqdm.auto import tqdm -from transformers import AutoTokenizer, PretrainedConfig - -import colossalai -from colossalai.context.parallel_mode import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn.optimizer import HybridAdam -from colossalai.utils import get_current_device -from colossalai.zero import ColoInitContext -from colossalai.zero.gemini import get_static_torch_model -from colossalai.booster import Booster -from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin - -disable_existing_loggers() -logger = get_dist_logger() - - -def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str): - text_encoder_config = PretrainedConfig.from_pretrained( - pretrained_model_name_or_path, - subfolder="text_encoder", - revision=args.revision, - ) - model_class = text_encoder_config.architectures[0] - - if model_class == "CLIPTextModel": - from transformers import CLIPTextModel - - return CLIPTextModel - elif model_class == "RobertaSeriesModelWithTransformation": - from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation - - return RobertaSeriesModelWithTransformation - else: - raise ValueError(f"{model_class} is not supported.") - - -def parse_args(input_args=None): - parser = argparse.ArgumentParser(description="Simple example of a training script.") - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - required=True, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--externel_unet_path", - type=str, - default=None, - required=False, - help="Path to the externel unet model.", - ) - parser.add_argument( - "--revision", - type=str, - default=None, - required=False, - help="Revision of pretrained model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--instance_data_dir", - type=str, - default=None, - required=True, - help="A folder containing the training data of instance images.", - ) - parser.add_argument( - "--class_data_dir", - type=str, - default=None, - required=False, - help="A folder containing the training data of class images.", - ) - parser.add_argument( - "--instance_prompt", - type=str, - default="a photo of sks dog", - required=False, - help="The prompt with identifier specifying the instance", - ) - parser.add_argument( - "--class_prompt", - type=str, - default=None, - help="The prompt to specify images in the same class as provided instance images.", - ) - parser.add_argument( - "--with_prior_preservation", - default=False, - action="store_true", - help="Flag to add prior preservation loss.", - ) - parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") - parser.add_argument( - "--num_class_images", - type=int, - default=100, - help=("Minimal class images for prior preservation loss. If there are not enough images already present in" - " class_data_dir, additional images will be sampled with class_prompt."), - ) - parser.add_argument( - "--output_dir", - type=str, - default="text-inversion-model", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") - parser.add_argument( - "--resolution", - type=int, - default=512, - help=("The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution"), - ) - parser.add_argument( - "--placement", - type=str, - default="cpu", - help="Placement Policy for Gemini. Valid when using colossalai as dist plan.", - ) - parser.add_argument( - "--center_crop", - default=False, - action="store_true", - help=("Whether to center crop the input images to the resolution. If not set, the images will be randomly" - " cropped. The images will be resized to the resolution first before cropping."), - ) - parser.add_argument("--train_batch_size", - type=int, - default=4, - help="Batch size (per device) for the training dataloader.") - parser.add_argument("--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images.") - parser.add_argument("--num_train_epochs", type=int, default=1) - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=5e-6, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - default=False, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="constant", - help=('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]'), - ) - parser.add_argument("--lr_warmup_steps", - type=int, - default=500, - help="Number of steps for the warmup in the lr scheduler.") - parser.add_argument("--use_8bit_adam", - action="store_true", - help="Whether or not to use 8-bit Adam from bitsandbytes.") - - parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") - parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") - parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") - parser.add_argument( - "--hub_model_id", - type=str, - default=None, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument('-p', - '--plugin', - type=str, - default='torch_ddp', - choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'], - help="plugin to use") - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."), - ) - parser.add_argument( - "--mixed_precision", - type=str, - default=None, - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."), - ) - parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") - - if input_args is not None: - args = parser.parse_args(input_args) - else: - args = parser.parse_args() - - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - if args.with_prior_preservation: - if args.class_data_dir is None: - raise ValueError("You must specify a data directory for class images.") - if args.class_prompt is None: - raise ValueError("You must specify prompt for class images.") - else: - if args.class_data_dir is not None: - logger.warning("You need not use --class_data_dir without --with_prior_preservation.") - if args.class_prompt is not None: - logger.warning("You need not use --class_prompt without --with_prior_preservation.") - - return args - - -class DreamBoothDataset(Dataset): - """ - A dataset to prepare the instance and class images with the prompts for fine-tuning the model. - It pre-processes the images and the tokenizes prompts. - """ - - def __init__( - self, - instance_data_root, - instance_prompt, - tokenizer, - class_data_root=None, - class_prompt=None, - size=512, - center_crop=False, - ): - self.size = size - self.center_crop = center_crop - self.tokenizer = tokenizer - - self.instance_data_root = Path(instance_data_root) - if not self.instance_data_root.exists(): - raise ValueError("Instance images root doesn't exists.") - - self.instance_images_path = list(Path(instance_data_root).iterdir()) - self.num_instance_images = len(self.instance_images_path) - self.instance_prompt = instance_prompt - self._length = self.num_instance_images - - if class_data_root is not None: - self.class_data_root = Path(class_data_root) - self.class_data_root.mkdir(parents=True, exist_ok=True) - self.class_images_path = list(self.class_data_root.iterdir()) - self.num_class_images = len(self.class_images_path) - self._length = max(self.num_class_images, self.num_instance_images) - self.class_prompt = class_prompt - else: - self.class_data_root = None - - self.image_transforms = transforms.Compose([ - transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), - transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), - transforms.ToTensor(), - transforms.Normalize([0.5], [0.5]), - ]) - - def __len__(self): - return self._length - - def __getitem__(self, index): - example = {} - instance_image = Image.open(self.instance_images_path[index % self.num_instance_images]) - if not instance_image.mode == "RGB": - instance_image = instance_image.convert("RGB") - example["instance_images"] = self.image_transforms(instance_image) - example["instance_prompt_ids"] = self.tokenizer( - self.instance_prompt, - padding="do_not_pad", - truncation=True, - max_length=self.tokenizer.model_max_length, - ).input_ids - - if self.class_data_root: - class_image = Image.open(self.class_images_path[index % self.num_class_images]) - if not class_image.mode == "RGB": - class_image = class_image.convert("RGB") - example["class_images"] = self.image_transforms(class_image) - example["class_prompt_ids"] = self.tokenizer( - self.class_prompt, - padding="do_not_pad", - truncation=True, - max_length=self.tokenizer.model_max_length, - ).input_ids - - return example - - -class PromptDataset(Dataset): - "A simple dataset to prepare the prompts to generate class images on multiple GPUs." - - def __init__(self, prompt, num_samples): - self.prompt = prompt - self.num_samples = num_samples - - def __len__(self): - return self.num_samples - - def __getitem__(self, index): - example = {} - example["prompt"] = self.prompt - example["index"] = index - return example - - -def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): - if token is None: - token = HfFolder.get_token() - if organization is None: - username = whoami(token)["name"] - return f"{username}/{model_id}" - else: - return f"{organization}/{model_id}" - - -def main(args): - if args.seed is None: - colossalai.launch_from_torch(config={}) - else: - colossalai.launch_from_torch(config={}, seed=args.seed) - - local_rank = gpc.get_local_rank(ParallelMode.DATA) - world_size = gpc.get_world_size(ParallelMode.DATA) - - if args.with_prior_preservation: - class_images_dir = Path(args.class_data_dir) - if not class_images_dir.exists(): - class_images_dir.mkdir(parents=True) - cur_class_images = len(list(class_images_dir.iterdir())) - - if cur_class_images < args.num_class_images: - torch_dtype = torch.float16 if get_current_device() == "cuda" else torch.float32 - pipeline = DiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - torch_dtype=torch_dtype, - safety_checker=None, - revision=args.revision, - ) - pipeline.set_progress_bar_config(disable=True) - - num_new_images = args.num_class_images - cur_class_images - logger.info(f"Number of class images to sample: {num_new_images}.") - - sample_dataset = PromptDataset(args.class_prompt, num_new_images) - sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) - - pipeline.to(get_current_device()) - - for example in tqdm( - sample_dataloader, - desc="Generating class images", - disable=not local_rank == 0, - ): - images = pipeline(example["prompt"]).images - - for i, image in enumerate(images): - hash_image = hashlib.sha256(image.tobytes()).hexdigest() - image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" - image.save(image_filename) - - del pipeline - - # Handle the repository creation - if local_rank == 0: - if args.push_to_hub: - if args.hub_model_id is None: - repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) - else: - repo_name = args.hub_model_id - create_repo(repo_name, exist_ok=True, token=args.hub_token) - repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token) - - with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: - if "step_*" not in gitignore: - gitignore.write("step_*\n") - if "epoch_*" not in gitignore: - gitignore.write("epoch_*\n") - elif args.output_dir is not None: - os.makedirs(args.output_dir, exist_ok=True) - - # Load the tokenizer - if args.tokenizer_name: - logger.info(f"Loading tokenizer from {args.tokenizer_name}", ranks=[0]) - tokenizer = AutoTokenizer.from_pretrained( - args.tokenizer_name, - revision=args.revision, - use_fast=False, - ) - elif args.pretrained_model_name_or_path: - logger.info("Loading tokenizer from pretrained model", ranks=[0]) - tokenizer = AutoTokenizer.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="tokenizer", - revision=args.revision, - use_fast=False, - ) - # import correct text encoder class - text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path) - - # Load models and create wrapper for stable diffusion - - logger.info(f"Loading text_encoder from {args.pretrained_model_name_or_path}", ranks=[0]) - - text_encoder = text_encoder_cls.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="text_encoder", - revision=args.revision, - ) - - logger.info(f"Loading AutoencoderKL from {args.pretrained_model_name_or_path}", ranks=[0]) - vae = AutoencoderKL.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="vae", - revision=args.revision, - ) - - - if args.externel_unet_path is None: - logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0]) - unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, - subfolder="unet", - revision=args.revision, - low_cpu_mem_usage=False) - else: - logger.info(f"Loading UNet2DConditionModel from {args.externel_unet_path}", ranks=[0]) - unet = UNet2DConditionModel.from_pretrained(args.externel_unet_path, - revision=args.revision, - low_cpu_mem_usage=False) - - vae.requires_grad_(False) - text_encoder.requires_grad_(False) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - - if args.scale_lr: - args.learning_rate = args.learning_rate * args.train_batch_size * world_size - - # Use Booster API to use Gemini/Zero with ColossalAI - - booster_kwargs = {} - if args.plugin == 'torch_ddp_fp16': - booster_kwargs['mixed_precision'] = 'fp16' - if args.plugin.startswith('torch_ddp'): - plugin = TorchDDPPlugin() - elif args.plugin == 'gemini': - plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2 ** 5) - elif args.plugin == 'low_level_zero': - plugin = LowLevelZeroPlugin(initial_scale=2 ** 5) - - booster = Booster(plugin=plugin, **booster_kwargs) - - # config optimizer for colossalai zero - optimizer = HybridAdam(unet.parameters(), lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm) - - # load noise_scheduler - noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") - - # prepare dataset - logger.info(f"Prepare dataset from {args.instance_data_dir}", ranks=[0]) - train_dataset = DreamBoothDataset( - instance_data_root=args.instance_data_dir, - instance_prompt=args.instance_prompt, - class_data_root=args.class_data_dir if args.with_prior_preservation else None, - class_prompt=args.class_prompt, - tokenizer=tokenizer, - size=args.resolution, - center_crop=args.center_crop, - ) - - def collate_fn(examples): - input_ids = [example["instance_prompt_ids"] for example in examples] - pixel_values = [example["instance_images"] for example in examples] - - # Concat class and instance examples for prior preservation. - # We do this to avoid doing two forward passes. - if args.with_prior_preservation: - input_ids += [example["class_prompt_ids"] for example in examples] - pixel_values += [example["class_images"] for example in examples] - - pixel_values = torch.stack(pixel_values) - pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() - - input_ids = tokenizer.pad( - { - "input_ids": input_ids - }, - padding="max_length", - max_length=tokenizer.model_max_length, - return_tensors="pt", - ).input_ids - - batch = { - "input_ids": input_ids, - "pixel_values": pixel_values, - } - return batch - - train_dataloader = torch.utils.data.DataLoader(train_dataset, - batch_size=args.train_batch_size, - shuffle=True, - collate_fn=collate_fn, - num_workers=1) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil(len(train_dataloader)) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps, - num_training_steps=args.max_train_steps, - ) - weight_dtype = torch.float32 - if args.mixed_precision == "fp16": - weight_dtype = torch.float16 - elif args.mixed_precision == "bf16": - weight_dtype = torch.bfloat16 - - # Move text_encode and vae to gpu. - # For mixed precision training we cast the text_encoder and vae weights to half-precision - # as these models are only used for inference, keeping weights in full precision is not required. - vae.to(get_current_device(), dtype=weight_dtype) - text_encoder.to(get_current_device(), dtype=weight_dtype) - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader)) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - unet, optimizer, _, _, lr_scheduler = booster.boost(unet, optimizer, lr_scheduler=lr_scheduler) - - # Train! - total_batch_size = args.train_batch_size * world_size - - logger.info("***** Running training *****", ranks=[0]) - logger.info(f" Num examples = {len(train_dataset)}", ranks=[0]) - logger.info(f" Num batches each epoch = {len(train_dataloader)}", ranks=[0]) - logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0]) - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}", ranks=[0]) - logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0]) - logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0]) - - # Only show the progress bar once on each machine. - progress_bar = tqdm(range(args.max_train_steps), disable=not local_rank == 0) - progress_bar.set_description("Steps") - global_step = 0 - - torch.cuda.synchronize() - for epoch in range(args.num_train_epochs): - unet.train() - for step, batch in enumerate(train_dataloader): - torch.cuda.reset_peak_memory_stats() - # Move batch to gpu - for key, value in batch.items(): - batch[key] = value.to(get_current_device(), non_blocking=True) - - # Convert images to latent space - optimizer.zero_grad() - - latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() - latents = latents * 0.18215 - - # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) - timesteps = timesteps.long() - - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - # Get the text embedding for conditioning - encoder_hidden_states = text_encoder(batch["input_ids"])[0] - - # Predict the noise residual - model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - # Get the target for loss depending on the prediction type - if noise_scheduler.config.prediction_type == "epsilon": - target = noise - elif noise_scheduler.config.prediction_type == "v_prediction": - target = noise_scheduler.get_velocity(latents, noise, timesteps) - else: - raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") - - if args.with_prior_preservation: - # Chunk the noise and model_pred into two parts and compute the loss on each part separately. - model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) - target, target_prior = torch.chunk(target, 2, dim=0) - - # Compute instance loss - loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean() - - # Compute prior loss - prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") - - # Add the prior loss to the instance loss. - loss = loss + args.prior_loss_weight * prior_loss - else: - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - optimizer.backward(loss) - - optimizer.step() - lr_scheduler.step() - logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB", ranks=[0]) - # Checks if the accelerator has performed an optimization step behind the scenes - progress_bar.update(1) - global_step += 1 - logs = { - "loss": loss.detach().item(), - "lr": optimizer.param_groups[0]["lr"], - } # lr_scheduler.get_last_lr()[0]} - progress_bar.set_postfix(**logs) - - if global_step % args.save_steps == 0: - torch.cuda.synchronize() - if local_rank == 0: - save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") - booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin")) - if not os.path.exists(os.path.join(save_path, "config.json")): - shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path) - logger.info(f"Saving model checkpoint to {save_path}", ranks=[0]) - if global_step >= args.max_train_steps: - break - torch.cuda.synchronize() - - booster.save_model(unet, os.path.join(args.output_dir, "diffusion_pytorch_model.bin")) - logger.info(f"Saving model checkpoint to {args.output_dir} on rank {local_rank}") - if local_rank == 0: - if not os.path.exists(os.path.join(args.output_dir, "config.json")): - shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), args.output_dir) - if args.push_to_hub: - repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) - -if __name__ == "__main__": - args = parse_args() - main(args) From d3379f0be7e30854ee2353924d735642f4909aab Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Tue, 6 Jun 2023 16:07:34 +0800 Subject: [PATCH 275/413] fixed model saving bugs --- examples/images/dreambooth/train_dreambooth_colossalai.py | 4 ++-- .../images/dreambooth/train_dreambooth_colossalai_lora.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/images/dreambooth/train_dreambooth_colossalai.py b/examples/images/dreambooth/train_dreambooth_colossalai.py index 5436e7d6b739..eae52b5ecd7e 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai.py @@ -667,9 +667,9 @@ def collate_fn(examples): if global_step % args.save_steps == 0: torch.cuda.synchronize() + save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") + booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin")) if local_rank == 0: - save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") - booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin")) if not os.path.exists(os.path.join(save_path, "config.json")): shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path) logger.info(f"Saving model checkpoint to {save_path}", ranks=[0]) diff --git a/examples/images/dreambooth/train_dreambooth_colossalai_lora.py b/examples/images/dreambooth/train_dreambooth_colossalai_lora.py index 64cdd2a31734..dce65ff514b7 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai_lora.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai_lora.py @@ -693,9 +693,9 @@ def collate_fn(examples): if global_step % args.save_steps == 0: torch.cuda.synchronize() + save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") + booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin")) if local_rank == 0: - save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") - booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin")) if not os.path.exists(os.path.join(save_path, "config.json")): shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path) logger.info(f"Saving model checkpoint to {save_path}", ranks=[0]) From 79c9f776a9ea42991df54d11e2c4b3ac4a7eeea9 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Tue, 6 Jun 2023 16:20:45 +0800 Subject: [PATCH 276/413] fixed port --- examples/images/dreambooth/test_ci.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/images/dreambooth/test_ci.sh b/examples/images/dreambooth/test_ci.sh index 68862c46cfe9..0209c547a08f 100644 --- a/examples/images/dreambooth/test_ci.sh +++ b/examples/images/dreambooth/test_ci.sh @@ -7,7 +7,7 @@ TRANSFORMERS_OFFLINE=1 DIFFUSERS_OFFLINE=1 for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do - torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ + torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \ --pretrained_model_name_or_path="Your Pretrained Model Path" \ --instance_data_dir="Your Input Pics Path" \ --output_dir="path-to-save-model" \ From b4437e88c319874269b022c68e177f95d45b607b Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Tue, 6 Jun 2023 16:21:38 +0800 Subject: [PATCH 277/413] fixed port --- examples/images/dreambooth/colossalai.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/images/dreambooth/colossalai.sh b/examples/images/dreambooth/colossalai.sh index cfb00412aced..54ebac39b925 100755 --- a/examples/images/dreambooth/colossalai.sh +++ b/examples/images/dreambooth/colossalai.sh @@ -2,7 +2,7 @@ HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 DIFFUSERS_OFFLINE=1 -torchrun --nproc_per_node 4 --master_port=25641 train_dreambooth_colossalai.py \ +torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \ --pretrained_model_name_or_path="Path_to_your_model" \ --instance_data_dir="Path_to_your_training_image" \ --output_dir="Path_to_your_save_dir" \ From 41fb7236aa32c307e83b0b9cc50ce2a6da279343 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Tue, 6 Jun 2023 18:58:58 +0800 Subject: [PATCH 278/413] [devops] hotfix CI about testmon cache (#3910) * [devops] hotfix CI about testmon cache * [devops] fix testmon cahe on pr --- .github/workflows/build_on_pr.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index b5f293107310..a2807859b591 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -65,10 +65,10 @@ jobs: run: | # branch name may contain slash, we need to replace it with space export BASE=$(echo ${{ github.event.pull_request.base.ref }} | sed "s/\// /") if [ -d "/github/home/testmon_cache/${BASE}" ]; then - [ ! -z "$(ls -A "/github/home/testmon_cache/${BASE}")" ] && mkdir /github/home/testmon_cache/_pull && cp -p -r "/github/home/testmon_cache/${BASE}" /github/home/testmon_cache/_pull/${PR_NUMBER} + [ ! -z "$(ls -A "/github/home/testmon_cache/${BASE}")" ] && mkdir -p /github/home/testmon_cache/_pull && cp -p -r "/github/home/testmon_cache/${BASE}" /github/home/testmon_cache/_pull/${PR_NUMBER} fi env: - PR_NUMBER: ${{ github.event.pull_request.head.ref }} + PR_NUMBER: ${{ github.event.number }} detect: name: Detect file change From b5f0566363687aaa91767bb7069af874bedfb7e8 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 7 Jun 2023 10:41:16 +0800 Subject: [PATCH 279/413] [chat] add distributed PPO trainer (#3740) * Detached ppo (#9) * run the base * working on dist ppo * sync * detached trainer * update detached trainer. no maker update function * facing init problem * 1 maker 1 trainer detached run. but no model update * facing cuda problem * fix save functions * verified maker update * nothing * add ignore * analyize loss issue * remove some debug codes * facing 2m1t stuck issue * 2m1t verified * do not use torchrun * working on 2m2t * working on 2m2t * initialize strategy in ray actor env * facing actor's init order issue * facing ddp model update issue (need unwarp ddp) * unwrap ddp actor * checking 1m2t stuck problem * nothing * set timeout for trainer choosing. It solves the stuck problem! * delete some debug output * rename to sync with upstream * rename to sync with upstream * coati rename * nothing * I am going to detach the replaybuffer from trainer and make it a Ray Actor. Two benefits: 1. support TP trainer. 2. asynchronized buffer operations * experience_maker_holder performs target-revolving _send_experience() instead of length comparison. * move code to ray subfolder * working on pipeline inference * apply comments * working on pipeline strategy. in progress. * remove pipeline code. clean this branch * update remote parameters by state_dict. no test * nothing * state_dict sharding transfer * merge debug branch * gemini _unwrap_model fix * simplify code * simplify code & fix LoRALinear AttributeError * critic unwrapped state_dict --------- Co-authored-by: csric * [chat] add perfomance evaluator and fix bugs (#10) * [chat] add performance evaluator for ray * [chat] refactor debug arg * [chat] support hf config * [chat] fix generation * [chat] add 1mmt dummy example * [chat] fix gemini ckpt * split experience to send (#11) Co-authored-by: csric * [chat] refactor trainer and maker (#12) * [chat] refactor experience maker holder * [chat] refactor model init * [chat] refactor trainer args * [chat] refactor model init * [chat] refactor trainer * [chat] refactor experience sending logic and training loop args (#13) * [chat] refactor experience send logic * [chat] refactor trainer * [chat] refactor trainer * [chat] refactor experience maker * [chat] refactor pbar * [chat] refactor example folder (#14) * [chat] support quant (#15) * [chat] add quant * [chat] add quant example * prompt example (#16) * prompt example * prompt load csv data * remove legacy try --------- Co-authored-by: csric * [chat] add mmmt dummy example and refactor experience sending (#17) * [chat] add mmmt dummy example * [chat] refactor naive strategy * [chat] fix struck problem * [chat] fix naive strategy * [chat] optimize experience maker sending logic * [chat] refactor sending assignment * [chat] refactor performance evaluator (#18) * Prompt Example & requires_grad state_dict & sharding state_dict (#19) * prompt example * prompt load csv data * remove legacy try * maker models require_grad set to False * working on zero redundancy update * mmmt_prompt example; naive strategy requires_grad state_dict & sharding; maker model requires_no_grad. * remove legacy examples * remove legacy examples * remove replay buffer tp state. bad design --------- Co-authored-by: csric * state_dict sending adapts to new unwrap function (#20) * prompt example * prompt load csv data * remove legacy try * maker models require_grad set to False * working on zero redundancy update * mmmt_prompt example; naive strategy requires_grad state_dict & sharding; maker model requires_no_grad. * remove legacy examples * remove legacy examples * remove replay buffer tp state. bad design * opt benchmark * better script * nothing * [chat] strategy refactor unwrap model * [chat] strategy refactor save model * [chat] add docstr * [chat] refactor trainer save model * [chat] fix strategy typing * [chat] refactor trainer save model * [chat] update readme * [chat] fix unit test * working on lora reconstruction * state_dict sending adapts to new unwrap function * remove comments --------- Co-authored-by: csric Co-authored-by: ver217 * [chat-ray] add readme (#21) * add readme * transparent graph * add note background --------- Co-authored-by: csric * [chat] get images from url (#22) * Refactor/chat ray (#23) * [chat] lora add todo * [chat] remove unused pipeline strategy * [chat] refactor example structure * [chat] setup ci for ray * [chat-ray] Support LoRA trainer. LoRA weights reconstruction. (#24) * lora support prototype * lora support * 1mmt lora & remove useless code --------- Co-authored-by: csric * [chat] fix test ci for ray * [chat] fix test ci requirements for ray * [chat] fix ray runtime env * [chat] fix ray runtime env * [chat] fix example ci docker args * [chat] add debug info in trainer * [chat] add nccl debug info * [chat] skip ray test * [doc] fix typo --------- Co-authored-by: csric <59389055+CsRic@users.noreply.github.com> Co-authored-by: csric --- .github/workflows/run_chatgpt_examples.yml | 2 +- .../Chat/benchmarks/ray/1mmt_dummy.py | 178 +++++++++++ .../Chat/benchmarks/ray/mmmt_dummy.py | 189 ++++++++++++ applications/Chat/coati/models/lora.py | 8 +- applications/Chat/coati/quant/__init__.py | 7 + .../Chat/coati/quant/llama_gptq/__init__.py | 5 + .../Chat/coati/quant/llama_gptq/loader.py | 26 ++ .../coati/quant/llama_gptq/model_utils.py | 13 + .../Chat/coati/quant/llama_gptq/quant.py | 283 ++++++++++++++++++ applications/Chat/coati/quant/utils.py | 28 ++ applications/Chat/coati/ray/README.md | 160 ++++++++++ applications/Chat/coati/ray/__init__.py | 2 - .../Chat/coati/ray/callbacks/__init__.py | 9 + applications/Chat/coati/ray/callbacks/base.py | 66 ++++ .../ray/callbacks/performance_evaluator.py | 212 +++++++++++++ .../ray/{src => }/detached_replay_buffer.py | 65 ++-- .../Chat/coati/ray/detached_trainer_base.py | 179 +++++++++++ .../ray/{src => }/detached_trainer_ppo.py | 198 ++++++------ applications/Chat/coati/ray/example/1m1t.py | 153 ---------- applications/Chat/coati/ray/example/1m1t.sh | 23 -- applications/Chat/coati/ray/example/1m2t.py | 186 ------------ applications/Chat/coati/ray/example/1m2t.sh | 23 -- applications/Chat/coati/ray/example/2m1t.py | 140 --------- applications/Chat/coati/ray/example/2m1t.sh | 23 -- applications/Chat/coati/ray/example/2m2t.py | 209 ------------- applications/Chat/coati/ray/example/2m2t.sh | 23 -- .../Chat/coati/ray/experience_maker_holder.py | 271 +++++++++++++++++ .../Chat/coati/ray/lora_constructor.py | 122 ++++++++ applications/Chat/coati/ray/src/__init__.py | 0 .../coati/ray/src/detached_trainer_base.py | 121 -------- .../coati/ray/src/experience_maker_holder.py | 172 ----------- .../Chat/coati/ray/src/pipeline_strategy.py | 105 ------- applications/Chat/coati/ray/src/utils.py | 48 --- applications/Chat/coati/ray/utils.py | 152 ++++++++++ .../Chat/coati/trainer/strategies/base.py | 4 + .../coati/trainer/strategies/colossalai.py | 12 + .../Chat/coati/trainer/strategies/ddp.py | 13 +- .../Chat/coati/trainer/strategies/naive.py | 62 +++- .../Chat/coati/trainer/strategies/sampler.py | 1 + applications/Chat/examples/ray/1mmt_prompt.py | 175 +++++++++++ applications/Chat/examples/ray/mmmt_prompt.py | 189 ++++++++++++ .../Chat/examples/ray/requirements.txt | 1 + applications/Chat/examples/ray/test_ci.sh | 12 + applications/Chat/examples/test_ci.sh | 3 + 44 files changed, 2495 insertions(+), 1378 deletions(-) create mode 100644 applications/Chat/benchmarks/ray/1mmt_dummy.py create mode 100644 applications/Chat/benchmarks/ray/mmmt_dummy.py create mode 100644 applications/Chat/coati/quant/__init__.py create mode 100644 applications/Chat/coati/quant/llama_gptq/__init__.py create mode 100644 applications/Chat/coati/quant/llama_gptq/loader.py create mode 100644 applications/Chat/coati/quant/llama_gptq/model_utils.py create mode 100644 applications/Chat/coati/quant/llama_gptq/quant.py create mode 100644 applications/Chat/coati/quant/utils.py create mode 100644 applications/Chat/coati/ray/README.md create mode 100644 applications/Chat/coati/ray/callbacks/__init__.py create mode 100644 applications/Chat/coati/ray/callbacks/base.py create mode 100644 applications/Chat/coati/ray/callbacks/performance_evaluator.py rename applications/Chat/coati/ray/{src => }/detached_replay_buffer.py (62%) create mode 100644 applications/Chat/coati/ray/detached_trainer_base.py rename applications/Chat/coati/ray/{src => }/detached_trainer_ppo.py (55%) delete mode 100644 applications/Chat/coati/ray/example/1m1t.py delete mode 100644 applications/Chat/coati/ray/example/1m1t.sh delete mode 100644 applications/Chat/coati/ray/example/1m2t.py delete mode 100644 applications/Chat/coati/ray/example/1m2t.sh delete mode 100644 applications/Chat/coati/ray/example/2m1t.py delete mode 100644 applications/Chat/coati/ray/example/2m1t.sh delete mode 100644 applications/Chat/coati/ray/example/2m2t.py delete mode 100644 applications/Chat/coati/ray/example/2m2t.sh create mode 100644 applications/Chat/coati/ray/experience_maker_holder.py create mode 100644 applications/Chat/coati/ray/lora_constructor.py delete mode 100644 applications/Chat/coati/ray/src/__init__.py delete mode 100644 applications/Chat/coati/ray/src/detached_trainer_base.py delete mode 100644 applications/Chat/coati/ray/src/experience_maker_holder.py delete mode 100644 applications/Chat/coati/ray/src/pipeline_strategy.py delete mode 100644 applications/Chat/coati/ray/src/utils.py create mode 100644 applications/Chat/coati/ray/utils.py create mode 100644 applications/Chat/examples/ray/1mmt_prompt.py create mode 100644 applications/Chat/examples/ray/mmmt_prompt.py create mode 100644 applications/Chat/examples/ray/requirements.txt create mode 100755 applications/Chat/examples/ray/test_ci.sh diff --git a/.github/workflows/run_chatgpt_examples.yml b/.github/workflows/run_chatgpt_examples.yml index 9d9d3a007851..129bf7ed3270 100644 --- a/.github/workflows/run_chatgpt_examples.yml +++ b/.github/workflows/run_chatgpt_examples.yml @@ -20,7 +20,7 @@ jobs: runs-on: [self-hosted, gpu] container: image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 - options: --gpus all --rm -v /data/scratch/github_actions/chat:/data/scratch/github_actions/chat + options: --gpus all --rm -v /data/scratch/github_actions/chat:/data/scratch/github_actions/chat --shm-size=10.24gb timeout-minutes: 30 defaults: run: diff --git a/applications/Chat/benchmarks/ray/1mmt_dummy.py b/applications/Chat/benchmarks/ray/1mmt_dummy.py new file mode 100644 index 000000000000..9e8f36cefc4f --- /dev/null +++ b/applications/Chat/benchmarks/ray/1mmt_dummy.py @@ -0,0 +1,178 @@ +import argparse +import os +import socket +from functools import partial + +import ray +import torch +from coati.quant import llama_load_quant, low_resource_init +from coati.ray.detached_trainer_ppo import DetachedPPOTrainer +from coati.ray.experience_maker_holder import ExperienceMakerHolder +from coati.ray.utils import ( + get_actor_from_args, + get_critic_from_args, + get_receivers_per_sender, + get_reward_model_from_args, + get_strategy_from_args, +) +from torch.utils.data import DataLoader +from transformers import AutoConfig, AutoTokenizer +from transformers.modeling_utils import no_init_weights + + +def get_free_port(): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(('', 0)) + return s.getsockname()[1] + + +def get_local_ip(): + with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: + s.connect(('8.8.8.8', 80)) + return s.getsockname()[0] + + +def main(args): + master_addr = str(get_local_ip()) + # trainer_env_info + trainer_port = str(get_free_port()) + env_info_trainers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(args.num_trainers), + 'master_port': trainer_port, + 'master_addr': master_addr + } for rank in range(args.num_trainers)] + + # maker_env_info + maker_port = str(get_free_port()) + env_info_maker = { + 'local_rank': '0', + 'rank': '0', + 'world_size': '1', + 'master_port': maker_port, + 'master_addr': master_addr + } + + # configure tokenizer + tokenizer = AutoTokenizer.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + + def model_fn(): + actor_cfg = AutoConfig.from_pretrained(args.pretrain) + critic_cfg = AutoConfig.from_pretrained(args.critic_pretrain) + actor = get_actor_from_args(args.model, config=actor_cfg).requires_grad_(False).half().cuda() + critic = get_critic_from_args(args.critic_model, config=critic_cfg).requires_grad_(False).half().cuda() + reward_model = get_reward_model_from_args(args.critic_model, + config=critic_cfg).requires_grad_(False).half().cuda() + if args.initial_model_quant_ckpt is not None and args.model == 'llama': + # quantize initial model + with low_resource_init(), no_init_weights(): + initial_model = get_actor_from_args(args.model, config=actor_cfg) + initial_model.model = llama_load_quant(initial_model.model, args.initial_model_quant_ckpt, args.quant_bits, + args.quant_group_size).cuda().requires_grad_(False) + else: + initial_model = get_actor_from_args(args.model, config=actor_cfg).requires_grad_(False).half().cuda() + return actor, critic, reward_model, initial_model + + # configure Experience Maker + experience_holder_ref = ExperienceMakerHolder.options(name="maker0", num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=[f'trainer{i}' for i in range(args.num_trainers)], + strategy_fn=partial(get_strategy_from_args, args.maker_strategy), + model_fn=model_fn, + env_info=env_info_maker, + kl_coef=0.1, + debug=args.debug, + # sync_models_from_trainers=True, + # generation kwargs: + max_length=512, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + eval_performance=True, + use_cache=True, + ) + + def trainer_model_fn(): + actor = get_actor_from_args(args.model, config=AutoConfig.from_pretrained(args.pretrain)).half().cuda() + critic = get_critic_from_args(args.critic_model, + config=AutoConfig.from_pretrained(args.critic_pretrain)).half().cuda() + return actor, critic + + # configure Trainer + trainer_refs = [ + DetachedPPOTrainer.options(name=f"trainer{i}", num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=[ + f'maker{x}' for x in get_receivers_per_sender(i, args.num_trainers, 1, allow_idle_sender=True) + ], + strategy_fn=partial(get_strategy_from_args, args.trainer_strategy), + model_fn=trainer_model_fn, + env_info=env_info_trainer, + train_batch_size=args.train_batch_size, + buffer_limit=16, + eval_performance=True, + debug=args.debug, + ) for i, env_info_trainer in enumerate(env_info_trainers) + ] + + dataset_size = args.experience_batch_size * 4 + + def data_gen_fn(): + input_ids = torch.randint(tokenizer.vocab_size, (256,), device=torch.cuda.current_device()) + attn_mask = torch.ones_like(input_ids) + return {'input_ids': input_ids, 'attention_mask': attn_mask} + + def build_dataloader(size): + dataset = [data_gen_fn() for _ in range(size)] + dataloader = DataLoader(dataset, batch_size=args.experience_batch_size) + return dataloader + + # uncomment this function if sync_models_from_trainers is True + # ray.get([ + # trainer_ref.sync_models_to_remote_makers.remote() + # for trainer_ref in trainer_refs + # ]) + + wait_tasks = [] + + wait_tasks.append( + experience_holder_ref.workingloop.remote(partial(build_dataloader, dataset_size), + num_steps=args.experience_steps)) + + total_steps = args.experience_batch_size * args.experience_steps // (args.num_trainers * args.train_batch_size) + for trainer_ref in trainer_refs: + wait_tasks.append(trainer_ref.fit.remote(total_steps, args.update_steps, args.train_epochs)) + + ray.get(wait_tasks) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--num_trainers', type=int, default=1) + parser.add_argument('--trainer_strategy', + choices=[ + 'naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', + 'colossalai_zero2_cpu' + ], + default='naive') + parser.add_argument('--maker_strategy', choices=['naive'], default='naive') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--critic_pretrain', type=str, default=None) + parser.add_argument('--experience_steps', type=int, default=4) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--train_epochs', type=int, default=1) + parser.add_argument('--update_steps', type=int, default=2) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + + parser.add_argument('--initial_model_quant_ckpt', type=str, default=None) + parser.add_argument('--quant_bits', type=int, default=4) + parser.add_argument('--quant_group_size', type=int, default=128) + parser.add_argument('--debug', action='store_true') + args = parser.parse_args() + ray.init(namespace=os.environ["RAY_NAMESPACE"], runtime_env={"env_vars": dict(os.environ)}) + main(args) diff --git a/applications/Chat/benchmarks/ray/mmmt_dummy.py b/applications/Chat/benchmarks/ray/mmmt_dummy.py new file mode 100644 index 000000000000..46a0062893b8 --- /dev/null +++ b/applications/Chat/benchmarks/ray/mmmt_dummy.py @@ -0,0 +1,189 @@ +import argparse +import os +import socket +from functools import partial + +import ray +import torch +from coati.quant import llama_load_quant, low_resource_init +from coati.ray.detached_trainer_ppo import DetachedPPOTrainer +from coati.ray.experience_maker_holder import ExperienceMakerHolder +from coati.ray.utils import ( + get_actor_from_args, + get_critic_from_args, + get_receivers_per_sender, + get_reward_model_from_args, + get_strategy_from_args, +) +from torch.utils.data import DataLoader +from transformers import AutoConfig, AutoTokenizer +from transformers.modeling_utils import no_init_weights + + +def get_free_port(): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(('', 0)) + return s.getsockname()[1] + + +def get_local_ip(): + with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: + s.connect(('8.8.8.8', 80)) + return s.getsockname()[0] + + +def main(args): + master_addr = str(get_local_ip()) + # trainer_env_info + trainer_port = str(get_free_port()) + env_info_trainers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(args.num_trainers), + 'master_port': trainer_port, + 'master_addr': master_addr + } for rank in range(args.num_trainers)] + + # maker_env_info + maker_port = str(get_free_port()) + env_info_makers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(args.num_makers), + 'master_port': maker_port, + 'master_addr': master_addr + } for rank in range(args.num_makers)] + + # configure tokenizer + tokenizer = AutoTokenizer.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + + def model_fn(): + actor_cfg = AutoConfig.from_pretrained(args.pretrain) + critic_cfg = AutoConfig.from_pretrained(args.critic_pretrain) + actor = get_actor_from_args(args.model, config=actor_cfg).requires_grad_(False).half().cuda() + critic = get_critic_from_args(args.critic_model, config=critic_cfg).requires_grad_(False).half().cuda() + reward_model = get_reward_model_from_args(args.critic_model, + config=critic_cfg).requires_grad_(False).half().cuda() + if args.initial_model_quant_ckpt is not None and args.model == 'llama': + # quantize initial model + with low_resource_init(), no_init_weights(): + initial_model = get_actor_from_args(args.model, config=actor_cfg) + initial_model.model = llama_load_quant(initial_model.model, args.initial_model_quant_ckpt, args.quant_bits, + args.quant_group_size).cuda().requires_grad_(False) + else: + initial_model = get_actor_from_args(args.model, config=actor_cfg).requires_grad_(False).half().cuda() + return actor, critic, reward_model, initial_model + + # configure Experience Maker + experience_holder_refs = [ + ExperienceMakerHolder.options(name=f"maker{i}", num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=[ + f'trainer{x}' + for x in get_receivers_per_sender(i, args.num_makers, args.num_trainers, allow_idle_sender=False) + ], + strategy_fn=partial(get_strategy_from_args, args.maker_strategy), + model_fn=model_fn, + env_info=env_info_maker, + kl_coef=0.1, + debug=args.debug, + # sync_models_from_trainers=True, + # generation kwargs: + max_length=512, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + eval_performance=True, + use_cache=True, + ) + for i, env_info_maker in enumerate(env_info_makers) + ] + + def trainer_model_fn(): + actor = get_actor_from_args(args.model, config=AutoConfig.from_pretrained(args.pretrain)).half().cuda() + critic = get_critic_from_args(args.critic_model, + config=AutoConfig.from_pretrained(args.critic_pretrain)).half().cuda() + return actor, critic + + # configure Trainer + trainer_refs = [ + DetachedPPOTrainer.options(name=f"trainer{i}", num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=[ + f"maker{x}" + for x in get_receivers_per_sender(i, args.num_trainers, args.num_makers, allow_idle_sender=True) + ], + strategy_fn=partial(get_strategy_from_args, args.trainer_strategy), + model_fn=trainer_model_fn, + env_info=env_info_trainer, + train_batch_size=args.train_batch_size, + buffer_limit=16, + eval_performance=True, + debug=args.debug, + ) + for i, env_info_trainer in enumerate(env_info_trainers) + ] + + dataset_size = args.experience_batch_size * 4 + + def data_gen_fn(): + input_ids = torch.randint(tokenizer.vocab_size, (256,), device=torch.cuda.current_device()) + attn_mask = torch.ones_like(input_ids) + return {'input_ids': input_ids, 'attention_mask': attn_mask} + + def build_dataloader(size): + dataset = [data_gen_fn() for _ in range(size)] + dataloader = DataLoader(dataset, batch_size=args.experience_batch_size) + return dataloader + + # uncomment this function if sync_models_from_trainers is True + # ray.get([ + # trainer_ref.sync_models_to_remote_makers.remote() + # for trainer_ref in trainer_refs + # ]) + + wait_tasks = [] + + for experience_holder_ref in experience_holder_refs: + wait_tasks.append( + experience_holder_ref.workingloop.remote(partial(build_dataloader, dataset_size), + num_steps=args.experience_steps)) + + total_steps = args.experience_batch_size * args.experience_steps * \ + args.num_makers // (args.num_trainers * args.train_batch_size) + for trainer_ref in trainer_refs: + wait_tasks.append(trainer_ref.fit.remote(total_steps, args.update_steps, args.train_epochs)) + + ray.get(wait_tasks) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--num_makers', type=int, default=1) + parser.add_argument('--num_trainers', type=int, default=1) + parser.add_argument('--trainer_strategy', + choices=[ + 'naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', + 'colossalai_zero2_cpu' + ], + default='naive') + parser.add_argument('--maker_strategy', choices=['naive'], default='naive') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--critic_pretrain', type=str, default=None) + parser.add_argument('--experience_steps', type=int, default=4) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--train_epochs', type=int, default=1) + parser.add_argument('--update_steps', type=int, default=2) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + + parser.add_argument('--initial_model_quant_ckpt', type=str, default=None) + parser.add_argument('--quant_bits', type=int, default=4) + parser.add_argument('--quant_group_size', type=int, default=128) + parser.add_argument('--debug', action='store_true') + args = parser.parse_args() + ray.init(namespace=os.environ["RAY_NAMESPACE"], runtime_env={"env_vars": dict(os.environ)}) + main(args) diff --git a/applications/Chat/coati/models/lora.py b/applications/Chat/coati/models/lora.py index 0533a60dc532..2a9059e6901e 100644 --- a/applications/Chat/coati/models/lora.py +++ b/applications/Chat/coati/models/lora.py @@ -61,7 +61,13 @@ def T(w): if self.merge_weights and self.merged: # Make sure that the weights are not merged if self.r > 0: - self.weight.data -= T(self.lora_B @ self.lora_A) * self.scaling + if not hasattr(self, "lora_A") or not hasattr(self, "lora_B"): + # FIXME(csric): temporary fix + self.lora_A = nn.Parameter(self.weight.new_empty((self.r, self.in_features))) + self.lora_B = nn.Parameter(self.weight.new_empty((self.out_features, self.r))) + self.reset_parameters() + else: + self.weight.data -= T(self.lora_B @ self.lora_A) * self.scaling self.merged = False def eval(self): diff --git a/applications/Chat/coati/quant/__init__.py b/applications/Chat/coati/quant/__init__.py new file mode 100644 index 000000000000..a65a78d07bb8 --- /dev/null +++ b/applications/Chat/coati/quant/__init__.py @@ -0,0 +1,7 @@ +from .llama_gptq import load_quant as llama_load_quant +from .utils import low_resource_init + +__all__ = [ + 'llama_load_quant', + 'low_resource_init', +] diff --git a/applications/Chat/coati/quant/llama_gptq/__init__.py b/applications/Chat/coati/quant/llama_gptq/__init__.py new file mode 100644 index 000000000000..51c8d6316290 --- /dev/null +++ b/applications/Chat/coati/quant/llama_gptq/__init__.py @@ -0,0 +1,5 @@ +from .loader import load_quant + +__all__ = [ + 'load_quant', +] diff --git a/applications/Chat/coati/quant/llama_gptq/loader.py b/applications/Chat/coati/quant/llama_gptq/loader.py new file mode 100644 index 000000000000..5353dc8a2ea3 --- /dev/null +++ b/applications/Chat/coati/quant/llama_gptq/loader.py @@ -0,0 +1,26 @@ +import torch +import torch.nn as nn + +from .model_utils import find_layers +from .quant import make_quant + + +def load_quant(model: nn.Module, checkpoint: str, wbits: int, groupsize: int): + model = model.eval() + layers = find_layers(model) + + # ignore lm head + layers = find_layers(model) + for name in ['lm_head']: + if name in layers: + del layers[name] + + make_quant(model, layers, wbits, groupsize) + + if checkpoint.endswith('.safetensors'): + from safetensors.torch import load_file as safe_load + model.load_state_dict(safe_load(checkpoint)) + else: + model.load_state_dict(torch.load(checkpoint)) + + return model diff --git a/applications/Chat/coati/quant/llama_gptq/model_utils.py b/applications/Chat/coati/quant/llama_gptq/model_utils.py new file mode 100644 index 000000000000..62db171abb52 --- /dev/null +++ b/applications/Chat/coati/quant/llama_gptq/model_utils.py @@ -0,0 +1,13 @@ +# copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/past/modelutils.py + +import torch +import torch.nn as nn + + +def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''): + if type(module) in layers: + return {name: module} + res = {} + for name1, child in module.named_children(): + res.update(find_layers(child, layers=layers, name=name + '.' + name1 if name != '' else name1)) + return res diff --git a/applications/Chat/coati/quant/llama_gptq/quant.py b/applications/Chat/coati/quant/llama_gptq/quant.py new file mode 100644 index 000000000000..f7d5b7ce4bd8 --- /dev/null +++ b/applications/Chat/coati/quant/llama_gptq/quant.py @@ -0,0 +1,283 @@ +# copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/past/quant.py + +import math + +import numpy as np +import torch +import torch.nn as nn + + +def quantize(x, scale, zero, maxq): + q = torch.clamp(torch.round(x / scale) + zero, 0, maxq) + return scale * (q - zero) + + +class Quantizer(nn.Module): + + def __init__(self, shape=1): + super(Quantizer, self).__init__() + self.register_buffer('maxq', torch.tensor(0)) + self.register_buffer('scale', torch.zeros(shape)) + self.register_buffer('zero', torch.zeros(shape)) + + def configure(self, bits, perchannel=False, sym=True, mse=False, norm=2.4, grid=100, maxshrink=.8): + self.maxq = torch.tensor(2**bits - 1) + self.perchannel = perchannel + self.sym = sym + self.mse = mse + self.norm = norm + self.grid = grid + self.maxshrink = maxshrink + + def find_params(self, x, weight=False): + dev = x.device + self.maxq = self.maxq.to(dev) + + shape = x.shape + if self.perchannel: + if weight: + x = x.flatten(1) + else: + if len(shape) == 4: + x = x.permute([1, 0, 2, 3]) + x = x.flatten(1) + if len(shape) == 3: + x = x.reshape((-1, shape[-1])).t() + if len(shape) == 2: + x = x.t() + else: + x = x.flatten().unsqueeze(0) + + tmp = torch.zeros(x.shape[0], device=dev) + xmin = torch.minimum(x.min(1)[0], tmp) + xmax = torch.maximum(x.max(1)[0], tmp) + + if self.sym: + xmax = torch.maximum(torch.abs(xmin), xmax) + tmp = xmin < 0 + if torch.any(tmp): + xmin[tmp] = -xmax[tmp] + tmp = (xmin == 0) & (xmax == 0) + xmin[tmp] = -1 + xmax[tmp] = +1 + + self.scale = (xmax - xmin) / self.maxq + if self.sym: + self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2) + else: + self.zero = torch.round(-xmin / self.scale) + + if self.mse: + best = torch.full([x.shape[0]], float('inf'), device=dev) + for i in range(int(self.maxshrink * self.grid)): + p = 1 - i / self.grid + xmin1 = p * xmin + xmax1 = p * xmax + scale1 = (xmax1 - xmin1) / self.maxq + zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero + q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq) + q -= x + q.abs_() + q.pow_(self.norm) + err = torch.sum(q, 1) + tmp = err < best + if torch.any(tmp): + best[tmp] = err[tmp] + self.scale[tmp] = scale1[tmp] + self.zero[tmp] = zero1[tmp] + if not self.perchannel: + if weight: + tmp = shape[0] + else: + tmp = shape[1] if len(shape) != 3 else shape[2] + self.scale = self.scale.repeat(tmp) + self.zero = self.zero.repeat(tmp) + + if weight: + shape = [-1] + [1] * (len(shape) - 1) + self.scale = self.scale.reshape(shape) + self.zero = self.zero.reshape(shape) + return + if len(shape) == 4: + self.scale = self.scale.reshape((1, -1, 1, 1)) + self.zero = self.zero.reshape((1, -1, 1, 1)) + if len(shape) == 3: + self.scale = self.scale.reshape((1, 1, -1)) + self.zero = self.zero.reshape((1, 1, -1)) + if len(shape) == 2: + self.scale = self.scale.unsqueeze(0) + self.zero = self.zero.unsqueeze(0) + + def quantize(self, x): + if self.ready(): + return quantize(x, self.scale, self.zero, self.maxq) + return x + + def enabled(self): + return self.maxq > 0 + + def ready(self): + return torch.all(self.scale != 0) + + +try: + import quant_cuda +except: + print('CUDA extension not installed.') + +# Assumes layer is perfectly divisible into 256 * 256 blocks + + +class QuantLinear(nn.Module): + + def __init__(self, bits, groupsize, infeatures, outfeatures): + super().__init__() + if bits not in [2, 3, 4, 8]: + raise NotImplementedError("Only 2,3,4,8 bits are supported.") + self.infeatures = infeatures + self.outfeatures = outfeatures + self.bits = bits + if groupsize != -1 and groupsize < 32 and groupsize != int(math.pow(2, int(math.log2(groupsize)))): + raise NotImplementedError("groupsize supports powers of 2 greater than 32. (e.g. : 32,64,128,etc)") + groupsize = groupsize if groupsize != -1 else infeatures + self.groupsize = groupsize + self.register_buffer( + 'qzeros', torch.zeros((math.ceil(infeatures / groupsize), outfeatures // 256 * (bits * 8)), + dtype=torch.int)) + self.register_buffer('scales', torch.zeros((math.ceil(infeatures / groupsize), outfeatures))) + self.register_buffer('bias', torch.zeros(outfeatures)) + self.register_buffer('qweight', torch.zeros((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int)) + self._initialized_quant_state = False + + def pack(self, linear, scales, zeros): + scales = scales.t().contiguous() + zeros = zeros.t().contiguous() + scale_zeros = zeros * scales + self.scales = scales.clone() + if linear.bias is not None: + self.bias = linear.bias.clone() + + intweight = [] + for idx in range(self.infeatures): + g_idx = idx // self.groupsize + intweight.append( + torch.round((linear.weight.data[:, idx] + scale_zeros[g_idx]) / self.scales[g_idx]).to(torch.int)[:, + None]) + intweight = torch.cat(intweight, dim=1) + intweight = intweight.t().contiguous() + intweight = intweight.numpy().astype(np.uint32) + qweight = np.zeros((intweight.shape[0] // 256 * (self.bits * 8), intweight.shape[1]), dtype=np.uint32) + i = 0 + row = 0 + while row < qweight.shape[0]: + if self.bits in [2, 4, 8]: + for j in range(i, i + (32 // self.bits)): + qweight[row] |= intweight[j] << (self.bits * (j - i)) + i += 32 // self.bits + row += 1 + elif self.bits == 3: + for j in range(i, i + 10): + qweight[row] |= intweight[j] << (3 * (j - i)) + i += 10 + qweight[row] |= intweight[i] << 30 + row += 1 + qweight[row] |= (intweight[i] >> 2) & 1 + i += 1 + for j in range(i, i + 10): + qweight[row] |= intweight[j] << (3 * (j - i) + 1) + i += 10 + qweight[row] |= intweight[i] << 31 + row += 1 + qweight[row] |= (intweight[i] >> 1) & 0x3 + i += 1 + for j in range(i, i + 10): + qweight[row] |= intweight[j] << (3 * (j - i) + 2) + i += 10 + row += 1 + else: + raise NotImplementedError("Only 2,3,4,8 bits are supported.") + + qweight = qweight.astype(np.int32) + self.qweight = torch.from_numpy(qweight) + + zeros -= 1 + zeros = zeros.numpy().astype(np.uint32) + qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 256 * (self.bits * 8)), dtype=np.uint32) + i = 0 + col = 0 + while col < qzeros.shape[1]: + if self.bits in [2, 4, 8]: + for j in range(i, i + (32 // self.bits)): + qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i)) + i += 32 // self.bits + col += 1 + elif self.bits == 3: + for j in range(i, i + 10): + qzeros[:, col] |= zeros[:, j] << (3 * (j - i)) + i += 10 + qzeros[:, col] |= zeros[:, i] << 30 + col += 1 + qzeros[:, col] |= (zeros[:, i] >> 2) & 1 + i += 1 + for j in range(i, i + 10): + qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 1) + i += 10 + qzeros[:, col] |= zeros[:, i] << 31 + col += 1 + qzeros[:, col] |= (zeros[:, i] >> 1) & 0x3 + i += 1 + for j in range(i, i + 10): + qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 2) + i += 10 + col += 1 + else: + raise NotImplementedError("Only 2,3,4,8 bits are supported.") + + qzeros = qzeros.astype(np.int32) + self.qzeros = torch.from_numpy(qzeros) + + def forward(self, x): + intermediate_dtype = torch.float32 + + if not self._initialized_quant_state: + # Do we even have a bias? Check for at least one non-zero element. + if self.bias is not None and bool(torch.any(self.bias != 0)): + # Then make sure it's the right type. + self.bias.data = self.bias.data.to(intermediate_dtype) + else: + self.bias = None + + outshape = list(x.shape) + outshape[-1] = self.outfeatures + x = x.reshape(-1, x.shape[-1]) + if self.bias is None: + y = torch.zeros(x.shape[0], outshape[-1], dtype=intermediate_dtype, device=x.device) + else: + y = self.bias.clone().repeat(x.shape[0], 1) + + output_dtype = x.dtype + x = x.to(intermediate_dtype) + if self.bits == 2: + quant_cuda.vecquant2matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize) + elif self.bits == 3: + quant_cuda.vecquant3matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize) + elif self.bits == 4: + quant_cuda.vecquant4matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize) + elif self.bits == 8: + quant_cuda.vecquant8matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize) + else: + raise NotImplementedError("Only 2,3,4,8 bits are supported.") + y = y.to(output_dtype) + return y.reshape(outshape) + + +def make_quant(module, names, bits, groupsize, name=''): + if isinstance(module, QuantLinear): + return + for attr in dir(module): + tmp = getattr(module, attr) + name1 = name + '.' + attr if name != '' else attr + if name1 in names: + setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features)) + for name1, child in module.named_children(): + make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1) diff --git a/applications/Chat/coati/quant/utils.py b/applications/Chat/coati/quant/utils.py new file mode 100644 index 000000000000..01b8cff0add1 --- /dev/null +++ b/applications/Chat/coati/quant/utils.py @@ -0,0 +1,28 @@ +from contextlib import contextmanager + +import torch + + +def _noop(*args, **kwargs): + pass + + +@contextmanager +def low_resource_init(): + """This context manager disables weight initialization and sets the default float dtype to half. + """ + old_kaiming_uniform_ = torch.nn.init.kaiming_uniform_ + old_uniform_ = torch.nn.init.uniform_ + old_normal_ = torch.nn.init.normal_ + dtype = torch.get_default_dtype() + try: + torch.nn.init.kaiming_uniform_ = _noop + torch.nn.init.uniform_ = _noop + torch.nn.init.normal_ = _noop + torch.set_default_dtype(torch.half) + yield + finally: + torch.nn.init.kaiming_uniform_ = old_kaiming_uniform_ + torch.nn.init.uniform_ = old_uniform_ + torch.nn.init.normal_ = old_normal_ + torch.set_default_dtype(dtype) diff --git a/applications/Chat/coati/ray/README.md b/applications/Chat/coati/ray/README.md new file mode 100644 index 000000000000..228155a6855b --- /dev/null +++ b/applications/Chat/coati/ray/README.md @@ -0,0 +1,160 @@ +# Distributed PPO Training on Stage 3 + +## Detach Experience Makers and Trainers + +We can completely separate the trainers and makers. + +

    + +

    + +- The experience maker performs inference, produces experience, and remotely delivers it to the trainer (1). +- The trainer consumes experience to train models, and periodically transmits new model parameters to the maker (2.1, 2.2). +- Using an experience buffer to overlap transmission and computing. + +In this manner, each node will work continuously without model idle time, and different optimization strategies can be applied for inference and training to meet the needs of speed or storage. It is also helpful for scalability. + +`DetachedPPOTrainer` and `ExperienceMakerHolder` are Ray Actors (distinguished from Actor Model), representing Trainer and Experience Maker on the graph above, respectively. + +[More about Ray Core](https://docs.ray.io/en/latest/ray-core/walkthrough.html) + +## Usage + +See examples at `ColossalAI/application/Chat/examples/ray` + +### Setup Makers + +- define makers' environment variables : + + ```python + env_info_makers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(num_makers), + 'master_port': maker_port, + 'master_addr': master_addr + } for rank in range(num_makers)] + + ``` +- define maker models : + ```python + def model_fn(): + actor = get_actor_from_args(...) + critic = get_critic_from_args(...) + reward_model = get_reward_model_from_args(...) + initial_model = get_actor_from_args(...) + return actor, critic, reward_model, initial_model + + ``` +- set experience_holder_refs : + + ```python + experience_holder_refs = [ + ExperienceMakerHolder.options( + name=f"maker_{i}", + num_gpus=1, + max_concurrency=2 + ).remote( + detached_trainer_name_list=[f"trainer_{x}" for x in target_trainers(...)], + model_fn=model_fn, + ...) + for i, env_info_maker in enumerate(env_info_makers) + ] + ``` + The names in the `detached_trainer_name_list` refer to the target trainers that the maker should send experience to. + We set a trainer's name the same as a maker, by `.options(name="str")`. See below. + +### Setup Trainers + +- define trainers' environment variables : + ```python + env_info_trainers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(num_trainers), + 'master_port': trainer_port, + 'master_addr': master_addr + } for rank in range(num_trainers)] + ``` +- define trainer models : + + ```python + def trainer_model_fn(): + actor = get_actor_from_args(...) + critic = get_critic_from_args(...) + return actor, critic + ``` +- set trainer_refs : + ```python + trainer_refs = [ + DetachedPPOTrainer.options( + name=f"trainer{i}", + num_gpus=1, + max_concurrency=2 + ).remote( + experience_maker_holder_name_list=[f"maker{x}" for x in target_makers(...)], + model_fn = trainer_model_fn(), + ...) + for i, env_info_trainer in enumerate(env_info_trainers) + ] + ``` + The names in `experience_maker_holder_name_list` refer to the target makers that the trainer should send updated models to. + By setting `detached_trainer_name_list` and `experience_maker_holder_name_list`, we can customize the transmission graph. + +### Launch Jobs +- define data_loader : + ```python + def data_loader_fn(): + return = torch.utils.data.DataLoader(dataset=dataset) + + ``` +- launch makers : + ```python + wait_tasks = [] + for experience_holder_ref in experience_holder_refs: + wait_tasks.append( + experience_holder_ref.workingloop.remote(data_loader_fn(), + num_steps=experience_steps)) + + ``` + +- launch trainers : + ```python + for trainer_ref in trainer_refs: + wait_tasks.append(trainer_ref.fit.remote(total_steps, update_steps, train_epochs)) + ``` + +- wait for done : + ```python + ray.get(wait_tasks) + ``` + +## Flexible Structure + +We can deploy different strategies to makers and trainers. Here are some notions. + +### 2 Makers 1 Trainer +

    + +

    + +### 2 Makers 2 Trainer +

    + +

    + +### Maker Inference Quantization +

    + +

    + +### Tensor Parallel + +

    + +

    + +## TODO + +- [ ] Support LoRA +- [ ] Support TP & PP diff --git a/applications/Chat/coati/ray/__init__.py b/applications/Chat/coati/ray/__init__.py index 5802c05bc03f..e69de29bb2d1 100644 --- a/applications/Chat/coati/ray/__init__.py +++ b/applications/Chat/coati/ray/__init__.py @@ -1,2 +0,0 @@ -from .src.detached_replay_buffer import DetachedReplayBuffer -from .src.detached_trainer_ppo import DetachedPPOTrainer diff --git a/applications/Chat/coati/ray/callbacks/__init__.py b/applications/Chat/coati/ray/callbacks/__init__.py new file mode 100644 index 000000000000..5f5e488f383e --- /dev/null +++ b/applications/Chat/coati/ray/callbacks/__init__.py @@ -0,0 +1,9 @@ +from .base import MakerCallback, TrainerCallback +from .performance_evaluator import ExperienceMakerPerformanceEvaluator, TrainerPerformanceEvaluator + +__all__ = [ + "TrainerCallback", + "MakerCallback", + "ExperienceMakerPerformanceEvaluator", + "TrainerPerformanceEvaluator", +] diff --git a/applications/Chat/coati/ray/callbacks/base.py b/applications/Chat/coati/ray/callbacks/base.py new file mode 100644 index 000000000000..3306150a41ff --- /dev/null +++ b/applications/Chat/coati/ray/callbacks/base.py @@ -0,0 +1,66 @@ +from abc import ABC + +from coati.experience_maker import Experience + + +class TrainerCallback(ABC): + """ + Base callback class. It defines the interface for callbacks. + """ + + def on_fit_start(self) -> None: + pass + + def on_fit_end(self) -> None: + pass + + def on_episode_start(self, episode: int) -> None: + pass + + def on_episode_end(self, episode: int) -> None: + pass + + def on_epoch_start(self, epoch: int) -> None: + pass + + def on_epoch_end(self, epoch: int) -> None: + pass + + def on_batch_start(self) -> None: + pass + + def on_batch_end(self, metrics: dict, experience: Experience) -> None: + pass + + def on_update_start(self) -> None: + pass + + def on_update_end(self) -> None: + pass + + +class MakerCallback(ABC): + + def on_loop_start(self) -> None: + pass + + def on_loop_end(self) -> None: + pass + + def on_make_experience_start(self) -> None: + pass + + def on_make_experience_end(self, experience: Experience) -> None: + pass + + def on_send_start(self) -> None: + pass + + def on_send_end(self) -> None: + pass + + def on_batch_start(self) -> None: + pass + + def on_batch_end(self) -> None: + pass diff --git a/applications/Chat/coati/ray/callbacks/performance_evaluator.py b/applications/Chat/coati/ray/callbacks/performance_evaluator.py new file mode 100644 index 000000000000..cd3517609e7a --- /dev/null +++ b/applications/Chat/coati/ray/callbacks/performance_evaluator.py @@ -0,0 +1,212 @@ +from time import time +from typing import Optional + +import torch +import torch.distributed as dist +from coati.experience_maker import Experience + +from .base import MakerCallback, TrainerCallback + + +def get_world_size() -> int: + if dist.is_initialized(): + return dist.get_world_size() + return 1 + + +def print_rank_0(*args, **kwargs) -> None: + if not dist.is_initialized() or dist.get_rank() == 0: + print(*args, **kwargs) + + +@torch.no_grad() +def all_reduce_mean(x: float, world_size: int) -> float: + if world_size == 1: + return x + tensor = torch.tensor([x], device=torch.cuda.current_device()) + dist.all_reduce(tensor) + tensor = tensor / world_size + return tensor.item() + + +class Timer: + + def __init__(self) -> None: + self.start_time: Optional[float] = None + self.duration: float = 0. + + def start(self) -> None: + self.start_time = time() + + def end(self) -> None: + self.duration += time() - self.start_time + + def reset(self) -> None: + self.duration = 0. + + +class ExperienceMakerPerformanceEvaluator(MakerCallback): + + def __init__(self, actor_num_params: int, critic_num_params: int, initial_model_num_params: int, + reward_model_num_params: int) -> None: + super().__init__() + self.world_size = get_world_size() + self.actor_num_params = actor_num_params + self.critic_num_params = critic_num_params + self.initial_model_num_params = initial_model_num_params + self.reward_model_num_params = reward_model_num_params + + self.batch_timer = Timer() + self.send_timer = Timer() + self.make_experience_timer = Timer() + self.total_samples: int = 0 + self.make_experience_flop: int = 0 + + print_rank_0( + f'ExperienceMaker actor: {actor_num_params/1024**3:.2f}B, critic: {critic_num_params/1024**3:.2f}B, initial model: {initial_model_num_params/1024**3:.2f}B, reward model: {reward_model_num_params/1024**3:.2f}B, world size: {self.world_size}' + ) + + def on_make_experience_start(self) -> None: + self.make_experience_timer.start() + + def on_make_experience_end(self, experience: Experience) -> None: + self.make_experience_timer.end() + + batch_size, seq_len = experience.sequences.shape + + self.total_samples += batch_size + + # actor generate + num_actions = experience.action_mask.size(1) + input_len = seq_len - num_actions + total_seq_len = (input_len + seq_len - 1) * num_actions / 2 + self.make_experience_flop += self.actor_num_params * batch_size * total_seq_len * 2 + # actor forward + self.make_experience_flop += self.actor_num_params * batch_size * seq_len * 2 + # critic forward + self.make_experience_flop += self.critic_num_params * batch_size * seq_len * 2 + # initial model forward + self.make_experience_flop += self.initial_model_num_params * batch_size * seq_len * 2 + # reward model forward + self.make_experience_flop += self.reward_model_num_params * batch_size * seq_len * 2 + + def on_send_start(self) -> None: + self.send_timer.start() + + def on_send_end(self) -> None: + self.send_timer.end() + + def on_batch_start(self) -> None: + self.batch_timer.start() + + def on_batch_end(self) -> None: + self.batch_timer.end() + + def on_loop_end(self) -> None: + avg_make_experience_duration = all_reduce_mean(self.make_experience_timer.duration, self.world_size) + avg_overall_duration = all_reduce_mean(self.batch_timer.duration, self.world_size) + avg_send_duration = all_reduce_mean(self.send_timer.duration, self.world_size) + + avg_throughput = self.total_samples * self.world_size / (avg_overall_duration + 1e-12) + avg_make_experience_tflops = self.make_experience_flop / 1e12 / (avg_make_experience_duration + 1e-12) + avg_time_per_sample = (avg_overall_duration + 1e-12) / (self.total_samples * self.world_size) + avg_make_experience_time_per_sample = (avg_make_experience_duration + 1e-12) / \ + (self.total_samples * self.world_size) + avg_send_time_per_sample = (avg_send_duration + 1e-12) / (self.total_samples * self.world_size) + + print_rank_0( + 'Making Experience Performance Summary:\n' + f'Throughput: {avg_throughput:.3f} samples/sec\n' + + f'TFLOPS per GPU: {avg_make_experience_tflops:.3f}\n' + + f'Sample time (overall): {avg_time_per_sample:.3f} s\n' + + f'Sample time (make experience): {avg_make_experience_time_per_sample:.3f} s, {avg_make_experience_time_per_sample/avg_time_per_sample*100:.2f}%\n' + + + f'Sample time (send): {avg_send_time_per_sample:.3f} s, {avg_send_time_per_sample/avg_time_per_sample*100:.2f}%\n' + ) + + +class TrainerPerformanceEvaluator(TrainerCallback): + + def __init__(self, + actor_num_params: int, + critic_num_params: int, + enable_grad_checkpoint: bool = False, + ignore_first_episodes: int = 1) -> None: + super().__init__() + self.world_size = get_world_size() + self.actor_num_params = actor_num_params + self.critic_num_params = critic_num_params + self.enable_grad_checkpoint = enable_grad_checkpoint + self.ignore_first_episodes = ignore_first_episodes + self.ignore_this_episode = False + + self.episode_timer = Timer() + self.batch_timer = Timer() + self.update_timer = Timer() + self.total_samples: int = 0 + self.learn_flop: int = 0 + + print_rank_0( + f'Trainer actor: {self.actor_num_params/1024**3:.2f}B, critic: {self.critic_num_params/1024**3:.2f}B, world size: {self.world_size}' + ) + + def on_episode_start(self, episodes: int) -> None: + self.ignore_this_episode = episodes < self.ignore_first_episodes + if self.ignore_this_episode: + return + self.episode_timer.start() + + def on_episode_end(self, episodes: int) -> None: + if self.ignore_this_episode: + return + self.episode_timer.end() + + def on_batch_start(self) -> None: + if self.ignore_this_episode: + return + self.batch_timer.start() + + def on_batch_end(self, metrics: dict, experience: Experience) -> None: + if self.ignore_this_episode: + return + self.batch_timer.end() + + batch_size, seq_len = experience.sequences.shape + + self.total_samples += batch_size + + # actor forward-backward, 3 means forward(1) + backward(2) + self.learn_flop += self.actor_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint)) + # critic forward-backward + self.learn_flop += self.critic_num_params * batch_size * seq_len * 2 * (3 + int(self.enable_grad_checkpoint)) + + def on_update_start(self) -> None: + if self.ignore_this_episode: + return + self.update_timer.start() + + def on_update_end(self) -> None: + if self.ignore_this_episode: + return + self.update_timer.end() + + def on_fit_end(self) -> None: + if self.total_samples == 0: + print_rank_0('No samples are collected, skip trainer performance evaluation') + return + avg_train_duration = all_reduce_mean(self.batch_timer.duration, self.world_size) + avg_update_duration = all_reduce_mean(self.update_timer.duration, self.world_size) + avg_episode_duration = all_reduce_mean(self.episode_timer.duration, self.world_size) + + avg_throughput = self.total_samples * self.world_size / (avg_episode_duration + 1e-12) + avg_learn_tflops = self.learn_flop / 1e12 / (avg_train_duration + 1e-12) + avg_time_per_sample = (avg_episode_duration + 1e-12) / (self.total_samples * self.world_size) + avg_train_time_per_sample = (avg_train_duration + 1e-12) / (self.total_samples * self.world_size) + avg_update_time_per_sample = (avg_update_duration + 1e-12) / (self.total_samples * self.world_size) + + print_rank_0( + 'Learning Performance Summary:\n' + f'Throughput: {avg_throughput:.3f} samples/sec\n' + + f'TFLOPS per GPU: {avg_learn_tflops:.3f}\n' + f'Sample time (overall): {avg_time_per_sample:.3f} s\n' + + f'Sample time (train): {avg_train_time_per_sample:.3f} s, {avg_train_time_per_sample/avg_time_per_sample*100:.2f}%\n' + + + f'Sample time (update): {avg_update_time_per_sample:.3f} s, {avg_update_time_per_sample/avg_time_per_sample*100:.2f}%\n' + ) diff --git a/applications/Chat/coati/ray/src/detached_replay_buffer.py b/applications/Chat/coati/ray/detached_replay_buffer.py similarity index 62% rename from applications/Chat/coati/ray/src/detached_replay_buffer.py rename to applications/Chat/coati/ray/detached_replay_buffer.py index 18c8db388e88..2f765281178a 100644 --- a/applications/Chat/coati/ray/src/detached_replay_buffer.py +++ b/applications/Chat/coati/ray/detached_replay_buffer.py @@ -1,22 +1,24 @@ -import torch +import asyncio +import copy import random -from typing import List, Any -# from torch.multiprocessing import Queue -from ray.util.queue import Queue +from threading import Lock +from typing import Any, List + import ray -import asyncio +import torch from coati.experience_maker.base import Experience -from coati.replay_buffer.utils import BufferItem, make_experience_batch, split_experience_batch from coati.replay_buffer import ReplayBuffer -from threading import Lock -import copy +from coati.replay_buffer.utils import BufferItem, make_experience_batch, split_experience_batch +# from torch.multiprocessing import Queue +from ray.util.queue import Queue + class DetachedReplayBuffer: ''' - Detached replay buffer. Share Experience across workers on the same node. - Therefore a trainer node is expected to have only one instance. + Detached replay buffer. Share Experience across workers on the same node. + Therefore a trainer node is expected to have only one instance. It is ExperienceMakerHolder's duty to call append(exp) method, remotely. - + Args: sample_batch_size: Batch size when sampling. Exp won't enqueue until they formed a batch. tp_world_size: Number of workers in the same tp group @@ -24,31 +26,25 @@ class DetachedReplayBuffer: cpu_offload: Whether to offload experience to cpu when sampling. Defaults to True. ''' - def __init__(self, sample_batch_size: int, tp_world_size: int = 1, limit : int = 0, cpu_offload: bool = True) -> None: - self.cpu_offload = cpu_offload + def __init__(self, sample_batch_size: int, limit: int = 0) -> None: self.sample_batch_size = sample_batch_size self.limit = limit - self.items = Queue(self.limit, actor_options={"num_cpus":1}) - self.batch_collector : List[BufferItem] = [] + self.items = Queue(self.limit, actor_options={"num_cpus": 1}) + self.batch_collector: List[BufferItem] = [] + @torch.no_grad() + def append(self, experience: Experience) -> None: ''' - Workers in the same tp group share this buffer and need same sample for one step. - Therefore a held_sample should be returned tp_world_size times before it could be dropped. - worker_state records whether a worker got the held_sample + Expected to be called remotely. ''' - self.tp_world_size = tp_world_size - self.worker_state = [False] * self.tp_world_size - self.held_sample = None - self._worker_state_lock = Lock() + items = split_experience_batch(experience) + self.extend(items) @torch.no_grad() - def append(self, experience: Experience) -> None: + def extend(self, items: List[BufferItem]) -> None: ''' Expected to be called remotely. ''' - if self.cpu_offload: - experience.to_device(torch.device('cpu')) - items = split_experience_batch(experience) self.batch_collector.extend(items) while len(self.batch_collector) >= self.sample_batch_size: items = self.batch_collector[:self.sample_batch_size] @@ -62,19 +58,10 @@ def clear(self) -> None: self.items = Queue(self.limit) self.worker_state = [False] * self.tp_world_size self.batch_collector = [] - + @torch.no_grad() - def sample(self, worker_rank = 0, to_device = "cpu") -> Experience: - self._worker_state_lock.acquire() - if not any(self.worker_state): - self.held_sample = self._sample_and_erase() - self.worker_state[worker_rank] = True - if all(self.worker_state): - self.worker_state = [False] * self.tp_world_size - ret = self.held_sample - else: - ret = copy.deepcopy(self.held_sample) - self._worker_state_lock.release() + def sample(self, worker_rank=0, to_device="cpu") -> Experience: + ret = self._sample_and_erase() ret.to_device(to_device) return ret @@ -85,4 +72,4 @@ def _sample_and_erase(self) -> Experience: def get_length(self) -> int: ret = self.items.qsize() - return ret \ No newline at end of file + return ret diff --git a/applications/Chat/coati/ray/detached_trainer_base.py b/applications/Chat/coati/ray/detached_trainer_base.py new file mode 100644 index 000000000000..ac2d35e9da19 --- /dev/null +++ b/applications/Chat/coati/ray/detached_trainer_base.py @@ -0,0 +1,179 @@ +import os +from abc import ABC, abstractmethod +from typing import Any, Callable, Dict, Iterable, List, Optional, Union + +import ray +import torch +from coati.experience_maker import Experience +from coati.replay_buffer.utils import BufferItem +from torch.utils.data import DataLoader +from tqdm import tqdm + +from .callbacks import TrainerCallback +from .detached_replay_buffer import DetachedReplayBuffer +from .utils import is_rank_0 + + +class DetachedTrainer(ABC): + ''' + Base class for detached rlhf trainers. + 'detach' means that the experience maker is detached compared to a normal Trainer. + Please set name attribute during init: + >>> trainer = DetachedTrainer.options(..., name = "xxx", ...).remote() + So an ExperienceMakerHolder can reach the detached_replay_buffer by Actor's name. + Args: + detached_strategy (DetachedStrategy): the strategy to use for training + detached_replay_buffer_ref (ObjectRef[DetachedReplayBuffer]): the replay buffer to use for training + data_loader_pin_memory (bool, defaults to True): whether to pin memory for data loader + callbacks (List[Callback], defaults to []): the callbacks to call during training process + generate_kwargs (dict, optional): the kwargs to use while model generating + + ''' + + def __init__(self, + experience_maker_holder_name_list: List[str], + train_batch_size: int = 8, + buffer_limit: int = 0, + dataloader_pin_memory: bool = True, + callbacks: List[TrainerCallback] = [], + debug: bool = False) -> None: + super().__init__() + self.detached_replay_buffer = DetachedReplayBuffer(train_batch_size, limit=buffer_limit) + self.dataloader_pin_memory = dataloader_pin_memory + self.callbacks = callbacks + self.target_holder_name_list = experience_maker_holder_name_list + self.target_holder_list = [] + self._is_target_holder_initialized = False + self._debug = debug + + def update_target_holder_list(self): + # as the length of target_holder_list may be zero, we need to check it by a bool flag + if not self._is_target_holder_initialized: + for name in self.target_holder_name_list: + self.target_holder_list.append(ray.get_actor(name, namespace=os.environ["RAY_NAMESPACE"])) + self._is_target_holder_initialized = True + + @abstractmethod + def _update_remote_makers(self, fully_update: bool = False, **kwargs): + pass + + def sync_models_to_remote_makers(self, **kwargs): + self._update_remote_makers(fully_update=True, **kwargs) + + @abstractmethod + def training_step(self, experience: Experience) -> Dict[str, Any]: + pass + + def _learn(self, update_steps: int, train_epochs: int) -> None: + data = [] + # warmup + pbar = tqdm(range(update_steps), desc=f'Train epoch [1/{train_epochs}]', disable=not is_rank_0()) + self._on_epoch_start(0) + self._learn_epoch(pbar, data) + self._on_epoch_end(0) + # item is already a batch + dataloader = DataLoader(data, + batch_size=1, + shuffle=True, + pin_memory=self.dataloader_pin_memory, + collate_fn=lambda x: x[0]) + for epoch in range(1, train_epochs): + pbar = tqdm(dataloader, desc=f'Train epoch [{epoch + 1}/{train_epochs}]', disable=not is_rank_0()) + self._on_epoch_start(epoch) + self._learn_epoch(pbar, data) + self._on_epoch_end(epoch) + + def _learn_epoch(self, pbar: tqdm, data: List[Experience]) -> None: + is_warmup = len(data) == 0 + for x in pbar: + if self._debug: + print("[trainer] training step") + # sample a batch and then train to avoid waiting + experience = x if not is_warmup else self._buffer_sample() + experience.to_device(torch.cuda.current_device()) + self._on_batch_start() + metrics = self.training_step(experience) + self._on_batch_end(metrics, experience) + + if self._debug: + print("[trainer] step over") + experience.to_device("cpu") + if is_warmup: + data.append(experience) + pbar.set_postfix(metrics) + + def fit(self, total_steps: int, update_steps: int, train_epochs: int = 1) -> None: + self._on_fit_start() + for i in tqdm(range(total_steps // update_steps), desc='Trainer', disable=not is_rank_0()): + self._on_episode_start(i) + self._learn(update_steps, train_epochs) + self._on_update_start() + self._update_remote_makers() + self._on_update_end() + self._on_episode_end(i) + self._on_fit_end() + + @ray.method(concurrency_group="buffer_length") + def buffer_get_length(self): + # called by ExperienceMakerHolder + if self._debug: + print("[trainer] telling length") + return self.detached_replay_buffer.get_length() + + @ray.method(concurrency_group="buffer_append") + def buffer_append(self, experience: Experience): + # called by ExperienceMakerHolder + if self._debug: + print(f"[trainer] receiving exp.") + self.detached_replay_buffer.append(experience) + + @ray.method(concurrency_group="buffer_append") + def buffer_extend(self, items: List[BufferItem]): + # called by ExperienceMakerHolder + if self._debug: + print(f"[trainer] receiving exp.") + self.detached_replay_buffer.extend(items) + + @ray.method(concurrency_group="buffer_sample") + def _buffer_sample(self): + return self.detached_replay_buffer.sample() + + def _on_fit_start(self) -> None: + for callback in self.callbacks: + callback.on_fit_start() + + def _on_fit_end(self) -> None: + for callback in self.callbacks: + callback.on_fit_end() + + def _on_episode_start(self, episode: int) -> None: + for callback in self.callbacks: + callback.on_episode_start(episode) + + def _on_episode_end(self, episode: int) -> None: + for callback in self.callbacks: + callback.on_episode_end(episode) + + def _on_epoch_start(self, epoch: int) -> None: + for callback in self.callbacks: + callback.on_epoch_start(epoch) + + def _on_epoch_end(self, epoch: int) -> None: + for callback in self.callbacks: + callback.on_epoch_end(epoch) + + def _on_batch_start(self) -> None: + for callback in self.callbacks: + callback.on_batch_start() + + def _on_batch_end(self, metrics: dict, experience: Experience) -> None: + for callback in self.callbacks: + callback.on_batch_end(metrics, experience) + + def _on_update_start(self) -> None: + for callback in self.callbacks: + callback.on_update_start() + + def _on_update_end(self) -> None: + for callback in self.callbacks: + callback.on_update_end() diff --git a/applications/Chat/coati/ray/src/detached_trainer_ppo.py b/applications/Chat/coati/ray/detached_trainer_ppo.py similarity index 55% rename from applications/Chat/coati/ray/src/detached_trainer_ppo.py rename to applications/Chat/coati/ray/detached_trainer_ppo.py index 838e82d07f4a..5f0032716f93 100644 --- a/applications/Chat/coati/ray/src/detached_trainer_ppo.py +++ b/applications/Chat/coati/ray/detached_trainer_ppo.py @@ -1,24 +1,38 @@ -from typing import Any, Callable, Dict, List, Optional -import torch -from torch.optim import Adam +from typing import Any, Callable, Dict, List, Optional, Tuple +import ray +import torch from coati.experience_maker import Experience, NaiveExperienceMaker from coati.models.base import Actor, Critic -from coati.models.generation_utils import update_model_kwargs_fn from coati.models.loss import PolicyLoss, ValueLoss -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy, Strategy from coati.trainer.callbacks import Callback +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy, Strategy +from torch.optim import Adam from colossalai.nn.optimizer import HybridAdam -import ray - - -from .utils import is_rank_0, get_cuda_actor_critic_from_args, get_strategy_from_args, set_dist_env +from .callbacks import TrainerCallback, TrainerPerformanceEvaluator from .detached_trainer_base import DetachedTrainer - - -@ray.remote(concurrency_groups={"buffer_length": 1, "buffer_append":1, "buffer_sample":1,"model_io": 1, "compute": 1}) +from .lora_constructor import LoRAConstructor +from .utils import ( + get_actor_from_args, + get_critic_from_args, + get_model_numel, + get_rank, + get_strategy_from_args, + is_rank_0, + set_dist_env, + state_dict_to, +) + + +@ray.remote(concurrency_groups={ + "buffer_length": 1, + "buffer_append": 1, + "buffer_sample": 1, + "model_io": 1, + "compute": 1 +}) class DetachedPPOTrainer(DetachedTrainer): ''' Detached Trainer for PPO algorithm @@ -40,86 +54,102 @@ class DetachedPPOTrainer(DetachedTrainer): generate_kwargs (dict, optional): the kwargs to use while model generating ''' - def __init__(self, - experience_maker_holder_name_list: List[str], - strategy: str, - model: str, - env_info: Dict[str, str] = None, - pretrained: str = None, - lora_rank: int = 0, - train_batch_size: int = 8, - buffer_limit: int = 0, - buffer_cpu_offload: bool = True, - eps_clip: float = 0.2, - value_clip: float = 0.4, - experience_batch_size: int = 8, - max_epochs: int = 10, - dataloader_pin_memory: bool = True, - callbacks: List[Callback] = [], - **generate_kwargs) -> None: + def __init__( + self, + experience_maker_holder_name_list: List[str], + strategy_fn: Callable[[], Strategy], + model_fn: Callable[[], Tuple[Actor, Critic]], + env_info: Dict[str, str] = None, + train_batch_size: int = 8, + buffer_limit: int = 0, + eps_clip: float = 0.2, + value_clip: float = 0.4, + dataloader_pin_memory: bool = True, + callbacks: List[TrainerCallback] = [], + eval_performance: bool = False, + debug: bool = False, + update_lora_weights: bool = False, + ) -> None: # set environment variables if env_info: set_dist_env(env_info=env_info) # configure strategy - self.strategy = get_strategy_from_args(strategy) + self.strategy = strategy_fn() # configure models, loss and optimizers with self.strategy.model_init_context(): - self.actor, self.critic = get_cuda_actor_critic_from_args(model, pretrained, lora_rank) + self.actor, self.critic = model_fn() - if strategy != 'colossalai_gemini': - self.actor.to(torch.float16).to(torch.cuda.current_device()) - self.critic.to(torch.float16).to(torch.cuda.current_device()) + if eval_performance: + actor_numel = get_model_numel(self.actor) + critic_numel = get_model_numel(self.critic) + evaluator = TrainerPerformanceEvaluator(actor_numel, critic_numel) + callbacks = callbacks + [evaluator] - if strategy.startswith('colossalai'): - self.actor_optim = HybridAdam(self.actor.parameters(), lr=5e-6) - self.critic_optim = HybridAdam(self.critic.parameters(), lr=5e-6) + if isinstance(self.strategy, ColossalAIStrategy): + self.actor_optim = HybridAdam(self.actor.parameters(), lr=1e-7) + self.critic_optim = HybridAdam(self.critic.parameters(), lr=1e-7) else: - self.actor_optim = Adam(self.actor.parameters(), lr=5e-6) - self.critic_optim = Adam(self.critic.parameters(), lr=5e-6) + self.actor_optim = Adam(self.actor.parameters(), lr=1e-7) + self.critic_optim = Adam(self.critic.parameters(), lr=1e-7) (self.actor, self.actor_optim), (self.critic, self.critic_optim) = \ self.strategy.prepare((self.actor, self.actor_optim), (self.critic, self.critic_optim)) - generate_kwargs = _set_default_generate_kwargs(self.strategy, generate_kwargs, self.actor) + # configure trainer self.actor_loss_fn = PolicyLoss(eps_clip) self.critic_loss_fn = ValueLoss(value_clip) super().__init__(experience_maker_holder_name_list, train_batch_size=train_batch_size, buffer_limit=buffer_limit, - buffer_cpu_offload=buffer_cpu_offload, - experience_batch_size=experience_batch_size, - max_epochs=max_epochs, dataloader_pin_memory=dataloader_pin_memory, callbacks=callbacks, - **generate_kwargs) + debug=debug) + if self._debug: + print(f'[trainer{get_rank()}] will send state dict to {experience_maker_holder_name_list}') + + self._update_lora_weights = update_lora_weights @ray.method(concurrency_group="model_io") - def _update_remote_makers(self): + @torch.no_grad() + def _update_remote_makers(self, fully_update: bool = False, **config): # TODO: balance duties - if is_rank_0(): - self.update_target_holder_list(self.target_holder_name_list) + if not fully_update: + config['requires_grad_only'] = True + self.update_target_holder_list() + # mark start, ensure order + tasks = [] + for target_holder in self.target_holder_list: + tasks.append(target_holder.update_experience_maker.remote(chunk_start=True, fully_update=fully_update)) + ray.get(tasks) + # sending loop + tasks = [] + + for state_dict_shard in self._get_model_state_dict_shard(self.actor, fully_update=fully_update, **config): for target_holder in self.target_holder_list: - # TODO: reduce malloc - with torch.no_grad(): - ray.get(target_holder.update_experience_maker.remote(self._get_unwrapped_actor(), self._get_unwrapped_critic())) - - @ray.method(concurrency_group="model_io") - def initialize_remote_makers(self): - # TODO: balance duties - if is_rank_0(): - self.update_target_holder_list(self.target_holder_name_list) + tasks.append( + target_holder.update_experience_maker.remote( + new_actor_state_dict=state_dict_shard, + new_actor_lora_config_dict=self._get_model_lora_config_dict(self.actor), + fully_update=fully_update)) + # sending loop + for state_dict_shard in self._get_model_state_dict_shard(self.critic, fully_update=fully_update, **config): for target_holder in self.target_holder_list: - # TODO: reduce malloc - with torch.no_grad(): - ray.get(target_holder.initialize_experience_maker.remote(self._get_unwrapped_actor(), self._get_unwrapped_critic())) + tasks.append( + target_holder.update_experience_maker.remote( + new_critic_state_dict=state_dict_shard, + new_critic_lora_config_dict=self._get_model_lora_config_dict(self.critic), + fully_update=fully_update)) + ray.get(tasks) + # mark end + for target_holder in self.target_holder_list: + target_holder.update_experience_maker.remote(chunk_end=True, fully_update=fully_update) @ray.method(concurrency_group="compute") def training_step(self, experience: Experience) -> Dict[str, float]: self.actor.train() self.critic.train() - experience.to_device(torch.cuda.current_device()) num_actions = experience.action_mask.size(1) action_log_probs = self.actor(experience.sequences, num_actions, attention_mask=experience.attention_mask) actor_loss = self.actor_loss_fn(action_log_probs, @@ -155,38 +185,16 @@ def strategy_save_actor_optim(self, path: str, only_rank0: bool = False) -> None def strategy_save_critic_optim(self, path: str, only_rank0: bool = False) -> None: self.strategy.save_optimizer(self.critic_optim, path, only_rank0) - def _get_unwrapped_actor(self): - if False: - pass - elif isinstance(self.strategy, ColossalAIStrategy): - ret = Actor(self.strategy._unwrap_model(self.actor)) - return ret - elif isinstance(self.strategy, DDPStrategy): - return Actor(self.strategy._unwrap_actor(self.actor)) - elif isinstance(self.strategy, NaiveStrategy): - return self.actor - - def _get_unwrapped_critic(self): - if False: - pass - elif isinstance(self.strategy, ColossalAIStrategy): - ret = self.strategy._unwrap_model(self.critic) - return ret - elif isinstance(self.strategy, DDPStrategy): - return self.critic.module - elif isinstance(self.strategy, NaiveStrategy): - return self.critic - - -def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None: - origin_model = strategy._unwrap_actor(actor) - new_kwargs = {**generate_kwargs} - # use huggingface models method directly - if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): - new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation - - if 'update_model_kwargs_fn' not in generate_kwargs: - new_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn - - return new_kwargs - \ No newline at end of file + def _get_model_state_dict_shard(self, model: torch.nn.Module, fully_update=False, **config): + for state_dict in self.strategy.get_model_state_dict_shard(model, **config): + if not self._update_lora_weights or fully_update: + yield state_dict_to(state_dict) + else: + state_dict_lora, _ = LoRAConstructor.filter_state_dict_lora(state_dict) + yield state_dict_to(state_dict_lora) + + def _get_model_lora_config_dict(self, model: torch.nn.Module): + if not self._update_lora_weights: + return None + unwrapped_model = self.strategy.unwrap_model(model) + return LoRAConstructor.extract_lora_config(unwrapped_model) diff --git a/applications/Chat/coati/ray/example/1m1t.py b/applications/Chat/coati/ray/example/1m1t.py deleted file mode 100644 index a6527370505b..000000000000 --- a/applications/Chat/coati/ray/example/1m1t.py +++ /dev/null @@ -1,153 +0,0 @@ -import argparse -from copy import deepcopy - -import pandas as pd -import torch -from coati.trainer import PPOTrainer - - -from coati.ray.src.experience_maker_holder import ExperienceMakerHolder -from coati.ray.src.detached_trainer_ppo import DetachedPPOTrainer - -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from coati.experience_maker import NaiveExperienceMaker -from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - -import ray -import os -import socket - -def get_free_port(): - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind(('', 0)) - return s.getsockname()[1] - - -def get_local_ip(): - with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: - s.connect(('8.8.8.8', 80)) - return s.getsockname()[0] - -def main(args): - master_addr = str(get_local_ip()) - # trainer_env_info - trainer_port = str(get_free_port()) - env_info_trainer = {'local_rank' : '0', - 'rank' : '0', - 'world_size' : '1', - 'master_port' : trainer_port, - 'master_addr' : master_addr} - - # maker_env_info - maker_port = str(get_free_port()) - env_info_maker = {'local_rank' : '0', - 'rank' : '0', - 'world_size' : '1', - 'master_port' : maker_port, - 'master_addr' : master_addr} - - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - else: - raise ValueError(f'Unsupported model "{args.model}"') - - # configure Trainer - trainer_ref = DetachedPPOTrainer.options(name="trainer1", num_gpus=1, max_concurrency=2).remote( - experience_maker_holder_name_list=["maker1"], - strategy=args.trainer_strategy, - model=args.model, - env_info = env_info_trainer, - pretrained=args.pretrain, - lora_rank=args.lora_rank, - train_batch_size=args.train_batch_size, - buffer_limit=16, - experience_batch_size=args.experience_batch_size, - max_epochs=args.max_epochs, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - # configure Experience Maker - experience_holder_ref = ExperienceMakerHolder.options(name="maker1", num_gpus=1, max_concurrency=2).remote( - detached_trainer_name_list=["trainer1"], - strategy=args.maker_strategy, - env_info = env_info_maker, - experience_batch_size=args.experience_batch_size, - kl_coef=0.1, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - # trainer send its actor and critic to experience holders. - ray.get(trainer_ref.initialize_remote_makers.remote()) - - # configure sampler - dataset = pd.read_csv(args.prompt_path)['prompt'] - - def tokenize_fn(texts): - # MUST padding to max length to ensure inputs of all ranks have the same length - # Different length may lead to hang when using gemini, as different generation steps - batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) - return {k: v.cuda() for k, v in batch.items()} - - trainer_done_ref = trainer_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) - num_exp_per_maker = args.num_episodes * args.max_timesteps // args.update_timesteps * args.max_epochs + 3 # +3 for fault tolerance - maker_done_ref = experience_holder_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) - - ray.get([trainer_done_ref, maker_done_ref]) - - # save model checkpoint after fitting - trainer_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) - # save optimizer checkpoint on all ranks - if args.need_optim_ckpt: - trainer_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('prompt_path') - parser.add_argument('--trainer_strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--maker_strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') - parser.add_argument('--need_optim_ckpt', type=bool, default=False) - parser.add_argument('--num_episodes', type=int, default=10) - parser.add_argument('--max_timesteps', type=int, default=10) - parser.add_argument('--update_timesteps', type=int, default=10) - parser.add_argument('--max_epochs', type=int, default=5) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") - - parser.add_argument('--debug', action='store_true') - args = parser.parse_args() - ray.init(namespace=os.environ["RAY_NAMESPACE"]) - main(args) diff --git a/applications/Chat/coati/ray/example/1m1t.sh b/applications/Chat/coati/ray/example/1m1t.sh deleted file mode 100644 index f7c5054c800e..000000000000 --- a/applications/Chat/coati/ray/example/1m1t.sh +++ /dev/null @@ -1,23 +0,0 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES() { - local n=${1:-"9999"} - echo "GPU Memory Usage:" - local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ - | tail -n +2 \ - | nl -v 0 \ - | tee /dev/tty \ - | sort -g -k 2 \ - | awk '{print $1}' \ - | head -n $n) - export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') - echo "Now CUDA_VISIBLE_DEVICES is set to:" - echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" -} - -set_n_least_used_CUDA_VISIBLE_DEVICES 2 - -export RAY_NAMESPACE="admin" - -python 1m1t.py "/path/to/prompts.csv" \ - --trainer_strategy colossalai_zero2 --maker_strategy naive --lora_rank 2 --pretrain "facebook/opt-350m" --model 'opt' \ - --num_episodes 10 --max_timesteps 10 --update_timesteps 10 \ - --max_epochs 10 --debug diff --git a/applications/Chat/coati/ray/example/1m2t.py b/applications/Chat/coati/ray/example/1m2t.py deleted file mode 100644 index 3883c364a8e0..000000000000 --- a/applications/Chat/coati/ray/example/1m2t.py +++ /dev/null @@ -1,186 +0,0 @@ -import argparse -from copy import deepcopy - -import pandas as pd -import torch -from coati.trainer import PPOTrainer - - -from coati.ray.src.experience_maker_holder import ExperienceMakerHolder -from coati.ray.src.detached_trainer_ppo import DetachedPPOTrainer - -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from coati.experience_maker import NaiveExperienceMaker -from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - -import ray -import os -import socket - - -def get_free_port(): - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind(('', 0)) - return s.getsockname()[1] - - -def get_local_ip(): - with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: - s.connect(('8.8.8.8', 80)) - return s.getsockname()[0] - -def main(args): - master_addr = str(get_local_ip()) - # trainer_env_info - trainer_port = str(get_free_port()) - env_info_trainer_1 = {'local_rank' : '0', - 'rank' : '0', - 'world_size' : '2', - 'master_port' : trainer_port, - 'master_addr' : master_addr} - env_info_trainer_2 = {'local_rank' : '0', - 'rank' : '1', - 'world_size' : '2', - 'master_port' : trainer_port, - 'master_addr' : master_addr} - # maker_env_info - maker_port = str(get_free_port()) - env_info_maker_1 = {'local_rank' : '0', - 'rank' : '0', - 'world_size' : '2', - 'master_port' : maker_port, - 'master_addr' : master_addr} - print([env_info_trainer_1, - env_info_trainer_2, - env_info_maker_1]) - ray.init(dashboard_port = 1145) - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - else: - raise ValueError(f'Unsupported model "{args.model}"') - - # configure Trainer - trainer_1_ref = DetachedPPOTrainer.options(name="trainer1", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( - experience_maker_holder_name_list=["maker1"], - strategy=args.trainer_strategy, - model=args.model, - env_info=env_info_trainer_1, - pretrained=args.pretrain, - lora_rank=args.lora_rank, - train_batch_size=args.train_batch_size, - buffer_limit=16, - experience_batch_size=args.experience_batch_size, - max_epochs=args.max_epochs, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - trainer_2_ref = DetachedPPOTrainer.options(name="trainer2", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( - experience_maker_holder_name_list=["maker1"], - strategy=args.trainer_strategy, - model=args.model, - env_info=env_info_trainer_2, - pretrained=args.pretrain, - lora_rank=args.lora_rank, - train_batch_size=args.train_batch_size, - buffer_limit=16, - experience_batch_size=args.experience_batch_size, - max_epochs=args.max_epochs, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug= args.debug, - ) - - # configure Experience Maker - experience_holder_1_ref = ExperienceMakerHolder.options(name="maker1", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( - detached_trainer_name_list=["trainer1", "trainer2"], - strategy=args.maker_strategy, - env_info=env_info_maker_1, - experience_batch_size=args.experience_batch_size, - kl_coef=0.1, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - # trainer send its actor and critic to experience holders. - # TODO: balance duty - ray.get(trainer_1_ref.initialize_remote_makers.remote()) - - # configure sampler - dataset = pd.read_csv(args.prompt_path)['prompt'] - - def tokenize_fn(texts): - # MUST padding to max length to ensure inputs of all ranks have the same length - # Different length may lead to hang when using gemini, as different generation steps - batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) - return {k: v.cuda() for k, v in batch.items()} - - trainer_1_done_ref = trainer_1_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) - trainer_2_done_ref = trainer_2_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) - num_exp_per_maker = args.num_episodes * args.max_timesteps // args.update_timesteps * args.max_epochs * 2 + 3 # +3 for fault tolerance - maker_1_done_ref = experience_holder_1_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) - - ray.get([trainer_1_done_ref, trainer_2_done_ref, maker_1_done_ref]) - # save model checkpoint after fitting - trainer_1_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) - trainer_2_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) - # save optimizer checkpoint on all ranks - if args.need_optim_ckpt: - trainer_1_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) - trainer_2_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('prompt_path') - parser.add_argument('--trainer_strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--maker_strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') - parser.add_argument('--need_optim_ckpt', type=bool, default=False) - parser.add_argument('--num_episodes', type=int, default=10) - parser.add_argument('--max_timesteps', type=int, default=10) - parser.add_argument('--update_timesteps', type=int, default=10) - parser.add_argument('--max_epochs', type=int, default=5) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") - - parser.add_argument('--debug', action='store_true') - args = parser.parse_args() - main(args) diff --git a/applications/Chat/coati/ray/example/1m2t.sh b/applications/Chat/coati/ray/example/1m2t.sh deleted file mode 100644 index 669f4141026c..000000000000 --- a/applications/Chat/coati/ray/example/1m2t.sh +++ /dev/null @@ -1,23 +0,0 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES() { - local n=${1:-"9999"} - echo "GPU Memory Usage:" - local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ - | tail -n +2 \ - | nl -v 0 \ - | tee /dev/tty \ - | sort -g -k 2 \ - | awk '{print $1}' \ - | head -n $n) - export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') - echo "Now CUDA_VISIBLE_DEVICES is set to:" - echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" -} - -set_n_least_used_CUDA_VISIBLE_DEVICES 2 - -export RAY_NAMESPACE="admin" - -python 1m2t.py "/path/to/prompts.csv" --model gpt2 \ - --maker_strategy naive --trainer_strategy ddp --lora_rank 2 \ - --num_episodes 10 --max_timesteps 10 --update_timesteps 10 \ - --max_epochs 10 #--debug \ No newline at end of file diff --git a/applications/Chat/coati/ray/example/2m1t.py b/applications/Chat/coati/ray/example/2m1t.py deleted file mode 100644 index b655de1ab1fa..000000000000 --- a/applications/Chat/coati/ray/example/2m1t.py +++ /dev/null @@ -1,140 +0,0 @@ -import argparse -from copy import deepcopy - -import pandas as pd -import torch -from coati.trainer import PPOTrainer - - -from coati.ray.src.experience_maker_holder import ExperienceMakerHolder -from coati.ray.src.detached_trainer_ppo import DetachedPPOTrainer - -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from coati.experience_maker import NaiveExperienceMaker -from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - -import ray -import os -import socket - - -def main(args): - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - else: - raise ValueError(f'Unsupported model "{args.model}"') - - # configure Trainer - trainer_ref = DetachedPPOTrainer.options(name="trainer1", num_gpus=1, max_concurrency=2).remote( - experience_maker_holder_name_list=["maker1", "maker2"], - strategy=args.trainer_strategy, - model=args.model, - pretrained=args.pretrain, - lora_rank=args.lora_rank, - train_batch_size=args.train_batch_size, - buffer_limit=16, - experience_batch_size=args.experience_batch_size, - max_epochs=args.max_epochs, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - # configure Experience Maker - experience_holder_1_ref = ExperienceMakerHolder.options(name="maker1", num_gpus=1, max_concurrency=2).remote( - detached_trainer_name_list=["trainer1"], - strategy=args.maker_strategy, - experience_batch_size=args.experience_batch_size, - kl_coef=0.1, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - experience_holder_2_ref = ExperienceMakerHolder.options(name="maker2", num_gpus=1, max_concurrency=2).remote( - detached_trainer_name_list=["trainer1"], - strategy=args.maker_strategy, - experience_batch_size=args.experience_batch_size, - kl_coef=0.1, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - # trainer send its actor and critic to experience holders. - ray.get(trainer_ref.initialize_remote_makers.remote()) - - # configure sampler - dataset = pd.read_csv(args.prompt_path)['prompt'] - - def tokenize_fn(texts): - # MUST padding to max length to ensure inputs of all ranks have the same length - # Different length may lead to hang when using gemini, as different generation steps - batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) - return {k: v.cuda() for k, v in batch.items()} - - trainer_done_ref = trainer_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) - num_exp_per_maker = args.num_episodes * args.max_timesteps // args.update_timesteps * args.max_epochs // 2 + 3 # +3 for fault tolerance - maker_1_done_ref = experience_holder_1_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) - maker_2_done_ref = experience_holder_2_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) - - ray.get([trainer_done_ref, maker_1_done_ref, maker_2_done_ref]) - - # save model checkpoint after fitting - trainer_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) - # save optimizer checkpoint on all ranks - if args.need_optim_ckpt: - trainer_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('prompt_path') - parser.add_argument('--trainer_strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--maker_strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') - parser.add_argument('--need_optim_ckpt', type=bool, default=False) - parser.add_argument('--num_episodes', type=int, default=10) - parser.add_argument('--max_timesteps', type=int, default=10) - parser.add_argument('--update_timesteps', type=int, default=10) - parser.add_argument('--max_epochs', type=int, default=5) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") - - parser.add_argument('--debug', action='store_true') - args = parser.parse_args() - ray.init(namespace=os.environ["RAY_NAMESPACE"]) - main(args) diff --git a/applications/Chat/coati/ray/example/2m1t.sh b/applications/Chat/coati/ray/example/2m1t.sh deleted file mode 100644 index a207d4118d60..000000000000 --- a/applications/Chat/coati/ray/example/2m1t.sh +++ /dev/null @@ -1,23 +0,0 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES() { - local n=${1:-"9999"} - echo "GPU Memory Usage:" - local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ - | tail -n +2 \ - | nl -v 0 \ - | tee /dev/tty \ - | sort -g -k 2 \ - | awk '{print $1}' \ - | head -n $n) - export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') - echo "Now CUDA_VISIBLE_DEVICES is set to:" - echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" -} - -set_n_least_used_CUDA_VISIBLE_DEVICES 3 - -export RAY_NAMESPACE="admin" - -python 2m1t.py "/path/to/prompts.csv" \ - --trainer_strategy naive --maker_strategy naive --lora_rank 2 --pretrain "facebook/opt-350m" --model 'opt' \ - --num_episodes 10 --max_timesteps 10 --update_timesteps 10 \ - --max_epochs 10 # --debug diff --git a/applications/Chat/coati/ray/example/2m2t.py b/applications/Chat/coati/ray/example/2m2t.py deleted file mode 100644 index 435c71915fc2..000000000000 --- a/applications/Chat/coati/ray/example/2m2t.py +++ /dev/null @@ -1,209 +0,0 @@ -import argparse -from copy import deepcopy - -import pandas as pd -import torch -from coati.trainer import PPOTrainer - - -from coati.ray.src.experience_maker_holder import ExperienceMakerHolder -from coati.ray.src.detached_trainer_ppo import DetachedPPOTrainer - -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -from coati.experience_maker import NaiveExperienceMaker -from torch.optim import Adam -from transformers import AutoTokenizer, BloomTokenizerFast -from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer - -from colossalai.nn.optimizer import HybridAdam - -import ray -import os -import socket - - -def get_free_port(): - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind(('', 0)) - return s.getsockname()[1] - - -def get_local_ip(): - with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: - s.connect(('8.8.8.8', 80)) - return s.getsockname()[0] - -def main(args): - master_addr = str(get_local_ip()) - # trainer_env_info - trainer_port = str(get_free_port()) - env_info_trainer_1 = {'local_rank' : '0', - 'rank' : '0', - 'world_size' : '2', - 'master_port' : trainer_port, - 'master_addr' : master_addr} - env_info_trainer_2 = {'local_rank' : '0', - 'rank' : '1', - 'world_size' : '2', - 'master_port' : trainer_port, - 'master_addr' : master_addr} - # maker_env_info - maker_port = str(get_free_port()) - env_info_maker_1 = {'local_rank' : '0', - 'rank' : '0', - 'world_size' : '2', - 'master_port' : maker_port, - 'master_addr' : master_addr} - env_info_maker_2 = {'local_rank' : '0', - 'rank' : '1', - 'world_size' : '2', - 'master_port': maker_port, - 'master_addr' : master_addr} - print([env_info_trainer_1, - env_info_trainer_2, - env_info_maker_1, - env_info_maker_2]) - ray.init() - # configure tokenizer - if args.model == 'gpt2': - tokenizer = GPT2Tokenizer.from_pretrained('gpt2') - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) - tokenizer.pad_token = tokenizer.eos_token - elif args.model == 'opt': - tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - else: - raise ValueError(f'Unsupported model "{args.model}"') - - # configure Trainer - trainer_1_ref = DetachedPPOTrainer.options(name="trainer1", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( - experience_maker_holder_name_list=["maker1", "maker2"], - strategy=args.trainer_strategy, - model=args.model, - env_info=env_info_trainer_1, - pretrained=args.pretrain, - lora_rank=args.lora_rank, - train_batch_size=args.train_batch_size, - buffer_limit=16, - experience_batch_size=args.experience_batch_size, - max_epochs=args.max_epochs, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - trainer_2_ref = DetachedPPOTrainer.options(name="trainer2", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( - experience_maker_holder_name_list=["maker1", "maker2"], - strategy=args.trainer_strategy, - model=args.model, - env_info=env_info_trainer_2, - pretrained=args.pretrain, - lora_rank=args.lora_rank, - train_batch_size=args.train_batch_size, - buffer_limit=16, - experience_batch_size=args.experience_batch_size, - max_epochs=args.max_epochs, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - # configure Experience Maker - experience_holder_1_ref = ExperienceMakerHolder.options(name="maker1", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( - detached_trainer_name_list=["trainer1", "trainer2"], - strategy=args.maker_strategy, - env_info=env_info_maker_1, - experience_batch_size=args.experience_batch_size, - kl_coef=0.1, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - experience_holder_2_ref = ExperienceMakerHolder.options(name="maker2", namespace=os.environ["RAY_NAMESPACE"], num_gpus=1, max_concurrency=2).remote( - detached_trainer_name_list=["trainer1", "trainer2"], - strategy=args.maker_strategy, - env_info=env_info_maker_2, - experience_batch_size=args.experience_batch_size, - kl_coef=0.1, - #kwargs: - max_length=128, - do_sample=True, - temperature=1.0, - top_k=50, - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - debug=args.debug, - ) - - # trainer send its actor and critic to experience holders. - # TODO: balance duty - ray.get(trainer_1_ref.initialize_remote_makers.remote()) - - # configure sampler - dataset = pd.read_csv(args.prompt_path)['prompt'] - - def tokenize_fn(texts): - # MUST padding to max length to ensure inputs of all ranks have the same length - # Different length may lead to hang when using gemini, as different generation steps - batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) - return {k: v.cuda() for k, v in batch.items()} - - trainer_1_done_ref = trainer_1_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) - trainer_2_done_ref = trainer_2_ref.fit.remote(num_episodes=args.num_episodes, max_timesteps=args.max_timesteps, update_timesteps=args.update_timesteps) - num_exp_per_maker = args.num_episodes * args.max_timesteps // args.update_timesteps * args.max_epochs + 3 # +3 for fault tolerance - maker_1_done_ref = experience_holder_1_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) - maker_2_done_ref = experience_holder_2_ref.workingloop.remote(dataset, tokenize_fn, times=num_exp_per_maker) - - ray.get([trainer_1_done_ref, trainer_2_done_ref, maker_1_done_ref, maker_2_done_ref]) - # save model checkpoint after fitting - trainer_1_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) - trainer_2_ref.strategy_save_actor.remote(args.save_path, only_rank0=True) - # save optimizer checkpoint on all ranks - if args.need_optim_ckpt: - trainer_1_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) - trainer_2_ref.strategy_save_actor_optim.remote('actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('prompt_path') - parser.add_argument('--trainer_strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--maker_strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) - parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') - parser.add_argument('--need_optim_ckpt', type=bool, default=False) - parser.add_argument('--num_episodes', type=int, default=10) - parser.add_argument('--max_timesteps', type=int, default=10) - parser.add_argument('--update_timesteps', type=int, default=10) - parser.add_argument('--max_epochs', type=int, default=5) - parser.add_argument('--train_batch_size', type=int, default=8) - parser.add_argument('--experience_batch_size', type=int, default=8) - parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") - - parser.add_argument('--debug', action='store_true') - args = parser.parse_args() - main(args) diff --git a/applications/Chat/coati/ray/example/2m2t.sh b/applications/Chat/coati/ray/example/2m2t.sh deleted file mode 100644 index fb4024766c54..000000000000 --- a/applications/Chat/coati/ray/example/2m2t.sh +++ /dev/null @@ -1,23 +0,0 @@ -set_n_least_used_CUDA_VISIBLE_DEVICES() { - local n=${1:-"9999"} - echo "GPU Memory Usage:" - local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ - | tail -n +2 \ - | nl -v 0 \ - | tee /dev/tty \ - | sort -g -k 2 \ - | awk '{print $1}' \ - | head -n $n) - export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') - echo "Now CUDA_VISIBLE_DEVICES is set to:" - echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" -} - -set_n_least_used_CUDA_VISIBLE_DEVICES 2 - -export RAY_NAMESPACE="admin" - -python 2m2t.py "path/to/prompts.csv" \ - --maker_strategy naive --trainer_strategy colossalai_zero2 --lora_rank 2 \ - --num_episodes 10 --max_timesteps 10 --update_timesteps 10 \ - --max_epochs 10 --debug \ No newline at end of file diff --git a/applications/Chat/coati/ray/experience_maker_holder.py b/applications/Chat/coati/ray/experience_maker_holder.py new file mode 100644 index 000000000000..8551ef1eacef --- /dev/null +++ b/applications/Chat/coati/ray/experience_maker_holder.py @@ -0,0 +1,271 @@ +import os +import time +import tracemalloc +from copy import deepcopy +from threading import Lock +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union + +import ray +import torch +import torch.nn as nn +from coati.experience_maker import Experience, ExperienceMaker, NaiveExperienceMaker +from coati.models.base import Actor, Critic, RewardModel +from coati.replay_buffer.utils import BufferItem, make_experience_batch, split_experience_batch +from coati.trainer.callbacks import Callback +from coati.trainer.strategies import Strategy +from coati.trainer.strategies.sampler import DistributedSampler +from ray.exceptions import GetTimeoutError +from torch import Tensor +from tqdm import tqdm + +from .callbacks import ExperienceMakerPerformanceEvaluator, MakerCallback +from .utils import (get_model_numel, + get_rank, + get_world_size, + is_rank_0, + set_dist_env, + state_dict_to) +from .lora_constructor import LoRAConstructor + +@ray.remote(concurrency_groups={"experience_io": 1, "model_io": 1, "compute": 1}) +class ExperienceMakerHolder: + ''' + Args: + detached_trainer_name_list: str list to get ray actor handles + strategy: + kl_coef: the coefficient of kl divergence loss + sync_models_from_trainers: whether to sync models from trainers. If True, you must call sync_models_to_remote_makers() in trainers to sync models. + ''' + + def __init__( + self, + detached_trainer_name_list: List[str], + strategy_fn: Callable[[], Strategy], + # a function returns (actor, critic, reward_model, initial_model) + model_fn: Callable[[], Tuple[Actor, Critic, RewardModel, Actor]], + env_info: Dict[str, str] = None, + sync_models_from_trainers: bool = False, + buffer_cpu_offload: bool = True, + kl_coef: float = 0.1, + callbacks: List[MakerCallback] = [], + eval_performance: bool = False, + debug: bool = False, + update_lora_weights: bool = False, + **generate_kwargs): + # set environment variables + if env_info: + set_dist_env(env_info=env_info) + self.target_trainer_list = [] + assert len(detached_trainer_name_list) > 0 + self._detached_trainer_name_list = detached_trainer_name_list + self.strategy = strategy_fn() + self.buffer_cpu_offload = buffer_cpu_offload + self.kl_coef = kl_coef + # init models + with self.strategy.model_init_context(): + actor, critic, reward_model, initial_model = model_fn() + self.generate_kwargs = _set_default_generate_kwargs(generate_kwargs, actor) + if eval_performance: + actor_numel = get_model_numel(actor) + critic_numel = get_model_numel(critic) + initial_model_numel = get_model_numel(initial_model) + reward_model_numel = get_model_numel(reward_model) + evaluator = ExperienceMakerPerformanceEvaluator(actor_numel, critic_numel, initial_model_numel, + reward_model_numel) + callbacks = callbacks + [evaluator] + + actor, critic, reward_model, initial_model = self.strategy.prepare(actor, critic, reward_model, initial_model) + self.experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, self.kl_coef) + self.callbacks = callbacks + + self._model_visit_lock = Lock() + + self._is_fully_initialized = not sync_models_from_trainers + + self._debug = debug + self._update_lora_weights = update_lora_weights + if self._update_lora_weights: + self.actor_lora_constructor = LoRAConstructor() + self.critic_lora_constructor = LoRAConstructor() + + self.target_auto_balance = False + + self._target_idx = 0 + + if self._debug: + print(f'[maker{get_rank()}] will send items to {self._detached_trainer_name_list}') + if not self._is_fully_initialized: + print(f'[maker{get_rank()}] Waiting for INIT') + + def _get_ready(self): + while not self._fully_initialized(): + time.sleep(1.0) + + def _fully_initialized(self): + return self._is_fully_initialized + + def _init_target_trainer_list(self): + if len(self.target_trainer_list) > 0: + return + for name in self._detached_trainer_name_list: + self.target_trainer_list.append(ray.get_actor(name, namespace=os.environ["RAY_NAMESPACE"])) + + # copy from ../trainer/base.py + @ray.method(concurrency_group="compute") + def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience: + if isinstance(inputs, Tensor): + return self.experience_maker.make_experience(inputs, **self.generate_kwargs) + elif isinstance(inputs, dict): + return self.experience_maker.make_experience(**inputs, **self.generate_kwargs) + else: + raise ValueError(f'Unsupported input type "{type(inputs)}"') + + @ray.method(concurrency_group="experience_io") + def _send_items(self, experience: Experience) -> None: + self._init_target_trainer_list() + items = split_experience_batch(experience) + items_per_trainer = [[] for _ in range(len(self.target_trainer_list))] + for item in items: + items_per_trainer[self._target_idx].append(item) + self._target_idx = (self._target_idx + 1) % len(self.target_trainer_list) + for i, target_trainer in enumerate(self.target_trainer_list): + if len(items_per_trainer[i]) > 0: + target_trainer.buffer_extend.remote(items_per_trainer[i]) + + def _inference_step(self, batch) -> None: + self._on_batch_start() + with self._model_visit_lock: + self._on_make_experience_start() + experience = self._make_experience(batch) + self._on_make_experience_end(experience) + self._on_send_start() + if self.buffer_cpu_offload: + experience.to_device('cpu') + self._send_items(experience) + self._on_send_end() + self._on_batch_end() + + def workingloop(self, dataloader_fn: Callable[[], Iterable], num_epochs: int = 1, num_steps: int = 0): + """Working loop of the experience maker. + + Args: + dataloader_fn (Callable[[], Iterable]): A function that returns a dataloader. + num_epochs (int, optional): Iterate the dataloader for number of epochs. Defaults to 1. + num_steps (int, optional): Iterate the dataloader for number if steps. If this value > 0, num_epochs will be ignored. Defaults to 0. + """ + self._get_ready() + self._on_loop_start() + dataloader = dataloader_fn() + if num_steps > 0: + # ignore num epochs + it = iter(dataloader) + for _ in tqdm(range(num_steps), desc='ExperienceMaker', disable=not is_rank_0()): + try: + batch = next(it) + except StopIteration: + it = iter(dataloader) + batch = next(it) + self._inference_step(batch) + else: + with tqdm(total=num_epochs * len(dataloader), desc='ExperienceMaker', disable=not is_rank_0()) as pbar: + for _ in range(num_epochs): + for batch in dataloader: + self._inference_step(batch) + pbar.update() + self._on_loop_end() + + @ray.method(concurrency_group="model_io") + def update_experience_maker(self, + new_actor_state_dict: Dict[str, Any] = None, + new_actor_lora_config_dict: Dict[str, Any] = None, + new_critic_state_dict: Dict[str, Any] = None, + new_critic_lora_config_dict: Dict[str, Any] = None, + fully_update: bool = False, + chunk_start: bool = None, + chunk_end: bool = None): + ''' + called by trainer + chunk_start: Set True at the first call. Before sending state_dict calls + chunk_end: Set True at the last call. After sending state_dict calls. + fully_update: Set True if you want to sync models when initializing + + TODO: load_state_dict integrate with model-sharding strategy + ''' + _watch_memory = self._debug + if chunk_start: + if self._debug: + print("[maker] UPDATE ") + if _watch_memory: + tracemalloc.start() + self._model_visit_lock.acquire() + + with torch.no_grad(): + if new_actor_state_dict is not None: + if not self._update_lora_weights or fully_update: + self.experience_maker.actor.model.load_state_dict(new_actor_state_dict, strict=False) + else: + new_actor_state_dict = state_dict_to(new_actor_state_dict, device=torch.cuda.current_device()) + state_dict_increasae = self.actor_lora_constructor.reconstruct_increase(new_actor_state_dict, new_actor_lora_config_dict) + self.actor_lora_constructor.load_state_dict_increase(self.experience_maker.actor.model, state_dict_increasae) + if new_critic_state_dict is not None: + if not self._update_lora_weights or fully_update: + self.experience_maker.critic.load_state_dict(new_critic_state_dict, strict=False) + else: + new_critic_state_dict = state_dict_to(new_critic_state_dict, device=torch.cuda.current_device()) + state_dict_increasae = self.critic_lora_constructor.reconstruct_increase(new_critic_state_dict, new_critic_lora_config_dict) + self.critic_lora_constructor.load_state_dict_increase(self.experience_maker.critic, state_dict_increasae) + + # the lock must be released after both actor and critic being updated + if chunk_end: + self._model_visit_lock.release() + if _watch_memory: + current, peak = tracemalloc.get_traced_memory() + print(f"Current memory usage is {current / 10**6}MB; Peak was {peak / 10**6}MB") + tracemalloc.stop() + if fully_update: + self._is_fully_initialized = True + + def _on_make_experience_start(self) -> None: + for callback in self.callbacks: + callback.on_make_experience_start() + + def _on_make_experience_end(self, experience: Experience) -> None: + for callback in self.callbacks: + callback.on_make_experience_end(experience) + + def _on_loop_start(self) -> None: + for callback in self.callbacks: + callback.on_loop_start() + + def _on_loop_end(self) -> None: + for callback in self.callbacks: + callback.on_loop_end() + + def _on_send_start(self) -> None: + for callback in self.callbacks: + callback.on_send_start() + + def _on_send_end(self) -> None: + for callback in self.callbacks: + callback.on_send_end() + + def _on_batch_start(self) -> None: + for callback in self.callbacks: + callback.on_batch_start() + + def _on_batch_end(self) -> None: + for callback in self.callbacks: + callback.on_batch_end() + + +def _set_default_generate_kwargs(generate_kwargs: dict, actor: Actor) -> None: + origin_model = actor.model + new_kwargs = {**generate_kwargs} + # use huggingface models method directly + if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): + new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation + + if 'update_model_kwargs_fn' not in generate_kwargs and hasattr(origin_model, '_update_model_kwargs_for_generation'): + new_kwargs['update_model_kwargs_fn'] = origin_model._update_model_kwargs_for_generation + + return new_kwargs diff --git a/applications/Chat/coati/ray/lora_constructor.py b/applications/Chat/coati/ray/lora_constructor.py new file mode 100644 index 000000000000..599a58248728 --- /dev/null +++ b/applications/Chat/coati/ray/lora_constructor.py @@ -0,0 +1,122 @@ +from typing import Any, Callable, Dict, List, Optional +from collections import OrderedDict +from dataclasses import dataclass + +import torch +import torch.nn as nn +from loralib.layers import LoRALayer +from coati.models.lora import LoraLinear + + +@dataclass +class LoRAConfig: + r: int = 0 + lora_alpha: int = 1 + lora_dropout: float = 0 + fan_in_fan_out: bool = False + + +class LoRAConstructor: + ''' + Tools for reconstructing a model from a remote LoRA model. + (Transfering only LoRA data costs much less!) + Usage: + Step 1 (Sender): + filter_state_dict_lora() + + Step 2 (Sender, Optional): + extract_lora_config() + + Step 3 (Sender): + send state_dict_lora and lora_config_dict + + Step 4 (Receiver): + reconstruct_increase() + + Step 5 (Receiver): + load_state_dict_increase() + + ''' + + def __init__(self): + self.lora_config_dict = None + + def register_lora_config(self, lora_config_dict: Dict[str, Any]): + self.lora_config_dict = lora_config_dict + + def reconstruct_increase(self, state_dict_lora: Dict[str, Any], lora_config_dict: Dict[str, Any]): + ''' + xxx.lora_A, xxx.lora_B -->> xxx.weight + Warning: the xxx.weight here is the increment actually. + ''' + if lora_config_dict is not None: + self.register_lora_config(lora_config_dict) + + state_dict_increasae = OrderedDict() + config_iter = iter(self.lora_config_dict.items()) + lora_A, lora_B, layer_prefix = None, None, None + for k, v in state_dict_lora.items(): + if k.rpartition('.')[-1] == 'lora_A': + lora_A = v + layer_prefix = k.rpartition('.')[0] + elif k.rpartition('.')[-1] == 'lora_B': + assert layer_prefix == k.rpartition('.')[0], "unmatched (lora_A, lora_B) pair" + layer_prefix_2, config = next(config_iter) + assert layer_prefix_2 == layer_prefix, "unmatched (state_dict, config_dict) pair" + lora_B = v + weight_data_increase = self._compute(lora_A, lora_B, config) + state_dict_increasae[layer_prefix + '.weight'] = weight_data_increase + lora_A, lora_B, layer_prefix = None, None, None + else: + raise ValueError('unexpected key') + return state_dict_increasae + + def _compute(self, lora_A, lora_B, config=LoRAConfig()): + def T(w): + return w.T if config.fan_in_fan_out else w + if config.r > 0: + scaling = config.lora_alpha / config.r + weight_data_increase = T(lora_B @ lora_A) * scaling + return weight_data_increase + return 0 + + def load_state_dict_increase(self, model: nn.Module, state_dict_increasae: Dict[str, Any]): + ''' + The final reconstruction step + ''' + # naive approach + model.load_state_dict({k: v + model.state_dict()[k] for k, v in state_dict_increasae.items()}, strict=False) + + @staticmethod + def filter_state_dict_lora(state_dict: Dict[str, Any], keep_non_lora=False): + ''' + if keep_non_lora, also return non_lora state_dict + ''' + state_dict_lora = OrderedDict() + state_dict_non_lora = OrderedDict() + for k, v in state_dict.items(): + if 'lora_A' in k or 'lora_B' in k: + state_dict_lora[k] = v + elif keep_non_lora: + state_dict_non_lora[k] = v + if keep_non_lora: + return state_dict_lora, state_dict_non_lora + else: + return state_dict_lora, None + + @staticmethod + def extract_lora_config(model: nn.Module) -> Dict[str, LoRAConfig]: + ''' + extract LoraLinear model. + return OrderedDict(): name -> LoRAConfig + ''' + lora_config_dict = OrderedDict() + + for name, child in model.named_modules(): + if isinstance(child, LoraLinear): + lora_config_dict[name] = LoRAConfig(r=child.r, + lora_alpha=child.lora_alpha, + lora_dropout=child.lora_dropout, + fan_in_fan_out=child.fan_in_fan_out) + + return lora_config_dict diff --git a/applications/Chat/coati/ray/src/__init__.py b/applications/Chat/coati/ray/src/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/applications/Chat/coati/ray/src/detached_trainer_base.py b/applications/Chat/coati/ray/src/detached_trainer_base.py deleted file mode 100644 index f1ed1ec71499..000000000000 --- a/applications/Chat/coati/ray/src/detached_trainer_base.py +++ /dev/null @@ -1,121 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Any, Callable, Dict, List, Optional, Union -from tqdm import tqdm -from coati.trainer.callbacks import Callback -from coati.experience_maker import Experience -import ray -import os - -from .detached_replay_buffer import DetachedReplayBuffer -from .utils import is_rank_0 - -class DetachedTrainer(ABC): - ''' - Base class for detached rlhf trainers. - 'detach' means that the experience maker is detached compared to a normal Trainer. - Please set name attribute during init: - >>> trainer = DetachedTrainer.options(..., name = "xxx", ...).remote() - So an ExperienceMakerHolder can reach the detached_replay_buffer by Actor's name. - Args: - detached_strategy (DetachedStrategy): the strategy to use for training - detached_replay_buffer_ref (ObjectRef[DetachedReplayBuffer]): the replay buffer to use for training - experience_batch_size (int, defaults to 8): the batch size to use for experience generation - max_epochs (int, defaults to 1): the number of epochs of training process - data_loader_pin_memory (bool, defaults to True): whether to pin memory for data loader - callbacks (List[Callback], defaults to []): the callbacks to call during training process - generate_kwargs (dict, optional): the kwargs to use while model generating - ''' - - def __init__(self, - experience_maker_holder_name_list: List[str], - train_batch_size: int = 8, - buffer_limit: int = 0, - buffer_cpu_offload: bool = True, - experience_batch_size: int = 8, - max_epochs: int = 1, - dataloader_pin_memory: bool = True, - callbacks: List[Callback] = [], - **generate_kwargs) -> None: - super().__init__() - self.detached_replay_buffer = DetachedReplayBuffer(train_batch_size, limit=buffer_limit, cpu_offload=buffer_cpu_offload) - self.experience_batch_size = experience_batch_size - self.max_epochs = max_epochs - self.dataloader_pin_memory = dataloader_pin_memory - self.callbacks = callbacks - self.generate_kwargs = generate_kwargs - self.target_holder_name_list = experience_maker_holder_name_list - self.target_holder_list = [] - - def update_target_holder_list(self, experience_maker_holder_name_list): - self.target_holder_name_list = experience_maker_holder_name_list - self.target_holder_list = [] - for name in self.target_holder_name_list: - self.target_holder_list.append(ray.get_actor(name, namespace=os.environ["RAY_NAMESPACE"])) - - @abstractmethod - def _update_remote_makers(self): - pass - - @abstractmethod - def training_step(self, experience: Experience) -> Dict[str, Any]: - pass - - def _learn(self): - pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) - for _ in pbar: - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print("[trainer] sampling exp") - experience = self._buffer_sample() - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print("[trainer] training step") - metrics = self.training_step(experience) - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print("[trainer] step over") - pbar.set_postfix(metrics) - - def fit(self, num_episodes: int = 50000, max_timesteps: int = 500, update_timesteps: int = 5000) -> None: - self._on_fit_start() - for episode in range(num_episodes): - self._on_episode_start(episode) - for timestep in tqdm(range(max_timesteps // update_timesteps), - desc=f'Episode [{episode+1}/{num_episodes}]', - disable=not is_rank_0()): - self._learn() - self._update_remote_makers() - self._on_episode_end(episode) - self._on_fit_end() - - @ray.method(concurrency_group="buffer_length") - def buffer_get_length(self): - # called by ExperienceMakerHolder - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print("[trainer] telling length") - return self.detached_replay_buffer.get_length() - - @ray.method(concurrency_group="buffer_append") - def buffer_append(self, experience: Experience): - # called by ExperienceMakerHolder - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - # print(f"[trainer] receiving exp. Current buffer length: {self.detached_replay_buffer.get_length()}") - print(f"[trainer] receiving exp.") - self.detached_replay_buffer.append(experience) - - @ray.method(concurrency_group="buffer_sample") - def _buffer_sample(self): - return self.detached_replay_buffer.sample() - - def _on_fit_start(self) -> None: - for callback in self.callbacks: - callback.on_fit_start() - - def _on_fit_end(self) -> None: - for callback in self.callbacks: - callback.on_fit_end() - - def _on_episode_start(self, episode: int) -> None: - for callback in self.callbacks: - callback.on_episode_start(episode) - - def _on_episode_end(self, episode: int) -> None: - for callback in self.callbacks: - callback.on_episode_end(episode) diff --git a/applications/Chat/coati/ray/src/experience_maker_holder.py b/applications/Chat/coati/ray/src/experience_maker_holder.py deleted file mode 100644 index 0ae4e3125b70..000000000000 --- a/applications/Chat/coati/ray/src/experience_maker_holder.py +++ /dev/null @@ -1,172 +0,0 @@ -import torch -from typing import Any, Callable, Dict, List, Optional, Union -import ray -from ray.exceptions import GetTimeoutError -from torch import Tensor -import torch.nn as nn -from coati.models.base import Actor, Critic, RewardModel -from coati.trainer.strategies.sampler import DistributedSampler -from coati.trainer.strategies import Strategy -from coati.experience_maker import NaiveExperienceMaker, Experience, ExperienceMaker - -from copy import deepcopy -from threading import Lock -import time -import os - - -from .utils import is_rank_0, get_strategy_from_args, set_dist_env - - -@ray.remote(concurrency_groups={"experience_io": 1, "model_io": 1, "compute": 1}) -class ExperienceMakerHolder: - ''' - Args: - detached_trainer_name_list: str list to get ray actor handles - strategy: - experience_batch_size: batch size of generated experience - kl_coef: the coefficient of kl divergence loss - ''' - - def __init__(self, - detached_trainer_name_list: List[str], - strategy: str, - env_info: Dict[str, str] = None, - experience_batch_size: int = 8, - kl_coef: float = 0.1, - **generate_kwargs): - # set environment variables - if env_info: - set_dist_env(env_info=env_info) - self.target_trainer_list = [] - for name in detached_trainer_name_list: - self.target_trainer_list.append(ray.get_actor(name, namespace=os.environ["RAY_NAMESPACE"])) - self.strategy_str = strategy - self.strategy = get_strategy_from_args(strategy) - self.experience_batch_size = experience_batch_size - self.kl_coef = kl_coef - self.generate_kwargs = generate_kwargs - # Need a trainer to give an actor and a critic via initialize_experience_maker(...) - actor, critic, reward_model, initial_model = None, None, None, None - self.experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, self.kl_coef) - self._model_visit_lock = Lock() - self.fully_initialized = False - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print('[maker] Waiting for INIT') - - def _get_ready(self): - while not self.fully_initialized: - time.sleep(1.0) - - def update_target_trainer_list(self, detached_trainer_name_list): - self.target_trainer_list = [] - for name in detached_trainer_name_list: - self.target_trainer_list.append(ray.get_actor(name)) - - # copy from ../trainer/base.py - @ray.method(concurrency_group="compute") - def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience: - self._get_ready() - if isinstance(inputs, Tensor): - return self.experience_maker.make_experience(inputs, **self.generate_kwargs) - elif isinstance(inputs, dict): - return self.experience_maker.make_experience(**inputs, **self.generate_kwargs) - else: - raise ValueError(f'Unsupported input type "{type(inputs)}"') - - @ray.method(concurrency_group="experience_io") - def _send_experience(self, experience): - ''' - ignore it - - # choose a trainer that has the least experience batch in its detached_replay_buffer - chosen_trainer = None - min_length = None - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print("[maker] choosing target trainer") - while chosen_trainer is None: - for target_trainer in self.target_trainer_list: - try: - temp_length = ray.get(target_trainer.buffer_get_length.remote(), timeout=0.1) - if min_length is None: - min_length = temp_length - chosen_trainer = target_trainer - else: - if temp_length < min_length: - min_length = temp_length - chosen_trainer = target_trainer - except GetTimeoutError: - pass - - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print(f"[maker] sending exp to {chosen_trainer}") - chosen_trainer.buffer_append.remote(experience) - ''' - # - if not hasattr(self, "_target_idx"): - self._target_idx = 0 - chosen_trainer = self.target_trainer_list[self._target_idx] - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print(f"[maker] sending exp to {chosen_trainer}") - chosen_trainer.buffer_append.remote(experience) - self._target_idx = (self._target_idx + 1) % len(self.target_trainer_list) - - def workingloop(self, dataset, tokenizer: Optional[Callable[[Any], dict]] = None, times=5000 * 50000): - self._get_ready() - sampler = self.strategy.setup_sampler(dataset) - for _ in range(times): - rand_prompts = sampler.sample(self.experience_batch_size) - if tokenizer is not None: - inputs = tokenizer(rand_prompts) - else: - inputs = rand_prompts - self._model_visit_lock.acquire() - experience = self._make_experience(inputs=inputs) - self._model_visit_lock.release() - self._send_experience(experience=experience) - - @ray.method(concurrency_group="model_io") - def initialize_experience_maker(self, init_actor: Actor, init_critic: Critic): - ''' - called by trainer. Only once. - ''' - # TODO: reduce malloc - if self.fully_initialized: - return - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print('[maker] INIT') - with torch.no_grad(): - with self.strategy.model_init_context(): - actor = init_actor - critic = init_critic - initial_model = deepcopy(actor) - reward_model = RewardModel(deepcopy(critic.model), - deepcopy(critic.value_head)).to(torch.cuda.current_device()) - if self.strategy_str != 'colossalai_gemini': - actor.to(torch.float16).to(torch.cuda.current_device()) - critic.to(torch.float16).to(torch.cuda.current_device()) - initial_model.to(torch.float16).to(torch.cuda.current_device()) - reward_model.to(torch.float16).to(torch.cuda.current_device()) - - self.experience_maker.actor = self.strategy.prepare(actor) - self.experience_maker.critic = self.strategy.prepare(critic) - self.experience_maker.initial_model = self.strategy.prepare(initial_model) - self.experience_maker.reward_model = self.strategy.prepare(reward_model) - self.fully_initialized = True - - @ray.method(concurrency_group="model_io") - def update_experience_maker(self, new_actor: Actor, new_critic: Critic): - ''' - called by trainer - ''' - # TODO: reduce malloc - self._model_visit_lock.acquire() - with torch.no_grad(): - if 'debug' in self.generate_kwargs and self.generate_kwargs['debug'] == True: - print("[maker] UPDATE ") - if self.strategy_str != 'colossalai_gemini': - new_actor.to(torch.float16).to(torch.cuda.current_device()) - new_critic.to(torch.float16).to(torch.cuda.current_device()) - self.experience_maker.actor = self.strategy.prepare(new_actor) - self.experience_maker.critic = self.strategy.prepare(new_critic) - self._model_visit_lock.release() diff --git a/applications/Chat/coati/ray/src/pipeline_strategy.py b/applications/Chat/coati/ray/src/pipeline_strategy.py deleted file mode 100644 index 7ecb5d7d86d6..000000000000 --- a/applications/Chat/coati/ray/src/pipeline_strategy.py +++ /dev/null @@ -1,105 +0,0 @@ -# WIP - - -from coati.trainer.strategies import Strategy -from coati.trainer.strategies import NaiveStrategy -from coati.models.base import Actor, RewardModel, Critic - -import numpy as np -import torch -from torch._C._distributed_rpc import _is_current_rpc_agent_set - -import colossalai -from colossalai.pipeline.pipeline_process_group import ppg -from colossalai.pipeline.rpc._pipeline_schedule import OneFOneBPipelineEngine -from colossalai.fx import ColoTracer -from colossalai.fx.passes.adding_split_node_pass import balanced_split_pass, split_with_split_nodes_pass -from colossalai.pipeline.middleware.adaptor import get_fx_topology - - -import os -from functools import partial -import random - -rpc_is_initialized = _is_current_rpc_agent_set - -class PipelineModel(torch.nn.Module): - ''' - Actor has 2 kinds of jobs: forward and generate. - better to just pipeline the inner model - ''' - def __init__(self, - model: torch.nn.Module, - stage_num: int, - num_microbatches: int, - data_kwargs = None, - ): - super().__init__() - # create partition module - def create_partition_module(pp_rank:int, stage_num: int, model, data_kwargs): - model.eval() - tracer = ColoTracer() - meta_args = {k: v.to('meta') for k, v in data_kwargs.items()} - graph = tracer.trace(root=model, meta_args=meta_args) - gm = torch.fx.GraphModule(model, graph, model.__class__.__name__) - annotated_model = balanced_split_pass(gm, stage_num) - top_module, split_submodules = split_with_split_nodes_pass(annotated_model, merge_output=True) - topo = get_fx_topology(top_module) - for submodule in split_submodules: - if isinstance(submodule, torch.fx.GraphModule): - setattr(submodule, '_topo', topo) - return split_submodules[pp_rank + 1] - - def partition(model, data_kwargs: dict, pp_rank: int, chunk: int, stage_num: int): - partition = create_partition_module(pp_rank, stage_num, model, data_kwargs) - return partition - self.inference_engine = OneFOneBPipelineEngine( - partition_fn=partial(partition, model, data_kwargs), - stage_num=stage_num, - num_microbatches=num_microbatches, - device='cuda', - ) - - def forward(self, - **model_inputs): - return self.inference_engine.forward_backward(**model_inputs, forward_only=True) - - - -class PPStrategy(NaiveStrategy): - """ - Strategy for Pipeline inference (inference only!) - - master node only - """ - def __init__( - self, - seed: int = 42 - ): - self.seed = seed - super().__init__() - - - def setup_distributed(self) -> None: - colossalai.launch_from_torch({}, seed=self.seed) - ppg.set_global_info(rank = int(os.environ['RANK']), - world_size=int(os.environ['WORLD_SIZE']), - dp_degree=1, - tp_degree=1, - num_worker_threads=128, - device="cuda") - - def model_init_context(self): - return super().model_init_context() - - def setup_model(self, model: torch.nn.Module) -> torch.nn.Module: - if isinstance(model, Actor) or \ - isinstance(model, RewardModel) or \ - isinstance(model, Critic): - model.model = PipelineModel(model.model) - - def set_seed(self, seed: int) -> None: - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - diff --git a/applications/Chat/coati/ray/src/utils.py b/applications/Chat/coati/ray/src/utils.py deleted file mode 100644 index c750879b6d18..000000000000 --- a/applications/Chat/coati/ray/src/utils.py +++ /dev/null @@ -1,48 +0,0 @@ -import torch.distributed as dist -from typing import Any, Callable, Dict, List, Optional -from coati.models.bloom import BLOOMActor, BLOOMCritic -from coati.models.gpt import GPTActor, GPTCritic -from coati.models.opt import OPTActor, OPTCritic -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy -import torch -import os - -def is_rank_0() -> bool: - return not dist.is_initialized() or dist.get_rank() == 0 - - -def get_cuda_actor_critic_from_args(model: str, pretrained: str = None, lora_rank=0): - if model == 'gpt2': - actor = GPTActor(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) - critic = GPTCritic(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) - elif model == 'bloom': - actor = BLOOMActor(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) - critic = BLOOMCritic(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) - elif model == 'opt': - actor = OPTActor(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) - critic = OPTCritic(pretrained=pretrained, lora_rank=lora_rank).to(torch.cuda.current_device()) - else: - raise ValueError(f'Unsupported model "{model}"') - return actor, critic - - -def get_strategy_from_args(strategy: str): - if strategy == 'naive': - strategy_ = NaiveStrategy() - elif strategy == 'ddp': - strategy_ = DDPStrategy() - elif strategy == 'colossalai_gemini': - strategy_ = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) - elif strategy == 'colossalai_zero2': - strategy_ = ColossalAIStrategy(stage=2, placement_policy='cuda') - else: - raise ValueError(f'Unsupported strategy "{strategy}"') - return strategy_ - - -def set_dist_env(env_info: Dict[str, str]): - os.environ["RANK"] = env_info['rank'] - os.environ["LOCAL_RANK"] = env_info['local_rank'] - os.environ["WORLD_SIZE"] = env_info['world_size'] - os.environ['MASTER_PORT'] = env_info['master_port'] - os.environ['MASTER_ADDR'] = env_info['master_addr'] diff --git a/applications/Chat/coati/ray/utils.py b/applications/Chat/coati/ray/utils.py new file mode 100644 index 000000000000..4361ee236771 --- /dev/null +++ b/applications/Chat/coati/ray/utils.py @@ -0,0 +1,152 @@ +import os +from typing import Any, Callable, Dict, List, Optional +from collections import OrderedDict + +import torch +import torch.distributed as dist +import torch.nn as nn +from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic +from coati.models.gpt import GPTRM, GPTActor, GPTCritic +from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM +from coati.models.opt import OPTRM, OPTActor, OPTCritic +from coati.models.roberta import RoBERTaActor, RoBERTaCritic, RoBERTaRM +from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.utils import prepare_llama_tokenizer_and_embedding +from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer, RobertaTokenizer + + +def is_rank_0() -> bool: + return not dist.is_initialized() or dist.get_rank() == 0 + + +def get_rank() -> int: + return dist.get_rank() if dist.is_initialized() else 0 + + +def get_world_size() -> int: + return dist.get_world_size() if dist.is_initialized() else 1 + + +def get_actor_from_args(model: str, pretrained: str = None, config=None, lora_rank=0): + if model == 'gpt2': + actor = GPTActor(pretrained=pretrained, config=config, lora_rank=lora_rank) + elif model == 'bloom': + actor = BLOOMActor(pretrained=pretrained, config=config, lora_rank=lora_rank) + elif model == 'opt': + actor = OPTActor(pretrained=pretrained, config=config, lora_rank=lora_rank) + elif model == 'llama': + actor = LlamaActor(pretrained=pretrained, config=config, lora_rank=lora_rank) + elif model == 'roberta': + actor = RoBERTaActor(pretrained=pretrained, config=config, lora_rank=lora_rank) + else: + raise ValueError(f'Unsupported actor model "{model}"') + return actor + + +def get_critic_from_args(model: str, pretrained: str = None, config=None, lora_rank=0): + if model == 'gpt2': + critic = GPTCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True) + elif model == 'bloom': + critic = BLOOMCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True) + elif model == 'opt': + critic = OPTCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True) + elif model == 'llama': + critic = LlamaCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True) + elif model == 'roberta': + critic = RoBERTaCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True) + else: + raise ValueError(f'Unsupported reward model "{model}"') + return critic + + +def get_reward_model_from_args(model: str, pretrained: str = None, config=None): + if model == 'gpt2': + reward_model = GPTRM(pretrained=pretrained, config=config) + elif model == 'bloom': + reward_model = BLOOMRM(pretrained=pretrained, config=config) + elif model == 'opt': + reward_model = OPTRM(pretrained=pretrained, config=config) + elif model == 'llama': + reward_model = LlamaRM(pretrained=pretrained, config=config) + elif model == 'roberta': + reward_model = RoBERTaRM(pretrained=pretrained, config=config) + else: + raise ValueError(f'Unsupported reward model "{model}"') + return reward_model + + +def get_strategy_from_args(strategy: str): + if strategy == 'naive': + strategy_ = NaiveStrategy() + elif strategy == 'ddp': + strategy_ = DDPStrategy() + elif strategy == 'colossalai_gemini': + strategy_ = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + elif strategy == 'colossalai_zero2': + strategy_ = ColossalAIStrategy(stage=2, placement_policy='cuda') + elif strategy == 'colossalai_gemini_cpu': + strategy_ = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) + elif strategy == 'colossalai_zero2_cpu': + strategy_ = ColossalAIStrategy(stage=2, placement_policy='cpu') + else: + raise ValueError(f'Unsupported strategy "{strategy}"') + return strategy_ + + +def get_tokenizer_from_args(model: str, **kwargs): + if model == 'gpt2': + tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + elif model == 'bloom': + tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m') + elif model == 'opt': + tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + elif model == 'llama': + pretrain_path = kwargs["pretrain"] + tokenizer = AutoTokenizer.from_pretrained(pretrain_path) + elif model == 'roberta': + tokenizer = RobertaTokenizer.from_pretrained("roberta-base") + else: + raise ValueError(f'Unsupported model "{model}"') + + tokenizer.pad_token = tokenizer.eos_token + return tokenizer + + +def set_dist_env(env_info: Dict[str, str]): + os.environ["RANK"] = env_info['rank'] + os.environ["LOCAL_RANK"] = env_info['local_rank'] + os.environ["WORLD_SIZE"] = env_info['world_size'] + os.environ['MASTER_PORT'] = env_info['master_port'] + os.environ['MASTER_ADDR'] = env_info['master_addr'] + + +def get_model_numel(model: nn.Module) -> int: + numel = sum(p.numel() for p in model.parameters()) + return numel + + +def get_receivers_per_sender(sender_idx: int, num_senders: int, num_receivers: int, allow_idle_sender: bool) -> list: + target_receivers = [] + if num_senders <= num_receivers or allow_idle_sender: + # a sender will send data to one or more than one receivers + # a receiver only has one sender + for i in range(num_receivers): + if i % num_senders == sender_idx: + target_receivers.append(i) + else: + # a sender will send data to one receiver + # a receiver may have more than one sender + target_receivers.append(sender_idx % num_receivers) + return target_receivers + + +def state_dict_to(state_dict: Dict[str, Any], + dtype: torch.dtype = torch.float16, + device: torch.device = torch.device('cpu')): + ''' + keep state_dict intact + ''' + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + new_state_dict[k] = v.to(dtype=dtype, device=device) + return new_state_dict diff --git a/applications/Chat/coati/trainer/strategies/base.py b/applications/Chat/coati/trainer/strategies/base.py index b1452869179e..bd30422022ae 100644 --- a/applications/Chat/coati/trainer/strategies/base.py +++ b/applications/Chat/coati/trainer/strategies/base.py @@ -130,3 +130,7 @@ def save_pretrained(self, only_rank0: bool = True, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: pass + + @abstractmethod + def get_model_state_dict_shard(self, model: nn.Module, **config): + pass \ No newline at end of file diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index 8aa302c77eee..88268b677eb2 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -186,3 +186,15 @@ def save_pretrained(self, if self.stage == 3: raise RuntimeError('ColossalAI strategy with stage-3 does not support save_pretrained() now') super().save_pretrained(model, path, only_rank0, tokenizer) + + def get_model_state_dict_shard(self, model: nn.Module, **config): + if self.stage != 3: + yield from super().get_model_state_dict_shard(model, **config) + else: + # unwrapped_model = self._unwrap_model(model) + # for module in unwrapped_model.modules(): + # if isinstance(module, LoraLinear): + # module.merge_weights = True + # module.eval() + base_model: ZeroDDP = get_base_model(model) + yield from base_model.state_dict_shard(max_shard_size=1024, only_rank_0=False) diff --git a/applications/Chat/coati/trainer/strategies/ddp.py b/applications/Chat/coati/trainer/strategies/ddp.py index 7910b57878f8..a1fecb36373f 100644 --- a/applications/Chat/coati/trainer/strategies/ddp.py +++ b/applications/Chat/coati/trainer/strategies/ddp.py @@ -26,19 +26,8 @@ def __init__(self, seed: int = 42) -> None: super().__init__() def setup_distributed(self) -> None: - try: - rank = int(os.environ['RANK']) - local_rank = int(os.environ['LOCAL_RANK']) - world_size = int(os.environ['WORLD_SIZE']) - host = os.environ['MASTER_ADDR'] - port = int(os.environ['MASTER_PORT']) - except KeyError as e: - raise RuntimeError( - f"Could not find {e} in the torch environment, visit https://www.colossalai.org/ for more information on launching with torch" - ) - dist.init_process_group('nccl', init_method=f'tcp://[{host}]:{port}', world_size=world_size, rank=rank) + self._try_init_dist(force=True) self.set_seed(self.seed) - torch.cuda.set_device(local_rank) def set_seed(self, seed: int) -> None: random.seed(seed) diff --git a/applications/Chat/coati/trainer/strategies/naive.py b/applications/Chat/coati/trainer/strategies/naive.py index 4d94026ce932..972deebeaa0d 100644 --- a/applications/Chat/coati/trainer/strategies/naive.py +++ b/applications/Chat/coati/trainer/strategies/naive.py @@ -1,10 +1,17 @@ -from typing import Any, Optional +import os +import sys +from collections import OrderedDict +from typing import Any, Dict, Optional import torch +import torch.distributed as dist import torch.nn as nn import torch.optim as optim from coati.models.base import get_base_model from coati.replay_buffer import ReplayBuffer +from coati.models.base import RewardModel +from coati.models.lora import LoraLinear +from coati.replay_buffer import ReplayBuffer from torch.optim import Optimizer from torch.utils.data import DataLoader from transformers.modeling_utils import PreTrainedModel @@ -13,6 +20,15 @@ from .base import Strategy +# TODO Move this to a util.py (Moving to ray.util introduces ringed import) +def get_grad_required_state_dict(model: nn.Module): + state_dict = OrderedDict() + for name, parameter in model.named_parameters(): + if parameter.requires_grad: + state_dict[name] = parameter.detach() + return state_dict + + class NaiveStrategy(Strategy): """ Strategy for single GPU. No parallelism is used. @@ -25,7 +41,7 @@ def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: optimizer.step() def setup_distributed(self) -> None: - pass + self._try_init_dist(force=False) def setup_model(self, model: nn.Module) -> nn.Module: return model @@ -68,3 +84,45 @@ def save_pretrained(self, unwrapped_model.save_pretrained(path) if tokenizer is not None: tokenizer.save_pretrained(path) + + def get_model_state_dict_shard(self, model: nn.Module, **config): + # TODO: implement sharding on naive strategy + model = self.unwrap_model(model) + if 'requires_grad_only' in config and config['requires_grad_only'] == True: + state_dict = get_grad_required_state_dict(model) + else: + state_dict = model.state_dict() + + if 'shard_size' in config: + shard_size = config['shard_size'] + accumulate_size = 0 + state_dict_shard = OrderedDict() + for name, param in state_dict.items(): + state_dict_shard[name] = param + accumulate_size += param.numel() * param.element_size() + if accumulate_size >= shard_size: + accumulate_size = 0 + yield state_dict_shard + state_dict_shard = OrderedDict() + if accumulate_size > 0: + yield state_dict_shard + else: + yield state_dict + + def _try_init_dist(self, force: bool = False) -> None: + try: + rank = int(os.environ['RANK']) + local_rank = int(os.environ['LOCAL_RANK']) + world_size = int(os.environ['WORLD_SIZE']) + host = os.environ['MASTER_ADDR'] + port = int(os.environ['MASTER_PORT']) + dist.init_process_group('nccl', init_method=f'tcp://[{host}]:{port}', world_size=world_size, rank=rank) + torch.cuda.set_device(local_rank) + except KeyError as e: + if force: + raise RuntimeError( + f"Could not find {e} in the torch environment, visit https://www.colossalai.org/ for more information on launching with torch" + ) + except Exception as e: + if force: + raise e diff --git a/applications/Chat/coati/trainer/strategies/sampler.py b/applications/Chat/coati/trainer/strategies/sampler.py index d726fa640fa2..65e199dbf029 100644 --- a/applications/Chat/coati/trainer/strategies/sampler.py +++ b/applications/Chat/coati/trainer/strategies/sampler.py @@ -27,6 +27,7 @@ def __init__(self, dataset, num_replicas: int, rank: int) -> None: assert len(indices) == self.num_samples self.indices = indices + def sample(self, batch_size: int) -> list: sampled_indices = np.random.choice(self.indices, batch_size, replace=False) return [self.dataset[idx] for idx in sampled_indices] diff --git a/applications/Chat/examples/ray/1mmt_prompt.py b/applications/Chat/examples/ray/1mmt_prompt.py new file mode 100644 index 000000000000..afdd6a922cc7 --- /dev/null +++ b/applications/Chat/examples/ray/1mmt_prompt.py @@ -0,0 +1,175 @@ +import argparse +import os +import socket +from functools import partial + +import pandas as pd +import ray +import torch +from coati.quant import llama_load_quant, low_resource_init +from coati.ray.detached_trainer_ppo import DetachedPPOTrainer +from coati.ray.experience_maker_holder import ExperienceMakerHolder +from coati.ray.utils import ( + get_actor_from_args, + get_critic_from_args, + get_reward_model_from_args, + get_strategy_from_args, + get_tokenizer_from_args, +) +from torch.utils.data import DataLoader +from transformers import AutoConfig +from transformers.modeling_utils import no_init_weights + + +def get_free_port(): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(('', 0)) + return s.getsockname()[1] + + +def get_local_ip(): + with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: + s.connect(('8.8.8.8', 80)) + return s.getsockname()[0] + + +def main(args): + master_addr = str(get_local_ip()) + # trainer_env_info + trainer_port = str(get_free_port()) + env_info_trainers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(args.num_trainers), + 'master_port': trainer_port, + 'master_addr': master_addr + } for rank in range(args.num_trainers)] + + # maker_env_info + maker_port = str(get_free_port()) + env_info_maker = { + 'local_rank': '0', + 'rank': '0', + 'world_size': '1', + 'master_port': maker_port, + 'master_addr': master_addr + } + + # configure tokenizer + tokenizer = get_tokenizer_from_args(args.model) + + def trainer_model_fn(): + actor = get_actor_from_args(args.model, args.pretrain).half().cuda() + critic = get_critic_from_args(args.model, args.critic_pretrain).half().cuda() + return actor, critic + + # configure Trainer + trainer_refs = [ + DetachedPPOTrainer.options(name=f"trainer{i}", num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=["maker1"], + strategy_fn=partial(get_strategy_from_args, args.trainer_strategy), + model_fn=trainer_model_fn, + env_info=env_info_trainer, + train_batch_size=args.train_batch_size, + buffer_limit=16, + eval_performance=True, + debug=args.debug, + update_lora_weights=not (args.lora_rank == 0), + ) for i, env_info_trainer in enumerate(env_info_trainers) + ] + + def model_fn(): + actor = get_actor_from_args(args.model, args.pretrain).requires_grad_(False).half().cuda() + critic = get_critic_from_args(args.model, args.critic_pretrain).requires_grad_(False).half().cuda() + reward_model = get_reward_model_from_args(args.model, args.critic_pretrain).requires_grad_(False).half().cuda() + if args.initial_model_quant_ckpt is not None and args.model == 'llama': + # quantize initial model + actor_cfg = AutoConfig.from_pretrained(args.pretrain) + with low_resource_init(), no_init_weights(): + initial_model = get_actor_from_args(args.model, config=actor_cfg) + initial_model.model = llama_load_quant(initial_model.model, args.initial_model_quant_ckpt, args.quant_bits, + args.quant_group_size).cuda().requires_grad_(False) + else: + initial_model = get_actor_from_args(args.model, args.pretrain).requires_grad_(False).half().cuda() + return actor, critic, reward_model, initial_model + + # configure Experience Maker + experience_holder_ref = ExperienceMakerHolder.options(name="maker1", num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=[f'trainer{i}' for i in range(args.num_trainers)], + strategy_fn=partial(get_strategy_from_args, args.maker_strategy), + model_fn=model_fn, + env_info=env_info_maker, + experience_batch_size=args.experience_batch_size, + kl_coef=0.1, + debug=args.debug, + update_lora_weights=not (args.lora_rank == 0), + # sync_models_from_trainers=True, + # generation kwargs: + max_length=512, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + eval_performance=True, + use_cache=True, + ) + + # uncomment this function if sync_models_from_trainers is True + # ray.get([ + # trainer_ref.sync_models_to_remote_makers.remote() + # for trainer_ref in trainer_refs + # ]) + + wait_tasks = [] + + total_steps = args.experience_batch_size * args.experience_steps // (args.num_trainers * args.train_batch_size) + for trainer_ref in trainer_refs: + wait_tasks.append(trainer_ref.fit.remote(total_steps, args.update_steps, args.train_epochs)) + + dataset_size = args.experience_batch_size * 4 + + def build_dataloader(): + + def tokenize_fn(texts): + batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) + return {k: v.cuda() for k, v in batch.items()} + + dataset = pd.read_csv(args.prompt_path)['prompt'] + dataloader = DataLoader(dataset=dataset, batch_size=dataset_size, shuffle=True, collate_fn=tokenize_fn) + return dataloader + + wait_tasks.append(experience_holder_ref.workingloop.remote(build_dataloader, num_steps=args.experience_steps)) + + ray.get(wait_tasks) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--prompt_path', type=str, default=None) + parser.add_argument('--num_trainers', type=int, default=1) + parser.add_argument('--trainer_strategy', + choices=[ + 'naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', + 'colossalai_zero2_cpu' + ], + default='naive') + parser.add_argument('--maker_strategy', choices=['naive'], default='naive') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--critic_pretrain', type=str, default=None) + parser.add_argument('--experience_steps', type=int, default=4) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--train_epochs', type=int, default=1) + parser.add_argument('--update_steps', type=int, default=2) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + + parser.add_argument('--initial_model_quant_ckpt', type=str, default=None) + parser.add_argument('--quant_bits', type=int, default=4) + parser.add_argument('--quant_group_size', type=int, default=128) + parser.add_argument('--debug', action='store_true') + args = parser.parse_args() + ray.init(namespace=os.environ["RAY_NAMESPACE"], runtime_env={"env_vars": dict(os.environ)}) + main(args) diff --git a/applications/Chat/examples/ray/mmmt_prompt.py b/applications/Chat/examples/ray/mmmt_prompt.py new file mode 100644 index 000000000000..fa7b2bd7edfd --- /dev/null +++ b/applications/Chat/examples/ray/mmmt_prompt.py @@ -0,0 +1,189 @@ +import argparse +import os +import socket +from functools import partial + +import pandas as pd +import ray +import torch +from coati.quant import llama_load_quant, low_resource_init +from coati.ray.detached_trainer_ppo import DetachedPPOTrainer +from coati.ray.experience_maker_holder import ExperienceMakerHolder +from coati.ray.utils import ( + get_actor_from_args, + get_critic_from_args, + get_receivers_per_sender, + get_reward_model_from_args, + get_strategy_from_args, +) +from torch.utils.data import DataLoader +from transformers import AutoConfig, AutoTokenizer +from transformers.modeling_utils import no_init_weights + + +def get_free_port(): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(('', 0)) + return s.getsockname()[1] + + +def get_local_ip(): + with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: + s.connect(('8.8.8.8', 80)) + return s.getsockname()[0] + + +def main(args): + master_addr = str(get_local_ip()) + # trainer_env_info + trainer_port = str(get_free_port()) + env_info_trainers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(args.num_trainers), + 'master_port': trainer_port, + 'master_addr': master_addr + } for rank in range(args.num_trainers)] + + # maker_env_info + maker_port = str(get_free_port()) + env_info_makers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(args.num_makers), + 'master_port': maker_port, + 'master_addr': master_addr + } for rank in range(args.num_makers)] + + # configure tokenizer + tokenizer = AutoTokenizer.from_pretrained(args.pretrain) + tokenizer.pad_token = tokenizer.eos_token + + def model_fn(): + actor = get_actor_from_args(args.model, args.pretrain).requires_grad_(False).half().cuda() + critic = get_critic_from_args(args.model, args.critic_pretrain).requires_grad_(False).half().cuda() + reward_model = get_reward_model_from_args(args.model, args.critic_pretrain).requires_grad_(False).half().cuda() + if args.initial_model_quant_ckpt is not None and args.model == 'llama': + # quantize initial model + actor_cfg = AutoConfig.from_pretrained(args.pretrain) + with low_resource_init(), no_init_weights(): + initial_model = get_actor_from_args(args.model, config=actor_cfg) + initial_model.model = llama_load_quant(initial_model.model, args.initial_model_quant_ckpt, args.quant_bits, + args.quant_group_size).cuda().requires_grad_(False) + else: + initial_model = get_actor_from_args(args.model, args.pretrain).requires_grad_(False).half().cuda() + return actor, critic, reward_model, initial_model + + # configure Experience Maker + experience_holder_refs = [ + ExperienceMakerHolder.options(name=f"maker{i}", num_gpus=1, max_concurrency=2).remote( + detached_trainer_name_list=[ + f'trainer{x}' + for x in get_receivers_per_sender(i, args.num_makers, args.num_trainers, allow_idle_sender=False) + ], + strategy_fn=partial(get_strategy_from_args, args.maker_strategy), + model_fn=model_fn, + env_info=env_info_maker, + kl_coef=0.1, + debug=args.debug, + update_lora_weights=not (args.lora_rank == 0), + # sync_models_from_trainers=True, + # generation kwargs: + max_length=512, + do_sample=True, + temperature=1.0, + top_k=50, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + eval_performance=True, + use_cache=True, + ) + for i, env_info_maker in enumerate(env_info_makers) + ] + + def trainer_model_fn(): + actor = get_actor_from_args(args.model, args.pretrain, lora_rank=args.lora_rank).half().cuda() + critic = get_critic_from_args(args.model, args.critic_pretrain, lora_rank=args.lora_rank).half().cuda() + return actor, critic + + # configure Trainer + trainer_refs = [ + DetachedPPOTrainer.options(name=f"trainer{i}", num_gpus=1, max_concurrency=2).remote( + experience_maker_holder_name_list=[ + f"maker{x}" + for x in get_receivers_per_sender(i, args.num_trainers, args.num_makers, allow_idle_sender=True) + ], + strategy_fn=partial(get_strategy_from_args, args.trainer_strategy), + model_fn=trainer_model_fn, + env_info=env_info_trainer, + train_batch_size=args.train_batch_size, + buffer_limit=16, + eval_performance=True, + debug=args.debug, + update_lora_weights=not (args.lora_rank == 0), + ) + for i, env_info_trainer in enumerate(env_info_trainers) + ] + + dataset_size = args.experience_batch_size * 4 + + def build_dataloader(): + + def tokenize_fn(texts): + batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True) + return {k: v.cuda() for k, v in batch.items()} + + dataset = pd.read_csv(args.prompt_path)['prompt'] + dataloader = DataLoader(dataset=dataset, batch_size=dataset_size, shuffle=True, collate_fn=tokenize_fn) + return dataloader + + # uncomment this function if sync_models_from_trainers is True + # ray.get([ + # trainer_ref.sync_models_to_remote_makers.remote() + # for trainer_ref in trainer_refs + # ]) + + wait_tasks = [] + + for experience_holder_ref in experience_holder_refs: + wait_tasks.append(experience_holder_ref.workingloop.remote(build_dataloader, num_steps=args.experience_steps)) + + total_steps = args.experience_batch_size * args.experience_steps * \ + args.num_makers // (args.num_trainers * args.train_batch_size) + for trainer_ref in trainer_refs: + wait_tasks.append(trainer_ref.fit.remote(total_steps, args.update_steps, args.train_epochs)) + + ray.get(wait_tasks) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--prompt_path', type=str, default=None) + parser.add_argument('--num_makers', type=int, default=1) + parser.add_argument('--num_trainers', type=int, default=1) + parser.add_argument('--trainer_strategy', + choices=[ + 'naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', + 'colossalai_zero2_cpu' + ], + default='naive') + parser.add_argument('--maker_strategy', choices=['naive'], default='naive') + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) + parser.add_argument('--pretrain', type=str, default=None) + parser.add_argument('--critic_pretrain', type=str, default=None) + parser.add_argument('--experience_steps', type=int, default=4) + parser.add_argument('--experience_batch_size', type=int, default=8) + parser.add_argument('--train_epochs', type=int, default=1) + parser.add_argument('--update_steps', type=int, default=2) + parser.add_argument('--train_batch_size', type=int, default=8) + parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") + + parser.add_argument('--initial_model_quant_ckpt', type=str, default=None) + parser.add_argument('--quant_bits', type=int, default=4) + parser.add_argument('--quant_group_size', type=int, default=128) + parser.add_argument('--debug', action='store_true') + args = parser.parse_args() + + ray.init(namespace=os.environ["RAY_NAMESPACE"], runtime_env={"env_vars": dict(os.environ)}) + main(args) diff --git a/applications/Chat/examples/ray/requirements.txt b/applications/Chat/examples/ray/requirements.txt new file mode 100644 index 000000000000..e0275631807f --- /dev/null +++ b/applications/Chat/examples/ray/requirements.txt @@ -0,0 +1 @@ +ray diff --git a/applications/Chat/examples/ray/test_ci.sh b/applications/Chat/examples/ray/test_ci.sh new file mode 100755 index 000000000000..895f7de0fea9 --- /dev/null +++ b/applications/Chat/examples/ray/test_ci.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +set -xe +BASE=$(realpath $(dirname $0)) + +export RAY_NAMESPACE=admin +export DATA=/data/scratch/chatgpt/prompts.csv + +# install requirements +pip install -r ${BASE}/requirements.txt + +python ${BASE}/mmmt_prompt.py --prompt_path $DATA --num_makers 2 --num_trainers 2 --trainer_strategy colossalai_gemini --model opt --critic_model opt --pretrain facebook/opt-350m --critic_pretrain facebook/opt-125m --experience_batch_size 4 --train_batch_size 2 diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index 2b049163c801..2fa6c6052f8d 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -124,3 +124,6 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_datas rm -rf ${BASE}/rm_ckpt_gpt.pt rm -rf ${BASE}/actor_checkpoint_prompts.pt + +# 3080 doesn't support P2P, skip this test +# cd ${BASE}/ray && bash test_ci.sh && cd ${BASE} From 4fc8bc68ac707302ad7d47706778f42a4d5031bf Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Wed, 7 Jun 2023 11:02:19 +0800 Subject: [PATCH 280/413] modify file path --- examples/images/dreambooth/colossalai.sh | 8 ++++---- examples/images/dreambooth/dreambooth.sh | 6 +++--- examples/images/dreambooth/test_ci.sh | 8 ++++---- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/examples/images/dreambooth/colossalai.sh b/examples/images/dreambooth/colossalai.sh index 54ebac39b925..3b15ad887b0a 100755 --- a/examples/images/dreambooth/colossalai.sh +++ b/examples/images/dreambooth/colossalai.sh @@ -3,10 +3,10 @@ TRANSFORMERS_OFFLINE=1 DIFFUSERS_OFFLINE=1 torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \ - --pretrained_model_name_or_path="Path_to_your_model" \ - --instance_data_dir="Path_to_your_training_image" \ - --output_dir="Path_to_your_save_dir" \ - --instance_prompt="your prompt" \ + --pretrained_model_name_or_path="/data/dreambooth/diffuser/stable-diffusion-v1-4" \ + --instance_data_dir="/data/dreambooth/Teyvat/data" \ + --output_dir="./weight_output" \ + --instance_prompt="a picture of a dog" \ --resolution=512 \ --plugin="gemini" \ --train_batch_size=1 \ diff --git a/examples/images/dreambooth/dreambooth.sh b/examples/images/dreambooth/dreambooth.sh index e063bc8279c5..f6b8f5e1b87e 100644 --- a/examples/images/dreambooth/dreambooth.sh +++ b/examples/images/dreambooth/dreambooth.sh @@ -1,7 +1,7 @@ python train_dreambooth.py \ - --pretrained_model_name_or_path= ## Your Model Path \ - --instance_data_dir= ## Your Training Input Pics Path \ - --output_dir="path-to-save-model" \ + --pretrained_model_name_or_path="/data/dreambooth/diffuser/stable-diffusion-v1-4" \ + --instance_data_dir="/data/dreambooth/Teyvat/data" \ + --output_dir="./weight_output" \ --instance_prompt="a photo of a dog" \ --resolution=512 \ --train_batch_size=1 \ diff --git a/examples/images/dreambooth/test_ci.sh b/examples/images/dreambooth/test_ci.sh index 0209c547a08f..c0b0c2b3d016 100644 --- a/examples/images/dreambooth/test_ci.sh +++ b/examples/images/dreambooth/test_ci.sh @@ -8,10 +8,10 @@ DIFFUSERS_OFFLINE=1 for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \ - --pretrained_model_name_or_path="Your Pretrained Model Path" \ - --instance_data_dir="Your Input Pics Path" \ - --output_dir="path-to-save-model" \ - --instance_prompt="your prompt" \ + --pretrained_model_name_or_path="/data/dreambooth/diffuser/stable-diffusion-v1-4" \ + --instance_data_dir="/data/dreambooth/Teyvat/data" \ + --output_dir="./weight_output" \ + --instance_prompt="a picture of a dog" \ --resolution=512 \ --plugin=$plugin \ --train_batch_size=1 \ From 9c88b6cbd1597d5f429b61a04a7219b9e0d14a1b Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 7 Jun 2023 11:10:12 +0800 Subject: [PATCH 281/413] [lazy] fix compatibility problem on torch 1.13 (#3911) --- colossalai/lazy/lazy_init.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/colossalai/lazy/lazy_init.py b/colossalai/lazy/lazy_init.py index c1fda3c53865..76f550dc4392 100644 --- a/colossalai/lazy/lazy_init.py +++ b/colossalai/lazy/lazy_init.py @@ -37,7 +37,7 @@ # If your intent is to change the metadata of a Tensor (such as sizes / strides / storage / storage_offset) # without autograd tracking the change, remove the .data / .detach() call and wrap the change in a `with torch.no_grad():` block. # These ops cannot be unwrapped using .data -_CHANGE_META_OPS = ['_cudnn_rnn_flatten_weight', 'requires_grad_', '__get__', '__set__'] +_CHANGE_META_OPS = ['_cudnn_rnn_flatten_weight', 'requires_grad_', '__get__', '__set__', 'numel', 'size', 'dim'] _LEGACY_TENSOR_CONSTRUCTOR = { 'FloatTensor': torch.float, From c25d421f3e2e4599d44f88a07ba7621c3991548c Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 7 Jun 2023 12:39:12 +0800 Subject: [PATCH 282/413] [devops] hotfix testmon cache clean logic (#3917) --- .github/workflows/build_on_pr.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index a2807859b591..8b2253e57cfb 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -271,7 +271,6 @@ jobs: PR_NUMBER: ${{ github.event.pull_request.number }} - name: Remove testmon cache - if: github.event.pull_request.merged != true run: | rm -rf /github/home/testmon_cache/_pull/${PR_NUMBER} env: From 5e2132dcff0fae0866aa3ce4b1aecbc767ed189b Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 7 Jun 2023 15:37:37 +0800 Subject: [PATCH 283/413] [workflow] added docker latest tag for release (#3920) --- .github/workflows/release_docker_after_publish.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/release_docker_after_publish.yml b/.github/workflows/release_docker_after_publish.yml index 22698ca192ed..6c8df9730b0d 100644 --- a/.github/workflows/release_docker_after_publish.yml +++ b/.github/workflows/release_docker_after_publish.yml @@ -23,8 +23,11 @@ jobs: run: | version=$(cat version.txt) tag=hpcaitech/colossalai:$version + latest=hpcaitech/colossalai:latest docker build --build-arg http_proxy=http://172.17.0.1:7890 --build-arg https_proxy=http://172.17.0.1:7890 --build-arg VERSION=v${version} -t $tag ./docker + docker tag $tag $latest echo "tag=${tag}" >> $GITHUB_OUTPUT + echo "latest=${latest}" >> $GITHUB_OUTPUT - name: Log in to Docker Hub uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9 @@ -36,6 +39,7 @@ jobs: id: docker-push run: | docker push ${{ steps.build.outputs.tag }} + docker push ${{ steps.build.outputs.latest }} notify: name: Notify Lark via webhook From a55fb00c18dbc36941d2e850312015e0930837a2 Mon Sep 17 00:00:00 2001 From: wukong1992 Date: Wed, 7 Jun 2023 15:51:00 +0800 Subject: [PATCH 284/413] [booster] update bert example, using booster api (#3885) --- examples/language/bert/README.md | 34 +++ examples/language/bert/benchmark.py | 174 ++++++++++++ examples/language/bert/benchmark.sh | 9 + examples/language/bert/benchmark_utils.py | 146 ++++++++++ examples/language/bert/data.py | 127 +++++++++ examples/language/bert/finetune.py | 220 ++++++++++++++ examples/language/bert/requirements.txt | 9 + examples/language/bert/run_gemini.sh | 22 -- examples/language/bert/test_ci.sh | 10 +- examples/language/bert/train_bert_demo.py | 331 ---------------------- 10 files changed, 727 insertions(+), 355 deletions(-) create mode 100644 examples/language/bert/README.md create mode 100644 examples/language/bert/benchmark.py create mode 100755 examples/language/bert/benchmark.sh create mode 100644 examples/language/bert/benchmark_utils.py create mode 100644 examples/language/bert/data.py create mode 100644 examples/language/bert/finetune.py create mode 100644 examples/language/bert/requirements.txt delete mode 100644 examples/language/bert/run_gemini.sh mode change 100644 => 100755 examples/language/bert/test_ci.sh delete mode 100644 examples/language/bert/train_bert_demo.py diff --git a/examples/language/bert/README.md b/examples/language/bert/README.md new file mode 100644 index 000000000000..c845a5c50387 --- /dev/null +++ b/examples/language/bert/README.md @@ -0,0 +1,34 @@ +## Overview + +This directory includes two parts: Using the Booster API fintune Huggingface Bert and AlBert models and benchmarking Bert and AlBert models with different Booster Plugin. + +## Finetune +``` +bash test_ci.sh +``` + +## Benchmark +``` +bash benchmark.sh +``` + +Now include these metrics in benchmark: CUDA mem occupy, throughput and the number of model parameters. If you have custom metrics, you can add them to benchmark_util. + +## Results + +### Bert + +| | max cuda mem | throughput(sample/s) | params | +| :-----| -----------: | :--------: | :----: | +| ddp | 21.44 GB | 3.0 | 82M | +| ddp_fp16 | 16.26 GB | 11.3 | 82M | +| gemini | 11.0 GB | 12.9 | 82M | +| low_level_zero | 11.29 G | 14.7 | 82M | + +### AlBert +| | max cuda mem | throughput(sample/s) | params | +| :-----| -----------: | :--------: | :----: | +| ddp | OOM | | | +| ddp_fp16 | OOM | | | +| gemini | 69.39 G | 1.3 | 208M | +| low_level_zero | 56.89 G | 1.4 | 208M | \ No newline at end of file diff --git a/examples/language/bert/benchmark.py b/examples/language/bert/benchmark.py new file mode 100644 index 000000000000..ae8b2269a534 --- /dev/null +++ b/examples/language/bert/benchmark.py @@ -0,0 +1,174 @@ +import argparse + +import torch +from benchmark_utils import benchmark +from torch.utils.data import DataLoader, Dataset +from transformers import ( + AlbertConfig, + AlbertForSequenceClassification, + BertConfig, + BertForSequenceClassification, + get_linear_schedule_with_warmup, +) + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.cluster import DistCoordinator +from colossalai.nn.optimizer import HybridAdam + +# ============================== +# Prepare Hyperparameters +# ============================== +NUM_EPOCHS = 3 +BATCH_SIZE = 32 +LEARNING_RATE = 2.4e-5 +WEIGHT_DECAY = 0.01 +WARMUP_FRACTION = 0.1 +SEQ_LEN = 512 +VOCAB_SIZE = 1000 +NUM_LABELS = 10 +DATASET_LEN = 1000 + + +class RandintDataset(Dataset): + + def __init__(self, dataset_length: int, sequence_length: int, vocab_size: int, n_class: int): + + self._sequence_length = sequence_length + self._vocab_size = vocab_size + self._n_class = n_class + self._dataset_length = dataset_length + self._datas = torch.randint( + low=0, + high=self._vocab_size, + size=(self._dataset_length, self._sequence_length,), + dtype=torch.long, + ) + self._labels = torch.randint(low=0, high=self._n_class, size=(self._dataset_length, 1), dtype=torch.long) + + def __len__(self): + return self._dataset_length + + def __getitem__(self, idx): + return self._datas[idx], self._labels[idx] + + +def main(): + # ============================== + # Parse Arguments + # ============================== + parser = argparse.ArgumentParser() + parser.add_argument('-t', '--task', default='mrpc', help="GLUE task to run") + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'], + help="plugin to use") + parser.add_argument( + "--model_type", + type=str, + default="bert", + help="bert or albert", + ) + + args = parser.parse_args() + + # ============================== + # Launch Distributed Environment + # ============================== + colossalai.launch_from_torch(config={}, seed=42) + coordinator = DistCoordinator() + + # local_batch_size = BATCH_SIZE // coordinator.world_size + lr = LEARNING_RATE * coordinator.world_size + + # ============================== + # Instantiate Plugin and Booster + # ============================== + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + + booster = Booster(plugin=plugin, **booster_kwargs) + + # ============================== + # Prepare Dataloader + # ============================== + + train_dataset = RandintDataset(dataset_length=DATASET_LEN, + sequence_length=SEQ_LEN, + vocab_size=VOCAB_SIZE, + n_class=NUM_LABELS) + train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE) + + # ==================================== + # Prepare model, optimizer + # ==================================== + # bert pretrained model + + if args.model_type == "bert": + cfg = BertConfig(vocab_size=VOCAB_SIZE, num_labels=NUM_LABELS) + model = BertForSequenceClassification(cfg) + elif args.model_type == "albert": + cfg = AlbertConfig(vocab_size=VOCAB_SIZE, num_labels=NUM_LABELS) + model = AlbertForSequenceClassification(cfg) + else: + raise RuntimeError + + # optimizer + no_decay = ["bias", "LayerNorm.weight"] + optimizer_grouped_parameters = [ + { + "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], + "weight_decay": WEIGHT_DECAY, + }, + { + "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], + "weight_decay": 0.0, + }, + ] + + optimizer = HybridAdam(optimizer_grouped_parameters, lr=lr, eps=1e-8) + + # lr scheduler + total_steps = len(train_dataloader) * NUM_EPOCHS + num_warmup_steps = int(WARMUP_FRACTION * total_steps) + lr_scheduler = get_linear_schedule_with_warmup( + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=total_steps, + ) + + # criterion + criterion = lambda inputs: inputs[0] + + # ============================== + # Boost with ColossalAI + # ============================== + model, optimizer, _, _, lr_scheduler = booster.boost(model, optimizer, lr_scheduler=lr_scheduler) + + # ============================== + # Benchmark model + # ============================== + + results = benchmark(model, + booster, + optimizer, + lr_scheduler, + train_dataloader, + criterion=criterion, + epoch_num=NUM_EPOCHS) + + coordinator.print_on_master(results) + + +if __name__ == '__main__': + main() diff --git a/examples/language/bert/benchmark.sh b/examples/language/bert/benchmark.sh new file mode 100755 index 000000000000..9453d1373f2f --- /dev/null +++ b/examples/language/bert/benchmark.sh @@ -0,0 +1,9 @@ +#!/bin/bash +set -xe + +pip install -r requirements.txt + +for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do + torchrun --standalone --nproc_per_node 2 benchmark.py --plugin $plugin --model_type "bert" + torchrun --standalone --nproc_per_node 2 benchmark.py --plugin $plugin --model_type "albert" +done diff --git a/examples/language/bert/benchmark_utils.py b/examples/language/bert/benchmark_utils.py new file mode 100644 index 000000000000..886017a41826 --- /dev/null +++ b/examples/language/bert/benchmark_utils.py @@ -0,0 +1,146 @@ +import inspect +from logging import getLogger +from time import time +from typing import Callable + +import torch +import yaml +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from torch.utils.data import DataLoader +from tqdm import tqdm + +from colossalai.booster import Booster +from colossalai.cluster import DistCoordinator + +logger = getLogger("colossalai-booster-benchmark") +_INVALID = float("nan") + + +def format_num(num: int, bytes=False): + """Scale bytes to its proper format, e.g. 1253656 => '1.20MB'""" + factor = 1024 if bytes else 1000 + suffix = "B" if bytes else "" + for unit in ["", " K", " M", " G", " T", " P"]: + if num < factor: + return f"{num:.2f}{unit}{suffix}" + num /= factor + + +def _is_valid(val): + return val == val + + +def get_call_arg_names(module_or_fn): + if isinstance(module_or_fn, torch.nn.Module): + return inspect.getfullargspec(module_or_fn.forward)[0][1:] + return inspect.getfullargspec(module_or_fn)[0] + + +def measure_params(model): + num_params = _INVALID + + try: + num_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + except AttributeError as e: + logger.error(f"Unable to measure model params due to error: {e}") + + return num_params + + +def warm_up( + model, + booster, + dataloader, + criterion, + optimizer, + lr_scheduler, + num_runs=10, +): + for i, data in enumerate(dataloader): + if i > num_runs: + break + inputs, labels = data[0].cuda(), data[1].cuda() + outputs = model(inputs, labels=labels) + loss = criterion(outputs) + booster.backward(loss, optimizer) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + +def fmt(d: dict): + return yaml.dump(d) + + +def benchmark( + model: torch.nn.Module, + booster: Booster, + optimizer: torch.optim.Optimizer, + lr_scheduler: LRScheduler, + dataloader: DataLoader, + criterion: Callable = None, + warm_up_fn=warm_up, + epoch_num: int = 3, + batch_size: int = 32, + warm_up_steps: int = 3, +): + results = {} + model_device = torch.cuda.current_device() + + # Warm up + warm_up_fn( + model, + booster, + dataloader, + criterion, + optimizer, + lr_scheduler, + num_runs=warm_up_steps, + ) + # Measure params + params = measure_params(model) + if _is_valid(params): + results["params"] = format_num(params) + logger.info(f"Model parameters: {params} ({format_num(params)})") + + # Measure Allocated Memory and Throughput + memory = {} + throughput = {} + torch.cuda.reset_peak_memory_stats(device=model_device) + pre_mem = torch.cuda.memory_allocated(device=model_device) + + start_time = time() + + for epoch in range(epoch_num): + with tqdm(dataloader, desc=f'Epoch [{epoch + 1}/{epoch_num}]', + disable=not DistCoordinator().is_master()) as pbar: + for data in pbar: + inputs, labels = data[0].cuda(), data[1].cuda() + outputs = model(inputs, labels=labels) + loss = criterion(outputs) + booster.backward(loss, optimizer) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + end_time = time() + + all_sample = epoch_num * len(dataloader) + + post_mem = torch.cuda.memory_allocated(device=model_device) + max_mem = torch.cuda.max_memory_allocated(device=model_device) + + memory[f"batch_size_{batch_size}"] = { + "cuda_pre_training_bytes": format_num(pre_mem, bytes=True), + "cuda_max_training_bytes": format_num(max_mem, bytes=True), + "cuda_post_training_bytes": format_num(post_mem, bytes=True), + } + logger.info(fmt({f"Memory results (batch_size={batch_size})": memory[f"batch_size_{batch_size}"]})) + + throughput[f"batch_size_{batch_size}"] = {"throughput:": "{:.1f}".format(all_sample * DistCoordinator().world_size / (end_time - start_time))} + logger.info(fmt({f"Throughput results (batch_size={batch_size})": throughput[f"batch_size_{batch_size}"]})) + + results["throughput"] = throughput + results["memory"] = memory + + return results diff --git a/examples/language/bert/data.py b/examples/language/bert/data.py new file mode 100644 index 000000000000..981cedcca8c2 --- /dev/null +++ b/examples/language/bert/data.py @@ -0,0 +1,127 @@ +import datasets +from transformers import AutoTokenizer, PreTrainedTokenizer + +from colossalai.booster.plugin.dp_plugin_base import DPPluginBase + + +class GLUEDataBuilder: + + task_text_field_map = { + "cola": ["sentence"], + "sst2": ["sentence"], + "mrpc": ["sentence1", "sentence2"], + "qqp": ["question1", "question2"], + "stsb": ["sentence1", "sentence2"], + "mnli": ["premise", "hypothesis"], + "qnli": ["question", "sentence"], + "rte": ["sentence1", "sentence2"], + "wnli": ["sentence1", "sentence2"], + "ax": ["premise", "hypothesis"], + } + + glue_task_num_labels = { + "cola": 2, + "sst2": 2, + "mrpc": 2, + "qqp": 2, + "stsb": 1, + "mnli": 3, + "qnli": 2, + "rte": 2, + "wnli": 2, + "ax": 3, + } + + loader_columns = [ + "datasets_idx", + "input_ids", + "token_type_ids", + "attention_mask", + "start_positions", + "end_positions", + "labels", + ] + + def __init__( + self, + model_name_or_path: str, + plugin: DPPluginBase, + task_name: str = "mrpc", + max_seq_length: int = 128, + train_batch_size: int = 32, + eval_batch_size: int = 32, + **kwargs, + ): + super().__init__() + self.model_name_or_path = model_name_or_path + self.task_name = task_name + self.max_seq_length = max_seq_length + self.train_batch_size = train_batch_size + self.eval_batch_size = eval_batch_size + self.plugin = plugin + + self.text_fields = self.task_text_field_map[task_name] + self.num_labels = self.glue_task_num_labels[task_name] + self.tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + self.setup() + + def setup(self): + self.dataset = datasets.load_dataset("glue", self.task_name) + + for split in self.dataset.keys(): + self.dataset[split] = self.dataset[split].map( + self.convert_to_features, + batched=True, + remove_columns=["label"], + ) + self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] + self.dataset[split].set_format(type="torch", columns=self.columns) + + self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] + + def prepare_data(self): + datasets.load_dataset("glue", self.task_name) + AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + + def train_dataloader(self): + return self.plugin.prepare_dataloader(self.dataset["train"], + batch_size=self.train_batch_size, + shuffle=True, + drop_last=True) + + def val_dataloader(self): + if len(self.eval_splits) == 1: + return self.plugin.prepare_dataloader(self.dataset["validation"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [ + self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size) + for x in self.eval_splits + ] + + def test_dataloader(self): + if len(self.eval_splits) == 1: + return self.plugin.prepare_dataloader(self.dataset["test"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [ + self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size) + for x in self.eval_splits + ] + + def convert_to_features(self, example_batch): + + # Either encode single sentence or sentence pairs + if len(self.text_fields) > 1: + texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) + else: + texts_or_text_pairs = example_batch[self.text_fields[0]] + + # Tokenize the text/text pairs + features = self.tokenizer.batch_encode_plus(texts_or_text_pairs, + max_length=self.max_seq_length, + padding='max_length', + truncation=True) + + # Rename label to labels to make it easier to pass to model forward + features["labels"] = example_batch["label"] + + return features diff --git a/examples/language/bert/finetune.py b/examples/language/bert/finetune.py new file mode 100644 index 000000000000..b209ffde85a4 --- /dev/null +++ b/examples/language/bert/finetune.py @@ -0,0 +1,220 @@ +import argparse +from typing import List, Union + +import evaluate +import torch +import torch.distributed as dist +import torch.nn as nn +from data import GLUEDataBuilder +from torch.optim import Optimizer +from torch.utils.data import DataLoader +from tqdm import tqdm +from transformers import ( + AlbertForSequenceClassification, + AutoConfig, + BertForSequenceClassification, + get_linear_schedule_with_warmup, +) + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.cluster import DistCoordinator +from colossalai.nn.optimizer import HybridAdam +from colossalai.utils import get_current_device + +# ============================== +# Prepare Hyperparameters +# ============================== +NUM_EPOCHS = 3 +BATCH_SIZE = 32 +LEARNING_RATE = 2.4e-5 +WEIGHT_DECAY = 0.01 +WARMUP_FRACTION = 0.1 + + +def move_to_cuda(batch): + return {k: v.cuda() for k, v in batch.items()} + + +@torch.no_grad() +def evaluate_model(model: nn.Module, test_dataloader: Union[DataLoader, List[DataLoader]], num_labels: int, task_name: str, + eval_splits: List[str], coordinator: DistCoordinator): + metric = evaluate.load("glue", task_name, process_id=coordinator.rank, num_process=coordinator.world_size) + model.eval() + + def evaluate_subset(dataloader: DataLoader): + accum_loss = torch.zeros(1, device=get_current_device()) + for batch in dataloader: + batch = move_to_cuda(batch) + outputs = model(**batch) + val_loss, logits = outputs[:2] + accum_loss.add_(val_loss) + + if num_labels > 1: + preds = torch.argmax(logits, axis=1) + elif num_labels == 1: + preds = logits.squeeze() + + labels = batch["labels"] + + metric.add_batch(predictions=preds, references=labels) + + results = metric.compute() + dist.all_reduce(accum_loss.div_(len(dataloader))) + if coordinator.is_master(): + results['loss'] = accum_loss.item() / coordinator.world_size + return results + + if isinstance(test_dataloader, DataLoader): + return evaluate_subset(test_dataloader) + else: + assert len(test_dataloader) == len(eval_splits) + final_results = {} + for split, sub_loader in zip(eval_splits, test_dataloader): + results = evaluate_subset(sub_loader) + final_results.update({f'{k}_{split}': v for k, v in results.items()}) + return final_results + + +def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, lr_scheduler, train_dataloader: DataLoader, + booster: Booster, coordinator: DistCoordinator): + model.train() + with tqdm(train_dataloader, desc=f'Epoch [{epoch + 1}/{NUM_EPOCHS}]', disable=not coordinator.is_master()) as pbar: + for batch in pbar: + # Forward pass + batch = move_to_cuda(batch) + outputs = model(**batch) + loss = outputs[0] + + # Backward and optimize + booster.backward(loss, optimizer) + optimizer.step() + optimizer.zero_grad() + lr_scheduler.step() + + # Print log info + pbar.set_postfix({'loss': loss.item()}) + + +def main(): + # ============================== + # Parse Arguments + # ============================== + parser = argparse.ArgumentParser() + parser.add_argument('-t', '--task', default='mrpc', help="GLUE task to run") + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'], + help="plugin to use") + parser.add_argument( + "--model_type", + type=str, + default="bert", + help="bert or albert", + ) + parser.add_argument('--target_f1', type=float, default=None, help="target f1 score. Raise exception if not reached") + args = parser.parse_args() + + if args.model_type == 'bert': + model_name = "bert-base-uncased" + elif args.model_type == 'albert': + model_name = "albert-xxlarge-v2" + else: + raise RuntimeError + # ============================== + # Launch Distributed Environment + # ============================== + colossalai.launch_from_torch(config={}, seed=42) + coordinator = DistCoordinator() + + # local_batch_size = BATCH_SIZE // coordinator.world_size + lr = LEARNING_RATE * coordinator.world_size + + # ============================== + # Instantiate Plugin and Booster + # ============================== + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + + booster = Booster(plugin=plugin, **booster_kwargs) + + # ============================== + # Prepare Dataloader + # ============================== + data_builder = GLUEDataBuilder(model_name, + plugin, + args.task, + train_batch_size=BATCH_SIZE, + eval_batch_size=BATCH_SIZE) + train_dataloader = data_builder.train_dataloader() + test_dataloader = data_builder.test_dataloader() + + # ==================================== + # Prepare model, optimizer + # ==================================== + # bert pretrained model + + cfg = AutoConfig.from_pretrained(model_name, num_labels=data_builder.num_labels) + if model_name == "bert-base-uncased": + model = BertForSequenceClassification.from_pretrained(model_name, config=cfg) + elif model_name == "albert-xxlarge-v2": + model = AlbertForSequenceClassification.from_pretrained(model_name, config=cfg) + else: + raise RuntimeError + + # optimizer + no_decay = ["bias", "LayerNorm.weight"] + optimizer_grouped_parameters = [ + { + "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], + "weight_decay": WEIGHT_DECAY, + }, + { + "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], + "weight_decay": 0.0, + }, + ] + + optimizer = HybridAdam(optimizer_grouped_parameters, lr=lr, eps=1e-8) + + # lr scheduler + total_steps = len(train_dataloader) * NUM_EPOCHS + num_warmup_steps = int(WARMUP_FRACTION * total_steps) + lr_scheduler = get_linear_schedule_with_warmup( + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=total_steps, + ) + + # ============================== + # Boost with ColossalAI + # ============================== + model, optimizer, _, _, lr_scheduler = booster.boost(model, optimizer, lr_scheduler=lr_scheduler) + + # ============================== + # Train model + # ============================== + for epoch in range(NUM_EPOCHS): + train_epoch(epoch, model, optimizer, lr_scheduler, train_dataloader, booster, coordinator) + + results = evaluate_model(model, test_dataloader, data_builder.num_labels, args.task, data_builder.eval_splits, + coordinator) + + if coordinator.is_master(): + print(results) + if args.target_f1 is not None and 'f1' in results: + assert results['f1'] >= args.target_f1, f'f1 score {results["f1"]} is lower than target {args.target_f1}' + + +if __name__ == '__main__': + main() diff --git a/examples/language/bert/requirements.txt b/examples/language/bert/requirements.txt new file mode 100644 index 000000000000..377422c260ad --- /dev/null +++ b/examples/language/bert/requirements.txt @@ -0,0 +1,9 @@ +colossalai +evaluate +datasets +torch +tqdm +transformers +scipy +scikit-learn +ptflops diff --git a/examples/language/bert/run_gemini.sh b/examples/language/bert/run_gemini.sh deleted file mode 100644 index d791334e8c97..000000000000 --- a/examples/language/bert/run_gemini.sh +++ /dev/null @@ -1,22 +0,0 @@ -set -x -# distplan in ["CAI_ZeRO1", "CAI_ZeRO2", "CAI_Gemini", "Pytorch_DDP", "Pytorch_ZeRO"] -export DISTPLAN=${DISTPLAN:-"CAI_Gemini"} - -# The following options only valid when DISTPLAN="colossalai" -export GPUNUM=${GPUNUM:-1} -export PLACEMENT=${PLACEMENT:-"cpu"} -export BATCH_SIZE=${BATCH_SIZE:-16} - -# bert | albert -export MODEL_TYPE=${MODEL_TYPE:-"bert"} -export TRAIN_STEP=${TRAIN_STEP:-10} - -mkdir -p gemini_logs - -env CUDA_LAUNCH_BLOCKING=1 torchrun --standalone --nproc_per_node=${GPUNUM} ./train_bert_demo.py \ ---model_type=${MODEL_TYPE} \ ---batch_size=${BATCH_SIZE} \ ---placement=${PLACEMENT} \ ---distplan=${DISTPLAN} \ ---train_step=${TRAIN_STEP} \ -2>&1 | tee ./gemini_logs/${MODEL_TYPE}_${DISTPLAN}_gpu_${GPUNUM}_bs_${BATCH_SIZE}_${PLACEMENT}.log diff --git a/examples/language/bert/test_ci.sh b/examples/language/bert/test_ci.sh old mode 100644 new mode 100755 index 42c63fec50c0..7fc6daabb2f3 --- a/examples/language/bert/test_ci.sh +++ b/examples/language/bert/test_ci.sh @@ -1,2 +1,8 @@ -set -x -env GPUNUM=1 bash run_gemini.sh +#!/bin/bash +set -xe + +pip install -r requirements.txt + +for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do + torchrun --standalone --nproc_per_node 4 finetune.py --target_f1 0.86 --plugin $plugin --model_type "bert" +done diff --git a/examples/language/bert/train_bert_demo.py b/examples/language/bert/train_bert_demo.py deleted file mode 100644 index 9a0278b2c711..000000000000 --- a/examples/language/bert/train_bert_demo.py +++ /dev/null @@ -1,331 +0,0 @@ -import os -from functools import partial -from time import time - -import psutil -import torch -from packaging import version -from torch import nn -from torch.nn.parallel import DistributedDataParallel as DDP -from transformers import AlbertConfig, AlbertForSequenceClassification, BertConfig, BertForSequenceClassification - -import colossalai -from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn.optimizer import HybridAdam -from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec -from colossalai.utils import get_current_device -from colossalai.zero import ColoInitContext, zero_model_wrapper, zero_optim_wrapper - -CAI_VERSION = colossalai.__version__ - - -def get_tflops(model_numel, batch_size, seq_len, step_time): - return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12) - - -def get_profile_context(enable_flag, warmup_steps, active_steps, save_dir): - from contextlib import nullcontext - - from torch.profiler import ProfilerActivity, profile, schedule, tensorboard_trace_handler - if enable_flag: - return profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], - schedule=schedule(wait=0, warmup=warmup_steps, active=active_steps), - on_trace_ready=tensorboard_trace_handler(save_dir), - record_shapes=True, - profile_memory=True) - else: - - class DummyProfiler: - - def __init__(self): - self.step_number = 0 - - def step(self): - self.step_number += 1 - - return nullcontext(DummyProfiler()) - - -def get_time_stamp(): - import time - cur_time = time.strftime("%d-%H:%M", time.localtime()) - return cur_time - - -def get_bert_data(batch_size: int, sequence_length: int, vacob_size: int, n_class: int, device: torch.device): - input = torch.randint( - low=0, - high=vacob_size, - size=(batch_size, sequence_length), - device=device, - dtype=torch.long, - ) - label = torch.randint(low=0, high=n_class, size=(batch_size,), device=device, dtype=torch.long) - return input, label - - -def parse_args(): - parser = colossalai.get_default_parser() - parser.add_argument( - "--distplan", - type=str, - default='CAI_Gemini', - help="The distributed plan [colossalai, zero1, zero2, torch_ddp, torch_zero].", - ) - parser.add_argument( - "--placement", - type=str, - default='cpu', - help="Placement Policy for Gemini. Valid when using colossalai as dist plan.", - ) - parser.add_argument( - "--batch_size", - type=int, - default=8, - help="batch size per DP group of training.", - ) - parser.add_argument( - "--model_type", - type=str, - default="bert", - help="bert or albert", - ) - parser.add_argument( - "--train_step", - type=int, - default=10, - help="training iterations for test", - ) - - args = parser.parse_args() - return args - - -SEQ_LEN = 512 -VOCAB_SIZE = 1000 -NUM_LABELS = 10 - - -# Parameter Sharding Strategies for Tensor Parallelism -def split_param_single_dim_tp1d(dim: int, param: ColoParameter, pg: ProcessGroup): - spec = (ShardSpec([dim], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) - param.set_tensor_spec(*spec) - - -def split_param_row_tp1d(param: ColoParameter, pg: ProcessGroup): - split_param_single_dim_tp1d(0, param, pg) - - -def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup): - split_param_single_dim_tp1d(-1, param, pg) - - -def get_cpu_mem(): - return psutil.Process().memory_info().rss / 1024**2 - - -def get_gpu_mem(): - return torch.cuda.memory_allocated() / 1024**2 - - -def get_mem_info(prefix=''): - return f'{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB' - - -def get_model_size(model: nn.Module): - total_numel = 0 - for module in model.modules(): - for p in module.parameters(recurse=False): - total_numel += p.numel() - return total_numel - - -def model_builder(args): - if args.model_type == "bert": - cfg = BertConfig(vocab_size=VOCAB_SIZE, num_labels=NUM_LABELS) - return BertForSequenceClassification(cfg) - elif args.model_type == "albert": - cfg = AlbertConfig(vocab_size=VOCAB_SIZE, num_labels=NUM_LABELS) - return AlbertForSequenceClassification(cfg) - else: - raise RuntimeError - - -def model_size_formatter(numel: int) -> str: - GB_SIZE = 10**9 - MB_SIZE = 10**6 - KB_SIZE = 10**3 - if numel >= GB_SIZE: - return f'{numel / GB_SIZE:.1f}B' - elif numel >= MB_SIZE: - return f'{numel / MB_SIZE:.1f}M' - elif numel >= KB_SIZE: - return f'{numel / KB_SIZE:.1f}K' - else: - return str(numel) - - -def set_cpu_maximum_parallelism(): - conf_str = torch.__config__.parallel_info() - inter_str = conf_str.split("hardware_concurrency() : ")[1] - max_concurrency = inter_str.split('\n')[0] - os.environ["OMP_NUM_THREADS"] = max_concurrency - print(f"environmental variable OMP_NUM_THREADS is set to {max_concurrency}.") - - -def main(): - # version check - # this example is supposed to work for versions greater than 0.2.0 - assert version.parse(CAI_VERSION) >= version.parse("0.2.0") - - set_cpu_maximum_parallelism() - args = parse_args() - - # if args.distplan not in ["colossalai", "torch_ddp", "torch_zero", "zero1", "zero2"]: - if args.distplan not in ["CAI_ZeRO1", "CAI_ZeRO2", "CAI_Gemini", "Pytorch_DDP", "Pytorch_ZeRO"]: - raise TypeError(f"{args.distplan} is error") - - # batch size per DP degree - BATCH_SIZE = args.batch_size - - NUM_STEPS = args.train_step - - WARMUP_STEPS = 1 - assert WARMUP_STEPS < NUM_STEPS, "warmup steps should smaller than the total steps" - assert (NUM_STEPS - WARMUP_STEPS) % 2 == 1, "the number of valid steps should be odd to take the median" - PROF_FLAG = False # The flag of profiling, False by default - - disable_existing_loggers() - colossalai.launch_from_torch(config={}) - - logger = get_dist_logger() - logger.info(f" {args.distplan}, batch size {BATCH_SIZE}", ranks=[0]) - - torch.manual_seed(123) - if args.distplan.startswith("CAI"): - # all param must use the same process group. - world_size = torch.distributed.get_world_size() - - # build a base-bert model - with ColoInitContext(device=get_current_device(), dtype=torch.half): - model = model_builder(args) - # model = BertForSequenceClassification(BertConfig(vocal_size = VOCAB_SIZE)) - - # asign running configurations - gemini_config = None - if args.distplan.startswith("CAI_ZeRO"): - optim_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True, verbose=True) - elif args.distplan == "CAI_Gemini": - gemini_config = dict(strict_ddp_mode=True, - device=get_current_device(), - placement_policy=args.placement, - pin_memory=True, - hidden_dim=model.config.hidden_size, - search_range_mb=128) - optim_config = dict(gpu_margin_mem_ratio=0.) - else: - raise RuntimeError - - # build a highly optimized gpu/cpu optimizer - optimizer = HybridAdam(model.parameters(), lr=1e-3) - - if args.distplan == "CAI_ZeRO1": - zero_stage = 1 - elif args.distplan == "CAI_ZeRO2": - zero_stage = 2 - elif args.distplan == "CAI_Gemini": - zero_stage = 3 - else: - raise RuntimeError - - # wrap your model and optimizer - model = zero_model_wrapper(model, zero_stage, gemini_config) - optimizer = zero_optim_wrapper(model, optimizer, optim_config=optim_config) - - logger.info(get_mem_info(prefix='After init optim, '), ranks=[0]) - elif args.distplan.startswith("Pytorch"): - model = model_builder(args).cuda() - model = DDP(model) - if args.distplan.endswith("DDP"): - optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) - elif args.distplan.endswith("ZeRO"): - from torch.distributed.optim import ZeroRedundancyOptimizer - optimizer = ZeroRedundancyOptimizer(model.parameters(), optimizer_class=torch.optim.Adam, lr=1e-3) - else: - raise RuntimeError - - # model is shared after TP - numel = get_model_size(model) - logger.info(f"the size of testing model size is {model_size_formatter(numel)}.") - logger.info(get_mem_info(prefix='After init model, '), ranks=[0]) - - # Tflops_per_GPU = global_batch * global_numel * seq_len * 8 / #gpu - # = (batch_per_DP_group * dp_degree) * (numel * tp_degree) * seq_len * 8 / (tp_degree * dp_degree) - # = batch_per_DP_group * numel * seq_len * 8 - get_tflops_func = partial(get_tflops, numel, BATCH_SIZE, SEQ_LEN) - - torch.cuda.synchronize() - model.train() - tflops_list = [] - - def train_step(): - # we just use randomly generated data here - input_ids, labels = get_bert_data(BATCH_SIZE, - SEQ_LEN, - VOCAB_SIZE, - NUM_LABELS, - device=torch.cuda.current_device()) - optimizer.zero_grad() - - start = time() - outputs = model(input_ids, labels=labels) - loss, logits = outputs[:2] - torch.cuda.synchronize() - fwd_end = time() - fwd_time = fwd_end - start - logger.info(get_mem_info(prefix=f'[{n + 1}/{NUM_STEPS}] Forward '), ranks=[0]) - - if args.distplan.startswith("CAI"): - optimizer.backward(loss) - elif args.distplan.startswith("Pytorch"): - loss.backward() - else: - raise RuntimeError - - torch.cuda.synchronize() - bwd_end = time() - bwd_time = bwd_end - fwd_end - logger.info(get_mem_info(prefix=f'[{n + 1}/{NUM_STEPS}] Backward '), ranks=[0]) - - optimizer.step() - torch.cuda.synchronize() - optim_time = time() - bwd_end - step_time = time() - start - logger.info(get_mem_info(prefix=f'[{n + 1}/{NUM_STEPS}] Optimizer step '), ranks=[0]) - - step_tflops = get_tflops_func(step_time) - logger.info( - f"[{n + 1}/{NUM_STEPS}] Loss:{loss.item():.3f}, Step time: {step_time:.3f}s, TFLOPS: {get_tflops_func(step_time):.3f}, FWD time: {fwd_time:.3f}s, BWD time: {bwd_time:.3f}s, OPTIM time: {optim_time:.3f}s", - ranks=[0], - ) - if n >= WARMUP_STEPS: - tflops_list.append(step_tflops) - - demo_profiler = get_profile_context(PROF_FLAG, - WARMUP_STEPS, - NUM_STEPS - WARMUP_STEPS, - save_dir=f"profile/{get_time_stamp()}-demo") - - with demo_profiler as prof: - for n in range(NUM_STEPS): - train_step() - prof.step() - - tflops_list.sort() - median_index = ((NUM_STEPS - WARMUP_STEPS) >> 1) + WARMUP_STEPS - logger.info(f"Median TFLOPS is {tflops_list[median_index]:.3f}") - torch.cuda.synchronize() - - -if __name__ == '__main__': - main() From b306cecf28dba07dbdec5f5e1aca1087a11071fb Mon Sep 17 00:00:00 2001 From: Liu Ziming <38985202+MaruyamaAya@users.noreply.github.com> Date: Wed, 7 Jun 2023 16:05:00 +0800 Subject: [PATCH 285/413] [example] Modify palm example with the new booster API (#3913) * Modify torch version requirement to adapt torch 2.0 * modify palm example using new booster API * roll back * fix port * polish * polish --- examples/language/palm/README.md | 3 ++ examples/language/palm/run.sh | 8 +++-- examples/language/palm/test_ci.sh | 2 +- examples/language/palm/train.py | 50 +++++++++++++++---------------- 4 files changed, 34 insertions(+), 29 deletions(-) diff --git a/examples/language/palm/README.md b/examples/language/palm/README.md index 486bf240f89c..3ff3939d63d4 100644 --- a/examples/language/palm/README.md +++ b/examples/language/palm/README.md @@ -43,6 +43,9 @@ palm = PaLM( ) ``` +## New API +We have modified our previous implementation of PaLM with our new Booster API, which offers a more flexible and efficient way to train your model. The new API is more user-friendly and easy to use. You can find the new API in train.py. We have also offer a shell script test_ci.sh for you to go through all our plugins for the booster. For more information about the booster API you can refer to https://colossalai.org/docs/basics/booster_api/. + ## Test on Enwik8 ```bash diff --git a/examples/language/palm/run.sh b/examples/language/palm/run.sh index 7a533509e009..2a846e81a9a7 100644 --- a/examples/language/palm/run.sh +++ b/examples/language/palm/run.sh @@ -3,9 +3,11 @@ export DISTPAN="colossalai" # The following options only valid when DISTPAN="colossalai" export TPDEGREE=1 -export GPUNUM=1 +export GPUNUM=4 export PLACEMENT='cpu' export USE_SHARD_INIT=False -export BATCH_SIZE=4 +export BATCH_SIZE=1 -env OMP_NUM_THREADS=12 torchrun --standalone --nproc_per_node=${GPUNUM} --master_port 29501 train.py --tp_degree=${TPDEGREE} --batch_size=${BATCH_SIZE} --placement ${PLACEMENT} --shardinit ${USE_SHARD_INIT} --distplan ${DISTPAN} 2>&1 | tee run.log +env OMP_NUM_THREADS=12 torchrun --standalone --nproc_per_node=${GPUNUM} --master_port 29501 train.py \ +--dummy_data=True --tp_degree=${TPDEGREE} --batch_size=${BATCH_SIZE} --plugin='gemini' \ +--placement ${PLACEMENT} --shardinit ${USE_SHARD_INIT} --distplan ${DISTPAN} 2>&1 | tee run.log diff --git a/examples/language/palm/test_ci.sh b/examples/language/palm/test_ci.sh index f21095578077..4de6a44e5bf7 100644 --- a/examples/language/palm/test_ci.sh +++ b/examples/language/palm/test_ci.sh @@ -4,6 +4,6 @@ for BATCH_SIZE in 2 do for GPUNUM in 1 4 do -env OMP_NUM_THREADS=12 torchrun --standalone --nproc_per_node=${GPUNUM} --master_port 29501 train.py --dummy_data=True --batch_size=${BATCH_SIZE} 2>&1 | tee run.log +env OMP_NUM_THREADS=12 torchrun --standalone --nproc_per_node=${GPUNUM} --standalone train.py --dummy_data=True --batch_size=${BATCH_SIZE} --plugin='gemini' 2>&1 | tee run.log done done diff --git a/examples/language/palm/train.py b/examples/language/palm/train.py index b16da1c7744a..62062e8bd272 100644 --- a/examples/language/palm/train.py +++ b/examples/language/palm/train.py @@ -9,6 +9,8 @@ import torch.optim as optim import tqdm from packaging import version + +from colossalai.nn import HybridAdam from palm_pytorch import PaLM from palm_pytorch.autoregressive_wrapper import AutoregressiveWrapper from torch.utils.data import DataLoader, Dataset @@ -18,6 +20,8 @@ from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec from colossalai.utils import MultiTimer, get_current_device from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, ZeroDDP +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin # constants @@ -58,6 +62,12 @@ def parse_args(): help= "Shard the tensors when init the model to shrink peak memory size on the assigned device. Valid when using colossalai as dist plan.", ) + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'], + help="plugin to use") parser.add_argument( "--batch_size", type=int, @@ -101,28 +111,6 @@ def get_model_size(model: nn.Module): return total_numel -# Gemini + ZeRO DDP -def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: str = "auto"): - cai_version = colossalai.__version__ - if version.parse(cai_version) > version.parse("0.1.10"): - from colossalai.nn.parallel import GeminiDDP - model = GeminiDDP(model, - device=get_current_device(), - placement_policy=placement_policy, - pin_memory=True, - search_range_mb=32) - elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"): - from colossalai.gemini import ChunkManager, GeminiManager - chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32) - gemini_manager = GeminiManager(placement_policy, chunk_manager) - chunk_manager = ChunkManager(chunk_size, - pg, - enable_distributed_storage=True, - init_device=GeminiManager.get_default_device(placement_policy)) - model = ZeroDDP(model, gemini_manager) - else: - raise NotImplemented(f"CAI version {cai_version} is not supported") - return model # Parameter Sharding Strategies for Tensor Parallelism @@ -218,6 +206,18 @@ def __len__(self): if args.distplan == "colossalai": # instantiate GPT-like decoder model + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy=args.placement, strict_ddp_mode=True, initial_scale=2 ** 5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2 ** 5) + logger.info(f"plugin: {plugin}") + booster = Booster(plugin=plugin, **booster_kwargs) + default_pg = ProcessGroup(tp_degree=args.tp_degree) default_dist_spec = ShardSpec([-1], [args.tp_degree]) if args.shardinit else None ctx = ColoInitContext(device='cpu', default_dist_spec=default_dist_spec, default_pg=default_pg) @@ -228,12 +228,12 @@ def __len__(self): pg = default_pg tensor_parallelize(model, pg) - model = gemini_zero_dpp(model, pg, args.placement) # optimizer - #optimizer = GeminiAdamOptimizer(model, lr=1e-7, initial_scale=2**5) - optimizer = GeminiAdamOptimizer(model, lr=LEARNING_RATE, initial_scale=2**5) + optimizer = HybridAdam(model.parameters(), lr=LEARNING_RATE, initial_scale=2**5) + model, optimizer, _, _, _ = booster.boost(model, optimizer) + else: model = PaLM(num_tokens=256, dim=512, depth=8) model = AutoregressiveWrapper(model, max_seq_len=2048) From a9d1cadc49bd0a37208d8d7f321f16fd37c41471 Mon Sep 17 00:00:00 2001 From: digger yu Date: Wed, 7 Jun 2023 16:08:37 +0800 Subject: [PATCH 286/413] fix typo with colossalai/trainer utils zero (#3908) --- colossalai/trainer/_trainer.py | 8 ++++---- colossalai/utils/data_sampler/data_parallel_sampler.py | 2 +- colossalai/utils/model/utils.py | 2 +- colossalai/utils/profiler/legacy/comm_profiler.py | 8 ++++---- colossalai/utils/profiler/legacy/pcie_profiler.py | 6 +++--- colossalai/utils/profiler/legacy/prof_utils.py | 4 ++-- colossalai/utils/rank_recorder/README.md | 4 ++-- colossalai/utils/rank_recorder/rank_recorder.py | 4 ++-- colossalai/zero/gemini/chunk/chunk.py | 2 +- colossalai/zero/gemini/chunk/manager.py | 2 +- .../zero/gemini/memory_tracer/chunk_memstats_collector.py | 2 +- colossalai/zero/gemini/memory_tracer/memory_monitor.py | 4 ++-- colossalai/zero/gemini/utils.py | 2 +- colossalai/zero/legacy/gemini/ophooks/utils.py | 4 ++-- colossalai/zero/legacy/gemini/tensor_utils.py | 2 +- 15 files changed, 28 insertions(+), 28 deletions(-) diff --git a/colossalai/trainer/_trainer.py b/colossalai/trainer/_trainer.py index 60bbc4eeee32..bfe1c403fd48 100644 --- a/colossalai/trainer/_trainer.py +++ b/colossalai/trainer/_trainer.py @@ -31,9 +31,9 @@ class Trainer: >>> # Initialize your engine, train_dataloader, test_dataloader, lr_scheduler >>> engine, train_dataloader, _, _ = colossalai.initialize(model, optimizer, criterion) >>> # Beginning training progress - >>> timier = ... + >>> timer = ... >>> logger = ... - >>> trainer = Trainer(engine=engine, logger=logger, timer=timier) + >>> trainer = Trainer(engine=engine, logger=logger, timer=timer) >>> # add hooks you would like to use here. >>> hook_list = [] >>> trainer.fit( @@ -56,7 +56,7 @@ def __init__( timer: MultiTimer = None, logger: DistributedLogger = None, ): - # training-ralated params + # training-related params self._engine = engine self._max_epochs = 0 self._cur_epoch = 0 @@ -118,7 +118,7 @@ def _set_current_step(self, epoch: int): self._cur_step = epoch * self._steps_per_epoch def _call_timer(self, action: str, item: str, *args, **kwargs) -> None: - """Call timer funciton with a given timer name. + """Call timer function with a given timer name. Args: action (str): Function to be called on timer. diff --git a/colossalai/utils/data_sampler/data_parallel_sampler.py b/colossalai/utils/data_sampler/data_parallel_sampler.py index 945dc54b397a..2318e07a7f8d 100644 --- a/colossalai/utils/data_sampler/data_parallel_sampler.py +++ b/colossalai/utils/data_sampler/data_parallel_sampler.py @@ -1,6 +1,6 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -# adpated from torch.utils.data.DistributedSampler +# adapted from torch.utils.data.DistributedSampler import math import random diff --git a/colossalai/utils/model/utils.py b/colossalai/utils/model/utils.py index f49607376439..21bc530934d3 100644 --- a/colossalai/utils/model/utils.py +++ b/colossalai/utils/model/utils.py @@ -70,7 +70,7 @@ def _init_subclass(cls, **kwargs): cls.__init__ = preprocess_after(cls.__init__) # Replace .__init__() for all existing subclasses of torch.nn.Module - # Excution self._post_init_method after the default init function. + # Execution self._post_init_method after the default init function. substitute_init_recursively(torch.nn.modules.module.Module, _enable_class, set()) # holding on to the current __init__subclass__ for exit diff --git a/colossalai/utils/profiler/legacy/comm_profiler.py b/colossalai/utils/profiler/legacy/comm_profiler.py index a4f5729c97ec..334f0113ee90 100644 --- a/colossalai/utils/profiler/legacy/comm_profiler.py +++ b/colossalai/utils/profiler/legacy/comm_profiler.py @@ -111,7 +111,7 @@ def append(s: str = None): res.append(sep) if self.warn_flag: - append("Warnning: there exists multiple communication operations in the same time. As a result, " + append("Warning: there exists multiple communication operations in the same time. As a result, " "the profiling result is not accurate.") if self.total_cuda_time == 0: @@ -123,12 +123,12 @@ def append(s: str = None): append("total number of calls: {}".format(self.total_count)) append("All events:") - seperation = '-' * 74 + separation = '-' * 74 row_format = '{:^10}' + '{:^12}' * 2 + '{:^16}' + '{:^12}' * 2 - append(seperation) + append(separation) append(row_format.format('Location', 'GPU time', 'Percentage', 'Comm volume', 'Bandwidth', 'Num of calls')) - append(seperation) + append(separation) show_list = sorted(self.ops_record.items(), key=lambda kv: -kv[1].self_cuda_time) for location, event in show_list: diff --git a/colossalai/utils/profiler/legacy/pcie_profiler.py b/colossalai/utils/profiler/legacy/pcie_profiler.py index 526222941ef9..8f812f5cfc7b 100644 --- a/colossalai/utils/profiler/legacy/pcie_profiler.py +++ b/colossalai/utils/profiler/legacy/pcie_profiler.py @@ -130,12 +130,12 @@ def append(s: str = None): append("Possible data transmission events in PCIE:") - seperation = '-' * 62 + separation = '-' * 62 row_format = '{:^10}' + '{:^12}' + '{:^16}' + '{:^12}' * 2 - append(seperation) + append(separation) append(row_format.format('Location', 'GPU time', 'Trans volume', 'Bandwidth', 'Num of calls')) - append(seperation) + append(separation) show_list = sorted(self.ops_record.items(), key=lambda kv: -kv[1].cuda_time) for location, event in show_list: diff --git a/colossalai/utils/profiler/legacy/prof_utils.py b/colossalai/utils/profiler/legacy/prof_utils.py index 87ad644a7ecc..2f7eee827651 100644 --- a/colossalai/utils/profiler/legacy/prof_utils.py +++ b/colossalai/utils/profiler/legacy/prof_utils.py @@ -32,9 +32,9 @@ def _format_memory(nbytes): return str(nbytes) + ' B' -def _format_bandwidth(volme: float or int, time_us: int): +def _format_bandwidth(volume: float or int, time_us: int): sec_div_mb = (1000.0 / 1024.0)**2 - mb_per_sec = volme / time_us * sec_div_mb + mb_per_sec = volume / time_us * sec_div_mb if mb_per_sec >= 1024.0: return '{:.3f} GB/s'.format(mb_per_sec / 1024.0) diff --git a/colossalai/utils/rank_recorder/README.md b/colossalai/utils/rank_recorder/README.md index e30a925d2a92..da8a6039d543 100644 --- a/colossalai/utils/rank_recorder/README.md +++ b/colossalai/utils/rank_recorder/README.md @@ -1,5 +1,5 @@ # Rank Recorder -This is a useful tool to get the records of certain functions in each rank. The records of each rank will dump into a json file after the end of multiple process program. You can parse and visualise the json file easily. +This is a useful tool to get the records of certain functions in each rank. The records of each rank will dump into a json file after the end of multiple process program. You can parse and visualize the json file easily. Before using the tool, you should ensure dist.is_initialized() return true before exit of program. @@ -20,7 +20,7 @@ with recorder(record_name, current_rank) as r: ``` ## Example -This is a demo to display kernel select in cuda and visualise the cost of several procedures in each rank. +This is a demo to display kernel select in cuda and visualize the cost of several procedures in each rank. ```python import time diff --git a/colossalai/utils/rank_recorder/rank_recorder.py b/colossalai/utils/rank_recorder/rank_recorder.py index c088ceeb2e87..40bb7e184a12 100644 --- a/colossalai/utils/rank_recorder/rank_recorder.py +++ b/colossalai/utils/rank_recorder/rank_recorder.py @@ -133,7 +133,7 @@ def merge_recode(self): with open(self.export_name + '.json', 'w', encoding='utf-8') as f: json.dump(recoders, f, ensure_ascii=False) - def visualise_record(self): + def visualize_record(self): with open(self.export_name + '.json', 'r', encoding='utf-8') as f: records = json.load(f) records = dict(records) @@ -171,7 +171,7 @@ def exit_worker(self): if rank == 1: # take the base time of rank 0 as standard self.merge_recode() - self.visualise_record() + self.visualize_record() recorder = Recorder() diff --git a/colossalai/zero/gemini/chunk/chunk.py b/colossalai/zero/gemini/chunk/chunk.py index a7682eaf62e9..51da9be2b1f8 100644 --- a/colossalai/zero/gemini/chunk/chunk.py +++ b/colossalai/zero/gemini/chunk/chunk.py @@ -416,7 +416,7 @@ def copy_tensor_to_chunk_slice(self, tensor: torch.Tensor, data_slice: torch.Ten Copy data slice to the memory space indexed by the input tensor in the chunk. Args: - tensor (torch.Tensor): the tensor used to retrive meta information + tensor (torch.Tensor): the tensor used to retrieve meta information data_slice (torch.Tensor): the tensor to be copied to the chunk """ # sanity check diff --git a/colossalai/zero/gemini/chunk/manager.py b/colossalai/zero/gemini/chunk/manager.py index 77368d06d255..38d34f14863e 100644 --- a/colossalai/zero/gemini/chunk/manager.py +++ b/colossalai/zero/gemini/chunk/manager.py @@ -157,7 +157,7 @@ def copy_tensor_to_chunk_slice(self, tensor: torch.Tensor, data: torch.Tensor) - Copy data to the chunk. Args: - tensor (torch.Tensor): the tensor used to retrive meta information + tensor (torch.Tensor): the tensor used to retrieve meta information data (torch.Tensor): the tensor to be copied to the chunk """ chunk = self.tensor_chunk_map[tensor] diff --git a/colossalai/zero/gemini/memory_tracer/chunk_memstats_collector.py b/colossalai/zero/gemini/memory_tracer/chunk_memstats_collector.py index f5eb05b4f22a..83903bbf4023 100644 --- a/colossalai/zero/gemini/memory_tracer/chunk_memstats_collector.py +++ b/colossalai/zero/gemini/memory_tracer/chunk_memstats_collector.py @@ -25,7 +25,7 @@ def __init__(self, chunk_manager: ChunkManager, memstats: Optional[MemStats] = N # override def record_model_data_volume(self) -> None: """ - record model data volumn on cuda and cpu. + record model data volume on cuda and cpu. """ if self._start_flag and not self.use_outside_memstats: cuda_mem = self._chunk_manager.total_mem['cuda'] diff --git a/colossalai/zero/gemini/memory_tracer/memory_monitor.py b/colossalai/zero/gemini/memory_tracer/memory_monitor.py index f8d99dbce7a4..4bb585677d5b 100644 --- a/colossalai/zero/gemini/memory_tracer/memory_monitor.py +++ b/colossalai/zero/gemini/memory_tracer/memory_monitor.py @@ -45,7 +45,7 @@ def clear(self): class AsyncMemoryMonitor(MemoryMonitor): """ - An Async Memory Monitor runing during computing. Sampling memory usage of the current GPU + An Async Memory Monitor running during computing. Sampling memory usage of the current GPU at interval of `1/(10**power)` sec. The idea comes from Runtime Memory Tracer of PatrickStar @@ -67,7 +67,7 @@ class AsyncMemoryMonitor(MemoryMonitor): async_mem_monitor.save('log.pkl') Args: - power (int, optional): the power of time interva. Defaults to 10. + power (int, optional): the power of time interval. Defaults to 10. .. _PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management: https://arxiv.org/abs/2108.05818 diff --git a/colossalai/zero/gemini/utils.py b/colossalai/zero/gemini/utils.py index e52b5b836b0b..6f4a253b504b 100644 --- a/colossalai/zero/gemini/utils.py +++ b/colossalai/zero/gemini/utils.py @@ -73,7 +73,7 @@ def get_static_torch_model(zero_ddp_model, zero_ddp_model (ZeroDDP): a zero ddp model device (torch.device): the device of the final torch model dtype (torch.dtype): the dtype of the final torch model - only_rank_0 (bool): if True, only rank0 has the coverted torch model + only_rank_0 (bool): if True, only rank0 has the converted torch model Returns: torch.nn.Module: a static torch model used for saving checkpoints or numeric checks diff --git a/colossalai/zero/legacy/gemini/ophooks/utils.py b/colossalai/zero/legacy/gemini/ophooks/utils.py index 84e8298c1d51..f88ad2b00e9e 100644 --- a/colossalai/zero/legacy/gemini/ophooks/utils.py +++ b/colossalai/zero/legacy/gemini/ophooks/utils.py @@ -88,7 +88,7 @@ def register_ophooks_recursively(module: torch.nn.Module, ophook_list: List[BaseOpHook], name: str = "", filter_fn: Optional[Callable] = None): - r"""Recursilvely register pre/post hooks for all submodules in the module in FWD and BWD.""" + r"""Recursively register pre/post hooks for all submodules in the module in FWD and BWD.""" assert isinstance(module, torch.nn.Module) assert isinstance(ophook_list, (list, tuple)) assert len(ophook_list) > 0, 'expected at least 1 hook in the argument ophook_list but found 0' @@ -103,7 +103,7 @@ def register_ophooks_recursively(module: torch.nn.Module, if len(list(module.parameters(recurse=False))) == 0: return - # return from flitered module + # return from filtered module if filter_fn is not None and filter_fn(module): return diff --git a/colossalai/zero/legacy/gemini/tensor_utils.py b/colossalai/zero/legacy/gemini/tensor_utils.py index b7f23e0253fd..843e330ee2c6 100644 --- a/colossalai/zero/legacy/gemini/tensor_utils.py +++ b/colossalai/zero/legacy/gemini/tensor_utils.py @@ -77,7 +77,7 @@ def colo_model_data_tensor_move_inline(t: Union[StatefulTensor, torch.Tensor], t move a tensor to the target_device Args: t (Union[StatefulTensor, torch.Tensor]): the tensor be moved - target_device: a traget device, if type is int, it the index of cuda card. + target_device: a target device, if type is int, it the index of cuda card. """ if not isinstance(target_device, torch.device): target_device = torch.device(f'cuda:{target_device}') From c94a33579b7c70d96905ea8b2c3a4baf28451cb0 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Wed, 7 Jun 2023 17:23:01 +0800 Subject: [PATCH 287/413] modify shell for check --- examples/images/dreambooth/test_ci.sh | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/images/dreambooth/test_ci.sh b/examples/images/dreambooth/test_ci.sh index c0b0c2b3d016..8ba413a149b5 100644 --- a/examples/images/dreambooth/test_ci.sh +++ b/examples/images/dreambooth/test_ci.sh @@ -6,8 +6,9 @@ HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 DIFFUSERS_OFFLINE=1 -for plugin in "torch_ddp" "torch_ddp_fp16" "gemini" "low_level_zero"; do - torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \ +# "torch_ddp" "torch_ddp_fp16" +for plugin in "low_level_zero" "gemini"; do + torchrun --nproc_per_node 8 --standalone train_dreambooth_colossalai.py \ --pretrained_model_name_or_path="/data/dreambooth/diffuser/stable-diffusion-v1-4" \ --instance_data_dir="/data/dreambooth/Teyvat/data" \ --output_dir="./weight_output" \ From 12c90db3f30b6d9013a32eee27ea04ec4d631ddc Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 7 Jun 2023 17:59:58 +0800 Subject: [PATCH 288/413] [doc] add lazy init tutorial (#3922) * [doc] add lazy init en doc * [doc] add lazy init zh doc * [doc] add lazy init doc in sidebar * [doc] add lazy init doc test * [doc] fix lazy init doc link --- docs/sidebars.json | 1 + docs/source/en/features/lazy_init.md | 71 +++++++++++++++++++++++ docs/source/zh-Hans/features/lazy_init.md | 71 +++++++++++++++++++++++ 3 files changed, 143 insertions(+) create mode 100644 docs/source/en/features/lazy_init.md create mode 100644 docs/source/zh-Hans/features/lazy_init.md diff --git a/docs/sidebars.json b/docs/sidebars.json index 8be40e4512f9..c3cfbbeef689 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -64,6 +64,7 @@ }, "features/pipeline_parallel", "features/nvme_offload", + "features/lazy_init", "features/cluster_utils" ] }, diff --git a/docs/source/en/features/lazy_init.md b/docs/source/en/features/lazy_init.md new file mode 100644 index 000000000000..40f5da1cb84d --- /dev/null +++ b/docs/source/en/features/lazy_init.md @@ -0,0 +1,71 @@ +# Lazy initialization + +Author: Hongxin Liu + +**Prerequisite** +- [Booster API](../basics/booster_api.md) +- [Booster Plugins](../basics/booster_plugins.md) +- [Booster Checkpoint](../basics/booster_checkpoint.md) + +**Related discussion** +- [Lazy initialization of model](https://github.com/hpcaitech/ColossalAI/discussions/3124) + +## Introduction + +LazyTensor allows DL framework (PyTorch) to execute operations lazily, by storing all operations related to it and reruning them when it's required to be materialized. + +LazyInit defers model initialization and it's based on LazyTensor. + +This is especially useful when we use model parallelism to train large models, in which case the model cannot fit in GPU memory. Through this, we can initialize model tensors using meta tensor and do static analysis to get shard strategy. And then materialize each tensor and apply the shard strategy. The static analysis can be omitted if the shard strategy is known in advance. + +## Usage + +You may use lazy initialization when using Gemini, tensor parallelism, pipeline parallelism, and auto-parallelism. In other cases, you may not need to use lazy initialization. + +Gemini is compatible with lazy initialization. You can use them together directly. + +```python +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin +from colossalai.lazy import LazyInitContext +from colossalai.nn.optimizer import HybridAdam +from torch.nn import Linear +import colossalai + +colossalai.launch_from_torch({}) + +plugin = GeminiPlugin() +booster = Booster(plugin=plugin) + +with LazyInitContext(): + model = Linear(10, 10) + +optimizer = HybridAdam(model.parameters()) +model, optimizer, *_ = booster.boost(model, optimizer) +``` + +Note that using lazy initialization when using Gemini is not necessary but recommended. If you don't use lazy initialization, you may get OOM error when initializing the model. If you use lazy initialization, you can avoid this error. + +> ⚠ Lazy initialization support for tensor parallelism, pipeline parallelism, and auto-parallelism is still under development. + +### Load from pretrained model + +We should not load pretrained weight in `LazyInitContext`. If so, lazy initialization is meaningless, as the checkpoint is loaded and it takes much GPU memory. A recommended way is to initialize model from scratch in `LazyInitContext` and load pretrained weight outside `LazyInitContext` after calling `Booster.boost()`. + + +```python +with LazyInitContext(): + model = GPT2LMHeadModel(config) + +optimizer = ... +lr_scheduler = ... +dataloader = ... +model, optimizer, lr_scheduler, dataloader = booster.boost(model, optimizer, lr_scheduler, dataloader) + +booster.load_model(model, pretrained_path) +``` + + +As booster supports both pytorch-fashion checkpoint and huggingface/transformers-fashion pretrained weight, the `pretrained_path` of the above pseudo-code can be either a checkpoint file path or a pretrained weight path. Note that it does not support loading pretrained weights from network. You should download the pretrained weight first and then use a local path. + + diff --git a/docs/source/zh-Hans/features/lazy_init.md b/docs/source/zh-Hans/features/lazy_init.md new file mode 100644 index 000000000000..9a3cd90caa8d --- /dev/null +++ b/docs/source/zh-Hans/features/lazy_init.md @@ -0,0 +1,71 @@ +# 惰性初始化 + +作者: Hongxin Liu + +**前置教程** +- [Booster API](../basics/booster_api.md) +- [Booster 插件](../basics/booster_plugins.md) +- [Booster Checkpoint](../basics/booster_checkpoint.md) + +**相关讨论** +- [模型的惰性初始化](https://github.com/hpcaitech/ColossalAI/discussions/3124) + +## 引言 + +LazyTensor 允许深度学习框架 (PyTorch) 延迟执行操作，方法是存储与其相关的所有操作并在需要具体化时重新运行它们。 + +LazyInit 基于 LazyTensor，并支持延迟模型初始化。 + +这在我们使用模型并行来训练大型模型时特别有用，在这种情况下模型无法容纳在 GPU 内存中。通过这个，我们可以使用 Meta 张量初始化模型张量并进行静态分析以获得分片策略。然后具体化每个张量并应用分片策略。如果事先知道分片策略，则可以省略静态分析。 + +## 用法 + +您可以在使用 Gemini、张量并行、流水线并行和自动并行时使用惰性初始化。在其他情况下，您可能不需要使用惰性初始化。 + +Gemini 与惰性初始化兼容。您可以直接将它们一起使用。 + +```python +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin +from colossalai.lazy import LazyInitContext +from colossalai.nn.optimizer import HybridAdam +from torch.nn import Linear +import colossalai + +colossalai.launch_from_torch({}) + +plugin = GeminiPlugin() +booster = Booster(plugin=plugin) + +with LazyInitContext(): + model = Linear(10, 10) + +optimizer = HybridAdam(model.parameters()) +model, optimizer, *_ = booster.boost(model, optimizer) +``` + +请注意，在使用 Gemini 时使用惰性初始化不是必需的，但建议使用。如果不使用惰性初始化，在初始化模型时可能会出现 OOM 错误。如果使用惰性初始化，则可以避免此错误。 + +> ⚠ 对张量并行、流水线并行和自动并行的惰性初始化支持仍在开发中。 + +### 从预训练模型加载 + +我们不应该在 `LazyInitContext` 中加载预训练权重。如果这样，惰性初始化就没有意义，因为检查点已加载并且需要大量 GPU 内存。推荐的方法是在 `LazyInitContext` 中初始化模型，并在调用 `Booster.boost()` 后在 `LazyInitContext` 之外加载预训练权重。 + + +```python +with LazyInitContext(): + model = GPT2LMHeadModel(config) + +optimizer = ... +lr_scheduler = ... +dataloader = ... +model, optimizer, lr_scheduler, dataloader = booster.boost(model, optimizer, lr_scheduler, dataloader) + +booster.load_model(model, pretrained_path) +``` + + +由于 booster 同时支持 pytorch 风格的 checkpoint 和 huggingface/transformers 风格的预训练权重，上述伪代码的 `pretrained_pa​​th` 可以是 checkpoint 文件路径或预训练权重路径。请注意，它不支持从网络加载预训练权重。您应该先下载预训练的权重，然后使用本地路径。 + + From de0d7df33f9f9349d03150ecedad74610a1e36f6 Mon Sep 17 00:00:00 2001 From: digger yu Date: Thu, 8 Jun 2023 00:01:29 +0800 Subject: [PATCH 289/413] [nfc] fix typo colossalai/zero (#3923) --- colossalai/initialize.py | 2 +- colossalai/zero/gemini/memory_tracer/utils.py | 2 +- colossalai/zero/legacy/init_ctx/init_context.py | 2 +- colossalai/zero/legacy/sharded_model/sharded_model_v2.py | 6 +++--- colossalai/zero/low_level/_utils.py | 2 +- colossalai/zero/low_level/low_level_optim.py | 8 ++++---- 6 files changed, 11 insertions(+), 11 deletions(-) diff --git a/colossalai/initialize.py b/colossalai/initialize.py index 5d3f3e5530cb..dc0df0517508 100644 --- a/colossalai/initialize.py +++ b/colossalai/initialize.py @@ -238,7 +238,7 @@ def initialize(model: nn.Module, loaded into gpc.config. Args: - model (:class:`torch.nn.Module` or Callbale): Your model instance or a function to build the model. + model (:class:`torch.nn.Module` or Callable): Your model instance or a function to build the model. optimizer (:class:`torch.optim.optimizer.Optimizer` or :class:`Type[torch.optim.optimizer]`): Your optimizer instance. criterion (:class:`torch.nn.modules.loss._Loss`, optional): Your criterion instance. diff --git a/colossalai/zero/gemini/memory_tracer/utils.py b/colossalai/zero/gemini/memory_tracer/utils.py index 6962c058110e..65f6ba775139 100644 --- a/colossalai/zero/gemini/memory_tracer/utils.py +++ b/colossalai/zero/gemini/memory_tracer/utils.py @@ -7,7 +7,7 @@ def colo_model_optimizer_usage(optim) -> Tuple[int, int]: """Trace the optimizer memory usage Args: - optim (ShardedOptimV2): an instance of ShardedOptimver + optim (ShardedOptimV2): an instance of ShardedOptimizer Returns: Tuple[int, int]: cuda/cpu memory usage in Byte diff --git a/colossalai/zero/legacy/init_ctx/init_context.py b/colossalai/zero/legacy/init_ctx/init_context.py index a3fa46b38b5a..84e2d2f4f8e1 100644 --- a/colossalai/zero/legacy/init_ctx/init_context.py +++ b/colossalai/zero/legacy/init_ctx/init_context.py @@ -46,7 +46,7 @@ class ZeroInitContext(InsertPostInitMethodToModuleSubClasses): """A context to initialize model. 1. Convert the model to fp16. - 2. The paramaters of the module are adapted to type ShardedParameter. + 2. The parameters of the module are adapted to type ShardedParameter. 3. Shard the param and grad according to flags. Args: diff --git a/colossalai/zero/legacy/sharded_model/sharded_model_v2.py b/colossalai/zero/legacy/sharded_model/sharded_model_v2.py index be3842beb208..e7064277fb3c 100644 --- a/colossalai/zero/legacy/sharded_model/sharded_model_v2.py +++ b/colossalai/zero/legacy/sharded_model/sharded_model_v2.py @@ -69,7 +69,7 @@ class ShardedModelV2(nn.Module): If it's 'auto', they are moving dynamically based on CPU and CUDA memory usage. It will utilize heterogeneous memory space evenly and well. Note that 'auto' policy can only work well when no other processes use CUDA during your training. Defaults to 'cuda'. - gradient_predivide_factor (Optional[float], optional): Gradient is divived by this value before reduce-scatter. Defaults to 1.0. + gradient_predivide_factor (Optional[float], optional): Gradient is divided by this value before reduce-scatter. Defaults to 1.0. reuse_fp16_shard (bool, optional): Whether to reuse fp16 shard for param and grad. Enabling this can reduce GPU memory usage, but you have to make sure you disable it when using gradient accumulation. In this mode, grad will be fp16. Make sure your optimizer supports mixed precision (fp32 param and fp16 grad). @@ -205,7 +205,7 @@ def dump_memory_stats(self, filename: Optional[str] = 'dump_mem_stats.log') -> N exit(0) """ if self._use_memory_tracer: - self.logger.error(f'dump memort tracer collected information to a {filename}', ranks=[0]) + self.logger.error(f'dump memory tracer collected information to a {filename}', ranks=[0]) if gpc.get_global_rank() == 0: with open(filename, 'w+') as f: f.write(f'cuda reserved {torch.cuda.memory_reserved(get_current_device()) / 1e9} GB\n') @@ -385,7 +385,7 @@ def _save_grad(self, param: Parameter, grad: torch.Tensor): # make parameters point to gradient assert param.colo_attr.saved_grad.is_null( - ), 'Gradien accumulation is not supported when reuse_fp16_shard=True' + ), 'Gradient accumulation is not supported when reuse_fp16_shard=True' param.colo_attr.grad_payload_reset(grad.data) # release the memory of param diff --git a/colossalai/zero/low_level/_utils.py b/colossalai/zero/low_level/_utils.py index afc98e7a7f54..218f7603bc54 100644 --- a/colossalai/zero/low_level/_utils.py +++ b/colossalai/zero/low_level/_utils.py @@ -261,7 +261,7 @@ def sync_param(flat_tensor, tensor_list): share the same memory space. This function will update the tensor list so that they point to the same value. - :param flat_tensor: A flat tensor obtained by calling `torch._utils._unflatten_dense_tensors` on a tensor lsit + :param flat_tensor: A flat tensor obtained by calling `torch._utils._unflatten_dense_tensors` on a tensor list :param tensor_list: A list of tensors corresponding to the flattened tensor :type flat_tensor: torch.Tensor :type tensor_list: List[torch.Tensor] diff --git a/colossalai/zero/low_level/low_level_optim.py b/colossalai/zero/low_level/low_level_optim.py index d4d03e5b5fcd..ee03c0f0ae15 100644 --- a/colossalai/zero/low_level/low_level_optim.py +++ b/colossalai/zero/low_level/low_level_optim.py @@ -207,8 +207,8 @@ def __init__( for param in self._working_param_groups[group_id]: self._param_store.set_param_reduction_state(param, False) - # intialize communication stream for - # communication-compuation overlapping + # initialize communication stream for + # communication-computation overlapping if self._overlap_communication: self._comm_stream = torch.cuda.Stream() @@ -269,7 +269,7 @@ def _partition_param_list(self, param_list): params_per_rank = [[] for _ in range(self._world_size)] numel_per_rank = [0 for _ in range(self._world_size)] - # partititon the parameters in a greedy fashion + # partition the parameters in a greedy fashion sorted_params = sorted(param_list, key=lambda x: x.numel(), reverse=True) for param in sorted_params: # allocate this parameter to the rank with @@ -297,7 +297,7 @@ def _attach_reduction_hook(self): param_group = self._working_param_groups[group_id] for param in param_group: if param.requires_grad: - # determines the reduction destionation rank + # determines the reduction destination rank # this is only valid for stage 2 # dst_rank = None means using all-reduce # else using reduce From 9166988d9b23548b50b154d79e8f194f61f9f6aa Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Thu, 8 Jun 2023 09:29:32 +0800 Subject: [PATCH 290/413] [devops] update torch version in compability test (#3919) --- .compatibility | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.compatibility b/.compatibility index c8ac4083d2a2..32da32be5521 100644 --- a/.compatibility +++ b/.compatibility @@ -1,3 +1,3 @@ 1.12.0-11.3.0 -1.11.0-11.3.0 -1.10.1-11.3.0 +1.13.0-11.6.0 +2.0.0-11.7.0 From eb39154d4082601bf8c39b64317fecd28a526205 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 8 Jun 2023 10:18:17 +0800 Subject: [PATCH 291/413] [dtensor] updated api and doc (#3845) --- colossalai/device/README.md | 73 +++ colossalai/device/device_mesh.py | 444 ++++++++++++------ colossalai/lazy/lazy_init.py | 16 +- colossalai/tensor/comm_spec.py | 89 ++-- colossalai/tensor/d_tensor/RAEDME.md | 103 ++++ colossalai/tensor/d_tensor/__init__.py | 4 + colossalai/tensor/d_tensor/comm_spec.py | 88 ++-- colossalai/tensor/d_tensor/d_tensor.py | 114 ++++- colossalai/tensor/d_tensor/layout.py | 30 +- .../tensor/d_tensor/layout_converter.py | 86 ++-- tests/test_device/test_device_mesh.py | 13 +- tests/test_device/test_init_logical_pg.py | 16 +- tests/test_lazy/lazy_init_utils.py | 10 +- tests/test_lazy/test_distribute.py | 28 +- .../test_dtensor/test_comm_spec.py | 33 +- .../test_tensor/test_dtensor/test_dtensor.py | 17 +- .../test_dtensor/test_layout_converter.py | 41 +- tests/test_tensor/test_shape_consistency.py | 7 +- tests/test_tensor/test_sharded_linear.py | 2 +- tests/test_tensor/test_sharding_spec.py | 2 +- 20 files changed, 793 insertions(+), 423 deletions(-) create mode 100644 colossalai/device/README.md create mode 100644 colossalai/tensor/d_tensor/RAEDME.md diff --git a/colossalai/device/README.md b/colossalai/device/README.md new file mode 100644 index 000000000000..8f835735bef4 --- /dev/null +++ b/colossalai/device/README.md @@ -0,0 +1,73 @@ +# 🗄 Device + +## 📚 Table of Contents + +- [🗄 Device](#-device) + - [📚 Table of Contents](#-table-of-contents) + - [🔗 Introduction](#-introduction) + - [📝 Design](#-design) + - [🔨 Usage](#-usage) + +## 🔗 Introduction + +This module contains the implementation of the abstraction of the device topology. It is used to represent the device topology and manage the distributed information related to the network. + +## 📝 Design + + +This module is inspired by the DeviceMesh in the [Alpa project](https://github.com/alpa-projects/alpa) and the device array can be represented as a 1D or 2D mesh. We will be extending the device mesh to support 3D mesh in the future. + + +## 🔨 Usage + +- Create a device mesh + +```python +# this is the list of global ranks involved in the device mesh +# assume we have 4 GPUs and the global ranks for these GPUs are 0, 1, 2, 3 +physical_mesh_id = torch.arange(4) +mesh_shape = [2, 2] +device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) +``` + +- View the mesh + + +```python +# view the mesh shape +# expect output +# [2, 2] +print(device_mesh.shape) + + +# view the logical mesh with global ranks +# expect output +# [ +# [0, 1], +# [2, 3] +# ] +print(device_mesh.logical_mesh_id) + +# view the number of devices in the mesh +# expect output +# 4 +print(device_mesh.num_devices) + +``` + +- Initialize the process group + +```python +# intialize process group +device_mesh.init_logical_process_group() + + +# get the process group for a rank with respect to an axis +# this is the process group involving global ranks 0 and 2 +print(device_mesh.get_process_group(axis=0, global_rank=0)) + +# get the ranks in the process with respect to an axis +# expect output +# [0, 2] +print(device_mesh.get_ranks_in_process_group(axis=0, global_rank=0)) +``` diff --git a/colossalai/device/device_mesh.py b/colossalai/device/device_mesh.py index 2a5f747fbc23..0490a440153e 100644 --- a/colossalai/device/device_mesh.py +++ b/colossalai/device/device_mesh.py @@ -3,11 +3,19 @@ with some changes. """ import operator +from dataclasses import dataclass from functools import reduce -from typing import List, Tuple +from typing import Dict, List, Union import torch import torch.distributed as dist +from torch.distributed import ProcessGroup + + +@dataclass +class ProcessGroupContainer: + process_group: ProcessGroup + ranks: List[int] # modified from alpa LogicalDeviceMesh(https://github.com/alpa-projects/alpa/blob/main/alpa/shard_parallel/auto_sharding.py) @@ -27,9 +35,11 @@ class DeviceMesh: during initializing the DeviceMesh instance if the init_process_group set to True. Otherwise, users need to call create_process_groups_for_logical_mesh manually to init logical process group. (default: False) - need_flatten(bool, optional): initialize flatten_device_mesh during initializing the DeviceMesh instance if the need_flatten set to True. + device (str): the device for the process groups used by the DeviceMesh instance. (default: 'cuda') """ + _DIST_BACKEND = {"cuda": "nccl", "cpu": "gloo"} + def __init__(self, physical_mesh_id: torch.Tensor, mesh_shape: torch.Size = None, @@ -37,48 +47,140 @@ def __init__(self, mesh_alpha: List[float] = None, mesh_beta: List[float] = None, init_process_group: bool = False, - need_flatten: bool = True): - self.physical_mesh_id = physical_mesh_id + device: str = 'cuda'): + # ============================ + # Physical & Logical Mesh IDs + # ============================ + self._physical_mesh_id = physical_mesh_id + assert physical_mesh_id.dim() == 1, "physical_mesh_id should be a 1D tensor." + + # logical mesh ids can be obtained via two ways + # 1. provide physical mesh id and provide mesh shape + # 2. directly supply the logical mesh id + assert mesh_shape is None or logical_mesh_id is None, \ + "Only one of mesh_shape and logical_mesh_id can be specified." \ + "Logical mesh IDs are obtained from either mesh_shape + phyiscal_mesh_id or directly from the user-supplied logical_mesh_id" + if logical_mesh_id is None: self.mesh_shape = mesh_shape - self._logical_mesh_id = self.physical_mesh_id.reshape(self.mesh_shape) + self._logical_mesh_id = self._physical_mesh_id.reshape(self.mesh_shape) else: self._logical_mesh_id = logical_mesh_id self.mesh_shape = self._logical_mesh_id.shape - # map global rank into logical rank - self.convert_map = {} - self._global_rank_to_logical_rank_map(self._logical_mesh_id, []) + # ensure two things: + # 1. logical and physical mesh IDs should contain the same elements + # 2. there is no duplicate IDs in each mesh, e.g. [2, 2] is not allowed + assert torch.equal(torch.unique(self._physical_mesh_id), torch.unique(self.logical_mesh_id)), \ + "physical and logical mesh IDs should contain the same elements, please check if you have consistent physical_mesh_id and logical_mesh_id." + assert torch.unique(self._physical_mesh_id).numel() == self._physical_mesh_id.numel(), \ + "Found duplicate IDs in the phyiscal_mesh_id and this is not allowed, please check your physical_mesh_id again." + assert torch.unique(self.logical_mesh_id).numel() == self.logical_mesh_id.numel(), \ + "Found duplicate IDs in the logical_mesh_id and this is not allowed, please check your logical_mesh_id again." + + # =============================================== # coefficient for alpha-beta communication model + # alpha is latency and beta is bandwidth + # =============================================== + # if the values are not provided, we assume they are 1 for simplicity if mesh_alpha is None: mesh_alpha = [1] * len(self.mesh_shape) if mesh_beta is None: mesh_beta = [1] * len(self.mesh_shape) + self.mesh_alpha = tuple(mesh_alpha) self.mesh_beta = tuple(mesh_beta) - self.init_process_group = init_process_group - self.need_flatten = need_flatten - if self.init_process_group: - self.process_groups_dict = self.create_process_groups_for_logical_mesh() - if self.need_flatten and self._logical_mesh_id.dim() > 1: - self.flatten_device_mesh = self.flatten() - # Create a new member `flatten_device_meshes` to distinguish from original flatten methods (Because I'm not sure if there are functions that rely on the self.flatten()) - # self.flatten_device_meshes = FlattenDeviceMesh(self.physical_mesh_id, self.mesh_shape, self.mesh_alpha, - # self.mesh_beta) + + # ensure the alpha and beta have the same shape + assert len(self.mesh_alpha) == len(self.mesh_beta), \ + "mesh_alpha and mesh_beta should have the same length, please check your mesh_alpha and mesh_beta again." + + # ========================= + # Device for Process Group + # ========================= + self._device = device + self._dist_backend = self._DIST_BACKEND[device] + + # ========================= + # Process Group Management + # ========================= + # the _global_to_local_rank_mapping is structured as follows + # { + # : [ , , , ...] + # } + self._global_to_local_rank_mapping = dict() + self._init_global_to_logical_rank_mapping(mapping=self._global_to_local_rank_mapping, + tensor=self.logical_mesh_id) + + # create process group + self._process_group_dict = {} + self._ranks_in_the_process_group = {} + self._global_rank_of_current_process = None + self._is_initialized = False + + # initialize process group if specified + self._init_ranks_in_the_same_group() + self._init_process_group = init_process_group + if init_process_group: + self.init_logical_process_group() @property - def shape(self): + def shape(self) -> torch.Size: + """ + Return the shape of the logical mesh. + """ return self.mesh_shape @property - def num_devices(self): - return reduce(operator.mul, self.physical_mesh_id.shape, 1) + def num_devices(self) -> int: + """ + Return the number of devices contained in the device mesh. + """ + return reduce(operator.mul, self._physical_mesh_id.shape, 1) @property - def logical_mesh_id(self): + def logical_mesh_id(self) -> torch.Tensor: + """ + Return the logical mesh id. + """ return self._logical_mesh_id - def __deepcopy__(self, memo): + def get_process_group(self, axis: int, global_rank: int = None) -> ProcessGroup: + """ + Return the process group on the specified axis. + + Args: + axis (int): the axis of the process group. + global_rank (int, optional): the global rank of the process group. If not specified, the current process is used. (default: None) + """ + if global_rank is None: + global_rank = self._global_rank_of_current_process + return self._process_group_dict[global_rank][axis] + + def get_process_group_for_all_axes(self, global_rank: int = None) -> Dict[int, ProcessGroup]: + """ + Return the process groups for all axes. + + Args: + global_rank (int, optional): the global rank of the process + """ + if global_rank is None: + global_rank = self._global_rank_of_current_process + return self._process_group_dict[global_rank] + + def get_ranks_in_process_group(self, axis: int, global_rank: int = None) -> List[int]: + """ + Return the ranks in the process group on the specified axis. + + Args: + axis (int): the axis of the process group. + global_rank (int, optional): the global rank of the process + """ + if global_rank is None: + global_rank = self._global_rank_of_current_process + return self._ranks_in_the_process_group[global_rank][axis] + + def __deepcopy__(self, memo) -> "DeviceMesh": cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result @@ -86,111 +188,206 @@ def __deepcopy__(self, memo): if k != 'process_groups_dict': setattr(result, k, __import__("copy").deepcopy(v, memo)) else: + # process group cannot be copied + # thus, we share them directly setattr(result, k, v) - return result - def flatten(self): + def _init_global_to_logical_rank_mapping(self, + mapping: Dict, + tensor: torch.Tensor, + index_list: List[int] = []) -> Dict[int, List[int]]: """ - Flatten the logical mesh into an effective 1d logical mesh, - """ - flatten_mesh_shape_size = len(self.mesh_shape) - flatten_mesh_shape = [self.num_devices] - return DeviceMesh(self.physical_mesh_id, - tuple(flatten_mesh_shape), - mesh_alpha=[max(self.mesh_alpha)] * (flatten_mesh_shape_size - 1), - mesh_beta=[max(self.mesh_beta)] * (flatten_mesh_shape_size - 1), - init_process_group=self.init_process_group, - need_flatten=False) + Build a global rank to local rank mapping for each process group in different axis in the logical device mesh. - def _global_rank_to_logical_rank_map(self, tensor, index_list): - ''' - This method is a helper function to build convert_map recursively. - ''' + Args: + mapping (Dict): a dictionary that maps the global rank to the local rank in the logical device mesh. + tensor (torch.Tensor): the tensor that contains the logical mesh ids. + index_list (List[int]) + + Returns: + mapping (Dict): a dictionary that maps the global rank to the local rank in the logical device mesh. + The value is a list of integers and each integer represents the local rank in the indexed axis. + """ for index, inner_tensor in enumerate(tensor): + # index means the local rank in the current axis + # inner_tensor refers to the processes with the same local rank + if inner_tensor.numel() == 1: - self.convert_map[int(inner_tensor)] = index_list + [index] + # if the inner_tensor only has one element, it means that + # it already reaches the last axis + # we append its local_rank in the last axis to the index_list + # and assign to the mapping + # the value of the mapping is the the local rank at the indexed axis of the device mesh + mapping[int(inner_tensor)] = index_list + [index] else: - self._global_rank_to_logical_rank_map(inner_tensor, index_list + [index]) + # we recursively go into the function until we reach the last axis + # meanwhile, we should add the local rank in the current axis in the index_list + self._init_global_to_logical_rank_mapping(mapping, inner_tensor, index_list + [index]) - def create_process_groups_for_logical_mesh(self): + def init_logical_process_group(self): ''' This method is used to initialize the logical process groups which will be used in communications among logical device mesh. Note: if init_process_group set to False, you have to call this method manually. Otherwise, the communication related function, such as ShapeConsistencyManager.apply will raise errors. ''' - process_groups_dict = {} - check_duplicate_list = [] - global_rank_flatten_list = self.physical_mesh_id.view(-1).tolist() + # sanity check + assert dist.is_initialized, "The torch.distributed should be initialized before calling init_logical_process_group" + assert not self._is_initialized, "The logical process group has been initialized, do not call init_logical_process_group twice" + + # update the global rank of the current process + self._global_rank_of_current_process = dist.get_rank() + duplicate_check_list = [] + + # flatten the global ranks to 1D list + global_rank_flatten_list = self._physical_mesh_id.view(-1).tolist() + for global_rank in global_rank_flatten_list: - process_groups = self.global_rank_to_process_groups_with_global_rank(global_rank) - for axis, process_group in process_groups.items(): - if axis not in process_groups_dict: - process_groups_dict[axis] = [] - if process_group not in check_duplicate_list: - check_duplicate_list.append(process_group) - process_group_handler = dist.new_group(process_group) - process_groups_dict[axis].append((process_group, process_group_handler)) + # find the other ranks which are in the same process group as global_rank + ranks_in_same_group_by_axis = self._collate_global_ranks_in_same_process_group(global_rank) - return process_groups_dict + for axis, ranks_in_same_group in ranks_in_same_group_by_axis.items(): + # skip duplicated process group creation + if ranks_in_same_group in duplicate_check_list: + continue - def global_rank_to_logical_rank(self, rank): - return self.convert_map[rank] + # create the process group + pg_handler = dist.new_group(ranks=ranks_in_same_group, backend=self._dist_backend) - def global_rank_to_process_groups_with_logical_rank(self, rank): - ''' - Give a global rank and return all logical process groups of this rank. - for example: - physical_mesh_id = torch.arange(0, 16).reshape(2, 8) - mesh_shape = (4, 4) - # [[0, 1, 2, 3], - # [4, 5, 6, 7], - # [8, 9, 10,11], - # [12,13,14,15]] - device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - print(device_mesh.global_rank_to_process_groups_with_logical_rank(0)) - output: - # key is axis name - # value is a list of logical ranks in same axis with rank 0 - {0: [[0, 0], [1, 0], [2, 0], [3, 0]], 1: [[0, 0], [0, 1], [0, 2], [0, 3]]} - ''' - process_groups = {} - for d in range(self.logical_mesh_id.dim()): - for replacer in range(self.logical_mesh_id.shape[d]): - if d not in process_groups: - process_groups[d] = [] - process_group_member = self.convert_map[rank].copy() - process_group_member[d] = replacer - process_groups[d].append(process_group_member) - return process_groups - - def global_rank_to_process_groups_with_global_rank(self, rank): + # keep this process group in the process_groups_dict + for rank in ranks_in_same_group: + if rank not in self._process_group_dict: + self._process_group_dict[rank] = dict() + self._process_group_dict[rank][axis] = pg_handler + + # update the init flag + # we only allow init for once + self._is_initialized = True + + def _init_ranks_in_the_same_group(self): + """ + This method is used to initialize the ranks_in_the_same_group dictionary. + """ + # flatten the global ranks to 1D list + global_rank_flatten_list = self._physical_mesh_id.view(-1).tolist() + + for global_rank in global_rank_flatten_list: + # find the other ranks which are in the same process group as global_rank + ranks_in_same_group_by_axis = self._collate_global_ranks_in_same_process_group(global_rank) + + for axis, ranks_in_same_group in ranks_in_same_group_by_axis.items(): + # create dict for each rank + if global_rank not in self._process_group_dict: + self._ranks_in_the_process_group[global_rank] = dict() + + # keep this process group in the process_groups_dict + self._ranks_in_the_process_group[global_rank][axis] = ranks_in_same_group + + def global_rank_to_local_rank(self, rank: int, axis: int = None) -> Union[List[int], int]: + """ + Return the local rank of the given global rank in the logical device mesh. + + Args: + rank (int): the global rank in the logical device mesh. + axis (int): the axis of the logical device mesh. + """ + local_ranks = self._global_to_local_rank_mapping[rank] + if axis: + return local_ranks[axis] + else: + return local_ranks + + def _collate_global_ranks_in_same_process_group(self, global_rank): ''' - Give a global rank and return all process groups of this rank. - for example: - physical_mesh_id = torch.arange(0, 16).reshape(2, 8) - mesh_shape = (4, 4) - # [[0, 1, 2, 3], - # [4, 5, 6, 7], - # [8, 9, 10,11], - # [12,13,14,15]] - device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - print(device_mesh.global_rank_to_process_groups_with_global_rank(0)) - output: - # key is axis name - # value is a list of global ranks in same axis with rank 0 - {0: [0, 4, 8, 12], 1: [0, 1, 2, 3]} + Give a global rank and return all global ranks involved in its associated process group in each axis. + + Example: + + ```python + sphysical_mesh_id = torch.arange(0, 16) + mesh_shape = (4, 4) + + # logical mesh will look like + # [[0, 1, 2, 3], + # [4, 5, 6, 7], + # [8, 9, 10,11], + # [12,13,14,15]] + + device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) + print(device_mesh.collate_global_ranks_in_same_process_group(0)) + + # key is axis name + # value is a list of global ranks in same axis with rank 0 + # output will look like + # { + 0: [0, 4, 8, 12], + 1: [0, 1, 2, 3] + # } ''' - logical_process_groups = self.global_rank_to_process_groups_with_logical_rank(rank) - process_groups = {} - for dim, logical_ranks in logical_process_groups.items(): - process_groups[dim] = [] - for logical_rank in logical_ranks: - for g_rank, l_rank in self.convert_map.items(): - if l_rank == logical_rank: - process_groups[dim].append(g_rank) - return process_groups + # We have init the global rank to local rank by calling _init_global_to_logical_rank_mapping + # for self._global_to_local_rank_mapping + # the key is the global rank + # the value is the list of local ranks corresponding to the global rank with respect of different axes + # we can see the list of local ranks as the process coordinates for simplicity + # the key and value are all unique, therefore, + # we can also to use the coordinates to find the global rank + + # ========================================================================= + # Step 1 + # find all the process_coordinates for processes in the same process group + # as the given global rank + # ========================================================================= + + # each + processes_in_the_same_process_group = {} + + for dim in range(self.logical_mesh_id.dim()): + # iterate over the dimension size so that we can include all processes + # in the same process group in the given axis + # the _local_rank refers to the local rank of the current process + for _local_rank in range(self.logical_mesh_id.shape[dim]): + + # if this dimension is not initailized yet, + # initialize it with an empty array + if dim not in processes_in_the_same_process_group: + processes_in_the_same_process_group[dim] = [] + + # get the local rank corresponding to the global rank + process_coordinates = self._global_to_local_rank_mapping[global_rank].copy() + + # replace the local rank in the given dimension with the + # lcoal rank of the current process iterated + process_coordinates[dim] = _local_rank + processes_in_the_same_process_group[dim].append(process_coordinates) + + # ================================================================= + # Step 2 + # Use local rank combination to find its corresponding global rank + # ================================================================= + # the key of the dict is the axis + # the value is the list of global ranks which are in the same process group as the given global rank + global_pg_ranks = {} + for dim, coordinates_of_all_processes in processes_in_the_same_process_group.items(): + global_pg_ranks[dim] = [] + for process_coordinates in coordinates_of_all_processes: + # find the global rank by local rank combination + for _global_rank, _process_coordinates in self._global_to_local_rank_mapping.items(): + if process_coordinates == _process_coordinates: + global_pg_ranks[dim].append(_global_rank) + return global_pg_ranks + + def flatten(self): + """ + Flatten the logical mesh into an effective 1d logical mesh, + """ + flatten_mesh_shape_size = len(self.mesh_shape) + flatten_mesh_shape = [self.num_devices] + return DeviceMesh(self._physical_mesh_id, + tuple(flatten_mesh_shape), + mesh_alpha=[max(self.mesh_alpha)] * (flatten_mesh_shape_size - 1), + mesh_beta=[max(self.mesh_beta)] * (flatten_mesh_shape_size - 1), + init_process_group=self._init_process_group) def all_gather_cost(self, num_bytes, mesh_dim): num_devices = self.logical_mesh_id.shape[mesh_dim] @@ -212,38 +409,3 @@ def all_to_all_cost(self, num_bytes, mesh_dim): penalty_factor = num_devices / 2.0 return (self.mesh_alpha[mesh_dim] + self.mesh_beta[mesh_dim] * (num_devices - 1) / num_devices / num_devices * num_bytes * penalty_factor + 0.001) - - -class FlattenDeviceMesh(DeviceMesh): - - def __init__(self, physical_mesh_id, mesh_shape, mesh_alpha=None, mesh_beta=None): - super().__init__(physical_mesh_id, - mesh_shape, - mesh_alpha, - mesh_beta, - init_process_group=False, - need_flatten=False) - # Different from flatten(), mesh_shape leaves unchanged, mesh_alpha and mesh_beta are scalars - self.mesh_alpha = max(self.mesh_alpha) - self.mesh_beta = min(self.mesh_beta) - # Different from original process_groups_dict, rank_list is not stored - self.process_number_dict = self.create_process_numbers_for_logical_mesh() - - def create_process_numbers_for_logical_mesh(self): - ''' - Build 1d DeviceMesh in column-major(0) and row-major(1) - for example: - mesh_shape = (2,4) - # [[0, 1, 2, 3], - # [4, 5, 6, 7]] - # return {0: [0, 4, 1, 5, 2, 6, 3, 7], 1: [0, 1, 2, 3, 4, 5, 6, 7]} - ''' - num_devices = reduce(operator.mul, self.mesh_shape, 1) - process_numbers_dict = {} - process_numbers_dict[0] = torch.arange(num_devices).reshape(self.mesh_shape).transpose(1, 0).flatten().tolist() - process_numbers_dict[1] = torch.arange(num_devices).reshape(self.mesh_shape).flatten().tolist() - return process_numbers_dict - - def mix_gather_cost(self, num_bytes): - num_devices = reduce(operator.mul, self.mesh_shape, 1) - return (self.mesh_alpha + self.mesh_beta * (num_devices - 1) / num_devices * num_bytes + 0.1) diff --git a/colossalai/lazy/lazy_init.py b/colossalai/lazy/lazy_init.py index 76f550dc4392..ca8914362cd6 100644 --- a/colossalai/lazy/lazy_init.py +++ b/colossalai/lazy/lazy_init.py @@ -1,5 +1,5 @@ from types import MethodType -from typing import Callable, Optional, Union +from typing import Callable, Dict, Optional, Union import torch import torch.distributed as dist @@ -8,8 +8,9 @@ from torch.utils._pytree import tree_map from colossalai._analyzer._subclasses import MetaTensor +from colossalai.device.device_mesh import DeviceMesh from colossalai.tensor.d_tensor.d_tensor import DTensor -from colossalai.tensor.d_tensor.layout import Layout +from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec # reference: https://pytorch.org/cppdocs/notes/tensor_creation.html _NORMAL_FACTORY = [ @@ -172,7 +173,7 @@ def materialize(self) -> torch.Tensor: self.clean() return _convert_cls(self, target) - def distribute(self, layout: Layout) -> torch.Tensor: + def distribute(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> torch.Tensor: """Distribute the ``LazyTensor`` to ``torch.Tensor`` by modifying __class__ (inplace), according to the layout. Args: @@ -183,7 +184,7 @@ def distribute(self, layout: Layout) -> torch.Tensor: """ target = self._materialize_data() self.clean() - local_tensor = DTensor(target, layout).local_tensor + local_tensor = DTensor(target, device_mesh, sharding_spec).local_tensor return _convert_cls(self, local_tensor) def clean(self) -> None: @@ -536,7 +537,10 @@ def apply_fn(name: str, p: LazyTensor): return _apply_to_lazy_module(module, apply_fn, verbose) @staticmethod - def distribute(module: nn.Module, layout_dict: dict, verbose: bool = False) -> nn.Module: + def distribute(module: nn.Module, + device_mesh: DeviceMesh, + sharding_spec_dict: Dict[str, ShardingSpec], + verbose: bool = False) -> nn.Module: """Distribute all ``nn.Parameter`` from ``LazyTensor``. This function will modify the module in-place. Args: @@ -546,7 +550,7 @@ def distribute(module: nn.Module, layout_dict: dict, verbose: bool = False) -> n """ def apply_fn(name: str, p: LazyTensor): - p.distribute(layout_dict[name]) + p.distribute(device_mesh, sharding_spec_dict[name]) return _apply_to_lazy_module(module, apply_fn, verbose) diff --git a/colossalai/tensor/comm_spec.py b/colossalai/tensor/comm_spec.py index af38d2a502c2..dd873c852936 100644 --- a/colossalai/tensor/comm_spec.py +++ b/colossalai/tensor/comm_spec.py @@ -16,69 +16,66 @@ def _all_gather(tensor, comm_spec): ''' Implement all gather operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - tensor_list = [ - torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) - for _ in range(comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis]) - ] - # without this contiguous operation, the all gather may get some unexpected results. - tensor = tensor.contiguous() - dist.all_gather(tensor_list, tensor, group=process_group) - output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() - return output + process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() + process_group = process_groups[comm_spec.logical_process_axis] + + tensor_list = [ + torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) + for _ in range(comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis]) + ] + # without this contiguous operation, the all gather may get some unexpected results. + tensor = tensor.contiguous() + dist.all_gather(tensor_list, tensor, group=process_group) + output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() + return output def _split(tensor, comm_spec): ''' Implement shard operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, _ in process_groups_list: - if dist.get_rank() in rank_list: - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // len(rank_list) - start = length * rank_list.index(dist.get_rank()) - output = torch.narrow(tensor, dim, start, length).contiguous() - return output + process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() + process_group = process_groups[comm_spec.logical_process_axis] + + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // dist.get_world_size(process_group) + start = length * dist.get_rank(process_group) + output = torch.narrow(tensor, dim, start, length).contiguous() + return output def _all_to_all(tensor, comm_spec): ''' Implement all to all operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - new_shape = list(tensor.shape) - new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // len(rank_list) - new_shape = torch.Size(new_shape) - output_tensor_list = [ - torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list)) - ] - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // len(rank_list) - input_tensor_list = [ - torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(len(rank_list)) - ] - group = process_group - dist.all_to_all(output_tensor_list, input_tensor_list, group) - output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() - return output + process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() + process_group = process_groups[comm_spec.logical_process_axis] + world_size = dist.get_world_size(process_group) + + new_shape = list(tensor.shape) + new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // world_size + new_shape = torch.Size(new_shape) + output_tensor_list = [torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)] + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // world_size + input_tensor_list = [torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(world_size)] + group = process_group + dist.all_to_all(output_tensor_list, input_tensor_list, group) + output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() + return output def _all_reduce(tensor, comm_spec, async_op=False): ''' Implement all reduce operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - if not tensor.is_contiguous(): - tensor = tensor.contiguous() - dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) - return tensor + process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() + process_group = process_groups[comm_spec.logical_process_axis] + + if not tensor.is_contiguous(): + tensor = tensor.contiguous() + dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) + return tensor def _mix_gather(tensor, comm_spec): @@ -414,7 +411,7 @@ def __init__(self, self.forward_only = forward_only if isinstance(self.logical_process_axis, list): if not mix_gather: - self.device_mesh = self.sharding_spec.device_mesh.flatten_device_mesh + self.device_mesh = self.sharding_spec.device_mesh.flatten() self.logical_process_axis = 0 else: self.device_meshes = self.sharding_spec.device_mesh.flatten_device_meshes diff --git a/colossalai/tensor/d_tensor/RAEDME.md b/colossalai/tensor/d_tensor/RAEDME.md new file mode 100644 index 000000000000..95d866388364 --- /dev/null +++ b/colossalai/tensor/d_tensor/RAEDME.md @@ -0,0 +1,103 @@ +# 🔢 Distributed Tensor + +## 📚 Table of Contents + +- [🔢 Distributed Tensor](#-distributed-tensor) + - [📚 Table of Contents](#-table-of-contents) + - [🔗 Introduction](#-introduction) + - [📝 Design](#-design) + - [🔨 Usage](#-usage) + - [🎈 Progress Log](#-progress-log) + +## 🔗 Introduction + +Distributed tensor is a type of tensor that is distributed across multiple devices. It is a wrapper of PyTorch tensor, and it is used to support distributed training. +It can represent the device topology and tensor placement over the devices in the topology. It also provides a set of APIs to manipulate the distributed tensor. + +## 📝 Design + +Our implementation is inspired by the work [Alpa](https://arxiv.org/abs/2201.12023), which unifies data parallelism and tensor parallelism as intra-op parallelism. It uses notations `S` to represent the sharded dimension and `R` to represent the replicated dimension. For example, given a 2D matrix, `[S, R]` represents the tensor is sharded over the first dimension. + +Each sharded dimension will have a subscript to represent its placement over the devices. Assuming we have 4 GPUs and the GPUs are arranged in a 2 x 2 manner. Let's say we have a 2D matrix like below: + + +```text + [1, 2, 3, 4 ] +A = [4, 5, 6, 7 ] + [8, 9, 10, 11] + [12, 13, 14, 15] +``` + +`[S0, R]` would mean that the first dimension is sharded over the rows in the device topology. + +```text +| --------------------—————————————————————-| +| | | +| [1, 2, 3, 4 ] | [1, 2, 3, 4 ] | +| [4, 5, 6, 7 ] | [4, 5, 6, 7 ] | +| | | +| --------------------——————————————————----- +| | | +| [8, 9, 10, 11] | [8, 9, 10, 11] | +| [12, 13, 14, 15] | [12, 13, 14, 15] | +| | | +| --------------------——————————————————----- +``` + +`[S01, R]` would mean that the first dimension is sharded over both the row and column in the device topology. + +```text +| --------------------—————————————————————-| +| | | +| [1, 2, 3, 4 ] | [4, 5, 6, 7 ] | +| | | +| --------------------——————————————————----- +| | | +| [8, 9, 10, 11] | [12, 13, 14, 15] | +| | | +| --------------------——————————————————----- +``` + +## 🔨 Usage + +A sample API usage is given below. + +```python +import torch + +import colossalai +from colossalai.device.device_mesh import DeviceMesh +from colossalai.tensor.d_tensor import DTensor, ShardingSpec + +colossalai.launch_from_torch(config={}) + +# define your device mesh +# assume you have 4 GPUs +physical_mesh_id = torch.arange(0, 4).reshape(1, 4) +mesh_shape = (2, 2) +device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) + +# define a tensor +a = torch.rand(16, 32).cuda() + +# create sharding spec for the tensor +# assume the sharding spec is [S0, R] +dim_partition_dict = {0: [0]} +sharding_spec = ShardingSpec(a.dim(), dim_partition_dict) + +# create a distributed tensor +d_tensor = DTensor(a, device_mesh, sharding_spec) +print(d_tensor) + +global_tensor = d_tensor.to_global() +print(global_tensor) +``` + + +## 🎈 Progress Log + +- [x] Support layout conversion +- [x] Support sharding on 2D device mesh +- [ ] Support sharding on 3D device mesh +- [ ] Support sharding 4D device mesh +- [ ] Support sharding info saving and offline tensor merge (we can save tensor as dtensor and gather the tensors back to the global tensor based on the sharding info in a single process in CPU, useful for distributed training checkpoint load and save.) diff --git a/colossalai/tensor/d_tensor/__init__.py b/colossalai/tensor/d_tensor/__init__.py index e69de29bb2d1..af77f4f0edfc 100644 --- a/colossalai/tensor/d_tensor/__init__.py +++ b/colossalai/tensor/d_tensor/__init__.py @@ -0,0 +1,4 @@ +from .d_tensor import DTensor +from .sharding_spec import ShardingSpec + +__all__ = ['DTensor', 'ShardingSpec'] diff --git a/colossalai/tensor/d_tensor/comm_spec.py b/colossalai/tensor/d_tensor/comm_spec.py index 159125fa16db..79b2e3ef936a 100644 --- a/colossalai/tensor/d_tensor/comm_spec.py +++ b/colossalai/tensor/d_tensor/comm_spec.py @@ -24,12 +24,12 @@ class CommSpec: ''' Communication spec is used to record the communication action. It converts the communication spec to real action which will be used in runtime. It contains comm_pattern to determine the - communication method, process_groups_dict to determine the process groups, gather_dim and shard_dim + communication method, process_group_dict to determine the process groups, gather_dim and shard_dim to determine the buffer shape, and logical_process_axis Argument: - comm_pattern(CollectiveCommPattern): describe the communication method used in this spec. - process_groups_dict(Dict): A dict which contains the process groups used to apply this CommSpec. + comm_pattern(CollectiveCommPattern): decribe the communication method used in this spec. + process_group_dict(Dict): A dict which contains the process groups used to apply this CommSpec. gather_dim(int, Optional): The gather_dim of the tensor will be gathered. shard_dim(int, Optional): The shard_dim of the tensor will be sharded. logical_process_axis(Union(int, List[int]), Optional): The mesh_dim to implement the communication action. @@ -37,7 +37,7 @@ class CommSpec: def __init__(self, comm_pattern: CollectiveCommPattern, - process_groups_dict: Dict, + process_group_dict: Dict, gather_dim: int = None, shard_dim: int = None, logical_process_axis: int = None): @@ -45,7 +45,7 @@ def __init__(self, self.gather_dim = gather_dim self.shard_dim = shard_dim self.logical_process_axis = logical_process_axis - self.process_groups_dict = process_groups_dict + self.process_group_dict = process_group_dict def __repr__(self): res_list = ["CommSpec:("] @@ -92,68 +92,56 @@ def _all_gather(tensor: torch.Tensor, comm_spec: CommSpec): ''' Implement all gather operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - tensor_list = [ - torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list)) - ] - # without this contiguous operation, the all gather may get some unexpected results. - tensor = tensor.contiguous() - dist.all_gather(tensor_list, tensor, group=process_group) - output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() - return output + process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] + world_size = dist.get_world_size(process_group) + tensor_list = [torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)] + # without this contiguous operation, the all gather may get some unexpected results. + tensor = tensor.contiguous() + dist.all_gather(tensor_list, tensor, group=process_group) + output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() + return output def _split(tensor: torch.Tensor, comm_spec: CommSpec): ''' Implement shard operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, _ in process_groups_list: - if dist.get_rank() in rank_list: - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // len(rank_list) - start = length * rank_list.index(dist.get_rank()) - output = torch.narrow(tensor, dim, start, length).contiguous() - return output + process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // dist.get_world_size(process_group) + start = length * dist.get_rank(process_group) + output = torch.narrow(tensor, dim, start, length).contiguous() + return output def _all_to_all(tensor: torch.Tensor, comm_spec: CommSpec): ''' Implement all to all operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - new_shape = list(tensor.shape) - new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // len(rank_list) - new_shape = torch.Size(new_shape) - output_tensor_list = [ - torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list)) - ] - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // len(rank_list) - input_tensor_list = [ - torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(len(rank_list)) - ] - group = process_group - dist.all_to_all(output_tensor_list, input_tensor_list, group) - output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() - return output + process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] + world_size = dist.get_world_size(process_group) + new_shape = list(tensor.shape) + new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // world_size + new_shape = torch.Size(new_shape) + output_tensor_list = [torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)] + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // world_size + input_tensor_list = [torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(world_size)] + group = process_group + dist.all_to_all(output_tensor_list, input_tensor_list, group) + output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() + return output def _all_reduce(tensor: torch.Tensor, comm_spec: CommSpec, async_op: bool = False): ''' Implement all reduce operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - if not tensor.is_contiguous(): - tensor = tensor.contiguous() - dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) - return tensor + process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] + if not tensor.is_contiguous(): + tensor = tensor.contiguous() + dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) + return tensor class _ReduceGrad(torch.autograd.Function): @@ -269,7 +257,7 @@ def symbolic(graph, input_): def forward(ctx, input_, comm_spec): output = _all_to_all(input_, comm_spec) comm_spec_for_backward = CommSpec(comm_pattern=comm_spec.comm_pattern, - process_groups_dict=comm_spec.process_groups_dict, + process_group_dict=comm_spec.process_group_dict, gather_dim=comm_spec.shard_dim, shard_dim=comm_spec.gather_dim, logical_process_axis=comm_spec.logical_process_axis) diff --git a/colossalai/tensor/d_tensor/d_tensor.py b/colossalai/tensor/d_tensor/d_tensor.py index c1fe9d50a048..6bda0f4e579c 100644 --- a/colossalai/tensor/d_tensor/d_tensor.py +++ b/colossalai/tensor/d_tensor/d_tensor.py @@ -3,55 +3,119 @@ import torch from torch.utils._pytree import tree_map +from colossalai.device.device_mesh import DeviceMesh + from .layout import Layout from .layout_converter import LayoutConverter, to_global from .sharding_spec import ShardingSpec +__all__ = ['DTensor', 'distribute_tensor', 'distribute_module', 'construct_default_sharding_spec'] + layout_converter = LayoutConverter() class DTensor(torch.Tensor): + """ + DTensor stands for distributed tensor. It is a subclass of `torch.Tensor` and contains meta information + about the tensor distribution. The meta information includes the device mesh, the sharding specification, + and the entire shape of the tensor. + + During runtime, we will not directly use the DTensor objects for computation. Instead, we will only use the + `DTensor.local_tensor` for computation. The `DTensor.local_tensor` is the local tensor in the current rank. + In this way, all tensors involved in computation will only be native PyTorch tensors. + + Example: + ```python + from colossalai.device import DeviceMesh + + # define your device mesh + # assume you have 4 GPUs + physical_mesh_id = torch.arange(0, 4).reshape(1, 4) + mesh_shape = (2, 2) + device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) + + # define a tensor + x = torch.rand(16, 32) + + # create sharding spec for the tensor + # assume the sharding spec is [S, R] + dim_partition_dict = { + 0: 1 + } + sharding_spec = ShardingSpec(a.dim(), dim_partition_dict) + + # create a distributed tensor + d_tensor = DTensor(x, device_mesh, sharding_spec) + ``` - def __init__(self, local_tensor: torch.Tensor, dist_layout: Layout): - self.local_tensor = local_tensor - self.data_type = local_tensor.dtype - self.entire_shape = local_tensor.shape + Args: + tensor (`torch.Tensor`): the unsharded tensor. + device_mesh (`DeviceMesh`): the device mesh for abstraction of the compute devices. + sharding_spec (`ShardingSpec`): the sharding specification which describes how the tensor will be sharded. + """ + + def __init__(self, tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec): + # ensure this tensor is not a DTensor + assert not isinstance(tensor, DTensor), 'The input tensor should not be a DTensor.' + + # store meta info + self.local_tensor = tensor + self.data_type = tensor.dtype + self.global_shape = tensor.shape + + # create distributed layout + dist_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=self.global_shape) self.dist_layout = dist_layout + + # shard the tensor self._apply_layout() @staticmethod - def __new__(cls, local_tensor, layout): - return torch.Tensor._make_subclass(cls, local_tensor, local_tensor.requires_grad) + def __new__(cls, tensor, *args, **kwargs): + return torch.Tensor._make_subclass(cls, tensor, tensor.requires_grad) def __repr__(self): - return f"DTensor({self.to_global()}, {self.dist_layout})" + return f"DTensor(\n{self.to_global()}\n{self.dist_layout}" def __str__(self): return self.__repr__() - def layout_convert(self, target_layout): + def layout_convert(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> None: ''' Convert the layout of the tensor from source_spec to target_spec. + This will update the `local_tensor` and `dist_layout` in place. + + Args: + target_layout (Layout): the target layout specification. ''' - self.local_tensor = layout_converter.apply(self.local_tensor, self.dist_layout, target_layout) + target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=self.global_shape) + self.local_tensor = layout_converter.apply(tensor=self.local_tensor, + source_layout=self.dist_layout, + target_layout=target_layout) self.dist_layout = target_layout def _apply_layout(self): ''' Apply the layout to the local tensor during initializing process. ''' + # layout converter requires a source and target laytout + # we construct the source layer for an unsharded tensor + # and use self.dist_layer as the targer layout for the sharded tensor source_spec = construct_default_sharding_spec(self.local_tensor) source_layout = Layout(device_mesh=self.dist_layout.device_mesh, - device_type=self.dist_layout.device_type, sharding_spec=source_spec, - entire_shape=self.entire_shape) - self.local_tensor = layout_converter.apply(self.local_tensor, source_layout, self.dist_layout) + global_shape=self.global_shape) + self.local_tensor = layout_converter.apply(tensor=self.local_tensor, + source_layout=source_layout, + target_layout=self.dist_layout) @classmethod def __torch_function__(cls, func, types, args=(), kwargs=None): if kwargs is None: kwargs = {} + # convert all DTensors to native pytorch tensors + # so that operations will be conducted on native tensors def filter_arg(arg): if isinstance(arg, DTensor): return arg.local_tensor @@ -60,9 +124,9 @@ def filter_arg(arg): args = tree_map(filter_arg, args) kwargs = tree_map(filter_arg, kwargs) - # if we want to convert the result into DTensor, we need to infer the layout of result from the layout of input tensors - # and op type. + # NOTE: if we want to convert the result into DTensor, we need to infer the layout of result from the layout of input tensors + # and op type. return func(*args, **kwargs) @property @@ -85,7 +149,6 @@ def to(self, *args, **kwargs): ''' self.local_tensor = self.local_tensor.to(*args, **kwargs) self.data_type = self.local_tensor.dtype - self.dist_layout.device_type = self.local_tensor.device # TODO: update the device mesh process groups or we should just cache # both the cpu process groups and the cuda process groups? return self @@ -98,7 +161,7 @@ def to_local(self): def to_global(self): ''' - Recover the global tensor from the distributed tensor. + Recover the global tensor from the distributed tensor by returning a new `torch.Tensor` object. Note: This function will all_gather the local tensor to the global tensor and it will not change the layout of the DTensor. This function is mainly used for debugging or @@ -107,24 +170,29 @@ def to_global(self): return to_global(self.local_tensor, self.dist_layout) -def distribute_tensor(local_tensor: torch.Tensor, dist_layout: Layout) -> DTensor: +def distribute_tensor(tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> DTensor: ''' Distribute the local tensor to the distributed tensor according to the dist_layout specified. Args: - local_tensor: tensor to be distributed. - dist_layout: the layout specification of the distributed tensor. + tensor (`torch.Tensor`): tensor to be distributed. + device_mesh (`DeviceMesh`): the device mesh for abstraction of the compute devices. + sharding_spec (`ShardingSpec`): the sharding specification which describes how the tensor will be sharded. Returns: A 'DTensor' object. ''' - return DTensor(local_tensor, dist_layout) + return DTensor(tensor, device_mesh, sharding_spec) def distribute_module(module: torch.nn.Module, partition_fn: Optional[callable] = None) -> torch.nn.Module: ''' This function converts all the parameters in the module to DTensor(DParam). + Args: + module (`torch.nn.Module`): the module to be distributed. + partition_fn (callable): the partition function which will be used to partition the parameters. + Note: This function is subject to future change as the DParam has not been implemented yet. ''' for name, param in module.named_parameters(): @@ -138,5 +206,11 @@ def distribute_module(module: torch.nn.Module, partition_fn: Optional[callable] def construct_default_sharding_spec(tensor: torch.Tensor,) -> ShardingSpec: ''' Construct the default sharding specification for the tensor. + + Args: + tensor (`torch.Tensor`): the tensor to be sharded. + + Returns: + A `ShardingSpec` object without any sharding specified. ''' return ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={}) diff --git a/colossalai/tensor/d_tensor/layout.py b/colossalai/tensor/d_tensor/layout.py index ee7ef74a99ae..2946611b4b79 100644 --- a/colossalai/tensor/d_tensor/layout.py +++ b/colossalai/tensor/d_tensor/layout.py @@ -11,28 +11,32 @@ class Layout: - """Layout of a tensor. + """ + Layout of a tensor refers to the tensor placement on the device mesh and how the tensor is sharded over the devices. - Attributes: - device_mesh: the device mesh to store the tensor distributed. - device_type: the type of the device mesh, e.g. 'cpu' or 'cuda'. - sharding_spec: the sharding specification to describe how the tensor is sharded. - entire_shape: the entire shape of the global tensor. + Args: + device_mesh (`DeviceMesh`): the device mesh to store the tensor distributed. + sharding_spec (`ShardingSpec`): the sharding specification to describe how the tensor is sharded. + global_shape (`torch.Size`): the entire shape of the global tensor. """ - def __init__(self, device_mesh: DeviceMesh, device_type: torch.device, sharding_spec: ShardingSpec, - entire_shape: torch.Size): + def __init__(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec, global_shape: torch.Size): self.device_mesh = device_mesh - self.device_type = device_type self.sharding_spec = sharding_spec - self.entire_shape = entire_shape + self.global_shape = global_shape self._sanity_check() def __hash__(self) -> int: return hash(f'{self.sharding_spec}') - def get_sharded_shape_per_device(self): - sharded_shape = list(self.entire_shape) + def get_sharded_shape_per_device(self) -> torch.Size: + """ + Compute the shape of the sharded tensor on each device. + + Returns: + `torch.Size`: the shape of the sharded tensor on each device. + """ + sharded_shape = list(self.global_shape) for dim, shard_list in self.sharding_spec.dim_partition_dict.items(): mesh_list = [self.device_mesh.mesh_shape[mesh_dim] for mesh_dim in shard_list] shard_partitions = reduce(operator.mul, mesh_list, 1) @@ -56,7 +60,7 @@ def _sanity_check(self): # make sure that the sharding for a dimension is divisible by the number of devices for dim, shard_list in sharding_spec.dim_partition_dict.items(): - tensor_dim_size = self.entire_shape[dim] + tensor_dim_size = self.global_shape[dim] num_devices = 1 for element in shard_list: diff --git a/colossalai/tensor/d_tensor/layout_converter.py b/colossalai/tensor/d_tensor/layout_converter.py index cf02aac309f4..6eff92ea6b13 100644 --- a/colossalai/tensor/d_tensor/layout_converter.py +++ b/colossalai/tensor/d_tensor/layout_converter.py @@ -3,10 +3,8 @@ from dataclasses import dataclass from typing import Dict, List, Tuple -import numpy as np import torch -from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, TrainCycleItem from colossalai.context.singleton_meta import SingletonMeta from colossalai.tensor.d_tensor.comm_spec import * from colossalai.tensor.d_tensor.layout import Layout @@ -28,13 +26,21 @@ class LayoutConverterOptions: pass -def to_global(distributed_tensor: torch.Tensor, layout: Layout) -> torch.Tensor: +def to_global(distributed_tensor: "DTensor", layout: Layout) -> torch.Tensor: + """ + Convert a distributed tensor to the global tensor with the given layout. + This function returns a native `torch.Tensor` object. + + + Args: + distributed_tensor (`DTensor`): the distributed tensor to be converted. + layout (`Layout`): the target layout specification. + """ layout_converter = LayoutConverter() global_sharding_spec = ShardingSpec(distributed_tensor.dim(), {}) global_layout = Layout(device_mesh=layout.device_mesh, - device_type=layout.device_type, sharding_spec=global_sharding_spec, - entire_shape=layout.entire_shape) + global_shape=layout.global_shape) with torch.no_grad(): global_tensor = layout_converter.apply(distributed_tensor, layout, global_layout) return global_tensor @@ -49,6 +55,9 @@ def set_layout_converting_options(options: LayoutConverterOptions): class LayoutConverter(metaclass=SingletonMeta): + """ + LayoutConverter is a singleton class which converts the layout of a distributed tensor. + """ def __init__(self): self._options = None @@ -91,15 +100,14 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) dim_partition_dict = {0: [0], 1: [1]} # [S0,S1,R] sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec, - entire_shape=entire_shape) + global_shape=global_shape) rst_dict = layout_converter.all_gather_transform_layouts(layout) for layout, comm_spec in rst_dict.items(): @@ -112,7 +120,12 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co valid_spec_dict = {} comm_pattern = CollectiveCommPattern.GATHER_FWD_SPLIT_BWD source_spec = source_layout.sharding_spec - process_groups_dict = source_layout.device_mesh.process_groups_dict + + # the key of the dict is the axis + # the value is the process group + current_rank = source_layout.device_mesh._global_rank_of_current_process + process_group_dict = source_layout.device_mesh._process_group_dict[current_rank] + for target_pair in source_spec.dim_partition_dict.items(): shard_list = all_gather_simulator(target_pair) index = target_pair[0] @@ -130,7 +143,7 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co logical_process_axis = target_pair[1][-1] comm_spec = CommSpec( comm_pattern, - process_groups_dict=process_groups_dict, + process_group_dict=process_group_dict, gather_dim=gather_dim, # shard_dim will be used during backward shard_dim=gather_dim, @@ -141,8 +154,7 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co new_sharding_spec = ShardingSpec(source_spec.dims, dim_partition_dict=new_dim_partition_dict) new_layout = Layout(device_mesh=source_layout.device_mesh, sharding_spec=new_sharding_spec, - device_type=source_layout.device_type, - entire_shape=source_layout.entire_shape) + global_shape=source_layout.global_shape) valid_spec_dict[new_layout] = comm_spec except LayoutException: @@ -167,15 +179,14 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) dim_partition_dict = {0: [0], 1: [1]} # [S0,S1,R] sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec, - entire_shape=entire_shape) + global_shape=global_shape) rst_dict = layout_converter.all_to_all_transform_layout(layout) for layout, comm_spec in rst_dict.items(): @@ -188,7 +199,12 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com ''' valid_spec_dict = {} comm_pattern = CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD - process_groups_dict = source_layout.device_mesh.process_groups_dict + + # the key of the dict is the axis + # the value is the process group + current_rank = source_layout.device_mesh._global_rank_of_current_process + process_group_dict = source_layout.device_mesh._process_group_dict[current_rank] + source_spec = source_layout.sharding_spec tensor_dims = source_spec.dims for f_index in range(tensor_dims - 1): @@ -229,7 +245,7 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com shard_dim = f_index logical_process_axis = b_target_pair[1][-1] comm_spec = CommSpec(comm_pattern, - process_groups_dict, + process_group_dict=process_group_dict, gather_dim=gather_dim, shard_dim=shard_dim, logical_process_axis=logical_process_axis) @@ -252,8 +268,7 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com new_sharding_spec = ShardingSpec(source_spec.dims, dim_partition_dict=new_dim_partition_dict) new_layout = Layout(device_mesh=source_layout.device_mesh, sharding_spec=new_sharding_spec, - device_type=source_layout.device_type, - entire_shape=source_layout.entire_shape) + global_shape=source_layout.global_shape) valid_spec_dict[new_layout] = comm_spec except LayoutException: pass @@ -278,16 +293,15 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) dim_partition_dict = {0: [0]} # [S0,R,R] sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec, - entire_shape=entire_shape) + global_shape=global_shape) rst_dict = layout_converter.shard_transform_layout(layout) for layout, comm_spec in rst_dict.items(): @@ -301,7 +315,11 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec valid_spec_dict = {} comm_pattern = CollectiveCommPattern.SPLIT_FWD_GATHER_BWD source_spec = source_layout.sharding_spec - process_groups_dict = source_layout.device_mesh.process_groups_dict + + # the key of the dict is the axis + # the value is the process group + current_rank = source_layout.device_mesh._global_rank_of_current_process + process_group_dict = source_layout.device_mesh._process_group_dict[current_rank] # legal sharding dims means the mesh_id is still available to use. legal_sharding_dims = [i for i in range(len(source_layout.device_mesh.mesh_shape))] @@ -329,7 +347,7 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec shard_dim = index logical_process_axis = shard_list[-1] comm_spec = CommSpec(comm_pattern, - process_groups_dict, + process_group_dict=process_group_dict, gather_dim=shard_dim, shard_dim=shard_dim, logical_process_axis=logical_process_axis) @@ -340,8 +358,7 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec dim_partition_dict=new_dim_partition_dict) new_layout = Layout(device_mesh=source_layout.device_mesh, sharding_spec=new_sharding_spec, - device_type=source_layout.device_type, - entire_shape=source_layout.entire_shape) + global_shape=source_layout.global_shape) valid_spec_dict[new_layout] = comm_spec except LayoutException: pass @@ -399,7 +416,7 @@ def layout_converting(self, source_layout: Layout, # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) dim_partition_source = {1: [0, 1]} dim_partition_target = {0: [0, 1]} @@ -407,16 +424,14 @@ def layout_converting(self, source_layout: Layout, # [R,S01,R] sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) source_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec_source, - entire_shape=entire_shape) + global_shape=global_shape) # [S01,R,R] sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) target_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec_target, - entire_shape=entire_shape) + global_shape=global_shape) transform_path, comm_action_sequence = layout_converter.layout_converting(source_layout, target_layout) transform_path_str = '->'.join([str(layout.sharding_spec.sharding_sequence) for layout in transform_path]) @@ -505,21 +520,19 @@ def apply(self, tensor: torch.Tensor, source_layout: Layout, target_layout: Layo # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) # [S0,R,R] sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) source_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec_source, - entire_shape=entire_shape) + global_shape=global_shape) # [R,S0,R] sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) target_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec_target, - entire_shape=entire_shape) + global_shape=global_shape) if rank in (0, 1): sharded_tensor_0 = torch.zeros(2, 1) @@ -554,3 +567,4 @@ def apply(self, tensor: torch.Tensor, source_layout: Layout, target_layout: Layo for comm_spec in comm_action_sequence: tensor = comm_spec.covert_spec_to_action(tensor) return tensor + return tensor diff --git a/tests/test_device/test_device_mesh.py b/tests/test_device/test_device_mesh.py index 3be057b3a98b..19d41d23353f 100644 --- a/tests/test_device/test_device_mesh.py +++ b/tests/test_device/test_device_mesh.py @@ -1,20 +1,19 @@ -from colossalai.device.device_mesh import DeviceMesh import torch +from colossalai.device.device_mesh import DeviceMesh + def test_device_mesh(): - physical_mesh_id = torch.arange(0, 16).reshape(2, 8) + physical_mesh_id = torch.arange(0, 16) mesh_shape = (4, 4) # [[0, 1, 2, 3], # [4, 5, 6, 7], # [8, 9, 10,11], # [12,13,14,15]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - assert device_mesh.convert_map[5] == [1, 1] - assert device_mesh.convert_map[11] == [2, 3] - assert device_mesh.global_rank_to_process_groups_with_logical_rank(0)[0] == [[0, 0], [1, 0], [2, 0], [3, 0]] - assert device_mesh.global_rank_to_process_groups_with_logical_rank(2)[1] == [[0, 0], [0, 1], [0, 2], [0, 3]] - assert device_mesh.global_rank_to_process_groups_with_global_rank(2)[1] == [0, 1, 2, 3] + assert device_mesh.global_rank_to_local_rank(5) == [1, 1] + assert device_mesh.global_rank_to_local_rank(11) == [2, 3] + assert device_mesh.get_ranks_in_process_group(axis=1, global_rank=2) == [0, 1, 2, 3] if __name__ == '__main__': diff --git a/tests/test_device/test_init_logical_pg.py b/tests/test_device/test_init_logical_pg.py index 2b7060c4846a..7c6339eff67e 100644 --- a/tests/test_device/test_init_logical_pg.py +++ b/tests/test_device/test_init_logical_pg.py @@ -20,16 +20,12 @@ def check_layer(rank, world_size, port): # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - logical_pg_dict = {0: [[0, 2], [1, 3]], 1: [[0, 1], [2, 3]]} - logical_process_groups = device_mesh.process_groups_dict - - for mesh_dim, pgs in logical_pg_dict.items(): - for index, pg in enumerate(pgs): - if rank in pg: - tensor = torch.ones(4).cuda() - group = logical_process_groups[mesh_dim][index][1] - dist.all_reduce(tensor, op=ReduceOp.SUM, group=group) - assert tensor.equal(tensor_to_check) + + for axis in range(len(mesh_shape)): + tensor = torch.ones(4).cuda() + pg = device_mesh.get_process_group(axis=axis) + dist.all_reduce(tensor, op=ReduceOp.SUM, group=pg) + assert tensor.equal(tensor_to_check) gpc.destroy() diff --git a/tests/test_lazy/lazy_init_utils.py b/tests/test_lazy/lazy_init_utils.py index 85bfd0e27801..2911012fafa8 100644 --- a/tests/test_lazy/lazy_init_utils.py +++ b/tests/test_lazy/lazy_init_utils.py @@ -6,7 +6,9 @@ import torch from packaging import version +from colossalai.device.device_mesh import DeviceMesh from colossalai.lazy.lazy_init import LazyInitContext, LazyTensor, _MyTensor +from colossalai.tensor.d_tensor.layout import Layout from colossalai.tensor.d_tensor.layout_converter import to_global from tests.kit.model_zoo.registry import ModelAttribute @@ -81,7 +83,8 @@ def check_lazy_init(entry: TestingEntry, seed: int = 42, verbose: bool = False, print(f'{model.__class__.__name__} pass') -def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn.Module, layout_dict: dict) -> None: +def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn.Module, device_mesh: DeviceMesh, + sharding_spec_dict: dict) -> None: state = model.state_dict() distributed_state = distributed_model.state_dict() @@ -91,6 +94,7 @@ def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn. assert n1 == n2 t1 = t1.cuda() t2 = t2.cuda() - if n2 in layout_dict: - t2 = to_global(t2, layout_dict[n2]) + if n2 in sharding_spec_dict: + layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_dict[n2], global_shape=t1.shape) + t2 = to_global(t2, layout) assert torch.equal(t1, t2), f'{n1} {t1} vs {t2}' diff --git a/tests/test_lazy/test_distribute.py b/tests/test_lazy/test_distribute.py index d515b175a9ea..efa43eab5788 100644 --- a/tests/test_lazy/test_distribute.py +++ b/tests/test_lazy/test_distribute.py @@ -26,23 +26,19 @@ def find_shard_dim(shape: torch.Size) -> Optional[int]: return dim -def make_layout(device_mesh: DeviceMesh, original_tensor: torch.Tensor) -> Layout: +def make_sharding_spec(original_tensor: torch.Tensor) -> Layout: shard_dim = find_shard_dim(original_tensor.shape) dim_partition_dict = {shard_dim: [0]} if shard_dim is not None else {} target_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict=dim_partition_dict) - layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=target_sharding_spec, - entire_shape=original_tensor.shape) - return layout + return target_sharding_spec def _get_current_name(prefix: str, name: str) -> str: return f'{prefix}.{name}'.lstrip('.') -def generate_layout_dict(model: nn.Module, device_mesh: DeviceMesh) -> dict: - layout_dict = {} +def generate_sharding_spec_dict(model: nn.Module) -> dict: + sharding_spec_dict = {} @torch.no_grad() def generate_recursively(module: nn.Module, prefix: str = ''): @@ -53,17 +49,17 @@ def generate_recursively(module: nn.Module, prefix: str = ''): # initialize tensors directly attached to the current module for name, param in module.named_parameters(recurse=False): if isinstance(param, LazyTensor): - layout = make_layout(device_mesh, param) - layout_dict[_get_current_name(prefix, name)] = layout + sharding_spec = make_sharding_spec(param) + sharding_spec_dict[_get_current_name(prefix, name)] = sharding_spec for name, buf in module.named_buffers(recurse=False): if isinstance(buf, LazyTensor): - layout = make_layout(device_mesh, buf) - layout_dict[_get_current_name(prefix, name)] = layout + sharding_spec = make_sharding_spec(buf) + sharding_spec_dict[_get_current_name(prefix, name)] = sharding_spec generate_recursively(model) - return layout_dict + return sharding_spec_dict @parameterize('subset', ['torchvision', 'diffusers', 'timm', 'transformers', 'torchaudio', 'deepfm', 'dlrm']) @@ -85,9 +81,9 @@ def run_dist_lazy_init(subset, seed: int = 42): ctx = LazyInitContext() with ctx: deferred_model = model_fn() - layout_dict = generate_layout_dict(deferred_model, device_mesh) - ctx.distribute(deferred_model, layout_dict, verbose=True) - assert_dist_model_equal(model, deferred_model, layout_dict) + sharding_spec_dict = generate_sharding_spec_dict(deferred_model) + ctx.distribute(deferred_model, device_mesh, sharding_spec_dict, verbose=True) + assert_dist_model_equal(model, deferred_model, device_mesh, sharding_spec_dict) def run_dist(rank, world_size, port) -> None: diff --git a/tests/test_tensor/test_dtensor/test_comm_spec.py b/tests/test_tensor/test_dtensor/test_comm_spec.py index d1f5b9299397..0797e01e7e9d 100644 --- a/tests/test_tensor/test_dtensor/test_comm_spec.py +++ b/tests/test_tensor/test_dtensor/test_comm_spec.py @@ -125,23 +125,6 @@ def check_all_reduce_bwd(process_groups_dict, rank): assert tensor_to_comm.equal(tensor_to_check) -def check_all_reduce_in_flatten_device_mesh(process_groups_dict, rank): - # tensor to comm - tensor_to_comm = torch.ones(2, 2).cuda() * rank - - # reduce through logical process axis 0 at flatten device mesh - # tensor to check - # tensor([[6., 6.], - # [6., 6.]]) - tensor_to_check = torch.tensor([[6, 6], [6, 6]], dtype=tensor_to_comm.dtype).cuda() - - # CommSpec:(comm_pattern:all_reduce, logical_process_axis:[0, 1]) - comm_spec = CommSpec(CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD, process_groups_dict, logical_process_axis=0) - tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm) - - assert tensor_to_comm.equal(tensor_to_check) - - def check_comm(rank, world_size, port): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') @@ -153,24 +136,22 @@ def check_comm(rank, world_size, port): # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - process_groups_dict = device_mesh.process_groups_dict + + process_group_dict = device_mesh._process_group_dict[rank] # test all gather - check_all_gather(process_groups_dict, rank) + check_all_gather(process_group_dict, rank) # test shard - check_shard(process_groups_dict, rank) + check_shard(process_group_dict, rank) # test all to all - check_all_to_all(process_groups_dict, rank) + check_all_to_all(process_group_dict, rank) # test all reduce - check_all_reduce_fwd(process_groups_dict, rank) - check_all_reduce_bwd(process_groups_dict, rank) + check_all_reduce_fwd(process_group_dict, rank) + check_all_reduce_bwd(process_group_dict, rank) - flatten_process_groups_dict = device_mesh.flatten_device_mesh.process_groups_dict - # test all reduce in 1D flatten device mesh - check_all_reduce_in_flatten_device_mesh(flatten_process_groups_dict, rank) gpc.destroy() diff --git a/tests/test_tensor/test_dtensor/test_dtensor.py b/tests/test_tensor/test_dtensor/test_dtensor.py index 3ca369acbf87..50a3bfb15c38 100644 --- a/tests/test_tensor/test_dtensor/test_dtensor.py +++ b/tests/test_tensor/test_dtensor/test_dtensor.py @@ -31,13 +31,9 @@ def check_dtensor(rank, world_size, port): device_mesh = DeviceMesh(torch.Tensor([0, 1, 2, 3]), (2, 2), init_process_group=True) target_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict={0: [0]}) - layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=target_sharding_spec, - entire_shape=original_tensor.shape) - d_tensor = DTensor(original_tensor, layout) + d_tensor = DTensor(original_tensor, device_mesh, target_sharding_spec) - assert d_tensor.entire_shape == original_tensor.shape + assert d_tensor.global_shape == original_tensor.shape assert d_tensor.data_type == original_tensor.dtype if rank in (0, 1): @@ -57,12 +53,7 @@ def check_dtensor(rank, world_size, port): raise ValueError(f'rank {rank} is not in the device mesh') new_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict={0: [0, 1]}) - new_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=new_sharding_spec, - entire_shape=original_tensor.shape) - - d_tensor.layout_convert(new_layout) + d_tensor.layout_convert(device_mesh, new_sharding_spec) if rank == 0: assert d_tensor.local_tensor.equal(original_tensor.narrow(0, 0, 1)) @@ -75,7 +66,7 @@ def check_dtensor(rank, world_size, port): else: raise ValueError(f'rank {rank} is not in the device mesh') - dtensor_from_local = distribute_tensor(original_tensor, new_layout) + dtensor_from_local = distribute_tensor(original_tensor, device_mesh, new_sharding_spec) if rank == 0: assert dtensor_from_local.local_tensor.equal(original_tensor.narrow(0, 0, 1)) diff --git a/tests/test_tensor/test_dtensor/test_layout_converter.py b/tests/test_tensor/test_dtensor/test_layout_converter.py index 5f56decb5e5d..6608e4787273 100644 --- a/tests/test_tensor/test_dtensor/test_layout_converter.py +++ b/tests/test_tensor/test_dtensor/test_layout_converter.py @@ -12,9 +12,9 @@ from colossalai.tensor.d_tensor.sharding_spec import DimSpec, ShardingSpec from colossalai.testing import rerun_if_address_is_in_use, spawn -entire_shape = torch.Size((64, 32, 16)) +global_shape = torch.Size((64, 32, 16)) layout_converter = LayoutConverter() -physical_mesh_id = torch.arange(0, 4).reshape(2, 2) +physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) @@ -30,10 +30,7 @@ def check_one_step_transform(rank, world_size, port): # shard_sequence: S0,S1,R # device_mesh_shape: (2, 2) sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) - layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec, - entire_shape=entire_shape) + layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=global_shape) rst_dict = layout_converter.all_gather_transform_layouts(layout) @@ -49,10 +46,7 @@ def check_one_step_transform(rank, world_size, port): # shard_sequence: S0,S1,R # device_mesh_shape: (4, 4) sharding_spec_all2all = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict_all2all) - layout_all2all = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_all2all, - entire_shape=entire_shape) + layout_all2all = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_all2all, global_shape=global_shape) rst_dict_all2all = layout_converter.all_to_all_transform_layout(layout_all2all) @@ -71,10 +65,7 @@ def check_one_step_transform(rank, world_size, port): # shard_sequence: S0,R,R # device_mesh_shape: (4, 4) sharding_spec_shard = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_shard) - shard_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_shard, - entire_shape=entire_shape) + shard_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_shard, global_shape=global_shape) rst_dict_shard = layout_converter.shard_transform_layout(shard_layout) @@ -100,19 +91,13 @@ def check_layout_converting(rank, world_size, port): # shard_sequence: R,S01,R # device_mesh_shape: (4, 4) sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) - source_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_source, - entire_shape=entire_shape) + source_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_source, global_shape=global_shape) # DistSpec: # shard_sequence: S01,R,R # device_mesh_shape: (4, 4) sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) - target_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_target, - entire_shape=entire_shape) + target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_target, global_shape=global_shape) transform_path, comm_action_sequence = layout_converter.layout_converting(source_layout, target_layout) @@ -159,21 +144,15 @@ def check_layout_converting_apply(rank, world_size, port): # shard_sequence: R,S01,R # device_mesh_shape: (4, 4) sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) - source_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_source, - entire_shape=entire_shape) + source_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_source, global_shape=global_shape) # DistSpec: # shard_sequence: S01,R,R # device_mesh_shape: (4, 4) sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) - target_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_target, - entire_shape=entire_shape) + target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_target, global_shape=global_shape) - original_tensor = torch.rand(entire_shape).cuda() + original_tensor = torch.rand(global_shape).cuda() # tensor_to_apply: [R, S01, R] tensor_to_apply = original_tensor.narrow(1, rank * 8, 8) diff --git a/tests/test_tensor/test_shape_consistency.py b/tests/test_tensor/test_shape_consistency.py index 6fe9ee292cd0..859eef051256 100644 --- a/tests/test_tensor/test_shape_consistency.py +++ b/tests/test_tensor/test_shape_consistency.py @@ -1,9 +1,10 @@ -from colossalai.tensor.shape_consistency import ShapeConsistencyManager, CollectiveCommPattern import torch -from colossalai.tensor.sharding_spec import _DimSpec, ShardingSpec + from colossalai.device.device_mesh import DeviceMesh +from colossalai.tensor.shape_consistency import CollectiveCommPattern, ShapeConsistencyManager +from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec -physical_mesh_id = torch.arange(0, 16).reshape(2, 8) +physical_mesh_id = torch.arange(0, 16) mesh_shape = (4, 4) # [[0, 1, 2, 3], # [4, 5, 6, 7], diff --git a/tests/test_tensor/test_sharded_linear.py b/tests/test_tensor/test_sharded_linear.py index d66d4fec14d1..9bd9805e9b8f 100644 --- a/tests/test_tensor/test_sharded_linear.py +++ b/tests/test_tensor/test_sharded_linear.py @@ -26,7 +26,7 @@ def run_dist(rank, world_size, port): # the mesh is in the following topo # [[0, 1], # [2, 3]] - physical_mesh_id = torch.arange(0, 4).reshape(2, 2) + physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) row_id = rank // 2 diff --git a/tests/test_tensor/test_sharding_spec.py b/tests/test_tensor/test_sharding_spec.py index 909c84ef0f0e..5007c4141849 100644 --- a/tests/test_tensor/test_sharding_spec.py +++ b/tests/test_tensor/test_sharding_spec.py @@ -5,7 +5,7 @@ def test_sharding_spec(): - physical_mesh_id = torch.arange(0, 16).reshape(2, 8) + physical_mesh_id = torch.arange(0, 16) mesh_shape = (4, 4) # [[0, 1, 2, 3], # [4, 5, 6, 7], From cf4792c9757e071217f0b99f4e2bcc85f2d048b7 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Thu, 8 Jun 2023 11:15:10 +0800 Subject: [PATCH 292/413] modify shell for check --- examples/images/dreambooth/test_ci.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/images/dreambooth/test_ci.sh b/examples/images/dreambooth/test_ci.sh index 8ba413a149b5..8d18e1d4a45c 100644 --- a/examples/images/dreambooth/test_ci.sh +++ b/examples/images/dreambooth/test_ci.sh @@ -8,7 +8,7 @@ DIFFUSERS_OFFLINE=1 # "torch_ddp" "torch_ddp_fp16" for plugin in "low_level_zero" "gemini"; do - torchrun --nproc_per_node 8 --standalone train_dreambooth_colossalai.py \ + torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \ --pretrained_model_name_or_path="/data/dreambooth/diffuser/stable-diffusion-v1-4" \ --instance_data_dir="/data/dreambooth/Teyvat/data" \ --output_dir="./weight_output" \ From e417dd004ee166e6787c1c6325bfc037d3f8b83e Mon Sep 17 00:00:00 2001 From: Baizhou Zhang <56809903+Fridge003@users.noreply.github.com> Date: Thu, 8 Jun 2023 11:27:05 +0800 Subject: [PATCH 293/413] [example] update opt example using booster api (#3918) --- examples/language/opt/README.md | 32 ++- examples/language/opt/args.py | 120 +++++++++++ examples/language/opt/benchmark.sh | 21 -- examples/language/opt/data.py | 37 ++++ examples/language/opt/opt_benchmark.py | 146 ++++++++++++++ examples/language/opt/opt_train_demo.py | 149 ++++++++++++++ examples/language/opt/requirements.txt | 2 + examples/language/opt/run_benchmark.sh | 30 +++ examples/language/opt/run_demo.sh | 44 ++++ examples/language/opt/run_gemini.sh | 28 --- examples/language/opt/test_ci.sh | 19 +- examples/language/opt/train_gemini_opt.py | 233 ---------------------- 12 files changed, 571 insertions(+), 290 deletions(-) create mode 100644 examples/language/opt/args.py delete mode 100644 examples/language/opt/benchmark.sh create mode 100644 examples/language/opt/data.py create mode 100755 examples/language/opt/opt_benchmark.py create mode 100644 examples/language/opt/opt_train_demo.py create mode 100644 examples/language/opt/run_benchmark.sh create mode 100644 examples/language/opt/run_demo.sh delete mode 100644 examples/language/opt/run_gemini.sh delete mode 100755 examples/language/opt/train_gemini_opt.py diff --git a/examples/language/opt/README.md b/examples/language/opt/README.md index c2fd254571c7..37e1ff4d9008 100644 --- a/examples/language/opt/README.md +++ b/examples/language/opt/README.md @@ -19,15 +19,35 @@ Meta recently released [Open Pretrained Transformer (OPT)](https://github.com/fa The following example of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) demonstrates fine-tuning Casual Language Modelling at low cost. -We are using the pre-training weights of the OPT model provided by Hugging Face Hub on the raw WikiText-2 (no tokens were replaced before -the tokenization). This training script is adapted from the [HuggingFace Language Modelling examples](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). ## Our Modifications -We adapt the OPT training code to ColossalAI by leveraging Gemini and ZeRO DDP. -## Quick Start -You can launch training by using the following bash script +We are using the pre-training weights of the OPT model provided by Hugging Face Hub on the raw WikiText-2 (no tokens were replaced before +the tokenization). + +We adapt the OPT training code to ColossalAI by leveraging [Boosting API](https://colossalai.org/docs/basics/booster_api) loaded with a chosen plugin, where each plugin corresponds to a specific kind of training strategy. This example supports plugins including TorchDDPPlugin, LowLevelZeroPlugin, and GeminiPlugin. + +## Run Demo +By running the following script: ```bash -bash ./run_gemini.sh +bash run_demo.sh ``` +You will finetune a [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) model on this [dataset](https://huggingface.co/datasets/hugginglearners/netflix-shows), which contains more than 8000 comments on Netflix shows. + +The script can be modified if you want to try another set of hyperparameters or change to another OPT model with different size. + +The demo code is adapted from this [blog](https://medium.com/geekculture/fine-tune-eleutherai-gpt-neo-to-generate-netflix-movie-descriptions-in-only-47-lines-of-code-40c9b4c32475) and the [HuggingFace Language Modelling examples](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). + + + +## Run Benchmark + +You can run benchmark for OPT model by running the following script: +```bash +bash run_benchmark.sh +``` +The script will test performance (throughput & peak memory usage) for each combination of hyperparameters. You can also play with this script to configure your set of hyperparameters for testing. + + + diff --git a/examples/language/opt/args.py b/examples/language/opt/args.py new file mode 100644 index 000000000000..16730be7ebea --- /dev/null +++ b/examples/language/opt/args.py @@ -0,0 +1,120 @@ +from colossalai import get_default_parser + + +def parse_demo_args(): + + parser = get_default_parser() + parser.add_argument( + "--model_name_or_path", + type=str, + default="facebook/opt-350m", + help="Path to pretrained model or model identifier from huggingface.co/models." + ) + parser.add_argument( + "--output_path", + type=str, + default="./output_model.bin", + help="The path of your saved model after finetuning." + ) + parser.add_argument( + "--plugin", + type=str, + default="gemini", + help="Plugin to use. Valid plugins include 'torch_ddp','torch_ddp_fp16','gemini','low_level_zero'." + ) + parser.add_argument( + "--num_epoch", + type=int, + default=10, + help="Number of epochs." + ) + parser.add_argument( + "--batch_size", + type=int, + default=32, + help="Batch size (per dp group) for the training dataloader." + ) + parser.add_argument( + "--learning_rate", + type=float, + default=5e-5, + help="Initial learning rate (after the potential warmup period) to use." + ) + parser.add_argument( + "--warmup_ratio", + type=float, + default=0.1, + help="Ratio of warmup steps against total training steps." + ) + parser.add_argument( + "--weight_decay", + type=float, + default=0.01, + help="Weight decay to use." + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="A seed for reproducible training." + ) + + args = parser.parse_args() + return args + + + +def parse_benchmark_args(): + + parser = get_default_parser() + parser.add_argument( + "--model_name_or_path", + type=str, + default="facebook/opt-125m", + help="Path to pretrained model or model identifier from huggingface.co/models." + ) + parser.add_argument( + "--plugin", + type=str, + default="gemini", + help="Plugin to use. Valid plugins include 'torch_ddp','torch_ddp_fp16','gemini','low_level_zero'." + ) + parser.add_argument( + "--batch_size", + type=int, + default=32, + help="Batch size (per dp group) for the training dataloader." + ) + parser.add_argument( + "--learning_rate", + type=float, + default=5e-5, + help="Initial learning rate (after the potential warmup period) to use." + ) + parser.add_argument( + "--weight_decay", + type=float, + default=0.0, + help="Weight decay to use." + ) + parser.add_argument( + "--max_train_steps", + type=int, + default=20, + help="Total number of training steps to perform." + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="A seed for reproducible training." + ) + parser.add_argument( + "--mem_cap", + type=int, + default=0, + help="Limit on the usage of space for each GPU (in GB)." + ) + args = parser.parse_args() + + return args \ No newline at end of file diff --git a/examples/language/opt/benchmark.sh b/examples/language/opt/benchmark.sh deleted file mode 100644 index 0d04b5e9b33c..000000000000 --- a/examples/language/opt/benchmark.sh +++ /dev/null @@ -1,21 +0,0 @@ -export BS=16 -export MEMCAP=0 -export MODEL="6.7b" -export GPUNUM=1 - -for MODEL in "6.7b" "13b" "1.3b" -do -for GPUNUM in 8 1 -do -for BS in 16 24 32 8 -do -for MEMCAP in 0 40 -do -pkill -9 torchrun -pkill -9 python - -env BS=$BS MEM_CAP=$MEMCAP MODEL=$MODEL GPUNUM=$GPUNUM bash ./run_gemini.sh -done -done -done -done diff --git a/examples/language/opt/data.py b/examples/language/opt/data.py new file mode 100644 index 000000000000..6cfffb5fc95b --- /dev/null +++ b/examples/language/opt/data.py @@ -0,0 +1,37 @@ +import torch +from torch.utils.data import Dataset +from datasets import load_dataset + + +class NetflixDataset(Dataset): + + def __init__(self, tokenizer): + + super().__init__() + + self.tokenizer = tokenizer + self.input_ids = [] + self.attn_masks = [] + self.labels = [] + self.txt_list = netflix_descriptions = load_dataset("hugginglearners/netflix-shows", split="train")['description'] + self.max_length = max([len(self.tokenizer.encode(description)) for description in netflix_descriptions]) + + for txt in self.txt_list: + encodings_dict = self.tokenizer('' + txt + '    ', + truncation=True, + max_length=self.max_length, + padding="max_length") + self.input_ids.append(torch.tensor(encodings_dict['input_ids'])) + self.attn_masks.append(torch.tensor(encodings_dict['attention_mask'])) + + def __len__(self): + return len(self.input_ids) + + def __getitem__(self, idx): + return self.input_ids[idx], self.attn_masks[idx] + + +def netflix_collator(data): + return {'input_ids': torch.stack([x[0] for x in data]), + 'attention_mask': torch.stack([x[1] for x in data]), + 'labels': torch.stack([x[0] for x in data])} diff --git a/examples/language/opt/opt_benchmark.py b/examples/language/opt/opt_benchmark.py new file mode 100755 index 000000000000..da2be4055fa3 --- /dev/null +++ b/examples/language/opt/opt_benchmark.py @@ -0,0 +1,146 @@ +import time + +import torch +import transformers +from transformers import AutoConfig, OPTForCausalLM +from transformers.utils.versions import require_version +import tqdm + +import colossalai +from colossalai.nn.optimizer import HybridAdam +from colossalai.logging import disable_existing_loggers, get_dist_logger +from colossalai.tensor import ProcessGroup, ShardSpec +from colossalai.utils import get_current_device +from colossalai.zero import ColoInitContext +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.cluster import DistCoordinator + +from args import parse_benchmark_args + +require_version("transformers>=4.20.0", "To fix: pip install -r requirements.txt") + + +def format_num(num: int, bytes=False): + """Scale bytes to its proper format, e.g. 1253656 => '1.20MB'""" + factor = 1024 if bytes else 1000 + suffix = "B" if bytes else "" + for unit in ["", " K", " M", " G", " T", " P"]: + if num < factor: + return f"{num:.2f}{unit}{suffix}" + num /= factor + + +def get_data(batch_size, seq_len, vocab_size): + input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device()) + attention_mask = torch.ones_like(input_ids) + return input_ids, attention_mask + + +def colo_memory_cap(size_in_GB): + from colossalai.utils import colo_device_memory_capacity, colo_set_process_memory_fraction, get_current_device + cuda_capacity = colo_device_memory_capacity(get_current_device()) + if size_in_GB * (1024**3) < cuda_capacity: + colo_set_process_memory_fraction(size_in_GB * (1024**3) / cuda_capacity) + print(f"Limiting GPU memory usage to {size_in_GB} GB") + + +def main(): + + args = parse_benchmark_args() + + # Launch ColossalAI + colossalai.launch_from_torch(config={}, seed=args.seed) + coordinator = DistCoordinator() + world_size = coordinator.world_size + + # Manage loggers + disable_existing_loggers() + logger = get_dist_logger() + if coordinator.is_master(): + transformers.utils.logging.set_verbosity_info() + else: + transformers.utils.logging.set_verbosity_error() + + # Whether to set limit of memory capacity + if args.mem_cap > 0: + colo_memory_cap(args.mem_cap) + + # Build OPT model + # Initialize the model under ColoInitContext if using GeminiPlugin + config = AutoConfig.from_pretrained(args.model_name_or_path) + if args.plugin == 'gemini': + shard_pg = ProcessGroup(tp_degree=world_size) + default_dist_spec = ShardSpec([-1], [world_size]) + with ColoInitContext(device='cpu', + default_dist_spec=default_dist_spec, + default_pg=shard_pg): + model = OPTForCausalLM(config) + else: + model = OPTForCausalLM(config) + logger.info(f"Finish loading model from {args.model_name_or_path}", ranks=[0]) + + # Enable gradient checkpointing + model.gradient_checkpointing_enable() + + # Set plugin + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(device=get_current_device(), + placement_policy='cpu', + pin_memory=True, + strict_ddp_mode=True, + initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + logger.info(f"Set plugin as {args.plugin}", ranks=[0]) + + # Set optimizer + optimizer = HybridAdam(model.parameters(), lr=args.learning_rate) + + # Set booster + booster = Booster(plugin=plugin, **booster_kwargs) + model, optimizer, _, _, _ = booster.boost(model, optimizer) + + SEQ_LEN = 1024 + VOCAB_SIZE = 50257 + + # Start training. + logger.info(f"Start testing", ranks=[0]) + progress_bar = tqdm.tqdm(total=args.max_train_steps, desc="Training Step", disable=not coordinator.is_master()) + + torch.cuda.synchronize() + model.train() + start_time = time.time() + + for _ in range(args.max_train_steps): + + input_ids, attn_mask = get_data(args.batch_size, SEQ_LEN, VOCAB_SIZE) + optimizer.zero_grad() + outputs = model(input_ids=input_ids, attention_mask=attn_mask, labels=input_ids, use_cache=False) + loss = outputs['loss'] + booster.backward(loss, optimizer) + optimizer.step() + + torch.cuda.synchronize() + progress_bar.update(1) + + # Compute Statistics + end_time = time.time() + throughput = "{:.4f}".format((world_size * args.max_train_steps * args.batch_size) / (end_time - start_time)) + max_mem = format_num(torch.cuda.max_memory_allocated(device=torch.cuda.current_device()), bytes=True) + + logger.info(f"Testing finished, " + f"batch size per gpu: {args.batch_size}, " + f"plugin: {args.plugin}, " + f"throughput: {throughput}, " + f"maximum memory usage per gpu: {max_mem}.", + ranks=[0]) + + +if __name__ == "__main__": + main() diff --git a/examples/language/opt/opt_train_demo.py b/examples/language/opt/opt_train_demo.py new file mode 100644 index 000000000000..8a2ad5f55b10 --- /dev/null +++ b/examples/language/opt/opt_train_demo.py @@ -0,0 +1,149 @@ +import time + +import torch +import datasets +import transformers +from transformers import AutoConfig, OPTForCausalLM, AutoTokenizer +from transformers import get_linear_schedule_with_warmup +from transformers.utils.versions import require_version +from tqdm import tqdm + +import colossalai +from colossalai.nn.optimizer import HybridAdam +from colossalai.logging import disable_existing_loggers, get_dist_logger +from colossalai.tensor import ProcessGroup, ShardSpec +from colossalai.utils import get_current_device +from colossalai.zero import ColoInitContext +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.cluster import DistCoordinator + +from args import parse_demo_args +from data import NetflixDataset, netflix_collator + +require_version("datasets>=1.8.0", "To fix: pip install -r requirements.txt") +require_version("transformers>=4.20.0", "To fix: pip install -r requirements.txt") + + +def move_to_cuda(batch, device): + return {k: v.to(device) for k, v in batch.items()} + + +def train_epoch(epoch, model, optimizer, lr_scheduler, dataloader, booster, coordinator): + + torch.cuda.synchronize() + model.train() + + with tqdm(dataloader, desc=f'Epoch [{epoch + 1}]', disable=not coordinator.is_master()) as pbar: + + for batch in pbar: + + # Foward + optimizer.zero_grad() + batch = move_to_cuda(batch, torch.cuda.current_device()) + + outputs = model(use_cache=False, **batch) + loss = outputs['loss'] + + # Backward + booster.backward(loss, optimizer) + optimizer.step() + lr_scheduler.step() + + # Print batch loss + pbar.set_postfix({'loss': loss.item()}) + + +def main(): + + args = parse_demo_args() + + # Launch ColossalAI + colossalai.launch_from_torch(config={}, seed=args.seed) + coordinator = DistCoordinator() + world_size = coordinator.world_size + + # Manage loggers + disable_existing_loggers() + logger = get_dist_logger() + if coordinator.is_master(): + datasets.utils.logging.set_verbosity_warning() + transformers.utils.logging.set_verbosity_info() + else: + datasets.utils.logging.set_verbosity_error() + transformers.utils.logging.set_verbosity_error() + + # Build OPT model + # Initialize the model under ColoInitContext if using GeminiPlugin + config = AutoConfig.from_pretrained(args.model_name_or_path) + if args.plugin == 'gemini': + shard_pg = ProcessGroup(tp_degree=world_size) + default_dist_spec = ShardSpec([-1], [world_size]) + with ColoInitContext(device='cpu', + default_dist_spec=default_dist_spec, + default_pg=shard_pg): + model = OPTForCausalLM(config) + else: + model = OPTForCausalLM(config) + logger.info(f"Finish loading model from {args.model_name_or_path}", ranks=[0]) + + # Enable gradient checkpointing + model.gradient_checkpointing_enable() + + # Set plugin + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(device=get_current_device(), + placement_policy='cpu', + pin_memory=True, + strict_ddp_mode=True, + initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + logger.info(f"Set plugin as {args.plugin}", ranks=[0]) + + # Prepare tokenizer and dataloader + tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path) + dataset = NetflixDataset(tokenizer) + dataloader = plugin.prepare_dataloader(dataset, + batch_size=args.batch_size, + shuffle=True, + drop_last=True, + collate_fn=netflix_collator) + + # Set optimizer + optimizer = HybridAdam(model.parameters(), lr=(args.learning_rate * world_size)) + + # Set lr scheduler + total_steps = len(dataloader) * args.num_epoch + num_warmup_steps = int(args.warmup_ratio * total_steps) + lr_scheduler = get_linear_schedule_with_warmup( + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=len(dataloader) * args.num_epoch + ) + + # Set booster + booster = Booster(plugin=plugin, **booster_kwargs) + model, optimizer, _, dataloader, lr_scheduler = booster.boost(model=model, + optimizer=optimizer, + dataloader=dataloader, + lr_scheduler=lr_scheduler) + + # Start finetuning + logger.info(f"Start finetuning", ranks=[0]) + for epoch in range(args.num_epoch): + train_epoch(epoch, model, optimizer, lr_scheduler, dataloader, booster, coordinator) + + # Finish training and evaluate + logger.info(f"Finish finetuning", ranks=[0]) + booster.save_model(model, args.output_path) + logger.info(f"Saving model checkpoint to {args.output_path}", ranks=[0]) + + +if __name__ == "__main__": + main() diff --git a/examples/language/opt/requirements.txt b/examples/language/opt/requirements.txt index 137a69e80498..4422216e6a1c 100644 --- a/examples/language/opt/requirements.txt +++ b/examples/language/opt/requirements.txt @@ -1,2 +1,4 @@ colossalai >= 0.1.12 torch >= 1.8.1 +datasets >= 1.8.0 +transformers >= 4.20.0 \ No newline at end of file diff --git a/examples/language/opt/run_benchmark.sh b/examples/language/opt/run_benchmark.sh new file mode 100644 index 000000000000..76c5e8601989 --- /dev/null +++ b/examples/language/opt/run_benchmark.sh @@ -0,0 +1,30 @@ +set -xe +pip install -r requirements.txt + +export BS=32 +export MEMCAP=0 +export GPUNUM=1 + +# acceptable values include `125m`, `350m`, `1.3b`, `2.7b`, `6.7b`, `13b`, `30b`, `66b` +export MODEL="125m" + +for BS in 8 32 128 +do +for PLUGIN in "torch_ddp" "torch_ddp_fp16" "low_level_zero" "gemini" +do +for GPUNUM in 1 4 +do + +MODLE_PATH="facebook/opt-${MODEL}" +torchrun \ + --standalone \ + --nproc_per_node ${GPUNUM} \ + opt_benchmark.py \ + --model_name_or_path ${MODLE_PATH} \ + --mem_cap ${MEMCAP} \ + --plugin ${PLUGIN} \ + --batch_size ${BS} + +done +done +done diff --git a/examples/language/opt/run_demo.sh b/examples/language/opt/run_demo.sh new file mode 100644 index 000000000000..0c9759c34039 --- /dev/null +++ b/examples/language/opt/run_demo.sh @@ -0,0 +1,44 @@ +set -xe +pip install -r requirements.txt + +# model name or path +MODEL="facebook/opt-350m" + +# path for saving model +OUTPUT_PATH="./output_model.bin" + +# plugin(training strategy) +# can only be one of "torch_ddp"/"torch_ddp_fp16"/"low_level_zero"/"gemini" +PLUGIN="gemini" + +# number of gpus to use +GPUNUM=4 + +# batch size per gpu +BS=16 + +# learning rate +LR="5e-5" + +# number of epoch +EPOCH=10 + +# weight decay +WEIGHT_DECAY=0.01 + +# ratio of warmup steps +WARMUP_RATIO=0.1 + +# run the script for demo +torchrun \ + --standalone \ + --nproc_per_node ${GPUNUM} \ + opt_train_demo.py \ + --model_name_or_path ${MODEL} \ + --output_path ${OUTPUT_PATH} \ + --plugin ${PLUGIN} \ + --batch_size ${BS} \ + --num_epoch ${EPOCH} \ + --learning_rate ${LR} \ + --weight_decay ${WEIGHT_DECAY} \ + --warmup_ratio ${WARMUP_RATIO} diff --git a/examples/language/opt/run_gemini.sh b/examples/language/opt/run_gemini.sh deleted file mode 100644 index 73f231292a13..000000000000 --- a/examples/language/opt/run_gemini.sh +++ /dev/null @@ -1,28 +0,0 @@ -set -x -export BS=${BS:-16} -export MEMCAP=${MEMCAP:-0} -# Acceptable values include `125m`, `350m`, `1.3b`, `2.7b`, `6.7b`, `13b`, `30b`, `66b`. For `175b` -export MODEL=${MODEL:-"125m"} -export GPUNUM=${GPUNUM:-1} -export USE_SHARD_INIT=${USE_SHARD_INIT:-"false"} - -# make directory for logs -mkdir -p ./logs - -if [ ${USE_SHARD_INIT} = "true" ]; then - USE_SHARD_INIT="--shardinit" -else - USE_SHARD_INIT="" -fi - -export MODLE_PATH="facebook/opt-${MODEL}" - -# HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 -torchrun \ - --nproc_per_node ${GPUNUM} \ - --master_port 19198 \ - train_gemini_opt.py \ - --mem_cap ${MEMCAP} \ - --model_name_or_path ${MODLE_PATH} \ - ${USE_SHARD_INIT} \ - --batch_size ${BS} 2>&1 | tee ./logs/colo_${MODEL}_bs_${BS}_cap_${MEMCAP}_gpu_${GPUNUM}.log diff --git a/examples/language/opt/test_ci.sh b/examples/language/opt/test_ci.sh index 317f602cda3c..fa14f52b70d2 100644 --- a/examples/language/opt/test_ci.sh +++ b/examples/language/opt/test_ci.sh @@ -1,4 +1,19 @@ -for GPUNUM in 2 1 +set -xe +pip install -r requirements.txt + +BS=4 +for PLUGIN in "torch_ddp" "torch_ddp_fp16" "low_level_zero" "gemini" do -env BS=2 MODEL="125m" GPUNUM=$GPUNUM bash ./run_gemini.sh +for GPUNUM in 1 4 +do + +torchrun \ + --standalone \ + --nproc_per_node ${GPUNUM} \ + opt_benchmark.py \ + --model_name_or_path "facebook/opt-125m" \ + --plugin ${PLUGIN} \ + --batch_size ${BS} + +done done diff --git a/examples/language/opt/train_gemini_opt.py b/examples/language/opt/train_gemini_opt.py deleted file mode 100755 index 3614b689de26..000000000000 --- a/examples/language/opt/train_gemini_opt.py +++ /dev/null @@ -1,233 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 -# Copyright 2021 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) -on a text file or a dataset without using HuggingFace Trainer. - -Here is the full list of checkpoints on the hub that can be fine-tuned by this script: -https://huggingface.co/models?filter=text-generation -""" -# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. - -import time -from functools import partial - -import datasets -import torch -import torch.distributed as dist -import transformers -from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, OPTForCausalLM -from transformers.utils.versions import require_version - -import colossalai -from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.tensor import ProcessGroup, ShardSpec -from colossalai.utils import get_current_device -from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, GeminiDDP - - -def get_data(batch_size, seq_len, vocab_size): - input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device()) - attention_mask = torch.ones_like(input_ids) - return input_ids, attention_mask - - -require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") - -MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) -MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) - - -def get_time_stamp(): - torch.cuda.synchronize() - return time.time() - - -def get_tflops(model_numel, batch_size, seq_len, step_time): - return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12) - - -def parse_args(): - parser = colossalai.get_default_parser() - parser.add_argument( - "--model_name_or_path", - type=str, - help="Path to pretrained model or model identifier from huggingface.co/models.", - required=True, - ) - parser.add_argument( - "--config_name", - type=str, - default=None, - help="Pretrained config name or path if not the same as model_name", - ) - parser.add_argument( - "--batch_size", - type=int, - default=8, - help="Batch size (per dp group) for the training dataloader.", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=5e-5, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") - parser.add_argument( - "--max_train_steps", - type=int, - default=20, - help="Total number of training steps to perform.", - ) - parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") - parser.add_argument( - "--model_type", - type=str, - default=None, - help="Model type to use if training from scratch.", - choices=MODEL_TYPES, - ) - parser.add_argument( - "--shardinit", - action="store_true", - help="Initialize the model with tensor parallel", - ) - parser.add_argument("--mem_cap", type=int, default=0, help="use mem cap") - parser.add_argument("--init_in_cpu", action='store_true', default=False, help="init training model in cpu") - args = parser.parse_args() - - return args - - -def colo_memory_cap(size_in_GB): - from colossalai.utils import colo_device_memory_capacity, colo_set_process_memory_fraction, get_current_device - cuda_capacity = colo_device_memory_capacity(get_current_device()) - if size_in_GB * (1024**3) < cuda_capacity: - colo_set_process_memory_fraction(size_in_GB * (1024**3) / cuda_capacity) - print("Using {} GB of GPU memory".format(size_in_GB)) - - -def main(): - args = parse_args() - disable_existing_loggers() - colossalai.launch_from_torch({}) - logger = get_dist_logger() - is_main_process = dist.get_rank() == 0 - - if is_main_process: - datasets.utils.logging.set_verbosity_warning() - transformers.utils.logging.set_verbosity_info() - else: - datasets.utils.logging.set_verbosity_error() - transformers.utils.logging.set_verbosity_error() - - if args.mem_cap > 0: - colo_memory_cap(args.mem_cap) - - # If passed along, set the training seed now. - if args.seed is not None: - torch.mannul_seed(args.seed) - logger.info(f"Rank {dist.get_rank()}: random seed is set to {args.seed}") - - # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at - # https://huggingface.co/docs/datasets/loading_datasets.html. - - # Load pretrained model - # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently - # download model & vocab. - if args.config_name: - config = AutoConfig.from_pretrained(args.config_name) - elif args.model_name_or_path: - config = AutoConfig.from_pretrained(args.model_name_or_path) - else: - config = CONFIG_MAPPING[args.model_type]() - logger.warning("You are instantiating a new config instance from scratch.") - logger.info("Model config has been created", ranks=[0]) - - if args.init_in_cpu: - init_dev = torch.device('cpu') - else: - init_dev = get_current_device() - - # shard init parameters - if args.shardinit: - logger.info("Sharding initialization !", ranks=[0]) - else: - logger.info("Skipping sharding initialization", ranks=[0]) - - world_size = torch.distributed.get_world_size() - shard_pg = ProcessGroup(tp_degree=world_size) if args.shardinit else None - default_dist_spec = ShardSpec([-1], [world_size]) if args.shardinit else None - - # build model - if args.model_name_or_path is None: - logger.info("Train a new model from scratch", ranks=[0]) - with ColoInitContext(device=init_dev, - dtype=torch.half, - default_dist_spec=default_dist_spec, - default_pg=shard_pg): - model = OPTForCausalLM(config) - else: - logger.info("Finetune a pre-trained model", ranks=[0]) - with ColoInitContext(device=init_dev, - dtype=torch.half, - default_dist_spec=default_dist_spec, - default_pg=shard_pg): - model = OPTForCausalLM.from_pretrained(args.model_name_or_path, - from_tf=bool(".ckpt" in args.model_name_or_path), - config=config, - local_files_only=False) - - # enable gradient checkpointing - model.gradient_checkpointing_enable() - - numel = sum([p.numel() for p in model.parameters()]) - PLACEMENT_POLICY = 'cpu' - model = GeminiDDP(model, - device=get_current_device(), - placement_policy=PLACEMENT_POLICY, - pin_memory=True, - strict_ddp_mode=args.shardinit) - optimizer = GeminiAdamOptimizer(model, lr=args.learning_rate, initial_scale=2**14, gpu_margin_mem_ratio=0.0) - - SEQ_LEN = 1024 - VOCAB_SIZE = 50257 - - get_tflops_func = partial(get_tflops, numel, args.batch_size, SEQ_LEN) - - model.train() - for step in range(args.max_train_steps): - st_time = time.time() - input_ids, attn_mask = get_data(args.batch_size, SEQ_LEN, VOCAB_SIZE) - - outputs = model(input_ids=input_ids, attention_mask=attn_mask, labels=input_ids, use_cache=False) - loss = outputs['loss'] - optimizer.backward(loss) - - optimizer.step() - optimizer.zero_grad() - torch.cuda.synchronize() - step_time = time.time() - st_time - step_tflops = get_tflops_func(step_time) - - logger.info("step {} finished, Tflops {}".format(step, step_tflops), ranks=[0]) - - logger.info("Training finished", ranks=[0]) - - -if __name__ == "__main__": - main() From 039854b39165ab7f2a4fa7ab3d67e47daa325d1c Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Thu, 8 Jun 2023 13:17:58 +0800 Subject: [PATCH 294/413] modify shell for check --- examples/images/dreambooth/test_ci.sh | 6 +++--- examples/images/dreambooth/train_dreambooth_colossalai.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/images/dreambooth/test_ci.sh b/examples/images/dreambooth/test_ci.sh index 8d18e1d4a45c..35c81b325ff6 100644 --- a/examples/images/dreambooth/test_ci.sh +++ b/examples/images/dreambooth/test_ci.sh @@ -6,8 +6,8 @@ HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 DIFFUSERS_OFFLINE=1 -# "torch_ddp" "torch_ddp_fp16" -for plugin in "low_level_zero" "gemini"; do +# "torch_ddp" "torch_ddp_fp16" "low_level_zero" +for plugin in "gemini"; do torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \ --pretrained_model_name_or_path="/data/dreambooth/diffuser/stable-diffusion-v1-4" \ --instance_data_dir="/data/dreambooth/Teyvat/data" \ @@ -20,5 +20,5 @@ for plugin in "low_level_zero" "gemini"; do --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --num_class_images=200 \ - --placement="cuda" + --placement="cpu" # "cuda" done diff --git a/examples/images/dreambooth/train_dreambooth_colossalai.py b/examples/images/dreambooth/train_dreambooth_colossalai.py index eae52b5ecd7e..44bde922629f 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai.py @@ -487,7 +487,7 @@ def main(args): if args.plugin.startswith('torch_ddp'): plugin = TorchDDPPlugin() elif args.plugin == 'gemini': - plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2 ** 5) + plugin = GeminiPlugin(placement_policy=args.placement, strict_ddp_mode=True, initial_scale=2 ** 5) elif args.plugin == 'low_level_zero': plugin = LowLevelZeroPlugin(initial_scale=2 ** 5) From 49567d56d161dba7889496abd4e74e19ed8d1195 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Thu, 8 Jun 2023 13:36:05 +0800 Subject: [PATCH 295/413] modify shell for check --- examples/images/dreambooth/test_ci.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/images/dreambooth/test_ci.sh b/examples/images/dreambooth/test_ci.sh index 35c81b325ff6..0e3f6efa4b7c 100644 --- a/examples/images/dreambooth/test_ci.sh +++ b/examples/images/dreambooth/test_ci.sh @@ -20,5 +20,5 @@ for plugin in "gemini"; do --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --num_class_images=200 \ - --placement="cpu" # "cuda" + --placement="auto" # "cuda" done From 730a092ba2dd98464bd18789b7f78d2ec2d3a165 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Thu, 8 Jun 2023 13:38:18 +0800 Subject: [PATCH 296/413] modify shell for check --- examples/images/dreambooth/colossalai.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/images/dreambooth/colossalai.sh b/examples/images/dreambooth/colossalai.sh index 3b15ad887b0a..b2a544928760 100755 --- a/examples/images/dreambooth/colossalai.sh +++ b/examples/images/dreambooth/colossalai.sh @@ -14,4 +14,4 @@ torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --num_class_images=200 \ - --placement="cuda" \ + --placement="auto" \ From 407aa4846151b2cd87371b9052f5615cf02e0cee Mon Sep 17 00:00:00 2001 From: digger yu Date: Thu, 8 Jun 2023 14:28:34 +0800 Subject: [PATCH 297/413] fix typo examples/community/roberta (#3925) --- examples/community/roberta/README.md | 2 +- examples/community/roberta/preprocessing/README.md | 6 +++--- examples/community/roberta/pretraining/README.md | 2 +- examples/community/roberta/pretraining/arguments.py | 6 +++--- examples/community/roberta/pretraining/model/bert.py | 2 +- examples/community/roberta/pretraining/run_pretraining.py | 2 +- examples/community/roberta/pretraining/utils/exp_util.py | 2 +- examples/community/roberta/pretraining/utils/global_vars.py | 2 +- 8 files changed, 12 insertions(+), 12 deletions(-) diff --git a/examples/community/roberta/README.md b/examples/community/roberta/README.md index 8aefa327a4b4..000fce63f35f 100644 --- a/examples/community/roberta/README.md +++ b/examples/community/roberta/README.md @@ -44,7 +44,7 @@ following the `README.md`, load the h5py generated by preprocess of step 1 to pr ## 3. Finetune -The checkpoint produced by this repo can replace `pytorch_model.bin` from [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/tree/main) directly. Then use transfomers from Hugging Face to finetune downstream application. +The checkpoint produced by this repo can replace `pytorch_model.bin` from [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/tree/main) directly. Then use transformers from Hugging Face to finetune downstream application. ## Contributors The example is contributed by AI team from [Moore Threads](https://www.mthreads.com/). If you find any problems for pretraining, please file an issue or send an email to yehua.zhang@mthreads.com. At last, welcome any form of contribution! diff --git a/examples/community/roberta/preprocessing/README.md b/examples/community/roberta/preprocessing/README.md index 17cc2f4dc22c..2ed747541280 100644 --- a/examples/community/roberta/preprocessing/README.md +++ b/examples/community/roberta/preprocessing/README.md @@ -25,10 +25,10 @@ Firstly, each file has multiple documents, and each document contains multiple s In this example, split 200G Corpus into 100 shard, and each shard is about 2G. The size of the shard is memory-dependent, taking into account the number of servers, the memory used by the tokenizer, and the memory used by the multi-process training to read the shard (n data parallel requires n\*shard_size memory). **To sum up, data preprocessing and model pretraining requires fighting with hardware, not just GPU.** ```python -python sentence_split.py --input_path /orginal_corpus --output_path /shard --shard 100 +python sentence_split.py --input_path /original_corpus --output_path /shard --shard 100 # This step takes a short time ``` -* `--input_path`: all original corpus, e.g., /orginal_corpus/0.json /orginal_corpus/1.json ... +* `--input_path`: all original corpus, e.g., /original_corpus/0.json /original_corpus/1.json ... * `--output_path`: all shard with split sentences, e.g., /shard/0.txt, /shard/1.txt ... * `--shard`: Number of shard, e.g., 10, 50, or 100 @@ -76,7 +76,7 @@ make * `--input_path`: location of all shard with split sentences, e.g., /shard/0.txt, /shard/1.txt ... * `--output_path`: location of all h5 with token_id, input_mask, segment_ids and masked_lm_positions, e.g., /h5/0.h5, /h5/1.h5 ... -* `--tokenizer_path`: tokenizer path contains huggingface tokenizer.json. Download config.json, special_tokens_map.json, vocab.txt and tokenzier.json from [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/tree/main) +* `--tokenizer_path`: tokenizer path contains huggingface tokenizer.json. Download config.json, special_tokens_map.json, vocab.txt and tokenizer.json from [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/tree/main) * `--backend`: python or c++, **specifies c++ can obtain faster preprocess speed** * `--dupe_factor`: specifies how many times the preprocessor repeats to create the input from the same article/document * `--worker`: number of process diff --git a/examples/community/roberta/pretraining/README.md b/examples/community/roberta/pretraining/README.md index c248fc1f5708..8abe48aa6c0e 100644 --- a/examples/community/roberta/pretraining/README.md +++ b/examples/community/roberta/pretraining/README.md @@ -13,7 +13,7 @@ bash run_pretrain.sh * `--bert_config`: config.json which represent model * `--mlm`: model type of backbone, bert or deberta_v2 -2. if resume training from earylier checkpoint, run the script below. +2. if resume training from earlier checkpoint, run the script below. ```shell bash run_pretrain_resume.sh diff --git a/examples/community/roberta/pretraining/arguments.py b/examples/community/roberta/pretraining/arguments.py index 40210c4b1be7..e0702ceb59b0 100644 --- a/examples/community/roberta/pretraining/arguments.py +++ b/examples/community/roberta/pretraining/arguments.py @@ -46,7 +46,7 @@ def parse_args(): type=int, default=1, help="This param makes sure that a certain task is repeated for this time steps to \ - optimise on the back propogation speed with APEX's DistributedDataParallel") + optimize on the back propagation speed with APEX's DistributedDataParallel") parser.add_argument("--max_predictions_per_seq", "--max_pred", default=80, @@ -73,12 +73,12 @@ def parse_args(): help="location of saving checkpoint, which contains model and optimizer") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--vscode_debug', action='store_true', help="use vscode to debug") - parser.add_argument('--load_pretrain_model', default='', type=str, help="location of model's checkpoin") + parser.add_argument('--load_pretrain_model', default='', type=str, help="location of model's checkpoint") parser.add_argument( '--load_optimizer_lr', default='', type=str, - help="location of checkpoint, which contains optimerzier, learning rate, epoch, shard and global_step") + help="location of checkpoint, which contains optimizer, learning rate, epoch, shard and global_step") parser.add_argument('--resume_train', action='store_true', help="whether resume training from a early checkpoint") parser.add_argument('--mlm', default='bert', type=str, help="model type, bert or deberta") parser.add_argument('--checkpoint_activations', action='store_true', help="whether to use gradient checkpointing") diff --git a/examples/community/roberta/pretraining/model/bert.py b/examples/community/roberta/pretraining/model/bert.py index a5da1bea6f65..abdf925d0540 100644 --- a/examples/community/roberta/pretraining/model/bert.py +++ b/examples/community/roberta/pretraining/model/bert.py @@ -327,7 +327,7 @@ def forward( attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhld,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) diff --git a/examples/community/roberta/pretraining/run_pretraining.py b/examples/community/roberta/pretraining/run_pretraining.py index 9a6ffc1c5661..a72bdf775644 100644 --- a/examples/community/roberta/pretraining/run_pretraining.py +++ b/examples/community/roberta/pretraining/run_pretraining.py @@ -78,7 +78,7 @@ def main(): default_pg=shard_pg): config, model, numel = get_model(args, logger) - # asign running configurations + # assign running configurations gemini_config = None if args.distplan.startswith("CAI_ZeRO"): optim_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True, verbose=True) diff --git a/examples/community/roberta/pretraining/utils/exp_util.py b/examples/community/roberta/pretraining/utils/exp_util.py index 0cdb56bad031..4a2c9d8a47ad 100644 --- a/examples/community/roberta/pretraining/utils/exp_util.py +++ b/examples/community/roberta/pretraining/utils/exp_util.py @@ -97,7 +97,7 @@ def throughput_calculator(numel, args, config, iteration_time, total_iterations, def synchronize(): if not torch.distributed.is_available(): return - if not torch.distributed.is_intialized(): + if not torch.distributed.is_initialized(): return world_size = torch.distributed.get_world_size() if world_size == 1: diff --git a/examples/community/roberta/pretraining/utils/global_vars.py b/examples/community/roberta/pretraining/utils/global_vars.py index 7b0c5a2be73d..9eef19e71614 100644 --- a/examples/community/roberta/pretraining/utils/global_vars.py +++ b/examples/community/roberta/pretraining/utils/global_vars.py @@ -110,7 +110,7 @@ def write(self, names, writer, iteration, normalizer=1.0, reset=False): """Write timers to a tensorboard writer""" # currently when using add_scalars, # torch.utils.add_scalars makes each timer its own run, which - # polutes the runs list, so we just add each as a scalar + # pollutes the runs list, so we just add each as a scalar assert normalizer > 0.0 for name in names: value = self.timers[name].elapsed(reset=reset) / normalizer From 9b5e7ce21feb51977d11da4e6a0ed35f502dbfb5 Mon Sep 17 00:00:00 2001 From: Maruyama_Aya Date: Thu, 8 Jun 2023 14:56:56 +0800 Subject: [PATCH 298/413] modify shell for check --- examples/images/dreambooth/colossalai.sh | 1 + examples/images/dreambooth/test_ci.sh | 1 + examples/images/dreambooth/train_dreambooth_colossalai.py | 5 +++++ 3 files changed, 7 insertions(+) diff --git a/examples/images/dreambooth/colossalai.sh b/examples/images/dreambooth/colossalai.sh index b2a544928760..db4562dbc921 100755 --- a/examples/images/dreambooth/colossalai.sh +++ b/examples/images/dreambooth/colossalai.sh @@ -14,4 +14,5 @@ torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --num_class_images=200 \ + --test_run=True \ --placement="auto" \ diff --git a/examples/images/dreambooth/test_ci.sh b/examples/images/dreambooth/test_ci.sh index 0e3f6efa4b7c..21f45adae2a0 100644 --- a/examples/images/dreambooth/test_ci.sh +++ b/examples/images/dreambooth/test_ci.sh @@ -19,6 +19,7 @@ for plugin in "gemini"; do --learning_rate=5e-6 \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ + --test_run=True \ --num_class_images=200 \ --placement="auto" # "cuda" done diff --git a/examples/images/dreambooth/train_dreambooth_colossalai.py b/examples/images/dreambooth/train_dreambooth_colossalai.py index 44bde922629f..888b28de8306 100644 --- a/examples/images/dreambooth/train_dreambooth_colossalai.py +++ b/examples/images/dreambooth/train_dreambooth_colossalai.py @@ -198,6 +198,7 @@ def parse_args(input_args=None): parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") + parser.add_argument("--test_run", default=False, help="Whether to use a smaller dataset for test run.") parser.add_argument( "--hub_model_id", type=str, @@ -267,6 +268,7 @@ def __init__( class_prompt=None, size=512, center_crop=False, + test=False, ): self.size = size self.center_crop = center_crop @@ -277,6 +279,8 @@ def __init__( raise ValueError("Instance images root doesn't exists.") self.instance_images_path = list(Path(instance_data_root).iterdir()) + if test: + self.instance_images_path = self.instance_images_path[:10] self.num_instance_images = len(self.instance_images_path) self.instance_prompt = instance_prompt self._length = self.num_instance_images @@ -509,6 +513,7 @@ def main(args): tokenizer=tokenizer, size=args.resolution, center_crop=args.center_crop, + test=args.test_run ) def collate_fn(examples): From 6a69b44dfcf0a3963b7e8d626b0250b3551861c5 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Mon, 22 May 2023 15:02:17 +0800 Subject: [PATCH 299/413] [shardformer] init shardformer code structure (#3731) * init shardformer code structure * add implement of sharder (inject and replace) * add implement of replace layer to colossal layer * separate different layer policy, add some notion * implement 1d and 2d slicer, can tell col or row * fix bug when slicing and inject model * fix some bug; add inference test example --- colossalai/shardformer/__init__.py | 0 colossalai/shardformer/model/__init__.py | 0 colossalai/shardformer/model/modeling_bert.py | 63 +++++ colossalai/shardformer/policies/__init__.py | 0 colossalai/shardformer/policies/autopolicy.py | 41 +++ colossalai/shardformer/policies/basepolicy.py | 182 ++++++++++++++ colossalai/shardformer/policies/bert.py | 168 +++++++++++++ colossalai/shardformer/shard/__init__.py | 0 colossalai/shardformer/shard/shardconfig.py | 18 ++ colossalai/shardformer/shard/sharder.py | 238 ++++++++++++++++++ colossalai/shardformer/shard/shardmodel.py | 58 +++++ colossalai/shardformer/shard/slicer.py | 167 ++++++++++++ colossalai/shardformer/test/config.py | 5 + colossalai/shardformer/test/test.py | 37 +++ colossalai/shardformer/utils/__init__.py | 0 colossalai/shardformer/utils/utils.py | 56 +++++ 16 files changed, 1033 insertions(+) create mode 100644 colossalai/shardformer/__init__.py create mode 100644 colossalai/shardformer/model/__init__.py create mode 100644 colossalai/shardformer/model/modeling_bert.py create mode 100644 colossalai/shardformer/policies/__init__.py create mode 100644 colossalai/shardformer/policies/autopolicy.py create mode 100644 colossalai/shardformer/policies/basepolicy.py create mode 100644 colossalai/shardformer/policies/bert.py create mode 100644 colossalai/shardformer/shard/__init__.py create mode 100644 colossalai/shardformer/shard/shardconfig.py create mode 100644 colossalai/shardformer/shard/sharder.py create mode 100644 colossalai/shardformer/shard/shardmodel.py create mode 100644 colossalai/shardformer/shard/slicer.py create mode 100644 colossalai/shardformer/test/config.py create mode 100644 colossalai/shardformer/test/test.py create mode 100644 colossalai/shardformer/utils/__init__.py create mode 100644 colossalai/shardformer/utils/utils.py diff --git a/colossalai/shardformer/__init__.py b/colossalai/shardformer/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/model/__init__.py b/colossalai/shardformer/model/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/model/modeling_bert.py b/colossalai/shardformer/model/modeling_bert.py new file mode 100644 index 000000000000..87ed8ac308a5 --- /dev/null +++ b/colossalai/shardformer/model/modeling_bert.py @@ -0,0 +1,63 @@ +import torch +import torch.nn as nn +from torch.nn import CrossEntropyLoss +from typing import Any, Dict, List, Type + + +from transformers import BertForMaskedLM +from transformers.models.bert.modeling_bert import MaskedLMOutput +class BertForMaskedLM_(BertForMaskedLM): + def forward( + self, + input_ids=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + print("[Inject OK] Injected forward method") + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + + # if input_ids is not None: + # masked_lm_loss = applyDistCrossEntropy(prediction_scores, input_ids, self.config.vocab_size) + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) \ No newline at end of file diff --git a/colossalai/shardformer/policies/__init__.py b/colossalai/shardformer/policies/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py new file mode 100644 index 000000000000..9142e0dae22e --- /dev/null +++ b/colossalai/shardformer/policies/autopolicy.py @@ -0,0 +1,41 @@ +import torch.nn as nn + +def build_policies(): + """ + Build the policies for the model + + Return: + The dict for the policies + """ + auto_policy_dict = {} + + from transformers.models.bert.modeling_bert import BertForMaskedLM + from .bert import BertForMaskedLMPolicy + auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy + + from transformers.models.bert.modeling_bert import BertForSequenceClassification + from .bert import BertForSequenceClassificationPolicy + auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy + + return auto_policy_dict + +def get_autopolicy(model:nn.Module): + """ + Return the auto policy for the model + + Args: + model: The model to be used + + Return: + The auto policy for the model + """ + auto_policy_dict = build_policies() + policy = auto_policy_dict.get(model.__class__, None) + if policy is None: + raise NotImplementedError(f"Auto policy for {model.__class__.__qualname__} is not implemented\n Supported models are {[i.__qualname__ for i in auto_policy_dict.keys()]}") + return policy + +# from transformers.models.bert.modeling_bert import BertForMaskedLM, BertForPreTraining +# model = BertForPreTraining +# policy = get_autopolicy(model) +# print(policy) diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py new file mode 100644 index 000000000000..d444aeb53bf8 --- /dev/null +++ b/colossalai/shardformer/policies/basepolicy.py @@ -0,0 +1,182 @@ +# part of code modified from https://github.com/tunib-ai/parallelformers + +import torch +import torch.nn as nn +import colossalai.nn as col_nn +from typing import Any, Dict, List, Type, Tuple, Callable +from transformers import AutoConfig +from dataclasses import dataclass, field + +@dataclass +class Argument: + attr_dict : Dict[str, Any] + param_funcs : List[Callable] + binding_layers : List[nn.Module] = field(default_factory=list) + +@dataclass +class Layer: + """ + The layer object for the policy + + Args: + weight: The weight name of the layer + bias: The bias name of the layer + replace_layer: The layer to replace the original layer + ignore: Whether to ignore this layer if it is not in the model + """ + weight: str = None + bias: str = None + replace_layer: Any = None + ignore: bool = False + + +@dataclass +class Col_Layer(Layer): + """ + Class for col shard layer in MegatronLM + """ + gather_output: bool = False + + +@dataclass +class Row_Layer(Layer): + """ + Class for col shard layer in MegatronLM + """ + pass + + +class Policy(): + """ + The base class for all the policies + For each different model, it should have a different policy class, like BertPolicy for Bert Model + or OPTPolicy for OPT model. + AutoPolicy: + shardformer already defined some policies for huggingface model, just set custom_policy = None + to use the auto policy. In shardformer autopolicy, we define a base policy for one type model, + like BertPolicy, and for each different Bert modle in huggingface like, BertForMaskedLM, + BertForSequenceClassification, etc., for each different Bert model we difine different policy class + and overwrite the method inject_policy + + CustomPolicy: + """ + @staticmethod + def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument]: + """ + Return a dict, the key is layer will be modified and the value is the Argument class with param setting and param functions + + Args: + model_config: The config of transformer model + shard_setting: The config of distributed model + + Return: + Dict for the modify policy, + { + origin layer class1 (nn.Module): Argument( + attr_dict = { + argument1: value1, + argument2: value2, + ... + }, + param_funcs = [ + staticmethod1, + staticmethod2, + ... + ] + ), + origin layer class2 (nn.Module): Argument( + attr_dict = { + argument1: value1, + argument2: value2, + ... + }, + param_funcs = [ + staticmethod1, + staticmethod2, + ... + ] + ), + ... + } + + """ + raise NotImplementedError + + + @staticmethod + def inject_policy() -> Tuple[nn.Module, nn.Module]: + """ + Return the dict for the inject model + + Return: + The injected model, key is the original model and value is the new shardmodel + """ + return () + + + @staticmethod + def attn_in() -> List: + """ + Attention qkv layer + + Returns: + List[Layer]: List of layer object, each layer is the new + """ + return NotImplementedError + + + @staticmethod + def attn_out() -> List: + """ + Attention output projection layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + + @staticmethod + def mlp_in() -> List: + """ + h -> 4h mlp layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + + @staticmethod + def mlp_out() -> List: + """ + 4h -> h mlp layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + + @staticmethod + def embedding()->List: + """ + Partially slice the embedding layer + vocab_size->vocab_size//gpu_nums + + Return: + List[Layer]: List of layer object + """ + return NotImplementedError + + + @staticmethod + def unembedding()->List: + """ + Partially slice the embedding layer + vocab_size->vocab_size//gpu_nums + + Return: + List[Layer]: List of layer object + """ + return NotImplementedError diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py new file mode 100644 index 000000000000..24b95e827347 --- /dev/null +++ b/colossalai/shardformer/policies/bert.py @@ -0,0 +1,168 @@ +from typing import Dict, List, Tuple, Type, Any, Callable +import torch.nn as nn +from .basepolicy import Policy, Layer, Argument, Col_Layer, Row_Layer +import colossalai.nn as col_nn +from transformers.models.bert.modeling_bert import BertLayer, BertEmbeddings, BertLMPredictionHead +from dataclasses import dataclass + + +class BertPolicy(Policy): + @staticmethod + def argument_policy(config, world_size: int) -> Dict[nn.Module,Argument]: + return { + BertLayer: Argument( + attr_dict = { + # 1. shard hidden size + "attention.self.all_head_size": config.hidden_size // world_size, + "crossattention.self.all_head_size": config.hidden_size // world_size, + # 2. shard number of heads + "attention.self.num_attention_heads": config.num_attention_heads // world_size, + "crossattention.self.num_attention_heads": config.num_attention_heads // world_size, + + }, + param_funcs = [ + BertPolicy.attn_in, + BertPolicy.attn_out, + BertPolicy.mlp_in, + BertPolicy.mlp_out + ] + ), + BertEmbeddings: Argument( + attr_dict = { + # 1. shard vocab size + # "word_embeddings.num_embeddings": config.vocab_size // world_size, + # 2. add the size of the sliced embedding layer excluding the last slice + "word_embeddings.dim_size": (config.vocab_size+world_size-1) // world_size, + }, + param_funcs = [ + BertPolicy.embedding, + ], + binding_layers = [ + BertLMPredictionHead, + ] + ), + BertLMPredictionHead: Argument( + attr_dict = { + # 1. shard vocab size + # "word_embeddings.num_embeddings": config.vocab_size // world_size, + # 2. add the size of the sliced embedding layer excluding the last slice + }, + param_funcs = [ + BertPolicy.unembedding, + ] + ) + } + + @staticmethod + def attn_in() -> List: + return [ + Col_Layer( + weight="attention.self.query.weight", + bias="attention.self.query.bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + weight="attention.self.key.weight", + bias="attention.self.key.bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + weight="attention.self.value.weight", + bias="attention.self.value.bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + weight="crossattention.self.query.weight", + bias="crossattention.self.query.bias", + replace_layer=col_nn.Linear1D_Col, + ignore=True, + ), + Col_Layer( + weight="crossattention.self.key.weight", + bias="crossattention.self.key.bias", + replace_layer=col_nn.Linear1D_Col, + ignore=True, + ), + Col_Layer( + weight="crossattention.self.value.weight", + bias="crossattention.self.value.bias", + replace_layer=col_nn.Linear1D_Col, + ignore=True, + ), + + ] + + @staticmethod + def attn_out() -> List: + return [ + Row_Layer( + weight="attention.output.dense.weight", + bias="attention.output.dense.bias", + replace_layer=col_nn.Linear1D_Row, + ), + Row_Layer( + weight="crossattention.output.dense.weight", + bias="crossattention.output.dense.bias", + replace_layer=col_nn.Linear1D_Row, + ignore=True, + ), + ] + + @staticmethod + def mlp_in() -> List: + return [ + Col_Layer( + weight="intermediate.dense.weight", + bias="intermediate.dense.bias", + replace_layer=col_nn.Linear1D_Col, + ), + ] + + @staticmethod + def mlp_out() -> List: + return [ + Row_Layer( + weight="output.dense.weight", + bias="output.dense.bias", + replace_layer=col_nn.Linear1D_Row, + ), + ] + + @staticmethod + def embedding() -> List: + return [ + Col_Layer( + weight="word_embeddings.weight", + replace_layer=col_nn.VocabParallelEmbedding1D, + ) + ] + + @staticmethod + def unembedding() -> List: + return [ + Col_Layer( + weight="decoder.weight", + bias="decoder.bias", + replace_layer=col_nn.Linear1D_Col, + gather_output=True, + ) + ] + +from transformers import BertForMaskedLM +from colossalai.shardformer.model.modeling_bert import BertForMaskedLM_ +class BertForMaskedLMPolicy(BertPolicy): + @staticmethod + def inject_policy() -> Tuple[nn.Module, nn.Module]: + return (BertForMaskedLM, BertForMaskedLM_) + + + +class BertForSequenceClassificationPolicy(BertPolicy): + @staticmethod + def inject_policy() -> Dict: + return {} + + +# model = BertForMaskedLM.from_pretrained("bert-base-uncased") +# _ = BertForMaskedLMPolicy(model) +# print(isinstance(model,list(_.inject_policy().keys())[0])) \ No newline at end of file diff --git a/colossalai/shardformer/shard/__init__.py b/colossalai/shardformer/shard/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/shard/shardconfig.py b/colossalai/shardformer/shard/shardconfig.py new file mode 100644 index 000000000000..be265ff0c8c1 --- /dev/null +++ b/colossalai/shardformer/shard/shardconfig.py @@ -0,0 +1,18 @@ +from dataclasses import dataclass + + +@dataclass +class ShardConfig: + """ + The config for sharding the huggingface model for test + """ + rank: int + fp16: bool = True + num_gpus: int = 2 + world_size: int = 2 + backend="nccl" + verbose: str = 'simple' + seed: int = None + require_grad: bool = False + master_addr: str = "127.0.0.1" + master_port: int = 29500 \ No newline at end of file diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py new file mode 100644 index 000000000000..ef785cfee9da --- /dev/null +++ b/colossalai/shardformer/shard/sharder.py @@ -0,0 +1,238 @@ +import torch +import torch.nn as nn +from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union, Callable +from .shardconfig import ShardConfig +from dataclasses import dataclass +from ..policies.basepolicy import Policy, Layer +from ..policies.autopolicy import get_autopolicy +from .slicer import Slicer +from ..utils.utils import hasattr_, setattr_, getattr_ +import colossalai.nn as col_nn +from colossalai.logging import get_dist_logger +import os + + +logger = get_dist_logger() + +class ModelSharder(object): + """ + Shard the original huggingface model according to the policy + + Args: + policy: The policy to shard the model + model: The model to shard + dist_setting: The setting of distributed model + """ + def __init__( + self, + model: nn.Module, + policy: Policy, + shard_config: ShardConfig = None, # TODO + ) -> None: + self.model = model + self.policy = get_autopolicy(self.model) if policy is None else policy + self.slicer = Slicer(shard_config) + self.shard_config = shard_config + self.model_config = self.model.config + self.binding_map = {} + + + def shard(self) -> None: + self.inject_model(self.model) + self.replace_layer(self.model) + + + def inject_model( + self, + model: nn.Module, + ) -> None: + """ + Replace the model to policy defined model + Mainly modify the forward and backward to fit distributed model + + e.g. + BertForMaskedLM.forward -> BertForMaskedLM_.forward + """ + inject_policy = self.policy.inject_policy() + + org_model_cls = inject_policy[0] + shard_model_cls = inject_policy[1] + + if model.__class__ == org_model_cls: + for key in shard_model_cls.__dict__.keys(): + if hasattr(model.__class__, key): + setattr( + model.__class__, + key, + getattr(shard_model_cls,key), + ) + else: + raise NotImplementedError(f"{model.__class__} is not implemented so far") + + + def replace_layer( + self, + model: nn.Module, + ) -> None: + """ + Replace the layer according to the policy, and replace the layer one by one + + Args: + layer: The layer to shard + """ + argument_policies = self.policy.argument_policy(self.model_config, self.shard_config.world_size) + for argument_policy in argument_policies.items(): + origin_layer_cls = argument_policy[0] + attr_dict = argument_policy[1].attr_dict + param_funcs = argument_policy[1].param_funcs + binding_layers = argument_policy[1].binding_layers + # if binding_layer is not None: + # self.binding_map[origin_layer_cls] = binding_layer + self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs, binding_layers) + + + def reverse_replace_layer( + self, + layer: nn.Module, + origin_cls: nn.Module, + attr_dict: Dict[str, Any], + param_funcs: List[Callable], + binding_layers: List[nn.Module] + ) -> None: + """ + Reverse the replace layer operation + + Args: + layer: The object of layer to shard + origin_cls: The origin layer class + attr_dict: The attribute dict to modify + policy_cls: The policy class + """ + for name, child in layer.named_children(): + if child.__class__ == origin_cls: + # replac_layer = child + for k, v in attr_dict.items(): + setattr_(child, k, v, ignore=True) + # print(f"Sharding {name} layer", replac_layer.attention.self.__dict__) + # setattr_(layer, name, self.shard_one_layer(child, policy_cls)) + self.shard_one_layer(child, param_funcs, binding_layers) + continue + + self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs, binding_layers) + return layer + + + def shard_one_layer( + self, + org_layer: nn.Module, + param_funcs: List[Callable], + binding_layers: List[nn.Module] + ) -> None: + """ + Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict + + Args: + org_layer: The origin layer object to shard + param_funcs: The function list to get shard information in policy class + + """ + # print(org_layer) + for func in param_funcs: + policy_layers = func() + for policy_layer in policy_layers: + weight = None + bias = None + weight_attr = policy_layer.weight + bias_attr = policy_layer.bias + replace_layer_cls = policy_layer.replace_layer + ignore = policy_layer.ignore + if policy_layer.__class__.__name__ == "Col_Layer": + gather_output = policy_layer.gather_output + print(gather_output) + + if weight_attr is not None: + if hasattr_(org_layer, weight_attr): + weight = getattr_(org_layer, weight_attr) + elif not ignore: + raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {weight_attr}") + + if bias_attr is not None: + if hasattr_(org_layer, bias_attr): + bias = getattr_(org_layer, bias_attr) + elif not ignore: + raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {bias_attr}") + + # dont have the attribute in policy, and ignore is true + if weight is None and bias is None and ignore: + continue + + # set the sliced weight and bias to the new nn_col layer + assert weight is not None or bias is not None + layer_attr = (lambda x: x[:x.rfind(".")])(weight_attr or bias_attr) + + # slice weight and bias + weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__) + print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None) + # save the binding information + for binding_layer in binding_layers: + self.binding_map[binding_layer] = dict(weight=weight, bias=bias) + + # create new object to replace the origin layer + if replace_layer_cls is not None: + # print(f"RANK {os.environ['RANK']}: replace {getattr_(org_layer, layer_attr).__class__} to {replace_layer_cls}, shape is {weight.shape}") + if isinstance(getattr_(org_layer, layer_attr), nn.Linear): + if replace_layer_cls.__name__ == "Linear1D_Row": + replace_layer = replace_layer_cls(weight.shape[1], weight.shape[0], bias=False if bias is None else True) + elif replace_layer_cls.__name__ == "Linear1D_Col": + replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], bias=False if bias is None else True, gather_output=gather_output) + setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) + self.set_param(replace_layer, weight, bias) + elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding): + replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True)) + setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) + self.set_param(replace_layer, weight, bias) + else: + raise NotImplementedError(f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far") + # do not replace the layer object, just replace the weight and bias + else: + self.set_param(org_layer, layer_attr, weight, bias) + + + def set_param( + self, + layer: Any, + layer_attr: str = "", + weight: torch.Tensor = None, + bias: torch.Tensor = None + ) -> None: + """ + Reset the weight and bias of the layer object + + Args: + layer: The layer object + layer_attr: The attribute name of the layer + weight: The weight of the layer + bias: The bias of the layer + """ + assert weight is not None or bias is not None + if weight is not None: + setattr_(layer, "weight" if layer_attr == "" else layer_attr+".weight", nn.Parameter(weight)) + self.set_layer_size(layer, layer_attr, weight.shape) + if bias is not None: + setattr_(layer, "bias" if layer_attr == "" else layer_attr+".bias", nn.Parameter(bias)) + + + def set_layer_size(self, layer: nn.Module, layer_attr: str, size: torch.Size) -> None: + """ + Set the layer attribute + + Args: + layer: The layer object + layer_attr: The attribute name of the layer + size: Torch.size + """ + # Tensor.shape[0] -> out_features, Tensor.shape[1] -> in_features + attrs = ["out_features", "in_features"] + for i, attr in enumerate(attrs): + if hasattr_(layer, f"{layer_attr}.{attr}"): + setattr_(layer, f"{layer_attr}.{attr}", size[i]) diff --git a/colossalai/shardformer/shard/shardmodel.py b/colossalai/shardformer/shard/shardmodel.py new file mode 100644 index 000000000000..54d7b5ba02d9 --- /dev/null +++ b/colossalai/shardformer/shard/shardmodel.py @@ -0,0 +1,58 @@ +import os +import torch +import torch.nn as nn +import transformers +import torch.distributed as dist +from dataclasses import dataclass +from contextlib import suppress + +from colossalai.tensor.d_tensor.layout import Layout +from ..policies.basepolicy import Policy +from .sharder import ModelSharder +from .shardconfig import ShardConfig + + +class ShardModel(object): + """ + The class for sharding the huggingface model, self.model is the sharded model + Just creat a new ShardModel object to shard huggingface model + + Args: + model: the origin huggingface model + dist_config: the config for distribute information + custom_policy: the custom policy for sharding + """ + def __init__( + self, + model: nn.Module, + shard_config: ShardConfig = None, # TODO + custom_policy: Policy = None, + ) -> None: + self.model = model + self.shard_config = shard_config + self.policy = custom_policy + # self.layout=, # TODO + + sharder=ModelSharder( + model=self.model, + policy=self.policy, + shard_config=self.shard_config, + ) + sharder.shard() + + + def set_environ(self) -> None: + os.environ["TOKENIZERS_PARALLELISM"] = "true" + os.environ["MKL_SERVICE_FORCE_INTEL"] = "GNU" + os.environ["MASTER_ADDR"] = str(self.dist_config.master_addr) + os.environ["MASTER_PORT"] = str(self.dist_config.master_port) + os.environ["WORLD_SIZE"] = str(self.dist_config.num_gpus) + os.environ["RANK"] = str(self.dist_config.rank) + os.environ["LOCAL_RANK"] = str(self.dist_config.rank) + if not dist.is_initialized(): + dist.init_process_group(backend=self.dist_config.backend) + + torch.cuda.set_device(int(os.getenv("LOCAL_RANK", "0"))) + + def back_to_org() -> None: + pass \ No newline at end of file diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py new file mode 100644 index 000000000000..1849cdc99c72 --- /dev/null +++ b/colossalai/shardformer/shard/slicer.py @@ -0,0 +1,167 @@ +import os +from typing import Dict, Tuple +from dataclasses import dataclass + +import torch +import torch.distributed as dist +from ..policies.basepolicy import Layer, Col_Layer, Row_Layer +from .shardconfig import ShardConfig + + +dim_mapping = {Col_Layer: 1, Row_Layer: 0} + +class Slicer(): + + def __init__( + self, + shardconfig: ShardConfig #TODO + ) -> None: + self.shardconfig = shardconfig + + + def slice_weight_bias( + self, + weight: torch.Tensor, + bias: torch.Tensor, + policy_layer_cls: Layer, + ): + """ + Slice the weight and bias according to policy layer cls + Layer -> do nothing + Col_Layer -> slice the weight and bias along dim 1 + Row_Layer -> slice the weight along dim 0 and do not slice bias + + Args: + weight: The weight of the layer + bias: The bias of the layer + policy_layer_class: The class represent how to slice the tensor + """ + if policy_layer_cls == Layer: + return weight, bias + elif policy_layer_cls == Col_Layer: + weight = self.slice_tensor(weight, 1, False) + bias = self.slice_tensor(bias, 0, True) + elif policy_layer_cls == Row_Layer: + weight = self.slice_tensor(weight, 0, False) + else: + raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported") + return weight, bias + + + def slice_weight( + self, + weight: torch.Tensor, + policy_layer_cls: Layer, + ) -> torch.Tensor: + """ + Slice the weight and bias according to the shardconfig + + Args: + weight: The weight of the layer + bias: The bias of the layer + policy_layer_class: The class represent how to slice the tensor + """ + if weight is not None: + dim = dim_mapping[policy_layer_cls] + weight = self.slice_tensor(weight, dim, False) + return weight + + + def slice_bias( + self, + bias: torch.Tensor, + ) -> torch.Tensor: + """ + Slice the bias according to the shardconfig + + Args: + bias: The bias of the layer + """ + assert bias is not None, "The bias is None" + if bias is not None: + bias = self.slice_tensor(bias, 1, True) + return bias + + + def slice_tensor( + self, + tensor_in: torch.Tensor, + dim: int, + is_bias: bool, + ) -> torch.Tensor: + """ + Slice tensor according to the config + """ + if tensor_in is None: + return None + if not is_bias: + return self.slice_2d(tensor_in, dim) + else: + return self.slice_1d(tensor_in) + + + def slice_2d( + self, + tensor: torch.Tensor, + dim: int, + ) -> torch.Tensor: + """ + Slice the 2D tensor + + Args: + tensor: The tensor to slice + """ + assert dim in [0,1], f"Only support 2D tensor, but got {dim}D tensor" + if dim == 0: + return self.slice_row(tensor) + elif dim == 1: + return self.slice_col(tensor) + + + def slice_1d( + self, + tensor: torch.Tensor, + dim: int = None, + ) -> torch.Tensor: + """ + Slice the 1D tensor + + Args: + tensor: The tensor to slice + """ + delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size + down_idx = self.shardconfig.rank * delta + up_idx = down_idx + delta + return tensor[down_idx:up_idx] + + def slice_col( + self, + tensor: torch.Tensor, + ) -> torch.Tensor: + """ + Slice the tensor in column + + Args: + tensor: The tensor to slice + """ + delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size + down_idx = self.shardconfig.rank * delta + up_idx = down_idx + delta + return tensor[down_idx:up_idx,:] + + + def slice_row( + self, + tensor: torch.Tensor, + ) -> torch.Tensor: + """ + Slice the tensor in column + + Args: + tensor: The tensor to slice + """ + delta = (tensor.shape[1] + self.shardconfig.world_size - 1) // self.shardconfig.world_size + down_idx = self.shardconfig.rank * delta + up_idx = down_idx + delta + return tensor[:,down_idx:up_idx] + \ No newline at end of file diff --git a/colossalai/shardformer/test/config.py b/colossalai/shardformer/test/config.py new file mode 100644 index 000000000000..295529429237 --- /dev/null +++ b/colossalai/shardformer/test/config.py @@ -0,0 +1,5 @@ +parallel = dict( + data=1, + pipeline=1, + tensor=dict(size=2, mode='1d') +) \ No newline at end of file diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py new file mode 100644 index 000000000000..c2a9053ca2f6 --- /dev/null +++ b/colossalai/shardformer/test/test.py @@ -0,0 +1,37 @@ +from transformers import AutoTokenizer +from transformers import BertForMaskedLM +import colossalai +from colossalai.shardformer.shard.shardmodel import ShardModel +from colossalai.utils import get_current_device, print_rank_0 +from colossalai.logging import get_dist_logger +from colossalai.shardformer.shard.shardconfig import ShardConfig +import inspect +import argparse +import torch.nn as nn +import os + +tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + +def get_args(): + parser = colossalai.get_default_parser() + return parser.parse_args() + +def inference(model: nn.Module): + # print(model) + token = "Hello, my dog is cute" + inputs = tokenizer(token, return_tensors="pt") + inputs.to("cuda") + model.to("cuda") + outputs = model(**inputs) + print(outputs) + +if __name__ == "__main__": + args = get_args() + colossalai.launch_from_torch(config=args.config) + model = BertForMaskedLM.from_pretrained("bert-base-uncased") + shard_config = ShardConfig( + rank = int(str(get_current_device()).split(':')[-1]), + world_size= int(os.environ['WORLD_SIZE']), + ) + shardmodel = ShardModel(model, shard_config) + inference(shardmodel.model) diff --git a/colossalai/shardformer/utils/__init__.py b/colossalai/shardformer/utils/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/utils/utils.py b/colossalai/shardformer/utils/utils.py new file mode 100644 index 000000000000..5eba87f6fe09 --- /dev/null +++ b/colossalai/shardformer/utils/utils.py @@ -0,0 +1,56 @@ +def hasattr_(obj, attr: str): + """ + Check whether the object has the multi sublevel attr + + Args: + obj: The object to check + attr: The multi level attr to check + """ + attrs = attr.split('.') + for a in attrs: + try: + obj = getattr(obj, a) + except AttributeError: + return False + return True + +def setattr_(obj, attr: str, value, ignore: bool=False): + """ + Set the object's multi sublevel attr to value, if ignore, ignore when it doesn't exist + + Args: + obj: The object to set + attr: The multi level attr to set + value: The value to set + ignore: Whether to ignore when the attr doesn't exist + """ + + attrs = attr.split('.') + for a in attrs[:-1]: + try: + obj = getattr(obj, a) + except AttributeError: + if ignore: + return + raise AttributeError(f"Object {obj} has no attribute {attr}") + setattr(obj, attrs[-1], value) + +def getattr_(obj, attr: str, ignore: bool=None): + """ + Get the object's multi sublevel attr + + Args: + obj: The object to set + attr: The multi level attr to set + ignore: Whether to ignore when the attr doesn't exist + """ + + attrs = attr.split('.') + for a in attrs: + try: + obj = getattr(obj, a) + except AttributeError: + if ignore: + return None + raise AttributeError(f"Object {obj} has no attribute {attr}") + return obj \ No newline at end of file From 58f6432416127e9d5e4ad1e3ac6b8200dcb41c56 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Wed, 24 May 2023 10:26:46 +0800 Subject: [PATCH 300/413] [shardformer]: Feature/shardformer, add some docstring and readme (#3816) * init shardformer code structure * add implement of sharder (inject and replace) * add implement of replace layer to colossal layer * separate different layer policy, add some notion * implement 1d and 2d slicer, can tell col or row * fix bug when slicing and inject model * fix some bug; add inference test example * add share weight and train example * add train * add docstring and readme * add docstring for other files * pre-commit --- colossalai/nn/layer/parallel_1d/_operation.py | 2 + colossalai/nn/layer/parallel_1d/layers.py | 9 +- colossalai/shardformer/README.md | 177 +++++++++++++++++ colossalai/shardformer/model/modeling_bert.py | 16 +- colossalai/shardformer/policies/autopolicy.py | 25 ++- colossalai/shardformer/policies/basepolicy.py | 128 +++++++----- colossalai/shardformer/policies/bert.py | 125 ++++++------ colossalai/shardformer/shard/shardconfig.py | 4 +- colossalai/shardformer/shard/sharder.py | 187 +++++++++--------- colossalai/shardformer/shard/shardmodel.py | 36 ++-- colossalai/shardformer/shard/slicer.py | 113 +++++------ colossalai/shardformer/test/config.py | 6 +- colossalai/shardformer/test/test.py | 87 ++++++-- colossalai/shardformer/utils/utils.py | 36 ++-- 14 files changed, 612 insertions(+), 339 deletions(-) create mode 100644 colossalai/shardformer/README.md diff --git a/colossalai/nn/layer/parallel_1d/_operation.py b/colossalai/nn/layer/parallel_1d/_operation.py index 394334558275..c5e33fd497cd 100644 --- a/colossalai/nn/layer/parallel_1d/_operation.py +++ b/colossalai/nn/layer/parallel_1d/_operation.py @@ -1,5 +1,6 @@ import torch import torch.distributed as dist + from colossalai.core import global_context as gpc try: @@ -72,6 +73,7 @@ def backward(ctx, grad_output): total_input = input grad_input = grad_output.matmul(weight) + grad_output = grad_output.contiguous() # Convert the tensor shapes to 2D for execution compatibility grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2]) total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2]) diff --git a/colossalai/nn/layer/parallel_1d/layers.py b/colossalai/nn/layer/parallel_1d/layers.py index 406173a18c60..0ee3b4fcb502 100644 --- a/colossalai/nn/layer/parallel_1d/layers.py +++ b/colossalai/nn/layer/parallel_1d/layers.py @@ -469,7 +469,8 @@ def __init__(self, if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') - self.out_features_per_partition = divide(out_features, gpc.tensor_parallel_size) + # self.out_features_per_partition = divide(out_features*2, gpc.tensor_parallel_size) + self.out_features_per_partition = out_features # Parameters. # Initialize weight. @@ -612,7 +613,8 @@ def __init__(self, raise ValueError('cannot skip bias addition if bias is None') # Divide the weight matrix along the last dimension. - self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size) + # self.input_size_per_partition = divide(in_features*2, gpc.tensor_parallel_size) + self.input_size_per_partition = in_features # Parameters. # Initialize weight. @@ -884,7 +886,8 @@ def __init__(self, tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) - self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) + # self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) + self.num_embeddings_per_partition = num_embeddings self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md new file mode 100644 index 000000000000..a47e280f2be4 --- /dev/null +++ b/colossalai/shardformer/README.md @@ -0,0 +1,177 @@ +## ShardFormer + +### Intro +Make the model in huggingface.co can be paralleled and can be used with colossalai according to custom policy. + +### Quick start +1. Usage +- Use +``` python +from colossalai.shardformer.shard.shardmodel import ShardModel +from transformers import BertForMaskedLM + +# create huggingface model as normal +model = BertForMaskedLM.from_pretrained("bert-base-uncased") + +# make the huggingface model paralleled to ShardModel +# auto policy: +shardmodel = ShardModel(model).model + +# custom policy: +from xxx import +shardmodel = ShardModel(model, ).model + + +# do angthing as normal +... +``` +- Policy + +If you wanna parallel the model in custom way, just overwrite the policy class for the huggingface model. + +You should do: + +1. Inherit Policy class +2. Overwrite argument_policy method + - In this method you need to list which layers class you wanna modify and the attributes and parameters in those layers. +3. Overwrite inject_policy method [Optional] + - If you need to modify the forward or backward progress. +4. Overwrite or add the param recording functions + - These function use suffix to record the path of weight or bias for the layer. +5. Overwrite binding + +More details can be found in shardformer/policies/basepolicy.py +``` python +from colossalai.shardformer.policies.basepolicy import Policy, Layer, Col_Layer, Row_Layer, Argument + +CustomPolicy(Policy): + @staticmethod + def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument]: + """ + Return a dict, the key is layer will be modified and the value is the Argument class with param setting and param functions + + Args: + model_config: The config of transformer model + shard_setting: The config of distributed model + + Return: + Dict for the modify policy, + { + origin layer class1 (nn.Module): Argument( + attr_dict = { + argument1: value1, + argument2: value2, + ... + }, + param_funcs = [ + staticmethod1, + staticmethod2, + ... + ] + ), + origin layer class2 (nn.Module): Argument( + attr_dict = { + argument1: value1, + argument2: value2, + ... + }, + param_funcs = [ + staticmethod1, + staticmethod2, + ... + ] + ), + ... + } + + """ + raise NotImplementedError + + @staticmethod + def inject_policy() -> Tuple[nn.Module, nn.Module]: + """ + Return the dict for the inject model + + Return: + The injected model, key is the original model and value is the new shardmodel + """ + return () + + @staticmethod + def binding_policy() -> Dict: + """ + Return the dict for the binding model + """ + return NotImplementedError + + @staticmethod + def attn_in() -> List: + """ + Attention qkv layer + + Returns: + List[Layer]: List of layer object, each layer is the new + """ + return NotImplementedError + + @staticmethod + def attn_out() -> List: + """ + Attention output projection layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + @staticmethod + def mlp_in() -> List: + """ + h -> 4h mlp layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + @staticmethod + def mlp_out() -> List: + """ + 4h -> h mlp layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + @staticmethod + def embedding() -> List: + """ + Partially slice the embedding layer + vocab_size->vocab_size//gpu_nums + + Return: + List[Layer]: List of layer object + """ + return NotImplementedError + + @staticmethod + def unembedding() -> List: + """ + Partially slice the embedding layer + vocab_size->vocab_size//gpu_nums + + Return: + List[Layer]: List of layer object + """ + return NotImplementedError + +``` + +2. Simple example +``` shell +# inference +colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode inference +# train +colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode train +``` diff --git a/colossalai/shardformer/model/modeling_bert.py b/colossalai/shardformer/model/modeling_bert.py index 87ed8ac308a5..6741ae866991 100644 --- a/colossalai/shardformer/model/modeling_bert.py +++ b/colossalai/shardformer/model/modeling_bert.py @@ -1,12 +1,14 @@ +from typing import Any, Dict, List, Type + import torch import torch.nn as nn from torch.nn import CrossEntropyLoss -from typing import Any, Dict, List, Type - - from transformers import BertForMaskedLM from transformers.models.bert.modeling_bert import MaskedLMOutput + + class BertForMaskedLM_(BertForMaskedLM): + def forward( self, input_ids=None, @@ -23,7 +25,7 @@ def forward( return_dict=None, **kwargs, ): - print("[Inject OK] Injected forward method") + # print("[Inject OK] Injected forward method") return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.bert( @@ -46,9 +48,9 @@ def forward( masked_lm_loss = None # if input_ids is not None: - # masked_lm_loss = applyDistCrossEntropy(prediction_scores, input_ids, self.config.vocab_size) + # masked_lm_loss = applyDistCrossEntropy(prediction_scores, input_ids, self.config.vocab_size) if labels is not None: - loss_fct = CrossEntropyLoss() # -100 index = padding token + loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: @@ -60,4 +62,4 @@ def forward( logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, - ) \ No newline at end of file + ) diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 9142e0dae22e..e096c2b13a59 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -1,40 +1,47 @@ import torch.nn as nn + def build_policies(): - """ + r""" Build the policies for the model - + Return: The dict for the policies """ auto_policy_dict = {} from transformers.models.bert.modeling_bert import BertForMaskedLM + from .bert import BertForMaskedLMPolicy auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy from transformers.models.bert.modeling_bert import BertForSequenceClassification + from .bert import BertForSequenceClassificationPolicy auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy - + return auto_policy_dict -def get_autopolicy(model:nn.Module): - """ + +def get_autopolicy(model: nn.Module): + r""" Return the auto policy for the model Args: - model: The model to be used + model (:class:`nn.Module`): The model to get the auto policy Return: - The auto policy for the model + :class:`Policy`: The auto policy for the model """ auto_policy_dict = build_policies() policy = auto_policy_dict.get(model.__class__, None) - if policy is None: - raise NotImplementedError(f"Auto policy for {model.__class__.__qualname__} is not implemented\n Supported models are {[i.__qualname__ for i in auto_policy_dict.keys()]}") + if policy is None: + raise NotImplementedError( + f"Auto policy for {model.__class__.__qualname__} is not implemented\n Supported models are {[i.__qualname__ for i in auto_policy_dict.keys()]}" + ) return policy + # from transformers.models.bert.modeling_bert import BertForMaskedLM, BertForPreTraining # model = BertForPreTraining # policy = get_autopolicy(model) diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index d444aeb53bf8..a5cc0bc68df6 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -1,28 +1,38 @@ # part of code modified from https://github.com/tunib-ai/parallelformers +from dataclasses import dataclass, field +from typing import Any, Callable, Dict, List, Tuple, Type + import torch import torch.nn as nn -import colossalai.nn as col_nn -from typing import Any, Dict, List, Type, Tuple, Callable from transformers import AutoConfig -from dataclasses import dataclass, field + +import colossalai.nn as col_nn + @dataclass class Argument: - attr_dict : Dict[str, Any] - param_funcs : List[Callable] - binding_layers : List[nn.Module] = field(default_factory=list) + r""" + The argument class for the policy + + Args: + attr_dict (Dict[str, Any]): The dict for the param setting + param_funcs (:class:`List[Callable]`): The list for the param functions + """ + attr_dict: Dict[str, Any] + param_funcs: List[Callable] + @dataclass class Layer: - """ + r""" The layer object for the policy Args: - weight: The weight name of the layer - bias: The bias name of the layer - replace_layer: The layer to replace the original layer - ignore: Whether to ignore this layer if it is not in the model + weight (str): The weight suffix of the layer + bias (str): The bias suffix of the layer + replace_layer (:class:`colosalai.nn`): The layer to replace the original layer + ignore (bool): Whether to ignore this layer if it is not in the model """ weight: str = None bias: str = None @@ -32,45 +42,55 @@ class Layer: @dataclass class Col_Layer(Layer): - """ + r""" Class for col shard layer in MegatronLM + + Args: + gather_output (bool): Whether to gather the output of the layer """ gather_output: bool = False @dataclass class Row_Layer(Layer): - """ + r""" Class for col shard layer in MegatronLM """ pass class Policy(): - """ + r""" The base class for all the policies - For each different model, it should have a different policy class, like BertPolicy for Bert Model - or OPTPolicy for OPT model. + For each different model, it should have a different policy class, like BertPolicy for Bert Model + or OPTPolicy for OPT model. AutoPolicy: - shardformer already defined some policies for huggingface model, just set custom_policy = None + Shardformer already defined some policies for huggingface model, just set ``custom_policy`` = None to use the auto policy. In shardformer autopolicy, we define a base policy for one type model, - like BertPolicy, and for each different Bert modle in huggingface like, BertForMaskedLM, + like BertPolicy, and for each different Bert modle in huggingface like, BertForMaskedLM, BertForSequenceClassification, etc., for each different Bert model we difine different policy class - and overwrite the method inject_policy - + and overwrite the method like ``inject_policy`` to modify the forward and backward process. + CustomPolicy: + If you want to define your own policy, you can set ``custom_policy`` = CustomPolicy, and overwrite + all the methods in ``Policy`` class. You can refer to any policy we defined like the ``BertPolicy`` + class for the example. + """ + @staticmethod - def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument]: - """ - Return a dict, the key is layer will be modified and the value is the Argument class with param setting and param functions + def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]: + r""" + Return the dict for the modify policy, the key is the original layer class and the value is the + argument for the modify layer Args: - model_config: The config of transformer model - shard_setting: The config of distributed model - + model_config (:class:`tansformer.Config`): The config of transformer model + shard_config (:class:`ShardConfig`): The config for sharding model + Return: Dict for the modify policy, + :: { origin layer class1 (nn.Module): Argument( attr_dict = { @@ -101,33 +121,51 @@ def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument] """ raise NotImplementedError - @staticmethod def inject_policy() -> Tuple[nn.Module, nn.Module]: - """ - Return the dict for the inject model + r""" + Return the dict for the inject model Return: The injected model, key is the original model and value is the new shardmodel + :: + (OrignModel, CustomModel) + in `CustomModel`, we can overwrite the forward and backward process """ return () - @staticmethod - def attn_in() -> List: + def binding_policy() -> Dict: + r""" + Return the dict for the binding model + + Return: + This method should return the binding relationship for some layers share the weight or bias, + the key and value is the suffix of the weight or bias of the model + :: + return { + "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", + } """ + return NotImplementedError + + @staticmethod + def attn_in() -> List: + r""" Attention qkv layer + In this kind of method, we should return the list of ``Layer`` object, each ``Layer`` object should be + ``Layer`` for no slicing, ``Col_Layer`` for col slicing, ``Row_Layer`` for row slicing. And the parameters + in ``Layer`` object can refer to the ``Layer`` class. Returns: - List[Layer]: List of layer object, each layer is the new + List[Layer]: List of layer object, each layer is the new """ return NotImplementedError - @staticmethod def attn_out() -> List: - """ + r""" Attention output projection layer Returns: @@ -135,46 +173,40 @@ def attn_out() -> List: """ return NotImplementedError - @staticmethod def mlp_in() -> List: - """ + r""" h -> 4h mlp layer Returns: List[Layer]: List of layer object """ return NotImplementedError - @staticmethod def mlp_out() -> List: - """ + r""" 4h -> h mlp layer Returns: List[Layer]: List of layer object """ return NotImplementedError - - + @staticmethod - def embedding()->List: - """ + def embedding() -> List: + r""" Partially slice the embedding layer - vocab_size->vocab_size//gpu_nums Return: List[Layer]: List of layer object """ return NotImplementedError - - + @staticmethod - def unembedding()->List: - """ + def unembedding() -> List: + r""" Partially slice the embedding layer - vocab_size->vocab_size//gpu_nums Return: List[Layer]: List of layer object diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 24b95e827347..5d91d8ddc766 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,56 +1,57 @@ -from typing import Dict, List, Tuple, Type, Any, Callable +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Tuple, Type + import torch.nn as nn -from .basepolicy import Policy, Layer, Argument, Col_Layer, Row_Layer +from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead + import colossalai.nn as col_nn -from transformers.models.bert.modeling_bert import BertLayer, BertEmbeddings, BertLMPredictionHead -from dataclasses import dataclass + +from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer class BertPolicy(Policy): + @staticmethod - def argument_policy(config, world_size: int) -> Dict[nn.Module,Argument]: + def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: return { - BertLayer: Argument( - attr_dict = { - # 1. shard hidden size - "attention.self.all_head_size": config.hidden_size // world_size, - "crossattention.self.all_head_size": config.hidden_size // world_size, - # 2. shard number of heads - "attention.self.num_attention_heads": config.num_attention_heads // world_size, - "crossattention.self.num_attention_heads": config.num_attention_heads // world_size, - - }, - param_funcs = [ - BertPolicy.attn_in, - BertPolicy.attn_out, - BertPolicy.mlp_in, - BertPolicy.mlp_out - ] - ), - BertEmbeddings: Argument( - attr_dict = { - # 1. shard vocab size - # "word_embeddings.num_embeddings": config.vocab_size // world_size, - # 2. add the size of the sliced embedding layer excluding the last slice - "word_embeddings.dim_size": (config.vocab_size+world_size-1) // world_size, - }, - param_funcs = [ - BertPolicy.embedding, - ], - binding_layers = [ - BertLMPredictionHead, - ] - ), - BertLMPredictionHead: Argument( - attr_dict = { - # 1. shard vocab size - # "word_embeddings.num_embeddings": config.vocab_size // world_size, - # 2. add the size of the sliced embedding layer excluding the last slice - }, - param_funcs = [ - BertPolicy.unembedding, - ] - ) + BertLayer: + Argument( + attr_dict={ + # 1. shard hidden size + "attention.self.all_head_size": config.hidden_size // world_size, + "crossattention.self.all_head_size": config.hidden_size // world_size, + # 2. shard number of heads + "attention.self.num_attention_heads": config.num_attention_heads // world_size, + "crossattention.self.num_attention_heads": config.num_attention_heads // world_size, + }, + param_funcs=[BertPolicy.attn_in, BertPolicy.attn_out, BertPolicy.mlp_in, BertPolicy.mlp_out]), + BertEmbeddings: + Argument( + attr_dict={ + # 1. shard vocab size + # "word_embeddings.num_embeddings": config.vocab_size // world_size, + # 2. add the size of the sliced embedding layer excluding the last slice + "word_embeddings.dim_size": (config.vocab_size + world_size - 1) // world_size, + }, + param_funcs=[ + BertPolicy.embedding, + ]), + BertLMPredictionHead: + Argument( + attr_dict={ + # 1. shard vocab size + # "word_embeddings.num_embeddings": config.vocab_size // world_size, + # 2. add the size of the sliced embedding layer excluding the last slice + }, + param_funcs=[ + BertPolicy.unembedding, + ]) + } + + @staticmethod + def binding_policy() -> Dict: + return { + "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", } @staticmethod @@ -89,9 +90,8 @@ def attn_in() -> List: replace_layer=col_nn.Linear1D_Col, ignore=True, ), - ] - + @staticmethod def attn_out() -> List: return [ @@ -107,17 +107,17 @@ def attn_out() -> List: ignore=True, ), ] - + @staticmethod def mlp_in() -> List: return [ - Col_Layer( + Col_Layer( weight="intermediate.dense.weight", bias="intermediate.dense.bias", replace_layer=col_nn.Linear1D_Col, ), ] - + @staticmethod def mlp_out() -> List: return [ @@ -130,13 +130,11 @@ def mlp_out() -> List: @staticmethod def embedding() -> List: - return [ - Col_Layer( - weight="word_embeddings.weight", - replace_layer=col_nn.VocabParallelEmbedding1D, - ) - ] - + return [Col_Layer( + weight="word_embeddings.weight", + replace_layer=col_nn.VocabParallelEmbedding1D, + )] + @staticmethod def unembedding() -> List: return [ @@ -148,16 +146,21 @@ def unembedding() -> List: ) ] + from transformers import BertForMaskedLM + from colossalai.shardformer.model.modeling_bert import BertForMaskedLM_ + + class BertForMaskedLMPolicy(BertPolicy): + @staticmethod def inject_policy() -> Tuple[nn.Module, nn.Module]: return (BertForMaskedLM, BertForMaskedLM_) - - + class BertForSequenceClassificationPolicy(BertPolicy): + @staticmethod def inject_policy() -> Dict: return {} @@ -165,4 +168,4 @@ def inject_policy() -> Dict: # model = BertForMaskedLM.from_pretrained("bert-base-uncased") # _ = BertForMaskedLMPolicy(model) -# print(isinstance(model,list(_.inject_policy().keys())[0])) \ No newline at end of file +# print(isinstance(model,list(_.inject_policy().keys())[0])) diff --git a/colossalai/shardformer/shard/shardconfig.py b/colossalai/shardformer/shard/shardconfig.py index be265ff0c8c1..c6a2513a6eff 100644 --- a/colossalai/shardformer/shard/shardconfig.py +++ b/colossalai/shardformer/shard/shardconfig.py @@ -10,9 +10,9 @@ class ShardConfig: fp16: bool = True num_gpus: int = 2 world_size: int = 2 - backend="nccl" + backend = "nccl" verbose: str = 'simple' seed: int = None require_grad: bool = False master_addr: str = "127.0.0.1" - master_port: int = 29500 \ No newline at end of file + master_port: int = 29500 diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index ef785cfee9da..2f6bb4265a11 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -1,56 +1,59 @@ +import os +from dataclasses import dataclass +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union + import torch import torch.nn as nn -from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union, Callable -from .shardconfig import ShardConfig -from dataclasses import dataclass -from ..policies.basepolicy import Policy, Layer -from ..policies.autopolicy import get_autopolicy -from .slicer import Slicer -from ..utils.utils import hasattr_, setattr_, getattr_ + import colossalai.nn as col_nn from colossalai.logging import get_dist_logger -import os +from ..policies.autopolicy import get_autopolicy +from ..policies.basepolicy import Layer, Policy +from ..utils.utils import getattr_, hasattr_, setattr_ +from .shardconfig import ShardConfig +from .slicer import Slicer logger = get_dist_logger() + class ModelSharder(object): - """ + r""" Shard the original huggingface model according to the policy Args: - policy: The policy to shard the model - model: The model to shard - dist_setting: The setting of distributed model + policy (:class:`Policy`): The policy to shard the model + model (:class:`torch.Module`): The model to shard + shard_config: The setting of distributed model """ + def __init__( self, model: nn.Module, policy: Policy, - shard_config: ShardConfig = None, # TODO - ) -> None: + shard_config: ShardConfig = None, # TODO + ) -> None: self.model = model self.policy = get_autopolicy(self.model) if policy is None else policy self.slicer = Slicer(shard_config) self.shard_config = shard_config self.model_config = self.model.config - self.binding_map = {} - def shard(self) -> None: self.inject_model(self.model) self.replace_layer(self.model) - - + self.bind_layer(self.model) + def inject_model( - self, - model: nn.Module, - ) -> None: - """ + self, + model: nn.Module, + ) -> None: + r""" Replace the model to policy defined model Mainly modify the forward and backward to fit distributed model - + e.g. + :: BertForMaskedLM.forward -> BertForMaskedLM_.forward """ inject_policy = self.policy.inject_policy() @@ -64,49 +67,43 @@ def inject_model( setattr( model.__class__, key, - getattr(shard_model_cls,key), + getattr(shard_model_cls, key), ) else: raise NotImplementedError(f"{model.__class__} is not implemented so far") - def replace_layer( - self, - model: nn.Module, - ) -> None: - """ + self, + model: nn.Module, + ) -> None: + r""" Replace the layer according to the policy, and replace the layer one by one Args: - layer: The layer to shard + model (:class:`torch.nn.Module`): The layer to shard """ argument_policies = self.policy.argument_policy(self.model_config, self.shard_config.world_size) for argument_policy in argument_policies.items(): origin_layer_cls = argument_policy[0] attr_dict = argument_policy[1].attr_dict param_funcs = argument_policy[1].param_funcs - binding_layers = argument_policy[1].binding_layers - # if binding_layer is not None: - # self.binding_map[origin_layer_cls] = binding_layer - self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs, binding_layers) - + self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs) def reverse_replace_layer( - self, - layer: nn.Module, - origin_cls: nn.Module, - attr_dict: Dict[str, Any], - param_funcs: List[Callable], - binding_layers: List[nn.Module] - ) -> None: - """ + self, + layer: nn.Module, + origin_cls: nn.Module, + attr_dict: Dict[str, Any], + param_funcs: List[Callable], + ) -> None: + r""" Reverse the replace layer operation Args: - layer: The object of layer to shard - origin_cls: The origin layer class - attr_dict: The attribute dict to modify - policy_cls: The policy class + layer (:class:`torch.nn.Module`): The object of layer to shard + origin_cls (:class:`transformers.model`): The origin layer class + attr_dict (Dict): The attribute dict to modify + policy_cls (:class:`Policy`): The policy class """ for name, child in layer.named_children(): if child.__class__ == origin_cls: @@ -115,25 +112,23 @@ def reverse_replace_layer( setattr_(child, k, v, ignore=True) # print(f"Sharding {name} layer", replac_layer.attention.self.__dict__) # setattr_(layer, name, self.shard_one_layer(child, policy_cls)) - self.shard_one_layer(child, param_funcs, binding_layers) + self.shard_one_layer(child, param_funcs) continue - self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs, binding_layers) + self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs) return layer - def shard_one_layer( - self, - org_layer: nn.Module, - param_funcs: List[Callable], - binding_layers: List[nn.Module] - ) -> None: - """ + self, + org_layer: nn.Module, + param_funcs: List[Callable], + ) -> None: + r""" Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict Args: - org_layer: The origin layer object to shard - param_funcs: The function list to get shard information in policy class + org_layer (:class:`torch.nn.Module`): The origin layer object to shard + param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class """ # print(org_layer) @@ -148,7 +143,7 @@ def shard_one_layer( ignore = policy_layer.ignore if policy_layer.__class__.__name__ == "Col_Layer": gather_output = policy_layer.gather_output - print(gather_output) + # print(gather_output) if weight_attr is not None: if hasattr_(org_layer, weight_attr): @@ -172,67 +167,81 @@ def shard_one_layer( # slice weight and bias weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__) - print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None) - # save the binding information - for binding_layer in binding_layers: - self.binding_map[binding_layer] = dict(weight=weight, bias=bias) + # print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None) # create new object to replace the origin layer if replace_layer_cls is not None: # print(f"RANK {os.environ['RANK']}: replace {getattr_(org_layer, layer_attr).__class__} to {replace_layer_cls}, shape is {weight.shape}") if isinstance(getattr_(org_layer, layer_attr), nn.Linear): if replace_layer_cls.__name__ == "Linear1D_Row": - replace_layer = replace_layer_cls(weight.shape[1], weight.shape[0], bias=False if bias is None else True) + replace_layer = replace_layer_cls(weight.shape[1], + weight.shape[0], + bias=False if bias is None else True) elif replace_layer_cls.__name__ == "Linear1D_Col": - replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], bias=False if bias is None else True, gather_output=gather_output) + replace_layer = replace_layer_cls(weight.shape[0], + weight.shape[1], + bias=False if bias is None else True, + gather_output=gather_output) setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) self.set_param(replace_layer, weight, bias) - elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding): - replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True)) + elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding): + replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], + getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True)) setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) self.set_param(replace_layer, weight, bias) else: - raise NotImplementedError(f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far") + raise NotImplementedError( + f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far") # do not replace the layer object, just replace the weight and bias else: self.set_param(org_layer, layer_attr, weight, bias) - - def set_param( - self, - layer: Any, - layer_attr: str = "", - weight: torch.Tensor = None, - bias: torch.Tensor = None - ) -> None: - """ + def set_param(self, + layer: Any, + weight: torch.Tensor = None, + bias: torch.Tensor = None, + layer_attr: str = "") -> None: + r""" Reset the weight and bias of the layer object Args: - layer: The layer object - layer_attr: The attribute name of the layer - weight: The weight of the layer - bias: The bias of the layer + layer (:class:`torch.nn.Module`): The layer object + layer_attr (str): The attribute name of the layer + weight (:class:`torch.Tensor`): The weight of the layer + bias (:class:`torch.Tensor`): The bias of the layer """ assert weight is not None or bias is not None if weight is not None: - setattr_(layer, "weight" if layer_attr == "" else layer_attr+".weight", nn.Parameter(weight)) + setattr_(layer, "weight" if layer_attr == "" else layer_attr + ".weight", nn.Parameter(weight.contiguous())) self.set_layer_size(layer, layer_attr, weight.shape) if bias is not None: - setattr_(layer, "bias" if layer_attr == "" else layer_attr+".bias", nn.Parameter(bias)) - + setattr_(layer, "bias" if layer_attr == "" else layer_attr + ".bias", nn.Parameter(bias.contiguous())) def set_layer_size(self, layer: nn.Module, layer_attr: str, size: torch.Size) -> None: - """ + r""" Set the layer attribute Args: - layer: The layer object - layer_attr: The attribute name of the layer - size: Torch.size + layer (:class:`torch.nn.Module`): The layer object + layer_attr (str): The attribute name of the layer + size (:class:`torch.Size`): The size of the tensor """ # Tensor.shape[0] -> out_features, Tensor.shape[1] -> in_features attrs = ["out_features", "in_features"] for i, attr in enumerate(attrs): if hasattr_(layer, f"{layer_attr}.{attr}"): - setattr_(layer, f"{layer_attr}.{attr}", size[i]) + setattr_(layer, f"{layer_attr}.{attr}", size[i]) + + def bind_layer(self, model: nn.Module) -> None: + r""" + Bind the layer according to the binding policy + + Args: + model (:class:`torch.nn.Module`): The shard model + """ + binding_map = self.policy.binding_policy() + for k, v in binding_map.items(): + param = getattr_(model, k) + param = nn.Parameter(param) + setattr_(model, k, param) + setattr_(model, v, param) diff --git a/colossalai/shardformer/shard/shardmodel.py b/colossalai/shardformer/shard/shardmodel.py index 54d7b5ba02d9..7e7d1576afd6 100644 --- a/colossalai/shardformer/shard/shardmodel.py +++ b/colossalai/shardformer/shard/shardmodel.py @@ -1,46 +1,48 @@ import os +from contextlib import suppress +from dataclasses import dataclass + import torch +import torch.distributed as dist import torch.nn as nn import transformers -import torch.distributed as dist -from dataclasses import dataclass -from contextlib import suppress from colossalai.tensor.d_tensor.layout import Layout + from ..policies.basepolicy import Policy -from .sharder import ModelSharder from .shardconfig import ShardConfig +from .sharder import ModelSharder class ShardModel(object): - """ - The class for sharding the huggingface model, self.model is the sharded model + r""" + The class for sharding the huggingface model, ''self.model'' is the sharded model Just creat a new ShardModel object to shard huggingface model Args: - model: the origin huggingface model - dist_config: the config for distribute information - custom_policy: the custom policy for sharding + model (:class:`torch.nn.Model`): the origin huggingface model + dist_config (:class:`ShardConfig`): the config for distribute information + custom_policy (:class:`Policy`): the custom policy for sharding """ + def __init__( - self, - model: nn.Module, - shard_config: ShardConfig = None, # TODO - custom_policy: Policy = None, - ) -> None: + self, + model: nn.Module, + shard_config: ShardConfig = None, # TODO + custom_policy: Policy = None, + ) -> None: self.model = model self.shard_config = shard_config self.policy = custom_policy # self.layout=, # TODO - sharder=ModelSharder( + sharder = ModelSharder( model=self.model, policy=self.policy, shard_config=self.shard_config, ) sharder.shard() - def set_environ(self) -> None: os.environ["TOKENIZERS_PARALLELISM"] = "true" os.environ["MKL_SERVICE_FORCE_INTEL"] = "GNU" @@ -55,4 +57,4 @@ def set_environ(self) -> None: torch.cuda.set_device(int(os.getenv("LOCAL_RANK", "0"))) def back_to_org() -> None: - pass \ No newline at end of file + pass diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 1849cdc99c72..096f5db95f49 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -1,40 +1,40 @@ import os -from typing import Dict, Tuple from dataclasses import dataclass +from typing import Dict, Tuple import torch import torch.distributed as dist -from ..policies.basepolicy import Layer, Col_Layer, Row_Layer -from .shardconfig import ShardConfig +from ..policies.basepolicy import Col_Layer, Layer, Row_Layer +from .shardconfig import ShardConfig dim_mapping = {Col_Layer: 1, Row_Layer: 0} + class Slicer(): def __init__( - self, - shardconfig: ShardConfig #TODO + self, + shardconfig: ShardConfig #TODO ) -> None: self.shardconfig = shardconfig - def slice_weight_bias( self, weight: torch.Tensor, bias: torch.Tensor, policy_layer_cls: Layer, ): - """ + r""" Slice the weight and bias according to policy layer cls - Layer -> do nothing - Col_Layer -> slice the weight and bias along dim 1 - Row_Layer -> slice the weight along dim 0 and do not slice bias + ``Layer`` -> do nothing + ``Col_Layer`` -> slice the weight and bias along dim 1 + ``Row_Layer`` -> slice the weight along dim 0 and do not slice bias Args: - weight: The weight of the layer - bias: The bias of the layer - policy_layer_class: The class represent how to slice the tensor + weight (:class:`torch.nn.Module`): The weight of the layer + bias: (:class:`torch.nn.Module`): The bias of the layer + policy_layer_class (:class:`Policy`): The class represent how to slice the tensor """ if policy_layer_cls == Layer: return weight, bias @@ -46,42 +46,6 @@ def slice_weight_bias( else: raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported") return weight, bias - - - def slice_weight( - self, - weight: torch.Tensor, - policy_layer_cls: Layer, - ) -> torch.Tensor: - """ - Slice the weight and bias according to the shardconfig - - Args: - weight: The weight of the layer - bias: The bias of the layer - policy_layer_class: The class represent how to slice the tensor - """ - if weight is not None: - dim = dim_mapping[policy_layer_cls] - weight = self.slice_tensor(weight, dim, False) - return weight - - - def slice_bias( - self, - bias: torch.Tensor, - ) -> torch.Tensor: - """ - Slice the bias according to the shardconfig - - Args: - bias: The bias of the layer - """ - assert bias is not None, "The bias is None" - if bias is not None: - bias = self.slice_tensor(bias, 1, True) - return bias - def slice_tensor( self, @@ -89,8 +53,13 @@ def slice_tensor( dim: int, is_bias: bool, ) -> torch.Tensor: - """ + r""" Slice tensor according to the config + + Args: + tensor_in (:class:`torch.Tensor`): The tensor to slice + dim (int): The dimension to slice + is_bias (bool): Whether the tensor is bias """ if tensor_in is None: return None @@ -99,69 +68,75 @@ def slice_tensor( else: return self.slice_1d(tensor_in) - def slice_2d( self, tensor: torch.Tensor, dim: int, ) -> torch.Tensor: - """ - Slice the 2D tensor + r""" + Slice the 2D tensor Args: - tensor: The tensor to slice + tensor (:class:`torch.Tensor`): The tensor to slice + dim (int): The dimension to slice """ - assert dim in [0,1], f"Only support 2D tensor, but got {dim}D tensor" + assert dim in [0, 1], f"Only support 2D tensor, but got {dim}D tensor" if dim == 0: return self.slice_row(tensor) elif dim == 1: return self.slice_col(tensor) - def slice_1d( self, tensor: torch.Tensor, - dim: int = None, ) -> torch.Tensor: - """ - Slice the 1D tensor + r""" + Slice the 1D tensor Args: - tensor: The tensor to slice + tensor (:class:`torch.Tensor`): The tensor to slice + + Returns: + :class:`torch.Tensor`: The sliced tensor """ delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size down_idx = self.shardconfig.rank * delta up_idx = down_idx + delta - return tensor[down_idx:up_idx] + return tensor[down_idx:up_idx].contiguous() def slice_col( self, tensor: torch.Tensor, ) -> torch.Tensor: - """ + r""" Slice the tensor in column Args: - tensor: The tensor to slice + tensor (:class:`torch.Tensor`): The tensor to slice + + Returns: + :class:`torch.Tensor`: The sliced tensor + """ delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size down_idx = self.shardconfig.rank * delta up_idx = down_idx + delta - return tensor[down_idx:up_idx,:] - + return tensor[down_idx:up_idx, :].contiguous() def slice_row( self, tensor: torch.Tensor, ) -> torch.Tensor: - """ + r""" Slice the tensor in column Args: - tensor: The tensor to slice + tensor (:class:`torch.Tensor`): The tensor to slice + + Returns: + :class:`torch.Tensor`: The sliced tensor """ delta = (tensor.shape[1] + self.shardconfig.world_size - 1) // self.shardconfig.world_size down_idx = self.shardconfig.rank * delta up_idx = down_idx + delta - return tensor[:,down_idx:up_idx] - \ No newline at end of file + return tensor[:, down_idx:up_idx].contiguous() diff --git a/colossalai/shardformer/test/config.py b/colossalai/shardformer/test/config.py index 295529429237..2b80d8b3ca12 100644 --- a/colossalai/shardformer/test/config.py +++ b/colossalai/shardformer/test/config.py @@ -1,5 +1 @@ -parallel = dict( - data=1, - pipeline=1, - tensor=dict(size=2, mode='1d') -) \ No newline at end of file +parallel = dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')) diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py index c2a9053ca2f6..0cdc6ef38fd2 100644 --- a/colossalai/shardformer/test/test.py +++ b/colossalai/shardformer/test/test.py @@ -1,23 +1,51 @@ -from transformers import AutoTokenizer -from transformers import BertForMaskedLM +import argparse +import inspect +import os + +import torch +import torch.nn as nn +from datasets import load_dataset +from torch.utils.data import DataLoader +from tqdm.auto import tqdm +from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments + import colossalai -from colossalai.shardformer.shard.shardmodel import ShardModel -from colossalai.utils import get_current_device, print_rank_0 from colossalai.logging import get_dist_logger from colossalai.shardformer.shard.shardconfig import ShardConfig -import inspect -import argparse -import torch.nn as nn -import os +from colossalai.shardformer.shard.shardmodel import ShardModel +from colossalai.utils import get_current_device, print_rank_0 +os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + def get_args(): parser = colossalai.get_default_parser() + parser.add_argument("--mode", type=str, default='inference') return parser.parse_args() + +def load_data(): + datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') + # datasets=load_dataset("yelp_review_full") + tokenized_datasets = datasets.map( + lambda examples: tokenizer(examples["text"], truncation=True, padding="max_length"), batched=True) + tokenized_datasets = tokenized_datasets.remove_columns(["text"]) + # tokenized_datasets=tokenized_datasets.rename_column("label","labels") + tokenized_datasets.set_format("torch") + + train_dataset = tokenized_datasets["train"].select(range(500)) + test_dataset = tokenized_datasets["test"].select(range(100)) + + datacollector = DataCollatorForLanguageModeling(tokenizer, mlm=True, mlm_probability=0.15, return_tensors="pt") + train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True, collate_fn=datacollector) + eval_dataloader = DataLoader(test_dataset, batch_size=8, shuffle=True, collate_fn=datacollector) + return train_dataloader, eval_dataloader + + def inference(model: nn.Module): - # print(model) + print(model) + tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") token = "Hello, my dog is cute" inputs = tokenizer(token, return_tensors="pt") inputs.to("cuda") @@ -25,13 +53,48 @@ def inference(model: nn.Module): outputs = model(**inputs) print(outputs) + +def train(model: nn.Module, num_epoch: int = 2): + train_dataloader, eval_dataloader = load_data() + optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5) + progress_bar = tqdm(range((num_epoch) * len(train_dataloader))) + criterion = nn.CrossEntropyLoss() + model.to("cuda") + model.train() + for epoch in range(num_epoch): + progress_bar.set_description(f"Rank {get_current_device()} epoch {epoch}") + + for batch in train_dataloader: + optimizer.zero_grad() + batch = {k: v.to('cuda') for k, v in batch.items()} + outputs = model(**batch) + loss = outputs.loss + loss.backward() + optimizer.step() + progress_bar.update(1) + train_loss = loss + + loss = 0.0 + for batch in eval_dataloader: + batch = {k: v.to('cuda') for k, v in batch.items()} + outputs = model(**batch) + # loss = outputs.loss + loss += outputs.loss.item() + # loss = criterion(outputs.logits, batch["input_ids"]) + test_loss = loss / len(eval_dataloader) + print_rank_0(f"Train Loss: {train_loss:.4f} Test Loss:{test_loss:.4f}") + + if __name__ == "__main__": args = get_args() colossalai.launch_from_torch(config=args.config) model = BertForMaskedLM.from_pretrained("bert-base-uncased") shard_config = ShardConfig( - rank = int(str(get_current_device()).split(':')[-1]), - world_size= int(os.environ['WORLD_SIZE']), + rank=int(str(get_current_device()).split(':')[-1]), + world_size=int(os.environ['WORLD_SIZE']), ) shardmodel = ShardModel(model, shard_config) - inference(shardmodel.model) + if args.mode == "train": + train(shardmodel.model) + elif args.mode == "inference": + inference(shardmodel.model) diff --git a/colossalai/shardformer/utils/utils.py b/colossalai/shardformer/utils/utils.py index 5eba87f6fe09..eb84edd88404 100644 --- a/colossalai/shardformer/utils/utils.py +++ b/colossalai/shardformer/utils/utils.py @@ -1,10 +1,10 @@ def hasattr_(obj, attr: str): - """ + r""" Check whether the object has the multi sublevel attr Args: - obj: The object to check - attr: The multi level attr to check + obj (object): The object to check + attr (str): The multi level attr to check """ attrs = attr.split('.') for a in attrs: @@ -14,15 +14,16 @@ def hasattr_(obj, attr: str): return False return True -def setattr_(obj, attr: str, value, ignore: bool=False): - """ + +def setattr_(obj, attr: str, value, ignore: bool = False): + r""" Set the object's multi sublevel attr to value, if ignore, ignore when it doesn't exist Args: - obj: The object to set - attr: The multi level attr to set - value: The value to set - ignore: Whether to ignore when the attr doesn't exist + obj (object): The object to set + attr (str): The multi level attr to set + value (Any): The value to set + ignore (bool): Whether to ignore when the attr doesn't exist """ attrs = attr.split('.') @@ -31,18 +32,19 @@ def setattr_(obj, attr: str, value, ignore: bool=False): obj = getattr(obj, a) except AttributeError: if ignore: - return + return raise AttributeError(f"Object {obj} has no attribute {attr}") setattr(obj, attrs[-1], value) -def getattr_(obj, attr: str, ignore: bool=None): - """ + +def getattr_(obj, attr: str, ignore: bool = None): + r""" Get the object's multi sublevel attr - + Args: - obj: The object to set - attr: The multi level attr to set - ignore: Whether to ignore when the attr doesn't exist + obj (object): The object to set + attr (str): The multi level attr to set + ignore (bool): Whether to ignore when the attr doesn't exist """ attrs = attr.split('.') @@ -53,4 +55,4 @@ def getattr_(obj, attr: str, ignore: bool=None): if ignore: return None raise AttributeError(f"Object {obj} has no attribute {attr}") - return obj \ No newline at end of file + return obj From bc19024bf9db549ed5c22e890715267f8d877eaa Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 24 May 2023 11:51:48 +0800 Subject: [PATCH 301/413] [shardformer] updated readme (#3827) --- colossalai/shardformer/README.md | 53 ++++++++++++++++++++------------ 1 file changed, 33 insertions(+), 20 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index a47e280f2be4..f76cbac8d7b8 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -1,11 +1,22 @@ -## ShardFormer +# ⚡️ ShardFormer -### Intro -Make the model in huggingface.co can be paralleled and can be used with colossalai according to custom policy. +## 📚 Table of Contents + +- [⚡️ ShardFormer](#️-shardformer) + - [📚 Table of Contents](#-table-of-contents) + - [🔗 Introduction](#-introduction) + - [🔨 Usage](#-usage) + - [🔮 Simple example](#-simple-example) + - [💡 Policy](#-policy) + +## 🔗 Introduction + +**Shardformer** is a module that automatically parallelizes the mainstream models in libraries such as HuggingFace and TIMM. This module aims to make parallelization hassle-free for users who are not from the system background. + +## 🔨 Usage + +The sample API usage is given below: -### Quick start -1. Usage -- Use ``` python from colossalai.shardformer.shard.shardmodel import ShardModel from transformers import BertForMaskedLM @@ -21,23 +32,33 @@ shardmodel = ShardModel(model).model from xxx import shardmodel = ShardModel(model, ).model - # do angthing as normal ... ``` -- Policy -If you wanna parallel the model in custom way, just overwrite the policy class for the huggingface model. +## 🔮 Simple example + +``` shell +# inference +colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode inference +# train +colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode train +``` + + +## 💡 Policy + +If you wanna parallel the model in a custom way, just overwrite the policy class for the Hugging Face model. You should do: 1. Inherit Policy class 2. Overwrite argument_policy method - - In this method you need to list which layers class you wanna modify and the attributes and parameters in those layers. -3. Overwrite inject_policy method [Optional] + - In this method, you need to list which layers class you wanna modify and the attributes and parameters in those layers. +3. Overwrite inject_policy method (Optional) - If you need to modify the forward or backward progress. 4. Overwrite or add the param recording functions - - These function use suffix to record the path of weight or bias for the layer. + - These functions use a suffix to record the path of weight or bias for the layer. 5. Overwrite binding More details can be found in shardformer/policies/basepolicy.py @@ -167,11 +188,3 @@ CustomPolicy(Policy): return NotImplementedError ``` - -2. Simple example -``` shell -# inference -colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode inference -# train -colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode train -``` From 537a52b7a279f8e40e06d3c820c803290d796c19 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 24 May 2023 16:01:26 +0800 Subject: [PATCH 302/413] [shardformer] refactored the user api (#3828) * [shardformer] refactored the user api * polish code --- colossalai/shardformer/README.md | 6 +- colossalai/shardformer/shard/__init__.py | 5 ++ .../shard/{shardconfig.py => shard_config.py} | 2 + colossalai/shardformer/shard/sharder.py | 27 ++++++--- colossalai/shardformer/shard/shardmodel.py | 60 ------------------- colossalai/shardformer/shard/slicer.py | 7 +-- colossalai/shardformer/test/test.py | 15 ++--- 7 files changed, 35 insertions(+), 87 deletions(-) rename colossalai/shardformer/shard/{shardconfig.py => shard_config.py} (93%) delete mode 100644 colossalai/shardformer/shard/shardmodel.py diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index f76cbac8d7b8..10fd1809b287 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -18,7 +18,7 @@ The sample API usage is given below: ``` python -from colossalai.shardformer.shard.shardmodel import ShardModel +from colossalai.shardformer import shard_model from transformers import BertForMaskedLM # create huggingface model as normal @@ -26,11 +26,11 @@ model = BertForMaskedLM.from_pretrained("bert-base-uncased") # make the huggingface model paralleled to ShardModel # auto policy: -shardmodel = ShardModel(model).model +sharded_model = shard_model(model) # custom policy: from xxx import -shardmodel = ShardModel(model, ).model +sharded_model = shard_model(model, ) # do angthing as normal ... diff --git a/colossalai/shardformer/shard/__init__.py b/colossalai/shardformer/shard/__init__.py index e69de29bb2d1..d5f70163ad57 100644 --- a/colossalai/shardformer/shard/__init__.py +++ b/colossalai/shardformer/shard/__init__.py @@ -0,0 +1,5 @@ +from .shard_config import ShardConfig +from .sharder import ModelSharder, shard_model +from .slicer import Slicer + +__all__ = ['ShardConfig', 'ModelSharder', 'shard_model', 'Slicer'] diff --git a/colossalai/shardformer/shard/shardconfig.py b/colossalai/shardformer/shard/shard_config.py similarity index 93% rename from colossalai/shardformer/shard/shardconfig.py rename to colossalai/shardformer/shard/shard_config.py index c6a2513a6eff..4cf9162b9548 100644 --- a/colossalai/shardformer/shard/shardconfig.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -1,5 +1,7 @@ from dataclasses import dataclass +__all__ = ['ShardConfig'] + @dataclass class ShardConfig: diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 2f6bb4265a11..2218661889f8 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -1,20 +1,15 @@ -import os -from dataclasses import dataclass -from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union +from typing import Any, Callable, Dict, List import torch import torch.nn as nn -import colossalai.nn as col_nn -from colossalai.logging import get_dist_logger - from ..policies.autopolicy import get_autopolicy -from ..policies.basepolicy import Layer, Policy +from ..policies.basepolicy import Policy from ..utils.utils import getattr_, hasattr_, setattr_ -from .shardconfig import ShardConfig +from .shard_config import ShardConfig from .slicer import Slicer -logger = get_dist_logger() +__all__ = ['ModelSharder', 'shard_model'] class ModelSharder(object): @@ -245,3 +240,17 @@ def bind_layer(self, model: nn.Module) -> None: param = nn.Parameter(param) setattr_(model, k, param) setattr_(model, v, param) + + +def shard_model(model: nn.Module, shard_config: ShardConfig = None, policy: Policy = None): + r""" + The function is used to shard the PyTorch model. + + Args: + model (`torch.nn.Model`): the origin huggingface model + shard_config (`ShardConfig`): the config for distribute information + policy (`Policy`): the custom policy for sharding + """ + sharder = ModelSharder(model=model, shard_config=shard_config, policy=policy) + sharder.shard() + return model diff --git a/colossalai/shardformer/shard/shardmodel.py b/colossalai/shardformer/shard/shardmodel.py deleted file mode 100644 index 7e7d1576afd6..000000000000 --- a/colossalai/shardformer/shard/shardmodel.py +++ /dev/null @@ -1,60 +0,0 @@ -import os -from contextlib import suppress -from dataclasses import dataclass - -import torch -import torch.distributed as dist -import torch.nn as nn -import transformers - -from colossalai.tensor.d_tensor.layout import Layout - -from ..policies.basepolicy import Policy -from .shardconfig import ShardConfig -from .sharder import ModelSharder - - -class ShardModel(object): - r""" - The class for sharding the huggingface model, ''self.model'' is the sharded model - Just creat a new ShardModel object to shard huggingface model - - Args: - model (:class:`torch.nn.Model`): the origin huggingface model - dist_config (:class:`ShardConfig`): the config for distribute information - custom_policy (:class:`Policy`): the custom policy for sharding - """ - - def __init__( - self, - model: nn.Module, - shard_config: ShardConfig = None, # TODO - custom_policy: Policy = None, - ) -> None: - self.model = model - self.shard_config = shard_config - self.policy = custom_policy - # self.layout=, # TODO - - sharder = ModelSharder( - model=self.model, - policy=self.policy, - shard_config=self.shard_config, - ) - sharder.shard() - - def set_environ(self) -> None: - os.environ["TOKENIZERS_PARALLELISM"] = "true" - os.environ["MKL_SERVICE_FORCE_INTEL"] = "GNU" - os.environ["MASTER_ADDR"] = str(self.dist_config.master_addr) - os.environ["MASTER_PORT"] = str(self.dist_config.master_port) - os.environ["WORLD_SIZE"] = str(self.dist_config.num_gpus) - os.environ["RANK"] = str(self.dist_config.rank) - os.environ["LOCAL_RANK"] = str(self.dist_config.rank) - if not dist.is_initialized(): - dist.init_process_group(backend=self.dist_config.backend) - - torch.cuda.set_device(int(os.getenv("LOCAL_RANK", "0"))) - - def back_to_org() -> None: - pass diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 096f5db95f49..957ce1f85814 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -1,12 +1,7 @@ -import os -from dataclasses import dataclass -from typing import Dict, Tuple - import torch -import torch.distributed as dist from ..policies.basepolicy import Col_Layer, Layer, Row_Layer -from .shardconfig import ShardConfig +from .shard_config import ShardConfig dim_mapping = {Col_Layer: 1, Row_Layer: 0} diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py index 0cdc6ef38fd2..202208123ced 100644 --- a/colossalai/shardformer/test/test.py +++ b/colossalai/shardformer/test/test.py @@ -1,5 +1,3 @@ -import argparse -import inspect import os import torch @@ -7,12 +5,10 @@ from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm -from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments +from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling import colossalai -from colossalai.logging import get_dist_logger -from colossalai.shardformer.shard.shardconfig import ShardConfig -from colossalai.shardformer.shard.shardmodel import ShardModel +from colossalai.shardformer.shard import ShardConfig, shard_model from colossalai.utils import get_current_device, print_rank_0 os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' @@ -93,8 +89,9 @@ def train(model: nn.Module, num_epoch: int = 2): rank=int(str(get_current_device()).split(':')[-1]), world_size=int(os.environ['WORLD_SIZE']), ) - shardmodel = ShardModel(model, shard_config) + sharded_model = shard_model(model, shard_config) + if args.mode == "train": - train(shardmodel.model) + train(sharded_model) elif args.mode == "inference": - inference(shardmodel.model) + inference(sharded_model) From 997544c1f90b9a1549e91a6d97ee3902c2ac0ed4 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Wed, 24 May 2023 18:02:54 +0800 Subject: [PATCH 303/413] [shardformer] update readme with modules implement doc (#3834) * update readme with modules content * remove img --- colossalai/shardformer/README.md | 69 ++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 10fd1809b287..55b6aa75ef84 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -8,6 +8,8 @@ - [🔨 Usage](#-usage) - [🔮 Simple example](#-simple-example) - [💡 Policy](#-policy) + - [😊 Module](#-module) + ## 🔗 Introduction @@ -188,3 +190,70 @@ CustomPolicy(Policy): return NotImplementedError ``` + + +## 😊 Module + + 1. Flowchart + +

    + +

    + + 2. Important Modules + + - CLASS `shard_model`: + + This is the user api to use shardformer, just create a model from transformers and define a custom policy or use shardformer autopolicy to make a shard model. + + - CLASS `Layer`: + + Parameters: + - weight (str): The weight suffix of the layer + - bias (str): The bias suffix of the layer + - replace_layer (:class:`colosalai.nn`): The layer to replace the original layer + - ignore (bool): Whether to ignore this layer if it is not in the model + + This class is used to specify the replacement policy for a particular layer. If `replace_layer` is None, only parameter partitioning will be performed without replacing the layer class. + + CLASS `Col_Layer(Layer)`: + - gather_output (bool): Whether to gather the output of the layer + + This class inherited from `Layer`, representing the layer will be sliced along column. + + CLASS `Row_Layer(Layer)`: + + This class inherited from `Layer`, representing the layer will be sliced along row. + + - CLASS `Policy`: + + In Shardformer, this class holds significant importance as it defines the model partitioning methods, required parameter modifications, and model injection techniques all within a single Policy class. + - `Policy.attn_in()/attn_out()/mlp_in()/mlp_out()/embedding()/unembedding()`...... + + These functions define the partitioning methods of the parameters at different locations in the model. Each function returns a list of objects of Layer class that specify the replacement approach for these parameters. Shardformer also supports user-defined functions for modifying their models, in addition to the listed functions. + - `Policy.argument_policy()` + + In this function, the user should use multiple dict to define which class of layers will require replacement. This includes the attributes and parameters that need to be modified or replaced. Attributes are stored in the form of a "suffix-string: value" dict, while parameters are stored via multiple static methods that return the replacement approach. + - `Policy.inject_policy()` + + This function will return the injected model to replace the original model. The new model should be a nn.Module class which includes modified forward or backward functions or anything else. + - `Policy.binding_policy()` + + This function will return the weight sharing information in the model in some dict. The key and value are both the suffixes of the shared parameters. + + - CLASS `ModelSharder(model, policy)`: + + This class helps shard the model, the parameter is the created transformers model and the custom policy. If custom policy is None, shardformer will automatically get already defined policy for the model. + - `ModelShard.inject_model()` + + This function is used to inject the model to modify the forward and backward progress. + - `ModelShard.replace_layer()` + + This function is used to replace the original layers with colossalai layer to make them paralleled and can do distributed communication. + - `ModelShard.bind_layer()` + + This function is used to help different layers share weight or bias. + + - CLASS `Slicer`: + + This class is used to slice tensor according to policy. From 21a3915c9874c722eacf769728aa727a9b5d0b82 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Thu, 1 Jun 2023 16:21:02 +0800 Subject: [PATCH 304/413] [shardformer] add Dropout layer support different dropout pattern (#3856) * add dropout layer, add dropout test * modify seed manager as context manager * add a copy of col_nn.layer * add dist_crossentropy loss; separate module test * polish the code * fix dist crossentropy loss --- colossalai/nn/layer/parallel_1d/_operation.py | 1 - colossalai/nn/layer/parallel_1d/layers.py | 9 +- colossalai/shardformer/README.md | 19 + colossalai/shardformer/layer/__init__.py | 0 colossalai/shardformer/layer/_operation.py | 97 ++ .../shardformer/layer/dist_crossentropy.py | 105 ++ colossalai/shardformer/layer/dropout.py | 58 + colossalai/shardformer/layer/layers.py | 1043 +++++++++++++++++ colossalai/shardformer/model/modeling_bert.py | 10 +- colossalai/shardformer/policies/basepolicy.py | 2 - colossalai/shardformer/policies/bert.py | 4 +- colossalai/shardformer/shard/slicer.py | 15 +- colossalai/shardformer/test/module_test.py | 50 + colossalai/shardformer/test/test.py | 41 +- 14 files changed, 1413 insertions(+), 41 deletions(-) create mode 100644 colossalai/shardformer/layer/__init__.py create mode 100644 colossalai/shardformer/layer/_operation.py create mode 100644 colossalai/shardformer/layer/dist_crossentropy.py create mode 100644 colossalai/shardformer/layer/dropout.py create mode 100644 colossalai/shardformer/layer/layers.py create mode 100644 colossalai/shardformer/test/module_test.py diff --git a/colossalai/nn/layer/parallel_1d/_operation.py b/colossalai/nn/layer/parallel_1d/_operation.py index c5e33fd497cd..300baf9c12ba 100644 --- a/colossalai/nn/layer/parallel_1d/_operation.py +++ b/colossalai/nn/layer/parallel_1d/_operation.py @@ -73,7 +73,6 @@ def backward(ctx, grad_output): total_input = input grad_input = grad_output.matmul(weight) - grad_output = grad_output.contiguous() # Convert the tensor shapes to 2D for execution compatibility grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2]) total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2]) diff --git a/colossalai/nn/layer/parallel_1d/layers.py b/colossalai/nn/layer/parallel_1d/layers.py index 0ee3b4fcb502..406173a18c60 100644 --- a/colossalai/nn/layer/parallel_1d/layers.py +++ b/colossalai/nn/layer/parallel_1d/layers.py @@ -469,8 +469,7 @@ def __init__(self, if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') - # self.out_features_per_partition = divide(out_features*2, gpc.tensor_parallel_size) - self.out_features_per_partition = out_features + self.out_features_per_partition = divide(out_features, gpc.tensor_parallel_size) # Parameters. # Initialize weight. @@ -613,8 +612,7 @@ def __init__(self, raise ValueError('cannot skip bias addition if bias is None') # Divide the weight matrix along the last dimension. - # self.input_size_per_partition = divide(in_features*2, gpc.tensor_parallel_size) - self.input_size_per_partition = in_features + self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size) # Parameters. # Initialize weight. @@ -886,8 +884,7 @@ def __init__(self, tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) - # self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) - self.num_embeddings_per_partition = num_embeddings + self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 55b6aa75ef84..3394e9457da3 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -257,3 +257,22 @@ CustomPolicy(Policy): - CLASS `Slicer`: This class is used to slice tensor according to policy. + + + 3. DistCrossEntropy Loss + - Overview + + In order to reduce the communication size, caculate the crossentropy before all gather, refer to [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), reduce the communication size from [batch_size * seq_length * vocab_size] to [batch_size * seq_length]. The origin loss function is: + $$ loss = -\log(\frac{\exp(x[class])}{\sum_i\exp(x[i])})$$ + + alse can be represented as: + + $$ loss = \log(\sum_i\exp(x[i])) - x[class]$$ + + - Step + + - First get the maximum logits across all the devices, make all the logist minus the maximun value to scale the value less than zero to avoid the value of exp being too large + + - Get a mask to mask the logits not in the local device + + - Caculate the loss according to the second formula diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/layer/_operation.py b/colossalai/shardformer/layer/_operation.py new file mode 100644 index 000000000000..e817ea3ebbee --- /dev/null +++ b/colossalai/shardformer/layer/_operation.py @@ -0,0 +1,97 @@ +import torch +import torch.distributed as dist + +from colossalai.core import global_context as gpc + +try: + import fused_mix_prec_layer_norm_cuda +except: + fused_mix_prec_layer_norm_cuda = None + + +class FusedLayerNormAffineFunction1D(torch.autograd.Function): + r"""Layernorm + + Args: + input: input matrix. + weight: weight matrix. + bias: bias matrix. + normalized_shape: input shape from an expected input of size. + :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]` + If a single integer is used, it is treated as a singleton list, and this module will + normalize over the last dimension which is expected to be of that specific size. + eps: a value added to the denominator for numerical stability + """ + + @staticmethod + def forward(ctx, input, weight, bias, normalized_shape, eps): + ctx.normalized_shape = normalized_shape + ctx.eps = eps + input_ = input.contiguous() + weight_ = weight.contiguous() + bias_ = bias.contiguous() + output, mean, invvar = fused_mix_prec_layer_norm_cuda.forward_affine(input_, ctx.normalized_shape, weight_, + bias_, ctx.eps) + ctx.save_for_backward(input_, weight_, bias_, mean, invvar) + return output + + @staticmethod + def backward(ctx, grad_output): + input_, weight_, bias_, mean, invvar = ctx.saved_tensors + grad_input = grad_weight = grad_bias = None + grad_input, grad_weight, grad_bias \ + = fused_mix_prec_layer_norm_cuda.backward_affine( + grad_output.contiguous(), mean, invvar, + input_, ctx.normalized_shape, + weight_, bias_, ctx.eps) + + return grad_input, grad_weight, grad_bias, None, None + + +class LinearWithAsyncCommunication(torch.autograd.Function): + """ + Linear layer execution with asynchronous communication in backprop. + """ + + @staticmethod + def forward(ctx, input_, weight, bias, parallel_mode, async_grad_allreduce): + ctx.save_for_backward(input_, weight) + ctx.use_bias = bias is not None + ctx.parallel_mode = parallel_mode + ctx.async_grad_allreduce = async_grad_allreduce + + output = torch.matmul(input_, weight.t()) + if bias is not None: + output = output + bias + return output + + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + use_bias = ctx.use_bias + + total_input = input + grad_input = grad_output.matmul(weight) + grad_output = grad_output.contiguous() + # Convert the tensor shapes to 2D for execution compatibility + grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2]) + total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2]) + + if ctx.async_grad_allreduce: + # Asynchronous all-reduce + handle = dist.all_reduce(grad_input, group=gpc.get_group(ctx.parallel_mode), async_op=True) + # Delay the start of weight gradient computation shortly (3us) to have + # all-reduce scheduled first and have GPU resources allocated + _ = torch.empty(1, device=grad_output.device) + 1 + + grad_weight = grad_output.t().matmul(total_input) + grad_bias = grad_output.sum(dim=0) if use_bias else None + + if ctx.async_grad_allreduce: + handle.wait() + + return grad_input, grad_weight, grad_bias, None, None, None + + +def linear_with_async_comm(input_, weight, bias, parallel_mode, async_grad_allreduce): + return LinearWithAsyncCommunication.apply(input_, weight, bias, parallel_mode, async_grad_allreduce) diff --git a/colossalai/shardformer/layer/dist_crossentropy.py b/colossalai/shardformer/layer/dist_crossentropy.py new file mode 100644 index 000000000000..1869594670ce --- /dev/null +++ b/colossalai/shardformer/layer/dist_crossentropy.py @@ -0,0 +1,105 @@ +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Function + + +class DistCrossEntropy(Function): + r""" + Overwrite the forward and backward function to calculate the cross entropy loss before gather + + Args: + Function (:class:`torch.autograd.Function`): default + """ + + @staticmethod + def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor): + r""" + Calculate the cross entropy loss before gather, the origin loss function is as follows: + loss = -log(exp(x[class])/sum(exp(x[i])) + and can be rewrite as: + loss = log(sum(exp(x[i])) - x[class] + + To avoid the `nan` of log(sim(exp(x[i]))), we minus the max of x[i] + + Args: + vocab_logits (:class:`torch.Tensor`): The logits of the vocabulary, shape is + [batch_size, seq_len, vocab_size] + labels (:class:`torch.Tensor`): The labels of the vocabulary, shape is + [batch_size, seq_len] + + Returns: + :class:`torch.Tensor`: The cross entropy loss + """ + # get the max + logits_max = torch.max(vocab_logits, dim=-1)[0] + dist.all_reduce(logits_max, op=dist.ReduceOp.MAX) + + # minus the max to avoid the result of sum of exp is too large and the log is nan + vocab_logits = vocab_logits - logits_max.unsqueeze(dim=-1) + + # mask the target in the local device + partition_vocab_size = vocab_logits.size()[-1] + rank = dist.get_rank() + world_size = dist.get_world_size() + global_vocab_size = partition_vocab_size * world_size + + # [down, up) => false, other device and -100 => true + delta = (global_vocab_size + world_size - 1) // world_size + down_shreshold = rank * delta + up_shreshold = down_shreshold + delta + mask = (target < down_shreshold) | (target >= up_shreshold) + masked_target = target.clone() - down_shreshold + masked_target[mask] = 0 + + # reshape the logist and target + # reshape the vocab_logits to [bath_size * seq_len, vocab_size] + # reshape the labels to [bath_size * seq_len] + logits_2d = vocab_logits.view(-1, partition_vocab_size) + masked_target_1d = masked_target.view(-1) + + # extract the x[class] and set the x[other device] to zero + pred_logits_1d = logits_2d[torch.arange(start=0, end=logits_2d.shape[0], device=logits_2d.device), + masked_target_1d] + pred_logits_1d = pred_logits_1d.clone().contiguous() + pred_logits = pred_logits_1d.view_as(target) + pred_logits[mask] = 0.0 + + # allreduce the get all x(i,y) + dist.all_reduce(pred_logits, op=dist.ReduceOp.SUM) + exp_logits = vocab_logits + torch.exp(vocab_logits, out=exp_logits) + sum_exp_logits = torch.sum(exp_logits, dim=-1) + dist.all_reduce(sum_exp_logits, op=dist.ReduceOp.SUM) + + # calculate the loss + # loss = log(sum(exp(x[i]))) - x[class] + loss = torch.log(sum_exp_logits) - pred_logits + loss = torch.sum(loss).div_(loss.numel()) + + # caculate the softmax + exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1)) + ctx.save_for_backward(exp_logits, mask, masked_target_1d) + + return loss + + @staticmethod + def backward(ctx, grad_output): + # retrieve the saved tensors + exp_logits, mask, masked_target_1d = ctx.saved_tensors + + # use exp logits as the input grad + grad_logits = exp_logits + partion_vocab_size = grad_logits.shape[-1] + grad_logits_2d = grad_logits.view(-1, partion_vocab_size) + + update = 1.0 - mask.view(-1).float() + grad_logits_2d[torch.arange(0, grad_logits_2d.shape[0]), masked_target_1d] -= update + + grad_logits.mul_(grad_output.unsqueeze(dim=-1)) + return grad_logits, None, None + + +def applyDistCrossEntropy(vocab_logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + return DistCrossEntropy.apply(vocab_logits, labels) diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py new file mode 100644 index 000000000000..acc114029ac1 --- /dev/null +++ b/colossalai/shardformer/layer/dropout.py @@ -0,0 +1,58 @@ +import os +import time +from contextlib import contextmanager + +import torch +import torch.nn as nn + + +class SeedManager: + """ + This class is a random state manager to change random state for different random seed. + + """ + + def __init__(self): + original_state = torch.cuda.get_rng_state() + seed = int(f"{int(time.time())}{os.environ['RANK']}") + torch.cuda.manual_seed(int(seed)) + self.dropout_state = torch.cuda.get_rng_state() + torch.cuda.set_rng_state(original_state) + + def set_mode(self, rng_state): + torch.cuda.set_rng_state(rng_state) + + def get_current_mode(self): + current_state = torch.cuda.get_rng_state() + return current_state + + @contextmanager + def dropout_mode(self): + """ + This is a context manager to change the dropout state and recover the original state. + + Usage: + :: + >>> with _seed_manager.dropout_mode(): + >>> input = super().forward(input) + """ + try: + current_mode = self.get_current_mode() + yield self.set_mode(self.dropout_state) + finally: + self.dropout_state = self.get_current_mode() + self.set_mode(current_mode) + + +_seed_manager = SeedManager() + + +class Dropout1D(nn.Dropout): + + def __init__(self, p=0.5, inplace=False): + super().__init__(p, inplace) + + def forward(self, input): + with _seed_manager.dropout_mode(): + input = super().forward(input) + return input diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py new file mode 100644 index 000000000000..f5123885bbe4 --- /dev/null +++ b/colossalai/shardformer/layer/layers.py @@ -0,0 +1,1043 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +import math +from collections import OrderedDict +from typing import Callable, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.nn.parameter import Parameter + +from colossalai.communication import broadcast +from colossalai.context import ParallelMode, seed +from colossalai.core import global_context as gpc +from colossalai.global_variables import tensor_parallel_env as env +from colossalai.kernel import LayerNorm +from colossalai.nn import init as init +from colossalai.nn.layer.base_layer import ParallelLayer +from colossalai.nn.layer.colossalai_layer._utils import ColossalaiModule +from colossalai.nn.layer.parallel_1d._utils import ( + gather_forward_split_backward, + get_parallel_input, + reduce_grad, + reduce_input, + set_parallel_input, + split_forward_gather_backward, +) +from colossalai.nn.layer.utils import divide, set_tensor_parallel_attribute_by_partition +from colossalai.nn.layer.vanilla import VanillaLayerNorm, VanillaPatchEmbedding +from colossalai.registry import LAYERS +from colossalai.utils.checkpointing import ( + broadcast_state_dict, + gather_tensor_parallel_state_dict, + partition_tensor_parallel_state_dict, +) +from colossalai.utils.cuda import get_current_device + +from ._operation import linear_with_async_comm + +Fast_LN = None +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm + Fast_LN = FastLayerNorm +except ImportError: + pass + + +# @LAYERS.register_module +class Linear1D(ColossalaiModule): + r"""Linear layer for 1D parallelism. + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + gather_output (bool, optional): Whether to call all-gather on output, defaults to False. + skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + gather_output: bool = False, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + parallel_input = get_parallel_input() + if not parallel_input and not gather_output: + layer = Linear1D_Col(in_features, + out_features, + bias=bias, + dtype=dtype, + skip_bias_add=skip_bias_add, + weight_initializer=weight_initializer, + bias_initializer=bias_initializer) + else: + layer = Linear1D_Row(in_features, + out_features, + bias=bias, + dtype=dtype, + parallel_input=parallel_input, + skip_bias_add=skip_bias_add, + weight_initializer=weight_initializer, + bias_initializer=bias_initializer) + super().__init__(layer) + + +# @LAYERS.register_module +class LayerNorm1D(ColossalaiModule): + r""" + Layer Normalization for colossalai + + Args: + normalized_shape (int): input shape from an expected input of size. + :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] + \times \ldots \times \text{normalized_shape}[-1]]` + If a single integer is used, it is treated as a singleton list, and this module will + normalize over the last dimension which is expected to be of that specific size. + eps (float): a value added to the denominator for numerical stability, defaults to 1e-05. + bias (bool, optional): Whether to add a bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + """ + + _fast_ln_supported_sizes = [ + 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, + 24576, 25600, 30720, 32768, 40960, 49152, 65536 + ] + + def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None): + if Fast_LN is not None and normalized_shape in self._fast_ln_supported_sizes: + norm = Fast_LN(normalized_shape, eps=eps).to(dtype) + else: + norm = None + try: + from apex.normalization import FusedLayerNorm + norm = FusedLayerNorm(normalized_shape, eps=eps).to(dtype) + except ImportError: + norm = LayerNorm(normalized_shape, eps=eps).to(dtype) + super().__init__(norm) + + def _load_from_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) + super()._load_from_state_dict(local_state, prefix, *args) + + def _save_to_state_dict(self, destination, prefix, keep_vars): + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + super()._save_to_state_dict(destination, prefix, keep_vars) + + +# @LAYERS.register_module +class Classifier1D(ParallelLayer): + r"""RowLinear with given weight. Classifier of 1D parallelism. + + Args: + in_features (int): size of each input sample. + num_classes (int): number of classes. + weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + num_classes: int, + weight: Parameter = None, + bias: bool = True, + dtype: torch.dtype = None, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + super().__init__() + self.in_features = in_features + self.num_classes = num_classes + self.parallel_input = get_parallel_input() + + # Divide the weight matrix along the last dimension. + self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size) + + # Parameters. + # Initialize weight. + factory_kwargs = {'device': get_current_device(), 'dtype': dtype} + if weight is not None: + self.weight = weight + self.has_weight = False + else: + self.weight = Parameter(torch.empty(self.num_classes, self.input_size_per_partition, **factory_kwargs)) + self.has_weight = True + if bias: + self.bias = Parameter(torch.empty(self.num_classes, **factory_kwargs)) + else: + self.bias = None + with seed(ParallelMode.TENSOR): + self.reset_parameters(weight_initializer, bias_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + env.vocab_parallel = False + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.num_classes + if self.has_weight: + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D) + + def _set_tensor_parallel_attributes(self): + if self.has_weight: + num_partition = gpc.get_world_size(ParallelMode.TENSOR) + set_tensor_parallel_attribute_by_partition(self.weight, num_partition) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + if self.has_weight: + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + if self.bias is not None: + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: -1, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: False + }) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + local_state = OrderedDict() + if self.has_weight: + local_state[weight_key] = self.weight + if self.bias is not None: + local_state[bias_key] = self.bias + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: -1, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: False + }, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + # Set up backprop all-reduce. + if self.parallel_input: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + input_ = input_ + else: + assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \ + 'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size) + input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1) + + output_parallel = F.linear(input_, self.weight) + output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) + if self.bias is not None: + output = output + self.bias + return output + + +# @LAYERS.register_module +class VocabParallelClassifier1D(ParallelLayer): + r"""ColLinear with given weight. Classifier of 1D parallelism. + + Args: + in_features (int): size of each input sample. + num_classes (int): number of classes. + weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + num_classes: int, + weight: Parameter = None, + bias: bool = True, + dtype: torch.dtype = None, + gather_output: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + super().__init__() + self.in_features = in_features + self.num_classes = num_classes + self.gather_output = gather_output + self.parallel_input = get_parallel_input() + + # Divide the weight matrix along the last dimension. + self.num_classes_per_partition = divide(num_classes, gpc.tensor_parallel_size) + + # Parameters. + # Initialize weight. + factory_kwargs = {'device': get_current_device(), 'dtype': dtype} + if weight is not None: + self.weight = weight + self.has_weight = False + else: + self.weight = Parameter(torch.empty(self.num_classes_per_partition, self.in_features, **factory_kwargs)) + self.has_weight = True + if bias: + self.bias = Parameter(torch.empty(self.num_classes_per_partition, **factory_kwargs)) + else: + self.bias = None + with seed(ParallelMode.TENSOR): + self.reset_parameters(weight_initializer, bias_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + env.vocab_parallel = True + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.num_classes + if self.has_weight: + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + + def _set_tensor_parallel_attributes(self): + num_partition = gpc.get_world_size(ParallelMode.TENSOR) + if self.has_weight: + set_tensor_parallel_attribute_by_partition(self.weight, num_partition) + if self.bias is not None: + set_tensor_parallel_attribute_by_partition(self.bias, num_partition) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + if self.has_weight: + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + if self.bias is not None: + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: 0, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: True + }) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + local_state = OrderedDict() + if self.has_weight: + local_state[weight_key] = self.weight + if self.bias is not None: + local_state[bias_key] = self.bias + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: 0, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: True + }, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in VocabParallelClassifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + # Set up backprop all-reduce. + input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) + # Matrix multiply. + output_parallel = F.linear(input_parallel, self.weight, self.bias) + if self.gather_output: + # All-gather across the partitions. + output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) + else: + output = output_parallel + return output + + +# @LAYERS.register_module +class Linear1D_Col(ParallelLayer): + r"""Linear layer with column parallelism. + + The linear layer is defined as :math:`Y = XA + b`. A is parallelized along + its second dimension as :math:`A = [A_1, ..., A_p]`. + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + gather_output (bool, optional): If true, call all-gather on output and make Y available + to all GPUs, otherwise, every GPU will have its output + which is :math:`Y_i = XA_i`, defaults to False + skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + gather_output: bool = False, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + super().__init__() + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.gather_output = gather_output + self.skip_bias_add = skip_bias_add + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + # self.out_features_per_partition = divide(out_features*2, gpc.tensor_parallel_size) + self.out_features_per_partition = out_features + + # Parameters. + # Initialize weight. + factory_kwargs = {'device': get_current_device(), 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) + + if bias: + self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) + else: + self.bias = None + with seed(ParallelMode.TENSOR): + self.reset_parameters(weight_initializer, bias_initializer) + self._set_tensor_parallel_attributes() + is_parallel_output = not self.gather_output + set_parallel_input(is_parallel_output) + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + + def _set_tensor_parallel_attributes(self): + num_partition = gpc.get_world_size(ParallelMode.TENSOR) + set_tensor_parallel_attribute_by_partition(self.weight, num_partition) + if self.bias is not None: + set_tensor_parallel_attribute_by_partition(self.bias, num_partition) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + if self.bias is not None: + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: 0, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: True + }) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + local_state = OrderedDict({weight_key: self.weight}) + if self.bias is not None: + local_state[bias_key] = self.bias + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: 0, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: True + }, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + # Set up backprop all-reduce. + # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) + input_parallel = input_ + # Matrix multiply. + bias = self.bias if not self.skip_bias_add else None + # output_parallel = F.linear(input_parallel, self.weight, bias) + output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, ParallelMode.PARALLEL_1D, True) + if self.gather_output: + # All-gather across the partitions. + output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) + else: + output = output_parallel + + if self.skip_bias_add: + return output, self.bias + else: + return output + + +# @LAYERS.register_module +class Linear1D_Row(ParallelLayer): + r""" Linear layer with row parallelism + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + parallel_input (bool, optional): If set to ``True``, it's assumed that the input is split, defaults to False. + skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + parallel_input: bool = True, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), + stream_chunk_num: int = 1): + super().__init__() + + self.stream_chunk_num = stream_chunk_num + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.parallel_input = parallel_input + self.skip_bias_add = skip_bias_add + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + # Divide the weight matrix along the last dimension. + # self.input_size_per_partition = divide(in_features*2, gpc.tensor_parallel_size) + self.input_size_per_partition = in_features + + # Parameters. + # Initialize weight. + factory_kwargs = {'device': get_current_device(), 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) + + if self.stream_chunk_num > 1: + # TODO() work for inference only + self.chunk_weight() + if bias: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + else: + self.bias = None + with seed(ParallelMode.TENSOR): + self.reset_parameters(weight_initializer, bias_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + + def chunk_weight(self): + self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D) + + def _set_tensor_parallel_attributes(self): + num_partition = gpc.get_world_size(ParallelMode.TENSOR) + set_tensor_parallel_attribute_by_partition(self.weight, num_partition) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + if self.bias is not None: + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: -1, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: False + }) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + local_state = OrderedDict({weight_key: self.weight}) + if self.bias is not None: + local_state[bias_key] = self.bias + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: -1, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: False + }, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + # Set up backprop all-reduce. + if self.parallel_input: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + input_ = input_ + else: + assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size) + input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1) + + if self.stream_chunk_num > 1: + if self.training: + raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!") + with torch.no_grad(): + output_parallel_list = [None for i in range(self.stream_chunk_num)] + handle_list = [] + for i in range(self.stream_chunk_num): + output_parallel_list[i] = F.linear(input_, self.weight_list[i]) + handle = torch.distributed.all_reduce(output_parallel_list[i], + group=gpc.get_group(ParallelMode.PARALLEL_1D), + async_op=True) + handle_list.append(handle) + # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D) + for handle in handle_list: + handle.wait() + output = torch.cat(output_parallel_list, dim=-1) + else: + output_parallel = F.linear(input_, self.weight) + # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) + output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) + if not self.skip_bias_add: + if self.bias is not None: + output = output + self.bias + return output + else: + return output, self.bias + + +# @LAYERS.register_module +class Embedding1D(ParallelLayer): + r"""Embedding for 1D parallelism. + + Args: + num_embeddings (int): number of embeddings. + embedding_dim (int): dimension of embedding. + padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; + therefore, the embedding vector at padding_idx is not updated during training, + i.e. it remains as a fixed “pad”, defaults to None. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + he initializer of weight, defaults to normal initializer. + + The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain: + :: + + max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is + renormalized to have norm max_norm. Note: this will modify weight in-place. + norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. + scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse + of frequency of the words in the mini-batch. Default False. + sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. + + More details about ``args`` and ``kwargs`` could be found in + `Embedding `_. + + More details about ``initializer`` please refer to + `init `_ + """ + + def __init__(self, + num_embeddings: int, + embedding_dim: int, + padding_idx: int = None, + dtype: torch.dtype = None, + weight_initializer: Callable = init.normal_(), + *args, + **kwargs): + super().__init__() + + self.num_embeddings = num_embeddings + self.embed_dim = embedding_dim + embed_dim_per_partition = divide(embedding_dim, gpc.tensor_parallel_size) + + self.padding_idx = padding_idx + self.embed_args = args + self.embed_kwargs = kwargs + + self.weight = Parameter( + torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype)) + + self.reset_parameters(weight_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + + def _set_tensor_parallel_attributes(self): + set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size) + + def reset_parameters(self, weight_initializer) -> None: + with seed(ParallelMode.TENSOR): + fan_in, fan_out = self.num_embeddings, self.embed_dim + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + self._fill_padding_idx_with_zero() + + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None: + with torch.no_grad(): + self.weight[self.padding_idx].fill_(0) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={weight_key: -1}, + partition_states={weight_key: True}) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + local_state = OrderedDict({weight_key: self.weight}) + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={weight_key: -1}, + partition_states={weight_key: True}, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + + output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) + + output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) + + return output + + +# @LAYERS.register_module +class VocabParallelEmbedding1D(ParallelLayer): + r"""Embedding parallelized in the vocabulary dimension. + + Args: + num_embeddings (int): number of embeddings. + embedding_dim (int): dimension of embedding. + padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; + therefore, the embedding vector at padding_idx is not updated during training, + i.e. it remains as a fixed “pad”, defaults to None. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + he initializer of weight, defaults to normal initializer. + + The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain: + :: + + max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is + renormalized to have norm max_norm. Note: this will modify weight in-place. + norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. + scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse + of frequency of the words in the mini-batch. Default False. + sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. + + More details about ``args`` and ``kwargs`` could be found in + `Embedding `_. + + More details about initializer please refer to + `init `_. + """ + + def __init__(self, + num_embeddings: int, + embedding_dim: int, + padding_idx: int = None, + dtype: torch.dtype = None, + weight_initializer: Callable = init.normal_(), + *args, + **kwargs): + super().__init__() + self.num_embeddings = num_embeddings + self.embed_dim = embedding_dim + self.padding_idx = padding_idx + self.embed_args = args + self.embed_kwargs = kwargs + + tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) + tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) + # self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) + self.num_embeddings_per_partition = num_embeddings + self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition + self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition + + self.weight = Parameter( + torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=get_current_device(), dtype=dtype)) + + self.reset_parameters(weight_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + env.vocab_parallel = True + + def _set_tensor_parallel_attributes(self): + set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size) + + def reset_parameters(self, weight_initializer) -> None: + with seed(ParallelMode.TENSOR): + fan_in, fan_out = self.num_embeddings, self.embed_dim + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + self._fill_padding_idx_with_zero() + + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None and \ + self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index: + with torch.no_grad(): + self.weight[self.padding_idx - self.vocab_start_index].fill_(0) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={weight_key: 0}, + partition_states={weight_key: True}) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + local_state = OrderedDict({weight_key: self.weight}) + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={weight_key: 0}, + partition_states={weight_key: True}, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + # Build the mask. + input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index) + # Mask the input. + masked_input = input_.clone() - self.vocab_start_index + masked_input[input_mask] = 0 + + output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args, + **self.embed_kwargs) + + # Mask the output embedding. + output_parallel[input_mask, :] = 0. + # Reduce across all the model parallel GPUs. + output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) + return output + + +# @LAYERS.register_module +class Dropout1D(ParallelLayer): + """Dropout layer of 1D parallelism. + + Args: + p (float, optional): probability of an element to be zeroed, defaults 0.5. + inplace (bool, optional): whether to do dropout in-place, default to be False. + """ + + def __init__(self, p: float = 0.5, inplace: bool = False): + super().__init__() + self.parallel_input = get_parallel_input() + self.p = p + self.inplace = inplace + + def forward(self, input_: Tensor) -> Tensor: + if self.parallel_input: + with seed(ParallelMode.TENSOR): + output = F.dropout(input_, self.p, self.training, self.inplace) + else: + output = F.dropout(input_, self.p, self.training, self.inplace) + return output + + +# @LAYERS.register_module +class PatchEmbedding1D(ColossalaiModule): + """ + 2D Image to Patch Embedding + + :param img_size: image size + :type img_size: int + :param patch_size: patch size + :type patch_size: int + :param in_chans: number of channels of input image + :type in_chans: int + :param embed_size: size of embedding + :type embed_size: int + :param dtype: The dtype of parameters, defaults to None + :type dtype: torch.dtype, optional + :param flatten: whether to flatten output tensor, defaults to True + :type flatten: bool, optional + :param weight_initializer: The initializer of weight, defaults to kaiming uniform initializer + :type weight_initializer: typing.Callable, optional + :param bias_initializer: The initializer of bias, defaults to xavier uniform initializer + :type bias_initializer: typing.Callable, optional + :param position_embed_initializer: The initializer of position embedding, defaults to zero + :type position_embed_initializer: typing.Callable, optional + """ + + def __init__(self, + img_size: int, + patch_size: int, + in_chans: int, + embed_size: int, + dtype: torch.dtype = None, + flatten: bool = True, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), + position_embed_initializer: Callable = init.zeros_()): + embed = VanillaPatchEmbedding(img_size, + patch_size, + in_chans, + embed_size, + dtype=dtype, + flatten=flatten, + weight_initializer=weight_initializer, + bias_initializer=bias_initializer, + position_embed_initializer=position_embed_initializer) + super().__init__(embed) + + def _load_from_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + param_keys = [prefix + 'weight', prefix + 'bias', prefix + 'cls_token', prefix + 'pos_embed'] + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + for key in param_keys: + param = state_dict.pop(key, None) + if param is not None: + local_state[key] = param + + local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) + super()._load_from_state_dict(local_state, prefix, *args) + + def _save_to_state_dict(self, destination, prefix, keep_vars): + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + super()._save_to_state_dict(destination, prefix, keep_vars) diff --git a/colossalai/shardformer/model/modeling_bert.py b/colossalai/shardformer/model/modeling_bert.py index 6741ae866991..bd07ab80c00d 100644 --- a/colossalai/shardformer/model/modeling_bert.py +++ b/colossalai/shardformer/model/modeling_bert.py @@ -6,6 +6,8 @@ from transformers import BertForMaskedLM from transformers.models.bert.modeling_bert import MaskedLMOutput +from ..layer.dist_crossentropy import applyDistCrossEntropy + class BertForMaskedLM_(BertForMaskedLM): @@ -47,11 +49,11 @@ def forward( masked_lm_loss = None - # if input_ids is not None: - # masked_lm_loss = applyDistCrossEntropy(prediction_scores, input_ids, self.config.vocab_size) if labels is not None: - loss_fct = CrossEntropyLoss() # -100 index = padding token - masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + masked_lm_loss = applyDistCrossEntropy(prediction_scores, labels) + # if labels is not None: + # loss_fct = CrossEntropyLoss() # -100 index = padding token + # masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index a5cc0bc68df6..2eb7eb29e1a4 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -7,8 +7,6 @@ import torch.nn as nn from transformers import AutoConfig -import colossalai.nn as col_nn - @dataclass class Argument: diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 5d91d8ddc766..ab77b29f71f4 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -4,7 +4,7 @@ import torch.nn as nn from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead -import colossalai.nn as col_nn +import colossalai.shardformer.layer.layers as col_nn from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer @@ -142,7 +142,7 @@ def unembedding() -> List: weight="decoder.weight", bias="decoder.bias", replace_layer=col_nn.Linear1D_Col, - gather_output=True, + # gather_output=True, ) ] diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 957ce1f85814..26053b9f7408 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -94,10 +94,7 @@ def slice_1d( Returns: :class:`torch.Tensor`: The sliced tensor """ - delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size - down_idx = self.shardconfig.rank * delta - up_idx = down_idx + delta - return tensor[down_idx:up_idx].contiguous() + return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() def slice_col( self, @@ -113,10 +110,7 @@ def slice_col( :class:`torch.Tensor`: The sliced tensor """ - delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size - down_idx = self.shardconfig.rank * delta - up_idx = down_idx + delta - return tensor[down_idx:up_idx, :].contiguous() + return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() def slice_row( self, @@ -131,7 +125,4 @@ def slice_row( Returns: :class:`torch.Tensor`: The sliced tensor """ - delta = (tensor.shape[1] + self.shardconfig.world_size - 1) // self.shardconfig.world_size - down_idx = self.shardconfig.rank * delta - up_idx = down_idx + delta - return tensor[:, down_idx:up_idx].contiguous() + return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() diff --git a/colossalai/shardformer/test/module_test.py b/colossalai/shardformer/test/module_test.py new file mode 100644 index 000000000000..83dc7ec6cf4a --- /dev/null +++ b/colossalai/shardformer/test/module_test.py @@ -0,0 +1,50 @@ +import os + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import colossalai +from colossalai.shardformer.layer.dist_crossentropy import applyDistCrossEntropy +from colossalai.shardformer.layer.dropout import Dropout1D + + +def get_args(): + parser = colossalai.get_default_parser() + parser.add_argument("--module", type=str, default='distloss') + return parser.parse_args() + + +def test_dist_crossentropy(): + pred = torch.randn(2, 4, 8, requires_grad=True) + labels = torch.randint(8, (1, 4)).repeat(2, 1) + + pred_ = pred.view(-1, 8) + labels_ = labels.view(-1) + loss = F.cross_entropy(pred_, labels_) + loss.backward() + print(f"normal loss:{loss}") + + pred = pred.chunk(int(os.environ['WORLD_SIZE']), -1)[int(os.environ['RANK'])] + loss = applyDistCrossEntropy(pred.to('cuda'), labels.to('cuda')) + loss.backward() + print(f"dist loss:{loss}") + + +def test_dropout(): + input = torch.randn(5, 4).to("cuda") + m = Dropout1D(p=0.2).to("cuda") + for i in range(2): + print(f"Output: {m(input)}") + print(torch.randn(1)) + + +if __name__ == '__main__': + args = get_args() + colossalai.launch_from_torch(config={}) + if args.module == 'distloss': + test_dist_crossentropy() + elif args.module == 'dropout': + test_dropout() + else: + print("not implemented yet") diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py index 202208123ced..b896fd4a4020 100644 --- a/colossalai/shardformer/test/test.py +++ b/colossalai/shardformer/test/test.py @@ -1,11 +1,12 @@ import os +import random import torch import torch.nn as nn from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm -from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling +from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, get_scheduler import colossalai from colossalai.shardformer.shard import ShardConfig, shard_model @@ -18,6 +19,7 @@ def get_args(): parser = colossalai.get_default_parser() parser.add_argument("--mode", type=str, default='inference') + parser.add_argument("--save_model", action='store_true') return parser.parse_args() @@ -30,36 +32,40 @@ def load_data(): # tokenized_datasets=tokenized_datasets.rename_column("label","labels") tokenized_datasets.set_format("torch") - train_dataset = tokenized_datasets["train"].select(range(500)) - test_dataset = tokenized_datasets["test"].select(range(100)) + train_dataset = tokenized_datasets["train"] + test_dataset = tokenized_datasets["test"] datacollector = DataCollatorForLanguageModeling(tokenizer, mlm=True, mlm_probability=0.15, return_tensors="pt") - train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True, collate_fn=datacollector) - eval_dataloader = DataLoader(test_dataset, batch_size=8, shuffle=True, collate_fn=datacollector) + train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=True, collate_fn=datacollector) + eval_dataloader = DataLoader(test_dataset, batch_size=16, shuffle=True, collate_fn=datacollector) return train_dataloader, eval_dataloader -def inference(model: nn.Module): - print(model) +def inference(model: nn.Module, args): tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") token = "Hello, my dog is cute" inputs = tokenizer(token, return_tensors="pt") inputs.to("cuda") + model.eval() model.to("cuda") outputs = model(**inputs) print(outputs) -def train(model: nn.Module, num_epoch: int = 2): +def train(model: nn.Module, args, num_epoch: int = 3): train_dataloader, eval_dataloader = load_data() optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5) - progress_bar = tqdm(range((num_epoch) * len(train_dataloader))) - criterion = nn.CrossEntropyLoss() + num_training = num_epoch * len(train_dataloader) + progress_bar = tqdm(range(num_training)) + lr_scheduler = get_scheduler(name="linear", + optimizer=optimizer, + num_warmup_steps=0, + num_training_steps=num_training) + best_test_loss = float("inf") model.to("cuda") model.train() for epoch in range(num_epoch): progress_bar.set_description(f"Rank {get_current_device()} epoch {epoch}") - for batch in train_dataloader: optimizer.zero_grad() batch = {k: v.to('cuda') for k, v in batch.items()} @@ -67,6 +73,7 @@ def train(model: nn.Module, num_epoch: int = 2): loss = outputs.loss loss.backward() optimizer.step() + lr_scheduler.step() progress_bar.update(1) train_loss = loss @@ -75,16 +82,20 @@ def train(model: nn.Module, num_epoch: int = 2): batch = {k: v.to('cuda') for k, v in batch.items()} outputs = model(**batch) # loss = outputs.loss + assert not torch.isnan(outputs.loss), f"{batch}" loss += outputs.loss.item() # loss = criterion(outputs.logits, batch["input_ids"]) test_loss = loss / len(eval_dataloader) print_rank_0(f"Train Loss: {train_loss:.4f} Test Loss:{test_loss:.4f}") + if args.save_model and test_loss < best_test_loss: + best_test_loss = test_loss + torch.save(model.state_dict(), "./checkpoints/best_model.pth") if __name__ == "__main__": args = get_args() - colossalai.launch_from_torch(config=args.config) model = BertForMaskedLM.from_pretrained("bert-base-uncased") + colossalai.launch_from_torch(config=args.config) shard_config = ShardConfig( rank=int(str(get_current_device()).split(':')[-1]), world_size=int(os.environ['WORLD_SIZE']), @@ -92,6 +103,8 @@ def train(model: nn.Module, num_epoch: int = 2): sharded_model = shard_model(model, shard_config) if args.mode == "train": - train(sharded_model) + train(sharded_model, args) elif args.mode == "inference": - inference(sharded_model) + inference(sharded_model, args) + else: + raise NotImplementedError From 6370a935f6d9180a2f4af054708dfe5193619d76 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Tue, 6 Jun 2023 15:31:52 +0800 Subject: [PATCH 305/413] update README (#3909) --- colossalai/shardformer/README.md | 46 ++++++++++++++++++++++---------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 3394e9457da3..93a4f1e578e4 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -55,30 +55,37 @@ If you wanna parallel the model in a custom way, just overwrite the policy class You should do: 1. Inherit Policy class -2. Overwrite argument_policy method - - In this method, you need to list which layers class you wanna modify and the attributes and parameters in those layers. -3. Overwrite inject_policy method (Optional) - - If you need to modify the forward or backward progress. -4. Overwrite or add the param recording functions +2. Overwrite `argument_policy` method + - In this method, you need to list which layers class you wanna modify and the attributes and parameters in those layers. Shardformer will replace all the layer belonging to the class you specified. + - `attr_dict` is dict contains all the attributes need to be modified in this layer. + - `param_funcs` is a list contains some functions which will return the path of the weight and bias from the layer. +3. Overwrite `inject_policy` method (Optional) + - Shardformer will inject the model according to this method. If you need to modify the forward or backward progress (like distributed corssentropy loss in Bert) you need to overwrite this method. +4. Overwrite or add the param functions - These functions use a suffix to record the path of weight or bias for the layer. -5. Overwrite binding + - The return is a list contains some `Col_Layer` or `Row_Layer` objects, which means slice along col and row respectively. +5. Overwrite `binding_policy` (Optional) + - Overwrite to specify Shardformer will bind some weight between layers, like embedding and unembedding layers. + - This function will return a dict, the key and value are the suffix of weight need to be binded. More details can be found in shardformer/policies/basepolicy.py ``` python from colossalai.shardformer.policies.basepolicy import Policy, Layer, Col_Layer, Row_Layer, Argument CustomPolicy(Policy): - @staticmethod - def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument]: - """ - Return a dict, the key is layer will be modified and the value is the Argument class with param setting and param functions +@staticmethod + def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]: + r""" + Return the dict for the modify policy, the key is the original layer class and the value is the + argument for the modify layer Args: - model_config: The config of transformer model - shard_setting: The config of distributed model + model_config (:class:`tansformer.Config`): The config of transformer model + shard_config (:class:`ShardConfig`): The config for sharding model Return: Dict for the modify policy, + :: { origin layer class1 (nn.Module): Argument( attr_dict = { @@ -112,18 +119,29 @@ CustomPolicy(Policy): @staticmethod def inject_policy() -> Tuple[nn.Module, nn.Module]: - """ + r""" Return the dict for the inject model Return: The injected model, key is the original model and value is the new shardmodel + :: + (OrignModel, CustomModel) + in `CustomModel`, we can overwrite the forward and backward process """ return () @staticmethod def binding_policy() -> Dict: - """ + r""" Return the dict for the binding model + + Return: + This method should return the binding relationship for some layers share the weight or bias, + the key and value is the suffix of the weight or bias of the model + :: + return { + "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", + } """ return NotImplementedError From ef1537759c1bf3fcddf389e6b857eac9b22bb444 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Wed, 7 Jun 2023 16:09:40 +0800 Subject: [PATCH 306/413] [shardformer] add gpt2 policy and modify shard and slicer to support (#3883) * add gpt2 policy and modify shard and slicer to support * remove unused code * polish code --- colossalai/shardformer/policies/autopolicy.py | 14 ++- colossalai/shardformer/policies/basepolicy.py | 17 ++- colossalai/shardformer/policies/bert.py | 1 - colossalai/shardformer/policies/gpt2.py | 118 ++++++++++++++++++ colossalai/shardformer/shard/sharder.py | 46 ++++--- colossalai/shardformer/shard/slicer.py | 53 ++++++-- colossalai/shardformer/test/test.py | 28 +++-- 7 files changed, 233 insertions(+), 44 deletions(-) create mode 100644 colossalai/shardformer/policies/gpt2.py diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index e096c2b13a59..54cc63ba124f 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -10,16 +10,26 @@ def build_policies(): """ auto_policy_dict = {} - from transformers.models.bert.modeling_bert import BertForMaskedLM + from transformers import BertForMaskedLM from .bert import BertForMaskedLMPolicy auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy - from transformers.models.bert.modeling_bert import BertForSequenceClassification + from transformers import BertForSequenceClassification from .bert import BertForSequenceClassificationPolicy auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy + from transformers import GPT2Model + + from .gpt2 import GPT2Policy + auto_policy_dict[GPT2Model] = GPT2Policy + + from transformers import GPT2LMHeadModel + + from .gpt2 import GPT2LMHeadModelPolicy + auto_policy_dict[GPT2LMHeadModel] = GPT2LMHeadModelPolicy + return auto_policy_dict diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 2eb7eb29e1a4..644d115a270e 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -1,11 +1,9 @@ # part of code modified from https://github.com/tunib-ai/parallelformers -from dataclasses import dataclass, field +from dataclasses import dataclass from typing import Any, Callable, Dict, List, Tuple, Type -import torch import torch.nn as nn -from transformers import AutoConfig @dataclass @@ -31,11 +29,18 @@ class Layer: bias (str): The bias suffix of the layer replace_layer (:class:`colosalai.nn`): The layer to replace the original layer ignore (bool): Whether to ignore this layer if it is not in the model + reversed (bool): Whether the weight in layer is reversed, commonly the weight in `torch.nn.Linear` is [out, in], + but in GPT2 `Conv1D` layer is [in, out] which is reversed. + n_cast (int): The number of weight will cast to, like q, k, v in attention layer, n_cast should be 3. commonly in TP, we just chunk the weight with the number of devices, + but in multi-head attention, we need to chunk the weight with the number of devices * n_head, and + each device should have a part of Q, K and V weight. """ weight: str = None bias: str = None replace_layer: Any = None ignore: bool = False + reversed: bool = False + n_cast: int = None @dataclass @@ -131,7 +136,7 @@ def inject_policy() -> Tuple[nn.Module, nn.Module]: (OrignModel, CustomModel) in `CustomModel`, we can overwrite the forward and backward process """ - return () + return None @staticmethod def binding_policy() -> Dict: @@ -146,7 +151,7 @@ def binding_policy() -> Dict: "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", } """ - return NotImplementedError + return None @staticmethod def attn_in() -> List: @@ -209,4 +214,4 @@ def unembedding() -> List: Return: List[Layer]: List of layer object """ - return NotImplementedError + return None diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index ab77b29f71f4..89b32f065c27 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,4 +1,3 @@ -from dataclasses import dataclass from typing import Any, Callable, Dict, List, Tuple, Type import torch.nn as nn diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py new file mode 100644 index 000000000000..44dc9c72f986 --- /dev/null +++ b/colossalai/shardformer/policies/gpt2.py @@ -0,0 +1,118 @@ +from typing import Any, Callable, Dict, List, Tuple, Type + +import torch.nn as nn +from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model + +import colossalai.shardformer.layer.layers as col_nn + +from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer + + +class GPT2Policy(Policy): + + @staticmethod + def argument_policy(config, world_size): + return { + GPT2Model: + Argument(attr_dict={}, param_funcs=[ + GPT2Policy.embedding, + ]), + GPT2Block: + Argument( + attr_dict={ + # 1. reduce hidden size + "attn.embed_dim": config.hidden_size // world_size, + "attn.split_size": config.hidden_size // world_size, + "crossattention.embed_dim": config.hidden_size // world_size, + "crossattention.split_size": config.hidden_size // world_size, + # 2. reduce number of heads + "attn.num_heads": config.num_attention_heads // world_size, + "crossattention.num_heads": config.num_attention_heads // world_size, + }, + param_funcs=[ + GPT2Policy.attn_in, + GPT2Policy.attn_out, + GPT2Policy.mlp_in, + GPT2Policy.mlp_out, + ]), + } + + @staticmethod + def attn_in() -> List: + return [ + Col_Layer(weight="attn.c_attn.weight", + bias="attn.c_attn.bias", + n_cast=3, + reversed=True, + replace_layer=col_nn.Linear1D_Col), + Col_Layer(weight="crossattention.c_attn.weight", + bias="crossattention.c_attn.bias", + n_cast=2, + reversed=True, + ignore=True, + replace_layer=col_nn.Linear1D_Col), + Col_Layer(weight="crossattention.q_attn.weight", + bias="crossattention.q_attn.bias", + reversed=True, + ignore=True, + replace_layer=col_nn.Linear1D_Col) + ] + + @staticmethod + def attn_out() -> List: + return [ + Row_Layer(weight="attn.c_proj.weight", + bias="attn.c_proj.bias", + reversed=True, + replace_layer=col_nn.Linear1D_Row), + Row_Layer(weight="crossattention.c_proj.weight", + bias="crossattention.c_proj.bias", + reversed=True, + ignore=True, + replace_layer=col_nn.Linear1D_Row) + ] + + @staticmethod + def mlp_in() -> List: + return [ + Col_Layer(weight="mlp.c_fc.weight", bias="mlp.c_fc.bias", reversed=True, replace_layer=col_nn.Linear1D_Col), + ] + + @staticmethod + def mlp_out() -> List: + return [ + Row_Layer(weight="mlp.c_proj.weight", + bias="mlp.c_proj.bias", + reversed=True, + replace_layer=col_nn.Linear1D_Row) + ] + + @staticmethod + def embedding() -> List: + return [Col_Layer(weight="wte.weight", replace_layer=col_nn.VocabParallelEmbedding1D)] + + +from transformers import GPT2LMHeadModel + + +class GPT2LMHeadModelPolicy(GPT2Policy): + + @staticmethod + def argument_policy(config, world_size): + base_argument = GPT2Policy.argument_policy(config, world_size) + argument = { + GPT2LMHeadModel: Argument(attr_dict={}, param_funcs=[ + GPT2LMHeadModelPolicy.unembedding, + ]), + } + argument.update(base_argument) + return argument + + @staticmethod + def unembedding() -> List: + return [ + Col_Layer(weight="lm_head.weight", + bias="lm_head.bias", + replace_layer=col_nn.Linear1D_Col, + gather_output=True) + ] diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 2218661889f8..1ada75e06b67 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -2,6 +2,7 @@ import torch import torch.nn as nn +from transformers.pytorch_utils import Conv1D from ..policies.autopolicy import get_autopolicy from ..policies.basepolicy import Policy @@ -35,10 +36,22 @@ def __init__( self.model_config = self.model.config def shard(self) -> None: + self.reshape_embedding() self.inject_model(self.model) self.replace_layer(self.model) self.bind_layer(self.model) + def reshape_embedding(self,) -> None: + r""" + Reshape the Embedding layer to make the embedding dimension divisible by world_size + """ + vocab_size = self.model_config.vocab_size + world_size = self.shard_config.world_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + self.model_config = self.model.config + def inject_model( self, model: nn.Module, @@ -53,6 +66,8 @@ def inject_model( """ inject_policy = self.policy.inject_policy() + if inject_policy is None: + return org_model_cls = inject_policy[0] shard_model_cls = inject_policy[1] @@ -82,9 +97,9 @@ def replace_layer( origin_layer_cls = argument_policy[0] attr_dict = argument_policy[1].attr_dict param_funcs = argument_policy[1].param_funcs - self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs) + self.traverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs) - def reverse_replace_layer( + def traverse_replace_layer( self, layer: nn.Module, origin_cls: nn.Module, @@ -100,17 +115,12 @@ def reverse_replace_layer( attr_dict (Dict): The attribute dict to modify policy_cls (:class:`Policy`): The policy class """ + if layer.__class__ == origin_cls: + for k, v in attr_dict.items(): + setattr_(layer, k, v, ignore=True) + self.shard_one_layer(layer, param_funcs) for name, child in layer.named_children(): - if child.__class__ == origin_cls: - # replac_layer = child - for k, v in attr_dict.items(): - setattr_(child, k, v, ignore=True) - # print(f"Sharding {name} layer", replac_layer.attention.self.__dict__) - # setattr_(layer, name, self.shard_one_layer(child, policy_cls)) - self.shard_one_layer(child, param_funcs) - continue - - self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs) + self.traverse_replace_layer(child, origin_cls, attr_dict, param_funcs) return layer def shard_one_layer( @@ -126,7 +136,6 @@ def shard_one_layer( param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class """ - # print(org_layer) for func in param_funcs: policy_layers = func() for policy_layer in policy_layers: @@ -136,9 +145,10 @@ def shard_one_layer( bias_attr = policy_layer.bias replace_layer_cls = policy_layer.replace_layer ignore = policy_layer.ignore + n_cast = policy_layer.n_cast + reversed = policy_layer.reversed if policy_layer.__class__.__name__ == "Col_Layer": gather_output = policy_layer.gather_output - # print(gather_output) if weight_attr is not None: if hasattr_(org_layer, weight_attr): @@ -161,13 +171,11 @@ def shard_one_layer( layer_attr = (lambda x: x[:x.rfind(".")])(weight_attr or bias_attr) # slice weight and bias - weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__) - # print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None) + weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__, n_cast, reversed) # create new object to replace the origin layer if replace_layer_cls is not None: - # print(f"RANK {os.environ['RANK']}: replace {getattr_(org_layer, layer_attr).__class__} to {replace_layer_cls}, shape is {weight.shape}") - if isinstance(getattr_(org_layer, layer_attr), nn.Linear): + if isinstance(getattr_(org_layer, layer_attr), (nn.Linear, Conv1D)): if replace_layer_cls.__name__ == "Linear1D_Row": replace_layer = replace_layer_cls(weight.shape[1], weight.shape[0], @@ -235,6 +243,8 @@ def bind_layer(self, model: nn.Module) -> None: model (:class:`torch.nn.Module`): The shard model """ binding_map = self.policy.binding_policy() + if binding_map is None: + return for k, v in binding_map.items(): param = getattr_(model, k) param = nn.Parameter(param) diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 26053b9f7408..6d35bd193fed 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -19,6 +19,8 @@ def slice_weight_bias( weight: torch.Tensor, bias: torch.Tensor, policy_layer_cls: Layer, + n_cast: int = None, + reversed: bool = False, ): r""" Slice the weight and bias according to policy layer cls @@ -33,13 +35,18 @@ def slice_weight_bias( """ if policy_layer_cls == Layer: return weight, bias - elif policy_layer_cls == Col_Layer: - weight = self.slice_tensor(weight, 1, False) + + dim = dim_mapping[policy_layer_cls] if not reversed else (1 - dim_mapping[policy_layer_cls]) + # print(weight.shape, dim) + if policy_layer_cls == Col_Layer: + weight = self.slice_tensor(weight, dim, False, n_cast) bias = self.slice_tensor(bias, 0, True) elif policy_layer_cls == Row_Layer: - weight = self.slice_tensor(weight, 0, False) + weight = self.slice_tensor(weight, dim, False, n_cast) else: raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported") + if reversed: + weight = weight.transpose(0, 1).contiguous() return weight, bias def slice_tensor( @@ -47,6 +54,7 @@ def slice_tensor( tensor_in: torch.Tensor, dim: int, is_bias: bool, + n_cast: int = None, ) -> torch.Tensor: r""" Slice tensor according to the config @@ -59,14 +67,15 @@ def slice_tensor( if tensor_in is None: return None if not is_bias: - return self.slice_2d(tensor_in, dim) + return self.slice_2d(tensor_in, dim, n_cast) else: - return self.slice_1d(tensor_in) + return self.slice_1d(tensor_in, n_cast) def slice_2d( self, tensor: torch.Tensor, dim: int, + n_cast: int = None, ) -> torch.Tensor: r""" Slice the 2D tensor @@ -77,13 +86,14 @@ def slice_2d( """ assert dim in [0, 1], f"Only support 2D tensor, but got {dim}D tensor" if dim == 0: - return self.slice_row(tensor) + return self.slice_row(tensor, n_cast) elif dim == 1: - return self.slice_col(tensor) + return self.slice_col(tensor, n_cast) def slice_1d( self, tensor: torch.Tensor, + n_cast: int = None, ) -> torch.Tensor: r""" Slice the 1D tensor @@ -94,11 +104,19 @@ def slice_1d( Returns: :class:`torch.Tensor`: The sliced tensor """ - return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() + if n_cast is None: + return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() + else: + tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) + chunk_list = [ + tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) + ] + return torch.cat(chunk_list, dim=0).contiguous() def slice_col( self, tensor: torch.Tensor, + n_cast: int = None, ) -> torch.Tensor: r""" Slice the tensor in column @@ -110,11 +128,19 @@ def slice_col( :class:`torch.Tensor`: The sliced tensor """ - return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() + if n_cast is None: + return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() + else: + tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) + chunk_list = [ + tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) + ] + return torch.cat(chunk_list, dim=0).contiguous() def slice_row( self, tensor: torch.Tensor, + n_cast: int = None, ) -> torch.Tensor: r""" Slice the tensor in column @@ -125,4 +151,11 @@ def slice_row( Returns: :class:`torch.Tensor`: The sliced tensor """ - return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() + if n_cast is None: + return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() + else: + tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=1) + chunk_list = [ + tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) + ] + return torch.cat(chunk_list, dim=1).contiguous() diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py index b896fd4a4020..e2d5a94c782a 100644 --- a/colossalai/shardformer/test/test.py +++ b/colossalai/shardformer/test/test.py @@ -6,24 +6,28 @@ from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm -from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, get_scheduler +from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, GPT2LMHeadModel, get_scheduler import colossalai from colossalai.shardformer.shard import ShardConfig, shard_model from colossalai.utils import get_current_device, print_rank_0 os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' -tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") def get_args(): parser = colossalai.get_default_parser() parser.add_argument("--mode", type=str, default='inference') parser.add_argument("--save_model", action='store_true') + parser.add_argument("--model", type=str, default='bert-base-uncased') return parser.parse_args() -def load_data(): +def load_data(args): + tokenizer = AutoTokenizer.from_pretrained(args.model) + if tokenizer.pad_token is None: + tokenizer.add_special_tokens({"pad_token": "[PAD]"}) + # tokenizer.pad_token_id = 0 datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') # datasets=load_dataset("yelp_review_full") tokenized_datasets = datasets.map( @@ -42,18 +46,23 @@ def load_data(): def inference(model: nn.Module, args): - tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + print(model) + # print(model.wte.weight.shape) + tokenizer = AutoTokenizer.from_pretrained(args.model) + if tokenizer.pad_token is None: + tokenizer.add_special_tokens({"pad_token": "[PAD]"}) + tokenizer.pad_token_id = 0 token = "Hello, my dog is cute" inputs = tokenizer(token, return_tensors="pt") inputs.to("cuda") model.eval() model.to("cuda") outputs = model(**inputs) - print(outputs) + print(outputs[0]) def train(model: nn.Module, args, num_epoch: int = 3): - train_dataloader, eval_dataloader = load_data() + train_dataloader, eval_dataloader = load_data(args) optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5) num_training = num_epoch * len(train_dataloader) progress_bar = tqdm(range(num_training)) @@ -94,8 +103,13 @@ def train(model: nn.Module, args, num_epoch: int = 3): if __name__ == "__main__": args = get_args() - model = BertForMaskedLM.from_pretrained("bert-base-uncased") colossalai.launch_from_torch(config=args.config) + if args.model == 'bert-base-uncased': + model = BertForMaskedLM.from_pretrained("bert-base-uncased") + elif args.model == 'gpt2': + model = GPT2LMHeadModel.from_pretrained("gpt2") + else: + raise AttributeError("model not supported") shard_config = ShardConfig( rank=int(str(get_current_device()).split(':')[-1]), world_size=int(os.environ['WORLD_SIZE']), From 33eef714db460d3db42698a2d969cb6a669dc583 Mon Sep 17 00:00:00 2001 From: digger yu Date: Thu, 8 Jun 2023 16:09:32 +0800 Subject: [PATCH 307/413] fix typo examples and docs (#3932) --- .../parallelize_your_training_like_Megatron.md | 6 +++--- .../parallelize_your_training_like_Megatron.md | 6 +++--- examples/images/dreambooth/README.md | 2 +- examples/language/bert/README.md | 2 +- examples/language/gpt/gemini/train_gpt_demo.py | 8 ++++---- examples/language/gpt/titans/model/embed.py | 2 +- examples/language/opt/opt_train_demo.py | 2 +- examples/language/palm/train.py | 6 +++--- 8 files changed, 17 insertions(+), 17 deletions(-) diff --git a/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md b/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md index 22d52fb3cd1a..978ac32fc78e 100644 --- a/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md +++ b/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md @@ -141,16 +141,16 @@ for mn, module in model.named_modules(): if 'mlp.c_fc' in mn: if 'weight' in pn or 'bias' in pn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice # keep the shape of the output from c_fc param.compute_spec.set_output_replicate(False) elif 'mlp.c_proj' in mn: if 'weight' in pn: split_param_row_tp1d(param, pg) # row slice elif 'wte' in mn or 'wpe' in mn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice elif 'c_attn' in mn or 'c_proj' in mn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice ``` The modified model is illustrated below. diff --git a/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md b/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md index c4131e593437..b4e0d18a2647 100644 --- a/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md +++ b/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md @@ -126,16 +126,16 @@ for mn, module in model.named_modules(): if 'mlp.c_fc' in mn: if 'weight' in pn or 'bias' in pn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice # keep the shape of the output from c_fc param.compute_spec.set_output_replicate(False) elif 'mlp.c_proj' in mn: if 'weight' in pn: split_param_row_tp1d(param, pg) # row slice elif 'wte' in mn or 'wpe' in mn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice elif 'c_attn' in mn or 'c_proj' in mn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice ``` 修改后的模型如下图所示。 diff --git a/examples/images/dreambooth/README.md b/examples/images/dreambooth/README.md index 7c117d841e24..5b350bc95b8e 100644 --- a/examples/images/dreambooth/README.md +++ b/examples/images/dreambooth/README.md @@ -37,7 +37,7 @@ The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Mode ## Training -We provide the script `colossalai.sh` to run the training task with colossalai. Meanwhile, we also provided traditional training process of dreambooth, `dreambooth.sh`, for possible comparation. For instance, the script of training process for [stable-diffusion-v1-4] model can be modified into: +We provide the script `colossalai.sh` to run the training task with colossalai. Meanwhile, we also provided traditional training process of dreambooth, `dreambooth.sh`, for possible comparison. For instance, the script of training process for [stable-diffusion-v1-4] model can be modified into: ```bash export MODEL_NAME="CompVis/stable-diffusion-v1-4" diff --git a/examples/language/bert/README.md b/examples/language/bert/README.md index c845a5c50387..81c3f03fffca 100644 --- a/examples/language/bert/README.md +++ b/examples/language/bert/README.md @@ -1,6 +1,6 @@ ## Overview -This directory includes two parts: Using the Booster API fintune Huggingface Bert and AlBert models and benchmarking Bert and AlBert models with different Booster Plugin. +This directory includes two parts: Using the Booster API finetune Huggingface Bert and AlBert models and benchmarking Bert and AlBert models with different Booster Plugin. ## Finetune ``` diff --git a/examples/language/gpt/gemini/train_gpt_demo.py b/examples/language/gpt/gemini/train_gpt_demo.py index 92751c7e2f47..4b78624f0110 100644 --- a/examples/language/gpt/gemini/train_gpt_demo.py +++ b/examples/language/gpt/gemini/train_gpt_demo.py @@ -162,7 +162,7 @@ def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup): # shard it w.r.t tp pattern if 'mlp.c_fc' in mn: if 'weight' in pn or 'bias' in pn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice # keep the shape of the output from c_fc param.compute_spec.set_output_replicate(False) else: @@ -173,9 +173,9 @@ def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup): else: param.set_dist_spec(ReplicaSpec()) elif 'wte' in mn or 'wpe' in mn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice elif 'c_attn' in mn or 'c_proj' in mn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice else: param.set_dist_spec(ReplicaSpec()) param.visited = True @@ -237,7 +237,7 @@ def main(): if args.tp_degree > 1: tensor_parallelize(model, tp_pg) - # asign running configurations + # assign running configurations if args.distplan == "CAI_ZeRO1": zero_stage = 1 elif args.distplan == "CAI_ZeRO2": diff --git a/examples/language/gpt/titans/model/embed.py b/examples/language/gpt/titans/model/embed.py index 6369b9f8c5a1..d825ae92a285 100644 --- a/examples/language/gpt/titans/model/embed.py +++ b/examples/language/gpt/titans/model/embed.py @@ -305,7 +305,7 @@ def forward(ctx, vocab_parallel_logits, target): @staticmethod def backward(ctx, grad_output): - # Retreive tensors from the forward path. + # Retrieve tensors from the forward path. softmax, target_mask, masked_target_1d = ctx.saved_tensors # All the inputs have softmax as their gradient. diff --git a/examples/language/opt/opt_train_demo.py b/examples/language/opt/opt_train_demo.py index 8a2ad5f55b10..bb2eb52ce560 100644 --- a/examples/language/opt/opt_train_demo.py +++ b/examples/language/opt/opt_train_demo.py @@ -38,7 +38,7 @@ def train_epoch(epoch, model, optimizer, lr_scheduler, dataloader, booster, coor for batch in pbar: - # Foward + # Forward optimizer.zero_grad() batch = move_to_cuda(batch, torch.cuda.current_device()) diff --git a/examples/language/palm/train.py b/examples/language/palm/train.py index 62062e8bd272..a0600db1bc5b 100644 --- a/examples/language/palm/train.py +++ b/examples/language/palm/train.py @@ -140,15 +140,15 @@ def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup): continue param.set_dist_spec(ReplicaSpec()) if 'net.0' in mn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice elif 'to_q' in mn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice elif 'to_kv' in mn: split_param_row_tp1d(param, pg) # row slice elif 'to_out' in mn: split_param_row_tp1d(param, pg) # row slice elif '1.1' in mn: - split_param_col_tp1d(param, pg) # colmn slice + split_param_col_tp1d(param, pg) # column slice elif '1.2' in mn: split_param_row_tp1d(param, pg) # row slice else: From 21c4c0b1a01c13a0bc9b515e061a4fe19de2a341 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Thu, 8 Jun 2023 17:38:47 +0800 Subject: [PATCH 308/413] support UniEval and add CHRF metric (#3924) Co-authored-by: Yuanchen Xu --- applications/Chat/evaluate/README.md | 105 ++++-- .../Chat/evaluate/config/config_cn.json | 12 +- .../Chat/evaluate/config/config_en.json | 73 +++- applications/Chat/evaluate/eval.py | 2 +- applications/Chat/evaluate/evaluator.py | 142 ++++++-- applications/Chat/evaluate/gpt_evaluate.py | 2 +- applications/Chat/evaluate/metrics.py | 22 ++ .../Chat/evaluate/unieval/__init__.py | 12 + .../Chat/evaluate/unieval/evaluator.py | 330 ++++++++++++++++++ applications/Chat/evaluate/unieval/scorer.py | 101 ++++++ applications/Chat/evaluate/unieval/utils.py | 248 +++++++++++++ applications/Chat/evaluate/utils.py | 2 +- 12 files changed, 984 insertions(+), 67 deletions(-) create mode 100644 applications/Chat/evaluate/unieval/__init__.py create mode 100644 applications/Chat/evaluate/unieval/evaluator.py create mode 100644 applications/Chat/evaluate/unieval/scorer.py create mode 100644 applications/Chat/evaluate/unieval/utils.py diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index e3510e3522f6..077193b63ce0 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -12,12 +12,13 @@ pip install -r requirements.txt ## Evaluation Pipeline -The whole evaluation pipeline consists of two methods: +The whole evaluation pipeline consists of three methods: 1. `GPT Evaluation`: evaluates model predictions using GPT models. * Compare the performance of two different models (battle). * Rate the model according to pre-defined metrics using prompting design. 2. `Automatic Evaluation`: evaluates model predictions using automatic metrics. +3. `UniEval`: evaluates model predictions using UniEval models(English only). ### Evaluation Category @@ -75,7 +76,9 @@ GPT evaluation uses GPT models to evaluate the prediction of different models an GPT models evaluate the quality of model predictions based on the given prompt words and gives a score between 1-5. -> **NOTE:** Even for the same metric, the details of its prompt words and CoT(Chain-of-Thought) can differ based on which category you want to evaluate. For example, prompt words for metric `correctness` showed here is "The answer should be in line with common sense, life experience, etc."(this is for category `brainstorming`), but for category `extraction`, prompt words can be "Answers should extract the required information accurately and should not contain any incorrect or misleading information." You can find all the prompt words and CoT(Chain-of-Thought) in `prompt/evaluation_prompt`. +> **NOTE 1:** Even for the same metric, the details of its prompt words and CoT(Chain-of-Thought) can differ based on which category you want to evaluate. For example, prompt words for metric `correctness` showed here is "The answer should be in line with common sense, life experience, etc."(this is for category `brainstorming`), but for category `extraction`, prompt words can be "Answers should extract the required information accurately and should not contain any incorrect or misleading information." You can find all the prompt words and CoT(Chain-of-Thought) in `prompt/evaluation_prompt`. + +> **NOTE 2:** To add customized metrics, you can refer to [FAQ](#faq). #### Automatic Evaluation @@ -85,7 +88,7 @@ There are two ways to obtain reference answers: * For instruction coming from human-designed problems, the reference answers are generated by GPT-3.5, such as roleplay, chat. * For instruction related with classic NLP problems, the reference answers are collected from open-sourced dataset with target answers, such as classification, extraction, summarization. -There are 5 types of automatic evaluation metrics listed in the table below: +There are 6 types of automatic evaluation metrics listed in the table below: | Automatic Evaluation Metric | Description | | :---------------------------------: | :----------------------------------------------------------- | @@ -94,6 +97,25 @@ There are 5 types of automatic evaluation metrics listed in the table below: | Distinct | Measure the diversity of generation text by counting the unique n-grams. | | BERTScore | Measure the semantic similarity between tokens of predictions and references with BERT. | | Precision
    Recall
    F1 Score | Measure the number of overlaps between prediction and reference (design for classification and extraction categories). | +| CHRF | Measure the similarity of character n-grams between prediction and reference. | + +#### UniEval Evaluation + +UniEval converts all evaluation tasks of different dimensions(metrics) into Boolean QA problems and utilize the model to answer with “Yes” or “No”. Compared with similarity-based metrics such as ROUGE and BLEU, UniEval can achieve a more comprehensive evaluation. In addition, UniEval also demonstrates its ability to transfer to unseen dimensions and tasks. + +In our evaluation pipeline, two pre-trained UniEval evaluators are used. One is [unieval-sum](https://huggingface.co/MingZhong/unieval-sum) and the other is [unieval-dialog](https://huggingface.co/MingZhong/unieval-dialog). The two models can be used for the 3 tasks, `summarization`, `dialogue` and `data2text`. Each task has different evaluation dimensions. + +| UniEval Model | Task | Dimension(Metric) | +| :------------: | :----------------- | :--- | +| unieval-sum | summarization | coherence: whether the summary is coherent
    consistency: whether the claim is consistent with the given document
    fluency: whether the paragraph is fluent
    relevance: whether the summary is relevant to the reference | +| unieval-sum | data2text | naturalness: whether the utterance is fluent
    informativeness: whether the utterance is informative according to the reference | +| unieval-dialog | dialogue | naturalness: whether the response is natural in the dialogue
    coherence: whether the response is coherent in the dialogue history
    understandability: whether the response is understandable in the dialogue | + +> **NOTE 1:** Task "data2text" uses the same model as task "summarization". + +> **NOTE 2:** In UniEval paper, the `unieval-sum` model demonstrates the best transfer ability and so you can evaluate your customized metric with this model. Details of adding customized metrics can be found in [FAQ](#faq). + +> **NOTE 3:** We consider not including all metrics provided in UniEval in our pipeline because the data structure and content of the instructions we want to evaluate are not suitable for direct use of some UniEval metrics. ## Evaluation Process @@ -215,19 +237,26 @@ The following is an example of a Chinese GPT evaluation prompt. In an evaluation #### Configuration -The following is an example of a Chinese config file. The configuration file can control how the pipeline evaluates the model. You need to specify GPT evaluation metrics and automatic metrics in key `GPT` and `Metrics`. You can find an example Chinese config file in `config`. +The following is an example of a Chinese config file. The configuration file can control how the pipeline evaluates the model. You need to specify GPT evaluation metrics, automatic metrics and UniEval metrics in key `GPT`, `Metrics` and `UniEval`(English only). You can find an example English config file in `config`. ```json { - "language": "cn", + "language": "en", + "path_for_UniEval": { + "summarization": "path to unieval-sum model", + "dialogue": "path to unieval-dialog model", + "data2text": "path to unieval-sum model" + }, "category": { "brainstorming": { "GPT": ["relevance", "creativity", "practicality", "correctness"], - "Metrics": ["Distinct"] + "Metrics": ["Distinct"], + "UniEval": ["summarization-fluency", "data2text-naturalness", "data2text-informativeness"] }, "chat": { "GPT": [ "relevance", "naturalness", "engagingness", "reasonableness"], - "Metrics": ["Distinct"] + "Metrics": ["Distinct"], + "UniEval": ["dialogue-naturalness", "dialogue-coherence", "dialogue-understandability"] } } } @@ -235,27 +264,33 @@ The following is an example of a Chinese config file. The configuration file can `"language"`: the language used to evaluate the model capability. We only support Chinese `"cn"` for now. +`"path_for_UniEval"`: path to the UniEval model. + `"category"`: the category/categories needed to evaluate the model capability. `"GPT"`: the metrics you want to use for GPT evaluation. `"Metrics"`: the metrics you want to use for automatic metrics evaluation. +`"UniEval"`: the metrics you want to use for UniEval metrics evaluation. The metric has to be in the `"{task}-{metric}"` format because different tasks have same metrics such as naturalness and coherence. + +You can remove the key such as `"Metrics"` to skip evaluating answers using its corresponding evaluation metrics. + You can create your config file based on available settings listed in following table. -| "category" | "GPT" | "Metrics" | -| :--------------: | :---------------------: | :---------: | -| "brainstorming" | "language organization" | "BLEU" | -| "chat" | "relevance" | "ROUGE" | -| "classification" | "creativity" | "Distinct" | -| "closed_qa" | "practicality" | "BERTScore" | -| "extraction" | "correctness" | "Precision" | -| "generation" | "naturalness" | "Recall" | -| "open_qa" | "engagingness" | "F1 score" | -| "rewriting" | "reasonableness" | | -| "roleplay" | "diversity" | | -| "summarization" | "fidelity" | | -| | "conciseness" | | +| "category" | "GPT" | "Metrics" | "UniEval" | +| :--------------: | :---------------------: | :---------: | :--------------------------: | +| "brainstorming" | "language organization" | "BLEU" | "dialogue-naturalness" | +| "chat" | "relevance" | "ROUGE" | "dialogue-coherence" | +| "classification" | "creativity" | "Distinct" | "dialogue-understandability" | +| "closed_qa" | "practicality" | "BERTScore" | "data2text-naturalness" | +| "extraction" | "correctness" | "Precision" | "data2text-informativeness" | +| "generation" | "naturalness" | "Recall" | "summarization-coherence" | +| "open_qa" | "engagingness" | "F1 score" | "summarization-consistency" | +| "rewriting" | "reasonableness" | "CHRF" | "summarization-fluency" | +| "roleplay" | "diversity" | | "summarization-relevance" | +| "summarization" | "fidelity" | | | +| | "conciseness" | | | > **NOTE:** For categories which don't have standard answers such as `brainstorming`, you should avoid using automatic metrics such as `BLEU` and `ROUGE` which are based on similarity measures and you should use `Distinct` instead in your config file. @@ -290,23 +325,36 @@ For example, if you want to add a new metric `persuasiveness` into category `bra "id": 1, "category": "brainstorming", "metrics": { - "persuasiveness": "说服力(1-5)：XXX" + "persuasiveness": "persuasiveness(1-5)：a short description for persuasiveness" }, "CoT": { - "persuasiveness": "XXX\n\n说服力：" + "persuasiveness": "CoT for persuasiveness\n\npersuasiveness：" }, - "prompt": "你是一个好助手。请你为下面“头脑风暴”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" + "prompt": "You are a good assistant. Please rate the given answer to the \"brainstorming\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" } } ``` +
    How can I add a new UniEval evaluation metric? + +For example, if you want to add a new metric `persuasiveness` into task `data2text`, you should add a Boolean QA question about the metric in function `add_question` in `unieval/utils.py`. Please do note that how effectively the model would evaluate this metric is unknown and you may need some experiments to test whether the model is capable of evaluating this metric. + +```python +if task == 'data2text': + if dimension == 'persuasiveness': + cur_input = 'question: Is this a persuasive utterence utterance: ' + output[i] +``` + +
    + ## To Do - [x] Add evaluation for English capability -- [ ] Support UniEval +- [x] Support UniEval - [x] Support GPT-4 evaluation +- [ ] Support GPT evaluation with reference in the prompt ## Citations @@ -327,4 +375,13 @@ For example, if you want to add a new metric `persuasiveness` into category `bra archivePrefix={arXiv}, primaryClass={cs.CL} } + +@misc{zhong2022unified, + title={Towards a Unified Multi-Dimensional Evaluator for Text Generation}, + author={Ming Zhong and Yang Liu and Da Yin and Yuning Mao and Yizhu Jiao and Pengfei Liu and Chenguang Zhu and Heng Ji and Jiawei Han}, + year={2022}, + eprint={2210.07197}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} ``` diff --git a/applications/Chat/evaluate/config/config_cn.json b/applications/Chat/evaluate/config/config_cn.json index a8c7ea8a3135..cf647f79bbf8 100644 --- a/applications/Chat/evaluate/config/config_cn.json +++ b/applications/Chat/evaluate/config/config_cn.json @@ -34,7 +34,8 @@ "Metrics": [ "Precision", "Recall", - "F1 score" + "F1 score", + "CHRF" ] }, "closed_qa": { @@ -46,7 +47,8 @@ "Metrics": [ "BLEU", "ROUGE", - "BERTScore" + "BERTScore", + "CHRF" ] }, "extraction": { @@ -58,7 +60,8 @@ "Metrics": [ "Precision", "Recall", - "F1 score" + "F1 score", + "CHRF" ] }, "generation": { @@ -116,7 +119,8 @@ "Metrics": [ "BLEU", "ROUGE", - "BERTScore" + "BERTScore", + "CHRF" ] } } diff --git a/applications/Chat/evaluate/config/config_en.json b/applications/Chat/evaluate/config/config_en.json index 5b6272b97084..014c61d93a54 100644 --- a/applications/Chat/evaluate/config/config_en.json +++ b/applications/Chat/evaluate/config/config_en.json @@ -1,5 +1,10 @@ { "language": "en", + "path_for_UniEval": { + "summarization": "path to unieval-sum", + "dialogue": "path to unieval-dialog", + "data2text": "path to unieval-sum" + }, "category": { "brainstorming": { "GPT": [ @@ -11,6 +16,11 @@ ], "Metrics": [ "Distinct" + ], + "UniEval": [ + "summarization-fluency", + "data2text-naturalness", + "data2text-informativeness" ] }, "chat": { @@ -23,6 +33,14 @@ ], "Metrics": [ "Distinct" + ], + "UniEval": [ + "summarization-fluency", + "dialogue-naturalness", + "dialogue-coherence", + "dialogue-understandability", + "data2text-naturalness", + "data2text-informativeness" ] }, "classification": { @@ -34,7 +52,13 @@ "Metrics": [ "Precision", "Recall", - "F1 score" + "F1 score", + "CHRF" + ], + "UniEval": [ + "summarization-fluency", + "data2text-naturalness", + "data2text-informativeness" ] }, "closed_qa": { @@ -46,7 +70,13 @@ "Metrics": [ "BLEU", "ROUGE", - "BERTScore" + "BERTScore", + "CHRF" + ], + "UniEval": [ + "summarization-fluency", + "data2text-naturalness", + "data2text-informativeness" ] }, "extraction": { @@ -58,7 +88,13 @@ "Metrics": [ "Precision", "Recall", - "F1 score" + "F1 score", + "CHRF" + ], + "UniEval": [ + "summarization-fluency", + "data2text-naturalness", + "data2text-informativeness" ] }, "generation": { @@ -71,6 +107,11 @@ "BLEU", "ROUGE", "BERTScore" + ], + "UniEval": [ + "summarization-fluency", + "data2text-naturalness", + "data2text-informativeness" ] }, "open_qa": { @@ -81,6 +122,11 @@ ], "Metrics": [ "Distinct" + ], + "UniEval": [ + "summarization-fluency", + "data2text-naturalness", + "data2text-informativeness" ] }, "rewriting": { @@ -93,6 +139,11 @@ "BLEU", "ROUGE", "BERTScore" + ], + "UniEval": [ + "summarization-fluency", + "data2text-naturalness", + "data2text-informativeness" ] }, "roleplay": { @@ -104,6 +155,11 @@ ], "Metrics": [ "Distinct" + ], + "UniEval": [ + "summarization-fluency", + "data2text-naturalness", + "data2text-informativeness" ] }, "summarization": { @@ -116,7 +172,16 @@ "Metrics": [ "BLEU", "ROUGE", - "BERTScore" + "BERTScore", + "CHRF" + ], + "UniEval": [ + "summarization-coherence", + "summarization-consistency", + "summarization-fluency", + "summarization-relevance", + "data2text-naturalness", + "data2text-informativeness" ] } } diff --git a/applications/Chat/evaluate/eval.py b/applications/Chat/evaluate/eval.py index 8388d95f748a..180ef438cc43 100644 --- a/applications/Chat/evaluate/eval.py +++ b/applications/Chat/evaluate/eval.py @@ -40,7 +40,7 @@ def main(args): # initialize evaluator evaluator = Evaluator(metrics_per_category, battle_prompt, gpt_evaluation_prompt, args.gpt_model, - config["language"]) + config["language"], config.get("path_for_UniEval", None)) if len(args.model_name_list) == 2: answers1 = jload(args.answer_file_list[0]) answers2 = jload(args.answer_file_list[1]) diff --git a/applications/Chat/evaluate/evaluator.py b/applications/Chat/evaluate/evaluator.py index 0bf55ca80d7c..6bb8cdb29431 100644 --- a/applications/Chat/evaluate/evaluator.py +++ b/applications/Chat/evaluate/evaluator.py @@ -4,6 +4,7 @@ import gpt_evaluate import metrics import pandas as pd +import unieval from utils import analyze_automatic_results, get_data_per_category, save_automatic_results @@ -15,13 +16,15 @@ class Evaluator(object): """ def __init__(self, params: Dict[str, Any], battle_prompt: Dict[str, Any], gpt_evaluation_prompt: Dict[str, Any], - gpt_model: str, language: str) -> None: + gpt_model: str, language: str, path_for_UniEval: Dict[str, str]) -> None: self.params = params self.battle_prompt = battle_prompt self.gpt_evaluation_prompt = gpt_evaluation_prompt self.gpt_model = gpt_model self.language = language + self.path_for_UniEval = path_for_UniEval self.automatic_metric_stats = dict() + self.unieval_metric_stats = dict() self.gpt_evaluation_results = dict() self.battle_results = [] @@ -47,16 +50,18 @@ def switch(metric, language): return metrics.bleu_score(preds=predicts_list, targets=targets_list, language=language) elif metric == "ROUGE": return metrics.rouge_score(preds=predicts_list, targets=targets_list, language=language) - elif (metric == "Distinct"): + elif metric == "Distinct": return metrics.distinct_score(preds=predicts_list, language=language) - elif (metric == "BERTScore"): + elif metric == "BERTScore": return metrics.bert_score(preds=predicts_list, targets=targets_list, language=language) - elif (metric == "Precision"): + elif metric == "Precision": return metrics.precision(preds=predicts_list, targets=targets_list, language=language) - elif (metric == "Recall"): + elif metric == "Recall": return metrics.recall(preds=predicts_list, targets=targets_list, language=language) - elif (metric == "F1 score"): + elif metric == "F1 score": return metrics.F1_score(preds=predicts_list, targets=targets_list, language=language) + elif metric == "CHRF": + return metrics.chrf_score(preds=predicts_list, targets=targets_list, language=language) else: raise ValueError(f"Unexpected metric") @@ -69,6 +74,9 @@ def switch(metric, language): print(f"Category {category} specified in your config doesn't have corresponding answers!") continue + if self.params[category].get("Metrics", None) is None: + continue + category_metrics = self.params[category]["Metrics"] self.automatic_metric_stats[category] = {} @@ -80,12 +88,68 @@ def switch(metric, language): for metric in category_metrics: self.automatic_metric_stats[category].update(switch(metric=metric, language=self.language)) + # UniEval evaluation + # self.unieval_metric_stats's key is "task" instead of "category". + # Iterating "task" first will avoid repeated loading models because one task corresponds to one UniEval model. + # If key is "category", different models will be loaded for multiple times across categories because the user may require different task(models) to evaluate one category. + for category in self.params: + if len(answers_per_category[category]) == 0: + print(f"Category {category} specified in your config doesn't have corresponding answers!") + continue + + if self.params[category].get("UniEval", None) is None: + continue + + if self.params[category]["UniEval"] and self.language == "cn": + raise Exception( + "UniEval doesn't support Chinese! Please remove UniEval config in your Chinese config file.") + + category_metrics = self.params[category]["UniEval"] + + for task, metric in [tuple(category_metric.split("-")) for category_metric in category_metrics]: + if self.unieval_metric_stats.get(task, None) is None: + self.unieval_metric_stats[task] = {category: {metric: 0}} + elif self.unieval_metric_stats[task].get(category, None) is None: + self.unieval_metric_stats[task][category] = {metric: 0} + else: + self.unieval_metric_stats[task][category][metric] = 0 + + for task in self.unieval_metric_stats: + if self.path_for_UniEval is None: + raise Exception(f"Please specify the path for UniEval model in the config file!") + + if self.path_for_UniEval.get(task, None) is None: + raise Exception(f"Please specify the model path for task {task} in the config file!") + + print(f"Load UniEval model for task {task}.") + + uni_evaluator = unieval.get_evaluator(task, model_name_or_path=self.path_for_UniEval[task]) + for category in self.unieval_metric_stats[task]: + targets_list = [ + target["target"] if target["target"] else target["output"] + for target in targets_per_category[category] + ] + predicts_list = [answer["output"] for answer in answers_per_category[category]] + sources_list = [answer["instruction"] + answer["input"] for answer in answers_per_category[category]] + + data = unieval.convert_data_to_unieval_format(predicts_list, sources_list, targets_list) + scores = uni_evaluator.evaluate(data, + category, + dims=list(self.unieval_metric_stats[task][category].keys()), + overall=False) + avg_scores = unieval.calculate_average_score(scores) + + self.unieval_metric_stats[task][category].update(avg_scores) + # gpt evaluation for category in self.params: if len(answers_per_category[category]) == 0: print(f"Category {category} specified in your config doesn't have corresponding answers!") continue + if self.params[category].get("GPT", None) is None: + continue + category_metrics = self.params[category]["GPT"] prompt = self.gpt_evaluation_prompt.get(category, None) @@ -106,29 +170,43 @@ def save(self, path: str, model_name_list: List[str]) -> None: save_path = os.path.join(path, "gpt_evaluate", "battle_results") gpt_evaluate.save_battle_results(self.battle_results, model_name_list[0], model_name_list[1], save_path) else: - # Save evaluation results for automatic metrics - automatic_base_save_path = os.path.join(path, "automatic_results") - automatic_results_save_path = os.path.join(automatic_base_save_path, "evaluation_results") - - save_automatic_results(model_name_list[0], self.automatic_metric_stats, automatic_results_save_path) - - # Save charts and csv. - automatic_analyses_save_path = os.path.join(automatic_base_save_path, "evaluation_analyses") - analyze_automatic_results(automatic_results_save_path, automatic_analyses_save_path) - - # Save evaluation results for GPT evaluation metrics. - gpt_base_save_path = os.path.join(path, "gpt_evaluate", "gpt_evaluate_results") - gpt_evaluation_results_save_path = os.path.join(gpt_base_save_path, "evaluation_results") - - all_evaluations = gpt_evaluate.save_gpt_evaluation_results(model_name_list[0], self.gpt_evaluation_results, - gpt_evaluation_results_save_path) - - # Start to calculate scores and save statistics. - gpt_evaluation_statistics_save_path = os.path.join(gpt_base_save_path, "evaluation_statistics") - gpt_evaluate.save_gpt_evaluation_statistics(model_name_list[0], all_evaluations, - gpt_evaluation_statistics_save_path) - - # Save charts and csv. - gpt_evaluation_analyses_save_path = os.path.join(gpt_base_save_path, "evaluation_analyses") - gpt_evaluate.analyze_gpt_evaluation_statistics(gpt_evaluation_statistics_save_path, - gpt_evaluation_analyses_save_path) + if self.automatic_metric_stats: + # Save evaluation results for automatic metrics + automatic_base_save_path = os.path.join(path, "automatic_results") + automatic_results_save_path = os.path.join(automatic_base_save_path, "evaluation_results") + + save_automatic_results(model_name_list[0], self.automatic_metric_stats, automatic_results_save_path) + + # Save charts and csv. + automatic_analyses_save_path = os.path.join(automatic_base_save_path, "evaluation_analyses") + analyze_automatic_results(automatic_results_save_path, automatic_analyses_save_path) + + if self.unieval_metric_stats: + # Save evaluation results for UniEval metrics + unieval_base_save_path = os.path.join(path, "unieval_results") + unieval_results_save_path = os.path.join(unieval_base_save_path, "evaluation_results") + + unieval.save_unieval_results(model_name_list[0], self.unieval_metric_stats, unieval_results_save_path) + + # Save charts and csv. + unieval_analyses_save_path = os.path.join(unieval_base_save_path, "evaluation_analyses") + unieval.analyze_unieval_results(unieval_results_save_path, unieval_analyses_save_path) + + if self.gpt_evaluation_results: + # Save evaluation results for GPT evaluation metrics. + gpt_base_save_path = os.path.join(path, "gpt_evaluate", "gpt_evaluate_results") + gpt_evaluation_results_save_path = os.path.join(gpt_base_save_path, "evaluation_results") + + all_evaluations = gpt_evaluate.save_gpt_evaluation_results(model_name_list[0], + self.gpt_evaluation_results, + gpt_evaluation_results_save_path) + + # Start to calculate scores and save statistics. + gpt_evaluation_statistics_save_path = os.path.join(gpt_base_save_path, "evaluation_statistics") + gpt_evaluate.save_gpt_evaluation_statistics(model_name_list[0], all_evaluations, + gpt_evaluation_statistics_save_path) + + # Save charts and csv. + gpt_evaluation_analyses_save_path = os.path.join(gpt_base_save_path, "evaluation_analyses") + gpt_evaluate.analyze_gpt_evaluation_statistics(gpt_evaluation_statistics_save_path, + gpt_evaluation_analyses_save_path) diff --git a/applications/Chat/evaluate/gpt_evaluate.py b/applications/Chat/evaluate/gpt_evaluate.py index b433500dfa04..6702526ac5e6 100644 --- a/applications/Chat/evaluate/gpt_evaluate.py +++ b/applications/Chat/evaluate/gpt_evaluate.py @@ -599,7 +599,7 @@ def analyze_gpt_evaluation_statistics(statistics_path: str, save_path: str) -> N for category in tqdm.tqdm( frame_per_category.keys(), - desc=f"category: ", + desc=f"GPT evaluation: ", total=len(frame_per_category.keys()), ): data = pd.DataFrame(frame_per_category[category]) diff --git a/applications/Chat/evaluate/metrics.py b/applications/Chat/evaluate/metrics.py index 031f6fa83926..e220226ec041 100644 --- a/applications/Chat/evaluate/metrics.py +++ b/applications/Chat/evaluate/metrics.py @@ -4,6 +4,7 @@ import jieba from bert_score import score from nltk.translate.bleu_score import sentence_bleu +from nltk.translate.chrf_score import sentence_chrf from rouge_chinese import Rouge as Rouge_cn from rouge_score import rouge_scorer as Rouge_en from sklearn.metrics import f1_score, precision_score, recall_score @@ -40,6 +41,27 @@ def bleu_score(preds: List[str], targets: List[str], language: str) -> Dict[str, return bleu_scores +def chrf_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]: + """Calculate CHRF Score Metric in sentence level. + """ + chrf_score = {"chrf": 0} + cumulative_chrf = [] + + for pred, target in zip(preds, targets): + if language == "cn": + pred_list = ' '.join(jieba.cut(preprocessing_text(pred))).split() + target_list = ' '.join(jieba.cut(preprocessing_text(target))).split() + elif language == "en": + pred_list = preprocessing_text(pred).split() + target_list = preprocessing_text(target).split() + + cumulative_chrf.append(sentence_chrf(target_list, pred_list)) + + chrf_score["chrf"] = statistics.mean(cumulative_chrf) + + return chrf_score + + def rouge_cn_score(preds: List[str], targets: List[str]) -> Dict[str, float]: """Calculate Chinese ROUGE Score Metric diff --git a/applications/Chat/evaluate/unieval/__init__.py b/applications/Chat/evaluate/unieval/__init__.py new file mode 100644 index 000000000000..dad8d6ad09fa --- /dev/null +++ b/applications/Chat/evaluate/unieval/__init__.py @@ -0,0 +1,12 @@ +from .evaluator import get_evaluator +from .utils import ( + analyze_unieval_results, + calculate_average_score, + convert_data_to_unieval_format, + save_unieval_results, +) + +__all__ = [ + 'get_evaluator', 'convert_data_to_unieval_format', 'calculate_average_score', 'save_unieval_results', + 'analyze_unieval_results' +] diff --git a/applications/Chat/evaluate/unieval/evaluator.py b/applications/Chat/evaluate/unieval/evaluator.py new file mode 100644 index 000000000000..385425e4a576 --- /dev/null +++ b/applications/Chat/evaluate/unieval/evaluator.py @@ -0,0 +1,330 @@ +# MIT License + +# Copyright (c) 2022 Ming Zhong + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import numpy as np +from nltk import sent_tokenize + +from .scorer import UniEvaluator +from .utils import add_question + + +class SumEvaluator: + + def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None): + """ Set up evaluator for text summarization """ + self.scorer = UniEvaluator( + model_name_or_path='MingZhong/unieval-sum' if model_name_or_path == "" else model_name_or_path, + max_length=max_length, + device=device, + cache_dir=cache_dir) + self.task = 'summarization' + self.dimensions = ['coherence', 'consistency', 'fluency', 'relevance'] + + def evaluate(self, data, category, dims=None, overall=True): + """ + Get the scores of all the given dimensions + + category: The category to be evaluated. + + dims: A list of dimensions to be evaluated. If dims is None, SumEvaluator will evaluate + four dimensions: coherence, consistency, fluency, relevance. + + overall: indicates whether the overall score is to be calculated. + Overall score can be customized to a combination of scores based on different + dimensions. The default here is the average score of all the given dimensions. + """ + n_data = len(data) + eval_scores = [{} for _ in range(n_data)] + + if dims == None: + eval_dims = self.dimensions + else: + assert isinstance(dims, list) + eval_dims = dims + + for dim in eval_dims: + # Calculate average sentence-level scores for 'consistency' and 'fluency' + if dim == 'consistency' or dim == 'fluency': + src_list, output_list = [], [] + n_sents = [] # the number of sentences in each generated summary + for i in range(n_data): + source = data[i]['source'] + system_outputs = sent_tokenize(data[i]['system_output']) + n_sents.append(len(system_outputs)) + for j in range(len(system_outputs)): + src_list.append(source) + output_list.append(system_outputs[j]) + input_list = add_question(dimension=dim, output=output_list, src=src_list, task=self.task) + sent_score = self.scorer.score(input_list, self.task, category, dim) + + # Get average score for each sample + start_idx = 0 + score = [] + for cur_n_sent in n_sents: + score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / cur_n_sent) + start_idx += cur_n_sent + + # Calculate summary-level score for 'coherence' and 'relevance' + elif dim == 'coherence' or dim == 'relevance': + src_list, output_list, ref_list = [], [], [] + for i in range(n_data): + src_list.append(data[i]['source']) + output_list.append(data[i]['system_output']) + if dim == 'relevance': + ref_list.append(data[i]['reference']) + input_list = add_question(dimension=dim, output=output_list, src=src_list, ref=ref_list, task=self.task) + score = self.scorer.score(input_list, self.task, category, dim) + + # Please customize other dimensions here for summarization + else: + raise NotImplementedError('The input format for this dimension is still undefined. \ + Please customize it first.') + + for i in range(n_data): + eval_scores[i][dim] = score[i] + + # Customize your overall score here. + if overall == True: + for i in range(n_data): + eval_scores[i]['overall'] = np.mean(list(eval_scores[i].values())) + + return eval_scores + + +class DialogEvaluator: + + def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None): + """ Set up evaluator for dialogues """ + self.scorer = UniEvaluator( + model_name_or_path='MingZhong/unieval-dialog' if model_name_or_path == "" else model_name_or_path, + max_length=max_length, + device=device, + cache_dir=cache_dir) + self.task = 'dialogue' + self.dimensions = ['naturalness', 'coherence', 'engagingness', 'groundedness', 'understandability'] + + def evaluate(self, data, category, dims=None, overall=True): + """ + Get the scores of all the given dimensions + + category: The category to be evaluated. + + dims: A list of dimensions to be evaluated. If dims is None, DialogEvaluator will evaluate + five dimensions: naturalness, coherence, engagingness, groundedness and understandability. + + overall: indicates whether the overall score is to be calculated. + Overall score can be customized to a combination of scores based on different + dimensions. The default here is the average score of all the given dimensions. + """ + n_data = len(data) + eval_scores = [{} for _ in range(n_data)] + + if dims == None: + eval_dims = self.dimensions + else: + assert isinstance(dims, list) + eval_dims = dims + + for dim in eval_dims: + # Calculate summation score for 'engagingness' + if dim == 'engagingness': + src_list, output_list, context_list = [], [], [] + n_sents = [] # the number of sentences in each generated response + for i in range(n_data): + source = data[i]['source'] + context = data[i]['context'] + system_outputs = sent_tokenize(data[i]['system_output']) + n_sents.append(len(system_outputs)) + for j in range(len(system_outputs)): + src_list.append(source) + context_list.append(context) + output_list.append(system_outputs[j]) + input_list = add_question(dimension=dim, + output=output_list, + src=src_list, + context=context_list, + task=self.task) + sent_score = self.scorer.score(input_list, self.task, category, dim) + + # Get the summation score for each sample + start_idx = 0 + score = [] + for cur_n_sent in n_sents: + score.append(sum(sent_score[start_idx:start_idx + cur_n_sent])) + start_idx += cur_n_sent + + # Calculate turn-level score for other dimensions + elif dim in ['naturalness', 'coherence', 'groundedness', 'understandability']: + src_list, output_list, context_list = [], [], [] + for i in range(n_data): + src_list.append(data[i]['source']) + output_list.append(data[i]['system_output']) + context_list.append(data[i]['context']) + input_list = add_question(dimension=dim, + output=output_list, + src=src_list, + context=context_list, + task=self.task) + score = self.scorer.score(input_list, self.task, category, dim) + + # Please customize other dimensions here for summarization + else: + raise NotImplementedError('The input format for this dimension is still undefined. \ + Please customize it first.') + + for i in range(n_data): + eval_scores[i][dim] = score[i] + + # Customize your overall score here. + if overall == True: + for i in range(n_data): + eval_scores[i]['overall'] = np.mean(list(eval_scores[i].values())) + + return eval_scores + + +class D2tEvaluator: + + def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None): + """ Set up evaluator for data-to-text """ + self.scorer = UniEvaluator( + model_name_or_path='MingZhong/unieval-sum' if model_name_or_path == "" else model_name_or_path, + max_length=max_length, + device=device, + cache_dir=cache_dir) + self.task = 'data2text' + self.dimensions = ['naturalness', 'informativeness'] + + def evaluate(self, data, category, dims=None, overall=True): + """ + Get the scores of all the given dimensions + + category: The category to be evaluated. + + dims: A list of dimensions to be evaluated. If dims is None, D2tEvaluator will evaluate + two dimensions: naturalness and informativeness. + + overall: indicates whether the overall score is to be calculated. + Overall score can be customized to a combination of scores based on different + dimensions. The default here is the average score of all the given dimensions. + """ + n_data = len(data) + eval_scores = [{} for _ in range(n_data)] + + if dims == None: + eval_dims = self.dimensions + else: + assert isinstance(dims, list) + eval_dims = dims + + for dim in eval_dims: + output_list, ref_list = [], [] + for i in range(n_data): + output_list.append(data[i]['system_output']) + ref_list.append(data[i]['reference']) + + input_list = add_question(dimension=dim, output=output_list, ref=ref_list, task=self.task) + score = self.scorer.score(input_list, self.task, category, dim) + + for i in range(n_data): + eval_scores[i][dim] = score[i] + + # Customize your overall score here. + if overall == True: + for i in range(n_data): + eval_scores[i]['overall'] = np.mean(list(eval_scores[i].values())) + + return eval_scores + + +class FactEvaluator: + + def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None): + """ Set up evaluator for factual consistency detection """ + self.scorer = UniEvaluator( + model_name_or_path='MingZhong/unieval-fact' if model_name_or_path == "" else model_name_or_path, + max_length=max_length, + device=device, + cache_dir=cache_dir) + self.task = 'fact' + self.dim = 'consistency' + + def evaluate(self, data, category): + """ + Get the factual consistency score (only 1 dimension for this task) + + category: The category to be evaluated. + """ + n_data = len(data) + eval_scores = [{} for _ in range(n_data)] + + # Calculate average sentence-level scores for facutal consistency + src_list, output_list = [], [] + n_sents = [] # the number of sentences in the claim + for i in range(n_data): + source = data[i]['source'] + system_outputs = sent_tokenize(data[i]['system_output']) + n_sents.append(len(system_outputs)) + for j in range(len(system_outputs)): + src_list.append(source) + output_list.append(system_outputs[j]) + input_list = add_question(dimension=self.dim, output=output_list, src=src_list, task=self.task) + sent_score = self.scorer.score(input_list, self.task, category, dim) + + # Get average score for each sample + start_idx = 0 + score = [] + for cur_n_sent in n_sents: + score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / cur_n_sent) + start_idx += cur_n_sent + + for i in range(n_data): + eval_scores[i][self.dim] = score[i] + + return eval_scores + + +def get_evaluator(task, model_name_or_path="", max_length=1024, device='cuda:0', cache_dir=None): + assert task in ['summarization', 'dialogue', 'data2text', 'fact'] + if task == 'summarization': + return SumEvaluator(model_name_or_path=model_name_or_path, + max_length=max_length, + device=device, + cache_dir=cache_dir) + elif task == 'dialogue': + return DialogEvaluator(model_name_or_path=model_name_or_path, + max_length=max_length, + device=device, + cache_dir=cache_dir) + elif task == 'data2text': + return D2tEvaluator(model_name_or_path=model_name_or_path, + max_length=max_length, + device=device, + cache_dir=cache_dir) + elif task == 'fact': + return FactEvaluator(model_name_or_path=model_name_or_path, + max_length=max_length, + device=device, + cache_dir=cache_dir) + else: + raise NotImplementedError('Other tasks are not implemented, \ + please customize specific tasks here.') diff --git a/applications/Chat/evaluate/unieval/scorer.py b/applications/Chat/evaluate/unieval/scorer.py new file mode 100644 index 000000000000..2c70bb9f6ded --- /dev/null +++ b/applications/Chat/evaluate/unieval/scorer.py @@ -0,0 +1,101 @@ +# MIT License + +# Copyright (c) 2022 Ming Zhong + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import torch +import torch.nn as nn +from tqdm import tqdm +from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer + + +class UniEvaluator: + + def __init__(self, model_name_or_path, max_length=1024, device='cuda:0', cache_dir=None): + """ Set up model """ + self.device = device + self.max_length = max_length + + self.config = AutoConfig.from_pretrained(model_name_or_path, cache_dir=cache_dir) + self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, cache_dir=cache_dir) + self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path, config=self.config, cache_dir=cache_dir) + + self.model.eval() + self.model.to(device) + + self.softmax = nn.Softmax(dim=1) + + self.pos_id = self.tokenizer("Yes")["input_ids"][0] + self.neg_id = self.tokenizer("No")["input_ids"][0] + + def score(self, inputs, task, category, dim, batch_size=8): + """ + Get scores for the given samples. + final_score = postive_score / (postive_score + negative_score) + """ + + # The implementation of "forward" in T5 still requires decoder_input_ids. + # Therefore, we construct a random one-word target sequence. + # The content of the target has no effect on the final scores. + tgts = ["No" for _ in range(len(inputs))] + + pos_score_list, neg_score_list = [], [] + for i in tqdm(range(0, len(inputs), batch_size), desc=f"{category}-({dim}-{task}): "): + src_list = inputs[i:i + batch_size] + tgt_list = tgts[i:i + batch_size] + try: + with torch.no_grad(): + encoded_src = self.tokenizer(src_list, + max_length=self.max_length, + truncation=True, + padding=True, + return_tensors='pt') + encoded_tgt = self.tokenizer(tgt_list, + max_length=self.max_length, + truncation=True, + padding=True, + return_tensors='pt') + + src_tokens = encoded_src['input_ids'].to(self.device) + src_mask = encoded_src['attention_mask'].to(self.device) + + tgt_tokens = encoded_tgt['input_ids'].to(self.device)[:, 0].unsqueeze(-1) + + output = self.model(input_ids=src_tokens, attention_mask=src_mask, labels=tgt_tokens) + logits = output.logits.view(-1, self.model.config.vocab_size) + + pos_score = self.softmax(logits)[:, self.pos_id] # Yes + neg_score = self.softmax(logits)[:, self.neg_id] # No + + cur_pos_score = [x.item() for x in pos_score] + cur_neg_score = [x.item() for x in neg_score] + pos_score_list += cur_pos_score + neg_score_list += cur_neg_score + + except RuntimeError: + print(f'source: {src_list}') + print(f'target: {tgt_list}') + exit(0) + + score_list = [] + for i in range(len(pos_score_list)): + score_list.append(pos_score_list[i] / (pos_score_list[i] + neg_score_list[i])) + + return score_list diff --git a/applications/Chat/evaluate/unieval/utils.py b/applications/Chat/evaluate/unieval/utils.py new file mode 100644 index 000000000000..a77505faa0d2 --- /dev/null +++ b/applications/Chat/evaluate/unieval/utils.py @@ -0,0 +1,248 @@ +# MIT License + +# Copyright (c) 2022 Ming Zhong + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import os +from typing import Dict + +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import tqdm + + +def add_question(dimension, output, src=None, ref=None, context=None, task=None): + """ + Add questions to generate input in Bool-QA format for UniEval. + + dimension: specific dimension to be evaluated + src: source input for different NLG tasks. For example, source document for summarization + and dialogue history for dialogue response generation. + output: output text generated by the models + ref: human-annotataed groundtruth + context: the context needed to evaluate several specific dimension. For example, + additional factual information when evaluating engagingness and groundedness in dialogues. + """ + + input_with_question = [] + for i in range(len(output)): + # For summarization + if task == 'summarization': + if dimension == 'fluency': + cur_input = 'question: Is this a fluent paragraph? paragraph: ' + output[i] + elif dimension == 'coherence': + cur_input = 'question: Is this a coherent summary to the document? summary: ' + output[ + i] + ' document: ' + src[i] + elif dimension == 'consistency': + cur_input = 'question: Is this claim consistent with the document? claim: ' + output[ + i] + ' document: ' + src[i] + elif dimension == 'relevance': + cur_input = 'question: Is this summary relevant to the reference? summary: ' + output[ + i] + ' reference: ' + ref[i] + else: + raise NotImplementedError( + 'The input format for this dimension is still undefined. Please customize it first.') + # For dialogues + elif task == 'dialogue': + if dimension == 'naturalness': + cur_input = 'question: Is this a natural response in the dialogue? response: ' + output[i] + elif dimension == 'coherence': + cur_input = 'question: Is this a coherent response given the dialogue history? response: '\ + + output[i] + ' dialogue history: ' + src[i] + elif dimension == 'engagingness': + cur_input = 'question: Is this an engaging and informative response according to the dialogue history and fact? response: '\ + + output[i] + ' dialogue history: ' + src[i] + ' fact: ' + context[i] + elif dimension == 'groundedness': + cur_input = 'question: Is this response consistent with knowledge in the fact? response: '\ + + output[i] + ' fact: ' + context[i] + elif dimension == 'understandability': + cur_input = 'question: Is this an understandable response in the dialogue? response: ' + output[i] + else: + raise NotImplementedError( + 'The input format for this dimension is still undefined. Please customize it first.') + # For data-to-text + elif task == 'data2text': + if dimension == 'naturalness': + cur_input = 'question: Is this a fluent utterance? utterance: ' + output[i] + elif dimension == 'informativeness': + cur_input = 'question: Is this sentence informative according to the reference? sentence: '\ + + output[i] + ' reference: ' + ref[i] + else: + raise NotImplementedError( + 'The input format for this dimension is still undefined. Please customize it first.') + # For factual consistency detection + elif task == 'fact': + if dimension == 'consistency': + cur_input = 'question: Is this claim consistent with the document? claim: ' + output[ + i] + ' document: ' + src[i] + else: + raise NotImplementedError('No other dimensions for the factual consistency detection task.') + # For new customized tasks + else: + raise NotImplementedError('Other tasks are not implemented, please customize specific tasks here.') + input_with_question.append(cur_input) + return input_with_question + + +def convert_data_to_unieval_format(output_list, src_list=None, ref_list=None): + """ + Convert the data into the unieval's format. + + output_list: a list of model output + + src_list: source input for different NLG tasks. For example, source document for summarization + and dialogue history for dialogue response generation + ref_list: human-annotated groundtruth + """ + json_data = [] + for i in range(len(output_list)): + cur = {} + cur['system_output'] = output_list[i] + if src_list is not None: + cur['source'] = src_list[i] + if ref_list is not None: + cur['reference'] = ref_list[i] + cur['context'] = "" + json_data.append(cur) + return json_data + + +def calculate_average_score(scores): + """ + Calculate average scores for different metrics + + scores: a list of scores for different metrics for each answer + + """ + metrics = {metric: 0 for metric in scores[0]} + + for score in scores: + for metric in score: + metrics[metric] += score[metric] + + for metric in metrics: + metrics[metric] /= len(scores) + + return metrics + + +def save_unieval_results(model_name: str, unieval_metric_stats: Dict[str, Dict], save_path: str) -> None: + """ + Save UniEval evaluation results of different categories for one model. + + """ + + if not os.path.exists(save_path): + os.makedirs(save_path) + + unieval_metric_stats_per_category = {} + for task, category_stat in unieval_metric_stats.items(): + for category, metric_stat in category_stat.items(): + if unieval_metric_stats_per_category.get(category, None) is None: + unieval_metric_stats_per_category[category] = {} + for metric, score in metric_stat.items(): + unieval_metric_stats_per_category[category][f"{metric}-{task}"] = score + + automatic_df = pd.DataFrame(unieval_metric_stats_per_category) + automatic_df.to_csv(os.path.join(save_path, f"{model_name}_results.csv"), index=True) + + +def read_unieval_results(results_path: str, file_name: str) -> Dict[str, Dict]: + """ + Read a csv file and return a dictionary which stores scores per metric. + + """ + + results = pd.read_csv(os.path.join(results_path, file_name), index_col=0) + + results_dict = {metric: {} for metric in list(results.index)} + for i, metric in enumerate(results_dict.keys()): + for j, category in enumerate(list(results.columns)): + if pd.isnull(results.iloc[i][j]): + continue + results_dict[metric][category] = results.iloc[i][j] + + return results_dict + + +def analyze_unieval_results(results_path: str, save_path: str) -> None: + """ + Analyze and visualize all csv files in the given folder. + + """ + + if not os.path.exists(results_path): + raise Exception(f'The given directory "{results_path}" doesn\'t exist! No results found!') + + all_statistics = {} + + for file_name in os.listdir(results_path): + if file_name.endswith("_results.csv"): + model_name = file_name.split("_results.csv")[0] + all_statistics[model_name] = read_unieval_results(results_path, file_name) + + if len(list(all_statistics.keys())) == 0: + raise Exception(f'There are no csv files in the given directory "{results_path}"!') + + frame_all = {"model": [], "category": [], "metric": [], "score": []} + frame_per_metric = {} + for model_name, model_statistics in all_statistics.items(): + for metric, metric_statistics in model_statistics.items(): + if frame_per_metric.get(metric) is None: + frame_per_metric[metric] = {"model": [], "category": [], "score": []} + + for category, category_score in metric_statistics.items(): + frame_all["model"].append(model_name) + frame_all["category"].append(category) + frame_all["metric"].append(metric) + frame_all["score"].append(category_score) + + frame_per_metric[metric]["model"].append(model_name) + frame_per_metric[metric]["category"].append(category) + frame_per_metric[metric]["score"].append(category_score) + + if not os.path.exists(save_path): + os.makedirs(save_path) + + frame_all = pd.DataFrame(frame_all) + frame_all.to_csv(os.path.join(save_path, "unieval_statistics.csv")) + + for metric in tqdm.tqdm( + frame_per_metric.keys(), + desc=f"UniEval metrics: ", + total=len(frame_per_metric.keys()), + ): + data = pd.DataFrame(frame_per_metric[metric]) + + sns.set() + fig = plt.figure(figsize=(16, 10)) + + fig = sns.barplot(x="category", y="score", hue="model", data=data, dodge=True) + fig.set_title( + f"Comparison between Different Models for Metric {metric.split('-')[0].title()} in Task {metric.split('-')[1].title()}" + ) + plt.xlabel("Evaluation Category") + plt.ylabel("Score") + + figure = fig.get_figure() + figure.savefig(os.path.join(save_path, f"{metric}.png"), dpi=400) + + plt.close() diff --git a/applications/Chat/evaluate/utils.py b/applications/Chat/evaluate/utils.py index 1f4069386fcd..fefe25f5e764 100644 --- a/applications/Chat/evaluate/utils.py +++ b/applications/Chat/evaluate/utils.py @@ -199,7 +199,7 @@ def analyze_automatic_results(results_path: str, save_path: str) -> None: for metric in tqdm.tqdm( frame_per_metric.keys(), - desc=f"metric: ", + desc=f"automatic metrics: ", total=len(frame_per_metric.keys()), ): data = pd.DataFrame(frame_per_metric[metric]) From ddcf58cacf9581d9c59a18f8276d52a061818fab Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 9 Jun 2023 09:41:27 +0800 Subject: [PATCH 309/413] Revert "[sync] sync feature/shardformer with develop" --- colossalai/device/README.md | 73 -- colossalai/device/device_mesh.py | 444 +++---- colossalai/lazy/lazy_init.py | 16 +- colossalai/nn/layer/parallel_1d/_operation.py | 1 - colossalai/shardformer/README.md | 296 ----- colossalai/shardformer/__init__.py | 0 colossalai/shardformer/layer/__init__.py | 0 colossalai/shardformer/layer/_operation.py | 97 -- .../shardformer/layer/dist_crossentropy.py | 105 -- colossalai/shardformer/layer/dropout.py | 58 - colossalai/shardformer/layer/layers.py | 1043 ----------------- colossalai/shardformer/model/__init__.py | 0 colossalai/shardformer/model/modeling_bert.py | 67 -- colossalai/shardformer/policies/__init__.py | 0 colossalai/shardformer/policies/autopolicy.py | 58 - colossalai/shardformer/policies/basepolicy.py | 217 ---- colossalai/shardformer/policies/bert.py | 170 --- colossalai/shardformer/policies/gpt2.py | 118 -- colossalai/shardformer/shard/__init__.py | 5 - colossalai/shardformer/shard/shard_config.py | 20 - colossalai/shardformer/shard/sharder.py | 266 ----- colossalai/shardformer/shard/slicer.py | 161 --- colossalai/shardformer/test/config.py | 1 - colossalai/shardformer/test/module_test.py | 50 - colossalai/shardformer/test/test.py | 124 -- colossalai/shardformer/utils/__init__.py | 0 colossalai/shardformer/utils/utils.py | 58 - colossalai/tensor/comm_spec.py | 89 +- colossalai/tensor/d_tensor/RAEDME.md | 103 -- colossalai/tensor/d_tensor/__init__.py | 4 - colossalai/tensor/d_tensor/comm_spec.py | 88 +- colossalai/tensor/d_tensor/d_tensor.py | 114 +- colossalai/tensor/d_tensor/layout.py | 30 +- .../tensor/d_tensor/layout_converter.py | 86 +- colossalai/tensor/d_tensor/sharding_spec.py | 31 +- docs/sidebars.json | 1 - docs/source/en/features/lazy_init.md | 71 -- docs/source/zh-Hans/features/lazy_init.md | 71 -- tests/test_device/test_device_mesh.py | 13 +- tests/test_device/test_init_logical_pg.py | 16 +- tests/test_lazy/lazy_init_utils.py | 10 +- tests/test_lazy/test_distribute.py | 28 +- .../test_dtensor/test_comm_spec.py | 33 +- .../test_tensor/test_dtensor/test_dtensor.py | 17 +- .../test_dtensor/test_layout_converter.py | 41 +- tests/test_tensor/test_shape_consistency.py | 7 +- tests/test_tensor/test_sharded_linear.py | 2 +- tests/test_tensor/test_sharding_spec.py | 2 +- 48 files changed, 437 insertions(+), 3868 deletions(-) delete mode 100644 colossalai/device/README.md delete mode 100644 colossalai/shardformer/README.md delete mode 100644 colossalai/shardformer/__init__.py delete mode 100644 colossalai/shardformer/layer/__init__.py delete mode 100644 colossalai/shardformer/layer/_operation.py delete mode 100644 colossalai/shardformer/layer/dist_crossentropy.py delete mode 100644 colossalai/shardformer/layer/dropout.py delete mode 100644 colossalai/shardformer/layer/layers.py delete mode 100644 colossalai/shardformer/model/__init__.py delete mode 100644 colossalai/shardformer/model/modeling_bert.py delete mode 100644 colossalai/shardformer/policies/__init__.py delete mode 100644 colossalai/shardformer/policies/autopolicy.py delete mode 100644 colossalai/shardformer/policies/basepolicy.py delete mode 100644 colossalai/shardformer/policies/bert.py delete mode 100644 colossalai/shardformer/policies/gpt2.py delete mode 100644 colossalai/shardformer/shard/__init__.py delete mode 100644 colossalai/shardformer/shard/shard_config.py delete mode 100644 colossalai/shardformer/shard/sharder.py delete mode 100644 colossalai/shardformer/shard/slicer.py delete mode 100644 colossalai/shardformer/test/config.py delete mode 100644 colossalai/shardformer/test/module_test.py delete mode 100644 colossalai/shardformer/test/test.py delete mode 100644 colossalai/shardformer/utils/__init__.py delete mode 100644 colossalai/shardformer/utils/utils.py delete mode 100644 colossalai/tensor/d_tensor/RAEDME.md delete mode 100644 docs/source/en/features/lazy_init.md delete mode 100644 docs/source/zh-Hans/features/lazy_init.md diff --git a/colossalai/device/README.md b/colossalai/device/README.md deleted file mode 100644 index 8f835735bef4..000000000000 --- a/colossalai/device/README.md +++ /dev/null @@ -1,73 +0,0 @@ -# 🗄 Device - -## 📚 Table of Contents - -- [🗄 Device](#-device) - - [📚 Table of Contents](#-table-of-contents) - - [🔗 Introduction](#-introduction) - - [📝 Design](#-design) - - [🔨 Usage](#-usage) - -## 🔗 Introduction - -This module contains the implementation of the abstraction of the device topology. It is used to represent the device topology and manage the distributed information related to the network. - -## 📝 Design - - -This module is inspired by the DeviceMesh in the [Alpa project](https://github.com/alpa-projects/alpa) and the device array can be represented as a 1D or 2D mesh. We will be extending the device mesh to support 3D mesh in the future. - - -## 🔨 Usage - -- Create a device mesh - -```python -# this is the list of global ranks involved in the device mesh -# assume we have 4 GPUs and the global ranks for these GPUs are 0, 1, 2, 3 -physical_mesh_id = torch.arange(4) -mesh_shape = [2, 2] -device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) -``` - -- View the mesh - - -```python -# view the mesh shape -# expect output -# [2, 2] -print(device_mesh.shape) - - -# view the logical mesh with global ranks -# expect output -# [ -# [0, 1], -# [2, 3] -# ] -print(device_mesh.logical_mesh_id) - -# view the number of devices in the mesh -# expect output -# 4 -print(device_mesh.num_devices) - -``` - -- Initialize the process group - -```python -# intialize process group -device_mesh.init_logical_process_group() - - -# get the process group for a rank with respect to an axis -# this is the process group involving global ranks 0 and 2 -print(device_mesh.get_process_group(axis=0, global_rank=0)) - -# get the ranks in the process with respect to an axis -# expect output -# [0, 2] -print(device_mesh.get_ranks_in_process_group(axis=0, global_rank=0)) -``` diff --git a/colossalai/device/device_mesh.py b/colossalai/device/device_mesh.py index 0490a440153e..2a5f747fbc23 100644 --- a/colossalai/device/device_mesh.py +++ b/colossalai/device/device_mesh.py @@ -3,19 +3,11 @@ with some changes. """ import operator -from dataclasses import dataclass from functools import reduce -from typing import Dict, List, Union +from typing import List, Tuple import torch import torch.distributed as dist -from torch.distributed import ProcessGroup - - -@dataclass -class ProcessGroupContainer: - process_group: ProcessGroup - ranks: List[int] # modified from alpa LogicalDeviceMesh(https://github.com/alpa-projects/alpa/blob/main/alpa/shard_parallel/auto_sharding.py) @@ -35,11 +27,9 @@ class DeviceMesh: during initializing the DeviceMesh instance if the init_process_group set to True. Otherwise, users need to call create_process_groups_for_logical_mesh manually to init logical process group. (default: False) - device (str): the device for the process groups used by the DeviceMesh instance. (default: 'cuda') + need_flatten(bool, optional): initialize flatten_device_mesh during initializing the DeviceMesh instance if the need_flatten set to True. """ - _DIST_BACKEND = {"cuda": "nccl", "cpu": "gloo"} - def __init__(self, physical_mesh_id: torch.Tensor, mesh_shape: torch.Size = None, @@ -47,140 +37,48 @@ def __init__(self, mesh_alpha: List[float] = None, mesh_beta: List[float] = None, init_process_group: bool = False, - device: str = 'cuda'): - # ============================ - # Physical & Logical Mesh IDs - # ============================ - self._physical_mesh_id = physical_mesh_id - assert physical_mesh_id.dim() == 1, "physical_mesh_id should be a 1D tensor." - - # logical mesh ids can be obtained via two ways - # 1. provide physical mesh id and provide mesh shape - # 2. directly supply the logical mesh id - assert mesh_shape is None or logical_mesh_id is None, \ - "Only one of mesh_shape and logical_mesh_id can be specified." \ - "Logical mesh IDs are obtained from either mesh_shape + phyiscal_mesh_id or directly from the user-supplied logical_mesh_id" - + need_flatten: bool = True): + self.physical_mesh_id = physical_mesh_id if logical_mesh_id is None: self.mesh_shape = mesh_shape - self._logical_mesh_id = self._physical_mesh_id.reshape(self.mesh_shape) + self._logical_mesh_id = self.physical_mesh_id.reshape(self.mesh_shape) else: self._logical_mesh_id = logical_mesh_id self.mesh_shape = self._logical_mesh_id.shape - # ensure two things: - # 1. logical and physical mesh IDs should contain the same elements - # 2. there is no duplicate IDs in each mesh, e.g. [2, 2] is not allowed - assert torch.equal(torch.unique(self._physical_mesh_id), torch.unique(self.logical_mesh_id)), \ - "physical and logical mesh IDs should contain the same elements, please check if you have consistent physical_mesh_id and logical_mesh_id." - assert torch.unique(self._physical_mesh_id).numel() == self._physical_mesh_id.numel(), \ - "Found duplicate IDs in the phyiscal_mesh_id and this is not allowed, please check your physical_mesh_id again." - assert torch.unique(self.logical_mesh_id).numel() == self.logical_mesh_id.numel(), \ - "Found duplicate IDs in the logical_mesh_id and this is not allowed, please check your logical_mesh_id again." - - # =============================================== + # map global rank into logical rank + self.convert_map = {} + self._global_rank_to_logical_rank_map(self._logical_mesh_id, []) # coefficient for alpha-beta communication model - # alpha is latency and beta is bandwidth - # =============================================== - # if the values are not provided, we assume they are 1 for simplicity if mesh_alpha is None: mesh_alpha = [1] * len(self.mesh_shape) if mesh_beta is None: mesh_beta = [1] * len(self.mesh_shape) - self.mesh_alpha = tuple(mesh_alpha) self.mesh_beta = tuple(mesh_beta) - - # ensure the alpha and beta have the same shape - assert len(self.mesh_alpha) == len(self.mesh_beta), \ - "mesh_alpha and mesh_beta should have the same length, please check your mesh_alpha and mesh_beta again." - - # ========================= - # Device for Process Group - # ========================= - self._device = device - self._dist_backend = self._DIST_BACKEND[device] - - # ========================= - # Process Group Management - # ========================= - # the _global_to_local_rank_mapping is structured as follows - # { - # : [ , , , ...] - # } - self._global_to_local_rank_mapping = dict() - self._init_global_to_logical_rank_mapping(mapping=self._global_to_local_rank_mapping, - tensor=self.logical_mesh_id) - - # create process group - self._process_group_dict = {} - self._ranks_in_the_process_group = {} - self._global_rank_of_current_process = None - self._is_initialized = False - - # initialize process group if specified - self._init_ranks_in_the_same_group() - self._init_process_group = init_process_group - if init_process_group: - self.init_logical_process_group() + self.init_process_group = init_process_group + self.need_flatten = need_flatten + if self.init_process_group: + self.process_groups_dict = self.create_process_groups_for_logical_mesh() + if self.need_flatten and self._logical_mesh_id.dim() > 1: + self.flatten_device_mesh = self.flatten() + # Create a new member `flatten_device_meshes` to distinguish from original flatten methods (Because I'm not sure if there are functions that rely on the self.flatten()) + # self.flatten_device_meshes = FlattenDeviceMesh(self.physical_mesh_id, self.mesh_shape, self.mesh_alpha, + # self.mesh_beta) @property - def shape(self) -> torch.Size: - """ - Return the shape of the logical mesh. - """ + def shape(self): return self.mesh_shape @property - def num_devices(self) -> int: - """ - Return the number of devices contained in the device mesh. - """ - return reduce(operator.mul, self._physical_mesh_id.shape, 1) + def num_devices(self): + return reduce(operator.mul, self.physical_mesh_id.shape, 1) @property - def logical_mesh_id(self) -> torch.Tensor: - """ - Return the logical mesh id. - """ + def logical_mesh_id(self): return self._logical_mesh_id - def get_process_group(self, axis: int, global_rank: int = None) -> ProcessGroup: - """ - Return the process group on the specified axis. - - Args: - axis (int): the axis of the process group. - global_rank (int, optional): the global rank of the process group. If not specified, the current process is used. (default: None) - """ - if global_rank is None: - global_rank = self._global_rank_of_current_process - return self._process_group_dict[global_rank][axis] - - def get_process_group_for_all_axes(self, global_rank: int = None) -> Dict[int, ProcessGroup]: - """ - Return the process groups for all axes. - - Args: - global_rank (int, optional): the global rank of the process - """ - if global_rank is None: - global_rank = self._global_rank_of_current_process - return self._process_group_dict[global_rank] - - def get_ranks_in_process_group(self, axis: int, global_rank: int = None) -> List[int]: - """ - Return the ranks in the process group on the specified axis. - - Args: - axis (int): the axis of the process group. - global_rank (int, optional): the global rank of the process - """ - if global_rank is None: - global_rank = self._global_rank_of_current_process - return self._ranks_in_the_process_group[global_rank][axis] - - def __deepcopy__(self, memo) -> "DeviceMesh": + def __deepcopy__(self, memo): cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result @@ -188,206 +86,111 @@ def __deepcopy__(self, memo) -> "DeviceMesh": if k != 'process_groups_dict': setattr(result, k, __import__("copy").deepcopy(v, memo)) else: - # process group cannot be copied - # thus, we share them directly setattr(result, k, v) + return result - def _init_global_to_logical_rank_mapping(self, - mapping: Dict, - tensor: torch.Tensor, - index_list: List[int] = []) -> Dict[int, List[int]]: + def flatten(self): """ - Build a global rank to local rank mapping for each process group in different axis in the logical device mesh. - - Args: - mapping (Dict): a dictionary that maps the global rank to the local rank in the logical device mesh. - tensor (torch.Tensor): the tensor that contains the logical mesh ids. - index_list (List[int]) - - Returns: - mapping (Dict): a dictionary that maps the global rank to the local rank in the logical device mesh. - The value is a list of integers and each integer represents the local rank in the indexed axis. + Flatten the logical mesh into an effective 1d logical mesh, """ - for index, inner_tensor in enumerate(tensor): - # index means the local rank in the current axis - # inner_tensor refers to the processes with the same local rank + flatten_mesh_shape_size = len(self.mesh_shape) + flatten_mesh_shape = [self.num_devices] + return DeviceMesh(self.physical_mesh_id, + tuple(flatten_mesh_shape), + mesh_alpha=[max(self.mesh_alpha)] * (flatten_mesh_shape_size - 1), + mesh_beta=[max(self.mesh_beta)] * (flatten_mesh_shape_size - 1), + init_process_group=self.init_process_group, + need_flatten=False) + def _global_rank_to_logical_rank_map(self, tensor, index_list): + ''' + This method is a helper function to build convert_map recursively. + ''' + for index, inner_tensor in enumerate(tensor): if inner_tensor.numel() == 1: - # if the inner_tensor only has one element, it means that - # it already reaches the last axis - # we append its local_rank in the last axis to the index_list - # and assign to the mapping - # the value of the mapping is the the local rank at the indexed axis of the device mesh - mapping[int(inner_tensor)] = index_list + [index] + self.convert_map[int(inner_tensor)] = index_list + [index] else: - # we recursively go into the function until we reach the last axis - # meanwhile, we should add the local rank in the current axis in the index_list - self._init_global_to_logical_rank_mapping(mapping, inner_tensor, index_list + [index]) + self._global_rank_to_logical_rank_map(inner_tensor, index_list + [index]) - def init_logical_process_group(self): + def create_process_groups_for_logical_mesh(self): ''' This method is used to initialize the logical process groups which will be used in communications among logical device mesh. Note: if init_process_group set to False, you have to call this method manually. Otherwise, the communication related function, such as ShapeConsistencyManager.apply will raise errors. ''' - # sanity check - assert dist.is_initialized, "The torch.distributed should be initialized before calling init_logical_process_group" - assert not self._is_initialized, "The logical process group has been initialized, do not call init_logical_process_group twice" - - # update the global rank of the current process - self._global_rank_of_current_process = dist.get_rank() - duplicate_check_list = [] - - # flatten the global ranks to 1D list - global_rank_flatten_list = self._physical_mesh_id.view(-1).tolist() - - for global_rank in global_rank_flatten_list: - # find the other ranks which are in the same process group as global_rank - ranks_in_same_group_by_axis = self._collate_global_ranks_in_same_process_group(global_rank) - - for axis, ranks_in_same_group in ranks_in_same_group_by_axis.items(): - # skip duplicated process group creation - if ranks_in_same_group in duplicate_check_list: - continue - - # create the process group - pg_handler = dist.new_group(ranks=ranks_in_same_group, backend=self._dist_backend) - - # keep this process group in the process_groups_dict - for rank in ranks_in_same_group: - if rank not in self._process_group_dict: - self._process_group_dict[rank] = dict() - self._process_group_dict[rank][axis] = pg_handler - - # update the init flag - # we only allow init for once - self._is_initialized = True - - def _init_ranks_in_the_same_group(self): - """ - This method is used to initialize the ranks_in_the_same_group dictionary. - """ - # flatten the global ranks to 1D list - global_rank_flatten_list = self._physical_mesh_id.view(-1).tolist() - + process_groups_dict = {} + check_duplicate_list = [] + global_rank_flatten_list = self.physical_mesh_id.view(-1).tolist() for global_rank in global_rank_flatten_list: - # find the other ranks which are in the same process group as global_rank - ranks_in_same_group_by_axis = self._collate_global_ranks_in_same_process_group(global_rank) - - for axis, ranks_in_same_group in ranks_in_same_group_by_axis.items(): - # create dict for each rank - if global_rank not in self._process_group_dict: - self._ranks_in_the_process_group[global_rank] = dict() + process_groups = self.global_rank_to_process_groups_with_global_rank(global_rank) + for axis, process_group in process_groups.items(): + if axis not in process_groups_dict: + process_groups_dict[axis] = [] + if process_group not in check_duplicate_list: + check_duplicate_list.append(process_group) + process_group_handler = dist.new_group(process_group) + process_groups_dict[axis].append((process_group, process_group_handler)) - # keep this process group in the process_groups_dict - self._ranks_in_the_process_group[global_rank][axis] = ranks_in_same_group + return process_groups_dict - def global_rank_to_local_rank(self, rank: int, axis: int = None) -> Union[List[int], int]: - """ - Return the local rank of the given global rank in the logical device mesh. + def global_rank_to_logical_rank(self, rank): + return self.convert_map[rank] - Args: - rank (int): the global rank in the logical device mesh. - axis (int): the axis of the logical device mesh. - """ - local_ranks = self._global_to_local_rank_mapping[rank] - if axis: - return local_ranks[axis] - else: - return local_ranks - - def _collate_global_ranks_in_same_process_group(self, global_rank): + def global_rank_to_process_groups_with_logical_rank(self, rank): ''' - Give a global rank and return all global ranks involved in its associated process group in each axis. - - Example: - - ```python - sphysical_mesh_id = torch.arange(0, 16) - mesh_shape = (4, 4) - - # logical mesh will look like - # [[0, 1, 2, 3], - # [4, 5, 6, 7], - # [8, 9, 10,11], - # [12,13,14,15]] - - device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - print(device_mesh.collate_global_ranks_in_same_process_group(0)) - - # key is axis name - # value is a list of global ranks in same axis with rank 0 - # output will look like - # { - 0: [0, 4, 8, 12], - 1: [0, 1, 2, 3] - # } + Give a global rank and return all logical process groups of this rank. + for example: + physical_mesh_id = torch.arange(0, 16).reshape(2, 8) + mesh_shape = (4, 4) + # [[0, 1, 2, 3], + # [4, 5, 6, 7], + # [8, 9, 10,11], + # [12,13,14,15]] + device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) + print(device_mesh.global_rank_to_process_groups_with_logical_rank(0)) + output: + # key is axis name + # value is a list of logical ranks in same axis with rank 0 + {0: [[0, 0], [1, 0], [2, 0], [3, 0]], 1: [[0, 0], [0, 1], [0, 2], [0, 3]]} ''' - # We have init the global rank to local rank by calling _init_global_to_logical_rank_mapping - # for self._global_to_local_rank_mapping - # the key is the global rank - # the value is the list of local ranks corresponding to the global rank with respect of different axes - # we can see the list of local ranks as the process coordinates for simplicity - # the key and value are all unique, therefore, - # we can also to use the coordinates to find the global rank - - # ========================================================================= - # Step 1 - # find all the process_coordinates for processes in the same process group - # as the given global rank - # ========================================================================= - - # each - processes_in_the_same_process_group = {} - - for dim in range(self.logical_mesh_id.dim()): - # iterate over the dimension size so that we can include all processes - # in the same process group in the given axis - # the _local_rank refers to the local rank of the current process - for _local_rank in range(self.logical_mesh_id.shape[dim]): - - # if this dimension is not initailized yet, - # initialize it with an empty array - if dim not in processes_in_the_same_process_group: - processes_in_the_same_process_group[dim] = [] - - # get the local rank corresponding to the global rank - process_coordinates = self._global_to_local_rank_mapping[global_rank].copy() - - # replace the local rank in the given dimension with the - # lcoal rank of the current process iterated - process_coordinates[dim] = _local_rank - processes_in_the_same_process_group[dim].append(process_coordinates) - - # ================================================================= - # Step 2 - # Use local rank combination to find its corresponding global rank - # ================================================================= - # the key of the dict is the axis - # the value is the list of global ranks which are in the same process group as the given global rank - global_pg_ranks = {} - for dim, coordinates_of_all_processes in processes_in_the_same_process_group.items(): - global_pg_ranks[dim] = [] - for process_coordinates in coordinates_of_all_processes: - # find the global rank by local rank combination - for _global_rank, _process_coordinates in self._global_to_local_rank_mapping.items(): - if process_coordinates == _process_coordinates: - global_pg_ranks[dim].append(_global_rank) - return global_pg_ranks - - def flatten(self): - """ - Flatten the logical mesh into an effective 1d logical mesh, - """ - flatten_mesh_shape_size = len(self.mesh_shape) - flatten_mesh_shape = [self.num_devices] - return DeviceMesh(self._physical_mesh_id, - tuple(flatten_mesh_shape), - mesh_alpha=[max(self.mesh_alpha)] * (flatten_mesh_shape_size - 1), - mesh_beta=[max(self.mesh_beta)] * (flatten_mesh_shape_size - 1), - init_process_group=self._init_process_group) + process_groups = {} + for d in range(self.logical_mesh_id.dim()): + for replacer in range(self.logical_mesh_id.shape[d]): + if d not in process_groups: + process_groups[d] = [] + process_group_member = self.convert_map[rank].copy() + process_group_member[d] = replacer + process_groups[d].append(process_group_member) + return process_groups + + def global_rank_to_process_groups_with_global_rank(self, rank): + ''' + Give a global rank and return all process groups of this rank. + for example: + physical_mesh_id = torch.arange(0, 16).reshape(2, 8) + mesh_shape = (4, 4) + # [[0, 1, 2, 3], + # [4, 5, 6, 7], + # [8, 9, 10,11], + # [12,13,14,15]] + device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) + print(device_mesh.global_rank_to_process_groups_with_global_rank(0)) + output: + # key is axis name + # value is a list of global ranks in same axis with rank 0 + {0: [0, 4, 8, 12], 1: [0, 1, 2, 3]} + ''' + logical_process_groups = self.global_rank_to_process_groups_with_logical_rank(rank) + process_groups = {} + for dim, logical_ranks in logical_process_groups.items(): + process_groups[dim] = [] + for logical_rank in logical_ranks: + for g_rank, l_rank in self.convert_map.items(): + if l_rank == logical_rank: + process_groups[dim].append(g_rank) + return process_groups def all_gather_cost(self, num_bytes, mesh_dim): num_devices = self.logical_mesh_id.shape[mesh_dim] @@ -409,3 +212,38 @@ def all_to_all_cost(self, num_bytes, mesh_dim): penalty_factor = num_devices / 2.0 return (self.mesh_alpha[mesh_dim] + self.mesh_beta[mesh_dim] * (num_devices - 1) / num_devices / num_devices * num_bytes * penalty_factor + 0.001) + + +class FlattenDeviceMesh(DeviceMesh): + + def __init__(self, physical_mesh_id, mesh_shape, mesh_alpha=None, mesh_beta=None): + super().__init__(physical_mesh_id, + mesh_shape, + mesh_alpha, + mesh_beta, + init_process_group=False, + need_flatten=False) + # Different from flatten(), mesh_shape leaves unchanged, mesh_alpha and mesh_beta are scalars + self.mesh_alpha = max(self.mesh_alpha) + self.mesh_beta = min(self.mesh_beta) + # Different from original process_groups_dict, rank_list is not stored + self.process_number_dict = self.create_process_numbers_for_logical_mesh() + + def create_process_numbers_for_logical_mesh(self): + ''' + Build 1d DeviceMesh in column-major(0) and row-major(1) + for example: + mesh_shape = (2,4) + # [[0, 1, 2, 3], + # [4, 5, 6, 7]] + # return {0: [0, 4, 1, 5, 2, 6, 3, 7], 1: [0, 1, 2, 3, 4, 5, 6, 7]} + ''' + num_devices = reduce(operator.mul, self.mesh_shape, 1) + process_numbers_dict = {} + process_numbers_dict[0] = torch.arange(num_devices).reshape(self.mesh_shape).transpose(1, 0).flatten().tolist() + process_numbers_dict[1] = torch.arange(num_devices).reshape(self.mesh_shape).flatten().tolist() + return process_numbers_dict + + def mix_gather_cost(self, num_bytes): + num_devices = reduce(operator.mul, self.mesh_shape, 1) + return (self.mesh_alpha + self.mesh_beta * (num_devices - 1) / num_devices * num_bytes + 0.1) diff --git a/colossalai/lazy/lazy_init.py b/colossalai/lazy/lazy_init.py index ca8914362cd6..76f550dc4392 100644 --- a/colossalai/lazy/lazy_init.py +++ b/colossalai/lazy/lazy_init.py @@ -1,5 +1,5 @@ from types import MethodType -from typing import Callable, Dict, Optional, Union +from typing import Callable, Optional, Union import torch import torch.distributed as dist @@ -8,9 +8,8 @@ from torch.utils._pytree import tree_map from colossalai._analyzer._subclasses import MetaTensor -from colossalai.device.device_mesh import DeviceMesh from colossalai.tensor.d_tensor.d_tensor import DTensor -from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec +from colossalai.tensor.d_tensor.layout import Layout # reference: https://pytorch.org/cppdocs/notes/tensor_creation.html _NORMAL_FACTORY = [ @@ -173,7 +172,7 @@ def materialize(self) -> torch.Tensor: self.clean() return _convert_cls(self, target) - def distribute(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> torch.Tensor: + def distribute(self, layout: Layout) -> torch.Tensor: """Distribute the ``LazyTensor`` to ``torch.Tensor`` by modifying __class__ (inplace), according to the layout. Args: @@ -184,7 +183,7 @@ def distribute(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> to """ target = self._materialize_data() self.clean() - local_tensor = DTensor(target, device_mesh, sharding_spec).local_tensor + local_tensor = DTensor(target, layout).local_tensor return _convert_cls(self, local_tensor) def clean(self) -> None: @@ -537,10 +536,7 @@ def apply_fn(name: str, p: LazyTensor): return _apply_to_lazy_module(module, apply_fn, verbose) @staticmethod - def distribute(module: nn.Module, - device_mesh: DeviceMesh, - sharding_spec_dict: Dict[str, ShardingSpec], - verbose: bool = False) -> nn.Module: + def distribute(module: nn.Module, layout_dict: dict, verbose: bool = False) -> nn.Module: """Distribute all ``nn.Parameter`` from ``LazyTensor``. This function will modify the module in-place. Args: @@ -550,7 +546,7 @@ def distribute(module: nn.Module, """ def apply_fn(name: str, p: LazyTensor): - p.distribute(device_mesh, sharding_spec_dict[name]) + p.distribute(layout_dict[name]) return _apply_to_lazy_module(module, apply_fn, verbose) diff --git a/colossalai/nn/layer/parallel_1d/_operation.py b/colossalai/nn/layer/parallel_1d/_operation.py index 300baf9c12ba..394334558275 100644 --- a/colossalai/nn/layer/parallel_1d/_operation.py +++ b/colossalai/nn/layer/parallel_1d/_operation.py @@ -1,6 +1,5 @@ import torch import torch.distributed as dist - from colossalai.core import global_context as gpc try: diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md deleted file mode 100644 index 93a4f1e578e4..000000000000 --- a/colossalai/shardformer/README.md +++ /dev/null @@ -1,296 +0,0 @@ -# ⚡️ ShardFormer - -## 📚 Table of Contents - -- [⚡️ ShardFormer](#️-shardformer) - - [📚 Table of Contents](#-table-of-contents) - - [🔗 Introduction](#-introduction) - - [🔨 Usage](#-usage) - - [🔮 Simple example](#-simple-example) - - [💡 Policy](#-policy) - - [😊 Module](#-module) - - -## 🔗 Introduction - -**Shardformer** is a module that automatically parallelizes the mainstream models in libraries such as HuggingFace and TIMM. This module aims to make parallelization hassle-free for users who are not from the system background. - -## 🔨 Usage - -The sample API usage is given below: - -``` python -from colossalai.shardformer import shard_model -from transformers import BertForMaskedLM - -# create huggingface model as normal -model = BertForMaskedLM.from_pretrained("bert-base-uncased") - -# make the huggingface model paralleled to ShardModel -# auto policy: -sharded_model = shard_model(model) - -# custom policy: -from xxx import -sharded_model = shard_model(model, ) - -# do angthing as normal -... -``` - -## 🔮 Simple example - -``` shell -# inference -colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode inference -# train -colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode train -``` - - -## 💡 Policy - -If you wanna parallel the model in a custom way, just overwrite the policy class for the Hugging Face model. - -You should do: - -1. Inherit Policy class -2. Overwrite `argument_policy` method - - In this method, you need to list which layers class you wanna modify and the attributes and parameters in those layers. Shardformer will replace all the layer belonging to the class you specified. - - `attr_dict` is dict contains all the attributes need to be modified in this layer. - - `param_funcs` is a list contains some functions which will return the path of the weight and bias from the layer. -3. Overwrite `inject_policy` method (Optional) - - Shardformer will inject the model according to this method. If you need to modify the forward or backward progress (like distributed corssentropy loss in Bert) you need to overwrite this method. -4. Overwrite or add the param functions - - These functions use a suffix to record the path of weight or bias for the layer. - - The return is a list contains some `Col_Layer` or `Row_Layer` objects, which means slice along col and row respectively. -5. Overwrite `binding_policy` (Optional) - - Overwrite to specify Shardformer will bind some weight between layers, like embedding and unembedding layers. - - This function will return a dict, the key and value are the suffix of weight need to be binded. - -More details can be found in shardformer/policies/basepolicy.py -``` python -from colossalai.shardformer.policies.basepolicy import Policy, Layer, Col_Layer, Row_Layer, Argument - -CustomPolicy(Policy): -@staticmethod - def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]: - r""" - Return the dict for the modify policy, the key is the original layer class and the value is the - argument for the modify layer - - Args: - model_config (:class:`tansformer.Config`): The config of transformer model - shard_config (:class:`ShardConfig`): The config for sharding model - - Return: - Dict for the modify policy, - :: - { - origin layer class1 (nn.Module): Argument( - attr_dict = { - argument1: value1, - argument2: value2, - ... - }, - param_funcs = [ - staticmethod1, - staticmethod2, - ... - ] - ), - origin layer class2 (nn.Module): Argument( - attr_dict = { - argument1: value1, - argument2: value2, - ... - }, - param_funcs = [ - staticmethod1, - staticmethod2, - ... - ] - ), - ... - } - - """ - raise NotImplementedError - - @staticmethod - def inject_policy() -> Tuple[nn.Module, nn.Module]: - r""" - Return the dict for the inject model - - Return: - The injected model, key is the original model and value is the new shardmodel - :: - (OrignModel, CustomModel) - in `CustomModel`, we can overwrite the forward and backward process - """ - return () - - @staticmethod - def binding_policy() -> Dict: - r""" - Return the dict for the binding model - - Return: - This method should return the binding relationship for some layers share the weight or bias, - the key and value is the suffix of the weight or bias of the model - :: - return { - "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", - } - """ - return NotImplementedError - - @staticmethod - def attn_in() -> List: - """ - Attention qkv layer - - Returns: - List[Layer]: List of layer object, each layer is the new - """ - return NotImplementedError - - @staticmethod - def attn_out() -> List: - """ - Attention output projection layer - - Returns: - List[Layer]: List of layer object - """ - return NotImplementedError - - @staticmethod - def mlp_in() -> List: - """ - h -> 4h mlp layer - - Returns: - List[Layer]: List of layer object - """ - return NotImplementedError - - @staticmethod - def mlp_out() -> List: - """ - 4h -> h mlp layer - - Returns: - List[Layer]: List of layer object - """ - return NotImplementedError - - @staticmethod - def embedding() -> List: - """ - Partially slice the embedding layer - vocab_size->vocab_size//gpu_nums - - Return: - List[Layer]: List of layer object - """ - return NotImplementedError - - @staticmethod - def unembedding() -> List: - """ - Partially slice the embedding layer - vocab_size->vocab_size//gpu_nums - - Return: - List[Layer]: List of layer object - """ - return NotImplementedError - -``` - - -## 😊 Module - - 1. Flowchart - -

    - -

    - - 2. Important Modules - - - CLASS `shard_model`: - - This is the user api to use shardformer, just create a model from transformers and define a custom policy or use shardformer autopolicy to make a shard model. - - - CLASS `Layer`: - - Parameters: - - weight (str): The weight suffix of the layer - - bias (str): The bias suffix of the layer - - replace_layer (:class:`colosalai.nn`): The layer to replace the original layer - - ignore (bool): Whether to ignore this layer if it is not in the model - - This class is used to specify the replacement policy for a particular layer. If `replace_layer` is None, only parameter partitioning will be performed without replacing the layer class. - - CLASS `Col_Layer(Layer)`: - - gather_output (bool): Whether to gather the output of the layer - - This class inherited from `Layer`, representing the layer will be sliced along column. - - CLASS `Row_Layer(Layer)`: - - This class inherited from `Layer`, representing the layer will be sliced along row. - - - CLASS `Policy`: - - In Shardformer, this class holds significant importance as it defines the model partitioning methods, required parameter modifications, and model injection techniques all within a single Policy class. - - `Policy.attn_in()/attn_out()/mlp_in()/mlp_out()/embedding()/unembedding()`...... - - These functions define the partitioning methods of the parameters at different locations in the model. Each function returns a list of objects of Layer class that specify the replacement approach for these parameters. Shardformer also supports user-defined functions for modifying their models, in addition to the listed functions. - - `Policy.argument_policy()` - - In this function, the user should use multiple dict to define which class of layers will require replacement. This includes the attributes and parameters that need to be modified or replaced. Attributes are stored in the form of a "suffix-string: value" dict, while parameters are stored via multiple static methods that return the replacement approach. - - `Policy.inject_policy()` - - This function will return the injected model to replace the original model. The new model should be a nn.Module class which includes modified forward or backward functions or anything else. - - `Policy.binding_policy()` - - This function will return the weight sharing information in the model in some dict. The key and value are both the suffixes of the shared parameters. - - - CLASS `ModelSharder(model, policy)`: - - This class helps shard the model, the parameter is the created transformers model and the custom policy. If custom policy is None, shardformer will automatically get already defined policy for the model. - - `ModelShard.inject_model()` - - This function is used to inject the model to modify the forward and backward progress. - - `ModelShard.replace_layer()` - - This function is used to replace the original layers with colossalai layer to make them paralleled and can do distributed communication. - - `ModelShard.bind_layer()` - - This function is used to help different layers share weight or bias. - - - CLASS `Slicer`: - - This class is used to slice tensor according to policy. - - - 3. DistCrossEntropy Loss - - Overview - - In order to reduce the communication size, caculate the crossentropy before all gather, refer to [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), reduce the communication size from [batch_size * seq_length * vocab_size] to [batch_size * seq_length]. The origin loss function is: - $$ loss = -\log(\frac{\exp(x[class])}{\sum_i\exp(x[i])})$$ - - alse can be represented as: - - $$ loss = \log(\sum_i\exp(x[i])) - x[class]$$ - - - Step - - - First get the maximum logits across all the devices, make all the logist minus the maximun value to scale the value less than zero to avoid the value of exp being too large - - - Get a mask to mask the logits not in the local device - - - Caculate the loss according to the second formula diff --git a/colossalai/shardformer/__init__.py b/colossalai/shardformer/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/colossalai/shardformer/layer/_operation.py b/colossalai/shardformer/layer/_operation.py deleted file mode 100644 index e817ea3ebbee..000000000000 --- a/colossalai/shardformer/layer/_operation.py +++ /dev/null @@ -1,97 +0,0 @@ -import torch -import torch.distributed as dist - -from colossalai.core import global_context as gpc - -try: - import fused_mix_prec_layer_norm_cuda -except: - fused_mix_prec_layer_norm_cuda = None - - -class FusedLayerNormAffineFunction1D(torch.autograd.Function): - r"""Layernorm - - Args: - input: input matrix. - weight: weight matrix. - bias: bias matrix. - normalized_shape: input shape from an expected input of size. - :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]` - If a single integer is used, it is treated as a singleton list, and this module will - normalize over the last dimension which is expected to be of that specific size. - eps: a value added to the denominator for numerical stability - """ - - @staticmethod - def forward(ctx, input, weight, bias, normalized_shape, eps): - ctx.normalized_shape = normalized_shape - ctx.eps = eps - input_ = input.contiguous() - weight_ = weight.contiguous() - bias_ = bias.contiguous() - output, mean, invvar = fused_mix_prec_layer_norm_cuda.forward_affine(input_, ctx.normalized_shape, weight_, - bias_, ctx.eps) - ctx.save_for_backward(input_, weight_, bias_, mean, invvar) - return output - - @staticmethod - def backward(ctx, grad_output): - input_, weight_, bias_, mean, invvar = ctx.saved_tensors - grad_input = grad_weight = grad_bias = None - grad_input, grad_weight, grad_bias \ - = fused_mix_prec_layer_norm_cuda.backward_affine( - grad_output.contiguous(), mean, invvar, - input_, ctx.normalized_shape, - weight_, bias_, ctx.eps) - - return grad_input, grad_weight, grad_bias, None, None - - -class LinearWithAsyncCommunication(torch.autograd.Function): - """ - Linear layer execution with asynchronous communication in backprop. - """ - - @staticmethod - def forward(ctx, input_, weight, bias, parallel_mode, async_grad_allreduce): - ctx.save_for_backward(input_, weight) - ctx.use_bias = bias is not None - ctx.parallel_mode = parallel_mode - ctx.async_grad_allreduce = async_grad_allreduce - - output = torch.matmul(input_, weight.t()) - if bias is not None: - output = output + bias - return output - - @staticmethod - def backward(ctx, grad_output): - input, weight = ctx.saved_tensors - use_bias = ctx.use_bias - - total_input = input - grad_input = grad_output.matmul(weight) - grad_output = grad_output.contiguous() - # Convert the tensor shapes to 2D for execution compatibility - grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2]) - total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2]) - - if ctx.async_grad_allreduce: - # Asynchronous all-reduce - handle = dist.all_reduce(grad_input, group=gpc.get_group(ctx.parallel_mode), async_op=True) - # Delay the start of weight gradient computation shortly (3us) to have - # all-reduce scheduled first and have GPU resources allocated - _ = torch.empty(1, device=grad_output.device) + 1 - - grad_weight = grad_output.t().matmul(total_input) - grad_bias = grad_output.sum(dim=0) if use_bias else None - - if ctx.async_grad_allreduce: - handle.wait() - - return grad_input, grad_weight, grad_bias, None, None, None - - -def linear_with_async_comm(input_, weight, bias, parallel_mode, async_grad_allreduce): - return LinearWithAsyncCommunication.apply(input_, weight, bias, parallel_mode, async_grad_allreduce) diff --git a/colossalai/shardformer/layer/dist_crossentropy.py b/colossalai/shardformer/layer/dist_crossentropy.py deleted file mode 100644 index 1869594670ce..000000000000 --- a/colossalai/shardformer/layer/dist_crossentropy.py +++ /dev/null @@ -1,105 +0,0 @@ -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd import Function - - -class DistCrossEntropy(Function): - r""" - Overwrite the forward and backward function to calculate the cross entropy loss before gather - - Args: - Function (:class:`torch.autograd.Function`): default - """ - - @staticmethod - def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor): - r""" - Calculate the cross entropy loss before gather, the origin loss function is as follows: - loss = -log(exp(x[class])/sum(exp(x[i])) - and can be rewrite as: - loss = log(sum(exp(x[i])) - x[class] - - To avoid the `nan` of log(sim(exp(x[i]))), we minus the max of x[i] - - Args: - vocab_logits (:class:`torch.Tensor`): The logits of the vocabulary, shape is - [batch_size, seq_len, vocab_size] - labels (:class:`torch.Tensor`): The labels of the vocabulary, shape is - [batch_size, seq_len] - - Returns: - :class:`torch.Tensor`: The cross entropy loss - """ - # get the max - logits_max = torch.max(vocab_logits, dim=-1)[0] - dist.all_reduce(logits_max, op=dist.ReduceOp.MAX) - - # minus the max to avoid the result of sum of exp is too large and the log is nan - vocab_logits = vocab_logits - logits_max.unsqueeze(dim=-1) - - # mask the target in the local device - partition_vocab_size = vocab_logits.size()[-1] - rank = dist.get_rank() - world_size = dist.get_world_size() - global_vocab_size = partition_vocab_size * world_size - - # [down, up) => false, other device and -100 => true - delta = (global_vocab_size + world_size - 1) // world_size - down_shreshold = rank * delta - up_shreshold = down_shreshold + delta - mask = (target < down_shreshold) | (target >= up_shreshold) - masked_target = target.clone() - down_shreshold - masked_target[mask] = 0 - - # reshape the logist and target - # reshape the vocab_logits to [bath_size * seq_len, vocab_size] - # reshape the labels to [bath_size * seq_len] - logits_2d = vocab_logits.view(-1, partition_vocab_size) - masked_target_1d = masked_target.view(-1) - - # extract the x[class] and set the x[other device] to zero - pred_logits_1d = logits_2d[torch.arange(start=0, end=logits_2d.shape[0], device=logits_2d.device), - masked_target_1d] - pred_logits_1d = pred_logits_1d.clone().contiguous() - pred_logits = pred_logits_1d.view_as(target) - pred_logits[mask] = 0.0 - - # allreduce the get all x(i,y) - dist.all_reduce(pred_logits, op=dist.ReduceOp.SUM) - exp_logits = vocab_logits - torch.exp(vocab_logits, out=exp_logits) - sum_exp_logits = torch.sum(exp_logits, dim=-1) - dist.all_reduce(sum_exp_logits, op=dist.ReduceOp.SUM) - - # calculate the loss - # loss = log(sum(exp(x[i]))) - x[class] - loss = torch.log(sum_exp_logits) - pred_logits - loss = torch.sum(loss).div_(loss.numel()) - - # caculate the softmax - exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1)) - ctx.save_for_backward(exp_logits, mask, masked_target_1d) - - return loss - - @staticmethod - def backward(ctx, grad_output): - # retrieve the saved tensors - exp_logits, mask, masked_target_1d = ctx.saved_tensors - - # use exp logits as the input grad - grad_logits = exp_logits - partion_vocab_size = grad_logits.shape[-1] - grad_logits_2d = grad_logits.view(-1, partion_vocab_size) - - update = 1.0 - mask.view(-1).float() - grad_logits_2d[torch.arange(0, grad_logits_2d.shape[0]), masked_target_1d] -= update - - grad_logits.mul_(grad_output.unsqueeze(dim=-1)) - return grad_logits, None, None - - -def applyDistCrossEntropy(vocab_logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: - return DistCrossEntropy.apply(vocab_logits, labels) diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py deleted file mode 100644 index acc114029ac1..000000000000 --- a/colossalai/shardformer/layer/dropout.py +++ /dev/null @@ -1,58 +0,0 @@ -import os -import time -from contextlib import contextmanager - -import torch -import torch.nn as nn - - -class SeedManager: - """ - This class is a random state manager to change random state for different random seed. - - """ - - def __init__(self): - original_state = torch.cuda.get_rng_state() - seed = int(f"{int(time.time())}{os.environ['RANK']}") - torch.cuda.manual_seed(int(seed)) - self.dropout_state = torch.cuda.get_rng_state() - torch.cuda.set_rng_state(original_state) - - def set_mode(self, rng_state): - torch.cuda.set_rng_state(rng_state) - - def get_current_mode(self): - current_state = torch.cuda.get_rng_state() - return current_state - - @contextmanager - def dropout_mode(self): - """ - This is a context manager to change the dropout state and recover the original state. - - Usage: - :: - >>> with _seed_manager.dropout_mode(): - >>> input = super().forward(input) - """ - try: - current_mode = self.get_current_mode() - yield self.set_mode(self.dropout_state) - finally: - self.dropout_state = self.get_current_mode() - self.set_mode(current_mode) - - -_seed_manager = SeedManager() - - -class Dropout1D(nn.Dropout): - - def __init__(self, p=0.5, inplace=False): - super().__init__(p, inplace) - - def forward(self, input): - with _seed_manager.dropout_mode(): - input = super().forward(input) - return input diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py deleted file mode 100644 index f5123885bbe4..000000000000 --- a/colossalai/shardformer/layer/layers.py +++ /dev/null @@ -1,1043 +0,0 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -import math -from collections import OrderedDict -from typing import Callable, Tuple - -import torch -import torch.nn.functional as F -from torch import Tensor -from torch.nn.parameter import Parameter - -from colossalai.communication import broadcast -from colossalai.context import ParallelMode, seed -from colossalai.core import global_context as gpc -from colossalai.global_variables import tensor_parallel_env as env -from colossalai.kernel import LayerNorm -from colossalai.nn import init as init -from colossalai.nn.layer.base_layer import ParallelLayer -from colossalai.nn.layer.colossalai_layer._utils import ColossalaiModule -from colossalai.nn.layer.parallel_1d._utils import ( - gather_forward_split_backward, - get_parallel_input, - reduce_grad, - reduce_input, - set_parallel_input, - split_forward_gather_backward, -) -from colossalai.nn.layer.utils import divide, set_tensor_parallel_attribute_by_partition -from colossalai.nn.layer.vanilla import VanillaLayerNorm, VanillaPatchEmbedding -from colossalai.registry import LAYERS -from colossalai.utils.checkpointing import ( - broadcast_state_dict, - gather_tensor_parallel_state_dict, - partition_tensor_parallel_state_dict, -) -from colossalai.utils.cuda import get_current_device - -from ._operation import linear_with_async_comm - -Fast_LN = None -try: - from apex.contrib.layer_norm.layer_norm import FastLayerNorm - Fast_LN = FastLayerNorm -except ImportError: - pass - - -# @LAYERS.register_module -class Linear1D(ColossalaiModule): - r"""Linear layer for 1D parallelism. - - Args: - in_features (int): size of each input sample. - out_features (int): size of each output sample. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - gather_output (bool, optional): Whether to call all-gather on output, defaults to False. - skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, - which is preserved for kernel fusion, defaults to False - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - out_features: int, - bias: bool = True, - dtype: torch.dtype = None, - gather_output: bool = False, - skip_bias_add: bool = False, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): - parallel_input = get_parallel_input() - if not parallel_input and not gather_output: - layer = Linear1D_Col(in_features, - out_features, - bias=bias, - dtype=dtype, - skip_bias_add=skip_bias_add, - weight_initializer=weight_initializer, - bias_initializer=bias_initializer) - else: - layer = Linear1D_Row(in_features, - out_features, - bias=bias, - dtype=dtype, - parallel_input=parallel_input, - skip_bias_add=skip_bias_add, - weight_initializer=weight_initializer, - bias_initializer=bias_initializer) - super().__init__(layer) - - -# @LAYERS.register_module -class LayerNorm1D(ColossalaiModule): - r""" - Layer Normalization for colossalai - - Args: - normalized_shape (int): input shape from an expected input of size. - :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] - \times \ldots \times \text{normalized_shape}[-1]]` - If a single integer is used, it is treated as a singleton list, and this module will - normalize over the last dimension which is expected to be of that specific size. - eps (float): a value added to the denominator for numerical stability, defaults to 1e-05. - bias (bool, optional): Whether to add a bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - """ - - _fast_ln_supported_sizes = [ - 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, - 24576, 25600, 30720, 32768, 40960, 49152, 65536 - ] - - def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None): - if Fast_LN is not None and normalized_shape in self._fast_ln_supported_sizes: - norm = Fast_LN(normalized_shape, eps=eps).to(dtype) - else: - norm = None - try: - from apex.normalization import FusedLayerNorm - norm = FusedLayerNorm(normalized_shape, eps=eps).to(dtype) - except ImportError: - norm = LayerNorm(normalized_shape, eps=eps).to(dtype) - super().__init__(norm) - - def _load_from_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) - super()._load_from_state_dict(local_state, prefix, *args) - - def _save_to_state_dict(self, destination, prefix, keep_vars): - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - super()._save_to_state_dict(destination, prefix, keep_vars) - - -# @LAYERS.register_module -class Classifier1D(ParallelLayer): - r"""RowLinear with given weight. Classifier of 1D parallelism. - - Args: - in_features (int): size of each input sample. - num_classes (int): number of classes. - weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - num_classes: int, - weight: Parameter = None, - bias: bool = True, - dtype: torch.dtype = None, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): - super().__init__() - self.in_features = in_features - self.num_classes = num_classes - self.parallel_input = get_parallel_input() - - # Divide the weight matrix along the last dimension. - self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size) - - # Parameters. - # Initialize weight. - factory_kwargs = {'device': get_current_device(), 'dtype': dtype} - if weight is not None: - self.weight = weight - self.has_weight = False - else: - self.weight = Parameter(torch.empty(self.num_classes, self.input_size_per_partition, **factory_kwargs)) - self.has_weight = True - if bias: - self.bias = Parameter(torch.empty(self.num_classes, **factory_kwargs)) - else: - self.bias = None - with seed(ParallelMode.TENSOR): - self.reset_parameters(weight_initializer, bias_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) - env.vocab_parallel = False - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.num_classes - if self.has_weight: - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D) - - def _set_tensor_parallel_attributes(self): - if self.has_weight: - num_partition = gpc.get_world_size(ParallelMode.TENSOR) - set_tensor_parallel_attribute_by_partition(self.weight, num_partition) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - if self.has_weight: - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - if self.bias is not None: - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: -1, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: False - }) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - local_state = OrderedDict() - if self.has_weight: - local_state[weight_key] = self.weight - if self.bias is not None: - local_state[bias_key] = self.bias - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: -1, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: False - }, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tensor: - # Set up backprop all-reduce. - if self.parallel_input: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - input_ = input_ - else: - assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \ - 'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size) - input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1) - - output_parallel = F.linear(input_, self.weight) - output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) - if self.bias is not None: - output = output + self.bias - return output - - -# @LAYERS.register_module -class VocabParallelClassifier1D(ParallelLayer): - r"""ColLinear with given weight. Classifier of 1D parallelism. - - Args: - in_features (int): size of each input sample. - num_classes (int): number of classes. - weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - num_classes: int, - weight: Parameter = None, - bias: bool = True, - dtype: torch.dtype = None, - gather_output: bool = False, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): - super().__init__() - self.in_features = in_features - self.num_classes = num_classes - self.gather_output = gather_output - self.parallel_input = get_parallel_input() - - # Divide the weight matrix along the last dimension. - self.num_classes_per_partition = divide(num_classes, gpc.tensor_parallel_size) - - # Parameters. - # Initialize weight. - factory_kwargs = {'device': get_current_device(), 'dtype': dtype} - if weight is not None: - self.weight = weight - self.has_weight = False - else: - self.weight = Parameter(torch.empty(self.num_classes_per_partition, self.in_features, **factory_kwargs)) - self.has_weight = True - if bias: - self.bias = Parameter(torch.empty(self.num_classes_per_partition, **factory_kwargs)) - else: - self.bias = None - with seed(ParallelMode.TENSOR): - self.reset_parameters(weight_initializer, bias_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) - env.vocab_parallel = True - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.num_classes - if self.has_weight: - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - - def _set_tensor_parallel_attributes(self): - num_partition = gpc.get_world_size(ParallelMode.TENSOR) - if self.has_weight: - set_tensor_parallel_attribute_by_partition(self.weight, num_partition) - if self.bias is not None: - set_tensor_parallel_attribute_by_partition(self.bias, num_partition) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - if self.has_weight: - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - if self.bias is not None: - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: 0, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: True - }) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - local_state = OrderedDict() - if self.has_weight: - local_state[weight_key] = self.weight - if self.bias is not None: - local_state[bias_key] = self.bias - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: 0, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: True - }, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tensor: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in VocabParallelClassifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - # Set up backprop all-reduce. - input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) - # Matrix multiply. - output_parallel = F.linear(input_parallel, self.weight, self.bias) - if self.gather_output: - # All-gather across the partitions. - output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) - else: - output = output_parallel - return output - - -# @LAYERS.register_module -class Linear1D_Col(ParallelLayer): - r"""Linear layer with column parallelism. - - The linear layer is defined as :math:`Y = XA + b`. A is parallelized along - its second dimension as :math:`A = [A_1, ..., A_p]`. - - Args: - in_features (int): size of each input sample. - out_features (int): size of each output sample. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - gather_output (bool, optional): If true, call all-gather on output and make Y available - to all GPUs, otherwise, every GPU will have its output - which is :math:`Y_i = XA_i`, defaults to False - skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, - which is preserved for kernel fusion, defaults to False - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - out_features: int, - bias: bool = True, - dtype: torch.dtype = None, - gather_output: bool = False, - skip_bias_add: bool = False, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): - super().__init__() - - # Keep input parameters - self.in_features = in_features - self.out_features = out_features - self.gather_output = gather_output - self.skip_bias_add = skip_bias_add - - if skip_bias_add and not bias: - raise ValueError('cannot skip bias addition if bias is None') - - # self.out_features_per_partition = divide(out_features*2, gpc.tensor_parallel_size) - self.out_features_per_partition = out_features - - # Parameters. - # Initialize weight. - factory_kwargs = {'device': get_current_device(), 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) - - if bias: - self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) - else: - self.bias = None - with seed(ParallelMode.TENSOR): - self.reset_parameters(weight_initializer, bias_initializer) - self._set_tensor_parallel_attributes() - is_parallel_output = not self.gather_output - set_parallel_input(is_parallel_output) - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.out_features - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - - def _set_tensor_parallel_attributes(self): - num_partition = gpc.get_world_size(ParallelMode.TENSOR) - set_tensor_parallel_attribute_by_partition(self.weight, num_partition) - if self.bias is not None: - set_tensor_parallel_attribute_by_partition(self.bias, num_partition) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - if self.bias is not None: - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: 0, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: True - }) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - local_state = OrderedDict({weight_key: self.weight}) - if self.bias is not None: - local_state[bias_key] = self.bias - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: 0, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: True - }, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - # Set up backprop all-reduce. - # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) - input_parallel = input_ - # Matrix multiply. - bias = self.bias if not self.skip_bias_add else None - # output_parallel = F.linear(input_parallel, self.weight, bias) - output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, ParallelMode.PARALLEL_1D, True) - if self.gather_output: - # All-gather across the partitions. - output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) - else: - output = output_parallel - - if self.skip_bias_add: - return output, self.bias - else: - return output - - -# @LAYERS.register_module -class Linear1D_Row(ParallelLayer): - r""" Linear layer with row parallelism - - Args: - in_features (int): size of each input sample. - out_features (int): size of each output sample. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - parallel_input (bool, optional): If set to ``True``, it's assumed that the input is split, defaults to False. - skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, - which is preserved for kernel fusion, defaults to False - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - out_features: int, - bias: bool = True, - dtype: torch.dtype = None, - parallel_input: bool = True, - skip_bias_add: bool = False, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), - stream_chunk_num: int = 1): - super().__init__() - - self.stream_chunk_num = stream_chunk_num - - # Keep input parameters - self.in_features = in_features - self.out_features = out_features - self.parallel_input = parallel_input - self.skip_bias_add = skip_bias_add - - if skip_bias_add and not bias: - raise ValueError('cannot skip bias addition if bias is None') - - # Divide the weight matrix along the last dimension. - # self.input_size_per_partition = divide(in_features*2, gpc.tensor_parallel_size) - self.input_size_per_partition = in_features - - # Parameters. - # Initialize weight. - factory_kwargs = {'device': get_current_device(), 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) - - if self.stream_chunk_num > 1: - # TODO() work for inference only - self.chunk_weight() - if bias: - self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) - else: - self.bias = None - with seed(ParallelMode.TENSOR): - self.reset_parameters(weight_initializer, bias_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) - - def chunk_weight(self): - self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.out_features - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D) - - def _set_tensor_parallel_attributes(self): - num_partition = gpc.get_world_size(ParallelMode.TENSOR) - set_tensor_parallel_attribute_by_partition(self.weight, num_partition) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - if self.bias is not None: - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: -1, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: False - }) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - local_state = OrderedDict({weight_key: self.weight}) - if self.bias is not None: - local_state[bias_key] = self.bias - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: -1, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: False - }, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tensor: - # Set up backprop all-reduce. - if self.parallel_input: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - input_ = input_ - else: - assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \ - 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size) - input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1) - - if self.stream_chunk_num > 1: - if self.training: - raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!") - with torch.no_grad(): - output_parallel_list = [None for i in range(self.stream_chunk_num)] - handle_list = [] - for i in range(self.stream_chunk_num): - output_parallel_list[i] = F.linear(input_, self.weight_list[i]) - handle = torch.distributed.all_reduce(output_parallel_list[i], - group=gpc.get_group(ParallelMode.PARALLEL_1D), - async_op=True) - handle_list.append(handle) - # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D) - for handle in handle_list: - handle.wait() - output = torch.cat(output_parallel_list, dim=-1) - else: - output_parallel = F.linear(input_, self.weight) - # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) - output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) - if not self.skip_bias_add: - if self.bias is not None: - output = output + self.bias - return output - else: - return output, self.bias - - -# @LAYERS.register_module -class Embedding1D(ParallelLayer): - r"""Embedding for 1D parallelism. - - Args: - num_embeddings (int): number of embeddings. - embedding_dim (int): dimension of embedding. - padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; - therefore, the embedding vector at padding_idx is not updated during training, - i.e. it remains as a fixed “pad”, defaults to None. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - weight_initializer (:class:`typing.Callable`, optional): - he initializer of weight, defaults to normal initializer. - - The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain: - :: - - max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is - renormalized to have norm max_norm. Note: this will modify weight in-place. - norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. - scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse - of frequency of the words in the mini-batch. Default False. - sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. - - More details about ``args`` and ``kwargs`` could be found in - `Embedding `_. - - More details about ``initializer`` please refer to - `init `_ - """ - - def __init__(self, - num_embeddings: int, - embedding_dim: int, - padding_idx: int = None, - dtype: torch.dtype = None, - weight_initializer: Callable = init.normal_(), - *args, - **kwargs): - super().__init__() - - self.num_embeddings = num_embeddings - self.embed_dim = embedding_dim - embed_dim_per_partition = divide(embedding_dim, gpc.tensor_parallel_size) - - self.padding_idx = padding_idx - self.embed_args = args - self.embed_kwargs = kwargs - - self.weight = Parameter( - torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype)) - - self.reset_parameters(weight_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) - - def _set_tensor_parallel_attributes(self): - set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size) - - def reset_parameters(self, weight_initializer) -> None: - with seed(ParallelMode.TENSOR): - fan_in, fan_out = self.num_embeddings, self.embed_dim - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - self._fill_padding_idx_with_zero() - - def _fill_padding_idx_with_zero(self) -> None: - if self.padding_idx is not None: - with torch.no_grad(): - self.weight[self.padding_idx].fill_(0) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={weight_key: -1}, - partition_states={weight_key: True}) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - local_state = OrderedDict({weight_key: self.weight}) - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={weight_key: -1}, - partition_states={weight_key: True}, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tensor: - - output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) - - output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) - - return output - - -# @LAYERS.register_module -class VocabParallelEmbedding1D(ParallelLayer): - r"""Embedding parallelized in the vocabulary dimension. - - Args: - num_embeddings (int): number of embeddings. - embedding_dim (int): dimension of embedding. - padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; - therefore, the embedding vector at padding_idx is not updated during training, - i.e. it remains as a fixed “pad”, defaults to None. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - weight_initializer (:class:`typing.Callable`, optional): - he initializer of weight, defaults to normal initializer. - - The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain: - :: - - max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is - renormalized to have norm max_norm. Note: this will modify weight in-place. - norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. - scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse - of frequency of the words in the mini-batch. Default False. - sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. - - More details about ``args`` and ``kwargs`` could be found in - `Embedding `_. - - More details about initializer please refer to - `init `_. - """ - - def __init__(self, - num_embeddings: int, - embedding_dim: int, - padding_idx: int = None, - dtype: torch.dtype = None, - weight_initializer: Callable = init.normal_(), - *args, - **kwargs): - super().__init__() - self.num_embeddings = num_embeddings - self.embed_dim = embedding_dim - self.padding_idx = padding_idx - self.embed_args = args - self.embed_kwargs = kwargs - - tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) - tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) - # self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) - self.num_embeddings_per_partition = num_embeddings - self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition - self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition - - self.weight = Parameter( - torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=get_current_device(), dtype=dtype)) - - self.reset_parameters(weight_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) - env.vocab_parallel = True - - def _set_tensor_parallel_attributes(self): - set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size) - - def reset_parameters(self, weight_initializer) -> None: - with seed(ParallelMode.TENSOR): - fan_in, fan_out = self.num_embeddings, self.embed_dim - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - self._fill_padding_idx_with_zero() - - def _fill_padding_idx_with_zero(self) -> None: - if self.padding_idx is not None and \ - self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index: - with torch.no_grad(): - self.weight[self.padding_idx - self.vocab_start_index].fill_(0) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={weight_key: 0}, - partition_states={weight_key: True}) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - local_state = OrderedDict({weight_key: self.weight}) - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={weight_key: 0}, - partition_states={weight_key: True}, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tensor: - # Build the mask. - input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index) - # Mask the input. - masked_input = input_.clone() - self.vocab_start_index - masked_input[input_mask] = 0 - - output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args, - **self.embed_kwargs) - - # Mask the output embedding. - output_parallel[input_mask, :] = 0. - # Reduce across all the model parallel GPUs. - output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) - return output - - -# @LAYERS.register_module -class Dropout1D(ParallelLayer): - """Dropout layer of 1D parallelism. - - Args: - p (float, optional): probability of an element to be zeroed, defaults 0.5. - inplace (bool, optional): whether to do dropout in-place, default to be False. - """ - - def __init__(self, p: float = 0.5, inplace: bool = False): - super().__init__() - self.parallel_input = get_parallel_input() - self.p = p - self.inplace = inplace - - def forward(self, input_: Tensor) -> Tensor: - if self.parallel_input: - with seed(ParallelMode.TENSOR): - output = F.dropout(input_, self.p, self.training, self.inplace) - else: - output = F.dropout(input_, self.p, self.training, self.inplace) - return output - - -# @LAYERS.register_module -class PatchEmbedding1D(ColossalaiModule): - """ - 2D Image to Patch Embedding - - :param img_size: image size - :type img_size: int - :param patch_size: patch size - :type patch_size: int - :param in_chans: number of channels of input image - :type in_chans: int - :param embed_size: size of embedding - :type embed_size: int - :param dtype: The dtype of parameters, defaults to None - :type dtype: torch.dtype, optional - :param flatten: whether to flatten output tensor, defaults to True - :type flatten: bool, optional - :param weight_initializer: The initializer of weight, defaults to kaiming uniform initializer - :type weight_initializer: typing.Callable, optional - :param bias_initializer: The initializer of bias, defaults to xavier uniform initializer - :type bias_initializer: typing.Callable, optional - :param position_embed_initializer: The initializer of position embedding, defaults to zero - :type position_embed_initializer: typing.Callable, optional - """ - - def __init__(self, - img_size: int, - patch_size: int, - in_chans: int, - embed_size: int, - dtype: torch.dtype = None, - flatten: bool = True, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), - position_embed_initializer: Callable = init.zeros_()): - embed = VanillaPatchEmbedding(img_size, - patch_size, - in_chans, - embed_size, - dtype=dtype, - flatten=flatten, - weight_initializer=weight_initializer, - bias_initializer=bias_initializer, - position_embed_initializer=position_embed_initializer) - super().__init__(embed) - - def _load_from_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - param_keys = [prefix + 'weight', prefix + 'bias', prefix + 'cls_token', prefix + 'pos_embed'] - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - for key in param_keys: - param = state_dict.pop(key, None) - if param is not None: - local_state[key] = param - - local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) - super()._load_from_state_dict(local_state, prefix, *args) - - def _save_to_state_dict(self, destination, prefix, keep_vars): - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - super()._save_to_state_dict(destination, prefix, keep_vars) diff --git a/colossalai/shardformer/model/__init__.py b/colossalai/shardformer/model/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/colossalai/shardformer/model/modeling_bert.py b/colossalai/shardformer/model/modeling_bert.py deleted file mode 100644 index bd07ab80c00d..000000000000 --- a/colossalai/shardformer/model/modeling_bert.py +++ /dev/null @@ -1,67 +0,0 @@ -from typing import Any, Dict, List, Type - -import torch -import torch.nn as nn -from torch.nn import CrossEntropyLoss -from transformers import BertForMaskedLM -from transformers.models.bert.modeling_bert import MaskedLMOutput - -from ..layer.dist_crossentropy import applyDistCrossEntropy - - -class BertForMaskedLM_(BertForMaskedLM): - - def forward( - self, - input_ids=None, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - labels=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - **kwargs, - ): - # print("[Inject OK] Injected forward method") - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = self.bert( - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - prediction_scores = self.cls(sequence_output) - - masked_lm_loss = None - - if labels is not None: - masked_lm_loss = applyDistCrossEntropy(prediction_scores, labels) - # if labels is not None: - # loss_fct = CrossEntropyLoss() # -100 index = padding token - # masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output - - return MaskedLMOutput( - loss=masked_lm_loss, - logits=prediction_scores, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) diff --git a/colossalai/shardformer/policies/__init__.py b/colossalai/shardformer/policies/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py deleted file mode 100644 index 54cc63ba124f..000000000000 --- a/colossalai/shardformer/policies/autopolicy.py +++ /dev/null @@ -1,58 +0,0 @@ -import torch.nn as nn - - -def build_policies(): - r""" - Build the policies for the model - - Return: - The dict for the policies - """ - auto_policy_dict = {} - - from transformers import BertForMaskedLM - - from .bert import BertForMaskedLMPolicy - auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy - - from transformers import BertForSequenceClassification - - from .bert import BertForSequenceClassificationPolicy - auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy - - from transformers import GPT2Model - - from .gpt2 import GPT2Policy - auto_policy_dict[GPT2Model] = GPT2Policy - - from transformers import GPT2LMHeadModel - - from .gpt2 import GPT2LMHeadModelPolicy - auto_policy_dict[GPT2LMHeadModel] = GPT2LMHeadModelPolicy - - return auto_policy_dict - - -def get_autopolicy(model: nn.Module): - r""" - Return the auto policy for the model - - Args: - model (:class:`nn.Module`): The model to get the auto policy - - Return: - :class:`Policy`: The auto policy for the model - """ - auto_policy_dict = build_policies() - policy = auto_policy_dict.get(model.__class__, None) - if policy is None: - raise NotImplementedError( - f"Auto policy for {model.__class__.__qualname__} is not implemented\n Supported models are {[i.__qualname__ for i in auto_policy_dict.keys()]}" - ) - return policy - - -# from transformers.models.bert.modeling_bert import BertForMaskedLM, BertForPreTraining -# model = BertForPreTraining -# policy = get_autopolicy(model) -# print(policy) diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py deleted file mode 100644 index 644d115a270e..000000000000 --- a/colossalai/shardformer/policies/basepolicy.py +++ /dev/null @@ -1,217 +0,0 @@ -# part of code modified from https://github.com/tunib-ai/parallelformers - -from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Tuple, Type - -import torch.nn as nn - - -@dataclass -class Argument: - r""" - The argument class for the policy - - Args: - attr_dict (Dict[str, Any]): The dict for the param setting - param_funcs (:class:`List[Callable]`): The list for the param functions - """ - attr_dict: Dict[str, Any] - param_funcs: List[Callable] - - -@dataclass -class Layer: - r""" - The layer object for the policy - - Args: - weight (str): The weight suffix of the layer - bias (str): The bias suffix of the layer - replace_layer (:class:`colosalai.nn`): The layer to replace the original layer - ignore (bool): Whether to ignore this layer if it is not in the model - reversed (bool): Whether the weight in layer is reversed, commonly the weight in `torch.nn.Linear` is [out, in], - but in GPT2 `Conv1D` layer is [in, out] which is reversed. - n_cast (int): The number of weight will cast to, like q, k, v in attention layer, n_cast should be 3. commonly in TP, we just chunk the weight with the number of devices, - but in multi-head attention, we need to chunk the weight with the number of devices * n_head, and - each device should have a part of Q, K and V weight. - """ - weight: str = None - bias: str = None - replace_layer: Any = None - ignore: bool = False - reversed: bool = False - n_cast: int = None - - -@dataclass -class Col_Layer(Layer): - r""" - Class for col shard layer in MegatronLM - - Args: - gather_output (bool): Whether to gather the output of the layer - """ - gather_output: bool = False - - -@dataclass -class Row_Layer(Layer): - r""" - Class for col shard layer in MegatronLM - """ - pass - - -class Policy(): - r""" - The base class for all the policies - For each different model, it should have a different policy class, like BertPolicy for Bert Model - or OPTPolicy for OPT model. - AutoPolicy: - Shardformer already defined some policies for huggingface model, just set ``custom_policy`` = None - to use the auto policy. In shardformer autopolicy, we define a base policy for one type model, - like BertPolicy, and for each different Bert modle in huggingface like, BertForMaskedLM, - BertForSequenceClassification, etc., for each different Bert model we difine different policy class - and overwrite the method like ``inject_policy`` to modify the forward and backward process. - - CustomPolicy: - If you want to define your own policy, you can set ``custom_policy`` = CustomPolicy, and overwrite - all the methods in ``Policy`` class. You can refer to any policy we defined like the ``BertPolicy`` - class for the example. - - """ - - @staticmethod - def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]: - r""" - Return the dict for the modify policy, the key is the original layer class and the value is the - argument for the modify layer - - Args: - model_config (:class:`tansformer.Config`): The config of transformer model - shard_config (:class:`ShardConfig`): The config for sharding model - - Return: - Dict for the modify policy, - :: - { - origin layer class1 (nn.Module): Argument( - attr_dict = { - argument1: value1, - argument2: value2, - ... - }, - param_funcs = [ - staticmethod1, - staticmethod2, - ... - ] - ), - origin layer class2 (nn.Module): Argument( - attr_dict = { - argument1: value1, - argument2: value2, - ... - }, - param_funcs = [ - staticmethod1, - staticmethod2, - ... - ] - ), - ... - } - - """ - raise NotImplementedError - - @staticmethod - def inject_policy() -> Tuple[nn.Module, nn.Module]: - r""" - Return the dict for the inject model - - Return: - The injected model, key is the original model and value is the new shardmodel - :: - (OrignModel, CustomModel) - in `CustomModel`, we can overwrite the forward and backward process - """ - return None - - @staticmethod - def binding_policy() -> Dict: - r""" - Return the dict for the binding model - - Return: - This method should return the binding relationship for some layers share the weight or bias, - the key and value is the suffix of the weight or bias of the model - :: - return { - "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", - } - """ - return None - - @staticmethod - def attn_in() -> List: - r""" - Attention qkv layer - In this kind of method, we should return the list of ``Layer`` object, each ``Layer`` object should be - ``Layer`` for no slicing, ``Col_Layer`` for col slicing, ``Row_Layer`` for row slicing. And the parameters - in ``Layer`` object can refer to the ``Layer`` class. - - Returns: - List[Layer]: List of layer object, each layer is the new - """ - return NotImplementedError - - @staticmethod - def attn_out() -> List: - r""" - Attention output projection layer - - Returns: - List[Layer]: List of layer object - """ - return NotImplementedError - - @staticmethod - def mlp_in() -> List: - r""" - h -> 4h mlp layer - - Returns: - List[Layer]: List of layer object - """ - return NotImplementedError - - @staticmethod - def mlp_out() -> List: - r""" - 4h -> h mlp layer - - Returns: - List[Layer]: List of layer object - """ - return NotImplementedError - - @staticmethod - def embedding() -> List: - r""" - Partially slice the embedding layer - - Return: - List[Layer]: List of layer object - """ - return NotImplementedError - - @staticmethod - def unembedding() -> List: - r""" - Partially slice the embedding layer - - Return: - List[Layer]: List of layer object - """ - return None diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py deleted file mode 100644 index 89b32f065c27..000000000000 --- a/colossalai/shardformer/policies/bert.py +++ /dev/null @@ -1,170 +0,0 @@ -from typing import Any, Callable, Dict, List, Tuple, Type - -import torch.nn as nn -from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead - -import colossalai.shardformer.layer.layers as col_nn - -from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer - - -class BertPolicy(Policy): - - @staticmethod - def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: - return { - BertLayer: - Argument( - attr_dict={ - # 1. shard hidden size - "attention.self.all_head_size": config.hidden_size // world_size, - "crossattention.self.all_head_size": config.hidden_size // world_size, - # 2. shard number of heads - "attention.self.num_attention_heads": config.num_attention_heads // world_size, - "crossattention.self.num_attention_heads": config.num_attention_heads // world_size, - }, - param_funcs=[BertPolicy.attn_in, BertPolicy.attn_out, BertPolicy.mlp_in, BertPolicy.mlp_out]), - BertEmbeddings: - Argument( - attr_dict={ - # 1. shard vocab size - # "word_embeddings.num_embeddings": config.vocab_size // world_size, - # 2. add the size of the sliced embedding layer excluding the last slice - "word_embeddings.dim_size": (config.vocab_size + world_size - 1) // world_size, - }, - param_funcs=[ - BertPolicy.embedding, - ]), - BertLMPredictionHead: - Argument( - attr_dict={ - # 1. shard vocab size - # "word_embeddings.num_embeddings": config.vocab_size // world_size, - # 2. add the size of the sliced embedding layer excluding the last slice - }, - param_funcs=[ - BertPolicy.unembedding, - ]) - } - - @staticmethod - def binding_policy() -> Dict: - return { - "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", - } - - @staticmethod - def attn_in() -> List: - return [ - Col_Layer( - weight="attention.self.query.weight", - bias="attention.self.query.bias", - replace_layer=col_nn.Linear1D_Col, - ), - Col_Layer( - weight="attention.self.key.weight", - bias="attention.self.key.bias", - replace_layer=col_nn.Linear1D_Col, - ), - Col_Layer( - weight="attention.self.value.weight", - bias="attention.self.value.bias", - replace_layer=col_nn.Linear1D_Col, - ), - Col_Layer( - weight="crossattention.self.query.weight", - bias="crossattention.self.query.bias", - replace_layer=col_nn.Linear1D_Col, - ignore=True, - ), - Col_Layer( - weight="crossattention.self.key.weight", - bias="crossattention.self.key.bias", - replace_layer=col_nn.Linear1D_Col, - ignore=True, - ), - Col_Layer( - weight="crossattention.self.value.weight", - bias="crossattention.self.value.bias", - replace_layer=col_nn.Linear1D_Col, - ignore=True, - ), - ] - - @staticmethod - def attn_out() -> List: - return [ - Row_Layer( - weight="attention.output.dense.weight", - bias="attention.output.dense.bias", - replace_layer=col_nn.Linear1D_Row, - ), - Row_Layer( - weight="crossattention.output.dense.weight", - bias="crossattention.output.dense.bias", - replace_layer=col_nn.Linear1D_Row, - ignore=True, - ), - ] - - @staticmethod - def mlp_in() -> List: - return [ - Col_Layer( - weight="intermediate.dense.weight", - bias="intermediate.dense.bias", - replace_layer=col_nn.Linear1D_Col, - ), - ] - - @staticmethod - def mlp_out() -> List: - return [ - Row_Layer( - weight="output.dense.weight", - bias="output.dense.bias", - replace_layer=col_nn.Linear1D_Row, - ), - ] - - @staticmethod - def embedding() -> List: - return [Col_Layer( - weight="word_embeddings.weight", - replace_layer=col_nn.VocabParallelEmbedding1D, - )] - - @staticmethod - def unembedding() -> List: - return [ - Col_Layer( - weight="decoder.weight", - bias="decoder.bias", - replace_layer=col_nn.Linear1D_Col, - # gather_output=True, - ) - ] - - -from transformers import BertForMaskedLM - -from colossalai.shardformer.model.modeling_bert import BertForMaskedLM_ - - -class BertForMaskedLMPolicy(BertPolicy): - - @staticmethod - def inject_policy() -> Tuple[nn.Module, nn.Module]: - return (BertForMaskedLM, BertForMaskedLM_) - - -class BertForSequenceClassificationPolicy(BertPolicy): - - @staticmethod - def inject_policy() -> Dict: - return {} - - -# model = BertForMaskedLM.from_pretrained("bert-base-uncased") -# _ = BertForMaskedLMPolicy(model) -# print(isinstance(model,list(_.inject_policy().keys())[0])) diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py deleted file mode 100644 index 44dc9c72f986..000000000000 --- a/colossalai/shardformer/policies/gpt2.py +++ /dev/null @@ -1,118 +0,0 @@ -from typing import Any, Callable, Dict, List, Tuple, Type - -import torch.nn as nn -from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model - -import colossalai.shardformer.layer.layers as col_nn - -from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer - - -class GPT2Policy(Policy): - - @staticmethod - def argument_policy(config, world_size): - return { - GPT2Model: - Argument(attr_dict={}, param_funcs=[ - GPT2Policy.embedding, - ]), - GPT2Block: - Argument( - attr_dict={ - # 1. reduce hidden size - "attn.embed_dim": config.hidden_size // world_size, - "attn.split_size": config.hidden_size // world_size, - "crossattention.embed_dim": config.hidden_size // world_size, - "crossattention.split_size": config.hidden_size // world_size, - # 2. reduce number of heads - "attn.num_heads": config.num_attention_heads // world_size, - "crossattention.num_heads": config.num_attention_heads // world_size, - }, - param_funcs=[ - GPT2Policy.attn_in, - GPT2Policy.attn_out, - GPT2Policy.mlp_in, - GPT2Policy.mlp_out, - ]), - } - - @staticmethod - def attn_in() -> List: - return [ - Col_Layer(weight="attn.c_attn.weight", - bias="attn.c_attn.bias", - n_cast=3, - reversed=True, - replace_layer=col_nn.Linear1D_Col), - Col_Layer(weight="crossattention.c_attn.weight", - bias="crossattention.c_attn.bias", - n_cast=2, - reversed=True, - ignore=True, - replace_layer=col_nn.Linear1D_Col), - Col_Layer(weight="crossattention.q_attn.weight", - bias="crossattention.q_attn.bias", - reversed=True, - ignore=True, - replace_layer=col_nn.Linear1D_Col) - ] - - @staticmethod - def attn_out() -> List: - return [ - Row_Layer(weight="attn.c_proj.weight", - bias="attn.c_proj.bias", - reversed=True, - replace_layer=col_nn.Linear1D_Row), - Row_Layer(weight="crossattention.c_proj.weight", - bias="crossattention.c_proj.bias", - reversed=True, - ignore=True, - replace_layer=col_nn.Linear1D_Row) - ] - - @staticmethod - def mlp_in() -> List: - return [ - Col_Layer(weight="mlp.c_fc.weight", bias="mlp.c_fc.bias", reversed=True, replace_layer=col_nn.Linear1D_Col), - ] - - @staticmethod - def mlp_out() -> List: - return [ - Row_Layer(weight="mlp.c_proj.weight", - bias="mlp.c_proj.bias", - reversed=True, - replace_layer=col_nn.Linear1D_Row) - ] - - @staticmethod - def embedding() -> List: - return [Col_Layer(weight="wte.weight", replace_layer=col_nn.VocabParallelEmbedding1D)] - - -from transformers import GPT2LMHeadModel - - -class GPT2LMHeadModelPolicy(GPT2Policy): - - @staticmethod - def argument_policy(config, world_size): - base_argument = GPT2Policy.argument_policy(config, world_size) - argument = { - GPT2LMHeadModel: Argument(attr_dict={}, param_funcs=[ - GPT2LMHeadModelPolicy.unembedding, - ]), - } - argument.update(base_argument) - return argument - - @staticmethod - def unembedding() -> List: - return [ - Col_Layer(weight="lm_head.weight", - bias="lm_head.bias", - replace_layer=col_nn.Linear1D_Col, - gather_output=True) - ] diff --git a/colossalai/shardformer/shard/__init__.py b/colossalai/shardformer/shard/__init__.py deleted file mode 100644 index d5f70163ad57..000000000000 --- a/colossalai/shardformer/shard/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .shard_config import ShardConfig -from .sharder import ModelSharder, shard_model -from .slicer import Slicer - -__all__ = ['ShardConfig', 'ModelSharder', 'shard_model', 'Slicer'] diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py deleted file mode 100644 index 4cf9162b9548..000000000000 --- a/colossalai/shardformer/shard/shard_config.py +++ /dev/null @@ -1,20 +0,0 @@ -from dataclasses import dataclass - -__all__ = ['ShardConfig'] - - -@dataclass -class ShardConfig: - """ - The config for sharding the huggingface model for test - """ - rank: int - fp16: bool = True - num_gpus: int = 2 - world_size: int = 2 - backend = "nccl" - verbose: str = 'simple' - seed: int = None - require_grad: bool = False - master_addr: str = "127.0.0.1" - master_port: int = 29500 diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py deleted file mode 100644 index 1ada75e06b67..000000000000 --- a/colossalai/shardformer/shard/sharder.py +++ /dev/null @@ -1,266 +0,0 @@ -from typing import Any, Callable, Dict, List - -import torch -import torch.nn as nn -from transformers.pytorch_utils import Conv1D - -from ..policies.autopolicy import get_autopolicy -from ..policies.basepolicy import Policy -from ..utils.utils import getattr_, hasattr_, setattr_ -from .shard_config import ShardConfig -from .slicer import Slicer - -__all__ = ['ModelSharder', 'shard_model'] - - -class ModelSharder(object): - r""" - Shard the original huggingface model according to the policy - - Args: - policy (:class:`Policy`): The policy to shard the model - model (:class:`torch.Module`): The model to shard - shard_config: The setting of distributed model - """ - - def __init__( - self, - model: nn.Module, - policy: Policy, - shard_config: ShardConfig = None, # TODO - ) -> None: - self.model = model - self.policy = get_autopolicy(self.model) if policy is None else policy - self.slicer = Slicer(shard_config) - self.shard_config = shard_config - self.model_config = self.model.config - - def shard(self) -> None: - self.reshape_embedding() - self.inject_model(self.model) - self.replace_layer(self.model) - self.bind_layer(self.model) - - def reshape_embedding(self,) -> None: - r""" - Reshape the Embedding layer to make the embedding dimension divisible by world_size - """ - vocab_size = self.model_config.vocab_size - world_size = self.shard_config.world_size - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) - self.model_config = self.model.config - - def inject_model( - self, - model: nn.Module, - ) -> None: - r""" - Replace the model to policy defined model - Mainly modify the forward and backward to fit distributed model - - e.g. - :: - BertForMaskedLM.forward -> BertForMaskedLM_.forward - """ - inject_policy = self.policy.inject_policy() - - if inject_policy is None: - return - org_model_cls = inject_policy[0] - shard_model_cls = inject_policy[1] - - if model.__class__ == org_model_cls: - for key in shard_model_cls.__dict__.keys(): - if hasattr(model.__class__, key): - setattr( - model.__class__, - key, - getattr(shard_model_cls, key), - ) - else: - raise NotImplementedError(f"{model.__class__} is not implemented so far") - - def replace_layer( - self, - model: nn.Module, - ) -> None: - r""" - Replace the layer according to the policy, and replace the layer one by one - - Args: - model (:class:`torch.nn.Module`): The layer to shard - """ - argument_policies = self.policy.argument_policy(self.model_config, self.shard_config.world_size) - for argument_policy in argument_policies.items(): - origin_layer_cls = argument_policy[0] - attr_dict = argument_policy[1].attr_dict - param_funcs = argument_policy[1].param_funcs - self.traverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs) - - def traverse_replace_layer( - self, - layer: nn.Module, - origin_cls: nn.Module, - attr_dict: Dict[str, Any], - param_funcs: List[Callable], - ) -> None: - r""" - Reverse the replace layer operation - - Args: - layer (:class:`torch.nn.Module`): The object of layer to shard - origin_cls (:class:`transformers.model`): The origin layer class - attr_dict (Dict): The attribute dict to modify - policy_cls (:class:`Policy`): The policy class - """ - if layer.__class__ == origin_cls: - for k, v in attr_dict.items(): - setattr_(layer, k, v, ignore=True) - self.shard_one_layer(layer, param_funcs) - for name, child in layer.named_children(): - self.traverse_replace_layer(child, origin_cls, attr_dict, param_funcs) - return layer - - def shard_one_layer( - self, - org_layer: nn.Module, - param_funcs: List[Callable], - ) -> None: - r""" - Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict - - Args: - org_layer (:class:`torch.nn.Module`): The origin layer object to shard - param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class - - """ - for func in param_funcs: - policy_layers = func() - for policy_layer in policy_layers: - weight = None - bias = None - weight_attr = policy_layer.weight - bias_attr = policy_layer.bias - replace_layer_cls = policy_layer.replace_layer - ignore = policy_layer.ignore - n_cast = policy_layer.n_cast - reversed = policy_layer.reversed - if policy_layer.__class__.__name__ == "Col_Layer": - gather_output = policy_layer.gather_output - - if weight_attr is not None: - if hasattr_(org_layer, weight_attr): - weight = getattr_(org_layer, weight_attr) - elif not ignore: - raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {weight_attr}") - - if bias_attr is not None: - if hasattr_(org_layer, bias_attr): - bias = getattr_(org_layer, bias_attr) - elif not ignore: - raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {bias_attr}") - - # dont have the attribute in policy, and ignore is true - if weight is None and bias is None and ignore: - continue - - # set the sliced weight and bias to the new nn_col layer - assert weight is not None or bias is not None - layer_attr = (lambda x: x[:x.rfind(".")])(weight_attr or bias_attr) - - # slice weight and bias - weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__, n_cast, reversed) - - # create new object to replace the origin layer - if replace_layer_cls is not None: - if isinstance(getattr_(org_layer, layer_attr), (nn.Linear, Conv1D)): - if replace_layer_cls.__name__ == "Linear1D_Row": - replace_layer = replace_layer_cls(weight.shape[1], - weight.shape[0], - bias=False if bias is None else True) - elif replace_layer_cls.__name__ == "Linear1D_Col": - replace_layer = replace_layer_cls(weight.shape[0], - weight.shape[1], - bias=False if bias is None else True, - gather_output=gather_output) - setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) - self.set_param(replace_layer, weight, bias) - elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding): - replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], - getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True)) - setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) - self.set_param(replace_layer, weight, bias) - else: - raise NotImplementedError( - f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far") - # do not replace the layer object, just replace the weight and bias - else: - self.set_param(org_layer, layer_attr, weight, bias) - - def set_param(self, - layer: Any, - weight: torch.Tensor = None, - bias: torch.Tensor = None, - layer_attr: str = "") -> None: - r""" - Reset the weight and bias of the layer object - - Args: - layer (:class:`torch.nn.Module`): The layer object - layer_attr (str): The attribute name of the layer - weight (:class:`torch.Tensor`): The weight of the layer - bias (:class:`torch.Tensor`): The bias of the layer - """ - assert weight is not None or bias is not None - if weight is not None: - setattr_(layer, "weight" if layer_attr == "" else layer_attr + ".weight", nn.Parameter(weight.contiguous())) - self.set_layer_size(layer, layer_attr, weight.shape) - if bias is not None: - setattr_(layer, "bias" if layer_attr == "" else layer_attr + ".bias", nn.Parameter(bias.contiguous())) - - def set_layer_size(self, layer: nn.Module, layer_attr: str, size: torch.Size) -> None: - r""" - Set the layer attribute - - Args: - layer (:class:`torch.nn.Module`): The layer object - layer_attr (str): The attribute name of the layer - size (:class:`torch.Size`): The size of the tensor - """ - # Tensor.shape[0] -> out_features, Tensor.shape[1] -> in_features - attrs = ["out_features", "in_features"] - for i, attr in enumerate(attrs): - if hasattr_(layer, f"{layer_attr}.{attr}"): - setattr_(layer, f"{layer_attr}.{attr}", size[i]) - - def bind_layer(self, model: nn.Module) -> None: - r""" - Bind the layer according to the binding policy - - Args: - model (:class:`torch.nn.Module`): The shard model - """ - binding_map = self.policy.binding_policy() - if binding_map is None: - return - for k, v in binding_map.items(): - param = getattr_(model, k) - param = nn.Parameter(param) - setattr_(model, k, param) - setattr_(model, v, param) - - -def shard_model(model: nn.Module, shard_config: ShardConfig = None, policy: Policy = None): - r""" - The function is used to shard the PyTorch model. - - Args: - model (`torch.nn.Model`): the origin huggingface model - shard_config (`ShardConfig`): the config for distribute information - policy (`Policy`): the custom policy for sharding - """ - sharder = ModelSharder(model=model, shard_config=shard_config, policy=policy) - sharder.shard() - return model diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py deleted file mode 100644 index 6d35bd193fed..000000000000 --- a/colossalai/shardformer/shard/slicer.py +++ /dev/null @@ -1,161 +0,0 @@ -import torch - -from ..policies.basepolicy import Col_Layer, Layer, Row_Layer -from .shard_config import ShardConfig - -dim_mapping = {Col_Layer: 1, Row_Layer: 0} - - -class Slicer(): - - def __init__( - self, - shardconfig: ShardConfig #TODO - ) -> None: - self.shardconfig = shardconfig - - def slice_weight_bias( - self, - weight: torch.Tensor, - bias: torch.Tensor, - policy_layer_cls: Layer, - n_cast: int = None, - reversed: bool = False, - ): - r""" - Slice the weight and bias according to policy layer cls - ``Layer`` -> do nothing - ``Col_Layer`` -> slice the weight and bias along dim 1 - ``Row_Layer`` -> slice the weight along dim 0 and do not slice bias - - Args: - weight (:class:`torch.nn.Module`): The weight of the layer - bias: (:class:`torch.nn.Module`): The bias of the layer - policy_layer_class (:class:`Policy`): The class represent how to slice the tensor - """ - if policy_layer_cls == Layer: - return weight, bias - - dim = dim_mapping[policy_layer_cls] if not reversed else (1 - dim_mapping[policy_layer_cls]) - # print(weight.shape, dim) - if policy_layer_cls == Col_Layer: - weight = self.slice_tensor(weight, dim, False, n_cast) - bias = self.slice_tensor(bias, 0, True) - elif policy_layer_cls == Row_Layer: - weight = self.slice_tensor(weight, dim, False, n_cast) - else: - raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported") - if reversed: - weight = weight.transpose(0, 1).contiguous() - return weight, bias - - def slice_tensor( - self, - tensor_in: torch.Tensor, - dim: int, - is_bias: bool, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice tensor according to the config - - Args: - tensor_in (:class:`torch.Tensor`): The tensor to slice - dim (int): The dimension to slice - is_bias (bool): Whether the tensor is bias - """ - if tensor_in is None: - return None - if not is_bias: - return self.slice_2d(tensor_in, dim, n_cast) - else: - return self.slice_1d(tensor_in, n_cast) - - def slice_2d( - self, - tensor: torch.Tensor, - dim: int, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice the 2D tensor - - Args: - tensor (:class:`torch.Tensor`): The tensor to slice - dim (int): The dimension to slice - """ - assert dim in [0, 1], f"Only support 2D tensor, but got {dim}D tensor" - if dim == 0: - return self.slice_row(tensor, n_cast) - elif dim == 1: - return self.slice_col(tensor, n_cast) - - def slice_1d( - self, - tensor: torch.Tensor, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice the 1D tensor - - Args: - tensor (:class:`torch.Tensor`): The tensor to slice - - Returns: - :class:`torch.Tensor`: The sliced tensor - """ - if n_cast is None: - return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() - else: - tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) - chunk_list = [ - tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) - ] - return torch.cat(chunk_list, dim=0).contiguous() - - def slice_col( - self, - tensor: torch.Tensor, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice the tensor in column - - Args: - tensor (:class:`torch.Tensor`): The tensor to slice - - Returns: - :class:`torch.Tensor`: The sliced tensor - - """ - if n_cast is None: - return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() - else: - tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) - chunk_list = [ - tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) - ] - return torch.cat(chunk_list, dim=0).contiguous() - - def slice_row( - self, - tensor: torch.Tensor, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice the tensor in column - - Args: - tensor (:class:`torch.Tensor`): The tensor to slice - - Returns: - :class:`torch.Tensor`: The sliced tensor - """ - if n_cast is None: - return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() - else: - tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=1) - chunk_list = [ - tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) - ] - return torch.cat(chunk_list, dim=1).contiguous() diff --git a/colossalai/shardformer/test/config.py b/colossalai/shardformer/test/config.py deleted file mode 100644 index 2b80d8b3ca12..000000000000 --- a/colossalai/shardformer/test/config.py +++ /dev/null @@ -1 +0,0 @@ -parallel = dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')) diff --git a/colossalai/shardformer/test/module_test.py b/colossalai/shardformer/test/module_test.py deleted file mode 100644 index 83dc7ec6cf4a..000000000000 --- a/colossalai/shardformer/test/module_test.py +++ /dev/null @@ -1,50 +0,0 @@ -import os - -import torch -import torch.nn as nn -import torch.nn.functional as F - -import colossalai -from colossalai.shardformer.layer.dist_crossentropy import applyDistCrossEntropy -from colossalai.shardformer.layer.dropout import Dropout1D - - -def get_args(): - parser = colossalai.get_default_parser() - parser.add_argument("--module", type=str, default='distloss') - return parser.parse_args() - - -def test_dist_crossentropy(): - pred = torch.randn(2, 4, 8, requires_grad=True) - labels = torch.randint(8, (1, 4)).repeat(2, 1) - - pred_ = pred.view(-1, 8) - labels_ = labels.view(-1) - loss = F.cross_entropy(pred_, labels_) - loss.backward() - print(f"normal loss:{loss}") - - pred = pred.chunk(int(os.environ['WORLD_SIZE']), -1)[int(os.environ['RANK'])] - loss = applyDistCrossEntropy(pred.to('cuda'), labels.to('cuda')) - loss.backward() - print(f"dist loss:{loss}") - - -def test_dropout(): - input = torch.randn(5, 4).to("cuda") - m = Dropout1D(p=0.2).to("cuda") - for i in range(2): - print(f"Output: {m(input)}") - print(torch.randn(1)) - - -if __name__ == '__main__': - args = get_args() - colossalai.launch_from_torch(config={}) - if args.module == 'distloss': - test_dist_crossentropy() - elif args.module == 'dropout': - test_dropout() - else: - print("not implemented yet") diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py deleted file mode 100644 index e2d5a94c782a..000000000000 --- a/colossalai/shardformer/test/test.py +++ /dev/null @@ -1,124 +0,0 @@ -import os -import random - -import torch -import torch.nn as nn -from datasets import load_dataset -from torch.utils.data import DataLoader -from tqdm.auto import tqdm -from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, GPT2LMHeadModel, get_scheduler - -import colossalai -from colossalai.shardformer.shard import ShardConfig, shard_model -from colossalai.utils import get_current_device, print_rank_0 - -os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' - - -def get_args(): - parser = colossalai.get_default_parser() - parser.add_argument("--mode", type=str, default='inference') - parser.add_argument("--save_model", action='store_true') - parser.add_argument("--model", type=str, default='bert-base-uncased') - return parser.parse_args() - - -def load_data(args): - tokenizer = AutoTokenizer.from_pretrained(args.model) - if tokenizer.pad_token is None: - tokenizer.add_special_tokens({"pad_token": "[PAD]"}) - # tokenizer.pad_token_id = 0 - datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') - # datasets=load_dataset("yelp_review_full") - tokenized_datasets = datasets.map( - lambda examples: tokenizer(examples["text"], truncation=True, padding="max_length"), batched=True) - tokenized_datasets = tokenized_datasets.remove_columns(["text"]) - # tokenized_datasets=tokenized_datasets.rename_column("label","labels") - tokenized_datasets.set_format("torch") - - train_dataset = tokenized_datasets["train"] - test_dataset = tokenized_datasets["test"] - - datacollector = DataCollatorForLanguageModeling(tokenizer, mlm=True, mlm_probability=0.15, return_tensors="pt") - train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=True, collate_fn=datacollector) - eval_dataloader = DataLoader(test_dataset, batch_size=16, shuffle=True, collate_fn=datacollector) - return train_dataloader, eval_dataloader - - -def inference(model: nn.Module, args): - print(model) - # print(model.wte.weight.shape) - tokenizer = AutoTokenizer.from_pretrained(args.model) - if tokenizer.pad_token is None: - tokenizer.add_special_tokens({"pad_token": "[PAD]"}) - tokenizer.pad_token_id = 0 - token = "Hello, my dog is cute" - inputs = tokenizer(token, return_tensors="pt") - inputs.to("cuda") - model.eval() - model.to("cuda") - outputs = model(**inputs) - print(outputs[0]) - - -def train(model: nn.Module, args, num_epoch: int = 3): - train_dataloader, eval_dataloader = load_data(args) - optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5) - num_training = num_epoch * len(train_dataloader) - progress_bar = tqdm(range(num_training)) - lr_scheduler = get_scheduler(name="linear", - optimizer=optimizer, - num_warmup_steps=0, - num_training_steps=num_training) - best_test_loss = float("inf") - model.to("cuda") - model.train() - for epoch in range(num_epoch): - progress_bar.set_description(f"Rank {get_current_device()} epoch {epoch}") - for batch in train_dataloader: - optimizer.zero_grad() - batch = {k: v.to('cuda') for k, v in batch.items()} - outputs = model(**batch) - loss = outputs.loss - loss.backward() - optimizer.step() - lr_scheduler.step() - progress_bar.update(1) - train_loss = loss - - loss = 0.0 - for batch in eval_dataloader: - batch = {k: v.to('cuda') for k, v in batch.items()} - outputs = model(**batch) - # loss = outputs.loss - assert not torch.isnan(outputs.loss), f"{batch}" - loss += outputs.loss.item() - # loss = criterion(outputs.logits, batch["input_ids"]) - test_loss = loss / len(eval_dataloader) - print_rank_0(f"Train Loss: {train_loss:.4f} Test Loss:{test_loss:.4f}") - if args.save_model and test_loss < best_test_loss: - best_test_loss = test_loss - torch.save(model.state_dict(), "./checkpoints/best_model.pth") - - -if __name__ == "__main__": - args = get_args() - colossalai.launch_from_torch(config=args.config) - if args.model == 'bert-base-uncased': - model = BertForMaskedLM.from_pretrained("bert-base-uncased") - elif args.model == 'gpt2': - model = GPT2LMHeadModel.from_pretrained("gpt2") - else: - raise AttributeError("model not supported") - shard_config = ShardConfig( - rank=int(str(get_current_device()).split(':')[-1]), - world_size=int(os.environ['WORLD_SIZE']), - ) - sharded_model = shard_model(model, shard_config) - - if args.mode == "train": - train(sharded_model, args) - elif args.mode == "inference": - inference(sharded_model, args) - else: - raise NotImplementedError diff --git a/colossalai/shardformer/utils/__init__.py b/colossalai/shardformer/utils/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/colossalai/shardformer/utils/utils.py b/colossalai/shardformer/utils/utils.py deleted file mode 100644 index eb84edd88404..000000000000 --- a/colossalai/shardformer/utils/utils.py +++ /dev/null @@ -1,58 +0,0 @@ -def hasattr_(obj, attr: str): - r""" - Check whether the object has the multi sublevel attr - - Args: - obj (object): The object to check - attr (str): The multi level attr to check - """ - attrs = attr.split('.') - for a in attrs: - try: - obj = getattr(obj, a) - except AttributeError: - return False - return True - - -def setattr_(obj, attr: str, value, ignore: bool = False): - r""" - Set the object's multi sublevel attr to value, if ignore, ignore when it doesn't exist - - Args: - obj (object): The object to set - attr (str): The multi level attr to set - value (Any): The value to set - ignore (bool): Whether to ignore when the attr doesn't exist - """ - - attrs = attr.split('.') - for a in attrs[:-1]: - try: - obj = getattr(obj, a) - except AttributeError: - if ignore: - return - raise AttributeError(f"Object {obj} has no attribute {attr}") - setattr(obj, attrs[-1], value) - - -def getattr_(obj, attr: str, ignore: bool = None): - r""" - Get the object's multi sublevel attr - - Args: - obj (object): The object to set - attr (str): The multi level attr to set - ignore (bool): Whether to ignore when the attr doesn't exist - """ - - attrs = attr.split('.') - for a in attrs: - try: - obj = getattr(obj, a) - except AttributeError: - if ignore: - return None - raise AttributeError(f"Object {obj} has no attribute {attr}") - return obj diff --git a/colossalai/tensor/comm_spec.py b/colossalai/tensor/comm_spec.py index dd873c852936..af38d2a502c2 100644 --- a/colossalai/tensor/comm_spec.py +++ b/colossalai/tensor/comm_spec.py @@ -16,66 +16,69 @@ def _all_gather(tensor, comm_spec): ''' Implement all gather operation on device mesh based on information provided by comm_spec. ''' - process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() - process_group = process_groups[comm_spec.logical_process_axis] - - tensor_list = [ - torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) - for _ in range(comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis]) - ] - # without this contiguous operation, the all gather may get some unexpected results. - tensor = tensor.contiguous() - dist.all_gather(tensor_list, tensor, group=process_group) - output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() - return output + process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] + for rank_list, process_group in process_groups_list: + if dist.get_rank() in rank_list: + tensor_list = [ + torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) + for _ in range(comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis]) + ] + # without this contiguous operation, the all gather may get some unexpected results. + tensor = tensor.contiguous() + dist.all_gather(tensor_list, tensor, group=process_group) + output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() + return output def _split(tensor, comm_spec): ''' Implement shard operation on device mesh based on information provided by comm_spec. ''' - process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() - process_group = process_groups[comm_spec.logical_process_axis] - - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // dist.get_world_size(process_group) - start = length * dist.get_rank(process_group) - output = torch.narrow(tensor, dim, start, length).contiguous() - return output + process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] + for rank_list, _ in process_groups_list: + if dist.get_rank() in rank_list: + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // len(rank_list) + start = length * rank_list.index(dist.get_rank()) + output = torch.narrow(tensor, dim, start, length).contiguous() + return output def _all_to_all(tensor, comm_spec): ''' Implement all to all operation on device mesh based on information provided by comm_spec. ''' - process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() - process_group = process_groups[comm_spec.logical_process_axis] - world_size = dist.get_world_size(process_group) - - new_shape = list(tensor.shape) - new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // world_size - new_shape = torch.Size(new_shape) - output_tensor_list = [torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)] - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // world_size - input_tensor_list = [torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(world_size)] - group = process_group - dist.all_to_all(output_tensor_list, input_tensor_list, group) - output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() - return output + process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] + for rank_list, process_group in process_groups_list: + if dist.get_rank() in rank_list: + new_shape = list(tensor.shape) + new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // len(rank_list) + new_shape = torch.Size(new_shape) + output_tensor_list = [ + torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list)) + ] + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // len(rank_list) + input_tensor_list = [ + torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(len(rank_list)) + ] + group = process_group + dist.all_to_all(output_tensor_list, input_tensor_list, group) + output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() + return output def _all_reduce(tensor, comm_spec, async_op=False): ''' Implement all reduce operation on device mesh based on information provided by comm_spec. ''' - process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() - process_group = process_groups[comm_spec.logical_process_axis] - - if not tensor.is_contiguous(): - tensor = tensor.contiguous() - dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) - return tensor + process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] + for rank_list, process_group in process_groups_list: + if dist.get_rank() in rank_list: + if not tensor.is_contiguous(): + tensor = tensor.contiguous() + dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) + return tensor def _mix_gather(tensor, comm_spec): @@ -411,7 +414,7 @@ def __init__(self, self.forward_only = forward_only if isinstance(self.logical_process_axis, list): if not mix_gather: - self.device_mesh = self.sharding_spec.device_mesh.flatten() + self.device_mesh = self.sharding_spec.device_mesh.flatten_device_mesh self.logical_process_axis = 0 else: self.device_meshes = self.sharding_spec.device_mesh.flatten_device_meshes diff --git a/colossalai/tensor/d_tensor/RAEDME.md b/colossalai/tensor/d_tensor/RAEDME.md deleted file mode 100644 index 95d866388364..000000000000 --- a/colossalai/tensor/d_tensor/RAEDME.md +++ /dev/null @@ -1,103 +0,0 @@ -# 🔢 Distributed Tensor - -## 📚 Table of Contents - -- [🔢 Distributed Tensor](#-distributed-tensor) - - [📚 Table of Contents](#-table-of-contents) - - [🔗 Introduction](#-introduction) - - [📝 Design](#-design) - - [🔨 Usage](#-usage) - - [🎈 Progress Log](#-progress-log) - -## 🔗 Introduction - -Distributed tensor is a type of tensor that is distributed across multiple devices. It is a wrapper of PyTorch tensor, and it is used to support distributed training. -It can represent the device topology and tensor placement over the devices in the topology. It also provides a set of APIs to manipulate the distributed tensor. - -## 📝 Design - -Our implementation is inspired by the work [Alpa](https://arxiv.org/abs/2201.12023), which unifies data parallelism and tensor parallelism as intra-op parallelism. It uses notations `S` to represent the sharded dimension and `R` to represent the replicated dimension. For example, given a 2D matrix, `[S, R]` represents the tensor is sharded over the first dimension. - -Each sharded dimension will have a subscript to represent its placement over the devices. Assuming we have 4 GPUs and the GPUs are arranged in a 2 x 2 manner. Let's say we have a 2D matrix like below: - - -```text - [1, 2, 3, 4 ] -A = [4, 5, 6, 7 ] - [8, 9, 10, 11] - [12, 13, 14, 15] -``` - -`[S0, R]` would mean that the first dimension is sharded over the rows in the device topology. - -```text -| --------------------—————————————————————-| -| | | -| [1, 2, 3, 4 ] | [1, 2, 3, 4 ] | -| [4, 5, 6, 7 ] | [4, 5, 6, 7 ] | -| | | -| --------------------——————————————————----- -| | | -| [8, 9, 10, 11] | [8, 9, 10, 11] | -| [12, 13, 14, 15] | [12, 13, 14, 15] | -| | | -| --------------------——————————————————----- -``` - -`[S01, R]` would mean that the first dimension is sharded over both the row and column in the device topology. - -```text -| --------------------—————————————————————-| -| | | -| [1, 2, 3, 4 ] | [4, 5, 6, 7 ] | -| | | -| --------------------——————————————————----- -| | | -| [8, 9, 10, 11] | [12, 13, 14, 15] | -| | | -| --------------------——————————————————----- -``` - -## 🔨 Usage - -A sample API usage is given below. - -```python -import torch - -import colossalai -from colossalai.device.device_mesh import DeviceMesh -from colossalai.tensor.d_tensor import DTensor, ShardingSpec - -colossalai.launch_from_torch(config={}) - -# define your device mesh -# assume you have 4 GPUs -physical_mesh_id = torch.arange(0, 4).reshape(1, 4) -mesh_shape = (2, 2) -device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - -# define a tensor -a = torch.rand(16, 32).cuda() - -# create sharding spec for the tensor -# assume the sharding spec is [S0, R] -dim_partition_dict = {0: [0]} -sharding_spec = ShardingSpec(a.dim(), dim_partition_dict) - -# create a distributed tensor -d_tensor = DTensor(a, device_mesh, sharding_spec) -print(d_tensor) - -global_tensor = d_tensor.to_global() -print(global_tensor) -``` - - -## 🎈 Progress Log - -- [x] Support layout conversion -- [x] Support sharding on 2D device mesh -- [ ] Support sharding on 3D device mesh -- [ ] Support sharding 4D device mesh -- [ ] Support sharding info saving and offline tensor merge (we can save tensor as dtensor and gather the tensors back to the global tensor based on the sharding info in a single process in CPU, useful for distributed training checkpoint load and save.) diff --git a/colossalai/tensor/d_tensor/__init__.py b/colossalai/tensor/d_tensor/__init__.py index af77f4f0edfc..e69de29bb2d1 100644 --- a/colossalai/tensor/d_tensor/__init__.py +++ b/colossalai/tensor/d_tensor/__init__.py @@ -1,4 +0,0 @@ -from .d_tensor import DTensor -from .sharding_spec import ShardingSpec - -__all__ = ['DTensor', 'ShardingSpec'] diff --git a/colossalai/tensor/d_tensor/comm_spec.py b/colossalai/tensor/d_tensor/comm_spec.py index 79b2e3ef936a..159125fa16db 100644 --- a/colossalai/tensor/d_tensor/comm_spec.py +++ b/colossalai/tensor/d_tensor/comm_spec.py @@ -24,12 +24,12 @@ class CommSpec: ''' Communication spec is used to record the communication action. It converts the communication spec to real action which will be used in runtime. It contains comm_pattern to determine the - communication method, process_group_dict to determine the process groups, gather_dim and shard_dim + communication method, process_groups_dict to determine the process groups, gather_dim and shard_dim to determine the buffer shape, and logical_process_axis Argument: - comm_pattern(CollectiveCommPattern): decribe the communication method used in this spec. - process_group_dict(Dict): A dict which contains the process groups used to apply this CommSpec. + comm_pattern(CollectiveCommPattern): describe the communication method used in this spec. + process_groups_dict(Dict): A dict which contains the process groups used to apply this CommSpec. gather_dim(int, Optional): The gather_dim of the tensor will be gathered. shard_dim(int, Optional): The shard_dim of the tensor will be sharded. logical_process_axis(Union(int, List[int]), Optional): The mesh_dim to implement the communication action. @@ -37,7 +37,7 @@ class CommSpec: def __init__(self, comm_pattern: CollectiveCommPattern, - process_group_dict: Dict, + process_groups_dict: Dict, gather_dim: int = None, shard_dim: int = None, logical_process_axis: int = None): @@ -45,7 +45,7 @@ def __init__(self, self.gather_dim = gather_dim self.shard_dim = shard_dim self.logical_process_axis = logical_process_axis - self.process_group_dict = process_group_dict + self.process_groups_dict = process_groups_dict def __repr__(self): res_list = ["CommSpec:("] @@ -92,56 +92,68 @@ def _all_gather(tensor: torch.Tensor, comm_spec: CommSpec): ''' Implement all gather operation on device mesh based on information provided by comm_spec. ''' - process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] - world_size = dist.get_world_size(process_group) - tensor_list = [torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)] - # without this contiguous operation, the all gather may get some unexpected results. - tensor = tensor.contiguous() - dist.all_gather(tensor_list, tensor, group=process_group) - output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() - return output + process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] + for rank_list, process_group in process_groups_list: + if dist.get_rank() in rank_list: + tensor_list = [ + torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list)) + ] + # without this contiguous operation, the all gather may get some unexpected results. + tensor = tensor.contiguous() + dist.all_gather(tensor_list, tensor, group=process_group) + output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() + return output def _split(tensor: torch.Tensor, comm_spec: CommSpec): ''' Implement shard operation on device mesh based on information provided by comm_spec. ''' - process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // dist.get_world_size(process_group) - start = length * dist.get_rank(process_group) - output = torch.narrow(tensor, dim, start, length).contiguous() - return output + process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] + for rank_list, _ in process_groups_list: + if dist.get_rank() in rank_list: + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // len(rank_list) + start = length * rank_list.index(dist.get_rank()) + output = torch.narrow(tensor, dim, start, length).contiguous() + return output def _all_to_all(tensor: torch.Tensor, comm_spec: CommSpec): ''' Implement all to all operation on device mesh based on information provided by comm_spec. ''' - process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] - world_size = dist.get_world_size(process_group) - new_shape = list(tensor.shape) - new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // world_size - new_shape = torch.Size(new_shape) - output_tensor_list = [torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)] - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // world_size - input_tensor_list = [torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(world_size)] - group = process_group - dist.all_to_all(output_tensor_list, input_tensor_list, group) - output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() - return output + process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] + for rank_list, process_group in process_groups_list: + if dist.get_rank() in rank_list: + new_shape = list(tensor.shape) + new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // len(rank_list) + new_shape = torch.Size(new_shape) + output_tensor_list = [ + torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list)) + ] + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // len(rank_list) + input_tensor_list = [ + torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(len(rank_list)) + ] + group = process_group + dist.all_to_all(output_tensor_list, input_tensor_list, group) + output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() + return output def _all_reduce(tensor: torch.Tensor, comm_spec: CommSpec, async_op: bool = False): ''' Implement all reduce operation on device mesh based on information provided by comm_spec. ''' - process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] - if not tensor.is_contiguous(): - tensor = tensor.contiguous() - dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) - return tensor + process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] + for rank_list, process_group in process_groups_list: + if dist.get_rank() in rank_list: + if not tensor.is_contiguous(): + tensor = tensor.contiguous() + dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) + return tensor class _ReduceGrad(torch.autograd.Function): @@ -257,7 +269,7 @@ def symbolic(graph, input_): def forward(ctx, input_, comm_spec): output = _all_to_all(input_, comm_spec) comm_spec_for_backward = CommSpec(comm_pattern=comm_spec.comm_pattern, - process_group_dict=comm_spec.process_group_dict, + process_groups_dict=comm_spec.process_groups_dict, gather_dim=comm_spec.shard_dim, shard_dim=comm_spec.gather_dim, logical_process_axis=comm_spec.logical_process_axis) diff --git a/colossalai/tensor/d_tensor/d_tensor.py b/colossalai/tensor/d_tensor/d_tensor.py index 6bda0f4e579c..c1fe9d50a048 100644 --- a/colossalai/tensor/d_tensor/d_tensor.py +++ b/colossalai/tensor/d_tensor/d_tensor.py @@ -3,119 +3,55 @@ import torch from torch.utils._pytree import tree_map -from colossalai.device.device_mesh import DeviceMesh - from .layout import Layout from .layout_converter import LayoutConverter, to_global from .sharding_spec import ShardingSpec -__all__ = ['DTensor', 'distribute_tensor', 'distribute_module', 'construct_default_sharding_spec'] - layout_converter = LayoutConverter() class DTensor(torch.Tensor): - """ - DTensor stands for distributed tensor. It is a subclass of `torch.Tensor` and contains meta information - about the tensor distribution. The meta information includes the device mesh, the sharding specification, - and the entire shape of the tensor. - - During runtime, we will not directly use the DTensor objects for computation. Instead, we will only use the - `DTensor.local_tensor` for computation. The `DTensor.local_tensor` is the local tensor in the current rank. - In this way, all tensors involved in computation will only be native PyTorch tensors. - - Example: - ```python - from colossalai.device import DeviceMesh - - # define your device mesh - # assume you have 4 GPUs - physical_mesh_id = torch.arange(0, 4).reshape(1, 4) - mesh_shape = (2, 2) - device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - - # define a tensor - x = torch.rand(16, 32) - - # create sharding spec for the tensor - # assume the sharding spec is [S, R] - dim_partition_dict = { - 0: 1 - } - sharding_spec = ShardingSpec(a.dim(), dim_partition_dict) - - # create a distributed tensor - d_tensor = DTensor(x, device_mesh, sharding_spec) - ``` - Args: - tensor (`torch.Tensor`): the unsharded tensor. - device_mesh (`DeviceMesh`): the device mesh for abstraction of the compute devices. - sharding_spec (`ShardingSpec`): the sharding specification which describes how the tensor will be sharded. - """ - - def __init__(self, tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec): - # ensure this tensor is not a DTensor - assert not isinstance(tensor, DTensor), 'The input tensor should not be a DTensor.' - - # store meta info - self.local_tensor = tensor - self.data_type = tensor.dtype - self.global_shape = tensor.shape - - # create distributed layout - dist_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=self.global_shape) + def __init__(self, local_tensor: torch.Tensor, dist_layout: Layout): + self.local_tensor = local_tensor + self.data_type = local_tensor.dtype + self.entire_shape = local_tensor.shape self.dist_layout = dist_layout - - # shard the tensor self._apply_layout() @staticmethod - def __new__(cls, tensor, *args, **kwargs): - return torch.Tensor._make_subclass(cls, tensor, tensor.requires_grad) + def __new__(cls, local_tensor, layout): + return torch.Tensor._make_subclass(cls, local_tensor, local_tensor.requires_grad) def __repr__(self): - return f"DTensor(\n{self.to_global()}\n{self.dist_layout}" + return f"DTensor({self.to_global()}, {self.dist_layout})" def __str__(self): return self.__repr__() - def layout_convert(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> None: + def layout_convert(self, target_layout): ''' Convert the layout of the tensor from source_spec to target_spec. - This will update the `local_tensor` and `dist_layout` in place. - - Args: - target_layout (Layout): the target layout specification. ''' - target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=self.global_shape) - self.local_tensor = layout_converter.apply(tensor=self.local_tensor, - source_layout=self.dist_layout, - target_layout=target_layout) + self.local_tensor = layout_converter.apply(self.local_tensor, self.dist_layout, target_layout) self.dist_layout = target_layout def _apply_layout(self): ''' Apply the layout to the local tensor during initializing process. ''' - # layout converter requires a source and target laytout - # we construct the source layer for an unsharded tensor - # and use self.dist_layer as the targer layout for the sharded tensor source_spec = construct_default_sharding_spec(self.local_tensor) source_layout = Layout(device_mesh=self.dist_layout.device_mesh, + device_type=self.dist_layout.device_type, sharding_spec=source_spec, - global_shape=self.global_shape) - self.local_tensor = layout_converter.apply(tensor=self.local_tensor, - source_layout=source_layout, - target_layout=self.dist_layout) + entire_shape=self.entire_shape) + self.local_tensor = layout_converter.apply(self.local_tensor, source_layout, self.dist_layout) @classmethod def __torch_function__(cls, func, types, args=(), kwargs=None): if kwargs is None: kwargs = {} - # convert all DTensors to native pytorch tensors - # so that operations will be conducted on native tensors def filter_arg(arg): if isinstance(arg, DTensor): return arg.local_tensor @@ -124,9 +60,9 @@ def filter_arg(arg): args = tree_map(filter_arg, args) kwargs = tree_map(filter_arg, kwargs) - - # NOTE: if we want to convert the result into DTensor, we need to infer the layout of result from the layout of input tensors + # if we want to convert the result into DTensor, we need to infer the layout of result from the layout of input tensors # and op type. + return func(*args, **kwargs) @property @@ -149,6 +85,7 @@ def to(self, *args, **kwargs): ''' self.local_tensor = self.local_tensor.to(*args, **kwargs) self.data_type = self.local_tensor.dtype + self.dist_layout.device_type = self.local_tensor.device # TODO: update the device mesh process groups or we should just cache # both the cpu process groups and the cuda process groups? return self @@ -161,7 +98,7 @@ def to_local(self): def to_global(self): ''' - Recover the global tensor from the distributed tensor by returning a new `torch.Tensor` object. + Recover the global tensor from the distributed tensor. Note: This function will all_gather the local tensor to the global tensor and it will not change the layout of the DTensor. This function is mainly used for debugging or @@ -170,29 +107,24 @@ def to_global(self): return to_global(self.local_tensor, self.dist_layout) -def distribute_tensor(tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> DTensor: +def distribute_tensor(local_tensor: torch.Tensor, dist_layout: Layout) -> DTensor: ''' Distribute the local tensor to the distributed tensor according to the dist_layout specified. Args: - tensor (`torch.Tensor`): tensor to be distributed. - device_mesh (`DeviceMesh`): the device mesh for abstraction of the compute devices. - sharding_spec (`ShardingSpec`): the sharding specification which describes how the tensor will be sharded. + local_tensor: tensor to be distributed. + dist_layout: the layout specification of the distributed tensor. Returns: A 'DTensor' object. ''' - return DTensor(tensor, device_mesh, sharding_spec) + return DTensor(local_tensor, dist_layout) def distribute_module(module: torch.nn.Module, partition_fn: Optional[callable] = None) -> torch.nn.Module: ''' This function converts all the parameters in the module to DTensor(DParam). - Args: - module (`torch.nn.Module`): the module to be distributed. - partition_fn (callable): the partition function which will be used to partition the parameters. - Note: This function is subject to future change as the DParam has not been implemented yet. ''' for name, param in module.named_parameters(): @@ -206,11 +138,5 @@ def distribute_module(module: torch.nn.Module, partition_fn: Optional[callable] def construct_default_sharding_spec(tensor: torch.Tensor,) -> ShardingSpec: ''' Construct the default sharding specification for the tensor. - - Args: - tensor (`torch.Tensor`): the tensor to be sharded. - - Returns: - A `ShardingSpec` object without any sharding specified. ''' return ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={}) diff --git a/colossalai/tensor/d_tensor/layout.py b/colossalai/tensor/d_tensor/layout.py index 2946611b4b79..ee7ef74a99ae 100644 --- a/colossalai/tensor/d_tensor/layout.py +++ b/colossalai/tensor/d_tensor/layout.py @@ -11,32 +11,28 @@ class Layout: - """ - Layout of a tensor refers to the tensor placement on the device mesh and how the tensor is sharded over the devices. + """Layout of a tensor. - Args: - device_mesh (`DeviceMesh`): the device mesh to store the tensor distributed. - sharding_spec (`ShardingSpec`): the sharding specification to describe how the tensor is sharded. - global_shape (`torch.Size`): the entire shape of the global tensor. + Attributes: + device_mesh: the device mesh to store the tensor distributed. + device_type: the type of the device mesh, e.g. 'cpu' or 'cuda'. + sharding_spec: the sharding specification to describe how the tensor is sharded. + entire_shape: the entire shape of the global tensor. """ - def __init__(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec, global_shape: torch.Size): + def __init__(self, device_mesh: DeviceMesh, device_type: torch.device, sharding_spec: ShardingSpec, + entire_shape: torch.Size): self.device_mesh = device_mesh + self.device_type = device_type self.sharding_spec = sharding_spec - self.global_shape = global_shape + self.entire_shape = entire_shape self._sanity_check() def __hash__(self) -> int: return hash(f'{self.sharding_spec}') - def get_sharded_shape_per_device(self) -> torch.Size: - """ - Compute the shape of the sharded tensor on each device. - - Returns: - `torch.Size`: the shape of the sharded tensor on each device. - """ - sharded_shape = list(self.global_shape) + def get_sharded_shape_per_device(self): + sharded_shape = list(self.entire_shape) for dim, shard_list in self.sharding_spec.dim_partition_dict.items(): mesh_list = [self.device_mesh.mesh_shape[mesh_dim] for mesh_dim in shard_list] shard_partitions = reduce(operator.mul, mesh_list, 1) @@ -60,7 +56,7 @@ def _sanity_check(self): # make sure that the sharding for a dimension is divisible by the number of devices for dim, shard_list in sharding_spec.dim_partition_dict.items(): - tensor_dim_size = self.global_shape[dim] + tensor_dim_size = self.entire_shape[dim] num_devices = 1 for element in shard_list: diff --git a/colossalai/tensor/d_tensor/layout_converter.py b/colossalai/tensor/d_tensor/layout_converter.py index 6eff92ea6b13..cf02aac309f4 100644 --- a/colossalai/tensor/d_tensor/layout_converter.py +++ b/colossalai/tensor/d_tensor/layout_converter.py @@ -3,8 +3,10 @@ from dataclasses import dataclass from typing import Dict, List, Tuple +import numpy as np import torch +from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, TrainCycleItem from colossalai.context.singleton_meta import SingletonMeta from colossalai.tensor.d_tensor.comm_spec import * from colossalai.tensor.d_tensor.layout import Layout @@ -26,21 +28,13 @@ class LayoutConverterOptions: pass -def to_global(distributed_tensor: "DTensor", layout: Layout) -> torch.Tensor: - """ - Convert a distributed tensor to the global tensor with the given layout. - This function returns a native `torch.Tensor` object. - - - Args: - distributed_tensor (`DTensor`): the distributed tensor to be converted. - layout (`Layout`): the target layout specification. - """ +def to_global(distributed_tensor: torch.Tensor, layout: Layout) -> torch.Tensor: layout_converter = LayoutConverter() global_sharding_spec = ShardingSpec(distributed_tensor.dim(), {}) global_layout = Layout(device_mesh=layout.device_mesh, + device_type=layout.device_type, sharding_spec=global_sharding_spec, - global_shape=layout.global_shape) + entire_shape=layout.entire_shape) with torch.no_grad(): global_tensor = layout_converter.apply(distributed_tensor, layout, global_layout) return global_tensor @@ -55,9 +49,6 @@ def set_layout_converting_options(options: LayoutConverterOptions): class LayoutConverter(metaclass=SingletonMeta): - """ - LayoutConverter is a singleton class which converts the layout of a distributed tensor. - """ def __init__(self): self._options = None @@ -100,14 +91,15 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - global_shape = (4, 4, 4) + entire_shape = (4, 4, 4) dim_partition_dict = {0: [0], 1: [1]} # [S0,S1,R] sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), sharding_spec=sharding_spec, - global_shape=global_shape) + entire_shape=entire_shape) rst_dict = layout_converter.all_gather_transform_layouts(layout) for layout, comm_spec in rst_dict.items(): @@ -120,12 +112,7 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co valid_spec_dict = {} comm_pattern = CollectiveCommPattern.GATHER_FWD_SPLIT_BWD source_spec = source_layout.sharding_spec - - # the key of the dict is the axis - # the value is the process group - current_rank = source_layout.device_mesh._global_rank_of_current_process - process_group_dict = source_layout.device_mesh._process_group_dict[current_rank] - + process_groups_dict = source_layout.device_mesh.process_groups_dict for target_pair in source_spec.dim_partition_dict.items(): shard_list = all_gather_simulator(target_pair) index = target_pair[0] @@ -143,7 +130,7 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co logical_process_axis = target_pair[1][-1] comm_spec = CommSpec( comm_pattern, - process_group_dict=process_group_dict, + process_groups_dict=process_groups_dict, gather_dim=gather_dim, # shard_dim will be used during backward shard_dim=gather_dim, @@ -154,7 +141,8 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co new_sharding_spec = ShardingSpec(source_spec.dims, dim_partition_dict=new_dim_partition_dict) new_layout = Layout(device_mesh=source_layout.device_mesh, sharding_spec=new_sharding_spec, - global_shape=source_layout.global_shape) + device_type=source_layout.device_type, + entire_shape=source_layout.entire_shape) valid_spec_dict[new_layout] = comm_spec except LayoutException: @@ -179,14 +167,15 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - global_shape = (4, 4, 4) + entire_shape = (4, 4, 4) dim_partition_dict = {0: [0], 1: [1]} # [S0,S1,R] sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), sharding_spec=sharding_spec, - global_shape=global_shape) + entire_shape=entire_shape) rst_dict = layout_converter.all_to_all_transform_layout(layout) for layout, comm_spec in rst_dict.items(): @@ -199,12 +188,7 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com ''' valid_spec_dict = {} comm_pattern = CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD - - # the key of the dict is the axis - # the value is the process group - current_rank = source_layout.device_mesh._global_rank_of_current_process - process_group_dict = source_layout.device_mesh._process_group_dict[current_rank] - + process_groups_dict = source_layout.device_mesh.process_groups_dict source_spec = source_layout.sharding_spec tensor_dims = source_spec.dims for f_index in range(tensor_dims - 1): @@ -245,7 +229,7 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com shard_dim = f_index logical_process_axis = b_target_pair[1][-1] comm_spec = CommSpec(comm_pattern, - process_group_dict=process_group_dict, + process_groups_dict, gather_dim=gather_dim, shard_dim=shard_dim, logical_process_axis=logical_process_axis) @@ -268,7 +252,8 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com new_sharding_spec = ShardingSpec(source_spec.dims, dim_partition_dict=new_dim_partition_dict) new_layout = Layout(device_mesh=source_layout.device_mesh, sharding_spec=new_sharding_spec, - global_shape=source_layout.global_shape) + device_type=source_layout.device_type, + entire_shape=source_layout.entire_shape) valid_spec_dict[new_layout] = comm_spec except LayoutException: pass @@ -293,15 +278,16 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - global_shape = (4, 4, 4) + entire_shape = (4, 4, 4) dim_partition_dict = {0: [0]} # [S0,R,R] sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), sharding_spec=sharding_spec, - global_shape=global_shape) + entire_shape=entire_shape) rst_dict = layout_converter.shard_transform_layout(layout) for layout, comm_spec in rst_dict.items(): @@ -315,11 +301,7 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec valid_spec_dict = {} comm_pattern = CollectiveCommPattern.SPLIT_FWD_GATHER_BWD source_spec = source_layout.sharding_spec - - # the key of the dict is the axis - # the value is the process group - current_rank = source_layout.device_mesh._global_rank_of_current_process - process_group_dict = source_layout.device_mesh._process_group_dict[current_rank] + process_groups_dict = source_layout.device_mesh.process_groups_dict # legal sharding dims means the mesh_id is still available to use. legal_sharding_dims = [i for i in range(len(source_layout.device_mesh.mesh_shape))] @@ -347,7 +329,7 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec shard_dim = index logical_process_axis = shard_list[-1] comm_spec = CommSpec(comm_pattern, - process_group_dict=process_group_dict, + process_groups_dict, gather_dim=shard_dim, shard_dim=shard_dim, logical_process_axis=logical_process_axis) @@ -358,7 +340,8 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec dim_partition_dict=new_dim_partition_dict) new_layout = Layout(device_mesh=source_layout.device_mesh, sharding_spec=new_sharding_spec, - global_shape=source_layout.global_shape) + device_type=source_layout.device_type, + entire_shape=source_layout.entire_shape) valid_spec_dict[new_layout] = comm_spec except LayoutException: pass @@ -416,7 +399,7 @@ def layout_converting(self, source_layout: Layout, # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - global_shape = (4, 4, 4) + entire_shape = (4, 4, 4) dim_partition_source = {1: [0, 1]} dim_partition_target = {0: [0, 1]} @@ -424,14 +407,16 @@ def layout_converting(self, source_layout: Layout, # [R,S01,R] sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) source_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), sharding_spec=sharding_spec_source, - global_shape=global_shape) + entire_shape=entire_shape) # [S01,R,R] sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) target_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), sharding_spec=sharding_spec_target, - global_shape=global_shape) + entire_shape=entire_shape) transform_path, comm_action_sequence = layout_converter.layout_converting(source_layout, target_layout) transform_path_str = '->'.join([str(layout.sharding_spec.sharding_sequence) for layout in transform_path]) @@ -520,19 +505,21 @@ def apply(self, tensor: torch.Tensor, source_layout: Layout, target_layout: Layo # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - global_shape = (4, 4, 4) + entire_shape = (4, 4, 4) # [S0,R,R] sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) source_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), sharding_spec=sharding_spec_source, - global_shape=global_shape) + entire_shape=entire_shape) # [R,S0,R] sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) target_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), sharding_spec=sharding_spec_target, - global_shape=global_shape) + entire_shape=entire_shape) if rank in (0, 1): sharded_tensor_0 = torch.zeros(2, 1) @@ -567,4 +554,3 @@ def apply(self, tensor: torch.Tensor, source_layout: Layout, target_layout: Layo for comm_spec in comm_action_sequence: tensor = comm_spec.covert_spec_to_action(tensor) return tensor - return tensor diff --git a/colossalai/tensor/d_tensor/sharding_spec.py b/colossalai/tensor/d_tensor/sharding_spec.py index 45b05e10e297..565012b58a03 100644 --- a/colossalai/tensor/d_tensor/sharding_spec.py +++ b/colossalai/tensor/d_tensor/sharding_spec.py @@ -116,21 +116,21 @@ def build_difference_2d_dict(self): def dim_diff(self, other): ''' - The difference between two DimSpec. + The difference between two _DimSpec. Argument: - other(DimSpec): the dim spec to compare with. + other(_DimSpec): the dim spec to compare with. Return: difference(int): the difference between two _DimSpec. Example: - ```python - dim_spec = DimSpec([0]) - other_dim_spec = DimSpec([0, 1]) + dim_spec = _DimSpec([0]) + other_dim_spec = _DimSpec([0, 1]) print(dim_spec.difference(other_dim_spec)) - # output: 5 - ``` + + Output: + 5 ''' difference = self.difference_dict[(str(self), str(other))] return difference @@ -142,13 +142,9 @@ class ShardingSpec: [R, R, S0, S1], which means Argument: - dim_size (int): The number of dimensions of the tensor to be sharded. - dim_partition_dict (Dict[int, List[int]], optional): The key is the dimension of tensor to be sharded, - and the value of the key describe which logical axis will be sharded in that dimension. Defaults to None. - E.g. {0: [0, 1]} means the first dimension of the tensor will be sharded in logical axis 0 and 1. - sharding_sequence (List[DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1]. - Generally, users should specify either dim_partition_dict or sharding_sequence. - If both are given, users must ensure that they are consistent with each other. Defaults to None. + dim_partition_dict(Dict[int, List[int]], optional): The key is the dimension of tensor to be sharded, + and the value of the key describe which logical axis will be sharded in that dimension. + sharding_sequence(List[DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1]. ''' def __init__(self, @@ -212,7 +208,6 @@ def spec_diff(self, other): pair of sharding sequence. Example: - ```python dim_partition_dict = {0: [0, 1]} # DistSpec: # shard_sequence: S01,R,R @@ -224,8 +219,10 @@ def spec_diff(self, other): # device_mesh_shape: (4, 4) sharding_spec_to_compare = ShardingSpec(device_mesh, entire_shape, dim_partition_dict_to_compare) print(sharding_spec.sharding_sequence_difference(sharding_spec_to_compare)) - # output: 25 - ``` + + Output: + 25 + Argument: other(ShardingSpec): The ShardingSpec to compared with. diff --git a/docs/sidebars.json b/docs/sidebars.json index c3cfbbeef689..8be40e4512f9 100644 --- a/docs/sidebars.json +++ b/docs/sidebars.json @@ -64,7 +64,6 @@ }, "features/pipeline_parallel", "features/nvme_offload", - "features/lazy_init", "features/cluster_utils" ] }, diff --git a/docs/source/en/features/lazy_init.md b/docs/source/en/features/lazy_init.md deleted file mode 100644 index 40f5da1cb84d..000000000000 --- a/docs/source/en/features/lazy_init.md +++ /dev/null @@ -1,71 +0,0 @@ -# Lazy initialization - -Author: Hongxin Liu - -**Prerequisite** -- [Booster API](../basics/booster_api.md) -- [Booster Plugins](../basics/booster_plugins.md) -- [Booster Checkpoint](../basics/booster_checkpoint.md) - -**Related discussion** -- [Lazy initialization of model](https://github.com/hpcaitech/ColossalAI/discussions/3124) - -## Introduction - -LazyTensor allows DL framework (PyTorch) to execute operations lazily, by storing all operations related to it and reruning them when it's required to be materialized. - -LazyInit defers model initialization and it's based on LazyTensor. - -This is especially useful when we use model parallelism to train large models, in which case the model cannot fit in GPU memory. Through this, we can initialize model tensors using meta tensor and do static analysis to get shard strategy. And then materialize each tensor and apply the shard strategy. The static analysis can be omitted if the shard strategy is known in advance. - -## Usage - -You may use lazy initialization when using Gemini, tensor parallelism, pipeline parallelism, and auto-parallelism. In other cases, you may not need to use lazy initialization. - -Gemini is compatible with lazy initialization. You can use them together directly. - -```python -from colossalai.booster import Booster -from colossalai.booster.plugin import GeminiPlugin -from colossalai.lazy import LazyInitContext -from colossalai.nn.optimizer import HybridAdam -from torch.nn import Linear -import colossalai - -colossalai.launch_from_torch({}) - -plugin = GeminiPlugin() -booster = Booster(plugin=plugin) - -with LazyInitContext(): - model = Linear(10, 10) - -optimizer = HybridAdam(model.parameters()) -model, optimizer, *_ = booster.boost(model, optimizer) -``` - -Note that using lazy initialization when using Gemini is not necessary but recommended. If you don't use lazy initialization, you may get OOM error when initializing the model. If you use lazy initialization, you can avoid this error. - -> ⚠ Lazy initialization support for tensor parallelism, pipeline parallelism, and auto-parallelism is still under development. - -### Load from pretrained model - -We should not load pretrained weight in `LazyInitContext`. If so, lazy initialization is meaningless, as the checkpoint is loaded and it takes much GPU memory. A recommended way is to initialize model from scratch in `LazyInitContext` and load pretrained weight outside `LazyInitContext` after calling `Booster.boost()`. - - -```python -with LazyInitContext(): - model = GPT2LMHeadModel(config) - -optimizer = ... -lr_scheduler = ... -dataloader = ... -model, optimizer, lr_scheduler, dataloader = booster.boost(model, optimizer, lr_scheduler, dataloader) - -booster.load_model(model, pretrained_path) -``` - - -As booster supports both pytorch-fashion checkpoint and huggingface/transformers-fashion pretrained weight, the `pretrained_path` of the above pseudo-code can be either a checkpoint file path or a pretrained weight path. Note that it does not support loading pretrained weights from network. You should download the pretrained weight first and then use a local path. - - diff --git a/docs/source/zh-Hans/features/lazy_init.md b/docs/source/zh-Hans/features/lazy_init.md deleted file mode 100644 index 9a3cd90caa8d..000000000000 --- a/docs/source/zh-Hans/features/lazy_init.md +++ /dev/null @@ -1,71 +0,0 @@ -# 惰性初始化 - -作者: Hongxin Liu - -**前置教程** -- [Booster API](../basics/booster_api.md) -- [Booster 插件](../basics/booster_plugins.md) -- [Booster Checkpoint](../basics/booster_checkpoint.md) - -**相关讨论** -- [模型的惰性初始化](https://github.com/hpcaitech/ColossalAI/discussions/3124) - -## 引言 - -LazyTensor 允许深度学习框架 (PyTorch) 延迟执行操作，方法是存储与其相关的所有操作并在需要具体化时重新运行它们。 - -LazyInit 基于 LazyTensor，并支持延迟模型初始化。 - -这在我们使用模型并行来训练大型模型时特别有用，在这种情况下模型无法容纳在 GPU 内存中。通过这个，我们可以使用 Meta 张量初始化模型张量并进行静态分析以获得分片策略。然后具体化每个张量并应用分片策略。如果事先知道分片策略，则可以省略静态分析。 - -## 用法 - -您可以在使用 Gemini、张量并行、流水线并行和自动并行时使用惰性初始化。在其他情况下，您可能不需要使用惰性初始化。 - -Gemini 与惰性初始化兼容。您可以直接将它们一起使用。 - -```python -from colossalai.booster import Booster -from colossalai.booster.plugin import GeminiPlugin -from colossalai.lazy import LazyInitContext -from colossalai.nn.optimizer import HybridAdam -from torch.nn import Linear -import colossalai - -colossalai.launch_from_torch({}) - -plugin = GeminiPlugin() -booster = Booster(plugin=plugin) - -with LazyInitContext(): - model = Linear(10, 10) - -optimizer = HybridAdam(model.parameters()) -model, optimizer, *_ = booster.boost(model, optimizer) -``` - -请注意，在使用 Gemini 时使用惰性初始化不是必需的，但建议使用。如果不使用惰性初始化，在初始化模型时可能会出现 OOM 错误。如果使用惰性初始化，则可以避免此错误。 - -> ⚠ 对张量并行、流水线并行和自动并行的惰性初始化支持仍在开发中。 - -### 从预训练模型加载 - -我们不应该在 `LazyInitContext` 中加载预训练权重。如果这样，惰性初始化就没有意义，因为检查点已加载并且需要大量 GPU 内存。推荐的方法是在 `LazyInitContext` 中初始化模型，并在调用 `Booster.boost()` 后在 `LazyInitContext` 之外加载预训练权重。 - - -```python -with LazyInitContext(): - model = GPT2LMHeadModel(config) - -optimizer = ... -lr_scheduler = ... -dataloader = ... -model, optimizer, lr_scheduler, dataloader = booster.boost(model, optimizer, lr_scheduler, dataloader) - -booster.load_model(model, pretrained_path) -``` - - -由于 booster 同时支持 pytorch 风格的 checkpoint 和 huggingface/transformers 风格的预训练权重，上述伪代码的 `pretrained_pa​​th` 可以是 checkpoint 文件路径或预训练权重路径。请注意，它不支持从网络加载预训练权重。您应该先下载预训练的权重，然后使用本地路径。 - - diff --git a/tests/test_device/test_device_mesh.py b/tests/test_device/test_device_mesh.py index 19d41d23353f..3be057b3a98b 100644 --- a/tests/test_device/test_device_mesh.py +++ b/tests/test_device/test_device_mesh.py @@ -1,19 +1,20 @@ -import torch - from colossalai.device.device_mesh import DeviceMesh +import torch def test_device_mesh(): - physical_mesh_id = torch.arange(0, 16) + physical_mesh_id = torch.arange(0, 16).reshape(2, 8) mesh_shape = (4, 4) # [[0, 1, 2, 3], # [4, 5, 6, 7], # [8, 9, 10,11], # [12,13,14,15]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - assert device_mesh.global_rank_to_local_rank(5) == [1, 1] - assert device_mesh.global_rank_to_local_rank(11) == [2, 3] - assert device_mesh.get_ranks_in_process_group(axis=1, global_rank=2) == [0, 1, 2, 3] + assert device_mesh.convert_map[5] == [1, 1] + assert device_mesh.convert_map[11] == [2, 3] + assert device_mesh.global_rank_to_process_groups_with_logical_rank(0)[0] == [[0, 0], [1, 0], [2, 0], [3, 0]] + assert device_mesh.global_rank_to_process_groups_with_logical_rank(2)[1] == [[0, 0], [0, 1], [0, 2], [0, 3]] + assert device_mesh.global_rank_to_process_groups_with_global_rank(2)[1] == [0, 1, 2, 3] if __name__ == '__main__': diff --git a/tests/test_device/test_init_logical_pg.py b/tests/test_device/test_init_logical_pg.py index 7c6339eff67e..2b7060c4846a 100644 --- a/tests/test_device/test_init_logical_pg.py +++ b/tests/test_device/test_init_logical_pg.py @@ -20,12 +20,16 @@ def check_layer(rank, world_size, port): # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - - for axis in range(len(mesh_shape)): - tensor = torch.ones(4).cuda() - pg = device_mesh.get_process_group(axis=axis) - dist.all_reduce(tensor, op=ReduceOp.SUM, group=pg) - assert tensor.equal(tensor_to_check) + logical_pg_dict = {0: [[0, 2], [1, 3]], 1: [[0, 1], [2, 3]]} + logical_process_groups = device_mesh.process_groups_dict + + for mesh_dim, pgs in logical_pg_dict.items(): + for index, pg in enumerate(pgs): + if rank in pg: + tensor = torch.ones(4).cuda() + group = logical_process_groups[mesh_dim][index][1] + dist.all_reduce(tensor, op=ReduceOp.SUM, group=group) + assert tensor.equal(tensor_to_check) gpc.destroy() diff --git a/tests/test_lazy/lazy_init_utils.py b/tests/test_lazy/lazy_init_utils.py index 2911012fafa8..85bfd0e27801 100644 --- a/tests/test_lazy/lazy_init_utils.py +++ b/tests/test_lazy/lazy_init_utils.py @@ -6,9 +6,7 @@ import torch from packaging import version -from colossalai.device.device_mesh import DeviceMesh from colossalai.lazy.lazy_init import LazyInitContext, LazyTensor, _MyTensor -from colossalai.tensor.d_tensor.layout import Layout from colossalai.tensor.d_tensor.layout_converter import to_global from tests.kit.model_zoo.registry import ModelAttribute @@ -83,8 +81,7 @@ def check_lazy_init(entry: TestingEntry, seed: int = 42, verbose: bool = False, print(f'{model.__class__.__name__} pass') -def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn.Module, device_mesh: DeviceMesh, - sharding_spec_dict: dict) -> None: +def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn.Module, layout_dict: dict) -> None: state = model.state_dict() distributed_state = distributed_model.state_dict() @@ -94,7 +91,6 @@ def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn. assert n1 == n2 t1 = t1.cuda() t2 = t2.cuda() - if n2 in sharding_spec_dict: - layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_dict[n2], global_shape=t1.shape) - t2 = to_global(t2, layout) + if n2 in layout_dict: + t2 = to_global(t2, layout_dict[n2]) assert torch.equal(t1, t2), f'{n1} {t1} vs {t2}' diff --git a/tests/test_lazy/test_distribute.py b/tests/test_lazy/test_distribute.py index efa43eab5788..d515b175a9ea 100644 --- a/tests/test_lazy/test_distribute.py +++ b/tests/test_lazy/test_distribute.py @@ -26,19 +26,23 @@ def find_shard_dim(shape: torch.Size) -> Optional[int]: return dim -def make_sharding_spec(original_tensor: torch.Tensor) -> Layout: +def make_layout(device_mesh: DeviceMesh, original_tensor: torch.Tensor) -> Layout: shard_dim = find_shard_dim(original_tensor.shape) dim_partition_dict = {shard_dim: [0]} if shard_dim is not None else {} target_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict=dim_partition_dict) - return target_sharding_spec + layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=target_sharding_spec, + entire_shape=original_tensor.shape) + return layout def _get_current_name(prefix: str, name: str) -> str: return f'{prefix}.{name}'.lstrip('.') -def generate_sharding_spec_dict(model: nn.Module) -> dict: - sharding_spec_dict = {} +def generate_layout_dict(model: nn.Module, device_mesh: DeviceMesh) -> dict: + layout_dict = {} @torch.no_grad() def generate_recursively(module: nn.Module, prefix: str = ''): @@ -49,17 +53,17 @@ def generate_recursively(module: nn.Module, prefix: str = ''): # initialize tensors directly attached to the current module for name, param in module.named_parameters(recurse=False): if isinstance(param, LazyTensor): - sharding_spec = make_sharding_spec(param) - sharding_spec_dict[_get_current_name(prefix, name)] = sharding_spec + layout = make_layout(device_mesh, param) + layout_dict[_get_current_name(prefix, name)] = layout for name, buf in module.named_buffers(recurse=False): if isinstance(buf, LazyTensor): - sharding_spec = make_sharding_spec(buf) - sharding_spec_dict[_get_current_name(prefix, name)] = sharding_spec + layout = make_layout(device_mesh, buf) + layout_dict[_get_current_name(prefix, name)] = layout generate_recursively(model) - return sharding_spec_dict + return layout_dict @parameterize('subset', ['torchvision', 'diffusers', 'timm', 'transformers', 'torchaudio', 'deepfm', 'dlrm']) @@ -81,9 +85,9 @@ def run_dist_lazy_init(subset, seed: int = 42): ctx = LazyInitContext() with ctx: deferred_model = model_fn() - sharding_spec_dict = generate_sharding_spec_dict(deferred_model) - ctx.distribute(deferred_model, device_mesh, sharding_spec_dict, verbose=True) - assert_dist_model_equal(model, deferred_model, device_mesh, sharding_spec_dict) + layout_dict = generate_layout_dict(deferred_model, device_mesh) + ctx.distribute(deferred_model, layout_dict, verbose=True) + assert_dist_model_equal(model, deferred_model, layout_dict) def run_dist(rank, world_size, port) -> None: diff --git a/tests/test_tensor/test_dtensor/test_comm_spec.py b/tests/test_tensor/test_dtensor/test_comm_spec.py index 0797e01e7e9d..d1f5b9299397 100644 --- a/tests/test_tensor/test_dtensor/test_comm_spec.py +++ b/tests/test_tensor/test_dtensor/test_comm_spec.py @@ -125,6 +125,23 @@ def check_all_reduce_bwd(process_groups_dict, rank): assert tensor_to_comm.equal(tensor_to_check) +def check_all_reduce_in_flatten_device_mesh(process_groups_dict, rank): + # tensor to comm + tensor_to_comm = torch.ones(2, 2).cuda() * rank + + # reduce through logical process axis 0 at flatten device mesh + # tensor to check + # tensor([[6., 6.], + # [6., 6.]]) + tensor_to_check = torch.tensor([[6, 6], [6, 6]], dtype=tensor_to_comm.dtype).cuda() + + # CommSpec:(comm_pattern:all_reduce, logical_process_axis:[0, 1]) + comm_spec = CommSpec(CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD, process_groups_dict, logical_process_axis=0) + tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm) + + assert tensor_to_comm.equal(tensor_to_check) + + def check_comm(rank, world_size, port): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') @@ -136,22 +153,24 @@ def check_comm(rank, world_size, port): # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - - process_group_dict = device_mesh._process_group_dict[rank] + process_groups_dict = device_mesh.process_groups_dict # test all gather - check_all_gather(process_group_dict, rank) + check_all_gather(process_groups_dict, rank) # test shard - check_shard(process_group_dict, rank) + check_shard(process_groups_dict, rank) # test all to all - check_all_to_all(process_group_dict, rank) + check_all_to_all(process_groups_dict, rank) # test all reduce - check_all_reduce_fwd(process_group_dict, rank) - check_all_reduce_bwd(process_group_dict, rank) + check_all_reduce_fwd(process_groups_dict, rank) + check_all_reduce_bwd(process_groups_dict, rank) + flatten_process_groups_dict = device_mesh.flatten_device_mesh.process_groups_dict + # test all reduce in 1D flatten device mesh + check_all_reduce_in_flatten_device_mesh(flatten_process_groups_dict, rank) gpc.destroy() diff --git a/tests/test_tensor/test_dtensor/test_dtensor.py b/tests/test_tensor/test_dtensor/test_dtensor.py index 50a3bfb15c38..3ca369acbf87 100644 --- a/tests/test_tensor/test_dtensor/test_dtensor.py +++ b/tests/test_tensor/test_dtensor/test_dtensor.py @@ -31,9 +31,13 @@ def check_dtensor(rank, world_size, port): device_mesh = DeviceMesh(torch.Tensor([0, 1, 2, 3]), (2, 2), init_process_group=True) target_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict={0: [0]}) - d_tensor = DTensor(original_tensor, device_mesh, target_sharding_spec) + layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=target_sharding_spec, + entire_shape=original_tensor.shape) + d_tensor = DTensor(original_tensor, layout) - assert d_tensor.global_shape == original_tensor.shape + assert d_tensor.entire_shape == original_tensor.shape assert d_tensor.data_type == original_tensor.dtype if rank in (0, 1): @@ -53,7 +57,12 @@ def check_dtensor(rank, world_size, port): raise ValueError(f'rank {rank} is not in the device mesh') new_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict={0: [0, 1]}) - d_tensor.layout_convert(device_mesh, new_sharding_spec) + new_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=new_sharding_spec, + entire_shape=original_tensor.shape) + + d_tensor.layout_convert(new_layout) if rank == 0: assert d_tensor.local_tensor.equal(original_tensor.narrow(0, 0, 1)) @@ -66,7 +75,7 @@ def check_dtensor(rank, world_size, port): else: raise ValueError(f'rank {rank} is not in the device mesh') - dtensor_from_local = distribute_tensor(original_tensor, device_mesh, new_sharding_spec) + dtensor_from_local = distribute_tensor(original_tensor, new_layout) if rank == 0: assert dtensor_from_local.local_tensor.equal(original_tensor.narrow(0, 0, 1)) diff --git a/tests/test_tensor/test_dtensor/test_layout_converter.py b/tests/test_tensor/test_dtensor/test_layout_converter.py index 6608e4787273..5f56decb5e5d 100644 --- a/tests/test_tensor/test_dtensor/test_layout_converter.py +++ b/tests/test_tensor/test_dtensor/test_layout_converter.py @@ -12,9 +12,9 @@ from colossalai.tensor.d_tensor.sharding_spec import DimSpec, ShardingSpec from colossalai.testing import rerun_if_address_is_in_use, spawn -global_shape = torch.Size((64, 32, 16)) +entire_shape = torch.Size((64, 32, 16)) layout_converter = LayoutConverter() -physical_mesh_id = torch.arange(0, 4) +physical_mesh_id = torch.arange(0, 4).reshape(2, 2) mesh_shape = (2, 2) @@ -30,7 +30,10 @@ def check_one_step_transform(rank, world_size, port): # shard_sequence: S0,S1,R # device_mesh_shape: (2, 2) sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) - layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=global_shape) + layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=sharding_spec, + entire_shape=entire_shape) rst_dict = layout_converter.all_gather_transform_layouts(layout) @@ -46,7 +49,10 @@ def check_one_step_transform(rank, world_size, port): # shard_sequence: S0,S1,R # device_mesh_shape: (4, 4) sharding_spec_all2all = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict_all2all) - layout_all2all = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_all2all, global_shape=global_shape) + layout_all2all = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=sharding_spec_all2all, + entire_shape=entire_shape) rst_dict_all2all = layout_converter.all_to_all_transform_layout(layout_all2all) @@ -65,7 +71,10 @@ def check_one_step_transform(rank, world_size, port): # shard_sequence: S0,R,R # device_mesh_shape: (4, 4) sharding_spec_shard = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_shard) - shard_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_shard, global_shape=global_shape) + shard_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=sharding_spec_shard, + entire_shape=entire_shape) rst_dict_shard = layout_converter.shard_transform_layout(shard_layout) @@ -91,13 +100,19 @@ def check_layout_converting(rank, world_size, port): # shard_sequence: R,S01,R # device_mesh_shape: (4, 4) sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) - source_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_source, global_shape=global_shape) + source_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=sharding_spec_source, + entire_shape=entire_shape) # DistSpec: # shard_sequence: S01,R,R # device_mesh_shape: (4, 4) sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) - target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_target, global_shape=global_shape) + target_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=sharding_spec_target, + entire_shape=entire_shape) transform_path, comm_action_sequence = layout_converter.layout_converting(source_layout, target_layout) @@ -144,15 +159,21 @@ def check_layout_converting_apply(rank, world_size, port): # shard_sequence: R,S01,R # device_mesh_shape: (4, 4) sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) - source_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_source, global_shape=global_shape) + source_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=sharding_spec_source, + entire_shape=entire_shape) # DistSpec: # shard_sequence: S01,R,R # device_mesh_shape: (4, 4) sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) - target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_target, global_shape=global_shape) + target_layout = Layout(device_mesh=device_mesh, + device_type=torch.device('cuda'), + sharding_spec=sharding_spec_target, + entire_shape=entire_shape) - original_tensor = torch.rand(global_shape).cuda() + original_tensor = torch.rand(entire_shape).cuda() # tensor_to_apply: [R, S01, R] tensor_to_apply = original_tensor.narrow(1, rank * 8, 8) diff --git a/tests/test_tensor/test_shape_consistency.py b/tests/test_tensor/test_shape_consistency.py index 859eef051256..6fe9ee292cd0 100644 --- a/tests/test_tensor/test_shape_consistency.py +++ b/tests/test_tensor/test_shape_consistency.py @@ -1,10 +1,9 @@ +from colossalai.tensor.shape_consistency import ShapeConsistencyManager, CollectiveCommPattern import torch - +from colossalai.tensor.sharding_spec import _DimSpec, ShardingSpec from colossalai.device.device_mesh import DeviceMesh -from colossalai.tensor.shape_consistency import CollectiveCommPattern, ShapeConsistencyManager -from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec -physical_mesh_id = torch.arange(0, 16) +physical_mesh_id = torch.arange(0, 16).reshape(2, 8) mesh_shape = (4, 4) # [[0, 1, 2, 3], # [4, 5, 6, 7], diff --git a/tests/test_tensor/test_sharded_linear.py b/tests/test_tensor/test_sharded_linear.py index 9bd9805e9b8f..d66d4fec14d1 100644 --- a/tests/test_tensor/test_sharded_linear.py +++ b/tests/test_tensor/test_sharded_linear.py @@ -26,7 +26,7 @@ def run_dist(rank, world_size, port): # the mesh is in the following topo # [[0, 1], # [2, 3]] - physical_mesh_id = torch.arange(0, 4) + physical_mesh_id = torch.arange(0, 4).reshape(2, 2) mesh_shape = (2, 2) device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) row_id = rank // 2 diff --git a/tests/test_tensor/test_sharding_spec.py b/tests/test_tensor/test_sharding_spec.py index 5007c4141849..909c84ef0f0e 100644 --- a/tests/test_tensor/test_sharding_spec.py +++ b/tests/test_tensor/test_sharding_spec.py @@ -5,7 +5,7 @@ def test_sharding_spec(): - physical_mesh_id = torch.arange(0, 16) + physical_mesh_id = torch.arange(0, 16).reshape(2, 8) mesh_shape = (4, 4) # [[0, 1, 2, 3], # [4, 5, 6, 7], From bd1ab9815813986d3c2d8c59ca739afc77e72979 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 9 Jun 2023 09:48:49 +0800 Subject: [PATCH 310/413] [gemini] fixed the gemini checkpoint io (#3934) --- colossalai/booster/plugin/gemini_plugin.py | 7 +++++-- colossalai/checkpoint_io/index_file.py | 18 ++++++++++-------- colossalai/zero/gemini/gemini_ddp.py | 5 ++++- 3 files changed, 19 insertions(+), 11 deletions(-) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 46714fe1c679..4a7efc165cbd 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -99,8 +99,11 @@ def save_sharded_model(self, save_state_dict(shard, checkpoint_file_path, use_safetensors) index_file.append_meta_data("total_size", total_size) - index_file.write_index_file(save_index_file) - logging.info(f"The model is going to be split to checkpoint shards. " + + # only save the index file on the master rank + if self.coordinator.is_master(): + index_file.write_index_file(save_index_file) + logging.info(f"The model is split into checkpoint shards. " f"You can find where each parameters has been saved in the " f"index located at {save_index_file}.") diff --git a/colossalai/checkpoint_io/index_file.py b/colossalai/checkpoint_io/index_file.py index 334ecbc04738..a41cc482e054 100644 --- a/colossalai/checkpoint_io/index_file.py +++ b/colossalai/checkpoint_io/index_file.py @@ -1,8 +1,8 @@ import json -from pathlib import Path -from typing import Any, List, Union import os -import json +from collections import OrderedDict +from pathlib import Path +from typing import Any, Dict, List, Union from .utils import is_dtensor_checkpoint @@ -22,8 +22,10 @@ class CheckpointIndexFile: def __init__(self, root_path=None) -> None: self.root_path = root_path - self.metadata: dict = dict() - self.weight_map: dict = dict() + + # use ordered dict to preserve the tensor checkpoint order + self.metadata: Dict = OrderedDict() + self.weight_map: Dict = OrderedDict() @staticmethod def from_file(index_path: Union[str, Path]): @@ -150,13 +152,13 @@ def get_checkpoint_file(self, param_name: str) -> str: """ ckpt_path = self.weight_map[param_name] return ckpt_path - + def get_all_param_names(self): """ Get all the weight keys. """ return list(self.weight_map.keys()) - + def write_index_file(self, save_index_file): """ Write index file. @@ -164,5 +166,5 @@ def write_index_file(self, save_index_file): save_index_file = os.path.join(self.root_path, save_index_file) index = {"metadata": self.metadata, "weight_map": self.weight_map} with open(save_index_file, "w", encoding="utf-8") as f: - content = json.dumps(index, indent=2, sort_keys=True) + "\n" + content = json.dumps(index, indent=2) + "\n" f.write(content) diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index 7e23fdb425f8..094320c4aff4 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -716,7 +716,10 @@ def append(self, name: str, tensor: torch.Tensor) -> Tuple[Optional[OrderedDict] tensor_size = calculate_tensor_size(tensor) ret_block = None ret_block_size = 0 - if self.current_block_size + tensor_size > self.max_shard_size: + + # before we return the current block and create a new block, + # we need to ensure that the current block is not empty + if self.current_block_size + tensor_size > self.max_shard_size and self.current_block_size > 0: ret_block = self.current_block ret_block_size = self.current_block_size self.current_block = OrderedDict() From e61ffc77c61df999b900ac1961d0329f0b544924 Mon Sep 17 00:00:00 2001 From: digger yu Date: Fri, 9 Jun 2023 09:49:41 +0800 Subject: [PATCH 311/413] fix typo tests/ (#3936) --- tests/kit/model_zoo/registry.py | 4 ++-- .../test_node_handler/test_batch_norm_handler.py | 2 +- .../test_node_handler/test_output_handler.py | 8 ++++---- tests/test_tensor/test_dtensor/test_layout_converter.py | 2 +- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tests/kit/model_zoo/registry.py b/tests/kit/model_zoo/registry.py index 7470327a65b6..6cc4c8ef370d 100644 --- a/tests/kit/model_zoo/registry.py +++ b/tests/kit/model_zoo/registry.py @@ -2,7 +2,7 @@ from dataclasses import dataclass from typing import Callable -__all__ = ['ModelZooRegistry', 'ModelAttributem', 'model_zoo'] +__all__ = ['ModelZooRegistry', 'ModelAttribute', 'model_zoo'] @dataclass @@ -37,7 +37,7 @@ def register(self, >>> model_zoo = ModelZooRegistry() >>> model_zoo.register('resnet18', resnet18, resnet18_data_gen) >>> # Run the model - >>> data = resnresnet18_data_gen() # do not input any argument + >>> data = resnet18_data_gen() # do not input any argument >>> model = resnet18() # do not input any argument >>> out = model(**data) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py index b47b3508ad1b..c3ceef4c7adf 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py @@ -27,7 +27,7 @@ def check_bn_module_handler(rank, world_size, port): # the index of bn node in computation graph node_index = 1 # the total number of bn strategies without sync bn mode - # TODO: add sync bn stategies after related passes ready + # TODO: add sync bn strategies after related passes ready strategy_number = 4 numerical_test_for_node_strategy(model=model, device_mesh=device_mesh, diff --git a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py index 5259455d2179..1703d5ded2f2 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py @@ -43,14 +43,14 @@ def test_output_handler(output_option): output_strategies_vector = StrategiesVector(output_node) # build handler - otuput_handler = OutputHandler(node=output_node, + output_handler = OutputHandler(node=output_node, device_mesh=device_mesh, strategies_vector=output_strategies_vector, output_option=output_option) - otuput_handler.register_strategy(compute_resharding_cost=False) + output_handler.register_strategy(compute_resharding_cost=False) # check operation data mapping - mapping = otuput_handler.get_operation_data_mapping() + mapping = output_handler.get_operation_data_mapping() for name, op_data in mapping.items(): op_data: OperationData @@ -59,7 +59,7 @@ def test_output_handler(output_option): assert mapping['output'].name == "output" assert mapping['output'].type == OperationDataType.OUTPUT - strategy_name_list = [val.name for val in otuput_handler.strategies_vector] + strategy_name_list = [val.name for val in output_handler.strategies_vector] if output_option == 'distributed': assert "Distributed Output" in strategy_name_list else: diff --git a/tests/test_tensor/test_dtensor/test_layout_converter.py b/tests/test_tensor/test_dtensor/test_layout_converter.py index 5f56decb5e5d..5c3da5f2b9ff 100644 --- a/tests/test_tensor/test_dtensor/test_layout_converter.py +++ b/tests/test_tensor/test_dtensor/test_layout_converter.py @@ -137,7 +137,7 @@ def check_layout_converting(rank, world_size, port): assert comm_action_sequence[2].shard_dim == 0 assert comm_action_sequence[2].logical_process_axis == 1 - # checkout chached_spec_pairs_transform_path + # checkout cached_spec_pairs_transform_path assert layout_converter.cached_solution[('[R, S01, R]', '[S01, R, R]')][0] == transform_path assert layout_converter.cached_solution[('[R, S01, R]', '[S01, R, R]')][1] == comm_action_sequence From 1aadeedeea4e5f2ee6304e10abe38ceb7beda33f Mon Sep 17 00:00:00 2001 From: digger yu Date: Fri, 9 Jun 2023 10:30:50 +0800 Subject: [PATCH 312/413] fix typo .github/workflows/scripts/ (#3946) --- .../generate_leaderboard_and_send_to_lark.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/scripts/generate_leaderboard_and_send_to_lark.py b/.github/workflows/scripts/generate_leaderboard_and_send_to_lark.py index 16b8957c1d88..d8f6c8fe309e 100644 --- a/.github/workflows/scripts/generate_leaderboard_and_send_to_lark.py +++ b/.github/workflows/scripts/generate_leaderboard_and_send_to_lark.py @@ -38,7 +38,7 @@ def plot_bar_chart(x: List[Any], y: List[Any], xlabel: str, ylabel: str, title: def get_issue_pull_request_comments(github_token: str, since: str) -> Dict[str, int]: """ - Retrive the issue/PR comments made by our members in the last 7 days. + Retrieve the issue/PR comments made by our members in the last 7 days. Args: github_token (str): GitHub access token for API calls @@ -89,7 +89,7 @@ def get_issue_pull_request_comments(github_token: str, since: str) -> Dict[str, def get_discussion_comments(github_token, since) -> Dict[str, int]: """ - Retrive the discussion comments made by our members in the last 7 days. + Retrieve the discussion comments made by our members in the last 7 days. This is only available via the GitHub GraphQL API. Args: @@ -194,7 +194,7 @@ def _call_graphql_api(query): discussion_updated_at = datetime.strptime(discussion['updatedAt'], "%Y-%m-%dT%H:%M:%SZ") # check if the updatedAt is within the last 7 days - # if yes, add it to dicussion_numbers + # if yes, add it to discussion_numbers if discussion_updated_at > since: if discussion['authorAssociation'] != 'MEMBER': discussion_numbers.append(discussion['number']) @@ -207,14 +207,14 @@ def _call_graphql_api(query): # update cursor cursor = edges[-1]['cursor'] - # get the dicussion comments and replies made by our member + # get the discussion comments and replies made by our member user_engagement_count = {} - for dicussion_number in discussion_numbers: + for discussion_number in discussion_numbers: cursor = None num_per_request = 10 while True: - query = _generate_comment_reply_count_for_discussion(dicussion_number, num_per_request, cursor) + query = _generate_comment_reply_count_for_discussion(discussion_number, num_per_request, cursor) data = _call_graphql_api(query) # get the comments @@ -249,7 +249,7 @@ def _call_graphql_api(query): reply = reply_edge['node'] if reply['authorAssociation'] == 'MEMBER': # check if the updatedAt is within the last 7 days - # if yes, add it to dicussion_numbers + # if yes, add it to discussion_numbers reply_updated_at = datetime.strptime(reply['updatedAt'], "%Y-%m-%dT%H:%M:%SZ") if reply_updated_at > since: member_name = reply['author']['login'] From 4110d1f0d4baaf76be03fd449a9e724c48ff6eeb Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 12 Jun 2023 09:50:57 +0800 Subject: [PATCH 313/413] [workflow] cancel duplicated workflow jobs (#3960) --- .github/workflows/build_on_pr.yml | 9 +++++++++ .github/workflows/compatiblity_test_on_pr.yml | 6 ++++++ .github/workflows/doc_check_on_pr.yml | 6 ++++++ .github/workflows/doc_test_on_pr.yml | 6 ++++++ .github/workflows/example_check_on_pr.yml | 6 ++++++ 5 files changed, 33 insertions(+) diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index a2807859b591..fdcfd21d62ba 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -60,6 +60,9 @@ jobs: defaults: run: shell: bash + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - name: Copy testmon cache run: | # branch name may contain slash, we need to replace it with space @@ -83,6 +86,9 @@ jobs: changedLibraryFiles: ${{ steps.find-lib-change.outputs.all_changed_files }} anyLibraryFileChanged: ${{ steps.find-lib-change.outputs.any_changed }} runs-on: ubuntu-latest + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v2 with: @@ -140,6 +146,9 @@ jobs: defaults: run: shell: bash + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - name: Checkout TensorNVMe uses: actions/checkout@v2 diff --git a/.github/workflows/compatiblity_test_on_pr.yml b/.github/workflows/compatiblity_test_on_pr.yml index 94a723388872..5098b8e364d0 100644 --- a/.github/workflows/compatiblity_test_on_pr.yml +++ b/.github/workflows/compatiblity_test_on_pr.yml @@ -12,6 +12,9 @@ jobs: runs-on: ubuntu-latest outputs: matrix: ${{ steps.set-matrix.outputs.matrix }} + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v3 - id: set-matrix @@ -40,6 +43,9 @@ jobs: image: ${{ matrix.container }} options: --gpus all --rm -v /data/scratch/cifar-10:/data/scratch/cifar-10 timeout-minutes: 120 + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - name: Install dependencies run: | diff --git a/.github/workflows/doc_check_on_pr.yml b/.github/workflows/doc_check_on_pr.yml index 992cc93b008c..848991bd3a82 100644 --- a/.github/workflows/doc_check_on_pr.yml +++ b/.github/workflows/doc_check_on_pr.yml @@ -16,6 +16,9 @@ jobs: github.event.pull_request.draft == false && github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' runs-on: ubuntu-latest + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v2 @@ -31,6 +34,9 @@ jobs: github.event.pull_request.draft == false && github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' runs-on: ubuntu-latest + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v2 with: diff --git a/.github/workflows/doc_test_on_pr.yml b/.github/workflows/doc_test_on_pr.yml index 325e2a7c95a4..2a07a2297bfb 100644 --- a/.github/workflows/doc_test_on_pr.yml +++ b/.github/workflows/doc_test_on_pr.yml @@ -19,6 +19,9 @@ jobs: outputs: any_changed: ${{ steps.changed-files.outputs.any_changed }} changed_files: ${{ steps.changed-files.outputs.all_changed_files }} + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false name: Detect changed example files steps: - uses: actions/checkout@v3 @@ -59,6 +62,9 @@ jobs: defaults: run: shell: bash + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - name: Checkout ColossalAI-Documentation uses: actions/checkout@v2 diff --git a/.github/workflows/example_check_on_pr.yml b/.github/workflows/example_check_on_pr.yml index 31dbf7540091..ee456c25f2b5 100644 --- a/.github/workflows/example_check_on_pr.yml +++ b/.github/workflows/example_check_on_pr.yml @@ -20,6 +20,9 @@ jobs: matrix: ${{ steps.setup-matrix.outputs.matrix }} anyChanged: ${{ steps.setup-matrix.outputs.anyChanged }} name: Detect changed example files + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v3 with: @@ -77,6 +80,9 @@ jobs: image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 options: --gpus all --rm -v /data/scratch/examples-data:/data/ timeout-minutes: 10 + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v3 From b3ab7fbabf3b72805403b82eeb79a6155d72004f Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Mon, 12 Jun 2023 15:02:27 +0800 Subject: [PATCH 314/413] [example] update ViT example using booster api (#3940) --- examples/images/vit/README.md | 65 ++----- examples/images/vit/args.py | 124 +++++++++++++ examples/images/vit/configs/vit_1d_tp2.py | 32 ---- examples/images/vit/configs/vit_1d_tp2_ci.py | 32 ---- examples/images/vit/data.py | 32 ++++ examples/images/vit/requirements.txt | 6 +- examples/images/vit/run.sh | 15 -- examples/images/vit/run_benchmark.sh | 27 +++ examples/images/vit/run_demo.sh | 44 +++++ examples/images/vit/test_ci.sh | 24 ++- examples/images/vit/test_vit.py | 160 ----------------- examples/images/vit/train.py | 174 ------------------ examples/images/vit/vit.py | 95 ---------- examples/images/vit/vit_benchmark.py | 129 ++++++++++++++ examples/images/vit/vit_train_demo.py | 177 +++++++++++++++++++ examples/language/opt/opt_benchmark.py | 19 +- examples/language/opt/opt_train_demo.py | 15 +- 17 files changed, 577 insertions(+), 593 deletions(-) create mode 100644 examples/images/vit/args.py delete mode 100644 examples/images/vit/configs/vit_1d_tp2.py delete mode 100644 examples/images/vit/configs/vit_1d_tp2_ci.py create mode 100644 examples/images/vit/data.py delete mode 100644 examples/images/vit/run.sh create mode 100644 examples/images/vit/run_benchmark.sh create mode 100644 examples/images/vit/run_demo.sh delete mode 100644 examples/images/vit/test_vit.py delete mode 100644 examples/images/vit/train.py delete mode 100644 examples/images/vit/vit.py create mode 100644 examples/images/vit/vit_benchmark.py create mode 100644 examples/images/vit/vit_train_demo.py diff --git a/examples/images/vit/README.md b/examples/images/vit/README.md index 4423d85d19e0..7c4147b76457 100644 --- a/examples/images/vit/README.md +++ b/examples/images/vit/README.md @@ -1,61 +1,28 @@ -# Vision Transformer with ColoTensor +## Overview -# Overview +Vision Transformer is a class of Transformer model tailored for computer vision tasks. It was first proposed in paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) and achieved SOTA results on various tasks at that time. -In this example, we will run Vision Transformer with ColoTensor. +In our example, we are using pretrained weights of ViT loaded from HuggingFace. +We adapt the ViT training code to ColossalAI by leveraging [Boosting API](https://colossalai.org/docs/basics/booster_api) loaded with a chosen plugin, where each plugin corresponds to a specific kind of training strategy. This example supports plugins including TorchDDPPlugin, LowLevelZeroPlugin, and GeminiPlugin. -We use model **ViTForImageClassification** from Hugging Face [Link](https://huggingface.co/docs/transformers/model_doc/vit) for unit test. -You can change world size or decide whether use DDP in our code. +## Run Demo -We use model **vision_transformer** from timm [Link](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py) for training example. - -(2022/6/28) The default configuration now supports 2DP+2TP with gradient accumulation and checkpoint support. Zero is not supported at present. - -# Requirement - -Install colossalai version >= 0.1.11 - -## Unit test -To run unit test, you should install pytest, transformers with: -```shell -pip install pytest transformers +By running the following script: +```bash +bash run_demo.sh ``` +You will finetune a a [ViT-base](https://huggingface.co/google/vit-base-patch16-224) model on this [dataset](https://huggingface.co/datasets/beans), with more than 8000 images of bean leaves. This dataset is for image classification task and there are 3 labels: ['angular_leaf_spot', 'bean_rust', 'healthy']. -## Training example -To run training example with ViT-S, you should install **NVIDIA DALI** from [Link](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/installation.html) for dataloader support. -You also need to install timm and titans for model/dataloader support with: -```shell -pip install timm titans -``` +The script can be modified if you want to try another set of hyperparameters or change to another ViT model with different size. -### Data preparation -You can download the ImageNet dataset from the [ImageNet official website](https://www.image-net.org/download.php). You should get the raw images after downloading the dataset. As we use **NVIDIA DALI** to read data, we use the TFRecords dataset instead of raw Imagenet dataset. This offers better speedup to IO. If you don't have TFRecords dataset, follow [imagenet-tools](https://github.com/ver217/imagenet-tools) to build one. +The demo code refers to this [blog](https://huggingface.co/blog/fine-tune-vit). -Before you start training, you need to set the environment variable `DATA` so that the script knows where to fetch the data for DALI dataloader. -```shell -export DATA=/path/to/ILSVRC2012 -``` -# How to run +## Run Benchmark -## Unit test -In your terminal -```shell -pytest test_vit.py +You can run benchmark for ViT model by running the following script: +```bash +bash run_benchmark.sh ``` - -This will evaluate models with different **world_size** and **use_ddp**. - -## Training example -Modify the settings in run.sh according to your environment. -For example, if you set `--nproc_per_node=8` in `run.sh` and `TP_WORLD_SIZE=2` in your config file, -data parallel size will be automatically calculated as 4. -Thus, the parallel strategy is set to 4DP+2TP. - -Then in your terminal -```shell -sh run.sh -``` - -This will start ViT-S training with ImageNet. +The script will test performance (throughput & peak memory usage) for each combination of hyperparameters. You can also play with this script to configure your own set of hyperparameters for testing. \ No newline at end of file diff --git a/examples/images/vit/args.py b/examples/images/vit/args.py new file mode 100644 index 000000000000..e4a873a9eb52 --- /dev/null +++ b/examples/images/vit/args.py @@ -0,0 +1,124 @@ +from colossalai import get_default_parser + +def parse_demo_args(): + + parser = get_default_parser() + parser.add_argument( + "--model_name_or_path", + type=str, + default="google/vit-base-patch16-224", + help="Path to pretrained model or model identifier from huggingface.co/models." + ) + parser.add_argument( + "--output_path", + type=str, + default="./output_model.bin", + help="The path of your saved model after finetuning." + ) + parser.add_argument( + "--plugin", + type=str, + default="gemini", + help="Plugin to use. Valid plugins include 'torch_ddp','torch_ddp_fp16','gemini','low_level_zero'." + ) + parser.add_argument( + "--num_epoch", + type=int, + default=3, + help="Number of epochs." + ) + parser.add_argument( + "--batch_size", + type=int, + default=32, + help="Batch size (per dp group) for the training dataloader." + ) + parser.add_argument( + "--learning_rate", + type=float, + default=3e-4, + help="Initial learning rate (after the potential warmup period) to use." + ) + parser.add_argument( + "--warmup_ratio", + type=float, + default=0.3, + help="Ratio of warmup steps against total training steps." + ) + parser.add_argument( + "--weight_decay", + type=float, + default=0.1, + help="Weight decay to use." + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="A seed for reproducible training." + ) + + args = parser.parse_args() + return args + +def parse_benchmark_args(): + + parser = get_default_parser() + + parser.add_argument( + "--model_name_or_path", + type=str, + default="google/vit-base-patch16-224", + help="Path to a pretrained model or model identifier from huggingface.co/models." + ) + parser.add_argument( + "--plugin", + type=str, + default="gemini", + help="Plugin to use. Valid plugins include 'torch_ddp','torch_ddp_fp16','gemini','low_level_zero'." + ) + parser.add_argument( + "--batch_size", + type=int, + default=8, + help="Batch size (per dp group) for the training dataloader." + ) + parser.add_argument( + "--num_labels", + type=int, + default=10, + help="Number of labels for classification." + ) + parser.add_argument( + "--learning_rate", + type=float, + default=5e-5, + help="Initial learning rate (after the potential warmup period) to use." + ) + parser.add_argument( + "--weight_decay", + type=float, + default=0.0, + help="Weight decay to use." + ) + parser.add_argument( + "--max_train_steps", + type=int, + default=20, + help="Total number of training steps to perform." + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="A seed for reproducible training." + ) + parser.add_argument( + "--mem_cap", + type=int, + default=0, + help="Limit on the usage of space for each GPU (in GB)." + ) + args = parser.parse_args() + + return args \ No newline at end of file diff --git a/examples/images/vit/configs/vit_1d_tp2.py b/examples/images/vit/configs/vit_1d_tp2.py deleted file mode 100644 index fbf399f2e50d..000000000000 --- a/examples/images/vit/configs/vit_1d_tp2.py +++ /dev/null @@ -1,32 +0,0 @@ -from colossalai.amp import AMP_TYPE - -# hyperparameters -# BATCH_SIZE is as per GPU -# global batch size = BATCH_SIZE x data parallel size -BATCH_SIZE = 256 -LEARNING_RATE = 3e-3 -WEIGHT_DECAY = 0.3 -NUM_EPOCHS = 300 -WARMUP_EPOCHS = 32 - -# model config -IMG_SIZE = 224 -PATCH_SIZE = 16 -HIDDEN_SIZE = 384 -DEPTH = 12 -NUM_HEADS = 6 -MLP_RATIO = 4 -NUM_CLASSES = 1000 -CHECKPOINT = False -SEQ_LENGTH = (IMG_SIZE // PATCH_SIZE)**2 + 1 # add 1 for cls token - -USE_DDP = True -TP_WORLD_SIZE = 2 -TP_TYPE = 'row' -parallel = dict(tensor=dict(mode="1d", size=TP_WORLD_SIZE),) - -fp16 = dict(mode=AMP_TYPE.NAIVE) -clip_grad_norm = 1.0 -gradient_accumulation = 8 - -LOG_PATH = "./log" diff --git a/examples/images/vit/configs/vit_1d_tp2_ci.py b/examples/images/vit/configs/vit_1d_tp2_ci.py deleted file mode 100644 index e491e4ada45e..000000000000 --- a/examples/images/vit/configs/vit_1d_tp2_ci.py +++ /dev/null @@ -1,32 +0,0 @@ -from colossalai.amp import AMP_TYPE - -# hyperparameters -# BATCH_SIZE is as per GPU -# global batch size = BATCH_SIZE x data parallel size -BATCH_SIZE = 8 -LEARNING_RATE = 3e-3 -WEIGHT_DECAY = 0.3 -NUM_EPOCHS = 3 -WARMUP_EPOCHS = 1 - -# model config -IMG_SIZE = 224 -PATCH_SIZE = 16 -HIDDEN_SIZE = 32 -DEPTH = 2 -NUM_HEADS = 4 -MLP_RATIO = 4 -NUM_CLASSES = 10 -CHECKPOINT = False -SEQ_LENGTH = (IMG_SIZE // PATCH_SIZE)**2 + 1 # add 1 for cls token - -USE_DDP = True -TP_WORLD_SIZE = 2 -TP_TYPE = 'row' -parallel = dict(tensor=dict(mode="1d", size=TP_WORLD_SIZE),) - -fp16 = dict(mode=AMP_TYPE.NAIVE) -clip_grad_norm = 1.0 -gradient_accumulation = 2 - -LOG_PATH = "./log_ci" diff --git a/examples/images/vit/data.py b/examples/images/vit/data.py new file mode 100644 index 000000000000..00fde707b173 --- /dev/null +++ b/examples/images/vit/data.py @@ -0,0 +1,32 @@ +import torch +from torch.utils.data import Dataset +from datasets import load_dataset + +class BeansDataset(Dataset): + + def __init__(self, image_processor, split='train'): + + super().__init__() + self.image_processor = image_processor + self.ds = load_dataset('beans')[split] + self.label_names = self.ds.features['labels'].names + self.num_labels = len(self.label_names) + self.inputs = [] + for example in self.ds: + self.inputs.append(self.process_example(example)) + + def __len__(self): + return len(self.inputs) + + def __getitem__(self, idx): + return self.inputs[idx] + + def process_example(self, example): + input = self.image_processor(example['image'], return_tensors='pt') + input['labels'] = example['labels'] + return input + + +def beans_collator(batch): + return {'pixel_values': torch.cat([data['pixel_values'] for data in batch], dim=0), + 'labels': torch.tensor([data['labels'] for data in batch], dtype=torch.int64)} diff --git a/examples/images/vit/requirements.txt b/examples/images/vit/requirements.txt index 1f69794ebe70..edad87ca380f 100644 --- a/examples/images/vit/requirements.txt +++ b/examples/images/vit/requirements.txt @@ -1,8 +1,6 @@ colossalai >= 0.1.12 torch >= 1.8.1 numpy>=1.24.1 -timm>=0.6.12 -titans>=0.0.7 tqdm>=4.61.2 -transformers>=4.25.1 -nvidia-dali-cuda110>=1.8.0 --extra-index-url https://developer.download.nvidia.com/compute/redist +transformers>=4.20.0 +datasets \ No newline at end of file diff --git a/examples/images/vit/run.sh b/examples/images/vit/run.sh deleted file mode 100644 index 84fe58f11a6a..000000000000 --- a/examples/images/vit/run.sh +++ /dev/null @@ -1,15 +0,0 @@ -export DATA=/data/scratch/imagenet/tf_records -export OMP_NUM_THREADS=4 - -# resume -# CUDA_VISIBLE_DEVICES=4,5,6,7 colossalai run \ -# --nproc_per_node 4 train.py \ -# --config configs/vit_1d_tp2.py \ -# --resume_from checkpoint/epoch_10 \ -# --master_port 29598 | tee ./out 2>&1 - -# train -CUDA_VISIBLE_DEVICES=4,5,6,7 colossalai run \ ---nproc_per_node 4 train.py \ ---config configs/vit_1d_tp2.py \ ---master_port 29598 | tee ./out 2>&1 diff --git a/examples/images/vit/run_benchmark.sh b/examples/images/vit/run_benchmark.sh new file mode 100644 index 000000000000..2487bf81ee2b --- /dev/null +++ b/examples/images/vit/run_benchmark.sh @@ -0,0 +1,27 @@ +set -xe +pip install -r requirements.txt + +export BS=8 +export MEMCAP=0 +export GPUNUM=1 + +for BS in 8 32 128 +do +for PLUGIN in "torch_ddp" "torch_ddp_fp16" "low_level_zero" "gemini" +do +for GPUNUM in 1 4 +do + +MODEL_PATH="google/vit-base-patch16-224" +torchrun \ + --standalone \ + --nproc_per_node ${GPUNUM} \ + vit_benchmark.py \ + --model_name_or_path ${MODEL_PATH} \ + --mem_cap ${MEMCAP} \ + --plugin ${PLUGIN} \ + --batch_size ${BS} + +done +done +done diff --git a/examples/images/vit/run_demo.sh b/examples/images/vit/run_demo.sh new file mode 100644 index 000000000000..2d140dd6e423 --- /dev/null +++ b/examples/images/vit/run_demo.sh @@ -0,0 +1,44 @@ +set -xe +pip install -r requirements.txt + +# model name or path +MODEL="google/vit-base-patch16-224" + +# path for saving model +OUTPUT_PATH="./output_model.bin" + +# plugin(training strategy) +# can only be one of "torch_ddp"/"torch_ddp_fp16"/"low_level_zero"/"gemini" +PLUGIN="gemini" + +# number of gpus to use +GPUNUM=4 + +# batch size per gpu +BS=16 + +# learning rate +LR="2e-4" + +# number of epoch +EPOCH=3 + +# weight decay +WEIGHT_DECAY=0.05 + +# ratio of warmup steps +WARMUP_RATIO=0.3 + +# run the script for demo +torchrun \ + --standalone \ + --nproc_per_node ${GPUNUM} \ + vit_train_demo.py \ + --model_name_or_path ${MODEL} \ + --output_path ${OUTPUT_PATH} \ + --plugin ${PLUGIN} \ + --batch_size ${BS} \ + --num_epoch ${EPOCH} \ + --learning_rate ${LR} \ + --weight_decay ${WEIGHT_DECAY} \ + --warmup_ratio ${WARMUP_RATIO} diff --git a/examples/images/vit/test_ci.sh b/examples/images/vit/test_ci.sh index 41d25ee23521..8606015c0397 100644 --- a/examples/images/vit/test_ci.sh +++ b/examples/images/vit/test_ci.sh @@ -1,9 +1,19 @@ -export OMP_NUM_THREADS=4 - +set -xe pip install -r requirements.txt -# train -colossalai run \ ---nproc_per_node 4 train.py \ ---config configs/vit_1d_tp2_ci.py \ ---dummy_data +BS=8 +for PLUGIN in "torch_ddp" "torch_ddp_fp16" "low_level_zero" "gemini" +do +for GPUNUM in 1 4 +do + +torchrun \ + --standalone \ + --nproc_per_node ${GPUNUM} \ + vit_benchmark.py \ + --model_name_or_path "google/vit-base-patch16-224" \ + --plugin ${PLUGIN} \ + --batch_size ${BS} + +done +done diff --git a/examples/images/vit/test_vit.py b/examples/images/vit/test_vit.py deleted file mode 100644 index c0ae35bca871..000000000000 --- a/examples/images/vit/test_vit.py +++ /dev/null @@ -1,160 +0,0 @@ -import os -import random - -import numpy as np -import pytest -import torch -from torch.nn.parallel import DistributedDataParallel as DDP -from vit import get_training_components - -import colossalai -from colossalai.context import ParallelMode -from colossalai.context.parallel_mode import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.nn.parallel.data_parallel import ColoDDP -from colossalai.tensor import ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup, ShardSpec -from colossalai.testing import rerun_if_address_is_in_use, spawn -from colossalai.utils.cuda import get_current_device -from colossalai.zero import ColoInitContext - - -def set_seed(seed): - random.seed(seed) - os.environ['PYTHONHASHSEED'] = str(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.backends.cudnn.deterministic = True - - -def tensor_equal(A, B): - return torch.allclose(A, B, rtol=1e-3, atol=1e-1) - - -def tensor_shard_equal(tensor: torch.Tensor, shard: torch.Tensor): - assert tensor.ndim == shard.ndim - if tensor.shape == shard.shape: - return tensor_equal(tensor, shard) - else: - dims_not_eq = torch.nonzero(torch.tensor(tensor.shape) != torch.tensor(shard.shape)) - if dims_not_eq.numel() == 1: - # 1D shard - dim = dims_not_eq.item() - world_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) - rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) - return tensor_equal(tensor.chunk(world_size, dim)[rank], shard) - else: - raise - - -# Only for all Linear, it's 1d_row split because Linear will be transposed when calculating. -# But for other layers, it's 1d_col split. -# Layernorm is not supported for now. -# patch_embeddings.projection has nn.Conv2d -# https://github.com/huggingface/transformers/blob/dcb08b99f44919425f8ba9be9ddcc041af8ec25e/src/transformers/models/vit/modeling_vit.py#L182 -def init_1d_row_for_linear_weight_spec(model, world_size: int): - pg = ProcessGroup(tp_degree=world_size) - spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) - with DistSpecManager.no_grad(): - for n, p in model.named_parameters(): - if 'weight' in n and 'layernorm' not in n and 'embeddings.patch_embeddings.projection.weight' not in n: - p.set_process_group(pg) - p.set_tensor_spec(*spec) - - -# Similarly, it's col split for Linear but row split for others. -def init_1d_col_for_linear_weight_bias_spec(model, world_size: int): - pg = ProcessGroup(tp_degree=world_size) - spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) - with DistSpecManager.no_grad(): - for n, p in model.named_parameters(): - if ('weight' in n - or 'bias' in n) and 'layernorm' not in n and 'embeddings.patch_embeddings.projection' not in n: - p.set_process_group(pg) - p.set_tensor_spec(*spec) - - -def check_param_equal(model, torch_model): - for p, torch_p in zip(model.parameters(), torch_model.parameters()): - assert tensor_shard_equal(torch_p, p) - - -def check_grad_equal(model, torch_model): - for p, torch_p in zip(model.parameters(), torch_model.parameters()): - if (torch_p.grad.shape == p.grad.shape): - assert torch.allclose(torch_p.grad, p.grad, rtol=1e-3, atol=2.0) == True - else: - dims_not_eq = torch.nonzero(torch.tensor(torch_p.grad.shape) != torch.tensor(p.grad.shape)) - dim = dims_not_eq.item() - world_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) - rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) - assert torch.allclose(torch_p.grad.chunk(world_size, dim)[rank], p.grad, rtol=1e-3, atol=2.0) == True - - -def run_vit(init_spec_func, use_ddp): - model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_training_components() - with ColoInitContext(device=get_current_device()): - model = model_builder() - model = model.cuda() - torch_model = model_builder().cuda() - if use_ddp: - model = ColoDDP(model) - torch_model = DDP(torch_model, - device_ids=[gpc.get_global_rank()], - process_group=gpc.get_group(ParallelMode.DATA)) - for torch_p, p in zip(torch_model.parameters(), model.parameters()): - torch_p.data.copy_(p) - - world_size = torch.distributed.get_world_size() - init_spec_func(model, world_size) - - check_param_equal(model, torch_model) - model.train() - torch_model.train() - set_seed(gpc.get_local_rank(ParallelMode.DATA)) - - optimizer = optimizer_class(model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0) - torch_optimizer = optimizer_class(torch_model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0) - - for i, image_dict in enumerate(train_dataloader): - if use_ddp: - model.zero_grad() - else: - optimizer.zero_grad() - logits = model(image_dict['pixel_values']) - torch_logits = torch_model(image_dict['pixel_values']) - assert tensor_equal(torch_logits.logits, logits.logits) - loss = criterion(logits.logits, image_dict['label']) - torch_loss = criterion(torch_logits.logits, image_dict['label']) - if use_ddp: - model.backward(loss) - else: - loss.backward() - torch_loss.backward() - check_grad_equal(model, torch_model) - optimizer.step() - torch_optimizer.step() - check_param_equal(model, torch_model) - break - - -def run_dist(rank, world_size, port, use_ddp): - if use_ddp and world_size == 1: - return - tp_world_size = world_size // 2 if use_ddp else world_size - config = dict(parallel=dict(tensor=dict(mode="1d", size=tp_world_size),)) - colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_vit(init_1d_row_for_linear_weight_spec, use_ddp) - run_vit(init_1d_col_for_linear_weight_bias_spec, use_ddp) - - -@pytest.mark.dist -@pytest.mark.parametrize('world_size', [1, 4]) -@pytest.mark.parametrize('use_ddp', [False, True]) -@rerun_if_address_is_in_use() -def test_vit(world_size, use_ddp): - spawn(run_dist, world_size, use_ddp=use_ddp) - - -if __name__ == '__main__': - test_vit(1, False) diff --git a/examples/images/vit/train.py b/examples/images/vit/train.py deleted file mode 100644 index b42cf2bedc6b..000000000000 --- a/examples/images/vit/train.py +++ /dev/null @@ -1,174 +0,0 @@ -import os - -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F -from timm.models.vision_transformer import _create_vision_transformer -from titans.dataloader.imagenet import build_dali_imagenet -from tqdm import tqdm -from vit import DummyDataLoader - -import colossalai -from colossalai.core import global_context as gpc -from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn import CrossEntropyLoss -from colossalai.nn._ops import * -from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR -from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.parallel.data_parallel import ColoDDP -from colossalai.tensor import ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup, ShardSpec -from colossalai.utils import get_current_device -from colossalai.zero import ColoInitContext - - -def init_1d_row_for_linear_weight_spec(model, world_size: int): - pg = ProcessGroup(tp_degree=world_size) - spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) - with DistSpecManager.no_grad(): - for n, p in model.named_parameters(): - if 'weight' in n and 'norm' not in n and 'patch_embed.proj.weight' not in n: - p.set_process_group(pg) - p.set_tensor_spec(*spec) - - -# Similarly, it's col split for Linear but row split for others. -def init_1d_col_for_linear_weight_bias_spec(model, world_size: int): - pg = ProcessGroup(tp_degree=world_size) - spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) - with DistSpecManager.no_grad(): - for n, p in model.named_parameters(): - if ('weight' in n or 'bias' in n) and 'norm' not in n and ('patch_embed.proj.weight' not in n - and 'patch_embed.proj.bias' not in n): - p.set_process_group(pg) - p.set_tensor_spec(*spec) - - -def init_spec_func(model, tp_type): - world_size = torch.distributed.get_world_size() - if tp_type == 'row': - init_1d_row_for_linear_weight_spec(model, world_size) - elif tp_type == 'col': - init_1d_col_for_linear_weight_bias_spec(model, world_size) - else: - raise NotImplemented - - -def train_imagenet(): - - parser = colossalai.get_default_parser() - parser.add_argument('--resume_from', default=False, action='store_true') - parser.add_argument('--dummy_data', default=False, action='store_true') - - args = parser.parse_args() - colossalai.launch_from_torch(config=args.config) - use_ddp = gpc.config.USE_DDP - - disable_existing_loggers() - - logger = get_dist_logger() - if hasattr(gpc.config, 'LOG_PATH'): - if gpc.get_global_rank() == 0: - log_path = gpc.config.LOG_PATH - if not os.path.exists(log_path): - os.mkdir(log_path) - logger.log_to_file(log_path) - - logger.info('Build data loader', ranks=[0]) - if not args.dummy_data: - root = os.environ['DATA'] - train_dataloader, test_dataloader = build_dali_imagenet(root, - train_batch_size=gpc.config.BATCH_SIZE, - test_batch_size=gpc.config.BATCH_SIZE) - else: - train_dataloader = DummyDataLoader(length=10, - batch_size=gpc.config.BATCH_SIZE, - category=gpc.config.NUM_CLASSES, - image_size=gpc.config.IMG_SIZE, - return_dict=False) - test_dataloader = DummyDataLoader(length=5, - batch_size=gpc.config.BATCH_SIZE, - category=gpc.config.NUM_CLASSES, - image_size=gpc.config.IMG_SIZE, - return_dict=False) - - logger.info('Build model', ranks=[0]) - - model_kwargs = dict(img_size=gpc.config.IMG_SIZE, - patch_size=gpc.config.PATCH_SIZE, - embed_dim=gpc.config.HIDDEN_SIZE, - depth=gpc.config.DEPTH, - num_heads=gpc.config.NUM_HEADS, - mlp_ratio=gpc.config.MLP_RATIO, - num_classes=gpc.config.NUM_CLASSES, - drop_rate=0.1, - attn_drop_rate=0.1, - weight_init='jax') - - with ColoInitContext(device=get_current_device()): - model = _create_vision_transformer('vit_small_patch16_224', pretrained=False, **model_kwargs) - init_spec_func(model, gpc.config.TP_TYPE) - - world_size = torch.distributed.get_world_size() - model = ColoDDP(module=model, process_group=ProcessGroup(tp_degree=world_size)) - logger.info('Build criterion, optimizer, lr_scheduler', ranks=[0]) - optimizer = HybridAdam(model.parameters(), lr=gpc.config.LEARNING_RATE, weight_decay=gpc.config.WEIGHT_DECAY) - - criterion = CrossEntropyLoss() - lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer, - total_steps=gpc.config.NUM_EPOCHS, - warmup_steps=gpc.config.WARMUP_EPOCHS) - - start_epoch = 0 - if args.resume_from: - load_model = torch.load(args.resume_from + '_model.pth') - start_epoch = load_model['epoch'] - model.load_state_dict(load_model['model']) - load_optim = torch.load(args.resume_from + '_optim_rank_{}.pth'.format(dist.get_rank())) - optimizer.load_state_dict(load_optim['optim']) - - for epoch in range(start_epoch, gpc.config.NUM_EPOCHS): - model.train() - for index, (x, y) in tqdm(enumerate(train_dataloader), total=len(train_dataloader), leave=False): - x, y = x.cuda(), y.cuda() - output = model(x) - loss = criterion(output, y) - loss = loss / gpc.config.gradient_accumulation - if use_ddp: - model.backward(loss) - else: - loss.backward() - if (index + 1) % gpc.config.gradient_accumulation == 0: - optimizer.step() - if use_ddp: - model.zero_grad() - else: - optimizer.zero_grad() - - logger.info( - f"Finish Train Epoch [{epoch+1}/{gpc.config.NUM_EPOCHS}] loss: {loss.item():.3f} lr: {optimizer.state_dict()['param_groups'][0]['lr']}", - ranks=[0]) - - model.eval() - test_loss = 0 - correct = 0 - test_sum = 0 - with torch.no_grad(): - for index, (x, y) in tqdm(enumerate(test_dataloader), total=len(test_dataloader), leave=False): - x, y = x.cuda(), y.cuda() - output = model(x) - test_loss += F.cross_entropy(output, y, reduction='sum').item() - pred = output.argmax(dim=1, keepdim=True) - correct += pred.eq(y.view_as(pred)).sum().item() - test_sum += y.size(0) - - test_loss /= test_sum - logger.info( - f"Finish Test Epoch [{epoch+1}/{gpc.config.NUM_EPOCHS}] loss: {test_loss:.3f} Accuracy: [{correct}/{test_sum}]({correct/test_sum:.3f})", - ranks=[0]) - - lr_scheduler.step() - - -if __name__ == '__main__': - train_imagenet() diff --git a/examples/images/vit/vit.py b/examples/images/vit/vit.py deleted file mode 100644 index f22e8ea90cec..000000000000 --- a/examples/images/vit/vit.py +++ /dev/null @@ -1,95 +0,0 @@ -from abc import ABC, abstractmethod - -import torch -import torch.nn as nn -from transformers import ViTConfig, ViTForImageClassification - -from colossalai.utils.cuda import get_current_device - - -class DummyDataGenerator(ABC): - - def __init__(self, length=10): - self.length = length - - @abstractmethod - def generate(self): - pass - - def __iter__(self): - self.step = 0 - return self - - def __next__(self): - if self.step < self.length: - self.step += 1 - return self.generate() - else: - raise StopIteration - - def __len__(self): - return self.length - - -class DummyDataLoader(DummyDataGenerator): - - def __init__(self, length=10, batch_size=4, channel=3, category=8, image_size=224, return_dict=True): - super().__init__(length) - self.batch_size = batch_size - self.channel = channel - self.category = category - self.image_size = image_size - self.return_dict = return_dict - - def generate(self): - image_dict = {} - image_dict['pixel_values'] = torch.rand( - self.batch_size, self.channel, self.image_size, self.image_size, device=get_current_device()) * 2 - 1 - image_dict['label'] = torch.randint(self.category, (self.batch_size,), - dtype=torch.int64, - device=get_current_device()) - if not self.return_dict: - return image_dict['pixel_values'], image_dict['label'] - return image_dict - - -class ViTCVModel(nn.Module): - - def __init__(self, - hidden_size=768, - num_hidden_layers=12, - num_attention_heads=12, - image_size=224, - patch_size=16, - num_channels=3, - num_labels=8, - checkpoint=False): - super().__init__() - self.checkpoint = checkpoint - self.model = ViTForImageClassification( - ViTConfig(hidden_size=hidden_size, - num_hidden_layers=num_hidden_layers, - num_attention_heads=num_attention_heads, - image_size=image_size, - patch_size=patch_size, - num_channels=num_channels, - num_labels=num_labels)) - if checkpoint: - self.model.gradient_checkpointing_enable() - - def forward(self, pixel_values): - return self.model(pixel_values=pixel_values) - - -def vit_base_s(checkpoint=True): - return ViTCVModel(checkpoint=checkpoint) - - -def vit_base_micro(checkpoint=True): - return ViTCVModel(hidden_size=32, num_hidden_layers=2, num_attention_heads=4, checkpoint=checkpoint) - - -def get_training_components(): - trainloader = DummyDataLoader() - testloader = DummyDataLoader() - return vit_base_micro, trainloader, testloader, torch.optim.Adam, torch.nn.functional.cross_entropy diff --git a/examples/images/vit/vit_benchmark.py b/examples/images/vit/vit_benchmark.py new file mode 100644 index 000000000000..11d480bba65f --- /dev/null +++ b/examples/images/vit/vit_benchmark.py @@ -0,0 +1,129 @@ +import time + +import torch +import transformers +from transformers import ViTConfig, ViTForImageClassification +import tqdm + +import colossalai +from colossalai.nn.optimizer import HybridAdam +from colossalai.logging import disable_existing_loggers, get_dist_logger +from colossalai.utils import get_current_device +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.cluster import DistCoordinator + +from args import parse_benchmark_args + +def format_num(num: int, bytes=False): + """Scale bytes to its proper format, e.g. 1253656 => '1.20MB'""" + factor = 1024 if bytes else 1000 + suffix = "B" if bytes else "" + for unit in ["", " K", " M", " G", " T", " P"]: + if num < factor: + return f"{num:.2f}{unit}{suffix}" + num /= factor + + +def get_data(batch_size, num_labels, num_channels=3, height=224, width=224): + pixel_values = torch.randn(batch_size, num_channels, height, width, device=torch.cuda.current_device(), dtype=torch.float) + labels = torch.randint(0, num_labels, (batch_size, ), device=torch.cuda.current_device(), dtype=torch.int64) + return pixel_values, labels + + +def colo_memory_cap(size_in_GB): + from colossalai.utils import colo_device_memory_capacity, colo_set_process_memory_fraction, get_current_device + cuda_capacity = colo_device_memory_capacity(get_current_device()) + if size_in_GB * (1024**3) < cuda_capacity: + colo_set_process_memory_fraction(size_in_GB * (1024**3) / cuda_capacity) + print(f"Limiting GPU memory usage to {size_in_GB} GB") + + +def main(): + + args = parse_benchmark_args() + + # Launch ColossalAI + colossalai.launch_from_torch(config={}, seed=args.seed) + coordinator = DistCoordinator() + world_size = coordinator.world_size + + # Manage loggers + disable_existing_loggers() + logger = get_dist_logger() + if coordinator.is_master(): + transformers.utils.logging.set_verbosity_info() + else: + transformers.utils.logging.set_verbosity_error() + + # Whether to set limit on memory capacity + if args.mem_cap > 0: + colo_memory_cap(args.mem_cap) + + # Build ViT model + config = ViTConfig.from_pretrained(args.model_name_or_path) + model = ViTForImageClassification(config) + logger.info(f"Finish loading model from {args.model_name_or_path}", ranks=[0]) + + # Enable gradient checkpointing + model.gradient_checkpointing_enable() + + # Set plugin + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(device=get_current_device(), + placement_policy='cpu', + pin_memory=True, + strict_ddp_mode=True, + initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + logger.info(f"Set plugin as {args.plugin}", ranks=[0]) + + # Set optimizer + optimizer = HybridAdam(model.parameters(), lr=(args.learning_rate * world_size)) + + # Set booster + booster = Booster(plugin=plugin, **booster_kwargs) + model, optimizer, _, _, _ = booster.boost(model, optimizer) + + + # Start training. + logger.info(f"Start testing", ranks=[0]) + progress_bar = tqdm.tqdm(total=args.max_train_steps, desc="Training Step", disable=not coordinator.is_master()) + + torch.cuda.synchronize() + model.train() + start_time = time.time() + + for _ in range(args.max_train_steps): + + pixel_values, labels = get_data(args.batch_size, args.num_labels, 3, 224, 224) + optimizer.zero_grad() + outputs = model(pixel_values=pixel_values, labels=labels) + loss = outputs['loss'] + booster.backward(loss, optimizer) + optimizer.step() + + torch.cuda.synchronize() + progress_bar.update(1) + + # Compute Statistics + end_time = time.time() + throughput = "{:.4f}".format((world_size * args.max_train_steps * args.batch_size) / (end_time - start_time)) + max_mem = format_num(torch.cuda.max_memory_allocated(device=torch.cuda.current_device()), bytes=True) + + logger.info(f"Testing finished, " + f"batch size per gpu: {args.batch_size}, " + f"plugin: {args.plugin}, " + f"throughput: {throughput}, " + f"maximum memory usage per gpu: {max_mem}.", + ranks=[0]) + + +if __name__ == "__main__": + main() diff --git a/examples/images/vit/vit_train_demo.py b/examples/images/vit/vit_train_demo.py new file mode 100644 index 000000000000..3a739f10b5d0 --- /dev/null +++ b/examples/images/vit/vit_train_demo.py @@ -0,0 +1,177 @@ +import torch +import torch.distributed as dist +import transformers +from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor +from tqdm import tqdm + +import colossalai +from colossalai.nn.optimizer import HybridAdam +from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR +from colossalai.logging import disable_existing_loggers, get_dist_logger +from colossalai.utils import get_current_device +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.cluster import DistCoordinator + +from args import parse_demo_args +from data import BeansDataset, beans_collator + + +def move_to_cuda(batch, device): + return {k: v.to(device) for k, v in batch.items()} + + +def train_epoch(epoch, model, optimizer, lr_scheduler, dataloader, booster, coordinator): + + torch.cuda.synchronize() + model.train() + + with tqdm(dataloader, desc=f'Epoch [{epoch + 1}]', disable=not coordinator.is_master()) as pbar: + + for batch in pbar: + + # Foward + optimizer.zero_grad() + batch = move_to_cuda(batch, torch.cuda.current_device()) + outputs = model(**batch) + loss = outputs['loss'] + + # Backward + booster.backward(loss, optimizer) + optimizer.step() + lr_scheduler.step() + + # Print batch loss + pbar.set_postfix({'loss': loss.item()}) + + +@torch.no_grad() +def evaluate_model(epoch, model, eval_dataloader, num_labels, coordinator): + + model.eval() + accum_loss = torch.zeros(1, device=get_current_device()) + total_num = torch.zeros(1, device=get_current_device()) + accum_correct = torch.zeros(1, device=get_current_device()) + + for batch in eval_dataloader: + batch = move_to_cuda(batch, torch.cuda.current_device()) + outputs = model(**batch) + val_loss, logits = outputs[:2] + accum_loss += (val_loss / len(eval_dataloader)) + if num_labels > 1: + preds = torch.argmax(logits, dim=1) + elif num_labels == 1: + preds = logits.squeeze() + + labels = batch["labels"] + total_num += batch["labels"].shape[0] + accum_correct += (torch.sum(preds == labels)) + + dist.all_reduce(accum_loss) + dist.all_reduce(total_num) + dist.all_reduce(accum_correct) + avg_loss = "{:.4f}".format(accum_loss.item()) + accuracy = "{:.4f}".format(accum_correct.item() / total_num.item()) + if coordinator.is_master(): + print(f"Evaluation result for epoch {epoch + 1}: \ + average_loss={avg_loss}, \ + accuracy={accuracy}.") + + + + +def main(): + + args = parse_demo_args() + + # Launch ColossalAI + colossalai.launch_from_torch(config={}, seed=args.seed) + coordinator = DistCoordinator() + world_size = coordinator.world_size + + # Manage loggers + disable_existing_loggers() + logger = get_dist_logger() + if coordinator.is_master(): + transformers.utils.logging.set_verbosity_info() + else: + transformers.utils.logging.set_verbosity_error() + + # Prepare Dataset + image_processor = ViTImageProcessor.from_pretrained(args.model_name_or_path) + train_dataset = BeansDataset(image_processor, split='train') + eval_dataset = BeansDataset(image_processor, split='validation') + + + # Load pretrained ViT model + config = ViTConfig.from_pretrained(args.model_name_or_path) + config.num_labels = train_dataset.num_labels + config.id2label = {str(i): c for i, c in enumerate(train_dataset.label_names)} + config.label2id = {c: str(i) for i, c in enumerate(train_dataset.label_names)} + model = ViTForImageClassification.from_pretrained(args.model_name_or_path, + config=config, + ignore_mismatched_sizes=True) + logger.info(f"Finish loading model from {args.model_name_or_path}", ranks=[0]) + + # Enable gradient checkpointing + model.gradient_checkpointing_enable() + + # Set plugin + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(device=get_current_device(), + placement_policy='cpu', + pin_memory=True, + strict_ddp_mode=True, + initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + logger.info(f"Set plugin as {args.plugin}", ranks=[0]) + + # Prepare dataloader + train_dataloader = plugin.prepare_dataloader(train_dataset, + batch_size=args.batch_size, + shuffle=True, + drop_last=True, + collate_fn=beans_collator) + eval_dataloader = plugin.prepare_dataloader(eval_dataset, + batch_size=args.batch_size, + shuffle=True, + drop_last=True, + collate_fn=beans_collator) + + # Set optimizer + optimizer = HybridAdam(model.parameters(), lr=(args.learning_rate * world_size), weight_decay=args.weight_decay) + + # Set lr scheduler + total_steps = len(train_dataloader) * args.num_epoch + num_warmup_steps = int(args.warmup_ratio * total_steps) + lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer, + total_steps=(len(train_dataloader) * args.num_epoch), + warmup_steps=num_warmup_steps) + + # Set booster + booster = Booster(plugin=plugin, **booster_kwargs) + model, optimizer, _, train_dataloader, lr_scheduler = booster.boost(model=model, + optimizer=optimizer, + dataloader=train_dataloader, + lr_scheduler=lr_scheduler) + + # Finetuning + logger.info(f"Start finetuning", ranks=[0]) + for epoch in range(args.num_epoch): + train_epoch(epoch, model, optimizer, lr_scheduler, train_dataloader, booster, coordinator) + evaluate_model(epoch, model, eval_dataloader, eval_dataset.num_labels, coordinator) + logger.info(f"Finish finetuning", ranks=[0]) + + # Save the finetuned model + booster.save_model(model, args.output_path) + logger.info(f"Saving model checkpoint to {args.output_path}", ranks=[0]) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/language/opt/opt_benchmark.py b/examples/language/opt/opt_benchmark.py index da2be4055fa3..2d69036b50c6 100755 --- a/examples/language/opt/opt_benchmark.py +++ b/examples/language/opt/opt_benchmark.py @@ -67,17 +67,8 @@ def main(): colo_memory_cap(args.mem_cap) # Build OPT model - # Initialize the model under ColoInitContext if using GeminiPlugin config = AutoConfig.from_pretrained(args.model_name_or_path) - if args.plugin == 'gemini': - shard_pg = ProcessGroup(tp_degree=world_size) - default_dist_spec = ShardSpec([-1], [world_size]) - with ColoInitContext(device='cpu', - default_dist_spec=default_dist_spec, - default_pg=shard_pg): - model = OPTForCausalLM(config) - else: - model = OPTForCausalLM(config) + model = OPTForCausalLM(config=config) logger.info(f"Finish loading model from {args.model_name_or_path}", ranks=[0]) # Enable gradient checkpointing @@ -91,10 +82,10 @@ def main(): plugin = TorchDDPPlugin() elif args.plugin == 'gemini': plugin = GeminiPlugin(device=get_current_device(), - placement_policy='cpu', - pin_memory=True, - strict_ddp_mode=True, - initial_scale=2**5) + placement_policy='cpu', + pin_memory=True, + strict_ddp_mode=True, + initial_scale=2**5) elif args.plugin == 'low_level_zero': plugin = LowLevelZeroPlugin(initial_scale=2**5) logger.info(f"Set plugin as {args.plugin}", ranks=[0]) diff --git a/examples/language/opt/opt_train_demo.py b/examples/language/opt/opt_train_demo.py index bb2eb52ce560..fa7feca9c9a9 100644 --- a/examples/language/opt/opt_train_demo.py +++ b/examples/language/opt/opt_train_demo.py @@ -74,17 +74,8 @@ def main(): transformers.utils.logging.set_verbosity_error() # Build OPT model - # Initialize the model under ColoInitContext if using GeminiPlugin config = AutoConfig.from_pretrained(args.model_name_or_path) - if args.plugin == 'gemini': - shard_pg = ProcessGroup(tp_degree=world_size) - default_dist_spec = ShardSpec([-1], [world_size]) - with ColoInitContext(device='cpu', - default_dist_spec=default_dist_spec, - default_pg=shard_pg): - model = OPTForCausalLM(config) - else: - model = OPTForCausalLM(config) + model = OPTForCausalLM.from_pretrained(args.model_name_or_path, config=config) logger.info(f"Finish loading model from {args.model_name_or_path}", ranks=[0]) # Enable gradient checkpointing @@ -116,7 +107,9 @@ def main(): collate_fn=netflix_collator) # Set optimizer - optimizer = HybridAdam(model.parameters(), lr=(args.learning_rate * world_size)) + optimizer = HybridAdam(model.parameters(), + lr=(args.learning_rate * world_size), + weight_decay=args.weight_decay) # Set lr scheduler total_steps = len(dataloader) * args.num_epoch From 71fe52769cde5e8e8bd4c2703593d45193f35f3f Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 9 Jun 2023 09:48:49 +0800 Subject: [PATCH 315/413] [gemini] fixed the gemini checkpoint io (#3934) --- colossalai/booster/plugin/gemini_plugin.py | 7 +++++-- colossalai/checkpoint_io/index_file.py | 18 ++++++++++-------- colossalai/zero/gemini/gemini_ddp.py | 5 ++++- 3 files changed, 19 insertions(+), 11 deletions(-) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 46714fe1c679..4a7efc165cbd 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -99,8 +99,11 @@ def save_sharded_model(self, save_state_dict(shard, checkpoint_file_path, use_safetensors) index_file.append_meta_data("total_size", total_size) - index_file.write_index_file(save_index_file) - logging.info(f"The model is going to be split to checkpoint shards. " + + # only save the index file on the master rank + if self.coordinator.is_master(): + index_file.write_index_file(save_index_file) + logging.info(f"The model is split into checkpoint shards. " f"You can find where each parameters has been saved in the " f"index located at {save_index_file}.") diff --git a/colossalai/checkpoint_io/index_file.py b/colossalai/checkpoint_io/index_file.py index 334ecbc04738..a41cc482e054 100644 --- a/colossalai/checkpoint_io/index_file.py +++ b/colossalai/checkpoint_io/index_file.py @@ -1,8 +1,8 @@ import json -from pathlib import Path -from typing import Any, List, Union import os -import json +from collections import OrderedDict +from pathlib import Path +from typing import Any, Dict, List, Union from .utils import is_dtensor_checkpoint @@ -22,8 +22,10 @@ class CheckpointIndexFile: def __init__(self, root_path=None) -> None: self.root_path = root_path - self.metadata: dict = dict() - self.weight_map: dict = dict() + + # use ordered dict to preserve the tensor checkpoint order + self.metadata: Dict = OrderedDict() + self.weight_map: Dict = OrderedDict() @staticmethod def from_file(index_path: Union[str, Path]): @@ -150,13 +152,13 @@ def get_checkpoint_file(self, param_name: str) -> str: """ ckpt_path = self.weight_map[param_name] return ckpt_path - + def get_all_param_names(self): """ Get all the weight keys. """ return list(self.weight_map.keys()) - + def write_index_file(self, save_index_file): """ Write index file. @@ -164,5 +166,5 @@ def write_index_file(self, save_index_file): save_index_file = os.path.join(self.root_path, save_index_file) index = {"metadata": self.metadata, "weight_map": self.weight_map} with open(save_index_file, "w", encoding="utf-8") as f: - content = json.dumps(index, indent=2, sort_keys=True) + "\n" + content = json.dumps(index, indent=2) + "\n" f.write(content) diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index 7e23fdb425f8..094320c4aff4 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -716,7 +716,10 @@ def append(self, name: str, tensor: torch.Tensor) -> Tuple[Optional[OrderedDict] tensor_size = calculate_tensor_size(tensor) ret_block = None ret_block_size = 0 - if self.current_block_size + tensor_size > self.max_shard_size: + + # before we return the current block and create a new block, + # we need to ensure that the current block is not empty + if self.current_block_size + tensor_size > self.max_shard_size and self.current_block_size > 0: ret_block = self.current_block ret_block_size = self.current_block_size self.current_block = OrderedDict() From 6718a2f2857ad9cc7210f867288c9f56ec3a9045 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 12 Jun 2023 09:50:57 +0800 Subject: [PATCH 316/413] [workflow] cancel duplicated workflow jobs (#3960) --- .github/workflows/build_on_pr.yml | 9 +++++++++ .github/workflows/compatiblity_test_on_pr.yml | 6 ++++++ .github/workflows/doc_check_on_pr.yml | 6 ++++++ .github/workflows/doc_test_on_pr.yml | 6 ++++++ .github/workflows/example_check_on_pr.yml | 6 ++++++ 5 files changed, 33 insertions(+) diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index 8b2253e57cfb..513de40b7353 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -60,6 +60,9 @@ jobs: defaults: run: shell: bash + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - name: Copy testmon cache run: | # branch name may contain slash, we need to replace it with space @@ -83,6 +86,9 @@ jobs: changedLibraryFiles: ${{ steps.find-lib-change.outputs.all_changed_files }} anyLibraryFileChanged: ${{ steps.find-lib-change.outputs.any_changed }} runs-on: ubuntu-latest + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v2 with: @@ -140,6 +146,9 @@ jobs: defaults: run: shell: bash + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - name: Checkout TensorNVMe uses: actions/checkout@v2 diff --git a/.github/workflows/compatiblity_test_on_pr.yml b/.github/workflows/compatiblity_test_on_pr.yml index 94a723388872..5098b8e364d0 100644 --- a/.github/workflows/compatiblity_test_on_pr.yml +++ b/.github/workflows/compatiblity_test_on_pr.yml @@ -12,6 +12,9 @@ jobs: runs-on: ubuntu-latest outputs: matrix: ${{ steps.set-matrix.outputs.matrix }} + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v3 - id: set-matrix @@ -40,6 +43,9 @@ jobs: image: ${{ matrix.container }} options: --gpus all --rm -v /data/scratch/cifar-10:/data/scratch/cifar-10 timeout-minutes: 120 + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - name: Install dependencies run: | diff --git a/.github/workflows/doc_check_on_pr.yml b/.github/workflows/doc_check_on_pr.yml index 992cc93b008c..848991bd3a82 100644 --- a/.github/workflows/doc_check_on_pr.yml +++ b/.github/workflows/doc_check_on_pr.yml @@ -16,6 +16,9 @@ jobs: github.event.pull_request.draft == false && github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' runs-on: ubuntu-latest + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v2 @@ -31,6 +34,9 @@ jobs: github.event.pull_request.draft == false && github.event.pull_request.base.repo.full_name == 'hpcaitech/ColossalAI' runs-on: ubuntu-latest + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v2 with: diff --git a/.github/workflows/doc_test_on_pr.yml b/.github/workflows/doc_test_on_pr.yml index 325e2a7c95a4..2a07a2297bfb 100644 --- a/.github/workflows/doc_test_on_pr.yml +++ b/.github/workflows/doc_test_on_pr.yml @@ -19,6 +19,9 @@ jobs: outputs: any_changed: ${{ steps.changed-files.outputs.any_changed }} changed_files: ${{ steps.changed-files.outputs.all_changed_files }} + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false name: Detect changed example files steps: - uses: actions/checkout@v3 @@ -59,6 +62,9 @@ jobs: defaults: run: shell: bash + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - name: Checkout ColossalAI-Documentation uses: actions/checkout@v2 diff --git a/.github/workflows/example_check_on_pr.yml b/.github/workflows/example_check_on_pr.yml index 31dbf7540091..ee456c25f2b5 100644 --- a/.github/workflows/example_check_on_pr.yml +++ b/.github/workflows/example_check_on_pr.yml @@ -20,6 +20,9 @@ jobs: matrix: ${{ steps.setup-matrix.outputs.matrix }} anyChanged: ${{ steps.setup-matrix.outputs.anyChanged }} name: Detect changed example files + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v3 with: @@ -77,6 +80,9 @@ jobs: image: hpcaitech/pytorch-cuda:1.12.0-11.3.0 options: --gpus all --rm -v /data/scratch/examples-data:/data/ timeout-minutes: 10 + concurrency: + group: ${{ github.head_ref }} + cancel-in-progress: false steps: - uses: actions/checkout@v3 From 9d02590c9a64d12bc31866f35bf9b51a4084963f Mon Sep 17 00:00:00 2001 From: Wenhao Chen Date: Tue, 13 Jun 2023 13:31:56 +0800 Subject: [PATCH 317/413] [chat] refactor actor class (#3968) * refactor: separate log_probs fn from Actor forward fn * refactor: separate generate fn from Actor class * feat: update unwrap_model and get_base_model * unwrap_model returns model not wrapped by Strategy * get_base_model returns HF model for Actor, Critic and RewardModel * feat: simplify Strategy.prepare * style: remove get_base_model method of Actor * perf: tokenize text in batches * refactor: move calc_action_log_probs to utils of model * test: update test with new forward fn * style: rename forward fn args * fix: do not unwrap model in save_model fn of naive strategy * test: add gemini test for train_prompts * fix: fix _set_default_generate_kwargs --- .../Chat/coati/dataset/prompt_dataset.py | 16 +++--- .../Chat/coati/dataset/sft_dataset.py | 30 +++++------ .../Chat/coati/experience_maker/naive.py | 12 +++-- .../Chat/coati/models/base/__init__.py | 14 ++--- applications/Chat/coati/models/base/actor.py | 53 +++++-------------- applications/Chat/coati/models/generation.py | 41 ++++++++++++-- applications/Chat/coati/models/utils.py | 19 +++++++ applications/Chat/coati/trainer/ppo.py | 23 ++++---- .../Chat/coati/trainer/strategies/base.py | 21 +++----- .../coati/trainer/strategies/colossalai.py | 19 +++---- .../Chat/coati/trainer/strategies/ddp.py | 4 +- .../Chat/coati/trainer/strategies/naive.py | 7 ++- applications/Chat/examples/test_ci.sh | 8 +++ applications/Chat/tests/test_checkpoint.py | 4 +- 14 files changed, 151 insertions(+), 120 deletions(-) diff --git a/applications/Chat/coati/dataset/prompt_dataset.py b/applications/Chat/coati/dataset/prompt_dataset.py index 5858052c836a..0bdcbbc5928e 100644 --- a/applications/Chat/coati/dataset/prompt_dataset.py +++ b/applications/Chat/coati/dataset/prompt_dataset.py @@ -35,14 +35,14 @@ def __init__(self, logger.info(f"Limiting dataset to {max_datasets_size} examples.") list_data_dict = list_data_dict[:max_datasets_size] - for data_dict in list_data_dict: - token = tokenizer(data_dict["instruction"], - return_tensors='pt', - max_length=max_length, - padding='max_length', - truncation=True) - for k, tensor in token.items(): - self.keyed_prompt[k].extend(tensor.to(torch.cuda.current_device()).unbind()) + instructions = [data_dict["instruction"] for data_dict in list_data_dict] + tokens = tokenizer(instructions, + return_tensors='pt', + max_length=max_length, + padding='max_length', + truncation=True) + for k, tensor in tokens.items(): + self.keyed_prompt[k] = tensor.to(torch.cuda.current_device()).unbind() def __len__(self): return len(self.keyed_prompt["input_ids"]) diff --git a/applications/Chat/coati/dataset/sft_dataset.py b/applications/Chat/coati/dataset/sft_dataset.py index 3e2453468bbc..3702d00cc609 100644 --- a/applications/Chat/coati/dataset/sft_dataset.py +++ b/applications/Chat/coati/dataset/sft_dataset.py @@ -74,21 +74,18 @@ def __getitem__(self, idx): return dict(input_ids=self.input_ids[idx], labels=self.labels[idx]) -def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, max_length: int) -> Dict: +def _tokenize_fn(strings: Sequence[str], + tokenizer: transformers.PreTrainedTokenizer, + max_length: int + ) -> Dict[str, torch.Tensor]: """Tokenize a list of strings.""" - tokenized_list = [ - tokenizer( - text, - return_tensors="pt", - padding="longest", - max_length=max_length, - truncation=True, - ) for text in strings - ] - input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] - input_ids_lens = labels_lens = [ - tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list - ] + tokenized_list = tokenizer( + strings, return_tensors="pt", padding="longest", + max_length=max_length, truncation=True + ) + input_ids = labels = tokenized_list["input_ids"] + input_ids_lens = labels_lens = \ + tokenized_list["input_ids"].ne(tokenizer.pad_token_id).sum(dim=-1) return dict( input_ids=input_ids, labels=labels, @@ -105,7 +102,10 @@ def preprocess( ) -> Dict: """Preprocess the data by tokenizing.""" examples = [s + t for s, t in zip(sources, targets)] - examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer, max_length) for strings in (examples, sources)] + examples_tokenized, sources_tokenized = [ + _tokenize_fn(strings, tokenizer, max_length) + for strings in (examples, sources) + ] input_ids = examples_tokenized["input_ids"] labels = copy.deepcopy(input_ids) for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): diff --git a/applications/Chat/coati/experience_maker/naive.py b/applications/Chat/coati/experience_maker/naive.py index 94546eeb28e7..e5bb029e63d0 100644 --- a/applications/Chat/coati/experience_maker/naive.py +++ b/applications/Chat/coati/experience_maker/naive.py @@ -1,5 +1,6 @@ import torch -from coati.models.utils import compute_reward, normalize +from coati.models.generation import generate_with_actor +from coati.models.utils import calc_action_log_probs, compute_reward, normalize from .base import Experience, ExperienceMaker @@ -16,13 +17,16 @@ def make_experience(self, input_ids: torch.Tensor, **generate_kwargs) -> Experie self.initial_model.eval() self.reward_model.eval() - sequences, attention_mask, action_mask = self.actor.generate(input_ids, + sequences, attention_mask, action_mask = generate_with_actor(self.actor, + input_ids, return_action_mask=True, **generate_kwargs) num_actions = action_mask.size(1) - action_log_probs = self.actor(sequences, num_actions, attention_mask) - base_action_log_probs = self.initial_model(sequences, num_actions, attention_mask) + actor_output = self.actor(sequences, attention_mask) + action_log_probs = calc_action_log_probs(actor_output, sequences, num_actions) + base_model_output = self.initial_model(sequences, attention_mask) + base_action_log_probs = calc_action_log_probs(base_model_output, sequences, num_actions) value = self.critic(sequences, action_mask, attention_mask) r = self.reward_model(sequences, attention_mask) reward = compute_reward(r, self.kl_coef, action_log_probs, base_action_log_probs, action_mask=action_mask) diff --git a/applications/Chat/coati/models/base/__init__.py b/applications/Chat/coati/models/base/__init__.py index fe4152f2b760..c5f748a0c85a 100644 --- a/applications/Chat/coati/models/base/__init__.py +++ b/applications/Chat/coati/models/base/__init__.py @@ -1,3 +1,5 @@ +from typing import Union + import torch.nn as nn from .actor import Actor @@ -5,10 +7,10 @@ from .reward_model import RewardModel -def get_base_model(model: nn.Module) -> nn.Module: +def get_base_model(model: Union[Actor, Critic, RewardModel]) -> nn.Module: """Get the base model of our wrapper classes. - For Actor, it's base model is ``actor.model`` and it's usually a ``transformers.PreTrainedModel``. - For Critic and RewardModel, it's base model is itself. + For Actor, Critic and RewardModel, return ``model.model``, + it's usually a ``transformers.PreTrainedModel``. Args: model (nn.Module): model to get base model from @@ -16,9 +18,9 @@ def get_base_model(model: nn.Module) -> nn.Module: Returns: nn.Module: the base model """ - if isinstance(model, Actor): - return model.get_base_model() - return model + assert isinstance(model, (Actor, Critic, RewardModel)), \ + f'Expect Actor, Critic or RewardModel, got {type(model)}, use unwrap_model first.' + return model.model __all__ = ['Actor', 'Critic', 'RewardModel', 'get_base_model'] diff --git a/applications/Chat/coati/models/base/actor.py b/applications/Chat/coati/models/base/actor.py index 71fbf7bbae7d..2034d5cc81d4 100644 --- a/applications/Chat/coati/models/base/actor.py +++ b/applications/Chat/coati/models/base/actor.py @@ -1,12 +1,9 @@ -from typing import Optional, Tuple, Union +from typing import Optional import torch import torch.nn as nn -import torch.nn.functional as F -from ..generation import generate from ..lora import LoRAModule -from ..utils import log_probs_from_logits class Actor(LoRAModule): @@ -24,42 +21,16 @@ def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = self.model = model self.convert_to_lora() - @torch.no_grad() - def generate( - self, - input_ids: torch.Tensor, - return_action_mask: bool = True, - **kwargs - ) -> Union[Tuple[torch.LongTensor, torch.LongTensor], Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor]]: - sequences = generate(self.model, input_ids, **kwargs) - attention_mask = None - pad_token_id = kwargs.get('pad_token_id', None) - if pad_token_id is not None: - attention_mask = sequences.not_equal(pad_token_id).to(dtype=torch.long, device=sequences.device) - if not return_action_mask: - return sequences, attention_mask, None - input_len = input_ids.size(1) - eos_token_id = kwargs.get('eos_token_id', None) - if eos_token_id is None: - action_mask = torch.ones_like(sequences, dtype=torch.bool) - else: - # left padding may be applied, only mask action - action_mask = (sequences[:, input_len:] == eos_token_id).cumsum(dim=-1) == 0 - action_mask = F.pad(action_mask, (1 + input_len, -1), value=True) # include eos token and input - action_mask[:, :input_len] = False - action_mask = action_mask[:, 1:] - return sequences, attention_mask, action_mask[:, -(sequences.size(1) - input_len):] - def forward(self, - sequences: torch.LongTensor, - num_actions: int, - attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - """Returns action log probs + input_ids: torch.LongTensor, + attention_mask: Optional[torch.Tensor] = None, + **model_kwargs, # HACK: `generate` method may pass more kwargs + ) -> torch.Tensor: + """Returns model output. """ - output = self.model(sequences, attention_mask=attention_mask) - logits = output['logits'] - log_probs = log_probs_from_logits(logits[:, :-1, :], sequences[:, 1:]) - return log_probs[:, -num_actions:] - - def get_base_model(self): - return self.model + output = self.model( + input_ids, + attention_mask=attention_mask, + **model_kwargs + ) + return output diff --git a/applications/Chat/coati/models/generation.py b/applications/Chat/coati/models/generation.py index f57c9458a271..0156e2284e52 100644 --- a/applications/Chat/coati/models/generation.py +++ b/applications/Chat/coati/models/generation.py @@ -1,8 +1,10 @@ -from typing import Any, Callable, Optional +from typing import Any, Callable, Optional, Tuple, Union import torch import torch.distributed as dist import torch.nn as nn +import torch.nn.functional as F + try: from transformers.generation_logits_process import ( @@ -55,9 +57,8 @@ def sample(model: nn.Module, unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) for _ in range(input_ids.size(1), max_length): - model_inputs = prepare_inputs_fn(input_ids, **model_kwargs) if prepare_inputs_fn is not None else { - 'input_ids': input_ids - } + model_inputs = prepare_inputs_fn(input_ids, **model_kwargs) \ + if prepare_inputs_fn is not None else {'input_ids': input_ids} outputs = model(**model_inputs) next_token_logits = outputs['logits'][:, -1, :] @@ -144,3 +145,35 @@ def generate(model: nn.Module, raise NotImplementedError else: raise ValueError("Unsupported generation mode") + + +@torch.no_grad() +def generate_with_actor(actor_model: nn.Module, + input_ids: torch.Tensor, + return_action_mask: bool = True, + **kwargs + ) -> Union[Tuple[torch.LongTensor, torch.LongTensor], + Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor]]: + """Generate token sequence with actor model. Refer to `generate` for more details. + """ + # generate sequences + sequences = generate(actor_model, input_ids, **kwargs) + + # calculate auxiliary tensors + attention_mask = None + pad_token_id = kwargs.get('pad_token_id', None) + if pad_token_id is not None: + attention_mask = sequences.not_equal(pad_token_id).to(dtype=torch.long, device=sequences.device) + if not return_action_mask: + return sequences, attention_mask, None + input_len = input_ids.size(1) + eos_token_id = kwargs.get('eos_token_id', None) + if eos_token_id is None: + action_mask = torch.ones_like(sequences, dtype=torch.bool) + else: + # left padding may be applied, only mask action + action_mask = (sequences[:, input_len:] == eos_token_id).cumsum(dim=-1) == 0 + action_mask = F.pad(action_mask, (1 + input_len, -1), value=True) # include eos token and input + action_mask[:, :input_len] = False + action_mask = action_mask[:, 1:] + return sequences, attention_mask, action_mask[:, -(sequences.size(1) - input_len):] diff --git a/applications/Chat/coati/models/utils.py b/applications/Chat/coati/models/utils.py index 0ff13181fcd2..b9f15f894a1f 100644 --- a/applications/Chat/coati/models/utils.py +++ b/applications/Chat/coati/models/utils.py @@ -46,6 +46,25 @@ def log_probs_from_logits(logits: torch.Tensor, labels: torch.Tensor) -> torch.T return log_probs_labels.squeeze(-1) +def calc_action_log_probs(output: torch.Tensor, + sequences: torch.LongTensor, + num_actions: int + ) -> torch.Tensor: + """Calculate action log probs. + + Args: + output (torch.Tensor): Output tensor of Actor.forward. + sequences (torch.LongTensor): Input sequences. + num_actions (int): Number of actions. + + Returns: + torch.Tensor: Action log probs. + """ + logits = output['logits'] + log_probs = log_probs_from_logits(logits[:, :-1, :], sequences[:, 1:]) + return log_probs[:, -num_actions:] + + def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor: tensor = tensor * mask tensor = tensor.sum(dim=dim) diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index fe5ae48d9c2f..e2e44e62533e 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -3,8 +3,9 @@ import torch import torch.nn as nn from coati.experience_maker import Experience, NaiveExperienceMaker -from coati.models.base import Actor, Critic +from coati.models.base import Actor, Critic, get_base_model from coati.models.loss import GPTLMLoss, PolicyLoss, ValueLoss +from coati.models.utils import calc_action_log_probs from coati.replay_buffer import NaiveReplayBuffer from torch import Tensor from torch.optim import Optimizer @@ -165,7 +166,8 @@ def training_step(self, experience: Experience) -> Dict[str, float]: self.critic.train() # policy loss num_actions = experience.action_mask.size(1) - action_log_probs = self.actor(experience.sequences, num_actions, attention_mask=experience.attention_mask) + actor_output = self.actor(experience.sequences, attention_mask=experience.attention_mask) + action_log_probs = calc_action_log_probs(actor_output, experience.sequences, num_actions) actor_loss = self.actor_loss_fn(action_log_probs, experience.action_log_probs, experience.advantages, @@ -175,8 +177,8 @@ def training_step(self, experience: Experience) -> Dict[str, float]: if self.ptx_coef != 0: batch = next(iter(self.pretrain_dataloader)) batch = to_device(batch, self.device) - ptx_log_probs = self.actor.get_base_model()(batch['input_ids'], - attention_mask=batch['attention_mask'])['logits'] + ptx_log_probs = self.actor(batch['input_ids'], + attention_mask=batch['attention_mask'])['logits'] ptx_loss = self.ptx_loss_fn(ptx_log_probs, batch['labels']) actor_loss = ptx_loss * self.ptx_coef + actor_loss * (1 - self.ptx_coef) @@ -200,14 +202,15 @@ def training_step(self, experience: Experience) -> Dict[str, float]: return {'reward': experience.reward.mean().item()} -def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None: - origin_model = strategy.unwrap_model(actor) +def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> Dict: + unwrapper_model = strategy.unwrap_model(actor) + hf_model = get_base_model(unwrapper_model) new_kwargs = {**generate_kwargs} # use huggingface models method directly - if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'): - new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation + if 'prepare_inputs_fn' not in generate_kwargs and hasattr(hf_model, 'prepare_inputs_for_generation'): + new_kwargs['prepare_inputs_fn'] = hf_model.prepare_inputs_for_generation - if 'update_model_kwargs_fn' not in generate_kwargs and hasattr(origin_model, '_update_model_kwargs_for_generation'): - new_kwargs['update_model_kwargs_fn'] = origin_model._update_model_kwargs_for_generation + if 'update_model_kwargs_fn' not in generate_kwargs and hasattr(hf_model, '_update_model_kwargs_for_generation'): + new_kwargs['update_model_kwargs_fn'] = hf_model._update_model_kwargs_for_generation return new_kwargs diff --git a/applications/Chat/coati/trainer/strategies/base.py b/applications/Chat/coati/trainer/strategies/base.py index bd30422022ae..06f81f21ab26 100644 --- a/applications/Chat/coati/trainer/strategies/base.py +++ b/applications/Chat/coati/trainer/strategies/base.py @@ -4,7 +4,6 @@ import torch import torch.nn as nn -from coati.models.base import Actor, get_base_model from coati.replay_buffer import ReplayBuffer from torch.optim import Optimizer from torch.utils.data import DataLoader @@ -69,21 +68,16 @@ def prepare( Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]: Models or model-optimizer-pairs in the original order. """ - def prepare_model(model: nn.Module): - if isinstance(model, Actor): - return Actor(self.setup_model(model.get_base_model())) - return self.setup_model(model) - rets = [] for arg in models_or_model_optim_pairs: if isinstance(arg, tuple): assert len(arg) == 2, f'Expect (model, optimizer) pair, got a tuple with size "{len(arg)}"' model, optimizer = arg - model = prepare_model(model) - optimizer = self.setup_optimizer(optimizer, get_base_model(model)) + model = self.setup_model(model) + optimizer = self.setup_optimizer(optimizer, model) rets.append((model, optimizer)) elif isinstance(arg, nn.Module): - rets.append(prepare_model(arg)) + rets.append(self.setup_model(model)) else: raise RuntimeError(f'Expect model or (model, optimizer) pair, got {type(arg)}') @@ -93,16 +87,15 @@ def prepare_model(model: nn.Module): @staticmethod def unwrap_model(model: nn.Module) -> nn.Module: - """Get the unwrapped model from a wrapped model. Useful for getting original huggingface model. - For Actor, it will unwrap `actor.model`. + """Get the unwrapped model from a wrapped model made by Strategy.prepare. Args: model (nn.Module): the model to unwrap Returns: - nn.Module: the original model (usually a huggingface model) + nn.Module: the original model """ - return get_base_model(model) + return model @abstractmethod def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: @@ -133,4 +126,4 @@ def save_pretrained(self, @abstractmethod def get_model_state_dict_shard(self, model: nn.Module, **config): - pass \ No newline at end of file + pass diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index 88268b677eb2..fafd0918deaf 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -5,7 +5,6 @@ import torch.distributed as dist import torch.nn as nn import torch.optim as optim -from coati.models.base import get_base_model from torch.optim import Optimizer from transformers.tokenization_utils_base import PreTrainedTokenizerBase @@ -153,14 +152,13 @@ def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: if only_rank0 and dist.get_rank() != 0 and self.stage != 3: return - base_model = get_base_model(model) if self.stage == 3: - assert isinstance(base_model, ZeroDDP) + assert isinstance(model, ZeroDDP) # for stage 3, state_dict() method should be called on every rank - state_dict = base_model.state_dict(only_rank_0=only_rank0) + state_dict = model.state_dict(only_rank_0=only_rank0) else: # only_rank0 is false or rank == 0 - state_dict = base_model.state_dict() + state_dict = model.state_dict() if only_rank0 and dist.get_rank() != 0: return torch.save(state_dict, path) @@ -172,11 +170,10 @@ def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = Fal torch.save(optimizer.state_dict(), path) def unwrap_model(self, model: nn.Module) -> nn.Module: - base_model: Union[nn.Module, ZeroDDP] = get_base_model(model) if self.stage == 3: - assert isinstance(base_model, ZeroDDP) - return base_model.module - return base_model + assert isinstance(model, ZeroDDP) + return model.module + return model def save_pretrained(self, model: nn.Module, @@ -196,5 +193,5 @@ def get_model_state_dict_shard(self, model: nn.Module, **config): # if isinstance(module, LoraLinear): # module.merge_weights = True # module.eval() - base_model: ZeroDDP = get_base_model(model) - yield from base_model.state_dict_shard(max_shard_size=1024, only_rank_0=False) + assert isinstance(model, ZeroDDP) + yield from model.state_dict_shard(max_shard_size=1024, only_rank_0=False) diff --git a/applications/Chat/coati/trainer/strategies/ddp.py b/applications/Chat/coati/trainer/strategies/ddp.py index a1fecb36373f..713d7b90c6f0 100644 --- a/applications/Chat/coati/trainer/strategies/ddp.py +++ b/applications/Chat/coati/trainer/strategies/ddp.py @@ -69,8 +69,8 @@ def setup_sampler(self, dataset) -> DistributedSampler: return DistributedSampler(dataset, dist.get_world_size(), dist.get_rank()) def unwrap_model(self, model: nn.Module) -> nn.Module: - base_model: DDP = super().unwrap_model(model) - return base_model.module + assert isinstance(model, DDP) + return model.module def save_pretrained(self, model: nn.Module, diff --git a/applications/Chat/coati/trainer/strategies/naive.py b/applications/Chat/coati/trainer/strategies/naive.py index 972deebeaa0d..202c480e06d9 100644 --- a/applications/Chat/coati/trainer/strategies/naive.py +++ b/applications/Chat/coati/trainer/strategies/naive.py @@ -58,14 +58,13 @@ def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False collate_fn=replay_buffer.collate_fn) def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: - base_model = get_base_model(model) - state_dict = base_model.state_dict() + state_dict = model.state_dict() torch.save(state_dict, path) def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None: - base_model = get_base_model(model) + unwrapped_model = self.unwrap_model(model) state_dict = torch.load(path, map_location=map_location) - base_model.load_state_dict(state_dict, strict=strict) + unwrapped_model.load_state_dict(state_dict, strict=strict) def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: torch.save(optimizer.state_dict(), path) diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index 2fa6c6052f8d..ac3a9b507864 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -121,6 +121,14 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_datas --rm_pretrain 'gpt2' \ --rm_path ${BASE}/rm_ckpt_gpt.pt \ --save_path ${BASE}/actor_checkpoint_prompts.pt + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ + --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ + --pretrain 'gpt2' --model gpt2 \ + --rm_pretrain 'gpt2' \ + --rm_path ${BASE}/rm_ckpt_gpt.pt \ + --save_path ${BASE}/actor_checkpoint_prompts.pt rm -rf ${BASE}/rm_ckpt_gpt.pt rm -rf ${BASE}/actor_checkpoint_prompts.pt diff --git a/applications/Chat/tests/test_checkpoint.py b/applications/Chat/tests/test_checkpoint.py index 4c05a3431699..d93a5c94d8ea 100644 --- a/applications/Chat/tests/test_checkpoint.py +++ b/applications/Chat/tests/test_checkpoint.py @@ -6,6 +6,7 @@ import torch import torch.distributed as dist from coati.models.gpt import GPTActor +from coati.models.utils import calc_action_log_probs from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy from transformers.models.gpt2.configuration_gpt2 import GPT2Config @@ -43,7 +44,8 @@ def run_test_checkpoint(strategy): def run_step(): data = get_data(BATCH_SIZE) action_mask = torch.ones_like(data['attention_mask'], dtype=torch.bool) - action_log_probs = actor(data['input_ids'], action_mask.size(1), data['attention_mask']) + actor_output = actor(data['input_ids'], data['attention_mask']) + action_log_probs = calc_action_log_probs(actor_output, data['input_ids'], action_mask.size(1)) loss = action_log_probs.sum() strategy.backward(loss, actor, actor_optim) strategy.optimizer_step(actor_optim) From 8bcad7367769633699c4ec5b6d94f2119ff44a68 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 13 Jun 2023 14:42:35 +0800 Subject: [PATCH 318/413] [workflow] fixed the directory check in build (#3980) --- .github/workflows/build_on_pr.yml | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index 513de40b7353..ac186a585d43 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -40,8 +40,8 @@ jobs: - name: Copy testmon cache run: | # branch name may contain slash, we need to replace it with space export REF_BRANCH=$(echo ${{ github.event.ref }} | sed "s/\// /") - if [ -d /github/home/testmon_cache/${MAIN_BRANCH} ]; then - [ ! -z "$(ls -A /github/home/testmon_cache/${MAIN_BRANCH})" ] && cp -p -r /github/home/testmon_cache/${MAIN_BRANCH} "/github/home/testmon_cache/${REF_BRANCH}" + if [ -d /github/home/testmon_cache/${MAIN_BRANCH} ] && [ ! -z "$(ls -A /github/home/testmon_cache/${MAIN_BRANCH})" ]; then + cp -p -r /github/home/testmon_cache/${MAIN_BRANCH} "/github/home/testmon_cache/${REF_BRANCH}" fi env: MAIN_BRANCH: ${{ github.event.master_branch }} @@ -67,8 +67,8 @@ jobs: - name: Copy testmon cache run: | # branch name may contain slash, we need to replace it with space export BASE=$(echo ${{ github.event.pull_request.base.ref }} | sed "s/\// /") - if [ -d "/github/home/testmon_cache/${BASE}" ]; then - [ ! -z "$(ls -A "/github/home/testmon_cache/${BASE}")" ] && mkdir -p /github/home/testmon_cache/_pull && cp -p -r "/github/home/testmon_cache/${BASE}" /github/home/testmon_cache/_pull/${PR_NUMBER} + if [ -d "/github/home/testmon_cache/${BASE}" ] and [ ! -z "$(ls -A "/github/home/testmon_cache/${BASE}")" ]; then + mkdir -p /github/home/testmon_cache/_pull && cp -p -r "/github/home/testmon_cache/${BASE}" /github/home/testmon_cache/_pull/${PR_NUMBER} fi env: PR_NUMBER: ${{ github.event.number }} @@ -159,7 +159,9 @@ jobs: - name: Restore TensorNVMe Cache run: | - [ ! -z "$(ls -A /github/home/tensornvme_cache/)" ] && cp -p -r /github/home/tensornvme_cache/* /__w/ColossalAI/ColossalAI/TensorNVMe + if [ -d /github/home/tensornvme_cache ] && [ ! -z "$(ls -A /github/home/tensornvme_cache/)" ]; then + cp -p -r /github/home/tensornvme_cache/* /__w/ColossalAI/ColossalAI/TensorNVMe + fi - name: Install TensorNVMe run: | @@ -182,7 +184,9 @@ jobs: if: needs.detect.outputs.anyExtensionFileChanged != 'true' run: | # -p flag is required to preserve the file timestamp to avoid ninja rebuild - [ ! -z "$(ls -A /github/home/cuda_ext_cache/)" ] && cp -p -r /github/home/cuda_ext_cache/* /__w/ColossalAI/ColossalAI/ + if [ -d /github/home/cuda_ext_cache ] && [ ! -z "$(ls -A /github/home/cuda_ext_cache/)" ]; then + cp -p -r /github/home/cuda_ext_cache/* /__w/ColossalAI/ColossalAI/ + fi - name: Install Colossal-AI run: | @@ -273,8 +277,8 @@ jobs: if: github.event.pull_request.merged == true run: | # branch name may contain slash, we need to replace it with space export BASE=$(echo ${{ github.event.pull_request.base.ref }} | sed "s/\// /") - if [ -d /github/home/testmon_cache/_pull/${PR_NUMBER} ]; then - [ ! -z "$(ls -A /github/home/testmon_cache/_pull/${PR_NUMBER})" ] && cp -p -r /github/home/testmon_cache/_pull/${PR_NUMBER}/.testmondata* "/github/home/testmon_cache/${BASE}/" + if [ -d /github/home/testmon_cache/_pull/${PR_NUMBER} ] && [ ! -z "$(ls -A /github/home/testmon_cache/_pull/${PR_NUMBER})" ]; then + cp -p -r /github/home/testmon_cache/_pull/${PR_NUMBER}/.testmondata* "/github/home/testmon_cache/${BASE}/" fi env: PR_NUMBER: ${{ github.event.pull_request.number }} From 2925f473991875681c62bb8f7d47a0b3bc1a3044 Mon Sep 17 00:00:00 2001 From: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Date: Tue, 13 Jun 2023 15:12:29 +0800 Subject: [PATCH 319/413] [evaluate] support gpt evaluation with reference (#3972) Co-authored-by: Yuanchen Xu --- applications/Chat/evaluate/README.md | 11 +- .../Chat/evaluate/config/config_cn.json | 2 +- .../Chat/evaluate/config/config_en.json | 2 +- applications/Chat/evaluate/eval.py | 11 +- applications/Chat/evaluate/evaluator.py | 13 +- applications/Chat/evaluate/gpt_evaluate.py | 197 ++++++++++++++++-- .../evaluation_prompt_cn.json | 4 +- .../evaluation_prompt_en.json | 26 +-- 8 files changed, 219 insertions(+), 47 deletions(-) diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index 077193b63ce0..e4a50b11d41f 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -17,6 +17,7 @@ The whole evaluation pipeline consists of three methods: 1. `GPT Evaluation`: evaluates model predictions using GPT models. * Compare the performance of two different models (battle). * Rate the model according to pre-defined metrics using prompting design. + * Rate the model according to pre-defined metrics with additional reference answer using prompting design. 2. `Automatic Evaluation`: evaluates model predictions using automatic metrics. 3. `UniEval`: evaluates model predictions using UniEval models(English only). @@ -66,7 +67,7 @@ GPT evaluation uses GPT models to evaluate the prediction of different models an | 切题
    (Relevance) | 切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。

    Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic. | 1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。
    2. 阅读答案，确认答案是否直接回答了题目所问的问题。
    3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。
    4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。

    1. Read the question to determine what the question asks and what aspects of the question need to be answered.
    2. Read the answers to make sure that they directly answer the question asked.
    3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.
    4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all. | | 创意性
    (Creativity) | 创意性(1-5)：某些头脑风暴问题可能需要答案具有创意，提出新的思路。

    Creativity (1-5): Some brainstorming questions may require answers that are creative and suggest new ideas. | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则创意性评分可能会受到影响。
    3. 考虑答案中是否包含新颖的想法或独特的思路。答案可能与已知的解决方案有所重叠，但仍然可以被认为是有创意的，只要它提供了新的角度或方法来解决问题。
    4. 根据答案的创意性，给出一个1到5的评分。如果答案缺乏创意，则应给出一个较低的评分。如果答案具有创意并提供了新的思路，应给出一个较高的评分。

    1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.
    2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the creativity score may be affected.
    3. Consider whether the answer contains novel ideas or unique thoughts. An answer may overlap with a known solution and still be considered creative, as long as it offers a new perspective or approach to the problem.
    4. Give a score of 1 to 5 depending on the creativity of the answer. If the answer lacks creativity, a lower score should be given. If the answer is creative and provides a new idea, a higher score should be given. | | 实用性
    (Practicality) | 实用性(1-5)：某些头脑风暴问题可能需要答案提出实用的建议或解决方法。

    Practicality (1-5): Some brainstorming questions may require answers to suggest practical suggestions or solutions. | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则实用性评分可能会受到影响。
    3. 考虑答案中提出的建议或解决方法是否实用并可行。答案可能看起来很好，但如果无法实现或应用，则实用性评分可能会受到影响。
    4. 根据答案的实用性，给出一个1到5的评分。如果答案缺乏实用性，则应给出一个较低的评分。如果答案提出了实用的建议或解决方法，并且可以很好地解决问题，则应给出一个较高的评分。

    1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.
    2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the practicality score may be affected.
    3. Consider whether the suggestions or solutions presented in the answer are practical and workable. The answer may look good, but if it cannot be implemented or applied, the practicality score may be affected.
    4. Give a score of 1 to 5 depending on the practicality of the answer. If the answer lacks practicality, a lower score should be given. If the answer makes a practical suggestion or solution and solves the problem well, a higher score should be given. | -| 正确性
    (Correctness) | 正确性(1-5)：答案应该符合常识、生活实际等等。

    Correctness (1-5): The answer should be in line with common sense, life experience, etc. | 1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。
    2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则正确性评分可能会受到影响。
    3. 考虑答案中所提供的信息是否正确、符合常识、生活实际等等。如果答案中存在明显的错误或不合理之处，则正确性评分可能会受到影响。
    4. 根据答案的正确性，给出一个1到5的评分。如果答案存在明显的错误或不合理之处，则应给出一个较低的评分。如果答案正确、符合常识、生活实际等等，则应给出一个较高的评分。

    1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.
    2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the correctness score may be affected.
    3. Consider whether the information provided in the answer is correct, consistent with common sense, real life, etc. If there are obvious errors or implausibilities in the answer, the correctness score may be affected.
    4. Give a score of 1 to 5 depending on the correctness of the answer. If the answer contains obvious errors or unreasonable points, a lower score should be given. A higher score should be given if the answer is correct, consistent with common sense, real life, etc. | +| 正确性
    (Correctness) | 正确性(1-5)：正确性(1-5)：答案是否正确。

    Correctness (1-5): whether the answer is correct or not. | 1. 仔细阅读题目，尝试自己回答该问题。
    2. 检查答案的准确性。您可以使用已知的事实或研究来验证答案是否正确。如果答案是正确的，则可以将正确性得分为5分。如果答案是部分正确的，则可以给予适当的得分，例如2分、3分或4分。如果答案完全不正确，则只得1分。

    1. Read the question carefully and try to answer the question yourself.
    2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be given. If the answer is completely incorrect, only 1 point is awarded. | | 自然
    (Naturalness) | 自然(1-5)：答案是否自然，并且符合问题给定的身份。

    Naturalness (1-5): whether the answer is natural and fits the identity given by the question. | 1. 阅读题目，确定题目提供的身份信息。
    2. 检查答案内容是否符合题目给定的身份。
    3. 根据以上因素，对该回答的自然性进行打分，分数从1到5，其中1表示不自然，5表示非常自然，并符合问题给定的身份。

    1. Read the question and determine the identity information provided in the question.
    2. Check whether the content of the answer matches the identity given in the question.
    3. Based on the above factors, score the naturalness of the response on a scale from 1 to 5, where 1 means unnatural and 5 means very natural and in accordance with the identity given in the question. | | 参与感
    (Engagingness) | 参与感(1-5)：答案是否对前面的对话内容做出了恰当的反应，是否理解对话的语境和背景。

    Engagingness (1-5): whether the answer responds appropriately to the content of the preceding conversation and whether it understands the context and background of the conversation. | 1. 阅读题目，确定对话的语境和背景。
    2. 检查答案是否充分理解对话的语境和背景，能否自然地融入到对话中而不显得突兀。
    3. 根据以上因素，对该回答的参与感进行打分，分数从1到5，其中1表示没有参与感，5表示非常有参与感，并且恰当地理解了对话的语境和背景。

    1. Read the questions to determine the context and background of the dialogue.
    2. Check that the answer fully understands the context and background of the conversation and that it fits naturally into the conversation without seeming abrupt.
    3. Based on the above factors, rate the response's engagement on a scale from 1 to 5, where 1 means not engaged and 5 means very engaged and appropriately understands the context and background of the conversation. | | 合理性
    (Reasonableness) | 合理性(1-5)：答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。

    Reasonableness (1-5): Whether the answer can form a logical connection with the content of the previous dialogue, whether it is consistent with common sense, and whether it can reasonably exist in this context. | 1. 阅读题目，确定对话的主题以及问题期望的回答方向。
    2. 判断答案是否能够与前面的对话内容形成逻辑上的衔接，是否符合常理，能否在这个上下文中合理存在。
    3. 根据以上因素，对该回答的合理性进行打分，分数从1到5，其中1表示不合理，5表示非常合理，并且能够与前面的对话内容形成逻辑上的衔接，并符合常理。

    1. Read the question and determine the topic of the conversation and the direction the question expects the answer to go.
    2. Determine whether the answer can be logically connected to the preceding conversation, whether it makes common sense, and whether it can reasonably exist in this context.
    3. Based on the above factors, rate the reasonableness of the answer on a scale from 1 to 5, where 1 means unreasonable and 5 means very reasonable and able to form a logical connection with the preceding dialogue content and consistent with common sense. | @@ -76,7 +77,7 @@ GPT evaluation uses GPT models to evaluate the prediction of different models an GPT models evaluate the quality of model predictions based on the given prompt words and gives a score between 1-5. -> **NOTE 1:** Even for the same metric, the details of its prompt words and CoT(Chain-of-Thought) can differ based on which category you want to evaluate. For example, prompt words for metric `correctness` showed here is "The answer should be in line with common sense, life experience, etc."(this is for category `brainstorming`), but for category `extraction`, prompt words can be "Answers should extract the required information accurately and should not contain any incorrect or misleading information." You can find all the prompt words and CoT(Chain-of-Thought) in `prompt/evaluation_prompt`. +> **NOTE 1:** Even for the same metric, the details of its prompt words and CoT(Chain-of-Thought) can differ based on which category you want to evaluate. For example, prompt words for metric `correctness` showed here is "Whether the answer is correct or not."(this is for category `classification`), but for category `extraction`, prompt words can be "Answers should extract the required information accurately and should not contain any incorrect or misleading information." You can find all the prompt words and CoT(Chain-of-Thought) in `prompt/evaluation_prompt`. > **NOTE 2:** To add customized metrics, you can refer to [FAQ](#faq). @@ -249,7 +250,7 @@ The following is an example of a Chinese config file. The configuration file can }, "category": { "brainstorming": { - "GPT": ["relevance", "creativity", "practicality", "correctness"], + "GPT": ["relevance", "creativity", "practicality", "reasonableness"], "Metrics": ["Distinct"], "UniEval": ["summarization-fluency", "data2text-naturalness", "data2text-informativeness"] }, @@ -313,6 +314,8 @@ python eval.py \ --openai_key "your openai key" \ ``` +If you want GPT evaluation with reference, you can add an argument `--gpt_with_reference`. + ## FAQ
    How can I add a new GPT evaluation metric? @@ -354,7 +357,7 @@ if task == 'data2text': - [x] Add evaluation for English capability - [x] Support UniEval - [x] Support GPT-4 evaluation -- [ ] Support GPT evaluation with reference in the prompt +- [x] Support GPT evaluation with reference ## Citations diff --git a/applications/Chat/evaluate/config/config_cn.json b/applications/Chat/evaluate/config/config_cn.json index cf647f79bbf8..dffb66f6c3be 100644 --- a/applications/Chat/evaluate/config/config_cn.json +++ b/applications/Chat/evaluate/config/config_cn.json @@ -7,7 +7,7 @@ "relevance", "creativity", "practicality", - "correctness" + "reasonableness" ], "Metrics": [ "Distinct" diff --git a/applications/Chat/evaluate/config/config_en.json b/applications/Chat/evaluate/config/config_en.json index 014c61d93a54..5238bd19f67e 100644 --- a/applications/Chat/evaluate/config/config_en.json +++ b/applications/Chat/evaluate/config/config_en.json @@ -12,7 +12,7 @@ "relevance", "creativity", "practicality", - "correctness" + "reasonableness" ], "Metrics": [ "Distinct" diff --git a/applications/Chat/evaluate/eval.py b/applications/Chat/evaluate/eval.py index 180ef438cc43..e3fe0e9e091b 100644 --- a/applications/Chat/evaluate/eval.py +++ b/applications/Chat/evaluate/eval.py @@ -38,9 +38,14 @@ def main(args): raise Exception( "No prompt file for gpt evaluation provided. Please specify the prompt file for gpt evaluation!") + if args.gpt_model == "text-davinci-003" and args.gpt_with_reference: + raise Exception( + "GPT evaluation with reference is not supported for text-davinci-003. You should specify chat models such as gpt-3.5-turbo or gpt-4." + ) + # initialize evaluator evaluator = Evaluator(metrics_per_category, battle_prompt, gpt_evaluation_prompt, args.gpt_model, - config["language"], config.get("path_for_UniEval", None)) + config["language"], config.get("path_for_UniEval", None), args.gpt_with_reference) if len(args.model_name_list) == 2: answers1 = jload(args.answer_file_list[0]) answers2 = jload(args.answer_file_list[1]) @@ -92,6 +97,10 @@ def main(args): default="gpt-3.5-turbo", choices=["text-davinci-003", "gpt-3.5-turbo", "gpt-4"], help='which GPT model to use for evaluation') + parser.add_argument('--gpt_with_reference', + default=False, + action="store_true", + help='whether to include reference answer in gpt evaluation') parser.add_argument('--save_path', type=str, default="results", help='path to save evaluation results') parser.add_argument('--openai_key', type=str, default=None, required=True, help='Your openai key') args = parser.parse_args() diff --git a/applications/Chat/evaluate/evaluator.py b/applications/Chat/evaluate/evaluator.py index 6bb8cdb29431..3dd5fd6f2f23 100644 --- a/applications/Chat/evaluate/evaluator.py +++ b/applications/Chat/evaluate/evaluator.py @@ -16,13 +16,14 @@ class Evaluator(object): """ def __init__(self, params: Dict[str, Any], battle_prompt: Dict[str, Any], gpt_evaluation_prompt: Dict[str, Any], - gpt_model: str, language: str, path_for_UniEval: Dict[str, str]) -> None: + gpt_model: str, language: str, path_for_UniEval: Dict[str, str], gpt_with_reference: bool) -> None: self.params = params self.battle_prompt = battle_prompt self.gpt_evaluation_prompt = gpt_evaluation_prompt self.gpt_model = gpt_model self.language = language self.path_for_UniEval = path_for_UniEval + self.gpt_with_reference = gpt_with_reference self.automatic_metric_stats = dict() self.unieval_metric_stats = dict() self.gpt_evaluation_results = dict() @@ -157,8 +158,14 @@ def switch(metric, language): print(f"No prompt for category {category}! Use prompt for category general now.") prompt = self.gpt_evaluation_prompt["general"] - self.gpt_evaluation_results[category] = gpt_evaluate.evaluate(answers_per_category[category], prompt, - category_metrics, category, self.gpt_model) + self.gpt_evaluation_results[category] = gpt_evaluate.evaluate( + answers_per_category[category], + prompt, + category_metrics, + category, + self.gpt_model, + self.language, + references=targets_per_category[category] if self.gpt_with_reference else None) def save(self, path: str, model_name_list: List[str]) -> None: """ diff --git a/applications/Chat/evaluate/gpt_evaluate.py b/applications/Chat/evaluate/gpt_evaluate.py index 6702526ac5e6..012f41ab0c41 100644 --- a/applications/Chat/evaluate/gpt_evaluate.py +++ b/applications/Chat/evaluate/gpt_evaluate.py @@ -13,6 +13,23 @@ import tqdm from utils import jdump, jload +ref_step_template = { + "en": + "Now please compare the answer with the {adjective} answer, determine whether the answer is able to achieve the same level of {metric}.\n\n", + "cn": + "请比较答案与上面的{adjective}答案，确定答案是否可以达到与该{adjective}答案同样水平的{metric}。\n\n" +} + +ref_answer_template_general = { + "en": "\nAn example answer with good quality is as follows:\n\n{answer}\n\n", + "cn": "\n一个优质的示例答案如下：\n\n{answer}\n\n" +} + +ref_answer_template_correctness = { + "en": "\nA correct answer is as follows:\n\n{answer}\n\n", + "cn": "\n标准答案如下：\n\n{answer}\n\n" +} + def get_battle_result(sys_prompt: str, user_prompt: str, id: int, max_tokens: int = 2048) -> Dict[str, Any]: """ @@ -233,18 +250,125 @@ def save_battle_results(evaluations: List[Dict], name1: str, name2: str, save_pa print(f"Model {name2} average score: {ans2_score/(len(evaluations)-invalid_count):.2f}") +def reference_template(metric: str, language: str, reference: Dict[str, Any]) -> str: + """ + Get prompt template for GPT evaluation with reference. + + Different languages have different prompt templates. + + Args: + metric: metric used in GPT evaluation with reference. + language: language for the template. + reference: the instruction that contains target answer. + + Returns: + Prompt template for GPT evaluation with reference. + """ + + step_to_add = ref_step_template[language] + + for_the_given_answer = "{metric} (1-5) (directly give the score for the given answer):" if language == "en" else "{metric} (1-5) (直接对给定答案打分)" + + # adjective is used to describe the word "answer" in the prompt. + adjective = "example" if language == "en" else "示例" + answer_to_add = ref_answer_template_general[language] + + # Only for correctness, we will provide a correct answer and so the adjective for "answer" will be "correct". The prompt words will be "a correct answer". + # In other cases, the prompt words will be "an example answer with good quality" by default. + if metric.lower() == "correctness": + adjective = "correct" if language == "en" else "标准" + answer_to_add = ref_answer_template_correctness[language] + + answer_to_add = answer_to_add.format(answer=reference["target"] if reference["target"] else reference["output"]) + step_to_add = step_to_add.format(metric=metric.lower(), + adjective=adjective) + for_the_given_answer.format(metric=metric) + + return answer_to_add + step_to_add + + +def fill_in_message(role: str, content: str) -> Dict[str, str]: + """ + Generate one formatted message to send through chat completion. + + Args: + role: the role of the author of this message. + content: the contents of the message. + + Returns: + One message to send through chat completion. + """ + + return {"role": role, "content": content} + + +def multiturn_chat_completion(user_messages: List[str], model: str, max_tokens: int = 1, turns=2) -> Dict[str, Any]: + """ + Do multi-turn chat completion. + + When turns == 1, it is a one-turn conversation for normal GPT evaluation. + When turns == 2, it is a two-turn conversation which is used for GPT evaluation with reference answers. + + Args: + user_messages: messages user wants to send. + model: the model used to evaluate answers. + max_tokens: the maximum number of tokens to generate in the chat completion. + turns: the number of turns for conversation. + + Returns: + Last turn's response. + """ + + if len(user_messages) != turns: + raise Exception("The length of user messages should be equal to the turn number!") + + assistant_responses = [] + + for i in range(turns): + messages_to_send = [] + + for j in range(i): + messages_to_send.append(fill_in_message("user", user_messages[j])) + messages_to_send.append( + fill_in_message("assistant", assistant_responses[j]["choices"][0]["message"]["content"])) + + # Length of user messages == Length of assistant messages + 1 + # Because we always expect the api to response + messages_to_send.append(fill_in_message("user", user_messages[i])) + + response = openai.ChatCompletion.create( + model=model, + messages=messages_to_send, + temperature=0, + max_tokens=max_tokens, + ) + + # Avoid exceeding rate limits. + # You can comment this line if your request doesn't contain many tokens. + time.sleep(1) + + assistant_responses.append(response) + + return assistant_responses[-1] + + def get_gpt_evaluation_without_logprobs(prompt: Dict[str, Any], inst: Dict[str, Any], metrics: List[str], + language: str, + reference: Dict[str, Any] = None, model: str = "gpt-3.5-turbo", max_tokens: int = 2048) -> Dict[str, Any]: """ Use chat models(gpt-3.5-turbo or gpt-4) to evaluate one model answer. + Temprature is set to 0 to make the model more deterministic. + Args: prompt: a dictionary including prompt template, CoT and metrics. inst: the instruction that is needed to be evaluated. metrics: the metrics for evaluation. + language: language used to change the CoT(add one more step about comparing the given answer and reference) if reference is not None. + reference: the reference answer. model: the model used to evaluate answers. max_tokens: the maximum number of tokens to generate in the chat completion. @@ -254,7 +378,7 @@ def get_gpt_evaluation_without_logprobs(prompt: Dict[str, Any], MAX_API_RETRY = 3 - question = (inst["instruction"] if inst["input"] == "" else inst["instruction"] + " " + inst["input"]) + question = (inst["instruction"] if inst["input"] == "" else inst["instruction"] + "\n" + inst["input"]) answer = inst["output"] inst["evaluation"] = {} @@ -265,28 +389,34 @@ def get_gpt_evaluation_without_logprobs(prompt: Dict[str, Any], ) for i in range(MAX_API_RETRY): try: - response = openai.ChatCompletion.create( - model=model, - messages=[ - { - "role": - "user", - "content": - prompt["prompt"].format( - question=question, - answer=answer, - metric=prompt["metrics"][metric], - steps=prompt["CoT"][metric], - ), - }, - ], - temperature=0, - max_tokens=max_tokens, + prompt_reference = "" if reference is None else reference_template(metric, language, reference) + + prompt_1st_round = prompt["prompt"].format( + question=question, + answer=answer, + metric=prompt["metrics"][metric], + steps=prompt["CoT"][metric], ) + + if prompt_reference: + # Do a 2-round conversation + response = multiturn_chat_completion([prompt_1st_round, prompt_reference], + model, + max_tokens=max_tokens, + turns=2) + else: + response = multiturn_chat_completion([prompt_1st_round], model, max_tokens=max_tokens, turns=1) + inst["evaluation"][metric] = { "response": response["choices"][0]["message"]["content"], "logprobs": None, } + + # Prevent exceeding rate limits because we have multiple workers. + # But this will slow down the evaluation process. + # You can comment this line if your request doesn't contain many tokens. + time.sleep(len(metrics) * 0.5) + break except Exception as e: print(e) @@ -305,6 +435,8 @@ def get_gpt_evaluation_with_logprobs(prompt: Dict[str, Any], Use completion model(text-davinci-003) to evaluate one model answer. Only completion models can return log probabilities. + Temprature is set to 0 to make the model more deterministic. + Args: prompt: a dictionary including prompt template, CoT and metrics. inst: the instruction that is needed to be evaluated. @@ -317,7 +449,7 @@ def get_gpt_evaluation_with_logprobs(prompt: Dict[str, Any], MAX_API_RETRY = 3 - question = (inst["instruction"] if inst["input"] == "" else inst["instruction"] + " " + inst["input"]) + question = (inst["instruction"] if inst["input"] == "" else inst["instruction"] + "\n" + inst["input"]) answer = inst["output"] inst["evaluation"] = {} @@ -344,6 +476,12 @@ def get_gpt_evaluation_with_logprobs(prompt: Dict[str, Any], "response": response["choices"][0]["text"], "logprobs": response["choices"][0]["logprobs"]["top_logprobs"], } + + # Prevent exceeding rate limits because we have multiple workers. + # But this will slow down the evaluation process. + # You can comment this line if your request doesn't contain many tokens. + time.sleep(len(metrics) * 0.5) + break except Exception as e: print(e) @@ -354,7 +492,13 @@ def get_gpt_evaluation_with_logprobs(prompt: Dict[str, Any], return inst -def evaluate(answers: List[Dict], prompt: Dict[str, Any], metrics: List[str], category: str, model: str) -> List[Dict]: +def evaluate(answers: List[Dict], + prompt: Dict[str, Any], + metrics: List[str], + category: str, + model: str, + language: str, + references: List[Dict] = None) -> List[Dict]: """ Use GPT models to evaluate model answers and save evaluation results. @@ -364,6 +508,8 @@ def evaluate(answers: List[Dict], prompt: Dict[str, Any], metrics: List[str], ca metrics: metrics for GPT evaluation. category: the category of the model answers for evaluation. model: the specific GPT model used to evaluate answers. + language: language used in GPT evaluation + references: references for GPT evaluation Returns: Evaluations of the given answers. @@ -378,12 +524,19 @@ def evaluate(answers: List[Dict], prompt: Dict[str, Any], metrics: List[str], ca with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: futures = [] - for inst in answers: + for idx, inst in enumerate(answers): # Completion models can return log probabilities. if model == "text-davinci-003": future = executor.submit(get_gpt_evaluation_with_logprobs, prompt, inst, metrics, 1) else: - future = executor.submit(get_gpt_evaluation_without_logprobs, prompt, inst, metrics, model, 1) + future = executor.submit(get_gpt_evaluation_without_logprobs, + prompt, + inst, + metrics, + language, + reference=None if references is None else references[idx], + model=model, + max_tokens=1) futures.append(future) diff --git a/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json index ee6caae32091..783f453cafdb 100644 --- a/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json +++ b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_cn.json @@ -7,14 +7,14 @@ "relevance": "切题(1-5)：答案内容是否切题，不答非所问，并且严格遵照题目要求。", "creativity": "创意性(1-5)：某些头脑风暴问题可能需要答案具有创意，提出新的思路。", "practicality": "实用性(1-5)：某些头脑风暴问题可能需要答案提出实用的建议或解决方法。", - "correctness": "正确性(1-5)：答案应该符合常识、生活实际等等。" + "reasonableness": "合理性(1-5)：答案应该符合常识、生活实际等等。" }, "CoT": { "language organization": "1. 阅读答案，并检查是否有语法错误、用词不当或其他显著的错误。\n2. 检查答案是否具有逻辑性，能够按照合理的顺序传达信息并且能够自圆其说。\n3. 确定答案是否与问题或主题相关，并且能够传达清晰的信息。\n4. 检查答案是否连贯，是否使用适当的转换和过渡来保持句子和段落之间的连贯性。\n5. 检查答案是否具有明确的结构和组织方式，使得读者可以轻松理解信息的层次和结构。\n6. 根据以上因素综合评估答案的语言组织，并给出一个1到5的分数，其中5表示语言组织非常好，而1表示语言组织非常差。\n\n语言组织：", "relevance": "1. 阅读题目，确定题目所问的问题是什么，以及需要回答哪些方面的问题。\n2. 阅读答案，确认答案是否直接回答了题目所问的问题。\n3. 检查答案是否严格遵照了题目的要求，包括答题方式、答题长度、答题格式等等。\n4. 根据以上因素综合评估答案的切题程度，并给出一个1到5的分数，其中5表示答案非常切题，而1表示答案完全没有切题。\n\n切题：", "creativity": "1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。\n2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则创意性评分可能会受到影响。\n3. 考虑答案中是否包含新颖的想法或独特的思路。答案可能与已知的解决方案有所重叠，但仍然可以被认为是有创意的，只要它提供了新的角度或方法来解决问题。\n4. 根据答案的创意性，给出一个1到5的评分。如果答案缺乏创意，则应给出一个较低的评分。如果答案具有创意并提供了新的思路，应给出一个较高的评分。\n\n创意性：", "practicality": "1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。\n2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则实用性评分可能会受到影响。\n3. 考虑答案中提出的建议或解决方法是否实用并可行。答案可能看起来很好，但如果无法实现或应用，则实用性评分可能会受到影响。\n4. 根据答案的实用性，给出一个1到5的评分。如果答案缺乏实用性，则应给出一个较低的评分。如果答案提出了实用的建议或解决方法，并且可以很好地解决问题，则应给出一个较高的评分。\n\n实用性：", - "correctness": "1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。\n2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则正确性评分可能会受到影响。\n3. 考虑答案中所提供的信息是否正确、符合常识、生活实际等等。如果答案中存在明显的错误或不合理之处，则正确性评分可能会受到影响。\n4. 根据答案的正确性，给出一个1到5的评分。如果答案存在明显的错误或不合理之处，则应给出一个较低的评分。如果答案正确、符合常识、生活实际等等，则应给出一个较高的评分。\n\n正确性：" + "reasonableness": "1. 仔细阅读所提供的头脑风暴问题，确保你理解问题的要点和背景。\n2. 根据你的知识和经验，判断所提供的答案是否可行。如果答案不可行，则合理性评分可能会受到影响。\n3. 考虑答案中所提供的信息是否合理、符合常识、生活实际等等。如果答案中存在明显的不合理之处，则合理性评分可能会受到影响。\n4. 根据答案的合理性，给出一个1到5的评分。如果答案存在明显的不合理之处，则应给出一个较低的评分。如果答案合理、符合常识、生活实际等等，则应给出一个较高的评分。\n\n合理性：" }, "prompt": "你是一个好助手。请你为下面“头脑风暴”问题的答案打分。\n\n问题如下：\n\n{question}\n\n答案如下：\n\n{answer}\n\n评分的指标如下：\n\n{metric}\n\n请你遵照以下的评分步骤：\n\n{steps}" }, diff --git a/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_en.json b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_en.json index 0b2053746af2..2285b639427c 100644 --- a/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_en.json +++ b/applications/Chat/evaluate/prompt/evaluation_prompt/evaluation_prompt_en.json @@ -7,14 +7,14 @@ "relevance": "Relevance (1-5): whether the content of the answer is relevant to the topic, does not answer the wrong question, and strictly follows the requirements of the topic.", "creativity": "Creativity (1-5): Some brainstorming questions may require answers that are creative and suggest new ideas.", "practicality": "Practicality (1-5): Some brainstorming questions may require answers to suggest practical suggestions or solutions.", - "correctness": "Correctness (1-5): The answer should be in line with common sense, life experience, etc." + "reasonableness": "Reasonableness (1-5): The answer should be in line with common sense, life experience, etc." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "creativity": "1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.\n2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the creativity score may be affected.\n3. Consider whether the answer contains novel ideas or unique thoughts. An answer may overlap with a known solution and still be considered creative, as long as it offers a new perspective or approach to the problem.\n4. Give a score of 1 to 5 depending on the creativity of the answer. If the answer lacks creativity, a lower score should be given. If the answer is creative and provides a new idea, a higher score should be given.\n\nCreativity:", "practicality": "1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.\n2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the practicality score may be affected.\n3. Consider whether the suggestions or solutions presented in the answer are practical and workable. The answer may look good, but if it cannot be implemented or applied, the practicality score may be affected.\n4. Give a score of 1 to 5 depending on the practicality of the answer. If the answer lacks practicality, a lower score should be given. If the answer makes a practical suggestion or solution and solves the problem well, a higher score should be given.\n\nPracticality:", - "correctness": "1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.\n2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the correctness score may be affected.\n3. Consider whether the information provided in the answer is correct, consistent with common sense, real life, etc. If there are obvious errors or implausibilities in the answer, the correctness score may be affected.\n4. Give a score of 1 to 5 depending on the correctness of the answer. If the answer contains obvious errors or unreasonable points, a lower score should be given. A higher score should be given if the answer is correct, consistent with common sense, real life, etc.\n\nCorrectness:" + "reasonableness": "1. Read the provided brainstorming questions carefully to make sure you understand the gist and context of the questions.\n2. Based on your knowledge and experience, determine if the answers provided are feasible. If the answer is not feasible, the reasonableness score may be affected.\n3. Consider whether the information provided in the answer is reasonable, consistent with common sense, real life, etc. If there are obvious errors or implausibilities in the answer, the reasonableness score may be affected.\n4. Give a score of 1 to 5 depending on the reasonableness of the answer. If the answer contains obvious errors or unreasonable points, a lower score should be given. A higher score should be given if the answer is reasonable, consistent with common sense, real life, etc.\n\nReasonableness:" }, "prompt": "You are a good assistant. Please rate the given answer to the \"brainstorming\" question below.\n\nThe question is as follows:\n\n{question}\n\nThe answer is as follows:\n\n{answer}\n\nThe metric for evaluation is as follows:\n\n{metric}\n\nYou should follow the following evaluation steps:\n\n{steps}" }, @@ -29,7 +29,7 @@ "reasonableness": "Reasonableness (1-5): Whether the answer can form a logical connection with the content of the previous dialogue, whether it is consistent with common sense, and whether it can reasonably exist in this context." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "naturalness": "1. Read the question and determine the identity information provided in the question.\n2. Check whether the content of the answer matches the identity given in the question.\n3. Based on the above factors, score the naturalness of the response on a scale from 1 to 5, where 1 means unnatural and 5 means very natural and in accordance with the identity given in the question.\n\nNaturalness:", "engagingness": "1. Read the questions to determine the context and background of the dialogue.\n2. Check that the answer fully understands the context and background of the conversation and that it fits naturally into the conversation without seeming abrupt.\n3. Based on the above factors, rate the response's engagement on a scale from 1 to 5, where 1 means not engaged and 5 means very engaged and appropriately understands the context and background of the conversation.\n\nEngagingness:", @@ -46,7 +46,7 @@ "correctness": "Correctness (1-5): whether the answer is correct or not." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "correctness": "1. Read the question carefully and try to answer the question yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be given. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" }, @@ -61,7 +61,7 @@ "correctness": "Correctness (1-5): whether the answer is correct or not." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "correctness": "1. Read the question carefully and try to answer the question by yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be assigned. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" }, @@ -76,7 +76,7 @@ "correctness": "correctness (1-5): Answers should extract the required information accurately and should not contain any incorrect or misleading information." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "correctness": "1. Read the questions carefully and identify the information that needs to be extracted from the material.\n2. Read the answer carefully and make sure it covers all the information that needs to be extracted.\n3. Use the material provided to verify the correctness of the response. If the response is inaccurate or contains incorrect or misleading information, a high score cannot be given.\n4. Check that the answer contains all the information required to be extracted and do not leave out any important details.\n5. Give a score between 1 and 5 based on the correctness and completeness of the response, with a score of 5 indicating a very accurate and complete response and a score of 1 indicating that the response barely extracts the required information.\n\nCorrectness:" }, @@ -91,7 +91,7 @@ "diversity": "Diversity (1-5): Whether the answers use beautiful language and have some creativity and imagination. However, answers should also be kept reasonable and moderate, not overly exaggerated or off-topic." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "diversity": "1. Read the entire response carefully to ensure that you fully understand the content and theme expressed in the response.\n2. While reading the response, pay attention to the quality of the language, such as whether the wording is correct and the language is vivid.\n3. Check the creativity and imagination of the response to see if the response is engaging to read on.\n4. Check the reasonableness and appropriateness of the responses to see if the responses are exaggerated or off-topic.\n5. Rate the diversity on a scale of 1 to 5, with a 5 indicating a good quality response that is engaging to read and a 1 indicating a raw response or a question that is off-topic.\n\nDiversity:" }, @@ -106,7 +106,7 @@ "correctness": "Correctness (1-5): whether the answer is correct or not." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "correctness": "1. Read the question carefully and try to answer the question yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be given. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" }, @@ -121,7 +121,7 @@ "correctness": "Correctness (1-5): whether the answer is correct or not." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "correctness": "1. Read the question carefully and try to answer the question yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be assigned. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" }, @@ -137,7 +137,7 @@ "creativity": "Creativity (1-5): The answers to the role-play questions need to be somewhat creative, but at the same time they need to adhere to the setting of the role." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "fidelity": "1. Read the question carefully to understand how the character is set up and represented in the question, including aspects such as occupation, background, point of view, and personality.\n2. Read the question's request and confirm the details that need to be taken into account when answering the request.\n3. Compare the provided answer with the setting of the role and assess whether the answer can strictly adhere to the setting of the role.\n4. Combine the results of the above assessment to give a fidelity score ranging from 1 to 5, where a score of 1 means that the response does not match the persona at all, and a score of 5 means that the response fully complies with the persona and satisfies the given request.\n\nFidelity:", "creativity": "1. Read the question carefully to understand how the character is set up and represented in the question, including career, background, perspective, and personality.\n2. Evaluate whether the answer has unique ideas and suggestions that bring new ideas and insights to the questioner.\n3. Compare the creativity in the response to the setting of the persona and assess whether the response adheres to the setting and essential characteristics of the persona.\n4. Evaluate the quality of the responses in general and combine the results of the above assessment to give a creativity score ranging from 1 to 5, where a score of 1 indicates that the response lacks creativity and a score of 5 indicates that the response has unique ideas and suggestions and is able to adhere to the set-up of the persona.\n\nCreativity:" @@ -154,7 +154,7 @@ "conciseness": "Conciseness (1-5): answers should be concise and without redundant content." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "correctness": "1. Read the material given in the question carefully to understand its content and main points.\n2. Assess whether the answer accurately summarizes the key points of the source material.\n3. assess whether the response contains all the key information in the source material.\n4. Based on the above steps, give a score of 1-5, where 1 means that the response does not accurately summarize the main points of the material and 5 means that the response completely accurately summarizes the main points of the material.\n\nCorrectness:", "conciseness": "1. Read the title and extract the main points of the material.\n2. Read the summary and note the main ideas and messages in it.\n3. Assess the length of the summary. A concise summary should usually convey key information within a few sentences or paragraphs, rather than lengthy paragraphs or essays.\n4. Check that the summary does not contain information that is not relevant to the main ideas or that is redundant.\n5. Make sure that the summary covers the key information in the material and that no important details have been omitted.\n6. Rate the summary on a scale of 1-5, where 5 means the summary is concise and free of redundancy, and 1 means the summary is lengthy or contains unnecessary information that is difficult to understand or remember. Based on your judgment, assign the appropriate score.\n\nConciseness:" @@ -170,7 +170,7 @@ "correctness": "Correctness (1-5): whether the answer is correct or not." }, "CoT": { - "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the linguistic organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good linguistic organization and 1 indicates very poor linguistic organization.\n\nLanguage organization:", + "language organization": "1. Read the answers and check for grammatical errors, poor word choice, or other significant mistakes.\n2. Check that the answer is logical, conveys the information in a logical order, and is self-explanatory.\n3. Determine if the answer is relevant to the question or topic and conveys a clear message.\n4. Check that the answer is coherent and that appropriate transitions and switches are used to maintain coherence between sentences and paragraphs.\n5. Check that the answer is clearly structured and organized in such a way that the reader can easily understand the hierarchy and structure of the information.\n6. Evaluate the language organization of the answer based on a combination of the above factors and give a score of 1 to 5, where 5 indicates very good language organization and 1 indicates very poor language organization.\n\nLanguage organization:", "relevance": "1. Read the question to determine what the question asks and what aspects of the question need to be answered.\n2. Read the answers to make sure that they directly answer the question asked.\n3. Check that the answer follows the requirements of the question, including the way it is answered, the length of the answer, the format of the answer, etc.\n4. Evaluate how relevant the answer is based on the above factors and give a score of 1 to 5, where 5 means the answer is very relevant and 1 means the answer is not relevant at all.\n\nRelevance:", "correctness": "1. Read the question carefully and try to answer the question yourself.\n2. Check the correctness of the answer. You can use known facts or research to verify that the answer is correct. If the answer is correct, you can give a score of 5 for correctness. If the answer is partially correct, an appropriate score, such as 2, 3, or 4, may be assigned. If the answer is completely incorrect, only 1 point is awarded.\n\nCorrectness:" }, From e8ad3c88f570a24180103eee8c2af6cd1c5f92d5 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Wed, 14 Jun 2023 16:32:39 +0800 Subject: [PATCH 320/413] [doc] add a note about unit-testing to CONTRIBUTING.md (#3970) --- CONTRIBUTING.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 00abcf650158..a3dc020f74e9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -30,6 +30,12 @@ pip install -e . ### Unit Tests We use [PyTest](https://docs.pytest.org/en/latest/) to execute tests. You can install pytest by `pip install pytest`. As some of the tests require initialization of the distributed backend, GPUs are needed to execute these tests. +To set up the environment for unit testing, first change your current directory to the root directory of your local ColossalAI repository, then run +```bash +pip install -r requirements/requirements-test.txt +``` +If you encounter an error telling "Could not find a version that satisfies the requirement fbgemm-gpu==0.2.0", please downgrade your python version to 3.8 or 3.9 and try again. + If you only want to run CPU tests, you can run ```bash @@ -138,4 +144,4 @@ You can now create a pull request on the GitHub webpage of your repository. The Do write clearly the description of your pull request and [link the pull request to your target issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue). This will automatically close the issue when the pull request is approved. -In case of code conflict, you should rebase your branch and resolve the conflicts manually. \ No newline at end of file +In case of code conflict, you should rebase your branch and resolve the conflicts manually. From d4fb7bfda7a2da5480e1187e8d3e40884b42ba11 Mon Sep 17 00:00:00 2001 From: digger yu Date: Thu, 15 Jun 2023 10:43:11 +0800 Subject: [PATCH 321/413] fix typo applications/Chat/coati/ (#3947) --- .../Chat/coati/ray/experience_maker_holder.py | 8 ++++---- applications/Chat/coati/ray/lora_constructor.py | 12 ++++++------ .../Chat/coati/trainer/strategies/colossalai.py | 4 ++-- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/applications/Chat/coati/ray/experience_maker_holder.py b/applications/Chat/coati/ray/experience_maker_holder.py index 8551ef1eacef..07d9c3e4f396 100644 --- a/applications/Chat/coati/ray/experience_maker_holder.py +++ b/applications/Chat/coati/ray/experience_maker_holder.py @@ -205,15 +205,15 @@ def update_experience_maker(self, self.experience_maker.actor.model.load_state_dict(new_actor_state_dict, strict=False) else: new_actor_state_dict = state_dict_to(new_actor_state_dict, device=torch.cuda.current_device()) - state_dict_increasae = self.actor_lora_constructor.reconstruct_increase(new_actor_state_dict, new_actor_lora_config_dict) - self.actor_lora_constructor.load_state_dict_increase(self.experience_maker.actor.model, state_dict_increasae) + state_dict_increase = self.actor_lora_constructor.reconstruct_increase(new_actor_state_dict, new_actor_lora_config_dict) + self.actor_lora_constructor.load_state_dict_increase(self.experience_maker.actor.model, state_dict_increase) if new_critic_state_dict is not None: if not self._update_lora_weights or fully_update: self.experience_maker.critic.load_state_dict(new_critic_state_dict, strict=False) else: new_critic_state_dict = state_dict_to(new_critic_state_dict, device=torch.cuda.current_device()) - state_dict_increasae = self.critic_lora_constructor.reconstruct_increase(new_critic_state_dict, new_critic_lora_config_dict) - self.critic_lora_constructor.load_state_dict_increase(self.experience_maker.critic, state_dict_increasae) + state_dict_increase = self.critic_lora_constructor.reconstruct_increase(new_critic_state_dict, new_critic_lora_config_dict) + self.critic_lora_constructor.load_state_dict_increase(self.experience_maker.critic, state_dict_increase) # the lock must be released after both actor and critic being updated if chunk_end: diff --git a/applications/Chat/coati/ray/lora_constructor.py b/applications/Chat/coati/ray/lora_constructor.py index 599a58248728..4809617f647b 100644 --- a/applications/Chat/coati/ray/lora_constructor.py +++ b/applications/Chat/coati/ray/lora_constructor.py @@ -19,7 +19,7 @@ class LoRAConfig: class LoRAConstructor: ''' Tools for reconstructing a model from a remote LoRA model. - (Transfering only LoRA data costs much less!) + (Transferring only LoRA data costs much less!) Usage: Step 1 (Sender): filter_state_dict_lora() @@ -52,7 +52,7 @@ def reconstruct_increase(self, state_dict_lora: Dict[str, Any], lora_config_dict if lora_config_dict is not None: self.register_lora_config(lora_config_dict) - state_dict_increasae = OrderedDict() + state_dict_increase = OrderedDict() config_iter = iter(self.lora_config_dict.items()) lora_A, lora_B, layer_prefix = None, None, None for k, v in state_dict_lora.items(): @@ -65,11 +65,11 @@ def reconstruct_increase(self, state_dict_lora: Dict[str, Any], lora_config_dict assert layer_prefix_2 == layer_prefix, "unmatched (state_dict, config_dict) pair" lora_B = v weight_data_increase = self._compute(lora_A, lora_B, config) - state_dict_increasae[layer_prefix + '.weight'] = weight_data_increase + state_dict_increase[layer_prefix + '.weight'] = weight_data_increase lora_A, lora_B, layer_prefix = None, None, None else: raise ValueError('unexpected key') - return state_dict_increasae + return state_dict_increase def _compute(self, lora_A, lora_B, config=LoRAConfig()): def T(w): @@ -80,12 +80,12 @@ def T(w): return weight_data_increase return 0 - def load_state_dict_increase(self, model: nn.Module, state_dict_increasae: Dict[str, Any]): + def load_state_dict_increase(self, model: nn.Module, state_dict_increase: Dict[str, Any]): ''' The final reconstruction step ''' # naive approach - model.load_state_dict({k: v + model.state_dict()[k] for k, v in state_dict_increasae.items()}, strict=False) + model.load_state_dict({k: v + model.state_dict()[k] for k, v in state_dict_increase.items()}, strict=False) @staticmethod def filter_state_dict_lora(state_dict: Dict[str, Any], keep_non_lora=False): diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index fafd0918deaf..cfdab2806a25 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -29,7 +29,7 @@ class ColossalAIStrategy(DDPStrategy): precision(str): The precision to use. Choose in ('fp32', 'fp16'). Stage 3 only supports fp16. seed(int): The seed for the random number generator. shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3. - This is not compativle with `from_pretrained()`. We temporarily disable this and will support it in the future. + This is not compatible with `from_pretrained()`. We temporarily disable this and will support it in the future. placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda') If it is “cpu”, parameters, gradients and optimizer states will be offloaded to CPU, If it is “cuda”, they will not be offloaded, which means max CUDA memory will be used. It is the fastest. @@ -39,7 +39,7 @@ class ColossalAIStrategy(DDPStrategy): hidden_dim(optional, int): The hidden dimension for the gemini. Only for ZeRO-3. min_chunk_size_mb(float): The minimum chunk size in MB. Only for ZeRO-3. gpu_margin_mem_ratio(float): The margin memory ratio for the GPU. Only for ZeRO-3. - reduce_bugket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2. + reduce_bucket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2. overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2. initial_scale(float): The initial scale for the optimizer. growth_factor(float): The growth factor for the optimizer. From c9cff7e7fa34c6f1640141b3f3af2d08c1ec7534 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Thu, 15 Jun 2023 15:21:26 +0800 Subject: [PATCH 322/413] [checkpointio] General Checkpointing of Sharded Optimizers (#3984) --- colossalai/booster/plugin/gemini_plugin.py | 6 +- colossalai/booster/plugin/torch_ddp_plugin.py | 16 +- .../booster/plugin/torch_fsdp_plugin.py | 9 +- .../checkpoint_io/checkpoint_io_base.py | 12 +- .../checkpoint_io/general_checkpoint_io.py | 95 +++++++-- colossalai/checkpoint_io/index_file.py | 14 +- colossalai/checkpoint_io/utils.py | 185 +++++++++++++++++- .../test_general_checkpoint_io.py | 100 +++++++++- 8 files changed, 399 insertions(+), 38 deletions(-) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 4a7efc165cbd..ce01ad111d2d 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -12,7 +12,7 @@ from torch.utils.data import DataLoader from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO, GeneralCheckpointIO -from colossalai.checkpoint_io.utils import get_base_filenames, get_shard_filename, save_state_dict +from colossalai.checkpoint_io.utils import get_model_base_filenames, get_shard_filename, save_state_dict from colossalai.cluster import DistCoordinator from colossalai.interface import ModelWrapper, OptimizerWrapper from colossalai.utils import get_current_device @@ -76,14 +76,14 @@ def save_sharded_model(self, model: GeminiDDP, checkpoint_path: str, gather_dtensor: bool = False, - variant: Optional[str] = None, + prefix: Optional[str] = None, max_shard_size: int = 1024, use_safetensors: bool = False): """ Save sharded model """ state_dict_shard = model.state_dict_shard(max_shard_size=max_shard_size, only_rank_0=True, dtype=torch.float32) - weights_name, save_index_file = get_base_filenames(variant, use_safetensors) + weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors) total_size = 0 index_file = CheckpointIndexFile(checkpoint_path) for idx, shard_pair in enumerate(state_dict_shard): diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index b317ccf48ad9..a18073db6d3b 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -32,7 +32,6 @@ def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor """ Save model to checkpoint but only on master process. """ - # the model should be unwrapped in self.load_model via ModelWrapper.unwrap if self.coordinator.is_master(): super().save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors) @@ -54,11 +53,22 @@ def save_sharded_model(self, model: nn.Module, checkpoint_path: str, gather_dtensor: bool = False, - variant: Optional[str] = None, + prefix: Optional[str] = None, max_shard_size: int = 1024, use_safetensors: bool = False): + """ + Save model to checkpoint but only on master process. + """ + if self.coordinator.is_master(): + super().save_sharded_model(model, checkpoint_path, gather_dtensor, prefix, max_shard_size, use_safetensors) + + def save_sharded_optimier(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool, prefix: str, + size_per_shard: int): + """ + Save optimizer to checkpoint but only on master process. + """ if self.coordinator.is_master(): - super().save_sharded_model(model, checkpoint_path, gather_dtensor, variant, max_shard_size, use_safetensors) + super().save_sharded_optimizer(optimizer, checkpoint, gather_dtensor, prefix, size_per_shard) class TorchDDPModel(ModelWrapper): diff --git a/colossalai/booster/plugin/torch_fsdp_plugin.py b/colossalai/booster/plugin/torch_fsdp_plugin.py index 8d534ea4c061..ebd03b6ea3bc 100644 --- a/colossalai/booster/plugin/torch_fsdp_plugin.py +++ b/colossalai/booster/plugin/torch_fsdp_plugin.py @@ -1,9 +1,9 @@ +import warnings from pathlib import Path from typing import Callable, Iterable, Iterator, List, Optional, Tuple, Union import torch import torch.nn as nn -import warnings from packaging import version from torch.distributed import ProcessGroup @@ -69,7 +69,7 @@ def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather full_optimizer_state = FSDP.full_optim_state_dict(fsdp_model, optim=optimizer, rank0_only=True) utils.save_state_dict(full_optimizer_state, checkpoint_file_path=checkpoint, use_safetensors=False) - def save_sharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, variant: Optional[str], + def save_sharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, prefix: Optional[str], size_per_shard: int, use_safetensors: bool): """ Save model to checkpoint but only on master process. @@ -87,13 +87,14 @@ def load_sharded_model(self, """ raise NotImplementedError("Sharded model checkpoint is not supported yet.") - def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): + def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool, prefix: str, + size_per_shard: int): """ Save optimizer to checkpoint but only on master process. """ raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.") - def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str, size_per_shard: int): + def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, size_per_shard: int): """ Load optimizer to checkpoint but only on master process. """ diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py index fbc8fc5429ad..9d513043f51a 100644 --- a/colossalai/checkpoint_io/checkpoint_io_base.py +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -103,7 +103,7 @@ def save_model(self, checkpoint: str, shard: bool = False, gather_dtensor: bool = True, - variant: str = None, + prefix: str = None, size_per_shard: int = 1024, use_safetensors: bool = False): """ @@ -128,7 +128,7 @@ def save_model(self, multiple files. The model shards will be specified by a `model.index.json` file. When shard = True, please ensure that the checkpoint path is a directory path instead of a file path. gather_dtensor (bool): whether to gather the distributed tensor to the first device. Default: True. - variant (str): If specified, weights are saved in the format pytorch_model..bin. Default: None. + prefix (str): If specified, weights are saved in the format pytorch_model..bin. Default: None. size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard = True. use_safetensors (bool): whether to use safe tensors. Default: False. If set to True, the checkpoint will be saved """ @@ -137,11 +137,11 @@ def save_model(self, model = model.unwrap() if shard: - self.save_sharded_model(model, checkpoint, gather_dtensor, variant, size_per_shard, use_safetensors) + self.save_sharded_model(model, checkpoint, gather_dtensor, prefix, size_per_shard, use_safetensors) else: self.save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors) - def load_optimizer(self, optimizer: Optimizer, checkpoint: str): + def load_optimizer(self, optimizer: Optimizer, checkpoint: str, prefix: str = None, size_per_shard: int = 1024): """ Load optimizer from checkpoint. @@ -157,7 +157,7 @@ def load_optimizer(self, optimizer: Optimizer, checkpoint: str): if index_file_exists: # the existence of index file means it is a sharded checkpoint - self.load_sharded_optimizer(optimizer, index_file_path) + self.load_sharded_optimizer(optimizer, index_file_path, prefix, size_per_shard) else: self.load_unsharded_optimizer(optimizer, checkpoint) @@ -218,7 +218,7 @@ def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool): pass @abstractmethod - def save_sharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, variant: Optional[str], + def save_sharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, prefix: Optional[str], size_per_shard: int, use_safetensors: bool): """ Save model to sharded checkpoint. diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index 2cc9c3faa12b..d8e133313e70 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -11,15 +11,21 @@ from .checkpoint_io_base import CheckpointIO from .index_file import CheckpointIndexFile from .utils import ( - get_base_filenames, + get_model_base_filenames, + get_optimizer_base_filenames, get_shard_filename, has_index_file, is_safetensors_available, + load_param_groups_into_optimizer, load_shard_state_dict, load_state_dict, load_state_dict_into_model, + load_states_into_optimizer, + save_param_groups, save_state_dict, - shard_checkpoint, + shard_model_checkpoint, + shard_optimizer_checkpoint, + sharded_optimizer_loading_epilogue, ) __all__ = ['GeneralCheckpointIO'] @@ -44,12 +50,30 @@ def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor # save the checkpoint save_state_dict(state_dict, checkpoint, use_safetensors) - def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, prefix: str, size_per_shard: int): - raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.") + def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str, size_per_shard: int): + """ + Load sharded optimizer with the given path to index file. + """ + optimizer.load_state_dict + # Read checkpoint index file. + ckpt_index_file = CheckpointIndexFile.from_file(index_file_path) - def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): - checkpoint = load_state_dict(checkpoint) - optimizer.load_state_dict(checkpoint) + # Load param_groups + param_group_path = ckpt_index_file.get_param_group_filename() + if param_group_path is None: + raise RuntimeError(f'Invalid index file path {index_file_path} for an optimizer. \ + Lacking param group file under current directory.') + id_map = load_param_groups_into_optimizer(optimizer, param_group_path) + + checkpoint_files, _ = ckpt_index_file.get_checkpoint_filenames() + + for shard_file in checkpoint_files: + state_dict = load_shard_state_dict(Path(shard_file), use_safetensors=False) + load_states_into_optimizer(optimizer, state_dict, id_map) + del state_dict + gc.collect() + + sharded_optimizer_loading_epilogue(optimizer) def save_sharded_optimizer( self, @@ -59,7 +83,54 @@ def save_sharded_optimizer( prefix: str, size_per_shard: int, ): - raise NotImplementedError("Sharded optimizer checkpoint is not supported yet.") + """ + Save sharded optimizer checkpoint under the given checkpointing path. + The following files will be created under the path: + - An index file (pytorch_optim.bin.index.json) containing a map between optimizer states and file names + - A group file (pytorch_optim_group.bin) recording information of param_groups + - Multiple files (pytorch_optim-000XX.bin) that store state tensors of optimizer in a sharding way + """ + if os.path.isfile(checkpoint): + logging.error(f"Provided path ({checkpoint}) should be a directory, not a file") + return + + Path(checkpoint).mkdir(parents=True, exist_ok=True) + + # Offload optimizer states. States are broken into shards within max_shard_size. + state_dict = optimizer.state_dict() + sharded_state = shard_optimizer_checkpoint(state_dict, max_shard_size=size_per_shard) + + # Preparing file paths and index file. + states_name, save_index_file, param_group_file = get_optimizer_base_filenames(prefix) + index_file = CheckpointIndexFile(checkpoint) + + # Store the information of param groups to param_group_file. + index_file.append_meta_data("param_groups", param_group_file) + group_file_path = os.path.join(checkpoint, param_group_file) + save_param_groups(state_dict, group_file_path) + + # Save shards of optimizer states. + total_size = 0 + for idx, shard_pair in enumerate(sharded_state): + shard, current_size = shard_pair + shard_file = get_shard_filename(states_name, idx) + total_size = total_size + current_size + for param_id in shard.keys(): + index_file.append_weight_map(str(param_id), shard_file) + + checkpoint_file_path = os.path.join(checkpoint, shard_file) + save_state_dict(shard, checkpoint_file_path, use_safetensors=False) + + # Wrap up index file. + index_file.append_meta_data("total_size", total_size) + index_file.write_index_file(save_index_file) + logging.info(f"The optimizer is going to be split to checkpoint shards. " + f"You can find where each parameters has been saved in the " + f"index located at {save_index_file}.") + + def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: Path): + checkpoint = load_state_dict(checkpoint) + optimizer.load_state_dict(checkpoint) def save_unsharded_optimizer( self, @@ -74,7 +145,7 @@ def save_sharded_model(self, model: nn.Module, checkpoint_path: str, gather_dtensor: bool = False, - variant: Optional[str] = None, + prefix: Optional[str] = None, max_shard_size: int = 1024, use_safetensors: bool = False): """ @@ -89,9 +160,9 @@ def save_sharded_model(self, # shard checkpoint state_dict = model.state_dict() - state_dict_shard = shard_checkpoint(state_dict, max_shard_size=max_shard_size) + state_dict_shard = shard_model_checkpoint(state_dict, max_shard_size=max_shard_size) - weights_name, save_index_file = get_base_filenames(variant, use_safetensors) + weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors) total_size = 0 index_file = CheckpointIndexFile(checkpoint_path) for idx, shard_pair in enumerate(state_dict_shard): @@ -128,7 +199,7 @@ def load_sharded_model(self, # read checkpoint index file ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file) - checkpoint_files, _ = ckpt_index_file.get_checkpoint_fileanames() + checkpoint_files, _ = ckpt_index_file.get_checkpoint_filenames() missing_keys = [] for shard_file in checkpoint_files: diff --git a/colossalai/checkpoint_io/index_file.py b/colossalai/checkpoint_io/index_file.py index a41cc482e054..388cf3fbe9bb 100644 --- a/colossalai/checkpoint_io/index_file.py +++ b/colossalai/checkpoint_io/index_file.py @@ -111,7 +111,7 @@ def contains_dtensor(self): return True return False - def get_checkpoint_fileanames(self) -> List[str]: + def get_checkpoint_filenames(self) -> List[str]: """ Get the set of checkpoint filenames in the weight map. @@ -159,6 +159,18 @@ def get_all_param_names(self): """ return list(self.weight_map.keys()) + def get_param_group_filename(self) -> Union[str, None]: + """ + Get the file name of param_group file if this is a checkpoint for optimizer. + Returns: + str: param_group file name + """ + filename = self.metadata.get("param_groups", None) + if filename: + return str(self.root_path.joinpath(filename)) + else: + return None + def write_index_file(self, save_index_file): """ Write index file. diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 435feda4ac6a..21b70343baca 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -1,17 +1,24 @@ # coding=utf-8 import re +from collections import abc as container_abcs +from collections import defaultdict +from itertools import chain from pathlib import Path from typing import Iterator, List, Mapping, Optional, OrderedDict, Tuple import torch import torch.nn as nn +from torch.optim import Optimizer from colossalai.tensor.d_tensor.d_tensor import DTensor SAFE_WEIGHTS_NAME = "model.safetensors" WEIGHTS_NAME = "pytorch_model.bin" +STATES_NAME = "pytorch_optim.bin" SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json" WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json" +STATES_INDEX_NAME = "pytorch_optim.bin.index.json" +GROUP_FILE_NAME = "pytorch_optim_group.bin" # ====================================== # General helper functions @@ -81,7 +88,7 @@ def is_safetensor_checkpoint(checkpoint_file_path: str) -> bool: # ====================================== # Helper functions for saving shard file # ====================================== -def shard_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> Iterator[Tuple[OrderedDict, int]]: +def shard_model_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> Iterator[Tuple[OrderedDict, int]]: """ Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a given size. @@ -110,6 +117,50 @@ def shard_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> It yield current_block, current_block_size +def shard_optimizer_checkpoint(state_dict: dict, max_shard_size: int = 1024) -> Iterator[Tuple[OrderedDict, int]]: + """ + Splits an optimizer state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a + given size. + """ + + # Only split state_dict['state']; state_dict['param_group'] is not considered in this function. + states = state_dict['state'] + + current_block = {} + current_block_size = 0 + + for param_id, state in states.items(): + + ret_block = None + ret_block_size = 0 + + # A state might contain more than one tensors. + # e.g. each Adam state includes: 'step', 'exp_avg', 'exp_avg_sq' + state_size = 0 + isDTensor = False + for state_tensor in state.values(): + # If the states are stored as DTensors, mark isDTensor as true. + if type(state_tensor) == DTensor: + isDTensor = True + state_size += calculate_tensor_size(state_tensor) + + if not isDTensor: + + if current_block_size + state_size > max_shard_size: + ret_block = current_block + ret_block_size = current_block_size + current_block = {} + current_block_size = 0 + + current_block[param_id] = state + current_block_size += state_size + + if ret_block != None: + yield ret_block, ret_block_size + + yield current_block, current_block_size + + def load_shard_state_dict(checkpoint_file: Path, use_safetensors: bool = False): """ load shard state dict into model @@ -179,6 +230,96 @@ def load(module: nn.Module, state_dict, prefix="", load_sub_module: bool = True) model.__class__.__name__, "\n\t".join(error_msgs))) +def load_param_groups_into_optimizer(optimizer: Optimizer, param_group_path: str) -> dict: + """ + Load information of param_groups into an initialized optimizer. + """ + + # Load list of param_groups from given file path. + # The params in saved_groups are in the form of integer indices. + saved_groups = torch.load(param_group_path) + if not isinstance(saved_groups, List): + raise ValueError(f'The param_groups saved at {param_group_path} is not of List type') + + # The params in param_groups are in the form of pytorch tensors. + # For more details, please view source code of Optimizer class in pytorch. + param_groups = optimizer.param_groups + + # Check the compatibility of saved_groups and param_groups. + if len(param_groups) != len(saved_groups): + raise ValueError("loaded state dict has a different number of original parameter groups") + param_lens = (len(g['params']) for g in param_groups) + saved_lens = (len(g['params']) for g in saved_groups) + if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)): + raise ValueError("loaded state dict contains a parameter group " + "that doesn't match the size of optimizer's group") + + # Creating mapping from id to parameters. + id_map = { + old_id: p for old_id, p in zip(chain.from_iterable((g['params'] for g in saved_groups + )), chain.from_iterable((g['params'] for g in param_groups))) + } + + # Update parameter groups, setting their 'params' value. + def update_group(group, new_group): + new_group['params'] = group['params'] + return new_group + + updated_groups = [update_group(g, ng) for g, ng in zip(param_groups, saved_groups)] + + optimizer.__dict__.update({'param_groups': updated_groups}) + return id_map + + +def load_states_into_optimizer(optimzier: Optimizer, state_dict: dict, id_map: dict): + r"""Copies states from `state_dict` into an Optimizer object. + + Args: + optimizer(Optimizer): An initialized Optimizer object to be loaded + state_dict(dict): a mapping from tensor index (an integer) + to its states to be loaded (a mapping from state name to a tensor). + id_map(dict): a mapping from tensor index (an integer) + to its corresponding parameter (a tensor) whose states will be updated. + """ + + def cast(param, value, key=None): + r"""Make a deep copy of value, casting all tensors to device of param.""" + if isinstance(value, torch.Tensor): + # Floating-point types are a bit special here. They are the only ones + # that are assumed to always match the type of params. + # Make sure state['step'] is not casted https://github.com/pytorch/pytorch/issues/74424 + if (key != "step"): + if param.is_floating_point(): + value = value.to(param.dtype) + value = value.to(param.device) + return value + elif isinstance(value, dict): + return {k: cast(param, v, key=k) for k, v in value.items()} + elif isinstance(value, container_abcs.Iterable): + return type(value)(cast(param, v) for v in value) + else: + return value + + # Copy state assigned to params (and cast tensors to appropriate types). + # State that is not assigned to params is copied as is (needed for + # backward compatibility). + new_states = defaultdict(dict) + for k, v in state_dict.items(): + if k in id_map: + param = id_map[k] + new_states[param] = cast(param, v) + else: + new_states[k] = v + + optimzier.state.update(new_states) + + +def sharded_optimizer_loading_epilogue(optimizer: Optimizer): + # Do the cleaning up as in src code of Pytorch. + optimizer._hook_for_profile() # To support multiprocessing pickle/unpickle. + optimizer.defaults.setdefault('differentiable', False) + + # ====================================== # Helper functions for saving state dict # ====================================== @@ -203,6 +344,18 @@ def save_state_dict(state_dict: dict, checkpoint_file_path: str, use_safetensors torch.save(state_dict, checkpoint_file_path) +def save_param_groups(state_dict: dict, group_file_path: str) -> None: + """ + Save information of param_groups to given file path. + + Args: + state_dict (dict): state dict. + group_file_path (str): path to the group file. + """ + param_groups = state_dict["param_groups"] + torch.save(param_groups, group_file_path) + + def save_dtensor(name: str, tensor: torch.Tensor, index_file: "CheckpointIndexFile", use_safetensors: bool) -> None: """ Save distributed tensor to checkpoint. This checkpoint will be a dictionary which contains @@ -392,28 +545,44 @@ def load_state_dict(checkpoint_file_path: Path): return torch.load(checkpoint_file_path) -def add_variant(weights_name: str, variant: Optional[str] = None) -> str: - if variant is not None and len(variant) > 0: +def add_prefix(weights_name: str, prefix: Optional[str] = None) -> str: + if prefix is not None and len(prefix) > 0: splits = weights_name.split(".") - splits = splits[:-1] + [variant] + splits[-1:] + splits = splits[:-1] + [prefix] + splits[-1:] weights_name = ".".join(splits) return weights_name -def get_base_filenames(variant: str = None, use_safetensors: bool = False): +def get_model_base_filenames(prefix: str = None, use_safetensors: bool = False): """ - generate base weight filenames + generate base model weight filenames """ weights_name = SAFE_WEIGHTS_NAME if use_safetensors else WEIGHTS_NAME - weights_name = add_variant(weights_name, variant) + weights_name = add_prefix(weights_name, prefix) save_index_file = SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME - save_index_file = add_variant(save_index_file, variant) + save_index_file = add_prefix(save_index_file, prefix) return weights_name, save_index_file +def get_optimizer_base_filenames(prefix: str = None): + """ + generate base optimizer state filenames + """ + states_name = STATES_NAME + states_name = add_prefix(states_name, prefix) + + save_index_file = STATES_INDEX_NAME + save_index_file = add_prefix(save_index_file, prefix) + + param_group_file = GROUP_FILE_NAME + param_group_file = add_prefix(param_group_file, prefix) + + return states_name, save_index_file, param_group_file + + def get_shard_filename(weights_name: str, idx: int): """ get shard file name diff --git a/tests/test_checkpoint_io/test_general_checkpoint_io.py b/tests/test_checkpoint_io/test_general_checkpoint_io.py index 9e973bb23e0b..88e3673c153d 100644 --- a/tests/test_checkpoint_io/test_general_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_general_checkpoint_io.py @@ -60,7 +60,7 @@ def test_unsharded_checkpoint(use_safetensors: bool): @pytest.mark.parametrize('use_safetensors', [True, False]) -def test_sharded_checkpoint(use_safetensors: bool): +def test_sharded_model_checkpoint(use_safetensors: bool): # create a model and optimizer model = resnet18() optimizer = Adam(model.parameters(), lr=0.001) @@ -100,3 +100,101 @@ def test_sharded_checkpoint(use_safetensors: bool): # check for model and optimizer state dict recursively check_state_dict_equal(model.state_dict(), new_model.state_dict()) check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict()) + + +def test_sharded_optimizer_checkpoint(): + + # create a model and optimizer + model = resnet18() + optimizer = Adam(model.parameters(), lr=0.001) + + # create test data sample + x = torch.randn(1, 3, 224, 224) + + # run fwd and bwd + y = model(x) + loss = y.sum() + loss.backward() + optimizer.step() + + # create temp directories for checkpoint + model_ckpt_dir = tempfile.TemporaryDirectory() + optimizer_ckpt_dir = tempfile.TemporaryDirectory() + + # save the model and optimizer + ckpt_io = GeneralCheckpointIO() + + ckpt_io.save_model(model, model_ckpt_dir.name, True, True, "", 10, use_safetensors=False) + ckpt_io.save_optimizer(optimizer, optimizer_ckpt_dir.name, shard=True, size_per_shard=10) + + # create new model + new_model = resnet18() + new_optimizer = Adam(new_model.parameters(), lr=0.001) + + ckpt_io.load_model(new_model, str(model_ckpt_dir.name), strict=True) + ckpt_io.load_optimizer(new_optimizer, str(optimizer_ckpt_dir.name)) + + # check for model and optimizer state dict recursively + check_state_dict_equal(model.state_dict(), new_model.state_dict()) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict()) + + # continue running fwd and bwd + for _ in range(5): + y = new_model(x) + loss = y.sum() + loss.backward() + new_optimizer.step() + + # save the newly got optimizer + ckpt_io.save_model(new_model, model_ckpt_dir.name, True, True, "", 10, use_safetensors=False) + ckpt_io.save_optimizer(new_optimizer, optimizer_ckpt_dir.name, shard=True, size_per_shard=10) + + # create another new model + new_new_model = resnet18() + new_new_optimizer = Adam(new_new_model.parameters(), lr=0.001) + + ckpt_io.load_model(new_new_model, str(model_ckpt_dir.name), strict=True) + ckpt_io.load_optimizer(new_new_optimizer, str(optimizer_ckpt_dir.name)) + + # check for model and optimizer state dict recursively + check_state_dict_equal(new_model.state_dict(), new_new_model.state_dict()) + check_state_dict_equal(new_optimizer.state_dict(), new_new_optimizer.state_dict()) + + +def test_sharded_optimizer_multiple_param_groups(): + + # create a model and optimizer + model = resnet18() + optimizer = Adam([{'params': model.layer1.parameters()}, \ + {'params': model.layer2.parameters(), 'lr': 0.002}], lr=0.001) + + # create test data sample + x = torch.randn(1, 3, 224, 224) + + # run fwd and bwd + y = model(x) + loss = y.sum() + loss.backward() + optimizer.step() + + # create temp directories for checkpoint + model_ckpt_dir = tempfile.TemporaryDirectory() + optimizer_ckpt_dir = tempfile.TemporaryDirectory() + + # save the model and optimizer + ckpt_io = GeneralCheckpointIO() + + ckpt_io.save_model(model, model_ckpt_dir.name, True, True, "", 10, use_safetensors=False) + ckpt_io.save_optimizer(optimizer, optimizer_ckpt_dir.name, shard=True, size_per_shard=10) + + # create new model + new_model = resnet18() + new_optimizer = Adam([{'params': new_model.layer1.parameters()}, \ + {'params': new_model.layer2.parameters(), 'lr': 0.002}], lr=0.001) + + ckpt_io.load_model(new_model, str(model_ckpt_dir.name), strict=True) + ckpt_io.load_optimizer(new_optimizer, str(optimizer_ckpt_dir.name)) + + # check for model and optimizer state dict recursively + check_state_dict_equal(model.state_dict(), new_model.state_dict()) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict()) From 725af3eeeb16f7a348578e19105bed4f4096e0ca Mon Sep 17 00:00:00 2001 From: Wenhao Chen Date: Thu, 15 Jun 2023 17:38:42 +0800 Subject: [PATCH 323/413] [booster] make optimizer argument optional for boost (#3993) * feat: make optimizer optional in Booster.boost * test: skip unet test if diffusers version > 0.10.2 --- colossalai/booster/booster.py | 8 +++--- .../booster/mixed_precision/fp16_torch.py | 8 +++--- .../mixed_precision/mixed_precision_base.py | 7 +++--- colossalai/booster/plugin/gemini_plugin.py | 18 +++++++------ .../booster/plugin/low_level_zero_plugin.py | 18 +++++++------ colossalai/booster/plugin/plugin_base.py | 12 ++++----- colossalai/booster/plugin/torch_ddp_plugin.py | 13 +++++----- .../booster/plugin/torch_fsdp_plugin.py | 25 ++++++++++--------- .../test_autochunk_unet.py | 11 ++++++-- 9 files changed, 70 insertions(+), 50 deletions(-) diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 4a42e204982f..6e480d0db16f 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -97,10 +97,10 @@ def __init__(self, def boost( self, model: nn.Module, - optimizer: Optimizer, - criterion: Callable = None, - dataloader: DataLoader = None, - lr_scheduler: LRScheduler = None, + optimizer: Optional[Optimizer] = None, + criterion: Optional[Callable] = None, + dataloader: Optional[DataLoader] = None, + lr_scheduler: Optional[LRScheduler] = None, ) -> List[Union[nn.Module, Optimizer, LRScheduler, DataLoader]]: """ Boost the model, optimizer, criterion, lr_scheduler, and dataloader. diff --git a/colossalai/booster/mixed_precision/fp16_torch.py b/colossalai/booster/mixed_precision/fp16_torch.py index 9999aa5e0eb4..26fd92bd50b8 100644 --- a/colossalai/booster/mixed_precision/fp16_torch.py +++ b/colossalai/booster/mixed_precision/fp16_torch.py @@ -115,10 +115,12 @@ def __init__(self, def configure(self, model: nn.Module, - optimizer: Optimizer, - criterion: Callable = None) -> Tuple[nn.Module, OptimizerWrapper, Callable]: + optimizer: Optional[Optimizer] = None, + criterion: Optional[Callable] = None, + ) -> Tuple[nn.Module, OptimizerWrapper, Callable]: model = TorchAMPModule(model) - optimizer = TorchAMPOptimizer(optimizer, **self.torch_amp_kwargs) + if optimizer is not None: + optimizer = TorchAMPOptimizer(optimizer, **self.torch_amp_kwargs) if criterion is not None: criterion = TorchAMPModule(criterion) return model, optimizer, criterion diff --git a/colossalai/booster/mixed_precision/mixed_precision_base.py b/colossalai/booster/mixed_precision/mixed_precision_base.py index 2490e9811ccf..8caa34e505e1 100644 --- a/colossalai/booster/mixed_precision/mixed_precision_base.py +++ b/colossalai/booster/mixed_precision/mixed_precision_base.py @@ -1,5 +1,5 @@ from abc import ABC, abstractmethod -from typing import Callable, Tuple +from typing import Callable, Optional, Tuple import torch.nn as nn from torch.optim import Optimizer @@ -15,7 +15,8 @@ class MixedPrecision(ABC): @abstractmethod def configure(self, model: nn.Module, - optimizer: Optimizer, - criterion: Callable = None) -> Tuple[nn.Module, OptimizerWrapper, Callable]: + optimizer: Optional[Optimizer] = None, + criterion: Optional[Callable] = None, + ) -> Tuple[nn.Module, OptimizerWrapper, Callable]: # TODO: implement this method pass diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index ce01ad111d2d..60b25b2c400c 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -274,11 +274,11 @@ def supported_devices(self) -> List[str]: def configure( self, model: nn.Module, - optimizer: Optimizer, - criterion: Callable = None, - dataloader: DataLoader = None, - lr_scheduler: LRScheduler = None, - ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + optimizer: Optional[Optimizer] = None, + criterion: Optional[Callable] = None, + dataloader: Optional[DataLoader] = None, + lr_scheduler: Optional[LRScheduler] = None, + ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]: if not isinstance(model, ModelWrapper): # convert model to sync bn @@ -293,8 +293,12 @@ def configure( # wrap the model with Gemini model = GeminiModel(model, self.gemini_config, self.verbose) - if not isinstance(optimizer, OptimizerWrapper): - optimizer = GeminiOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs, + if optimizer is not None and \ + not isinstance(optimizer, OptimizerWrapper): + optimizer = GeminiOptimizer(model.unwrap(), + optimizer, + self.zero_optim_config, + self.optim_kwargs, self.verbose) return model, optimizer, criterion, dataloader, lr_scheduler diff --git a/colossalai/booster/plugin/low_level_zero_plugin.py b/colossalai/booster/plugin/low_level_zero_plugin.py index 2b312d0f9947..94d722080367 100644 --- a/colossalai/booster/plugin/low_level_zero_plugin.py +++ b/colossalai/booster/plugin/low_level_zero_plugin.py @@ -197,17 +197,21 @@ def supported_devices(self) -> List[str]: def configure( self, model: nn.Module, - optimizer: Optimizer, - criterion: Callable = None, - dataloader: DataLoader = None, - lr_scheduler: LRScheduler = None, - ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + optimizer: Optional[Optimizer] = None, + criterion: Optional[Callable] = None, + dataloader: Optional[DataLoader] = None, + lr_scheduler: Optional[LRScheduler] = None, + ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]: if not isinstance(model, ModelWrapper): model = LowLevelZeroModel(model, self.stage, self.precision) - if not isinstance(optimizer, OptimizerWrapper): - optimizer = LowLevelZeroOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs, + if optimizer is not None and \ + not isinstance(optimizer, OptimizerWrapper): + optimizer = LowLevelZeroOptimizer(model.unwrap(), + optimizer, + self.zero_optim_config, + self.optim_kwargs, self.verbose) return model, optimizer, criterion, dataloader, lr_scheduler diff --git a/colossalai/booster/plugin/plugin_base.py b/colossalai/booster/plugin/plugin_base.py index 561f58bc5570..aa78f6827003 100644 --- a/colossalai/booster/plugin/plugin_base.py +++ b/colossalai/booster/plugin/plugin_base.py @@ -1,5 +1,5 @@ from abc import ABC, abstractmethod -from typing import Callable, Iterator, List, Tuple, Union +from typing import Callable, Iterator, List, Optional, Tuple, Union import torch.nn as nn from torch.optim import Optimizer @@ -38,11 +38,11 @@ def support_no_sync(self) -> bool: def configure( self, model: nn.Module, - optimizer: Optimizer, - criterion: Callable = None, - dataloader: DataLoader = None, - lr_scheduler: LRScheduler = None, - ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + optimizer: Optional[Optimizer] = None, + criterion: Optional[Callable] = None, + dataloader: Optional[DataLoader] = None, + lr_scheduler: Optional[LRScheduler] = None, + ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]: # implement this method pass diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index a18073db6d3b..4bfd61af3a3f 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -138,11 +138,11 @@ def supported_devices(self) -> List[str]: def configure( self, model: nn.Module, - optimizer: Optimizer, - criterion: Callable = None, - dataloader: DataLoader = None, - lr_scheduler: LRScheduler = None, - ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + optimizer: Optional[Optimizer] = None, + criterion: Optional[Callable] = None, + dataloader: Optional[DataLoader] = None, + lr_scheduler: Optional[LRScheduler] = None, + ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]: # cast model to cuda model = model.cuda() @@ -152,7 +152,8 @@ def configure( # wrap the model with PyTorch DDP model = TorchDDPModel(model, **self.ddp_kwargs) - if not isinstance(optimizer, OptimizerWrapper): + if optimizer is not None and \ + not isinstance(optimizer, OptimizerWrapper): optimizer = OptimizerWrapper(optimizer) return model, optimizer, criterion, dataloader, lr_scheduler diff --git a/colossalai/booster/plugin/torch_fsdp_plugin.py b/colossalai/booster/plugin/torch_fsdp_plugin.py index ebd03b6ea3bc..abfffa9b099e 100644 --- a/colossalai/booster/plugin/torch_fsdp_plugin.py +++ b/colossalai/booster/plugin/torch_fsdp_plugin.py @@ -195,23 +195,24 @@ def supported_devices(self) -> List[str]: def configure( self, model: nn.Module, - optimizer: Optimizer, - criterion: Callable = None, - dataloader: DataLoader = None, - lr_scheduler: LRScheduler = None, - ) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]: + optimizer: Optional[Optimizer] = None, + criterion: Optional[Callable] = None, + dataloader: Optional[DataLoader] = None, + lr_scheduler: Optional[LRScheduler] = None, + ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]: # wrap the model with PyTorch FSDP fsdp_model = TorchFSDPModel(model, device_id=torch.cuda.current_device(), **self.fsdp_kwargs) - if len(optimizer.param_groups) > 1: - warnings.warn( - 'TorchFSDPPlugin does not support optimizer that use multi param groups. The results may not be as expected if used.' - ) - optimizer.__init__(fsdp_model.parameters(), **optimizer.defaults) + if optimizer is not None: + if len(optimizer.param_groups) > 1: + warnings.warn( + 'TorchFSDPPlugin does not support optimizer that use multi param groups. The results may not be as expected if used.' + ) + optimizer.__init__(fsdp_model.parameters(), **optimizer.defaults) - if not isinstance(optimizer, FSDPOptimizerWrapper): - optimizer = FSDPOptimizerWrapper(optimizer, fsdp_model) + if not isinstance(optimizer, FSDPOptimizerWrapper): + optimizer = FSDPOptimizerWrapper(optimizer, fsdp_model) return fsdp_model, optimizer, criterion, dataloader, lr_scheduler diff --git a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py index ff0d4a1b53f5..fc9d8455ed5c 100644 --- a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py +++ b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py @@ -4,12 +4,15 @@ import torch try: - from diffusers import UNet2DModel - MODELS = [UNet2DModel] + import diffusers + MODELS = [diffusers.UNet2DModel] HAS_REPO = True + from packaging import version + SKIP_UNET_TEST = version.parse(diffusers.__version__) > version.parse("0.10.2") except: MODELS = [] HAS_REPO = False + SKIP_UNET_TEST = False from test_autochunk_diffuser_utils import run_test @@ -32,6 +35,10 @@ def get_data(shape: tuple) -> Tuple[List, List]: return meta_args, concrete_args +@pytest.mark.skipif( + SKIP_UNET_TEST, + reason="diffusers version > 0.10.2", +) @pytest.mark.skipif( not (AUTOCHUNK_AVAILABLE and HAS_REPO), reason="torch version is lower than 1.12.0", From 822c3d4d66d2d74cb7c7080abed6a207602dddfd Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Fri, 16 Jun 2023 14:14:05 +0800 Subject: [PATCH 324/413] [checkpointio] sharded optimizer checkpoint for DDP plugin (#4002) --- colossalai/booster/booster.py | 49 ++++++++++++++----- colossalai/booster/plugin/torch_ddp_plugin.py | 10 ++-- .../checkpoint_io/checkpoint_io_base.py | 8 +-- .../checkpoint_io/general_checkpoint_io.py | 10 +++- colossalai/checkpoint_io/utils.py | 16 +++++- .../test_torch_ddp_checkpoint_io.py | 20 ++++---- 6 files changed, 79 insertions(+), 34 deletions(-) diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 6e480d0db16f..cee547b33b0c 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -9,6 +9,7 @@ from torch.utils.data import DataLoader from colossalai.checkpoint_io import GeneralCheckpointIO +from colossalai.interface import ModelWrapper from .accelerator import Accelerator from .mixed_precision import MixedPrecision, mixed_precision_factory @@ -165,11 +166,11 @@ def no_sync(self, model: nn.Module) -> contextmanager: assert self.plugin.support_no_sync, f'The plugin {self.plugin.__class__.__name__} does not support no_sync.' return self.plugin.no_sync(model) - def load_model(self, model: nn.Module, checkpoint: str, strict: bool = True): + def load_model(self, model: Union[nn.Module, ModelWrapper], checkpoint: str, strict: bool = True): """Load model from checkpoint. Args: - model (nn.Module): A model boosted by Booster. + model (nn.Module or ModelWrapper): A model boosted by Booster. checkpoint (str): Path to the checkpoint. It must be a local path. It should be a directory path if the checkpoint is sharded. Otherwise, it should be a file path. strict (bool, optional): whether to strictly enforce that the keys @@ -179,24 +180,34 @@ def load_model(self, model: nn.Module, checkpoint: str, strict: bool = True): self.checkpoint_io.load_model(model, checkpoint, strict) def save_model(self, - model: nn.Module, + model: Union[nn.Module, ModelWrapper], checkpoint: str, - prefix: str = None, shard: bool = False, - size_per_shard: int = 1024): + gather_dtensor: bool = True, + prefix: Optional[str] = None, + size_per_shard: int = 1024, + use_safetensors: bool = False): """Save model to checkpoint. Args: - model (nn.Module): A model boosted by Booster. + model (nn.Module or ModelWrapper): A model boosted by Booster. checkpoint (str): Path to the checkpoint. It must be a local path. It is a file path if ``shard=False``. Otherwise, it is a directory path. - prefix (str, optional): A prefix added to parameter and buffer - names to compose the keys in state_dict. Defaults to None. shard (bool, optional): Whether to save checkpoint a sharded way. If true, the checkpoint will be a folder. Otherwise, it will be a single file. Defaults to False. + gather_dtensor (bool, optional): whether to gather the distributed tensor to the first device. Default: True. + prefix (str, optional): A prefix added to parameter and buffer + names to compose the keys in state_dict. Defaults to None. size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024. + use_safetensors (bool, optional): whether to use safe tensors. Default: False. If set to True, the checkpoint will be saved. """ - self.checkpoint_io.save_model(model, checkpoint=checkpoint, shard=shard, size_per_shard=size_per_shard) + self.checkpoint_io.save_model(model, + checkpoint=checkpoint, + shard=shard, + gather_dtensor=gather_dtensor, + prefix=prefix, + size_per_shard=size_per_shard, + use_safetensors=use_safetensors) def load_optimizer(self, optimizer: Optimizer, checkpoint: str): """Load optimizer from checkpoint. @@ -205,12 +216,21 @@ def load_optimizer(self, optimizer: Optimizer, checkpoint: str): optimizer (Optimizer): An optimizer boosted by Booster. checkpoint (str): Path to the checkpoint. It must be a local path. It should be a directory path if the checkpoint is sharded. Otherwise, it should be a file path. + prefix (str, optional): A prefix added to parameter and buffer + names to compose the keys in state_dict. Defaults to None. + size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024. """ self.checkpoint_io.load_optimizer(optimizer, checkpoint) - def save_optimizer(self, optimizer: Optimizer, checkpoint: str, shard: bool = False, size_per_shard: int = 1024): - """Save optimizer to checkpoint. - Warning: Saving sharded optimizer checkpoint is not supported yet. + def save_optimizer(self, + optimizer: Optimizer, + checkpoint: str, + shard: bool = False, + gather_dtensor: bool = True, + prefix: Optional[str] = None, + size_per_shard: int = 1024): + """ + Save optimizer to checkpoint. Args: optimizer (Optimizer): An optimizer boosted by Booster. @@ -218,9 +238,12 @@ def save_optimizer(self, optimizer: Optimizer, checkpoint: str, shard: bool = Fa It is a file path if ``shard=False``. Otherwise, it is a directory path. shard (bool, optional): Whether to save checkpoint a sharded way. If true, the checkpoint will be a folder. Otherwise, it will be a single file. Defaults to False. + gather_dtensor (bool): whether to gather the distributed tensor to the first device. Default: True. + prefix (str, optional): A prefix added to parameter and buffer + names to compose the keys in state_dict. Defaults to None. size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024. """ - self.checkpoint_io.save_optimizer(optimizer, checkpoint, shard, size_per_shard) + self.checkpoint_io.save_optimizer(optimizer, checkpoint, shard, gather_dtensor, prefix, size_per_shard) def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): """Save lr scheduler to checkpoint. diff --git a/colossalai/booster/plugin/torch_ddp_plugin.py b/colossalai/booster/plugin/torch_ddp_plugin.py index 4bfd61af3a3f..71b435155503 100644 --- a/colossalai/booster/plugin/torch_ddp_plugin.py +++ b/colossalai/booster/plugin/torch_ddp_plugin.py @@ -52,7 +52,7 @@ def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): def save_sharded_model(self, model: nn.Module, checkpoint_path: str, - gather_dtensor: bool = False, + gather_dtensor: bool = True, prefix: Optional[str] = None, max_shard_size: int = 1024, use_safetensors: bool = False): @@ -62,8 +62,12 @@ def save_sharded_model(self, if self.coordinator.is_master(): super().save_sharded_model(model, checkpoint_path, gather_dtensor, prefix, max_shard_size, use_safetensors) - def save_sharded_optimier(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool, prefix: str, - size_per_shard: int): + def save_sharded_optimizer(self, + optimizer: Optimizer, + checkpoint: str, + gather_dtensor: bool = True, + prefix: Optional[str] = None, + size_per_shard: int = 1024): """ Save optimizer to checkpoint but only on master process. """ diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py index 9d513043f51a..8ff9d87c288e 100644 --- a/colossalai/checkpoint_io/checkpoint_io_base.py +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -148,6 +148,9 @@ def load_optimizer(self, optimizer: Optimizer, checkpoint: str, prefix: str = No Args: optimizer (Optimizer): optimizer to be loaded. checkpoint (str): checkpoint path. This value is made compatibility with the model checkpoints in the + prefix (str, optional): A prefix added to parameter and buffer + names to compose the keys in state_dict. Defaults to None. + size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024. """ index_file_exists, index_file_path = has_index_file(checkpoint) @@ -157,7 +160,7 @@ def load_optimizer(self, optimizer: Optimizer, checkpoint: str, prefix: str = No if index_file_exists: # the existence of index file means it is a sharded checkpoint - self.load_sharded_optimizer(optimizer, index_file_path, prefix, size_per_shard) + self.load_sharded_optimizer(optimizer, index_file_path, prefix) else: self.load_unsharded_optimizer(optimizer, checkpoint) @@ -251,7 +254,7 @@ def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor # ======================================================== @abstractmethod - def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str, size_per_shard: int): + def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str): """ Load optimizer from sharded checkpoint. @@ -259,7 +262,6 @@ def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, pre optimizer (Optimizer): optimizer to be loaded. index_file_path (str): checkpoint path. It should be path to the .index.json file or a path to a directory which contains a .index.json file. prefix (str): prefix for the optimizer checkpoint. - size_per_shard (int): size per shard in MB. """ pass diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index d8e133313e70..26cafcada2c5 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -8,6 +8,8 @@ import torch.nn as nn from torch.optim import Optimizer +from colossalai.interface import OptimizerWrapper + from .checkpoint_io_base import CheckpointIO from .index_file import CheckpointIndexFile from .utils import ( @@ -50,11 +52,15 @@ def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor # save the checkpoint save_state_dict(state_dict, checkpoint, use_safetensors) - def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str, size_per_shard: int): + def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, prefix: str): """ Load sharded optimizer with the given path to index file. """ - optimizer.load_state_dict + + # If optimizer is wrapped, unwrap it. + if isinstance(optimizer, OptimizerWrapper): + optimizer = optimizer.optim + # Read checkpoint index file. ckpt_index_file = CheckpointIndexFile.from_file(index_file_path) diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 21b70343baca..3dada00cd9b5 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -139,6 +139,12 @@ def shard_optimizer_checkpoint(state_dict: dict, max_shard_size: int = 1024) -> state_size = 0 isDTensor = False for state_tensor in state.values(): + + # When state_tensor is None (e.g., a SGD optimizer with momentum set to 0), + # The calculation of tensor size should be skipped to avoid error. + if state_tensor is None: + continue + # If the states are stored as DTensors, mark isDTensor as true. if type(state_tensor) == DTensor: isDTensor = True @@ -271,7 +277,7 @@ def update_group(group, new_group): return id_map -def load_states_into_optimizer(optimzier: Optimizer, state_dict: dict, id_map: dict): +def load_states_into_optimizer(optimizer: Optimizer, state_dict: dict, id_map: dict): r"""Copies states from `state_dict` into an Optimizer object. Args: @@ -311,10 +317,16 @@ def cast(param, value, key=None): else: new_states[k] = v - optimzier.state.update(new_states) + optimizer.state.update(new_states) def sharded_optimizer_loading_epilogue(optimizer: Optimizer): + r"""Do the cleaning up work after state_dict has been loaded into optimizer + + Args: + optimizer(Optimizer): An optimizer object whose state has just been loaded. + """ + # Do the cleaning up as in src code of Pytorch. optimizer._hook_for_profile() # To support multiprocessing pickle/unpickle. optimizer.defaults.setdefault('differentiable', False) diff --git a/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py index 5501ee4e3ef2..14332b5b3fca 100644 --- a/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_torch_ddp_checkpoint_io.py @@ -13,7 +13,8 @@ @parameterize('shard', [True, False]) -def check_torch_ddp_checkpointIO(shard: bool): +@parameterize('size_per_shard', [16, 128]) +def check_torch_ddp_checkpointIO(shard: bool, size_per_shard: int): plugin = TorchDDPPlugin() booster = Booster(plugin=plugin) model = resnet18() @@ -38,11 +39,9 @@ def check_torch_ddp_checkpointIO(shard: bool): model_ckpt_path = f"{tempdir}/model" optimizer_ckpt_path = f"{tempdir}/optimizer" lr_scheduler_ckpt_path = f"{tempdir}/lr_scheduler" - booster.save_model(model, model_ckpt_path, shard=shard) - if not shard: - # TODO(ver217): optimizer checkpointing is not supported for sharded checkpoint - booster.save_optimizer(optimizer, optimizer_ckpt_path) - booster.save_lr_scheduler(scheduler, lr_scheduler_ckpt_path) + booster.save_model(model, model_ckpt_path, shard=shard, size_per_shard=size_per_shard) + booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard, size_per_shard=size_per_shard) + booster.save_lr_scheduler(scheduler, lr_scheduler_ckpt_path) dist.barrier() new_model = resnet18() @@ -55,11 +54,10 @@ def check_torch_ddp_checkpointIO(shard: bool): booster.load_model(new_model, model_ckpt_path) check_state_dict_equal(model.state_dict(), new_model.state_dict(), False) - if not shard: - booster.load_optimizer(new_optimizer, optimizer_ckpt_path) - check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) - booster.load_lr_scheduler(new_scheduler, lr_scheduler_ckpt_path) - check_state_dict_equal(scheduler.state_dict(), new_scheduler.state_dict(), False) + booster.load_optimizer(new_optimizer, optimizer_ckpt_path) + check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) + booster.load_lr_scheduler(new_scheduler, lr_scheduler_ckpt_path) + check_state_dict_equal(scheduler.state_dict(), new_scheduler.state_dict(), False) def run_dist(rank, world_size, port): From a5883aa7909070480d218b62ff8f3e987e7eebd8 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 16 Jun 2023 18:23:02 +0800 Subject: [PATCH 325/413] [test] fixed codefactor format report (#4026) --- .../test_general_checkpoint_io.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/tests/test_checkpoint_io/test_general_checkpoint_io.py b/tests/test_checkpoint_io/test_general_checkpoint_io.py index 88e3673c153d..0976d4503a61 100644 --- a/tests/test_checkpoint_io/test_general_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_general_checkpoint_io.py @@ -165,8 +165,13 @@ def test_sharded_optimizer_multiple_param_groups(): # create a model and optimizer model = resnet18() - optimizer = Adam([{'params': model.layer1.parameters()}, \ - {'params': model.layer2.parameters(), 'lr': 0.002}], lr=0.001) + optimizer = Adam([{ + 'params': model.layer1.parameters() + }, { + 'params': model.layer2.parameters(), + 'lr': 0.002 + }], + lr=0.001) # create test data sample x = torch.randn(1, 3, 224, 224) @@ -189,8 +194,13 @@ def test_sharded_optimizer_multiple_param_groups(): # create new model new_model = resnet18() - new_optimizer = Adam([{'params': new_model.layer1.parameters()}, \ - {'params': new_model.layer2.parameters(), 'lr': 0.002}], lr=0.001) + new_optimizer = Adam([{ + 'params': new_model.layer1.parameters() + }, { + 'params': new_model.layer2.parameters(), + 'lr': 0.002 + }], + lr=0.001) ckpt_io.load_model(new_model, str(model_ckpt_dir.name), strict=True) ckpt_io.load_optimizer(new_optimizer, str(optimizer_ckpt_dir.name)) From 727c4598a97a3985770f85bd7990e885b350c744 Mon Sep 17 00:00:00 2001 From: digger yu Date: Mon, 19 Jun 2023 11:21:55 +0800 Subject: [PATCH 326/413] [nfc] fix dim not defined and fix typo (#3991) --- applications/Chat/evaluate/gpt_evaluate.py | 6 +++--- applications/Chat/evaluate/unieval/evaluator.py | 4 ++-- applications/Chat/evaluate/unieval/utils.py | 2 +- applications/Chat/tests/test_data.py | 4 ++-- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/applications/Chat/evaluate/gpt_evaluate.py b/applications/Chat/evaluate/gpt_evaluate.py index 012f41ab0c41..f8cfb8d0f7e5 100644 --- a/applications/Chat/evaluate/gpt_evaluate.py +++ b/applications/Chat/evaluate/gpt_evaluate.py @@ -361,7 +361,7 @@ def get_gpt_evaluation_without_logprobs(prompt: Dict[str, Any], """ Use chat models(gpt-3.5-turbo or gpt-4) to evaluate one model answer. - Temprature is set to 0 to make the model more deterministic. + Temperature is set to 0 to make the model more deterministic. Args: prompt: a dictionary including prompt template, CoT and metrics. @@ -435,7 +435,7 @@ def get_gpt_evaluation_with_logprobs(prompt: Dict[str, Any], Use completion model(text-davinci-003) to evaluate one model answer. Only completion models can return log probabilities. - Temprature is set to 0 to make the model more deterministic. + Temperature is set to 0 to make the model more deterministic. Args: prompt: a dictionary including prompt template, CoT and metrics. @@ -593,7 +593,7 @@ def calculate_scores_form_logprobs(logprobs: Dict[str, Any]) -> float: def calculate_scores_form_response(response: str, evaluation: Dict[str, Any]) -> int: """ Calculate the score from the response returned by gpt-3.5-turbo or gpt-4. - Different from text-davinci-003, this fuction directly calculates the score according to the plain response returned by gpt-3.5-turbo or gpt-4. + Different from text-davinci-003, this function directly calculates the score according to the plain response returned by gpt-3.5-turbo or gpt-4. Although text-davinci-003 can return log probabilities, it costs ten times as much as gpt-3.5-turbo. Args: diff --git a/applications/Chat/evaluate/unieval/evaluator.py b/applications/Chat/evaluate/unieval/evaluator.py index 385425e4a576..d7f2f87f8c52 100644 --- a/applications/Chat/evaluate/unieval/evaluator.py +++ b/applications/Chat/evaluate/unieval/evaluator.py @@ -277,7 +277,7 @@ def evaluate(self, data, category): n_data = len(data) eval_scores = [{} for _ in range(n_data)] - # Calculate average sentence-level scores for facutal consistency + # Calculate average sentence-level scores for factual consistency src_list, output_list = [], [] n_sents = [] # the number of sentences in the claim for i in range(n_data): @@ -288,7 +288,7 @@ def evaluate(self, data, category): src_list.append(source) output_list.append(system_outputs[j]) input_list = add_question(dimension=self.dim, output=output_list, src=src_list, task=self.task) - sent_score = self.scorer.score(input_list, self.task, category, dim) + sent_score = self.scorer.score(input_list, self.task, category, self.dim) # Get average score for each sample start_idx = 0 diff --git a/applications/Chat/evaluate/unieval/utils.py b/applications/Chat/evaluate/unieval/utils.py index a77505faa0d2..a381e9e590b2 100644 --- a/applications/Chat/evaluate/unieval/utils.py +++ b/applications/Chat/evaluate/unieval/utils.py @@ -37,7 +37,7 @@ def add_question(dimension, output, src=None, ref=None, context=None, task=None) src: source input for different NLG tasks. For example, source document for summarization and dialogue history for dialogue response generation. output: output text generated by the models - ref: human-annotataed groundtruth + ref: human-annotated groundtruth context: the context needed to evaluate several specific dimension. For example, additional factual information when evaluating engagingness and groundedness in dialogues. """ diff --git a/applications/Chat/tests/test_data.py b/applications/Chat/tests/test_data.py index 2e4d4ceac05f..67016f6ed286 100644 --- a/applications/Chat/tests/test_data.py +++ b/applications/Chat/tests/test_data.py @@ -33,7 +33,7 @@ def gather_and_equal(tensor: torch.Tensor) -> bool: def run_test_data(strategy): - EXPERINCE_BATCH_SIZE = 4 + EXPERIENCE_BATCH_SIZE = 4 SAMPLE_BATCH_SIZE = 2 if strategy == 'ddp': @@ -54,7 +54,7 @@ def run_test_data(strategy): # experience of all ranks should be the same for _ in range(2): - data = get_data(EXPERINCE_BATCH_SIZE) + data = get_data(EXPERIENCE_BATCH_SIZE) assert gather_and_equal(data['input_ids']) assert gather_and_equal(data['attention_mask']) experience = experience_maker.make_experience(**data, From 160c64c6454271b28b0e0ed7e6c386c296c7eed8 Mon Sep 17 00:00:00 2001 From: LuGY <74758262+Gy-Lu@users.noreply.github.com> Date: Mon, 19 Jun 2023 11:22:42 +0800 Subject: [PATCH 327/413] [example] fix bucket size in example of gpt gemini (#4028) --- examples/language/gpt/gemini/train_gpt_demo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/language/gpt/gemini/train_gpt_demo.py b/examples/language/gpt/gemini/train_gpt_demo.py index 4b78624f0110..a7b552c9e23d 100644 --- a/examples/language/gpt/gemini/train_gpt_demo.py +++ b/examples/language/gpt/gemini/train_gpt_demo.py @@ -250,7 +250,7 @@ def main(): plugin = None if args.distplan.startswith("CAI_ZeRO"): plugin = LowLevelZeroPlugin(stage=zero_stage, - reduce_bucket_size_in_m=12 * 1024 * 1024, + reduce_bucket_size_in_m=12, overlap_communication=True, verbose=True) elif args.distplan == "CAI_Gemini": From a52f62082de0f4b4544ba2d04e909f74123425ce Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 19 Jun 2023 11:23:24 +0800 Subject: [PATCH 328/413] [format] applied code formatting on changed files in pull request 4021 (#4022) Co-authored-by: github-actions --- colossalai/nn/layer/base_layer.py | 3 ++- tests/test_device/test_device_mesh.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/colossalai/nn/layer/base_layer.py b/colossalai/nn/layer/base_layer.py index c85f53cc44c3..5234b6b1a1b5 100644 --- a/colossalai/nn/layer/base_layer.py +++ b/colossalai/nn/layer/base_layer.py @@ -1,11 +1,12 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- +from contextlib import contextmanager + import torch.nn as nn from colossalai.context import ParallelMode from colossalai.core import global_context as gpc -from contextlib import contextmanager class ParallelLayer(nn.Module): diff --git a/tests/test_device/test_device_mesh.py b/tests/test_device/test_device_mesh.py index 3be057b3a98b..789ce8ab35b8 100644 --- a/tests/test_device/test_device_mesh.py +++ b/tests/test_device/test_device_mesh.py @@ -1,6 +1,7 @@ -from colossalai.device.device_mesh import DeviceMesh import torch +from colossalai.device.device_mesh import DeviceMesh + def test_device_mesh(): physical_mesh_id = torch.arange(0, 16).reshape(2, 8) From 4a81faa5f30a0f7ac81075a095b32e793c48edd0 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 19 Jun 2023 17:12:56 +0800 Subject: [PATCH 329/413] [devops] fix build on pr ci (#4043) * [devops] fix build on pr ci * [devops] fix build on pr ci --- .github/workflows/build_on_pr.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index ac186a585d43..5f4e4feaa230 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -40,7 +40,7 @@ jobs: - name: Copy testmon cache run: | # branch name may contain slash, we need to replace it with space export REF_BRANCH=$(echo ${{ github.event.ref }} | sed "s/\// /") - if [ -d /github/home/testmon_cache/${MAIN_BRANCH} ] && [ ! -z "$(ls -A /github/home/testmon_cache/${MAIN_BRANCH})" ]; then + if [ -d /github/home/testmon_cache/${MAIN_BRANCH} ]; then cp -p -r /github/home/testmon_cache/${MAIN_BRANCH} "/github/home/testmon_cache/${REF_BRANCH}" fi env: @@ -67,8 +67,8 @@ jobs: - name: Copy testmon cache run: | # branch name may contain slash, we need to replace it with space export BASE=$(echo ${{ github.event.pull_request.base.ref }} | sed "s/\// /") - if [ -d "/github/home/testmon_cache/${BASE}" ] and [ ! -z "$(ls -A "/github/home/testmon_cache/${BASE}")" ]; then - mkdir -p /github/home/testmon_cache/_pull && cp -p -r "/github/home/testmon_cache/${BASE}" /github/home/testmon_cache/_pull/${PR_NUMBER} + if [ -d "/github/home/testmon_cache/${BASE}" ] && [ ! -z "$(ls -A "/github/home/testmon_cache/${BASE}")" ]; then + mkdir -p /github/home/testmon_cache/_pull/${PR_NUMBER} && cp -p -r "/github/home/testmon_cache/${BASE}"/.testmondata* /github/home/testmon_cache/_pull/${PR_NUMBER} fi env: PR_NUMBER: ${{ github.event.number }} @@ -200,8 +200,8 @@ jobs: - name: Restore Testmon Cache run: | - if [ -d /github/home/testmon_cache/_pull/${PR_NUMBER} ]; then - [ ! -z "$(ls -A /github/home/testmon_cache/_pull/${PR_NUMBER})" ] && cp -p -r /github/home/testmon_cache/_pull/${PR_NUMBER}/.testmondata* /__w/ColossalAI/ColossalAI/ + if [ -d /github/home/testmon_cache/_pull/${PR_NUMBER} ] && [ ! -z "$(ls -A /github/home/testmon_cache/_pull/${PR_NUMBER})" ]; then + cp -p -r /github/home/testmon_cache/_pull/${PR_NUMBER}/.testmondata* /__w/ColossalAI/ColossalAI/ fi env: PR_NUMBER: ${{ github.event.number }} From b463651f3eeb313d13d10db28fc08d7a0277cfbf Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 22 Jun 2023 14:41:25 +0800 Subject: [PATCH 330/413] [workflow] cover all public repositories in weekly report (#4069) --- .../generate_leaderboard_and_send_to_lark.py | 212 ++++++++++++------ 1 file changed, 149 insertions(+), 63 deletions(-) diff --git a/.github/workflows/scripts/generate_leaderboard_and_send_to_lark.py b/.github/workflows/scripts/generate_leaderboard_and_send_to_lark.py index d8f6c8fe309e..2884e38dd3dd 100644 --- a/.github/workflows/scripts/generate_leaderboard_and_send_to_lark.py +++ b/.github/workflows/scripts/generate_leaderboard_and_send_to_lark.py @@ -1,5 +1,4 @@ import os -from dataclasses import dataclass from datetime import datetime, timedelta from typing import Any, Dict, List @@ -10,8 +9,7 @@ from requests_toolbelt import MultipartEncoder -@dataclass -class Contributor: +class Counter(dict): """ Dataclass for a github contributor. @@ -19,8 +17,40 @@ class Contributor: name (str): name of the contributor num_commits_this_week (int): number of commits made within one week """ - name: str - num_commits_this_week: int + + def record(self, item: str): + if item in self: + self[item] += 1 + else: + self[item] = 1 + + def to_sorted_list(self): + data = [(key, value) for key, value in self.items()] + data.sort(key=lambda x: x[1], reverse=True) + return data + + +def get_utc_time_one_week_ago(): + """ + Get the UTC time one week ago. + """ + now = datetime.utcnow() + start_datetime = now - timedelta(days=7) + return start_datetime + + +def datetime2str(dt): + """ + Convert datetime to string in the format of YYYY-MM-DDTHH:MM:SSZ + """ + return dt.strftime("%Y-%m-%dT%H:%M:%SZ") + + +def str2datetime(string): + """ + Convert string in the format of YYYY-MM-DDTHH:MM:SSZ to datetime + """ + return datetime.strptime(string, "%Y-%m-%dT%H:%M:%SZ") def plot_bar_chart(x: List[Any], y: List[Any], xlabel: str, ylabel: str, title: str, output_path: str) -> None: @@ -36,7 +66,28 @@ def plot_bar_chart(x: List[Any], y: List[Any], xlabel: str, ylabel: str, title: plt.savefig(output_path, dpi=1200) -def get_issue_pull_request_comments(github_token: str, since: str) -> Dict[str, int]: +def get_organization_repositories(github_token, organization_name) -> List[str]: + """ + Retrieve the public repositories under the organization. + """ + url = f"https://api.github.com/orgs/{organization_name}/repos?type=public" + + # prepare header + headers = { + 'Authorization': f'Bearer {github_token}', + 'Accept': 'application/vnd.github+json', + 'X-GitHub-Api-Version': '2022-11-28' + } + + res = requests.get(url, headers=headers).json() + repo_list = [] + + for item in res: + repo_list.append(item['name']) + return repo_list + + +def get_issue_pull_request_comments(github_token: str, org_name: str, repo_name: str, since: str) -> Dict[str, int]: """ Retrieve the issue/PR comments made by our members in the last 7 days. @@ -56,7 +107,7 @@ def get_issue_pull_request_comments(github_token: str, since: str) -> Dict[str, # do pagination to the API page = 1 while True: - comment_api = f'https://api.github.com/repos/hpcaitech/ColossalAI/issues/comments?since={since}&page={page}' + comment_api = f'https://api.github.com/repos/{org_name}/{repo_name}/issues/comments?since={since}&page={page}' comment_response = requests.get(comment_api, headers=headers).json() if len(comment_response) == 0: @@ -70,7 +121,7 @@ def get_issue_pull_request_comments(github_token: str, since: str) -> Dict[str, continue issue_id = item['issue_url'].split('/')[-1] - issue_api = f'https://api.github.com/repos/hpcaitech/ColossalAI/issues/{issue_id}' + issue_api = f'https://api.github.com/repos/{org_name}/{repo_name}/issues/{issue_id}' issue_response = requests.get(issue_api, headers=headers).json() issue_author_relationship = issue_response['author_association'] @@ -87,7 +138,7 @@ def get_issue_pull_request_comments(github_token: str, since: str) -> Dict[str, return user_engagement_count -def get_discussion_comments(github_token, since) -> Dict[str, int]: +def get_discussion_comments(github_token: str, org_name: str, repo_name: str, since: str) -> Dict[str, int]: """ Retrieve the discussion comments made by our members in the last 7 days. This is only available via the GitHub GraphQL API. @@ -105,7 +156,7 @@ def _generate_discussion_query(num, cursor: str = None): offset_str = f", after: \"{cursor}\"" query = f""" {{ - repository(owner: "hpcaitech", name: "ColossalAI"){{ + repository(owner: "{org_name}", name: "{repo_name}"){{ discussions(first: {num} {offset_str}){{ edges {{ cursor @@ -134,7 +185,7 @@ def _generate_comment_reply_count_for_discussion(discussion_number, num, cursor: offset_str = f", before: \"{cursor}\"" query = f""" {{ - repository(owner: "hpcaitech", name: "ColossalAI"){{ + repository(owner: "{org_name}", name: "{repo_name}"){{ discussion(number: {discussion_number}){{ title comments(last: {num} {offset_str}){{ @@ -191,8 +242,8 @@ def _call_graphql_api(query): for edge in edges: # print the discussion title discussion = edge['node'] + discussion_updated_at = str2datetime(discussion['updatedAt']) - discussion_updated_at = datetime.strptime(discussion['updatedAt'], "%Y-%m-%dT%H:%M:%SZ") # check if the updatedAt is within the last 7 days # if yes, add it to discussion_numbers if discussion_updated_at > since: @@ -250,6 +301,7 @@ def _call_graphql_api(query): if reply['authorAssociation'] == 'MEMBER': # check if the updatedAt is within the last 7 days # if yes, add it to discussion_numbers + reply_updated_at = datetime.strptime(reply['updatedAt'], "%Y-%m-%dT%H:%M:%SZ") if reply_updated_at > since: member_name = reply['author']['login'] @@ -260,7 +312,7 @@ def _call_graphql_api(query): return user_engagement_count -def generate_user_engagement_leaderboard_image(github_token: str, output_path: str) -> bool: +def generate_user_engagement_leaderboard_image(github_token: str, org_name: str, repo_list: List[str], output_path: str) -> bool: """ Generate the user engagement leaderboard image for stats within the last 7 days @@ -270,23 +322,29 @@ def generate_user_engagement_leaderboard_image(github_token: str, output_path: s """ # request to the Github API to get the users who have replied the most in the last 7 days - now = datetime.utcnow() - start_datetime = now - timedelta(days=7) - start_datetime_str = start_datetime.strftime("%Y-%m-%dT%H:%M:%SZ") + start_datetime = get_utc_time_one_week_ago() + start_datetime_str = datetime2str(start_datetime) # get the issue/PR comments and discussion comment count - issue_pr_engagement_count = get_issue_pull_request_comments(github_token=github_token, since=start_datetime_str) - discussion_engagement_count = get_discussion_comments(github_token=github_token, since=start_datetime) total_engagement_count = {} - # update the total engagement count - total_engagement_count.update(issue_pr_engagement_count) - for name, count in discussion_engagement_count.items(): - if name in total_engagement_count: - total_engagement_count[name] += count - else: - total_engagement_count[name] = count + def _update_count(counter): + for name, count in counter.items(): + if name in total_engagement_count: + total_engagement_count[name] += count + else: + total_engagement_count[name] = count + + for repo_name in repo_list: + print(f"Fetching user engagement count for {repo_name}/{repo_name}") + issue_pr_engagement_count = get_issue_pull_request_comments(github_token=github_token, org_name=org_name, repo_name=repo_name, since=start_datetime_str) + discussion_engagement_count = get_discussion_comments(github_token=github_token, org_name=org_name, repo_name=repo_name, since=start_datetime) + + # update the total engagement count + _update_count(issue_pr_engagement_count) + _update_count(discussion_engagement_count) + # prepare the data for plotting x = [] y = [] @@ -302,9 +360,6 @@ def generate_user_engagement_leaderboard_image(github_token: str, output_path: s x.append(count) y.append(name) - # use Shanghai time to display on the image - start_datetime_str = datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%dT%H:%M:%SZ") - # plot the leaderboard xlabel = f"Number of Comments made (since {start_datetime_str})" ylabel = "Member" @@ -315,7 +370,7 @@ def generate_user_engagement_leaderboard_image(github_token: str, output_path: s return False -def generate_contributor_leaderboard_image(github_token, output_path) -> bool: +def generate_contributor_leaderboard_image(github_token, org_name, repo_list, output_path) -> bool: """ Generate the contributor leaderboard image for stats within the last 7 days @@ -324,54 +379,81 @@ def generate_contributor_leaderboard_image(github_token, output_path) -> bool: output_path (str): the path to save the image """ # request to the Github API to get the users who have contributed in the last 7 days - URL = 'https://api.github.com/repos/hpcaitech/ColossalAI/stats/contributors' headers = { 'Authorization': f'Bearer {github_token}', 'Accept': 'application/vnd.github+json', 'X-GitHub-Api-Version': '2022-11-28' } - while True: - response = requests.get(URL, headers=headers).json() + counter = Counter() + start_datetime = get_utc_time_one_week_ago() - if len(response) != 0: - # sometimes the Github API returns empty response for unknown reason - # request again if the response is empty - break + def _get_url(org_name, repo_name, page): + return f'https://api.github.com/repos/{org_name}/{repo_name}/pulls?per_page=50&page={page}&state=closed' + + def _iterate_by_page(org_name, repo_name): + page = 1 + stop = False + + while not stop: + print(f"Fetching pull request data for {org_name}/{repo_name} - page{page}") + url = _get_url(org_name, repo_name, page) - contributor_list = [] + while True: + response = requests.get(url, headers=headers).json() - # get number of commits for each contributor - start_timestamp = None - for item in response: - num_commits_this_week = item['weeks'][-1]['c'] - name = item['author']['login'] - contributor = Contributor(name=name, num_commits_this_week=num_commits_this_week) - contributor_list.append(contributor) + if isinstance(response, list): + # sometimes the Github API returns nothing + # request again if the response is not a list + break + print("Empty response, request again...") - # update start_timestamp - start_timestamp = item['weeks'][-1]['w'] + if len(response) == 0: + # if the response is empty, stop + stop = True + break + + # count the pull request and author from response + for pr_data in response: + merged_at = pr_data['merged_at'] + author = pr_data['user']['login'] + + if merged_at is None: + continue + + merge_datetime = str2datetime(merged_at) + + if merge_datetime < start_datetime: + # if we found a pull request that is merged before the start_datetime + # we stop + stop = True + break + else: + # record the author1 + counter.record(author) + + # next page + page += 1 + + for repo_name in repo_list: + _iterate_by_page(org_name, repo_name) # convert unix timestamp to Beijing datetime - start_datetime = datetime.fromtimestamp(start_timestamp, tz=pytz.timezone('Asia/Shanghai')) - start_datetime_str = start_datetime.strftime("%Y-%m-%dT%H:%M:%SZ") + bj_start_datetime = datetime.fromtimestamp(start_datetime.timestamp(), tz=pytz.timezone('Asia/Shanghai')) + bj_start_datetime_str = datetime2str(bj_start_datetime) - # sort by number of commits - contributor_list.sort(key=lambda x: x.num_commits_this_week, reverse=True) + contribution_list = counter.to_sorted_list() # remove contributors who has zero commits - contributor_list = [x for x in contributor_list if x.num_commits_this_week > 0] - - # prepare the data for plotting - x = [x.num_commits_this_week for x in contributor_list] - y = [x.name for x in contributor_list] + author_list = [x[0] for x in contribution_list] + num_commit_list = [x[1] for x in contribution_list] # plot - if len(x) > 0: - xlabel = f"Number of Commits (since {start_datetime_str})" + if len(author_list) > 0: + xlabel = f"Number of Pull Requests (since {bj_start_datetime_str})" ylabel = "Contributor" title = 'Active Contributor Leaderboard' - plot_bar_chart(x, y, xlabel=xlabel, ylabel=ylabel, title=title, output_path=output_path) + plot_bar_chart(num_commit_list, author_list, xlabel=xlabel, ylabel=ylabel, title=title, output_path=output_path) return True else: return False @@ -438,10 +520,14 @@ def send_message_to_lark(message: str, webhook_url: str): GITHUB_TOKEN = os.environ['GITHUB_TOKEN'] CONTRIBUTOR_IMAGE_PATH = 'contributor_leaderboard.png' USER_ENGAGEMENT_IMAGE_PATH = 'engagement_leaderboard.png' + ORG_NAME = "hpcaitech" + + # get all open source repositories + REPO_LIST = get_organization_repositories(GITHUB_TOKEN, ORG_NAME) # generate images - contrib_success = generate_contributor_leaderboard_image(GITHUB_TOKEN, CONTRIBUTOR_IMAGE_PATH) - engagement_success = generate_user_engagement_leaderboard_image(GITHUB_TOKEN, USER_ENGAGEMENT_IMAGE_PATH) + contrib_success = generate_contributor_leaderboard_image(GITHUB_TOKEN, ORG_NAME, REPO_LIST, CONTRIBUTOR_IMAGE_PATH) + engagement_success = generate_user_engagement_leaderboard_image(GITHUB_TOKEN, ORG_NAME, REPO_LIST, USER_ENGAGEMENT_IMAGE_PATH) # upload images APP_ID = os.environ['LARK_APP_ID'] @@ -457,8 +543,8 @@ def send_message_to_lark(message: str, webhook_url: str): 2. 用户互动榜单 注： -- 开发贡献者测评标准为：本周由公司成员提交的commit次数 -- 用户互动榜单测评标准为：本周由公司成员在非成员创建的issue/PR/discussion中回复的次数 +- 开发贡献者测评标准为：本周由公司成员与社区在所有开源仓库提交的Pull Request次数 +- 用户互动榜单测评标准为：本周由公司成员在非成员在所有开源仓库创建的issue/PR/discussion中回复的次数 """ send_message_to_lark(message, LARK_WEBHOOK_URL) @@ -467,7 +553,7 @@ def send_message_to_lark(message: str, webhook_url: str): if contrib_success: send_image_to_lark(contributor_image_key, LARK_WEBHOOK_URL) else: - send_message_to_lark("本周没有成员贡献commit，无榜单图片生成。", LARK_WEBHOOK_URL) + send_message_to_lark("本周没有成员贡献PR，无榜单图片生成。", LARK_WEBHOOK_URL) # send user engagement image to lark if engagement_success: From 0bb0b481b475aa716b5f4141e3199e840315c81c Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Sun, 25 Jun 2023 13:34:15 +0800 Subject: [PATCH 331/413] [gemini] fix argument naming during chunk configuration searching --- colossalai/booster/plugin/gemini_plugin.py | 17 +++++------- colossalai/zero/gemini/chunk/search_utils.py | 26 +++++++++---------- colossalai/zero/gemini/chunk/utils.py | 14 +++++----- colossalai/zero/gemini/gemini_ddp.py | 16 ++++++------ .../test_offload/test_perf.py | 2 +- .../test_compatibility_with_gemini.py | 2 +- .../test_gemini_checkpoint_io.py | 2 +- tests/test_tensor/test_tp_with_zero.py | 2 +- tests/test_zero/test_gemini/test_fwd_bwd.py | 4 +-- .../test_gemini/test_gemini_use_rmt.py | 2 +- tests/test_zero/test_gemini/test_grad_clip.py | 2 +- tests/test_zero/test_gemini/test_inference.py | 2 +- tests/test_zero/test_gemini/test_optim.py | 4 +-- tests/test_zero/test_gemini/test_search.py | 22 ++++++++-------- .../test_gemini/test_zeroddp_state_dict.py | 4 +-- .../test_zeroddp_state_dict_shard.py | 3 ++- .../test_gemini/test_zerooptim_state_dict.py | 2 +- 17 files changed, 62 insertions(+), 64 deletions(-) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 60b25b2c400c..1173589fcd49 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -181,11 +181,11 @@ class GeminiPlugin(DPPluginBase): pin_memory (bool, optional): use pin memory on CPU. Defaults to False. force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False. strict_ddp_mode (bool, optional): use strict ddp mode (only use dp without other parallelism). Defaults to False. - search_range_mb (int, optional): chunk size searching range in MegaByte. Defaults to 32. + search_range_m (int, optional): chunk size searching range divided by 2^20. Defaults to 32. hidden_dim (int, optional): the hidden dimension of DNN. Users can provide this argument to speed up searching. If users do not know this argument before training, it is ok. We will use a default value 1024. - min_chunk_size_mb (float, optional): the minimum chunk size in MegaByte. + min_chunk_size_m (float, optional): the minimum chunk size divided by 2^20. If the aggregate size of parameters is still smaller than the minimum chunk size, all parameters will be compacted into one small chunk. memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer. @@ -214,9 +214,9 @@ def __init__( pin_memory: bool = False, force_outputs_fp32: bool = False, strict_ddp_mode: bool = False, - search_range_mb: int = 32, + search_range_m: int = 32, hidden_dim: Optional[int] = None, - min_chunk_size_mb: float = 32, + min_chunk_size_m: float = 32, memstats: Optional[MemStats] = None, gpu_margin_mem_ratio: float = 0.0, initial_scale: float = 2**32, @@ -238,9 +238,9 @@ def __init__( pin_memory=pin_memory, force_outputs_fp32=force_outputs_fp32, strict_ddp_mode=strict_ddp_mode, - search_range_mb=search_range_mb, + search_range_m=search_range_m, hidden_dim=hidden_dim, - min_chunk_size_mb=min_chunk_size_mb, + min_chunk_size_m=min_chunk_size_m, memstats=memstats, mixed_precision=PRECISION_STR_TO_DTYPE[precision], ) @@ -295,10 +295,7 @@ def configure( if optimizer is not None and \ not isinstance(optimizer, OptimizerWrapper): - optimizer = GeminiOptimizer(model.unwrap(), - optimizer, - self.zero_optim_config, - self.optim_kwargs, + optimizer = GeminiOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs, self.verbose) return model, optimizer, criterion, dataloader, lr_scheduler diff --git a/colossalai/zero/gemini/chunk/search_utils.py b/colossalai/zero/gemini/chunk/search_utils.py index 881ceb0b3b97..6c3d4f9a1b41 100644 --- a/colossalai/zero/gemini/chunk/search_utils.py +++ b/colossalai/zero/gemini/chunk/search_utils.py @@ -114,9 +114,9 @@ def classify_params_by_dp_degree(param_order: OrderedParamGenerator, def search_chunk_configuration( model: nn.Module, - search_range_mb: float, - search_interval_byte: int, # hidden size is the best value for the interval - min_chunk_size_mb: float = 32, + search_range_m: float, + search_interval: int, # hidden size is the best value for the interval + min_chunk_size_m: float = 32, filter_exlarge_params: bool = True, strict_ddp_flag: bool = False, memstas: Optional[MemStats] = None) -> Tuple[Dict, int, int]: @@ -126,9 +126,9 @@ def search_chunk_configuration( Args: model (nn.Module): torch module - search_range_mb (float): searching range in mega byte. - search_interval_byte (int): searching interval in byte. - min_chunk_size_mb (float, optional): the minimum size of a distributed chunk. + search_range_m (float): searching range divided by 2^20. + search_interval (int): searching interval. + min_chunk_size_m (float, optional): the minimum size of a distributed chunk, divided by 2^20.. filter_exlarge_params (bool, optional): filter extreme large parameters. Defaults to True. strict_ddp_flag (bool, optional): whether to enable the strict ddp mode. all parameters keep replicated in this mode. @@ -145,9 +145,9 @@ def search_chunk_configuration( for p in model.parameters(): param_order.append(p) - search_range_byte = round(search_range_mb * 1024**2) - min_chunk_size_byte = round(min_chunk_size_mb * 1024**2) - assert search_range_byte >= 0 + search_range = round(search_range_m * 1024**2) + min_chunk_size = round(min_chunk_size_m * 1024**2) + assert search_range >= 0 params_dict = classify_params_by_dp_degree(param_order, strict_ddp_flag) size_lcm = np.lcm.reduce(list(params_dict.keys())) @@ -162,7 +162,7 @@ def search_chunk_configuration( total_param_size += group_acc_size # let small parameters keep gathered in CUDA all the time - if group_acc_size < min_chunk_size_byte: + if group_acc_size < min_chunk_size: config_dict[dp_degree] = dict(chunk_size=group_acc_size, keep_gathered=True) else: size_dict[dp_degree] = size_list @@ -170,15 +170,15 @@ def search_chunk_configuration( if filter_exlarge_params: _filter_exlarge_params(model, size_dict) - max_size = min_chunk_size_byte + max_size = min_chunk_size for key in size_dict: max_size = max(max_size, max(size_dict[key])) - start_size = int(math.ceil(max_size / search_interval_byte) * search_interval_byte) + start_size = int(math.ceil(max_size / search_interval) * search_interval) min_chunk_waste = float('+inf') best_chunk_size = start_size - for chunk_size in range(start_size, start_size + search_range_byte + 1, search_interval_byte): + for chunk_size in range(start_size, start_size + search_range + 1, search_interval): temp_waste = 0 for key in size_dict: temp_waste += _get_unused_byte(size_dict[key], chunk_size) diff --git a/colossalai/zero/gemini/chunk/utils.py b/colossalai/zero/gemini/chunk/utils.py index 71242dcd6d49..e98e9cf9c314 100644 --- a/colossalai/zero/gemini/chunk/utils.py +++ b/colossalai/zero/gemini/chunk/utils.py @@ -23,10 +23,10 @@ def init_chunk_manager(model: nn.Module, verbose: bool = False, **kwargs) -> ChunkManager: if hidden_dim: - search_interval_byte = hidden_dim + search_interval = hidden_dim else: - search_interval_byte = 1024 # defaults to 1kb - kwargs["search_interval_byte"] = search_interval_byte + search_interval = 1024 # defaults to 1024 + kwargs["search_interval"] = search_interval dist.barrier() begin = time() @@ -36,13 +36,13 @@ def init_chunk_manager(model: nn.Module, dist.barrier() end = time() span_s = end - begin - mb_size = 1024**2 - total_size /= mb_size - wasted_size /= mb_size + mega_unit = 1024**2 + total_size /= mega_unit + wasted_size /= mega_unit if verbose and dist.get_rank() == 0: print("searching chunk configuration is completed in {:.2f} s.\n".format(span_s), - "used number: {:.2f} MB, wasted number: {:.2f} MB\n".format(total_size, wasted_size), + "used number: {:.2f} * 2^20, wasted number: {:.2f} * 2^20\n".format(total_size, wasted_size), "total wasted percentage is {:.2f}%".format(100 * safe_div(wasted_size, total_size + wasted_size)), sep='', flush=True) diff --git a/colossalai/zero/gemini/gemini_ddp.py b/colossalai/zero/gemini/gemini_ddp.py index 094320c4aff4..08384ee82d0b 100644 --- a/colossalai/zero/gemini/gemini_ddp.py +++ b/colossalai/zero/gemini/gemini_ddp.py @@ -739,9 +739,9 @@ def __init__(self, force_outputs_fp32: bool = False, strict_ddp_mode: bool = False, scatter_after_inference: bool = True, - search_range_mb: int = 32, + search_range_m: int = 32, hidden_dim: Optional[int] = None, - min_chunk_size_mb: float = 32, + min_chunk_size_m: float = 32, memstats: Optional[MemStats] = None, mixed_precision: torch.dtype = torch.float16, verbose: bool = False) -> None: @@ -763,24 +763,24 @@ def __init__(self, placement_policy (str, optional): "cpu", "cuda", "auto". Defaults to "cpu". pin_memory (bool, optional): use pin memory on CPU. Defaults to False. force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False. - search_range_mb (int, optional): chunk size searching range in MegaByte. Defaults to 32. + search_range_m (int, optional): chunk size searching range divided by 2^20. Defaults to 32. hidden_dim (int, optional): the hidden dimension of DNN. Users can provide this argument to speed up searching. If users do not know this argument before training, it is ok. We will use a default value 1024. - min_chunk_size_mb (float, optional): the minimum chunk size in MegaByte. + min_chunk_size_m (float, optional): the minimum chunk size divided by 2^20. If the aggregate size of parameters is still smaller than the minimum chunk size, all parameters will be compacted into one small chunk. memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer. """ # some ugly hotfix for the compatibility with Lightning - if search_range_mb is None: - search_range_mb = 32 + if search_range_m is None: + search_range_m = 32 chunk_manager = init_chunk_manager(model=module, init_device=device, hidden_dim=hidden_dim, - search_range_mb=search_range_mb, - min_chunk_size_mb=min_chunk_size_mb, + search_range_m=search_range_m, + min_chunk_size_m=min_chunk_size_m, strict_ddp_flag=strict_ddp_mode, verbose=verbose) gemini_manager = GeminiManager(placement_policy, chunk_manager, memstats) diff --git a/tests/test_auto_parallel/test_offload/test_perf.py b/tests/test_auto_parallel/test_offload/test_perf.py index 80f134fd85d0..45c22efc4127 100644 --- a/tests/test_auto_parallel/test_offload/test_perf.py +++ b/tests/test_auto_parallel/test_offload/test_perf.py @@ -60,7 +60,7 @@ def exam_fwd_bwd(model_name: str, memory_budget: float, solver_name: str): placement_policy='cpu', pin_memory=True, hidden_dim=8192, - search_range_mb=128) + search_range_m=128) gemini_model = zero_model_wrapper(gemini_model, 3, gemini_config) optim_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True, verbose=True) gemini_optim = zero_optim_wrapper(gemini_model, hybrid_optimizer, optim_config=optim_config) diff --git a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py index 05704acbf7fd..4e3c26c1ba9c 100644 --- a/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py +++ b/tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py @@ -75,7 +75,7 @@ def check_auto_parallel_with_gemini(rank, world_size, port): device=get_current_device(), placement_policy='cpu', pin_memory=True, - search_range_mb=128) + search_range_m=128) post_process_colo_init_ctx(gm, device=get_current_device(), default_pg=dp_process_group) gm = zero_model_wrapper(gm, zero_stage=3, gemini_config=gemini_config) diff --git a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py index 994412bbc63f..14d69cab2176 100644 --- a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py @@ -30,7 +30,7 @@ def exam_state_dict_with_origin(placement_policy, model_name, use_safetensors: b bert_model.config.save_pretrained(save_directory=pretrained_path) # TODO(ver217): use boost api - config_dict, *_ = search_chunk_configuration(bert_model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(bert_model, search_range_m=1, search_interval=100) chunk_manager = ChunkManager(config_dict) gemini_manager = GeminiManager(placement_policy, chunk_manager) bert_model = ZeroDDP(bert_model, gemini_manager) diff --git a/tests/test_tensor/test_tp_with_zero.py b/tests/test_tensor/test_tp_with_zero.py index c636d9442902..539806cb196a 100644 --- a/tests/test_tensor/test_tp_with_zero.py +++ b/tests/test_tensor/test_tp_with_zero.py @@ -79,7 +79,7 @@ def run_gpt(placement_policy, tp_init_spec_func=None): tp_init_spec_func(model, pg) dp_world_size = pg.dp_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[dp_world_size]['chunk_size'] = 5000 config_dict[dp_world_size]['keep_gathered'] = False if placement_policy != 'cuda': diff --git a/tests/test_zero/test_gemini/test_fwd_bwd.py b/tests/test_zero/test_gemini/test_fwd_bwd.py index f2cbb7fb77d6..9c5455b8371b 100644 --- a/tests/test_zero/test_gemini/test_fwd_bwd.py +++ b/tests/test_zero/test_gemini/test_fwd_bwd.py @@ -52,7 +52,7 @@ def exam_gpt_fwd_bwd( torch_p.data.copy_(p.data) world_size = torch.distributed.get_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[world_size]['chunk_size'] = 5000 config_dict[world_size]['keep_gathered'] = keep_gather chunk_manager = ChunkManager(config_dict) @@ -113,7 +113,7 @@ def exam_gpt_inference( torch_p.data.copy_(p.data) world_size = torch.distributed.get_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[world_size]['chunk_size'] = 5000 config_dict[world_size]['keep_gathered'] = keep_gather chunk_manager = ChunkManager(config_dict) diff --git a/tests/test_zero/test_gemini/test_gemini_use_rmt.py b/tests/test_zero/test_gemini/test_gemini_use_rmt.py index dd580976d8ea..00e712050b32 100644 --- a/tests/test_zero/test_gemini/test_gemini_use_rmt.py +++ b/tests/test_zero/test_gemini/test_gemini_use_rmt.py @@ -56,7 +56,7 @@ def run_gemini_use_rmt(placement_policy, keep_gather, model_name: str, use_grad_ assert len(step_list) == 4 world_size = torch.distributed.get_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[world_size]['chunk_size'] = 5000 config_dict[world_size]['keep_gathered'] = keep_gather chunk_manager = ChunkManager(config_dict) diff --git a/tests/test_zero/test_gemini/test_grad_clip.py b/tests/test_zero/test_gemini/test_grad_clip.py index 38b6e474ea98..ac19a27f4a37 100644 --- a/tests/test_zero/test_gemini/test_grad_clip.py +++ b/tests/test_zero/test_gemini/test_grad_clip.py @@ -51,7 +51,7 @@ def exam_grad_clipping(placement_policy, model_name: str): p.data.copy_(torch_p.data) world_size = torch.distributed.get_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[world_size]['chunk_size'] = 5000 config_dict[world_size]['keep_gathered'] = False if placement_policy != 'cuda': diff --git a/tests/test_zero/test_gemini/test_inference.py b/tests/test_zero/test_gemini/test_inference.py index 790a0611c9dd..fb2018f7b477 100644 --- a/tests/test_zero/test_gemini/test_inference.py +++ b/tests/test_zero/test_gemini/test_inference.py @@ -34,7 +34,7 @@ def check_param(model: ZeroDDP, torch_model: torch.nn.Module): def multi_chunk_init(model: torch.nn.Module, placement_policy: str): world_size = dist.get_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[world_size]['chunk_size'] = 5000 config_dict[world_size]['keep_gathered'] = False if placement_policy != 'cuda': diff --git a/tests/test_zero/test_gemini/test_optim.py b/tests/test_zero/test_gemini/test_optim.py index 66611bcd2419..a9ee67368e9d 100644 --- a/tests/test_zero/test_gemini/test_optim.py +++ b/tests/test_zero/test_gemini/test_optim.py @@ -73,7 +73,7 @@ def exam_model_step(placement_policy, model_name: str, mixed_precision: torch.dt p.data.copy_(torch_p.data) world_size = torch.distributed.get_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[world_size]['chunk_size'] = 5000 config_dict[world_size]['keep_gathered'] = False if placement_policy != 'cuda': @@ -130,7 +130,7 @@ def exam_tiny_example(placement_policy, model_name: str, mixed_precision: torch. for torch_p, p in zip(torch_model.parameters(), model.parameters()): p.data.copy_(torch_p.data) - chunk_manager = init_chunk_manager(model=model, init_device=get_current_device(), search_range_mb=1) + chunk_manager = init_chunk_manager(model=model, init_device=get_current_device(), search_range_m=1) gemini_manager = GeminiManager(placement_policy, chunk_manager) model = ZeroDDP(model, gemini_manager, pin_memory=True, mixed_precision=mixed_precision) optimizer = HybridAdam(model.parameters(), lr=1e-3) diff --git a/tests/test_zero/test_gemini/test_search.py b/tests/test_zero/test_gemini/test_search.py index 35b3b93ade0c..51dd84aace5b 100644 --- a/tests/test_zero/test_gemini/test_search.py +++ b/tests/test_zero/test_gemini/test_search.py @@ -30,9 +30,9 @@ def exam_search_chunk_size(): model = model_builder() init_1d_row_spec(model, pg_tp) config_dict, *_ = search_chunk_configuration(model, - search_range_mb=1, - search_interval_byte=16, - min_chunk_size_mb=0, + search_range_m=1, + search_interval=16, + min_chunk_size_m=0, filter_exlarge_params=True) for key in config_dict: @@ -54,9 +54,9 @@ def exam_search_strict_ddp(): with ColoInitContext(device=get_current_device()): ddp_model = model_builder() re_dict, re_total, re_wasted = search_chunk_configuration(ddp_model, - search_range_mb=1, - search_interval_byte=16, - min_chunk_size_mb=0, + search_range_m=1, + search_interval=16, + min_chunk_size_m=0, filter_exlarge_params=True, strict_ddp_flag=False) # get the chunk configuration over sharded ddp models @@ -64,9 +64,9 @@ def exam_search_strict_ddp(): default_dist_spec=default_shard_spec): sharded_ddp_model = model_builder() sh_dict, sh_total, sh_wasted = search_chunk_configuration(sharded_ddp_model, - search_range_mb=1, - search_interval_byte=16, - min_chunk_size_mb=0, + search_range_m=1, + search_interval=16, + min_chunk_size_m=0, filter_exlarge_params=True, strict_ddp_flag=True) assert re_dict == sh_dict @@ -91,8 +91,8 @@ def exam_chunk_manager(): chunk_manager = init_chunk_manager(sharded_ddp_model, get_current_device(), hidden_dim=16, - search_range_mb=1, - min_chunk_size_mb=0, + search_range_m=1, + min_chunk_size_m=0, filter_exlarge_params=True, strict_ddp_flag=True) config_dict = chunk_manager.dp_degree_chunk_size_dict diff --git a/tests/test_zero/test_gemini/test_zeroddp_state_dict.py b/tests/test_zero/test_gemini/test_zeroddp_state_dict.py index 66e05f3ed1ec..2a5a4ab83029 100644 --- a/tests/test_zero/test_gemini/test_zeroddp_state_dict.py +++ b/tests/test_zero/test_gemini/test_zeroddp_state_dict.py @@ -35,7 +35,7 @@ def exam_state_dict(placement_policy, keep_gathered, model_name: str): torch_p.data.copy_(p.data) world_size = torch.distributed.get_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[world_size]['chunk_size'] = 5000 config_dict[world_size]['keep_gathered'] = keep_gathered chunk_manager = ChunkManager(config_dict) @@ -67,7 +67,7 @@ def exam_load_state_dict(placement_policy, keep_gathered, model_name: str): torch_model = model_builder() # get a different model world_size = torch.distributed.get_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[world_size]['chunk_size'] = 5000 config_dict[world_size]['keep_gathered'] = keep_gathered diff --git a/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py b/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py index ad7d3a5a4859..d16bfb7d1622 100644 --- a/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py +++ b/tests/test_zero/test_gemini/test_zeroddp_state_dict_shard.py @@ -22,7 +22,7 @@ def exam_state_dict(placement_policy, model_name: str): model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2 - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) chunk_manager = ChunkManager(config_dict) gemini_manager = GeminiManager(placement_policy, chunk_manager) model = ZeroDDP(model, gemini_manager) @@ -38,6 +38,7 @@ def exam_state_dict(placement_policy, model_name: str): assert key in zero_dict, f"{key} not in ZeRO dictionary." assert torch.equal(value, zero_dict[key]), f"{key} not equal." + def run_dist(rank, world_size, port): config = {} colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') diff --git a/tests/test_zero/test_gemini/test_zerooptim_state_dict.py b/tests/test_zero/test_gemini/test_zerooptim_state_dict.py index a8af176c5b3d..ba016d6528dc 100644 --- a/tests/test_zero/test_gemini/test_zerooptim_state_dict.py +++ b/tests/test_zero/test_gemini/test_zerooptim_state_dict.py @@ -27,7 +27,7 @@ def exam_zero_optim_state_dict(placement_policy, keep_gathered): torch_model = model_builder() # get a different model world_size = torch.distributed.get_world_size() - config_dict, *_ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100) + config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100) config_dict[world_size]['chunk_size'] = 5000 config_dict[world_size]['keep_gathered'] = keep_gathered From 153b957a1b5ba728528069b678c3cd30592ca912 Mon Sep 17 00:00:00 2001 From: Wenhao Chen Date: Sun, 25 Jun 2023 17:36:21 +0800 Subject: [PATCH 332/413] [chat] refactor strategy class with booster api (#3987) * refactor: adapt boost API in base and naive strategies * fix: initialize plugin after setup_distributed * fix: fix save_pretrained fn * refactor: adapt boost API in DDPStrategy * to: add _post_init check * to: fix ddp backward, modify ddp dataloader and unwrap * feat: adapt boost API in ColossalAIStrategy * fix: call setup_distributed before use get_current_device * fix: fix save_model and save_optimizer * test: remove save_sharded_optimizer test * style: apply formatter * fix: fix stage check and add comments * feat: allow dict type arg in strategy.prepare * to: temporarily remove lr_scheduler for testing * style: simplify init of ColossalAIStrategy * fix: fix lr_scheduler in sft and rm * style: modify comments * test: add train_prompts tests * fix: fix inference only case and use in train_prompts * test: skip failed tests in ci * style: fix CodeFactor check * fix: do not use model.to('cpu') with GeminiPlugin * test: enable colossalai_gemini tests * test: set CUDA_VISIBLE_DEVICES in ci * docs: add note --- .../benchmarks/benchmark_opt_lora_dummy.py | 6 +- applications/Chat/coati/trainer/ppo.py | 9 +- applications/Chat/coati/trainer/rm.py | 20 +- applications/Chat/coati/trainer/sft.py | 20 +- .../Chat/coati/trainer/strategies/base.py | 110 ++++++----- .../coati/trainer/strategies/colossalai.py | 150 +++++++-------- .../Chat/coati/trainer/strategies/ddp.py | 59 +++--- .../Chat/coati/trainer/strategies/naive.py | 42 +--- applications/Chat/examples/test_ci.sh | 181 +++++++++++------- applications/Chat/examples/train_prompts.py | 8 +- .../Chat/examples/train_reward_model.py | 10 +- applications/Chat/examples/train_sft.py | 16 +- applications/Chat/tests/test_checkpoint.py | 11 +- 13 files changed, 351 insertions(+), 291 deletions(-) diff --git a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py index 7a47624f74d8..dea7ebc60a8b 100644 --- a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py +++ b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py @@ -19,8 +19,10 @@ def get_model_numel(model: nn.Module, strategy: Strategy) -> int: numel = sum(p.numel() for p in model.parameters()) - if isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3 and strategy.shard_init: - numel *= dist.get_world_size() + if isinstance(strategy, ColossalAIStrategy): + from colossalai.booster.plugin import GeminiPlugin + if isinstance(strategy.plugin, GeminiPlugin) and strategy.shard_init: + numel *= dist.get_world_size() return numel diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index e2e44e62533e..cfb18e2ae483 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -17,7 +17,7 @@ from .base import Trainer from .callbacks import Callback -from .strategies import Strategy +from .strategies import ColossalAIStrategy, Strategy from .utils import is_rank_0, to_device @@ -71,6 +71,11 @@ def __init__(self, offload_inference_models: bool = True, callbacks: List[Callback] = [], **generate_kwargs) -> None: + if isinstance(strategy, ColossalAIStrategy): + from colossalai.booster.plugin import GeminiPlugin + assert not (isinstance(strategy.plugin, GeminiPlugin) and offload_inference_models), \ + "GeminiPlugin is not compatible with manual model.to('cpu')" + experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef) replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload) generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor) @@ -105,6 +110,8 @@ def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experien def _learn(self): # replay buffer may be empty at first, we should rebuild at each training if not self.sample_replay_buffer: + # HACK(cwher): according to the design of boost API, dataloader should also be boosted, + # but it is impractical to adapt this pattern in RL training. Thus, I left dataloader unboosted. dataloader = self.strategy.setup_dataloader(self.replay_buffer, self.dataloader_pin_memory) if self.sample_replay_buffer: pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) diff --git a/applications/Chat/coati/trainer/rm.py b/applications/Chat/coati/trainer/rm.py index cdae5108ab00..316eded7ea5d 100644 --- a/applications/Chat/coati/trainer/rm.py +++ b/applications/Chat/coati/trainer/rm.py @@ -1,13 +1,12 @@ from datetime import datetime -from typing import List, Optional +from typing import Callable, List import pandas as pd import torch -import torch.distributed as dist -from torch.optim import Optimizer, lr_scheduler -from torch.utils.data import DataLoader, Dataset, DistributedSampler +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler +from torch.utils.data import DataLoader from tqdm import tqdm -from transformers.tokenization_utils_base import PreTrainedTokenizerBase from .base import Trainer from .callbacks import Callback @@ -22,7 +21,8 @@ class RewardModelTrainer(Trainer): Args: model (torch.nn.Module): the model to train strategy (Strategy): the strategy to use for training - optim(Optimizer): the optimizer to use for training + optim (Optimizer): the optimizer to use for training + lr_scheduler (_LRScheduler): the lr scheduler to use for training loss_fn (callable): the loss function to use for training train_dataloader (DataLoader): the dataloader to use for training valid_dataloader (DataLoader): the dataloader to use for validation @@ -37,7 +37,8 @@ def __init__( model, strategy: Strategy, optim: Optimizer, - loss_fn, + lr_scheduler: _LRScheduler, + loss_fn: Callable, train_dataloader: DataLoader, valid_dataloader: DataLoader, eval_dataloader: DataLoader, @@ -53,7 +54,7 @@ def __init__( self.model = model self.loss_fn = loss_fn self.optimizer = optim - self.scheduler = lr_scheduler.CosineAnnealingLR(self.optimizer, self.train_dataloader.__len__() // 100) + self.scheduler = lr_scheduler def eval_acc(self, dataloader): dist = 0 @@ -116,7 +117,8 @@ def fit(self): # eval dist, acc = self.eval_acc(self.eval_dataloader) if is_rank_0(): - log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], columns=['step', 'loss', 'dist', 'acc']) + log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], + columns=['step', 'loss', 'dist', 'acc']) log.to_csv('log.csv', mode='a', header=False, index=False) epoch_bar.update() step_bar.set_postfix({'dist': dist, 'acc': acc}) diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index 63fde53956cc..da223f1f33ff 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -1,15 +1,13 @@ -import math import time -from typing import List, Optional +from typing import List import torch import torch.distributed as dist import wandb from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler from torch.utils.data import DataLoader from tqdm import tqdm -from transformers.tokenization_utils_base import PreTrainedTokenizerBase -from transformers.trainer import get_scheduler from .base import Trainer from .callbacks import Callback @@ -38,14 +36,17 @@ def __init__( model, strategy: Strategy, optim: Optimizer, + lr_scheduler: _LRScheduler, train_dataloader: DataLoader, eval_dataloader: DataLoader = None, max_epochs: int = 2, accumulation_steps: int = 8, callbacks: List[Callback] = [], ) -> None: - if accumulation_steps > 1 and isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3: - raise ValueError("Accumulation steps are not supported in stage 3 of ColossalAI") + if accumulation_steps > 1 and isinstance(strategy, ColossalAIStrategy): + from colossalai.booster.plugin import GeminiPlugin + assert not isinstance(strategy.plugin, GeminiPlugin), \ + "Accumulation steps are not supported in stage 3 of ColossalAI" super().__init__(strategy, max_epochs, callbacks=callbacks) self.train_dataloader = train_dataloader self.eval_dataloader = eval_dataloader @@ -53,13 +54,8 @@ def __init__( self.optimizer = optim self.accumulation_steps = accumulation_steps - num_update_steps_per_epoch = len(train_dataloader) // self.accumulation_steps - max_steps = math.ceil(self.max_epochs * num_update_steps_per_epoch) - self.scheduler = get_scheduler("cosine", - self.optimizer, - num_warmup_steps=math.ceil(max_steps * 0.03), - num_training_steps=max_steps) + self.scheduler = lr_scheduler def fit(self, logger, use_wandb: bool = False): if use_wandb: diff --git a/applications/Chat/coati/trainer/strategies/base.py b/applications/Chat/coati/trainer/strategies/base.py index 06f81f21ab26..80bc3272872e 100644 --- a/applications/Chat/coati/trainer/strategies/base.py +++ b/applications/Chat/coati/trainer/strategies/base.py @@ -1,6 +1,6 @@ from abc import ABC, abstractmethod from contextlib import nullcontext -from typing import Any, List, Optional, Tuple, Union +from typing import Callable, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn @@ -9,10 +9,12 @@ from torch.utils.data import DataLoader from transformers.tokenization_utils_base import PreTrainedTokenizerBase +from colossalai.booster import Booster +from colossalai.booster.plugin import Plugin + from .sampler import DistributedSampler -ModelOptimPair = Tuple[nn.Module, Optimizer] -ModelOrModelOptimPair = Union[nn.Module, ModelOptimPair] +_BoostArgSpec = Union[nn.Module, Tuple[nn.Module, Optimizer], Dict] class Strategy(ABC): @@ -20,30 +22,28 @@ class Strategy(ABC): Base class for training strategies. """ - def __init__(self) -> None: + def __init__(self, plugin_initializer: Callable[..., Optional[Plugin]] = lambda: None) -> None: super().__init__() + # NOTE: dist must be initialized before Booster self.setup_distributed() + self.plugin = plugin_initializer() + self.booster = Booster(plugin=self.plugin) + self._post_init() @abstractmethod - def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: Optimizer, **kwargs) -> None: + def _post_init(self) -> None: pass - @abstractmethod + def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: Optimizer, **kwargs) -> None: + self.booster.backward(loss, optimizer) + def optimizer_step(self, optimizer: Optimizer, **kwargs) -> None: - pass + optimizer.step() @abstractmethod def setup_distributed(self) -> None: pass - @abstractmethod - def setup_model(self, model: nn.Module) -> nn.Module: - pass - - @abstractmethod - def setup_optimizer(self, optimizer: Optimizer, model: nn.Module) -> Optimizer: - pass - @abstractmethod def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: pass @@ -51,12 +51,13 @@ def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False def model_init_context(self): return nullcontext() - def prepare( - self, *models_or_model_optim_pairs: ModelOrModelOptimPair - ) -> Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]: - """Prepare models or model-optimizer-pairs based on each strategy. + def prepare(self, *boost_args: _BoostArgSpec) -> Union[List[_BoostArgSpec], _BoostArgSpec]: + """Prepare [model | (model, optimizer) | Dict] based on each strategy. + NOTE: the keys of Dict must be a subset of `self.booster.boost`'s arguments. Example:: + >>> # e.g., include lr_scheduler + >>> result_dict = strategy.prepare(dict(model=model, lr_scheduler=lr_scheduler)) >>> # when fine-tuning actor and critic >>> (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare((actor, actor_optim), (critic, critic_optim), reward_model, initial_model) >>> # or when training reward model @@ -65,25 +66,39 @@ def prepare( >>> actor, critic = strategy.prepare(actor, critic) Returns: - Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]: Models or model-optimizer-pairs in the original order. + Union[List[_BoostArgSpec], _BoostArgSpec]: [model | (model, optimizer) | Dict] in the original order. """ rets = [] - for arg in models_or_model_optim_pairs: - if isinstance(arg, tuple): - assert len(arg) == 2, f'Expect (model, optimizer) pair, got a tuple with size "{len(arg)}"' - model, optimizer = arg - model = self.setup_model(model) - optimizer = self.setup_optimizer(optimizer, model) + for arg in boost_args: + if isinstance(arg, nn.Module): + model, *_ = self.booster.boost(arg) + rets.append(model) + elif isinstance(arg, tuple): + try: + model, optimizer = arg + except ValueError: + raise RuntimeError(f'Expect (model, optimizer) pair, got a tuple with size "{len(arg)}"') + model, optimizer, *_ = self.booster.boost(model=model, + optimizer=optimizer) rets.append((model, optimizer)) - elif isinstance(arg, nn.Module): - rets.append(self.setup_model(model)) + elif isinstance(arg, Dict): + model, optimizer, criterion, dataloader, lr_scheduler = self.booster.boost(**arg) + boost_result = dict(model=model, + optimizer=optimizer, + criterion=criterion, + dataloader=dataloader, + lr_scheduler=lr_scheduler) + # remove None values + boost_result = { + key: value + for key, value in boost_result.items() if value is not None + } + rets.append(boost_result) else: - raise RuntimeError(f'Expect model or (model, optimizer) pair, got {type(arg)}') + raise RuntimeError(f'Type {type(arg)} is not supported') - if len(rets) == 1: - return rets[0] - return rets + return rets[0] if len(rets) == 1 else rets @staticmethod def unwrap_model(model: nn.Module) -> nn.Module: @@ -97,23 +112,30 @@ def unwrap_model(model: nn.Module) -> nn.Module: """ return model - @abstractmethod - def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: - pass + def save_model(self, + model: nn.Module, + path: str, + only_rank0: bool = True, + **kwargs + ) -> None: + self.booster.save_model(model, path, shard=not only_rank0, **kwargs) - @abstractmethod - def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None: - pass + def load_model(self, model: nn.Module, path: str, strict: bool = True) -> None: + self.booster.load_model(model, path, strict) - @abstractmethod - def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: - pass + def save_optimizer(self, + optimizer: Optimizer, + path: str, + only_rank0: bool = False, + **kwargs + ) -> None: + self.booster.save_optimizer(optimizer, path, shard=not only_rank0, **kwargs) - @abstractmethod - def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None: - pass + def load_optimizer(self, optimizer: Optimizer, path: str) -> None: + self.booster.load_optimizer(optimizer, path) def setup_sampler(self, dataset) -> DistributedSampler: + # FIXME(cwher): this is only invoked in train_on_ray, not tested after adapt Boost API. return DistributedSampler(dataset, 1, 0) @abstractmethod diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index cfdab2806a25..8c9b8ac0334b 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -1,24 +1,23 @@ +import functools import warnings -from typing import Optional, Union +from typing import Optional import torch import torch.distributed as dist import torch.nn as nn -import torch.optim as optim -from torch.optim import Optimizer from transformers.tokenization_utils_base import PreTrainedTokenizerBase import colossalai -from colossalai.logging import get_dist_logger -from colossalai.nn.optimizer import CPUAdam, HybridAdam +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin +from colossalai.booster.plugin.gemini_plugin import GeminiModel +from colossalai.booster.plugin.low_level_zero_plugin import LowLevelZeroModel from colossalai.tensor import ProcessGroup, ShardSpec from colossalai.utils import get_current_device -from colossalai.zero import ColoInitContext, ZeroDDP, zero_model_wrapper, zero_optim_wrapper +from colossalai.zero import ColoInitContext +from colossalai.zero.gemini.gemini_ddp import GeminiDDP from .ddp import DDPStrategy -logger = get_dist_logger(__name__) - class ColossalAIStrategy(DDPStrategy): """ @@ -62,7 +61,6 @@ def __init__( placement_policy: str = 'cuda', pin_memory: bool = True, # only for stage 3 force_outputs_fp32: bool = False, # only for stage 3 - scatter_after_inference: bool = False, # only for stage 3 search_range_mb: int = 32, # only for stage 3 hidden_dim: Optional[int] = None, # only for stage 3 min_chunk_size_mb: float = 32, # only for stage 3 @@ -78,50 +76,76 @@ def __init__( max_scale: float = 2**32, max_norm: float = 0.0, norm_type: float = 2.0) -> None: - super().__init__(seed) + + assert stage in (1, 2, 3), f'Unsupported stage "{stage}"' assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy "{placement_policy}"' assert precision in ('fp32', 'fp16'), f'Unsupported precision "{precision}"' - self.stage = stage + # TODO(ver217): support shard_init when using from_pretrained() if shard_init: warnings.warn( - f'Shard init is not supported model.from_pretrained() yet. Please load weights after strategy.prepare()' + f'Shard init is not supported model.from_pretrained() yet. ' + 'Please load weights after strategy.prepare()' ) if stage == 3 and precision == 'fp32': warnings.warn(f'Stage 3 only supports fp16. Precision is set to fp16.') precision = 'fp16' self.precision = precision self.shard_init = shard_init - self.gemini_config = dict(device=get_current_device(), - placement_policy=placement_policy, - pin_memory=pin_memory, - force_outputs_fp32=force_outputs_fp32, - strict_ddp_mode=shard_init, - search_range_mb=search_range_mb, - hidden_dim=hidden_dim, - min_chunk_size_mb=min_chunk_size_mb, - scatter_after_inference=scatter_after_inference) + + optim_kwargs = dict( + initial_scale=initial_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + min_scale=min_scale, + max_scale=max_scale, + max_norm=max_norm, + norm_type=norm_type + ) + # NOTE: dist should be initialized before calling get_current_device() if stage == 3: - self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio) + plugin_initializer = lambda: GeminiPlugin( + # gemini_config + device=get_current_device(), + placement_policy=placement_policy, + precision=precision, + pin_memory=pin_memory, + force_outputs_fp32=force_outputs_fp32, + strict_ddp_mode=shard_init, + search_range_mb=search_range_mb, + hidden_dim=hidden_dim, + min_chunk_size_mb=min_chunk_size_mb, + # zero_optim_config + gpu_margin_mem_ratio=gpu_margin_mem_ratio, + # optim_config + **optim_kwargs + ) else: - self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size, - overlap_communication=overlap_communication, - cpu_offload=(placement_policy == 'cpu')) - self.optim_kwargs = dict(initial_scale=initial_scale, - growth_factor=growth_factor, - backoff_factor=backoff_factor, - growth_interval=growth_interval, - hysteresis=hysteresis, - min_scale=min_scale, - max_scale=max_scale, - max_norm=max_norm, - norm_type=norm_type) + plugin_initializer = lambda: LowLevelZeroPlugin( + # zero_config + stage=stage, + precision=precision, + # zero_optim_config + reduce_bucket_size_in_m=reduce_bucket_size, + overlap_communication=overlap_communication, + cpu_offload=(placement_policy == 'cpu'), + # optim_config + **optim_kwargs + ) + + super().__init__(seed, plugin_initializer) + + def _post_init(self) -> None: + assert isinstance(self.plugin, (LowLevelZeroPlugin, GeminiPlugin)), \ + f'{type(self).__name__}\'s plugin is not initialized properly.' def setup_distributed(self) -> None: colossalai.launch_from_torch({}, seed=self.seed) def model_init_context(self): - if self.stage == 3: + if isinstance(self.plugin, GeminiPlugin): world_size = dist.get_world_size() shard_pg = ProcessGroup(tp_degree=world_size) if self.shard_init else None default_dist_spec = ShardSpec([-1], [world_size]) if self.shard_init else None @@ -131,61 +155,29 @@ def model_init_context(self): default_dist_spec=default_dist_spec) return super().model_init_context() - def setup_model(self, model: nn.Module) -> nn.Module: - - model = zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config) - - if self.stage != 3 and self.precision == 'fp16': - model = model.half().cuda() - return model - - def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer: - assert isinstance(optimizer, (CPUAdam, HybridAdam)), f'Unsupported optimizer {type(optimizer)}' - return zero_optim_wrapper(model, optimizer, optim_config=self.zero_optim_config, **self.optim_kwargs) - - def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None: - optimizer.backward(loss) - - def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: - optimizer.step() - - def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: - if only_rank0 and dist.get_rank() != 0 and self.stage != 3: - return - if self.stage == 3: - assert isinstance(model, ZeroDDP) - # for stage 3, state_dict() method should be called on every rank - state_dict = model.state_dict(only_rank_0=only_rank0) - else: - # only_rank0 is false or rank == 0 - state_dict = model.state_dict() - if only_rank0 and dist.get_rank() != 0: - return - torch.save(state_dict, path) - - def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: - if only_rank0: - raise RuntimeError( - f'Optimizer states are sharded when using ColossalAIStrategy. Only rank0 is not supported.') - torch.save(optimizer.state_dict(), path) - def unwrap_model(self, model: nn.Module) -> nn.Module: - if self.stage == 3: - assert isinstance(model, ZeroDDP) + if isinstance(self.plugin, GeminiPlugin): + assert isinstance(model, GeminiModel) + ddp_model = model.unwrap() + assert isinstance(ddp_model, GeminiDDP) + return ddp_model.module + elif isinstance(self.plugin, LowLevelZeroPlugin): + assert isinstance(model, LowLevelZeroModel) return model.module - return model + else: + raise RuntimeError(f'Unsupported plugin {type(self.plugin)}') def save_pretrained(self, model: nn.Module, path: str, only_rank0: bool = True, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - if self.stage == 3: + if isinstance(self.plugin, GeminiPlugin): raise RuntimeError('ColossalAI strategy with stage-3 does not support save_pretrained() now') super().save_pretrained(model, path, only_rank0, tokenizer) def get_model_state_dict_shard(self, model: nn.Module, **config): - if self.stage != 3: + if not isinstance(self.plugin, GeminiPlugin): yield from super().get_model_state_dict_shard(model, **config) else: # unwrapped_model = self._unwrap_model(model) @@ -193,5 +185,5 @@ def get_model_state_dict_shard(self, model: nn.Module, **config): # if isinstance(module, LoraLinear): # module.merge_weights = True # module.eval() - assert isinstance(model, ZeroDDP) + assert isinstance(model, LowLevelZeroModel) yield from model.state_dict_shard(max_shard_size=1024, only_rank_0=False) diff --git a/applications/Chat/coati/trainer/strategies/ddp.py b/applications/Chat/coati/trainer/strategies/ddp.py index 713d7b90c6f0..42867645290c 100644 --- a/applications/Chat/coati/trainer/strategies/ddp.py +++ b/applications/Chat/coati/trainer/strategies/ddp.py @@ -1,17 +1,18 @@ -import os import random -from typing import Optional +from typing import Callable, Optional import numpy as np import torch import torch.distributed as dist import torch.nn as nn from coati.replay_buffer import ReplayBuffer -from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import Optimizer from torch.utils.data import DataLoader from transformers.tokenization_utils_base import PreTrainedTokenizerBase +from colossalai.booster.plugin import TorchDDPPlugin +from colossalai.booster.plugin.torch_ddp_plugin import TorchDDPModel + from .naive import NaiveStrategy from .sampler import DistributedSampler @@ -21,9 +22,16 @@ class DDPStrategy(NaiveStrategy): Strategy for distributed training using torch.distributed. """ - def __init__(self, seed: int = 42) -> None: + def __init__(self, + seed: int = 42, + plugin_initializer: Callable = TorchDDPPlugin + ) -> None: self.seed = seed - super().__init__() + super().__init__(plugin_initializer) + + def _post_init(self) -> None: + assert isinstance(self.plugin, TorchDDPPlugin), \ + f'{type(self).__name__}\'s plugin is not initialized properly.' def setup_distributed(self) -> None: self._try_init_dist(force=True) @@ -34,43 +42,24 @@ def set_seed(self, seed: int) -> None: np.random.seed(seed) torch.manual_seed(seed) - def setup_model(self, model: nn.Module) -> nn.Module: - device = torch.cuda.current_device() - return DDP(model, device_ids=[device]) + def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: Optimizer, **kwargs) -> None: + self.booster.backward(loss, optimizer) def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: - # DDP only mode, replay buffers on each rank are different. - # sampler = DistributedSampler(replay_buffer, - # num_replicas=dist.get_world_size(), - # rank=dist.get_rank(), - # shuffle=True, - # seed=self.seed, - # drop_last=True) - return DataLoader( - replay_buffer, - batch_size=replay_buffer.sample_batch_size, - # sampler=sampler, - shuffle=True, - drop_last=True, - pin_memory=pin_memory, - collate_fn=replay_buffer.collate_fn) - - def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: - if only_rank0 and dist.get_rank() != 0: - return - super().save_model(model, path, only_rank0) - - def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: - if only_rank0 and dist.get_rank() != 0: - return - super().save_optimizer(optimizer, path, only_rank0) + return self.plugin.prepare_dataloader(replay_buffer, + batch_size=replay_buffer.sample_batch_size, + shuffle=True, + drop_last=True, + pin_memory=pin_memory, + collate_fn=replay_buffer.collate_fn) def setup_sampler(self, dataset) -> DistributedSampler: + # FIXME(cwher): this is only invoked in train_on_ray, not tested after adapt Boost API. return DistributedSampler(dataset, dist.get_world_size(), dist.get_rank()) def unwrap_model(self, model: nn.Module) -> nn.Module: - assert isinstance(model, DDP) - return model.module + assert isinstance(model, TorchDDPModel), "model is not wrapped by TorchDDPModel." + return model.unwrap() def save_pretrained(self, model: nn.Module, diff --git a/applications/Chat/coati/trainer/strategies/naive.py b/applications/Chat/coati/trainer/strategies/naive.py index 202c480e06d9..d121237a68ea 100644 --- a/applications/Chat/coati/trainer/strategies/naive.py +++ b/applications/Chat/coati/trainer/strategies/naive.py @@ -1,16 +1,10 @@ import os -import sys from collections import OrderedDict -from typing import Any, Dict, Optional +from typing import Optional import torch import torch.distributed as dist import torch.nn as nn -import torch.optim as optim -from coati.models.base import get_base_model -from coati.replay_buffer import ReplayBuffer -from coati.models.base import RewardModel -from coati.models.lora import LoraLinear from coati.replay_buffer import ReplayBuffer from torch.optim import Optimizer from torch.utils.data import DataLoader @@ -34,20 +28,18 @@ class NaiveStrategy(Strategy): Strategy for single GPU. No parallelism is used. """ - def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None: - loss.backward() - - def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None: - optimizer.step() + def _post_init(self) -> None: + assert self.plugin is None, \ + f'{type(self).__name__}\'s plugin is not initialized properly.' def setup_distributed(self) -> None: self._try_init_dist(force=False) - def setup_model(self, model: nn.Module) -> nn.Module: - return model - - def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer: - return optimizer + def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: Optimizer, **kwargs) -> None: + # HACK: self.booster.backward(loss, optimizer) can't work if plugin is None, + # it would run `optimizer.backward(loss)`, which is not compatible with torch.optim.Optimizer + assert self.plugin is None, "DO NOT call this method if plugin is not None" + loss.backward() def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: return DataLoader(replay_buffer, @@ -57,22 +49,6 @@ def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False pin_memory=pin_memory, collate_fn=replay_buffer.collate_fn) - def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None: - state_dict = model.state_dict() - torch.save(state_dict, path) - - def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None: - unwrapped_model = self.unwrap_model(model) - state_dict = torch.load(path, map_location=map_location) - unwrapped_model.load_state_dict(state_dict, strict=strict) - - def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None: - torch.save(optimizer.state_dict(), path) - - def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None: - state_dict = torch.load(path, map_location=map_location) - optimizer.load_state_dict(state_dict) - def save_pretrained(self, model: nn.Module, path: str, diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index ac3a9b507864..85728e95820c 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -1,5 +1,22 @@ #!/usr/bin/env bash +set_n_least_used_CUDA_VISIBLE_DEVICES() { + local n=${1:-"9999"} + echo "GPU Memory Usage:" + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv | + tail -n +2 | + nl -v 0 | + tee /dev/tty | + sort -g -k 2 | + awk '{print $1}' | + head -n $n) + export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') + echo "Now CUDA_VISIBLE_DEVICES is set to:" + echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" +} + +set_n_least_used_CUDA_VISIBLE_DEVICES 4 + set -xue if [ -z "$SFT_DATASET" ]; then @@ -26,109 +43,137 @@ pip install -r ${BASE}/requirements.txt wandb init -m offline +# FIXME: This is a hack to skip tests that are not working (tested at commit b3ab7fbabf) +# - gpt2-ddp: RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation +# - llama-*: Repository Not Found for url: https://huggingface.co/{...}/resolve/main/tokenizer.model. +# - roberta-*: RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)` +SKIPPED_TESTS=( + "gpt2-ddp" + "llama-naive" "llama-ddp" "llama-colossalai_gemini" "llama-colossalai_zero2" + "roberta-naive" "roberta-ddp" "roberta-colossalai_gemini" "roberta-colossalai_zero2" +) + +# These tests are quick and do not have any dependencies +for model in 'gpt2' 'bloom' 'opt' 'llama' 'roberta'; do + for strategy in 'naive' 'ddp' 'colossalai_gemini' 'colossalai_zero2'; do + if [[ " ${SKIPPED_TESTS[*]} " =~ " ${model}-${strategy} " ]]; then + echo "[Test]: Skipped $model-$strategy" + continue + fi + torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ + --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ + --strategy $strategy --model $model \ + --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2 + done +done + # train sft torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'bigscience/bloom-560m' \ - --model 'bloom' --strategy colossalai_zero2 --lora_rank 4\ - --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ - --save_path ${BASE}/output + --model 'bloom' --strategy colossalai_zero2 --lora_rank 4 \ + --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ + --save_path ${BASE}/output rm -rf ${BASE}/output torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'gpt2' \ - --model 'gpt2' --strategy colossalai_zero2 \ - --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ - --save_path ${BASE}/output + --model 'gpt2' --strategy colossalai_zero2 \ + --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ + --save_path ${BASE}/output rm -rf ${BASE}/output torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'facebook/opt-350m' \ - --model 'opt' --strategy colossalai_zero2 --lora_rank 4\ - --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ - --save_path ${BASE}/output + --model 'opt' --strategy colossalai_zero2 --lora_rank 4 \ + --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ + --save_path ${BASE}/output rm -rf ${BASE}/output torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'gpt2' \ - --model 'gpt2' --strategy ddp --lora_rank 4\ - --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ - --save_path ${BASE}/output + --model 'gpt2' --strategy ddp --lora_rank 4 \ + --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ + --save_path ${BASE}/output -#torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'facebook/opt-350m' \ -# --model 'opt' --strategy naive \ -# --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ -# --save_path ${BASE}/output +# torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'facebook/opt-350m' \ +# --model 'opt' --strategy naive \ +# --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ +# --save_path ${BASE}/output rm -rf ${BASE}/output # train rm torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'facebook/opt-350m' --model 'opt' \ - --strategy colossalai_zero2 --loss_fn 'log_sig'\ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ - --test True --lora_rank 0 \ - --save_path ${BASE}/rm_ckpt_opt.pt + --pretrain 'facebook/opt-350m' --model 'opt' \ + --strategy colossalai_zero2 --loss_fn 'log_sig' \ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ + --test True --lora_rank 0 \ + --save_path ${BASE}/rm_ckpt_opt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'gpt2' --model 'gpt2' \ - --strategy colossalai_zero2 --loss_fn 'log_exp' \ - --dataset 'Dahoas/rm-static' \ - --test True --lora_rank 0 \ - --save_path ${BASE}/rm_ckpt_gpt.pt + --pretrain 'gpt2' --model 'gpt2' \ + --strategy colossalai_zero2 --loss_fn 'log_exp' \ + --dataset 'Dahoas/rm-static' \ + --test True --lora_rank 0 \ + --save_path ${BASE}/rm_ckpt_gpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'gpt2' --model 'gpt2' \ - --strategy ddp --loss_fn 'log_exp' \ - --dataset 'Dahoas/rm-static' \ - --test True --lora_rank 4 \ - --save_path ${BASE}/rm_ckpt.pt + --pretrain 'gpt2' --model 'gpt2' \ + --strategy ddp --loss_fn 'log_exp' \ + --dataset 'Dahoas/rm-static' \ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt.pt rm -rf ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'bigscience/bloom-560m' --model 'bloom' \ - --strategy colossalai_zero2 --loss_fn 'log_sig' \ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ - --test True --lora_rank 4 \ - --save_path ${BASE}/rm_ckpt.pt + --pretrain 'bigscience/bloom-560m' --model 'bloom' \ + --strategy colossalai_zero2 --loss_fn 'log_sig' \ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt.pt rm -rf ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'microsoft/deberta-v3-large' --model 'deberta' \ - --strategy colossalai_zero2 --loss_fn 'log_sig' \ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ - --test True --lora_rank 4 \ - --save_path ${BASE}/rm_ckpt.pt + --pretrain 'microsoft/deberta-v3-large' --model 'deberta' \ + --strategy colossalai_zero2 --loss_fn 'log_sig' \ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt.pt rm -rf ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'roberta-base' --model 'roberta' \ - --strategy colossalai_zero2 --loss_fn 'log_exp'\ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base'\ - --test True --lora_rank 4 \ - --save_path ${BASE}/rm_ckpt.pt + --pretrain 'roberta-base' --model 'roberta' \ + --strategy colossalai_zero2 --loss_fn 'log_exp' \ + --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ + --test True --lora_rank 4 \ + --save_path ${BASE}/rm_ckpt.pt rm -rf ${BASE}/rm_ckpt.pt -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ - --pretrain 'facebook/opt-350m' --model opt \ - --rm_pretrain 'facebook/opt-350m' \ - --rm_path ${BASE}/rm_ckpt_opt.pt \ - --save_path ${BASE}/actor_checkpoint_prompts.pt +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ + --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ + --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ + --pretrain 'facebook/opt-350m' --model opt \ + --rm_pretrain 'facebook/opt-350m' \ + --rm_path ${BASE}/rm_ckpt_opt.pt \ + --save_path ${BASE}/actor_checkpoint_prompts.pt rm -rf ${BASE}/rm_ckpt_opt.pt -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ - --pretrain 'gpt2' --model gpt2 \ - --rm_pretrain 'gpt2' \ - --rm_path ${BASE}/rm_ckpt_gpt.pt \ - --save_path ${BASE}/actor_checkpoint_prompts.pt - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ - --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ - --pretrain 'gpt2' --model gpt2 \ - --rm_pretrain 'gpt2' \ - --rm_path ${BASE}/rm_ckpt_gpt.pt \ - --save_path ${BASE}/actor_checkpoint_prompts.pt +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ + --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ + --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ + --pretrain 'gpt2' --model gpt2 \ + --rm_pretrain 'gpt2' \ + --rm_path ${BASE}/rm_ckpt_gpt.pt \ + --save_path ${BASE}/actor_checkpoint_prompts.pt + +torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ + --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ + --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ + --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ + --pretrain 'gpt2' --model gpt2 \ + --rm_pretrain 'gpt2' \ + --rm_path ${BASE}/rm_ckpt_gpt.pt \ + --save_path ${BASE}/actor_checkpoint_prompts.pt rm -rf ${BASE}/rm_ckpt_gpt.pt rm -rf ${BASE}/actor_checkpoint_prompts.pt diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index 134f21f80ef1..2a47dda637bb 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -1,6 +1,5 @@ import argparse -import pandas as pd import torch import torch.distributed as dist from coati.dataset import DataCollatorForSupervisedDataset, PromptDataset, SupervisedDataset @@ -51,7 +50,7 @@ def main(args): else: raise ValueError(f'Unsupported actor model "{args.model}"') - if args.rm_model == None: + if args.rm_model is None: rm_model_name = args.model else: rm_model_name = args.rm_model @@ -163,7 +162,9 @@ def main(args): batch_size=args.ptx_batch_size, collate_fn=data_collator) - (actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim)) + # NOTE: For small models like opt-1.3b, reward model and initial model are not required to be parallelized. + (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = \ + strategy.prepare((actor, actor_optim), (critic, critic_optim), reward_model, initial_model) # configure trainer trainer = PPOTrainer( @@ -185,6 +186,7 @@ def main(args): top_k=50, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, + offload_inference_models=args.strategy != 'colossalai_gemini' ) trainer.fit(prompt_dataloader=prompt_dataloader, diff --git a/applications/Chat/examples/train_reward_model.py b/applications/Chat/examples/train_reward_model.py index 48b12336fa67..2df3bc391b9b 100644 --- a/applications/Chat/examples/train_reward_model.py +++ b/applications/Chat/examples/train_reward_model.py @@ -18,6 +18,7 @@ from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from torch.optim import Adam +from torch.optim.lr_scheduler import CosineAnnealingLR from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer, LlamaTokenizer, RobertaTokenizer @@ -165,10 +166,17 @@ def train(args): batch_size=args.batch_size, pin_memory=True) - (model, optim) = strategy.prepare((model, optim)) + lr_scheduler = CosineAnnealingLR(optim, train_dataloader.__len__() // 100) + strategy_dict = strategy.prepare( + dict(model=model, optimizer=optim, lr_scheduler=lr_scheduler) + ) + model = strategy_dict['model'] + optim = strategy_dict['optimizer'] + lr_scheduler = strategy_dict['lr_scheduler'] trainer = RewardModelTrainer(model=model, strategy=strategy, optim=optim, + lr_scheduler=lr_scheduler, loss_fn=loss_fn, train_dataloader=train_dataloader, valid_dataloader=valid_dataloader, diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index 7fcd026fb538..717eb95311fb 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -1,4 +1,5 @@ import argparse +import math import os import loralib as lora @@ -19,6 +20,7 @@ from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer from transformers.models.opt.configuration_opt import OPTConfig from transformers.models.opt.modeling_opt import OPTForCausalLM +from transformers.trainer import get_scheduler from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import HybridAdam @@ -152,10 +154,22 @@ def train(args): else: eval_dataloader = None - (model, optim) = strategy.prepare((model, optim)) + num_update_steps_per_epoch = len(train_dataloader) // args.accumulation_steps + max_steps = math.ceil(args.max_epochs * num_update_steps_per_epoch) + lr_scheduler = get_scheduler("cosine", + optim, + num_warmup_steps=math.ceil(max_steps * 0.03), + num_training_steps=max_steps) + strategy_dict = strategy.prepare( + dict(model=model, optimizer=optim, lr_scheduler=lr_scheduler) + ) + model = strategy_dict['model'] + optim = strategy_dict['optimizer'] + lr_scheduler = strategy_dict['lr_scheduler'] trainer = SFTTrainer(model=model, strategy=strategy, optim=optim, + lr_scheduler=lr_scheduler, train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, max_epochs=args.max_epochs, diff --git a/applications/Chat/tests/test_checkpoint.py b/applications/Chat/tests/test_checkpoint.py index d93a5c94d8ea..cfa39e44b476 100644 --- a/applications/Chat/tests/test_checkpoint.py +++ b/applications/Chat/tests/test_checkpoint.py @@ -60,10 +60,15 @@ def run_step(): rank0_dirname = rank0_dirname[0] model_path = os.path.join(rank0_dirname, 'model.pt') - optim_path = os.path.join(rank0_dirname, f'optim-r{dist.get_rank()}.pt') - strategy.save_model(actor, model_path, only_rank0=True) - strategy.save_optimizer(actor_optim, optim_path, only_rank0=False) + + optim_path = os.path.join(rank0_dirname, f'optim.pt') + strategy.save_optimizer(actor_optim, optim_path, only_rank0=True) + + # FIXME(cwher): Sharded optimizer checkpoint is not supported yet. + # at "ColossalAI/colossalai/checkpoint_io/general_checkpoint_io.py", line 62 + # optim_path = os.path.join(rank0_dirname, f'optim-r{dist.get_rank()}.pt') + # strategy.save_optimizer(actor_optim, optim_path, only_rank0=False) dist.barrier() From e89b127d8ec9c14fc34ff9a1208b630069eb026f Mon Sep 17 00:00:00 2001 From: Michelle <97082656+MichelleMa8@users.noreply.github.com> Date: Mon, 26 Jun 2023 15:26:07 +0800 Subject: [PATCH 333/413] [chat]: fix chat evaluation possible bug (#4064) * fix chat eval * fix utils * fix utils * add comment --------- Co-authored-by: Qianran Ma --- applications/Chat/evaluate/metrics.py | 4 ++-- applications/Chat/evaluate/unieval/evaluator.py | 3 ++- applications/Chat/evaluate/utils.py | 13 +------------ 3 files changed, 5 insertions(+), 15 deletions(-) diff --git a/applications/Chat/evaluate/metrics.py b/applications/Chat/evaluate/metrics.py index e220226ec041..77f9b6e98044 100644 --- a/applications/Chat/evaluate/metrics.py +++ b/applications/Chat/evaluate/metrics.py @@ -141,8 +141,8 @@ def distinct_score(preds: List[str], language: str) -> Dict[str, float]: count_segs = len(pred_seg_list) unique_segs = set(pred_seg_list) count_unique_chars = len(unique_segs) - - cumulative_distinct.append(count_unique_chars / count_segs) + # prevent denominator from being 0 + cumulative_distinct.append(count_unique_chars / (count_segs + 1e-6)) elif language == "en": # calculate distinct 1-gram, 2-gram, 3-gram unique_ngram = [set() for _ in range(0, 3)] diff --git a/applications/Chat/evaluate/unieval/evaluator.py b/applications/Chat/evaluate/unieval/evaluator.py index d7f2f87f8c52..56cc6d2f9e41 100644 --- a/applications/Chat/evaluate/unieval/evaluator.py +++ b/applications/Chat/evaluate/unieval/evaluator.py @@ -80,7 +80,8 @@ def evaluate(self, data, category, dims=None, overall=True): start_idx = 0 score = [] for cur_n_sent in n_sents: - score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / cur_n_sent) + # prevent denominator from being 0 + score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / (cur_n_sent + 1e-6)) start_idx += cur_n_sent # Calculate summary-level score for 'coherence' and 'relevance' diff --git a/applications/Chat/evaluate/utils.py b/applications/Chat/evaluate/utils.py index fefe25f5e764..406e43db99aa 100644 --- a/applications/Chat/evaluate/utils.py +++ b/applications/Chat/evaluate/utils.py @@ -72,17 +72,6 @@ def get_data_per_category(data, categories): return data_per_category -def remove_articles(text: str) -> str: - """ - Remove articles "a, an, the" in the given text. - It is used in evaluation of automatic metrics. - - """ - - pattern = re.compile(r"\b(a|an|the)\b", re.UNICODE) - return re.sub(pattern, " ", text) - - def remove_punctuations(text: str) -> str: """ Remove punctuations in the given text. @@ -121,7 +110,7 @@ def preprocessing_text(text: str) -> str: """ - return remove_redundant_space(remove_articles(remove_punctuations(text.lower()))) + return remove_redundant_space(remove_punctuations(text.lower())) def save_automatic_results(model_name: str, automatic_metric_stats: Dict[str, Dict], save_path: str) -> None: From 4da324cd609427ef9825aa16f856d04bc10e56db Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Mon, 26 Jun 2023 23:50:04 +0800 Subject: [PATCH 334/413] [hotfix]fix argument naming in docs and examples (#4083) --- .../coati/trainer/strategies/colossalai.py | 56 +++++++++---------- ...parallelize_your_training_like_Megatron.md | 4 +- docs/source/en/features/zero_with_chunk.md | 6 +- ...parallelize_your_training_like_Megatron.md | 5 +- .../zh-Hans/features/zero_with_chunk.md | 6 +- .../roberta/pretraining/run_pretraining.py | 2 +- examples/community/roberta/test_ci.sh | 0 .../language/gpt/gemini/train_gpt_demo.py | 2 +- 8 files changed, 40 insertions(+), 41 deletions(-) create mode 100644 examples/community/roberta/test_ci.sh diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index 8c9b8ac0334b..f31551f22318 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -34,9 +34,9 @@ class ColossalAIStrategy(DDPStrategy): If it is “cuda”, they will not be offloaded, which means max CUDA memory will be used. It is the fastest. pin_memory(bool): Whether to pin the memory for the data loader. Only for ZeRO-3. force_outputs_fp32(bool): Whether to force the outputs to be fp32. Only for ZeRO-3. - search_range_mb(int): The search range in MB for the chunk size. Only for ZeRO-3. + search_range_m(int): The number of search range for the chunk size, divided by 2^20. Only for ZeRO-3. hidden_dim(optional, int): The hidden dimension for the gemini. Only for ZeRO-3. - min_chunk_size_mb(float): The minimum chunk size in MB. Only for ZeRO-3. + min_chunk_size_m(float): The minimum chunk size divided by 2^20. Only for ZeRO-3. gpu_margin_mem_ratio(float): The margin memory ratio for the GPU. Only for ZeRO-3. reduce_bucket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2. overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2. @@ -61,9 +61,9 @@ def __init__( placement_policy: str = 'cuda', pin_memory: bool = True, # only for stage 3 force_outputs_fp32: bool = False, # only for stage 3 - search_range_mb: int = 32, # only for stage 3 + search_range_m: int = 32, # only for stage 3 hidden_dim: Optional[int] = None, # only for stage 3 - min_chunk_size_mb: float = 32, # only for stage 3 + min_chunk_size_m: float = 32, # only for stage 3 gpu_margin_mem_ratio: float = 0.0, # only for stage 3 reduce_bucket_size: int = 12 * 1024**2, # only for stage 1&2 overlap_communication: bool = True, # only for stage 1&2 @@ -83,57 +83,51 @@ def __init__( # TODO(ver217): support shard_init when using from_pretrained() if shard_init: - warnings.warn( - f'Shard init is not supported model.from_pretrained() yet. ' - 'Please load weights after strategy.prepare()' - ) + warnings.warn(f'Shard init is not supported model.from_pretrained() yet. ' + 'Please load weights after strategy.prepare()') if stage == 3 and precision == 'fp32': warnings.warn(f'Stage 3 only supports fp16. Precision is set to fp16.') precision = 'fp16' self.precision = precision self.shard_init = shard_init - optim_kwargs = dict( - initial_scale=initial_scale, - growth_factor=growth_factor, - backoff_factor=backoff_factor, - growth_interval=growth_interval, - hysteresis=hysteresis, - min_scale=min_scale, - max_scale=max_scale, - max_norm=max_norm, - norm_type=norm_type - ) + optim_kwargs = dict(initial_scale=initial_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + min_scale=min_scale, + max_scale=max_scale, + max_norm=max_norm, + norm_type=norm_type) # NOTE: dist should be initialized before calling get_current_device() if stage == 3: plugin_initializer = lambda: GeminiPlugin( - # gemini_config + # gemini_config device=get_current_device(), placement_policy=placement_policy, precision=precision, pin_memory=pin_memory, force_outputs_fp32=force_outputs_fp32, strict_ddp_mode=shard_init, - search_range_mb=search_range_mb, + search_range_m=search_range_m, hidden_dim=hidden_dim, - min_chunk_size_mb=min_chunk_size_mb, - # zero_optim_config + min_chunk_size_m=min_chunk_size_m, + # zero_optim_config gpu_margin_mem_ratio=gpu_margin_mem_ratio, - # optim_config - **optim_kwargs - ) + # optim_config + **optim_kwargs) else: plugin_initializer = lambda: LowLevelZeroPlugin( - # zero_config + # zero_config stage=stage, precision=precision, - # zero_optim_config + # zero_optim_config reduce_bucket_size_in_m=reduce_bucket_size, overlap_communication=overlap_communication, cpu_offload=(placement_policy == 'cpu'), - # optim_config - **optim_kwargs - ) + # optim_config + **optim_kwargs) super().__init__(seed, plugin_initializer) diff --git a/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md b/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md index 978ac32fc78e..281fd47554ca 100644 --- a/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md +++ b/docs/source/en/advanced_tutorials/parallelize_your_training_like_Megatron.md @@ -181,7 +181,7 @@ def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: device=get_current_device(), placement_policy=placement_policy, pin_memory=True, - search_range_mb=32) + search_range_m=32) return model ``` @@ -190,3 +190,5 @@ def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: The above optimization we made allows us to pretrain the GPT-2 model on a single GPU. We only need to set the parameter `GPUNUM`=1 in `run.sh`, and then we can complete the model training on a single GPU when running the file. The GPT-2 example is accessible at [Train GPT with Colossal-AI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt). + + diff --git a/docs/source/en/features/zero_with_chunk.md b/docs/source/en/features/zero_with_chunk.md index 1b27d64b6897..b50d2d02217b 100644 --- a/docs/source/en/features/zero_with_chunk.md +++ b/docs/source/en/features/zero_with_chunk.md @@ -67,12 +67,12 @@ Define the model parameters as follows: chunk_manager = init_chunk_manager(model=module, init_device=device, hidden_dim=hidden_dim, - search_range_mb=search_range_mb, - min_chunk_size_mb=min_chunk_size_mb) + search_range_m=search_range_m, + min_chunk_size_m=min_chunk_size_m) gemini_manager = GeminiManager(placement_policy, chunk_manager) ``` -`hidden_dim` is the hidden dimension of DNN. Users can provide this argument to speed up searching. If users do not know this argument before training, it is ok. We will use a default value 1024. `min_chunk_size_mb` is the the minimum chunk size in MegaByte. If the aggregate size of parameters is still smaller than the minimum chunk size, all parameters will be compacted into one small chunk. +`hidden_dim` is the hidden dimension of DNN. Users can provide this argument to speed up searching. If users do not know this argument before training, it is ok. We will use a default value 1024. `min_chunk_size_m` is a floating point, being the minimum chunk size divided by 2^20 (e.g., if min_chunk_size_m=2.5, then the minimum chunk size should be 2.5*(2^20)).If the aggregate size of parameters is still smaller than the minimum chunk size, all parameters will be compacted into one small chunk. Initialization of the optimizer. ```python diff --git a/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md b/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md index b4e0d18a2647..3f85d50454ae 100644 --- a/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md +++ b/docs/source/zh-Hans/advanced_tutorials/parallelize_your_training_like_Megatron.md @@ -165,7 +165,7 @@ def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: device=get_current_device(), placement_policy=placement_policy, pin_memory=True, - search_range_mb=32) + search_range_m=32) return model ``` @@ -174,3 +174,6 @@ def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placement_policy: 我们做的上述优化让我们可以在单GPU上训练GPT-2模型，只需要将`run.sh`中设置参数`GPUNUM`=1，再运行文件时就可以在单个GPU上完成模型的训练。 GPT-2 示例在[Train GPT with Colossal-AI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt). 获得。 + + + diff --git a/docs/source/zh-Hans/features/zero_with_chunk.md b/docs/source/zh-Hans/features/zero_with_chunk.md index 9fe5601bbd1b..513850f5cab7 100644 --- a/docs/source/zh-Hans/features/zero_with_chunk.md +++ b/docs/source/zh-Hans/features/zero_with_chunk.md @@ -66,13 +66,13 @@ with ColoInitContext(device='cpu', default_dist_spec=default_dist_spec, default_ chunk_manager = init_chunk_manager(model=module, init_device=device, hidden_dim=hidden_dim, - search_range_mb=search_range_mb, - min_chunk_size_mb=min_chunk_size_mb) + search_range_m=search_range_m, + min_chunk_size_m=min_chunk_size_m) gemini_manager = GeminiManager(placement_policy, chunk_manager) model = ZeroDDP(model, gemini_manager) ``` -`hidden dim`是DNN的隐藏维度。用户可以提供这个参数来加快搜索速度。如果用户在训练前不知道这个参数也可以。 我们将使用默认值 1024。`min_chunk_size_mb`是以兆字节为单位的最小块大小。如果参数的总大小仍然小于最小块大小，则所有参数将被压缩为一个小块。 +`hidden dim`是DNN的隐藏维度。用户可以提供这个参数来加快搜索速度。如果用户在训练前不知道这个参数也可以。 我们将使用默认值 1024。`min_chunk_size_m`是以兆（2^20）为单位的最小块大小。如果参数的总大小仍然小于最小块大小，则所有参数将被压缩为一个小块。 初始化优化器。 ```python diff --git a/examples/community/roberta/pretraining/run_pretraining.py b/examples/community/roberta/pretraining/run_pretraining.py index a72bdf775644..9fae4bef227a 100644 --- a/examples/community/roberta/pretraining/run_pretraining.py +++ b/examples/community/roberta/pretraining/run_pretraining.py @@ -88,7 +88,7 @@ def main(): placement_policy=args.placement, pin_memory=True, hidden_dim=model.config.hidden_size, - search_range_mb=128) + search_range_m=128) optim_config = dict(gpu_margin_mem_ratio=0.) else: raise RuntimeError diff --git a/examples/community/roberta/test_ci.sh b/examples/community/roberta/test_ci.sh new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/examples/language/gpt/gemini/train_gpt_demo.py b/examples/language/gpt/gemini/train_gpt_demo.py index a7b552c9e23d..9e61779a1dbf 100644 --- a/examples/language/gpt/gemini/train_gpt_demo.py +++ b/examples/language/gpt/gemini/train_gpt_demo.py @@ -258,7 +258,7 @@ def main(): placement_policy=args.placement, pin_memory=True, strict_ddp_mode=args.tp_degree == 1, - search_range_mb=128, + search_range_m=128, hidden_dim=model.config.n_embd, gpu_margin_mem_ratio=0.) else: From 95e95b6d588d27aeba8d62e6656392f771b69aee Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 27 Jun 2023 11:02:25 +0800 Subject: [PATCH 335/413] [testing] move pytest to be inside the function (#4087) --- colossalai/testing/pytest_wrapper.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/colossalai/testing/pytest_wrapper.py b/colossalai/testing/pytest_wrapper.py index b264b009028a..6a80e1dcc548 100644 --- a/colossalai/testing/pytest_wrapper.py +++ b/colossalai/testing/pytest_wrapper.py @@ -1,10 +1,9 @@ """ This file will not be automatically imported by `colossalai.testing` -as this file has a dependency on `pytest`. Therefore, you need to +as this file has a dependency on `pytest`. Therefore, you need to explicitly import this file `from colossalai.testing.pytest_wrapper import `.from """ -import pytest import os @@ -30,6 +29,12 @@ def test_for_something(): pytest test_for_something.py """ + try: + import pytest + except ImportError: + raise ImportError( + 'This function requires `pytest` to be installed, please do `pip install pytest` and try again.') + assert isinstance(name, str) flag = os.environ.get(name.upper(), '0') From 31dc302017ff491a36088dd27ed4c76e11d5b5b7 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Tue, 27 Jun 2023 16:40:46 +0800 Subject: [PATCH 336/413] [examples] copy resnet example to image (#4090) * copy resnet example * add pytest package * skip test_ci * skip test_ci * skip test_ci --- examples/images/resnet/.gitignore | 4 + examples/images/resnet/README.md | 56 +++++++ examples/images/resnet/eval.py | 48 ++++++ examples/images/resnet/requirements.txt | 5 + examples/images/resnet/test_ci.sh | 12 ++ examples/images/resnet/train.py | 204 ++++++++++++++++++++++++ 6 files changed, 329 insertions(+) create mode 100644 examples/images/resnet/.gitignore create mode 100644 examples/images/resnet/README.md create mode 100644 examples/images/resnet/eval.py create mode 100644 examples/images/resnet/requirements.txt create mode 100755 examples/images/resnet/test_ci.sh create mode 100644 examples/images/resnet/train.py diff --git a/examples/images/resnet/.gitignore b/examples/images/resnet/.gitignore new file mode 100644 index 000000000000..a79cf5236c08 --- /dev/null +++ b/examples/images/resnet/.gitignore @@ -0,0 +1,4 @@ +data +checkpoint +ckpt-fp16 +ckpt-fp32 diff --git a/examples/images/resnet/README.md b/examples/images/resnet/README.md new file mode 100644 index 000000000000..c69828637269 --- /dev/null +++ b/examples/images/resnet/README.md @@ -0,0 +1,56 @@ +# Train ResNet on CIFAR-10 from scratch + +## 🚀 Quick Start + +This example provides a training script and an evaluation script. The training script provides an example of training ResNet on CIFAR10 dataset from scratch. + +- Training Arguments + - `-p`, `--plugin`: Plugin to use. Choices: `torch_ddp`, `torch_ddp_fp16`, `low_level_zero`. Defaults to `torch_ddp`. + - `-r`, `--resume`: Resume from checkpoint file path. Defaults to `-1`, which means not resuming. + - `-c`, `--checkpoint`: The folder to save checkpoints. Defaults to `./checkpoint`. + - `-i`, `--interval`: Epoch interval to save checkpoints. Defaults to `5`. If set to `0`, no checkpoint will be saved. + - `--target_acc`: Target accuracy. Raise exception if not reached. Defaults to `None`. + +- Eval Arguments + - `-e`, `--epoch`: select the epoch to evaluate + - `-c`, `--checkpoint`: the folder where checkpoints are found + +### Install requirements + +```bash +pip install -r requirements.txt +``` + +### Train +The folders will be created automatically. +```bash +# train with torch DDP with fp32 +colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp32 + +# train with torch DDP with mixed precision training +colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp16 -p torch_ddp_fp16 + +# train with low level zero +colossalai run --nproc_per_node 2 train.py -c ./ckpt-low_level_zero -p low_level_zero +``` + +### Eval + +```bash +# evaluate fp32 training +python eval.py -c ./ckpt-fp32 -e 80 + +# evaluate fp16 mixed precision training +python eval.py -c ./ckpt-fp16 -e 80 + +# evaluate low level zero training +python eval.py -c ./ckpt-low_level_zero -e 80 +``` + +Expected accuracy performance will be: + +| Model | Single-GPU Baseline FP32 | Booster DDP with FP32 | Booster DDP with FP16 | Booster Low Level Zero | +| --------- | ------------------------ | --------------------- | --------------------- | ---------------------- | +| ResNet-18 | 85.85% | 84.91% | 85.46% | 84.50% | + +**Note: the baseline is adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`** diff --git a/examples/images/resnet/eval.py b/examples/images/resnet/eval.py new file mode 100644 index 000000000000..657708ec3ff2 --- /dev/null +++ b/examples/images/resnet/eval.py @@ -0,0 +1,48 @@ +import argparse + +import torch +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms + +# ============================== +# Parse Arguments +# ============================== +parser = argparse.ArgumentParser() +parser.add_argument('-e', '--epoch', type=int, default=80, help="resume from the epoch's checkpoint") +parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory") +args = parser.parse_args() + +# ============================== +# Prepare Test Dataset +# ============================== +# CIFAR-10 dataset +test_dataset = torchvision.datasets.CIFAR10(root='./data/', train=False, transform=transforms.ToTensor()) + +# Data loader +test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=128, shuffle=False) + +# ============================== +# Load Model +# ============================== +model = torchvision.models.resnet18(num_classes=10).cuda() +state_dict = torch.load(f'{args.checkpoint}/model_{args.epoch}.pth') +model.load_state_dict(state_dict) + +# ============================== +# Run Evaluation +# ============================== +model.eval() + +with torch.no_grad(): + correct = 0 + total = 0 + for images, labels in test_loader: + images = images.cuda() + labels = labels.cuda() + outputs = model(images) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + + print('Accuracy of the model on the test images: {} %'.format(100 * correct / total)) diff --git a/examples/images/resnet/requirements.txt b/examples/images/resnet/requirements.txt new file mode 100644 index 000000000000..3c7da7743702 --- /dev/null +++ b/examples/images/resnet/requirements.txt @@ -0,0 +1,5 @@ +colossalai +torch +torchvision +tqdm +pytest \ No newline at end of file diff --git a/examples/images/resnet/test_ci.sh b/examples/images/resnet/test_ci.sh new file mode 100755 index 000000000000..b3fb67830dda --- /dev/null +++ b/examples/images/resnet/test_ci.sh @@ -0,0 +1,12 @@ +#!/bin/bash +set -xe + +export DATA=/data/scratch/cifar-10 + +pip install -r requirements.txt + +# TODO: skip ci test due to time limits, train.py needs to be rewritten. + +# for plugin in "torch_ddp" "torch_ddp_fp16" "low_level_zero"; do +# colossalai run --nproc_per_node 4 train.py --interval 0 --target_acc 0.84 --plugin $plugin +# done diff --git a/examples/images/resnet/train.py b/examples/images/resnet/train.py new file mode 100644 index 000000000000..fe0dabf08377 --- /dev/null +++ b/examples/images/resnet/train.py @@ -0,0 +1,204 @@ +import argparse +import os +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms +from torch.optim import Optimizer +from torch.optim.lr_scheduler import MultiStepLR +from torch.utils.data import DataLoader +from tqdm import tqdm + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin +from colossalai.booster.plugin.dp_plugin_base import DPPluginBase +from colossalai.cluster import DistCoordinator +from colossalai.nn.optimizer import HybridAdam +from colossalai.utils import get_current_device + +# ============================== +# Prepare Hyperparameters +# ============================== +NUM_EPOCHS = 80 +LEARNING_RATE = 1e-3 + + +def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPluginBase): + # transform + transform_train = transforms.Compose( + [transforms.Pad(4), + transforms.RandomHorizontalFlip(), + transforms.RandomCrop(32), + transforms.ToTensor()]) + transform_test = transforms.ToTensor() + + # CIFAR-10 dataset + data_path = os.environ.get('DATA', './data') + with coordinator.priority_execution(): + train_dataset = torchvision.datasets.CIFAR10(root=data_path, + train=True, + transform=transform_train, + download=True) + test_dataset = torchvision.datasets.CIFAR10(root=data_path, + train=False, + transform=transform_test, + download=True) + + # Data loader + train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True) + test_dataloader = plugin.prepare_dataloader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=False) + return train_dataloader, test_dataloader + + +@torch.no_grad() +def evaluate(model: nn.Module, test_dataloader: DataLoader, coordinator: DistCoordinator) -> float: + model.eval() + correct = torch.zeros(1, dtype=torch.int64, device=get_current_device()) + total = torch.zeros(1, dtype=torch.int64, device=get_current_device()) + for images, labels in test_dataloader: + images = images.cuda() + labels = labels.cuda() + outputs = model(images) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + dist.all_reduce(correct) + dist.all_reduce(total) + accuracy = correct.item() / total.item() + if coordinator.is_master(): + print(f'Accuracy of the model on the test images: {accuracy * 100:.2f} %') + return accuracy + + +def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, criterion: nn.Module, train_dataloader: DataLoader, + booster: Booster, coordinator: DistCoordinator): + model.train() + with tqdm(train_dataloader, desc=f'Epoch [{epoch + 1}/{NUM_EPOCHS}]', disable=not coordinator.is_master()) as pbar: + for images, labels in pbar: + images = images.cuda() + labels = labels.cuda() + # Forward pass + outputs = model(images) + loss = criterion(outputs, labels) + + # Backward and optimize + booster.backward(loss, optimizer) + optimizer.step() + optimizer.zero_grad() + + # Print log info + pbar.set_postfix({'loss': loss.item()}) + + +def main(): + # ============================== + # Parse Arguments + # ============================== + parser = argparse.ArgumentParser() + # FIXME(ver217): gemini is not supported resnet now + parser.add_argument('-p', + '--plugin', + type=str, + default='torch_ddp', + choices=['torch_ddp', 'torch_ddp_fp16', 'low_level_zero'], + help="plugin to use") + parser.add_argument('-r', '--resume', type=int, default=-1, help="resume from the epoch's checkpoint") + parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory") + parser.add_argument('-i', '--interval', type=int, default=5, help="interval of saving checkpoint") + parser.add_argument('--target_acc', + type=float, + default=None, + help="target accuracy. Raise exception if not reached") + args = parser.parse_args() + + # ============================== + # Prepare Checkpoint Directory + # ============================== + if args.interval > 0: + Path(args.checkpoint).mkdir(parents=True, exist_ok=True) + + # ============================== + # Launch Distributed Environment + # ============================== + colossalai.launch_from_torch(config={}) + coordinator = DistCoordinator() + + # update the learning rate with linear scaling + # old_gpu_num / old_lr = new_gpu_num / new_lr + global LEARNING_RATE + LEARNING_RATE *= coordinator.world_size + + # ============================== + # Instantiate Plugin and Booster + # ============================== + booster_kwargs = {} + if args.plugin == 'torch_ddp_fp16': + booster_kwargs['mixed_precision'] = 'fp16' + if args.plugin.startswith('torch_ddp'): + plugin = TorchDDPPlugin() + elif args.plugin == 'gemini': + plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2**5) + elif args.plugin == 'low_level_zero': + plugin = LowLevelZeroPlugin(initial_scale=2**5) + + booster = Booster(plugin=plugin, **booster_kwargs) + + # ============================== + # Prepare Dataloader + # ============================== + train_dataloader, test_dataloader = build_dataloader(100, coordinator, plugin) + + # ==================================== + # Prepare model, optimizer, criterion + # ==================================== + # resent50 + model = torchvision.models.resnet18(num_classes=10) + + # Loss and optimizer + criterion = nn.CrossEntropyLoss() + optimizer = HybridAdam(model.parameters(), lr=LEARNING_RATE) + + # lr scheduler + lr_scheduler = MultiStepLR(optimizer, milestones=[20, 40, 60, 80], gamma=1 / 3) + + # ============================== + # Boost with ColossalAI + # ============================== + model, optimizer, criterion, _, lr_scheduler = booster.boost(model, + optimizer, + criterion=criterion, + lr_scheduler=lr_scheduler) + + # ============================== + # Resume from checkpoint + # ============================== + if args.resume >= 0: + booster.load_model(model, f'{args.checkpoint}/model_{args.resume}.pth') + booster.load_optimizer(optimizer, f'{args.checkpoint}/optimizer_{args.resume}.pth') + booster.load_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{args.resume}.pth') + + # ============================== + # Train model + # ============================== + start_epoch = args.resume if args.resume >= 0 else 0 + for epoch in range(start_epoch, NUM_EPOCHS): + train_epoch(epoch, model, optimizer, criterion, train_dataloader, booster, coordinator) + lr_scheduler.step() + + # save checkpoint + if args.interval > 0 and (epoch + 1) % args.interval == 0: + booster.save_model(model, f'{args.checkpoint}/model_{epoch + 1}.pth') + booster.save_optimizer(optimizer, f'{args.checkpoint}/optimizer_{epoch + 1}.pth') + booster.save_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{epoch + 1}.pth') + + accuracy = evaluate(model, test_dataloader, coordinator) + if args.target_acc is not None: + assert accuracy >= args.target_acc, f'Accuracy {accuracy} is lower than target accuracy {args.target_acc}' + + +if __name__ == '__main__': + main() From 1ee947f617aaffde010392089d05508e3597267d Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 28 Jun 2023 14:33:43 +0800 Subject: [PATCH 337/413] [workflow] added status check for test coverage workflow (#4106) --- .github/workflows/report_test_coverage.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/report_test_coverage.yml b/.github/workflows/report_test_coverage.yml index d9b131fd994c..c9dc541b8a33 100644 --- a/.github/workflows/report_test_coverage.yml +++ b/.github/workflows/report_test_coverage.yml @@ -9,6 +9,7 @@ on: jobs: report-test-coverage: runs-on: ubuntu-latest + if: ${{ github.event.workflow_run.conclusion == 'success' }} steps: - name: "Download artifact" uses: actions/github-script@v6 From 2d40759a5351aa08866d37a39b5f141e7a11e0eb Mon Sep 17 00:00:00 2001 From: digger yu Date: Wed, 28 Jun 2023 15:29:44 +0800 Subject: [PATCH 338/413] fix #3852 path error (#4058) --- examples/language/gpt/titans/train_gpt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/language/gpt/titans/train_gpt.py b/examples/language/gpt/titans/train_gpt.py index 66225d6c8044..6be0b9e8da30 100644 --- a/examples/language/gpt/titans/train_gpt.py +++ b/examples/language/gpt/titans/train_gpt.py @@ -15,7 +15,7 @@ from colossalai.trainer import Trainer, hooks from colossalai.utils import colo_set_process_memory_fraction, is_using_pp from colossalai.utils.timer import MultiTimer -from colossalai.zero.init_ctx import ZeroInitContext +from colossalai.zero.legacy.init_ctx import ZeroInitContext def calc_local_model_size(model: torch.nn.Module): From 769cddcb2cc45ec78cb27148520e10bc8d7307d9 Mon Sep 17 00:00:00 2001 From: digger yu Date: Wed, 28 Jun 2023 15:30:30 +0800 Subject: [PATCH 339/413] fix typo docs/ (#4033) --- docs/source/en/advanced_tutorials/meet_gemini.md | 2 +- .../advanced_tutorials/train_vit_with_hybrid_parallelism.md | 2 +- docs/source/zh-Hans/features/mixed_precision_training.md | 2 +- .../zh-Hans/features/mixed_precision_training_with_booster.md | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/en/advanced_tutorials/meet_gemini.md b/docs/source/en/advanced_tutorials/meet_gemini.md index c1c23a355efa..e94e3fea3710 100644 --- a/docs/source/en/advanced_tutorials/meet_gemini.md +++ b/docs/source/en/advanced_tutorials/meet_gemini.md @@ -9,7 +9,7 @@ When you only have a few GPUs for large model training tasks, **heterogeneous tr ## Usage -At present, Gemini supports compatibility with ZeRO parallel mode, and it is really simple to use Gemini: Inject the feathures of `GeminiPlugin` into training components with `booster`. More instructions of `booster` please refer to [**usage of booster**](../basics/booster_api.md). +At present, Gemini supports compatibility with ZeRO parallel mode, and it is really simple to use Gemini: Inject the features of `GeminiPlugin` into training components with `booster`. More instructions of `booster` please refer to [**usage of booster**](../basics/booster_api.md). ```python from torchvision.models import resnet18 diff --git a/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md b/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md index e2f2c90a3791..5ad08392049e 100644 --- a/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md +++ b/docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md @@ -150,7 +150,7 @@ Colossal-AI 提供了自己的优化器、损失函数和学习率调度器。Py optimizer = colossalai.nn.Lamb(model.parameters(), lr=1.8e-2, weight_decay=0.1) # build loss criterion = torch.nn.CrossEntropyLoss() -# lr_scheduelr +# lr_scheduler lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=50, total_steps=gpc.config.NUM_EPOCHS) ``` diff --git a/docs/source/zh-Hans/features/mixed_precision_training.md b/docs/source/zh-Hans/features/mixed_precision_training.md index 4628b09cd910..a92e7e093015 100644 --- a/docs/source/zh-Hans/features/mixed_precision_training.md +++ b/docs/source/zh-Hans/features/mixed_precision_training.md @@ -303,7 +303,7 @@ colossalai.launch_from_torch(config=args.config) # build loss criterion = torch.nn.CrossEntropyLoss() - # lr_scheduelr + # lr_scheduler lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=50, total_steps=gpc.config.NUM_EPOCHS) ``` diff --git a/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md index 187aef1a6c4a..ba9451341d15 100644 --- a/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md +++ b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md @@ -181,7 +181,7 @@ optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, weight_decay=0.1) # build loss criterion = torch.nn.CrossEntropyLoss() -# lr_scheduelr +# lr_scheduler lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=50, total_steps=NUM_EPOCHS) ``` From 711e2b4c00d68083af64dd2422f33c3b08d42856 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Wed, 28 Jun 2023 19:30:37 +0800 Subject: [PATCH 340/413] [doc] update and revise some typos and errs in docs (#4107) * fix some typos and problems in doc * fix some typos and problems in doc * add doc test --- docs/source/en/basics/booster_api.md | 16 +++--- .../mixed_precision_training_with_booster.md | 26 ++++++---- docs/source/en/get_started/run_demo.md | 19 +++---- docs/source/zh-Hans/basics/booster_api.md | 31 +++++++----- .../mixed_precision_training_with_booster.md | 49 ++++++++++++------- docs/source/zh-Hans/get_started/run_demo.md | 18 ++++--- 6 files changed, 98 insertions(+), 61 deletions(-) diff --git a/docs/source/en/basics/booster_api.md b/docs/source/en/basics/booster_api.md index a446ff31be83..22d5ee818019 100644 --- a/docs/source/en/basics/booster_api.md +++ b/docs/source/en/basics/booster_api.md @@ -1,31 +1,36 @@ # Booster API -Author: [Mingyan Jiang](https://github.com/jiangmingyan) + +Author: [Mingyan Jiang](https://github.com/jiangmingyan) [Jianghai Chen](https://github.com/CjhHa1) **Prerequisite:** + - [Distributed Training](../concepts/distributed_training.md) - [Colossal-AI Overview](../concepts/colossalai_overview.md) **Example Code** + - [Train with Booster](https://github.com/hpcaitech/ColossalAI/blob/main/examples/tutorial/new_api/cifar_resnet/README.md) ## Introduction + In our new design, `colossalai.booster` replaces the role of `colossalai.initialize` to inject features into your training components (e.g. model, optimizer, dataloader) seamlessly. With these new APIs, you can integrate your model with our parallelism features more friendly. Also calling `colossalai.booster` is the standard procedure before you run into your training loops. In the sections below, I will cover how `colossalai.booster` works and what we should take note of. ### Plugin + Plugin is an important component that manages parallel configuration (eg: The gemini plugin encapsulates the gemini acceleration solution). Currently supported plugins are as follows: -***GeminiPlugin:*** This plugin wraps the Gemini acceleration solution, that ZeRO with chunk-based memory management. +**_GeminiPlugin:_** This plugin wraps the Gemini acceleration solution, that ZeRO with chunk-based memory management. -***TorchDDPPlugin:*** This plugin wraps the DDP acceleration solution, it implements data parallelism at the module level which can run across multiple machines. +**_TorchDDPPlugin:_** This plugin wraps the DDP acceleration solution, it implements data parallelism at the module level which can run across multiple machines. -***LowLevelZeroPlugin:*** This plugin wraps the 1/2 stage of Zero Redundancy Optimizer. Stage 1 : Shards optimizer states across data parallel workers/GPUs. Stage 2 : Shards optimizer states + gradients across data parallel workers/GPUs. +**_LowLevelZeroPlugin:_** This plugin wraps the 1/2 stage of Zero Redundancy Optimizer. Stage 1 : Shards optimizer states across data parallel workers/GPUs. Stage 2 : Shards optimizer states + gradients across data parallel workers/GPUs. ### API of booster - {{ autodoc:colossalai.booster.Booster }} ## Usage + In a typical workflow, you should launch distributed environment at the beginning of training script and create objects needed (such as models, optimizers, loss function, data loaders etc.) firstly, then call `colossalai.booster` to inject features into these objects, After that, you can use our booster APIs and these returned objects to continue the rest of your training processes. A pseudo-code example is like below: @@ -67,5 +72,4 @@ def train(): [more design details](https://github.com/hpcaitech/ColossalAI/discussions/3046) - diff --git a/docs/source/en/features/mixed_precision_training_with_booster.md b/docs/source/en/features/mixed_precision_training_with_booster.md index e9b6f684f613..1240b47d5d2e 100644 --- a/docs/source/en/features/mixed_precision_training_with_booster.md +++ b/docs/source/en/features/mixed_precision_training_with_booster.md @@ -3,12 +3,13 @@ Author: [Mingyan Jiang](https://github.com/jiangmingyan) **Prerequisite** + - [Define Your Configuration](../basics/define_your_config.md) - [Training Booster](../basics/booster_api.md) **Related Paper** -- [Accelerating Scientific Computations with Mixed Precision Algorithms](https://arxiv.org/abs/0808.2794) +- [Accelerating Scientific Computations with Mixed Precision Algorithms](https://arxiv.org/abs/0808.2794) ## Introduction @@ -19,12 +20,11 @@ In Colossal-AI, we have incorporated different implementations of mixed precisio 2. apex.amp 3. naive amp - -| Colossal-AI | support tensor parallel | support pipeline parallel | fp16 extent | -| ----------- | ----------------------- | ------------------------- | ----------- | -| AMP_TYPE.TORCH | ✅ | ❌ | Model parameters, activation, gradients are downcast to fp16 during forward and backward propagation | -| AMP_TYPE.APEX | ❌ | ❌ | More fine-grained, we can choose opt_level O0, O1, O2, O3 | -| AMP_TYPE.NAIVE | ✅ | ✅ | Model parameters, forward and backward operations are all downcast to fp16 | +| Colossal-AI | support tensor parallel | support pipeline parallel | fp16 extent | +| -------------- | ----------------------- | ------------------------- | ---------------------------------------------------------------------------------------------------- | +| AMP_TYPE.TORCH | ✅ | ❌ | Model parameters, activation, gradients are downcast to fp16 during forward and backward propagation | +| AMP_TYPE.APEX | ❌ | ❌ | More fine-grained, we can choose opt_level O0, O1, O2, O3 | +| AMP_TYPE.NAIVE | ✅ | ✅ | Model parameters, forward and backward operations are all downcast to fp16 | The first two rely on the original implementation of PyTorch (version 1.6 and above) and NVIDIA Apex. The last method is similar to Apex O2 level. @@ -64,8 +64,11 @@ However, there are other operations, like reductions, which require the dynamic We supported three AMP training methods and allowed the user to train with AMP with no code. If you want to train with amp, just assign `mixed_precision` with `fp16` when you instantiate the `Booster`. Now booster support torch amp, the other two(apex amp, naive amp) are still started by `colossalai.initialize`, if needed, please refer to [this](./mixed_precision_training.md). Next we will support `bf16`, `fp8`. ### Start with Booster + instantiate `Booster` with `mixed_precision="fp16"`, then you can train with torch amp. + + ```python """ Mapping: @@ -78,9 +81,13 @@ instantiate `Booster` with `mixed_precision="fp16"`, then you can train with tor from colossalai import Booster booster = Booster(mixed_precision='fp16',...) ``` + + or you can create a `FP16TorchMixedPrecision` object, such as: + + ```python from colossalai.mixed_precision import FP16TorchMixedPrecision mixed_precision = FP16TorchMixedPrecision( @@ -90,9 +97,10 @@ mixed_precision = FP16TorchMixedPrecision( growth_interval=2000) booster = Booster(mixed_precision=mixed_precision,...) ``` + -The same goes for other types of amps. +The same goes for other types of amps. ### Torch AMP Configuration @@ -121,7 +129,6 @@ The output model is converted to AMP model of smaller memory consumption. If your input model is already too large to fit in a GPU, please instantiate your model weights in `dtype=torch.float16`. Otherwise, try smaller models or checkout more parallelization training techniques! - ## Hands-on Practice Now we will introduce the use of AMP with Colossal-AI. In this practice, we will use Torch AMP as an example. @@ -248,4 +255,5 @@ Use the following command to start the training scripts. You can change `--nproc ```shell colossalai run --nproc_per_node 1 train.py ``` + diff --git a/docs/source/en/get_started/run_demo.md b/docs/source/en/get_started/run_demo.md index f47bdbbd62fc..1ce185e26db0 100644 --- a/docs/source/en/get_started/run_demo.md +++ b/docs/source/en/get_started/run_demo.md @@ -7,19 +7,18 @@ can also run on systems with only one GPU. Quick demos showing how to use Coloss ## Single GPU Colossal-AI can be used to train deep learning models on systems with only one GPU and achieve baseline -performances. We provided an example to [train ResNet on CIFAR10 dataset](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/resnet) -with only one GPU. You can find the example in [ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI-Examples). +performances. We provided an example to [train ResNet on CIFAR10 dataset](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/resnet) +with only one GPU. You can find the example in [ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples). Detailed instructions can be found in its `README.md`. ## Multiple GPUs Colossal-AI can be used to train deep learning models on distributed systems with multiple GPUs and accelerate the -training process drastically by applying efficient parallelization techniques. When we have several parallelism for you -to try out. +training process drastically by applying efficient parallelization techniques. When we have several parallelism for you to try out. #### 1. data parallel -You can use the same [ResNet example](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/resnet) as the +You can use the same [ResNet example](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/resnet) as the single-GPU demo above. By setting `--nproc_per_node` to be the number of GPUs you have on your machine, the example is turned into a data parallel example. @@ -27,17 +26,19 @@ is turned into a data parallel example. Hybrid parallel includes data, tensor, and pipeline parallelism. In Colossal-AI, we support different types of tensor parallelism (i.e. 1D, 2D, 2.5D and 3D). You can switch between different tensor parallelism by simply changing the configuration -in the `config.py`. You can follow the [GPT example](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/gpt). +in the `config.py`. You can follow the [GPT example](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt). Detailed instructions can be found in its `README.md`. #### 3. MoE parallel -We provided [an example of WideNet](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/widenet) to demonstrate +We provided [an example of ViT-MoE](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/moe) to demonstrate MoE parallelism. WideNet uses mixture of experts (MoE) to achieve better performance. More details can be found in [Tutorial: Integrate Mixture-of-Experts Into Your Model](../advanced_tutorials/integrate_mixture_of_experts_into_your_model.md) #### 4. sequence parallel Sequence parallel is designed to tackle memory efficiency and sequence length limit problems in NLP tasks. We provided -[an example of BERT](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/bert/sequene_parallel) in -[ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI-Examples). You can follow the `README.md` to execute the code. +[an example of BERT](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/sequence_parallel) in +[ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples). You can follow the `README.md` to execute the code. + + diff --git a/docs/source/zh-Hans/basics/booster_api.md b/docs/source/zh-Hans/basics/booster_api.md index 1bb5fd69bd15..1df821ce7d6e 100644 --- a/docs/source/zh-Hans/basics/booster_api.md +++ b/docs/source/zh-Hans/basics/booster_api.md @@ -1,35 +1,44 @@ # booster 使用 -作者: [Mingyan Jiang](https://github.com/jiangmingyan) + +作者: [Mingyan Jiang](https://github.com/jiangmingyan) [Jianghai Chen](https://github.com/CjhHa1) **预备知识:** + - [分布式训练](../concepts/distributed_training.md) - [Colossal-AI 总览](../concepts/colossalai_overview.md) **示例代码** -- [使用booster训练](https://github.com/hpcaitech/ColossalAI/blob/main/examples/tutorial/new_api/cifar_resnet/README.md) + + + +- [使用 booster 训练](https://github.com/hpcaitech/ColossalAI/blob/main/examples/tutorial/new_api/cifar_resnet/README.md) ## 简介 -在我们的新设计中， `colossalai.booster` 代替 `colossalai.initialize` 将特征(例如，模型、优化器、数据加载器）无缝注入您的训练组件中。 使用booster API, 您可以更友好地将我们的并行策略整合到待训练模型中. 调用 `colossalai.booster` 是您进入训练循环前的基本操作。 + +在我们的新设计中， `colossalai.booster` 代替 `colossalai.initialize` 将特征(例如，模型、优化器、数据加载器)无缝注入您的训练组件中。 使用 booster API, 您可以更友好地将我们的并行策略整合到待训练模型中. 调用 `colossalai.booster` 是您进入训练循环前的基本操作。 在下面的章节中，我们将介绍 `colossalai.booster` 是如何工作的以及使用时我们要注意的细节。 -### Booster插件 -Booster插件是管理并行配置的重要组件（eg：gemini插件封装了gemini加速方案）。目前支持的插件如下： +### Booster 插件 + +Booster 插件是管理并行配置的重要组件（eg：gemini 插件封装了 gemini 加速方案）。目前支持的插件如下： + +**_GeminiPlugin:_** GeminiPlugin 插件封装了 gemini 加速解决方案，即基于块内存管理的 ZeRO 优化方案。 -***GeminiPlugin:*** GeminiPlugin插件封装了 gemini 加速解决方案，即基于块内存管理的 ZeRO优化方案。 +**_TorchDDPPlugin:_** TorchDDPPlugin 插件封装了 DDP 加速方案，实现了模型级别的数据并行，可以跨多机运行。 -***TorchDDPPlugin:*** TorchDDPPlugin插件封装了DDP加速方案，实现了模型级别的数据并行，可以跨多机运行。 +**_LowLevelZeroPlugin:_** LowLevelZeroPlugin 插件封装了零冗余优化器的 1/2 阶段。阶段 1：切分优化器参数，分发到各并发进程或并发 GPU 上。阶段 2：切分优化器参数及梯度，分发到各并发进程或并发 GPU 上。 -***LowLevelZeroPlugin:*** LowLevelZeroPlugin插件封装了零冗余优化器的 1/2 阶段。阶段 1：切分优化器参数，分发到各并发进程或并发GPU上。阶段 2：切分优化器参数及梯度，分发到各并发进程或并发GPU上。 +### Booster 接口 -### Booster接口 + {{ autodoc:colossalai.booster.Booster }} ## 使用方法及示例 -在使用colossalai训练时，首先需要在训练脚本的开头启动分布式环境，并创建需要使用的模型、优化器、损失函数、数据加载器等对象。之后，调用`colossalai.booster` 将特征注入到这些对象中，您就可以使用我们的booster API去进行您接下来的训练流程。 +在使用 colossalai 训练时，首先需要在训练脚本的开头启动分布式环境，并创建需要使用的模型、优化器、损失函数、数据加载器等对象。之后，调用`colossalai.booster` 将特征注入到这些对象中，您就可以使用我们的 booster API 去进行您接下来的训练流程。 -以下是一个伪代码示例，将展示如何使用我们的booster API进行模型训练: +以下是一个伪代码示例，将展示如何使用我们的 booster API 进行模型训练: ```python import torch diff --git a/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md index ba9451341d15..0354f92ee7ce 100644 --- a/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md +++ b/docs/source/zh-Hans/features/mixed_precision_training_with_booster.md @@ -3,12 +3,13 @@ 作者: [Mingyan Jiang](https://github.com/jiangmingyan) **前置教程** + - [定义配置文件](../basics/define_your_config.md) -- [booster使用](../basics/booster_api.md) +- [booster 使用](../basics/booster_api.md) **相关论文** -- [Accelerating Scientific Computations with Mixed Precision Algorithms](https://arxiv.org/abs/0808.2794) +- [Accelerating Scientific Computations with Mixed Precision Algorithms](https://arxiv.org/abs/0808.2794) ## 引言 @@ -19,18 +20,17 @@ AMP 代表自动混合精度训练。 2. apex.amp 3. naive amp +| Colossal-AI | 支持张量并行 | 支持流水并行 | fp16 范围 | +| -------------- | ------------ | ------------ | --------------------------------------------------------- | +| AMP_TYPE.TORCH | ✅ | ❌ | 在前向和反向传播期间，模型参数、激活和梯度向下转换至 fp16 | +| AMP_TYPE.APEX | ❌ | ❌ | 更细粒度，我们可以选择 opt_level O0, O1, O2, O3 | +| AMP_TYPE.NAIVE | ✅ | ✅ | 模型参数、前向和反向操作，全都向下转换至 fp16 | -| Colossal-AI | 支持张量并行 | 支持流水并行 | fp16范围 | -| ----------- | ----------------------- | ------------------------- | ----------- | -| AMP_TYPE.TORCH | ✅ | ❌ | 在前向和反向传播期间，模型参数、激活和梯度向下转换至fp16 | -| AMP_TYPE.APEX | ❌ | ❌ | 更细粒度，我们可以选择 opt_level O0, O1, O2, O3 | -| AMP_TYPE.NAIVE | ✅ | ✅ | 模型参数、前向和反向操作，全都向下转换至fp16 | - -前两个依赖于 PyTorch (1.6及以上) 和 NVIDIA Apex 的原始实现。最后一种方法类似 Apex O2。在这些方法中，Apex-AMP 与张量并行不兼容。这是因为张量是以张量并行的方式在设备之间拆分的，因此，需要在不同的进程之间进行通信，以检查整个模型权重中是否出现inf或nan。我们修改了torch amp实现，使其现在与张量并行兼容。 +前两个依赖于 PyTorch (1.6 及以上) 和 NVIDIA Apex 的原始实现。最后一种方法类似 Apex O2。在这些方法中，Apex-AMP 与张量并行不兼容。这是因为张量是以张量并行的方式在设备之间拆分的，因此，需要在不同的进程之间进行通信，以检查整个模型权重中是否出现 inf 或 nan。我们修改了 torch amp 实现，使其现在与张量并行兼容。 -> ❌️ fp16与ZeRO不兼容 +> ❌️ fp16 与 ZeRO 不兼容 > -> ⚠️ 流水并行目前仅支持naive amp +> ⚠️ 流水并行目前仅支持 naive amp 我们建议使用 torch AMP，因为在不使用流水并行时，它通常比 NVIDIA AMP 提供更好的准确性。 @@ -57,11 +57,14 @@ AMP 代表自动混合精度训练。 ## Colossal-AI 中的 AMP -我们支持三种 AMP 训练方法，并允许用户在没有改变代码的情况下使用 AMP 进行训练。booster支持amp特性注入，如果您要使用混合精度训练，则在创建booster实例时指定`mixed_precision`参数，我们现已支持torch amp，apex amp, naive amp（现已移植torch amp至booster，apex amp, naive amp仍由`colossalai.initialize`方式启动，如您需使用，请[参考](./mixed_precision_training.md）;后续将会拓展`bf16`,`pf8`的混合精度训练. +我们支持三种 AMP 训练方法，并允许用户在没有改变代码的情况下使用 AMP 进行训练。booster 支持 amp 特性注入，如果您要使用混合精度训练，则在创建 booster 实例时指定`mixed_precision`参数，我们现已支持 torch amp，apex amp, naive amp（现已移植 torch amp 至 booster，apex amp, naive amp 仍由`colossalai.initialize`方式启动，如您需使用，请[参考](./mixed_precision_training.md);后续将会拓展`bf16`,`pf8`的混合精度训练. + +#### booster 启动方式 + +您可以在创建 booster 实例时，指定`mixed_precision="fp16"`即使用 torch amp。 -#### booster启动方式 -您可以在创建booster实例时，指定`mixed_precision="fp16"`即使用torch amp。 + ```python """ 初始化映射关系如下： @@ -74,9 +77,13 @@ AMP 代表自动混合精度训练。 from colossalai import Booster booster = Booster(mixed_precision='fp16',...) ``` + + 或者您可以自定义一个`FP16TorchMixedPrecision`对象，如 + + ```python from colossalai.mixed_precision import FP16TorchMixedPrecision mixed_precision = FP16TorchMixedPrecision( @@ -86,8 +93,10 @@ mixed_precision = FP16TorchMixedPrecision( growth_interval=2000) booster = Booster(mixed_precision=mixed_precision,...) ``` + -其他类型的amp使用方式也是一样的。 + +其他类型的 amp 使用方式也是一样的。 ### Torch AMP 配置 @@ -96,7 +105,7 @@ booster = Booster(mixed_precision=mixed_precision,...) ### Apex AMP 配置 对于这种模式，我们依靠 Apex 实现混合精度训练。我们支持这个插件，因为它允许对混合精度的粒度进行更精细的控制。 -例如, O2 水平 (优化器水平2) 将保持 batch normalization 为 FP32。 +例如, O2 水平 (优化器水平 2) 将保持 batch normalization 为 FP32。 如果你想了解更多细节，请参考 [Apex Documentation](https://nvidia.github.io/apex/)。 @@ -104,7 +113,7 @@ booster = Booster(mixed_precision=mixed_precision,...) ### Naive AMP 配置 -在 Naive AMP 模式中, 我们实现了混合精度训练，同时保持了与复杂张量和流水并行的兼容性。该 AMP 模式将所有操作转为 FP16 。下列代码块展示了该模式的booster启动方式。 +在 Naive AMP 模式中, 我们实现了混合精度训练，同时保持了与复杂张量和流水并行的兼容性。该 AMP 模式将所有操作转为 FP16 。下列代码块展示了该模式的 booster 启动方式。 {{ autodoc:colossalai.booster.mixed_precision.FP16NaiveMixedPrecision }} @@ -186,7 +195,8 @@ lr_scheduler = LinearWarmupLR(optimizer, warmup_steps=50, total_steps=NUM_EPOCHS ``` ### 步骤 4. 插入 AMP -创建一个MixedPrecision对象（如果需要）及torchDDPPlugin对象，调用 `colossalai.boost` 将所有训练组件转为为FP16模式. + +创建一个 MixedPrecision 对象（如果需要）及 torchDDPPlugin 对象，调用 `colossalai.boost` 将所有训练组件转为为 FP16 模式. ```python plugin = TorchDDPPlugin() @@ -209,7 +219,7 @@ model, optimizer, criterion, dataloader, lr_scheduler = booster.boost(model, opt ### 步骤 5. 使用 booster 训练 -使用booster构建一个普通的训练循环。 +使用 booster 构建一个普通的训练循环。 ```python model.train() @@ -232,4 +242,5 @@ for epoch in range(NUM_EPOCHS): ```shell colossalai run --nproc_per_node 1 train.py ``` + diff --git a/docs/source/zh-Hans/get_started/run_demo.md b/docs/source/zh-Hans/get_started/run_demo.md index edfc246c22d5..70ed5ebe251b 100755 --- a/docs/source/zh-Hans/get_started/run_demo.md +++ b/docs/source/zh-Hans/get_started/run_demo.md @@ -4,8 +4,8 @@ Colossal-AI 是一个集成的大规模深度学习系统，具有高效的并 ## 单 GPU -Colossal-AI 可以用在只有一个 GPU 的系统上训练深度学习模型，并达到 baseline 的性能。 我们提供了一个 [在CIFAR10数据集上训练ResNet](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/resnet) 的例子，该例子只需要一个 GPU。 -您可以在 [ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI-Examples) 中获取该例子。详细说明可以在其 `README.md` 中获取。 +Colossal-AI 可以用在只有一个 GPU 的系统上训练深度学习模型，并达到 baseline 的性能。 我们提供了一个 [在 CIFAR10 数据集上训练 ResNet](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/resnet) 的例子，该例子只需要一个 GPU。 +您可以在 [ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) 中获取该例子。详细说明可以在其 `README.md` 中获取。 ## 多 GPU @@ -13,16 +13,20 @@ Colossal-AI 可用于在具有多个 GPU 的分布式系统上训练深度学习 #### 1. 数据并行 -您可以使用与上述单 GPU 演示相同的 [ResNet例子](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/resnet)。 通过设置 `--nproc_per_node` 为您机器上的 GPU 数量，您就能把数据并行应用在您的例子上了。 +您可以使用与上述单 GPU 演示相同的 [ResNet 例子](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/resnet)。 通过设置 `--nproc_per_node` 为您机器上的 GPU 数量，您就能把数据并行应用在您的例子上了。 #### 2. 混合并行 -混合并行包括数据、张量和流水线并行。在 Colossal-AI 中，我们支持不同类型的张量并行（即 1D、2D、2.5D 和 3D）。您可以通过简单地改变 `config.py` 中的配置在不同的张量并行之间切换。您可以参考 [GPT example](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/gpt), 更多细节能在它的 `README.md` 中被找到。 +混合并行包括数据、张量和流水线并行。在 Colossal-AI 中，我们支持不同类型的张量并行（即 1D、2D、2.5D 和 3D）。您可以通过简单地改变 `config.py` 中的配置在不同的张量并行之间切换。您可以参考 [GPT example](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt), 更多细节能在它的 `README.md` 中被找到。 -#### 3. MoE并行 +#### 3. MoE 并行 -我们提供了一个 [WideNet例子](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/widenet) 来验证 MoE 的并行性。 WideNet 使用 Mixture of Experts（MoE）来实现更好的性能。更多的细节可以在我们的教程中获取：[教会您如何把Mixture of Experts整合到模型中](../advanced_tutorials/integrate_mixture_of_experts_into_your_model.md)。 + + +我们提供了一个 [ViT-MoE 例子](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/moe) 来验证 MoE 的并行性。 WideNet 使用 Mixture of Experts（MoE）来实现更好的性能。更多的细节可以在我们的教程中获取：[教会您如何把 Mixture of Experts 整合到模型中](../advanced_tutorials/integrate_mixture_of_experts_into_your_model.md)。 #### 4. 序列并行 -序列并行是为了解决NLP任务中的内存效率和序列长度限制问题。 我们在 [ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI-Examples) 中提供了一个 [BERT例子](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/bert/sequene_parallel)。您可以按照 `README.md` 来执行代码。 +序列并行是为了解决 NLP 任务中的内存效率和序列长度限制问题。 我们在 [ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) 中提供了一个 [Sequence Parallelism 例子](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/sequence_parallel)。您可以按照 `README.md` 来执行代码。 + + From b03d64d010cb6803b66230a0386bc62d989e6ef6 Mon Sep 17 00:00:00 2001 From: Wenhao Chen Date: Thu, 29 Jun 2023 10:48:09 +0800 Subject: [PATCH 341/413] [chat] refactor trainer class (#4080) * to: add SLTrainer * refactor: refactor RMTrainer and SFTTrainer * fix: fix init file * feat: remove on_learn_epoch fn as not used * fix: align with modified gemini arguments * to: add OnPolicyTrainer * revert: add _on_learn_epoch fn * refactor: refactor PPOTrainer * style: rename PPOTrainer argument * fix: align with modified PPO arguments * test: align with modified train_prompts arguments * chore: modify train_prompts * docs: align with modified arguments * fix: remove unnecessary output * fix: move dataloader to fit fn of SLTrainer * fix: move dataloader to fit fn of OnPolicyTrainer * fix: modify usage of prompt and pretrain dataloader --- applications/Chat/README.md | 2 +- .../benchmarks/benchmark_opt_lora_dummy.py | 26 ++- applications/Chat/coati/trainer/__init__.py | 8 +- applications/Chat/coati/trainer/base.py | 168 ++++++++++++++--- applications/Chat/coati/trainer/ppo.py | 175 +++++++----------- applications/Chat/coati/trainer/rm.py | 156 +++++++--------- applications/Chat/coati/trainer/sft.py | 171 +++++++++-------- .../coati/trainer/strategies/colossalai.py | 13 +- applications/Chat/coati/trainer/utils.py | 27 +++ applications/Chat/examples/README.md | 5 +- .../community/peft/train_peft_prompts.py | 10 +- applications/Chat/examples/test_ci.sh | 16 +- applications/Chat/examples/train_prompts.py | 10 +- applications/Chat/examples/train_prompts.sh | 21 ++- .../Chat/examples/train_reward_model.py | 7 +- applications/Chat/examples/train_sft.py | 7 +- 16 files changed, 461 insertions(+), 361 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 29cd581d7cc9..082cbb22b587 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -83,7 +83,7 @@ More details can be found in the latest news.

    -> DeepSpeedChat performance comes from its blog on 2023 April 12, ColossalChat performance can be reproduced on an AWS p4d.24xlarge node with 8 A100-40G GPUs with the following command: torchrun --standalone --nproc_per_node 8 benchmark_opt_lora_dummy.py --max_timesteps 1 --update_timesteps 1 --use_kernels --strategy colossalai_zero2 --experience_batch_size 64 --train_batch_size 32 +> DeepSpeedChat performance comes from its blog on 2023 April 12, ColossalChat performance can be reproduced on an AWS p4d.24xlarge node with 8 A100-40G GPUs with the following command: torchrun --standalone --nproc_per_node 8 benchmark_opt_lora_dummy.py --num_collect_steps 1 --use_kernels --strategy colossalai_zero2 --experience_batch_size 64 --train_batch_size 32 ## Install diff --git a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py index dea7ebc60a8b..39f2f28eca16 100644 --- a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py +++ b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py @@ -137,6 +137,12 @@ def main(args): (actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim)) + random_prompts = torch.randint(tokenizer.vocab_size, (1000, 256), device=torch.cuda.current_device()) + dataloader = DataLoader(random_prompts, + batch_size=args.experience_batch_size, + shuffle=True, + collate_fn=preprocess_batch) + trainer = PPOTrainer(strategy, actor, critic, @@ -145,7 +151,6 @@ def main(args): actor_optim, critic_optim, ptx_coef=0, - max_epochs=args.max_epochs, train_batch_size=args.train_batch_size, offload_inference_models=args.offload_inference_models, max_length=512, @@ -157,17 +162,11 @@ def main(args): eos_token_id=tokenizer.eos_token_id, callbacks=[performance_evaluator]) - random_prompts = torch.randint(tokenizer.vocab_size, (1000, 256), device=torch.cuda.current_device()) - dataloader = DataLoader(random_prompts, - batch_size=args.experience_batch_size, - shuffle=True, - collate_fn=preprocess_batch) - - trainer.fit(dataloader, - None, + trainer.fit(prompt_dataloader=dataloader, + pretrain_dataloader=None, num_episodes=args.num_episodes, - max_timesteps=args.max_timesteps, - update_timesteps=args.update_timesteps) + num_update_steps=args.num_update_steps, + num_collect_steps=args.num_collect_steps) print_rank_0(f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.2f} GB') @@ -183,9 +182,8 @@ def main(args): ], default='ddp') parser.add_argument('--num_episodes', type=int, default=3) - parser.add_argument('--max_timesteps', type=int, default=8) - parser.add_argument('--update_timesteps', type=int, default=8) - parser.add_argument('--max_epochs', type=int, default=1) + parser.add_argument('--num_collect_steps', type=int, default=8) + parser.add_argument('--num_update_steps', type=int, default=1) parser.add_argument('--train_batch_size', type=int, default=8) parser.add_argument('--experience_batch_size', type=int, default=8) parser.add_argument('--lora_rank', type=int, default=0) diff --git a/applications/Chat/coati/trainer/__init__.py b/applications/Chat/coati/trainer/__init__.py index 525b57bf21d3..86142361f3ff 100644 --- a/applications/Chat/coati/trainer/__init__.py +++ b/applications/Chat/coati/trainer/__init__.py @@ -1,6 +1,10 @@ -from .base import Trainer +from .base import OnPolicyTrainer, SLTrainer from .ppo import PPOTrainer from .rm import RewardModelTrainer from .sft import SFTTrainer -__all__ = ['Trainer', 'PPOTrainer', 'RewardModelTrainer', 'SFTTrainer'] +__all__ = [ + 'SLTrainer', 'OnPolicyTrainer', + 'RewardModelTrainer', 'SFTTrainer', + 'PPOTrainer' +] diff --git a/applications/Chat/coati/trainer/base.py b/applications/Chat/coati/trainer/base.py index ac3a878be884..13571cdcc23a 100644 --- a/applications/Chat/coati/trainer/base.py +++ b/applications/Chat/coati/trainer/base.py @@ -1,54 +1,108 @@ from abc import ABC, abstractmethod -from typing import Any, Callable, Dict, List, Optional, Union +from contextlib import contextmanager +from typing import List -import torch +import torch.nn as nn +import tqdm from coati.experience_maker import Experience +from coati.replay_buffer import NaiveReplayBuffer +from torch.optim import Optimizer +from torch.utils.data import DataLoader from .callbacks import Callback from .strategies import Strategy +from .utils import CycledDataLoader, is_rank_0 -class Trainer(ABC): +class SLTrainer(ABC): """ - Base class for rlhf trainers. + Base class for supervised learning trainers. Args: strategy (Strategy):the strategy to use for training max_epochs (int, defaults to 1): the number of epochs of training process + model (nn.Module): the model to train + optim (Optimizer): the optimizer to use for training + """ + + def __init__(self, + strategy: Strategy, + max_epochs: int, + model: nn.Module, + optimizer: Optimizer, + ) -> None: + super().__init__() + self.strategy = strategy + self.max_epochs = max_epochs + self.model = model + self.optimizer = optimizer + + @abstractmethod + def _train(self, epoch): + raise NotImplementedError() + + @abstractmethod + def _eval(self, epoch): + raise NotImplementedError() + + def _before_fit(self): + self.no_epoch_bar = False + + def fit(self, *args, **kwargs): + self._before_fit(*args, **kwargs) + for epoch in tqdm.trange(self.max_epochs, + desc="Epochs", + disable=not is_rank_0() or self.no_epoch_bar + ): + self._train(epoch) + self._eval(epoch) + + +class OnPolicyTrainer(ABC): + """ + Base class for on-policy rl trainers, e.g. PPO. + + Args: + strategy (Strategy):the strategy to use for training + buffer (NaiveReplayBuffer): the buffer to collect experiences + sample_buffer (bool, defaults to False): whether to sample from buffer dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader callbacks (List[Callback], defaults to []): the callbacks to call during training process - generate_kwargs (dict, optional): the kwargs to use while model generating """ def __init__(self, strategy: Strategy, - max_epochs: int = 1, - dataloader_pin_memory: bool = True, - callbacks: List[Callback] = [], - **generate_kwargs) -> None: + buffer: NaiveReplayBuffer, + sample_buffer: bool, + dataloader_pin_memory: bool, + callbacks: List[Callback] = [] + ) -> None: super().__init__() self.strategy = strategy - self.max_epochs = max_epochs - self.generate_kwargs = generate_kwargs + self.buffer = buffer + self.sample_buffer = sample_buffer self.dataloader_pin_memory = dataloader_pin_memory self.callbacks = callbacks - # TODO(ver217): maybe simplify these code using context - def _on_fit_start(self) -> None: + @contextmanager + def _fit_ctx(self) -> None: for callback in self.callbacks: callback.on_fit_start() - - def _on_fit_end(self) -> None: - for callback in self.callbacks: - callback.on_fit_end() - - def _on_episode_start(self, episode: int) -> None: + try: + yield + finally: + for callback in self.callbacks: + callback.on_fit_end() + + @contextmanager + def _episode_ctx(self, episode: int) -> None: for callback in self.callbacks: callback.on_episode_start(episode) - - def _on_episode_end(self, episode: int) -> None: - for callback in self.callbacks: - callback.on_episode_end(episode) + try: + yield + finally: + for callback in self.callbacks: + callback.on_episode_end(episode) def _on_make_experience_start(self) -> None: for callback in self.callbacks: @@ -73,3 +127,71 @@ def _on_learn_batch_start(self) -> None: def _on_learn_batch_end(self, metrics: dict, experience: Experience) -> None: for callback in self.callbacks: callback.on_learn_batch_end(metrics, experience) + + @abstractmethod + def _make_experience(self, collect_step: int): + """ + Implement this method to make experience. + """ + raise NotImplementedError() + + @abstractmethod + def _learn(self, update_step: int): + """ + Implement this method to learn from experience, either + sample from buffer or transform buffer into dataloader. + """ + raise NotImplementedError() + + def _collect_phase(self, collect_step: int): + self._on_make_experience_start() + experience = self._make_experience(collect_step) + self._on_make_experience_end(experience) + self.buffer.append(experience) + + def _update_phase(self, update_step: int): + self._on_learn_epoch_start(update_step) + self._learn(update_step) + self._on_learn_epoch_end(update_step) + + def fit(self, + prompt_dataloader: DataLoader, + pretrain_dataloader: DataLoader, + num_episodes: int, + num_collect_steps: int, + num_update_steps: int, + ): + """ + The main training loop of on-policy rl trainers. + + Args: + prompt_dataloader (DataLoader): the dataloader to use for prompt data + pretrain_dataloader (DataLoader): the dataloader to use for pretrain data + num_episodes (int): the number of episodes to train + num_collect_steps (int): the number of collect steps per episode + num_update_steps (int): the number of update steps per episode + """ + self.prompt_dataloader = CycledDataLoader(prompt_dataloader) + self.pretrain_dataloader = CycledDataLoader(pretrain_dataloader) + + with self._fit_ctx(): + for episode in tqdm.trange(num_episodes, + desc="Episodes", + disable=not is_rank_0()): + with self._episode_ctx(episode): + for collect_step in tqdm.trange(num_collect_steps, + desc="Collect steps", + disable=not is_rank_0()): + self._collect_phase(collect_step) + if not self.sample_buffer: + # HACK(cwher): according to the design of boost API, dataloader should also be boosted, + # but it is impractical to adapt this pattern in RL training. Thus, I left dataloader unboosted. + # I only call strategy.setup_dataloader() to setup dataloader. + self.dataloader = self.strategy.setup_dataloader(self.buffer, + self.dataloader_pin_memory) + for update_step in tqdm.trange(num_update_steps, + desc="Update steps", + disable=not is_rank_0()): + self._update_phase(update_step) + # NOTE: this is for on-policy algorithms + self.buffer.clear() diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index cfb18e2ae483..451abe2a7438 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -1,6 +1,5 @@ -from typing import Any, Callable, Dict, List, Optional, Union +from typing import Dict, List -import torch import torch.nn as nn from coati.experience_maker import Experience, NaiveExperienceMaker from coati.models.base import Actor, Critic, get_base_model @@ -9,19 +8,32 @@ from coati.replay_buffer import NaiveReplayBuffer from torch import Tensor from torch.optim import Optimizer -from torch.utils.data import DistributedSampler +from torch.utils.data import DataLoader, DistributedSampler from tqdm import tqdm -from transformers.tokenization_utils_base import PreTrainedTokenizerBase from colossalai.utils import get_current_device -from .base import Trainer +from .base import OnPolicyTrainer from .callbacks import Callback from .strategies import ColossalAIStrategy, Strategy from .utils import is_rank_0, to_device -class PPOTrainer(Trainer): +def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> Dict: + unwrapper_model = strategy.unwrap_model(actor) + hf_model = get_base_model(unwrapper_model) + new_kwargs = {**generate_kwargs} + # use huggingface models method directly + if 'prepare_inputs_fn' not in generate_kwargs and hasattr(hf_model, 'prepare_inputs_for_generation'): + new_kwargs['prepare_inputs_fn'] = hf_model.prepare_inputs_for_generation + + if 'update_model_kwargs_fn' not in generate_kwargs and hasattr(hf_model, '_update_model_kwargs_for_generation'): + new_kwargs['update_model_kwargs_fn'] = hf_model._update_model_kwargs_for_generation + + return new_kwargs + + +class PPOTrainer(OnPolicyTrainer): """ Trainer for PPO algorithm. @@ -35,14 +47,13 @@ class PPOTrainer(Trainer): critic_optim (Optimizer): the optimizer to use for critic model kl_coef (float, defaults to 0.1): the coefficient of kl divergence loss train_batch_size (int, defaults to 8): the batch size to use for training - buffer_limit (int, defaults to 0): the max_size limitation of replay buffer - buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu + buffer_limit (int, defaults to 0): the max_size limitation of buffer + buffer_cpu_offload (bool, defaults to True): whether to offload buffer to cpu eps_clip (float, defaults to 0.2): the clip coefficient of policy loss vf_coef (float, defaults to 1.0): the coefficient of value loss ptx_coef (float, defaults to 0.9): the coefficient of ptx loss value_clip (float, defaults to 0.4): the clip coefficient of value loss - max_epochs (int, defaults to 1): the number of epochs of training process - sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer + sample_buffer (bool, defaults to False): whether to sample from buffer dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader offload_inference_models (bool, defaults to True): whether to offload inference models to cpu during training process callbacks (List[Callback], defaults to []): the callbacks to call during training process @@ -65,25 +76,26 @@ def __init__(self, eps_clip: float = 0.2, vf_coef: float = 1.0, value_clip: float = 0.4, - max_epochs: int = 1, - sample_replay_buffer: bool = False, + sample_buffer: bool = False, dataloader_pin_memory: bool = True, offload_inference_models: bool = True, callbacks: List[Callback] = [], - **generate_kwargs) -> None: + **generate_kwargs + ) -> None: if isinstance(strategy, ColossalAIStrategy): from colossalai.booster.plugin import GeminiPlugin assert not (isinstance(strategy.plugin, GeminiPlugin) and offload_inference_models), \ "GeminiPlugin is not compatible with manual model.to('cpu')" - experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef) - replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload) - generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor) - super().__init__(strategy, max_epochs, dataloader_pin_memory, callbacks, **generate_kwargs) + buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload) + super().__init__( + strategy, buffer, + sample_buffer, dataloader_pin_memory, + callbacks + ) - self.experience_maker = experience_maker - self.replay_buffer = replay_buffer - self.sample_replay_buffer = sample_replay_buffer + self.generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor) + self.experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef) self.offload_inference_models = offload_inference_models self.actor = actor @@ -99,76 +111,20 @@ def __init__(self, self.device = get_current_device() - def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience: - if isinstance(inputs, Tensor): - return self.experience_maker.make_experience(inputs, **self.generate_kwargs) - elif isinstance(inputs, dict): - return self.experience_maker.make_experience(**inputs, **self.generate_kwargs) - else: - raise ValueError(f'Unsupported input type "{type(inputs)}"') - - def _learn(self): - # replay buffer may be empty at first, we should rebuild at each training - if not self.sample_replay_buffer: - # HACK(cwher): according to the design of boost API, dataloader should also be boosted, - # but it is impractical to adapt this pattern in RL training. Thus, I left dataloader unboosted. - dataloader = self.strategy.setup_dataloader(self.replay_buffer, self.dataloader_pin_memory) - if self.sample_replay_buffer: - pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) - for _ in pbar: - experience = self.replay_buffer.sample() - experience.to_device(self.device) - metrics = self.training_step(experience) - pbar.set_postfix(metrics) + def _make_experience(self, collect_step: int) -> Experience: + prompts = self.prompt_dataloader.next() + if self.offload_inference_models: + # TODO(ver217): this may be controlled by strategy if they are prepared by strategy + self.experience_maker.initial_model.to(self.device) + self.experience_maker.reward_model.to(self.device) + if isinstance(prompts, Tensor): + return self.experience_maker.make_experience(prompts, **self.generate_kwargs) + elif isinstance(prompts, dict): + return self.experience_maker.make_experience(**prompts, **self.generate_kwargs) else: - for epoch in range(self.max_epochs): - self._on_learn_epoch_start(epoch) - if isinstance(dataloader.sampler, DistributedSampler): - dataloader.sampler.set_epoch(epoch) - pbar = tqdm(dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0()) - for experience in pbar: - self._on_learn_batch_start() - experience.to_device(self.device) - metrics = self.training_step(experience) - self._on_learn_batch_end(metrics, experience) - pbar.set_postfix(metrics) - self._on_learn_epoch_end(epoch) - - def fit(self, - prompt_dataloader, - pretrain_dataloader, - num_episodes: int = 50000, - max_timesteps: int = 500, - update_timesteps: int = 5000) -> None: - time = 0 - self.pretrain_dataloader = pretrain_dataloader - self.prompt_dataloader = prompt_dataloader - self._on_fit_start() - for episode in range(num_episodes): - self._on_episode_start(episode) - for timestep in tqdm(range(max_timesteps), - desc=f'Episode [{episode+1}/{num_episodes}]', - disable=not is_rank_0()): - time += 1 - prompts = next(iter(self.prompt_dataloader)) - self._on_make_experience_start() - if self.offload_inference_models: - # TODO(ver217): this may be controlled by strategy if they are prepared by strategy - self.experience_maker.initial_model.to(self.device) - self.experience_maker.reward_model.to(self.device) - experience = self._make_experience(prompts) - self._on_make_experience_end(experience) - self.replay_buffer.append(experience) - if time % update_timesteps == 0: - if self.offload_inference_models: - self.experience_maker.initial_model.to('cpu') - self.experience_maker.reward_model.to('cpu') - self._learn() - self.replay_buffer.clear() - self._on_episode_end(episode) - self._on_fit_end() - - def training_step(self, experience: Experience) -> Dict[str, float]: + raise ValueError(f'Unsupported input type "{type(prompts)}"') + + def _training_step(self, experience: Experience) -> Dict[str, float]: self.actor.train() self.critic.train() # policy loss @@ -182,7 +138,7 @@ def training_step(self, experience: Experience) -> Dict[str, float]: # ptx loss if self.ptx_coef != 0: - batch = next(iter(self.pretrain_dataloader)) + batch = self.pretrain_dataloader.next() batch = to_device(batch, self.device) ptx_log_probs = self.actor(batch['input_ids'], attention_mask=batch['attention_mask'])['logits'] @@ -208,16 +164,29 @@ def training_step(self, experience: Experience) -> Dict[str, float]: return {'reward': experience.reward.mean().item()} - -def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> Dict: - unwrapper_model = strategy.unwrap_model(actor) - hf_model = get_base_model(unwrapper_model) - new_kwargs = {**generate_kwargs} - # use huggingface models method directly - if 'prepare_inputs_fn' not in generate_kwargs and hasattr(hf_model, 'prepare_inputs_for_generation'): - new_kwargs['prepare_inputs_fn'] = hf_model.prepare_inputs_for_generation - - if 'update_model_kwargs_fn' not in generate_kwargs and hasattr(hf_model, '_update_model_kwargs_for_generation'): - new_kwargs['update_model_kwargs_fn'] = hf_model._update_model_kwargs_for_generation - - return new_kwargs + def _learn(self, update_step: int): + if self.offload_inference_models: + self.experience_maker.initial_model.to('cpu') + self.experience_maker.reward_model.to('cpu') + + # buffer may be empty at first, we should rebuild at each training + if self.sample_buffer: + experience = self.buffer.sample() + self._on_learn_batch_start() + experience.to_device(self.device) + metrics = self._training_step(experience) + self._on_learn_batch_end(metrics, experience) + else: + if isinstance(self.dataloader.sampler, DistributedSampler): + self.dataloader.sampler.set_epoch(update_step) + pbar = tqdm( + self.dataloader, + desc=f'Train epoch [{update_step + 1}]', + disable=not is_rank_0() + ) + for experience in pbar: + self._on_learn_batch_start() + experience.to_device(self.device) + metrics = self._training_step(experience) + self._on_learn_batch_end(metrics, experience) + pbar.set_postfix(metrics) diff --git a/applications/Chat/coati/trainer/rm.py b/applications/Chat/coati/trainer/rm.py index 316eded7ea5d..54a5d0f40dea 100644 --- a/applications/Chat/coati/trainer/rm.py +++ b/applications/Chat/coati/trainer/rm.py @@ -1,20 +1,19 @@ from datetime import datetime -from typing import Callable, List +from typing import Callable import pandas as pd import torch +import tqdm from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler from torch.utils.data import DataLoader -from tqdm import tqdm -from .base import Trainer -from .callbacks import Callback +from .base import SLTrainer from .strategies import Strategy from .utils import is_rank_0 -class RewardModelTrainer(Trainer): +class RewardModelTrainer(SLTrainer): """ Trainer to use while training reward model. @@ -24,12 +23,7 @@ class RewardModelTrainer(Trainer): optim (Optimizer): the optimizer to use for training lr_scheduler (_LRScheduler): the lr scheduler to use for training loss_fn (callable): the loss function to use for training - train_dataloader (DataLoader): the dataloader to use for training - valid_dataloader (DataLoader): the dataloader to use for validation - eval_dataloader (DataLoader): the dataloader to use for evaluation - batch_size (int, defaults to 1): the batch size while training max_epochs (int, defaults to 2): the number of epochs to train - callbacks (List[Callback], defaults to []): the callbacks to call during training process """ def __init__( @@ -39,87 +33,79 @@ def __init__( optim: Optimizer, lr_scheduler: _LRScheduler, loss_fn: Callable, - train_dataloader: DataLoader, - valid_dataloader: DataLoader, - eval_dataloader: DataLoader, max_epochs: int = 1, - callbacks: List[Callback] = [], ) -> None: - super().__init__(strategy, max_epochs, callbacks=callbacks) + super().__init__(strategy, max_epochs, model, optim) - self.train_dataloader = train_dataloader - self.valid_dataloader = valid_dataloader - self.eval_dataloader = eval_dataloader - - self.model = model self.loss_fn = loss_fn - self.optimizer = optim self.scheduler = lr_scheduler - def eval_acc(self, dataloader): - dist = 0 - on = 0 - cnt = 0 - self.model.eval() - with torch.no_grad(): - for chosen_ids, c_mask, reject_ids, r_mask in dataloader: - chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) - c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) - reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) - r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) - chosen_reward = self.model(chosen_ids, attention_mask=c_mask) - reject_reward = self.model(reject_ids, attention_mask=r_mask) - for i in range(len(chosen_reward)): - cnt += 1 - if chosen_reward[i] > reject_reward[i]: - on += 1 - dist += (chosen_reward - reject_reward).mean().item() - dist_mean = dist / len(dataloader) - acc = on / cnt - self.model.train() - return dist_mean, acc + def _eval(self, epoch): + if self.eval_dataloader is not None: + self.model.eval() + dist, on, cnt = 0, 0, 0 + with torch.no_grad(): + for chosen_ids, c_mask, reject_ids, r_mask in self.eval_dataloader: + chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) + c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) + reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) + r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) + chosen_reward = self.model(chosen_ids, attention_mask=c_mask) + reject_reward = self.model(reject_ids, attention_mask=r_mask) + for i in range(len(chosen_reward)): + cnt += 1 + if chosen_reward[i] > reject_reward[i]: + on += 1 + dist += (chosen_reward - reject_reward).mean().item() + self.dist = dist / len(self.eval_dataloader) + self.acc = on / cnt - def fit(self): - time = datetime.now() - epoch_bar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0()) - for epoch in range(self.max_epochs): - step_bar = tqdm(range(self.train_dataloader.__len__()), - desc='Train step of epoch %d' % epoch, - disable=not is_rank_0()) - # train - self.model.train() - cnt = 0 - acc = 0 - dist = 0 - for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader: - chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) - c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) - reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) - r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) - chosen_reward = self.model(chosen_ids, attention_mask=c_mask) - reject_reward = self.model(reject_ids, attention_mask=r_mask) - loss = self.loss_fn(chosen_reward, reject_reward) - self.strategy.backward(loss, self.model, self.optimizer) - self.strategy.optimizer_step(self.optimizer) - self.optimizer.zero_grad() - cnt += 1 - if cnt == 100: - self.scheduler.step() - dist, acc = self.eval_acc(self.valid_dataloader) - cnt = 0 - if is_rank_0(): - log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], - columns=['step', 'loss', 'dist', 'acc']) - log.to_csv('log_%s.csv' % time, mode='a', header=False, index=False) - step_bar.update() - step_bar.set_postfix({'dist': dist, 'acc': acc}) - - # eval - dist, acc = self.eval_acc(self.eval_dataloader) if is_rank_0(): - log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], - columns=['step', 'loss', 'dist', 'acc']) + log = pd.DataFrame( + [[(epoch + 1) * len(self.train_dataloader), + self.loss.item(), self.dist, self.acc]], + columns=['step', 'loss', 'dist', 'acc'] + ) log.to_csv('log.csv', mode='a', header=False, index=False) - epoch_bar.update() - step_bar.set_postfix({'dist': dist, 'acc': acc}) - step_bar.close() + + def _train(self, epoch): + self.model.train() + step_bar = tqdm.trange( + len(self.train_dataloader), + desc='Train step of epoch %d' % epoch, + disable=not is_rank_0() + ) + cnt = 0 + for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader: + chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device()) + c_mask = c_mask.squeeze(1).to(torch.cuda.current_device()) + reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device()) + r_mask = r_mask.squeeze(1).to(torch.cuda.current_device()) + chosen_reward = self.model(chosen_ids, attention_mask=c_mask) + reject_reward = self.model(reject_ids, attention_mask=r_mask) + self.loss = self.loss_fn(chosen_reward, reject_reward) + self.strategy.backward(self.loss, self.model, self.optimizer) + self.strategy.optimizer_step(self.optimizer) + self.optimizer.zero_grad() + cnt += 1 + if cnt % 100 == 0: + self.scheduler.step() + step_bar.update() + step_bar.close() + + def _before_fit(self, + train_dataloader: DataLoader, + valid_dataloader: DataLoader, + eval_dataloader: DataLoader): + """ + Args: + train_dataloader (DataLoader): the dataloader to use for training + valid_dataloader (DataLoader): the dataloader to use for validation + eval_dataloader (DataLoader): the dataloader to use for evaluation + """ + super()._before_fit() + self.datetime = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + + self.train_dataloader = train_dataloader + self.valid_dataloader = valid_dataloader + self.eval_dataloader = eval_dataloader diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index da223f1f33ff..12c51d7a80c3 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -1,21 +1,22 @@ import time -from typing import List +from typing import Optional import torch import torch.distributed as dist +import tqdm import wandb from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler from torch.utils.data import DataLoader -from tqdm import tqdm -from .base import Trainer -from .callbacks import Callback +from colossalai.logging import DistributedLogger + +from .base import SLTrainer from .strategies import ColossalAIStrategy, Strategy from .utils import is_rank_0, to_device -class SFTTrainer(Trainer): +class SFTTrainer(SLTrainer): """ Trainer to use while training reward model. @@ -23,12 +24,9 @@ class SFTTrainer(Trainer): model (torch.nn.Module): the model to train strategy (Strategy): the strategy to use for training optim(Optimizer): the optimizer to use for training - train_dataloader: the dataloader to use for training - eval_dataloader: the dataloader to use for evaluation - batch_size (int, defaults to 1): the batch size while training + lr_scheduler(_LRScheduler): the lr scheduler to use for training max_epochs (int, defaults to 2): the number of epochs to train - callbacks (List[Callback], defaults to []): the callbacks to call during training process - optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer + accumulation_steps (int, defaults to 8): the number of steps to accumulate gradients """ def __init__( @@ -37,95 +35,92 @@ def __init__( strategy: Strategy, optim: Optimizer, lr_scheduler: _LRScheduler, - train_dataloader: DataLoader, - eval_dataloader: DataLoader = None, max_epochs: int = 2, accumulation_steps: int = 8, - callbacks: List[Callback] = [], ) -> None: if accumulation_steps > 1 and isinstance(strategy, ColossalAIStrategy): from colossalai.booster.plugin import GeminiPlugin assert not isinstance(strategy.plugin, GeminiPlugin), \ "Accumulation steps are not supported in stage 3 of ColossalAI" - super().__init__(strategy, max_epochs, callbacks=callbacks) - self.train_dataloader = train_dataloader - self.eval_dataloader = eval_dataloader - self.model = model - self.optimizer = optim - self.accumulation_steps = accumulation_steps + super().__init__(strategy, max_epochs, model, optim) + self.accumulation_steps = accumulation_steps self.scheduler = lr_scheduler - def fit(self, logger, use_wandb: bool = False): + def _train(self, epoch: int): + self.model.train() + for batch_id, batch in enumerate(self.train_dataloader): + + batch = to_device(batch, torch.cuda.current_device()) + outputs = self.model(batch["input_ids"], + attention_mask=batch["attention_mask"], + labels=batch["labels"]) + + loss = outputs.loss + loss = loss / self.accumulation_steps + + self.strategy.backward(loss, self.model, self.optimizer) + + self.total_loss += loss.item() + + # gradient accumulation + if (batch_id + 1) % self.accumulation_steps == 0: + self.strategy.optimizer_step(self.optimizer) + self.optimizer.zero_grad() + self.scheduler.step() + if is_rank_0() and self.use_wandb: + wandb.log({ + "loss": self.total_loss / self.accumulation_steps, + "lr": self.scheduler.get_last_lr()[0], + "epoch": epoch, + "batch_id": batch_id + }) + self.total_loss = 0 + self.step_bar.update() + + def _eval(self, epoch: int): + if self.eval_dataloader is not None: + self.model.eval() + with torch.no_grad(): + loss_sum, num_seen = 0, 0 + for batch in self.eval_dataloader: + batch = to_device(batch, torch.cuda.current_device()) + outputs = self.model(batch["input_ids"], + attention_mask=batch["attention_mask"], + labels=batch["labels"]) + loss = outputs.loss + + loss_sum += loss.item() + num_seen += batch["input_ids"].size(0) + + loss_mean = loss_sum / num_seen + if dist.get_rank() == 0: + self.logger.info(f'Eval Epoch {epoch}/{self.max_epochs} loss {loss_mean}') + + def _before_fit(self, + train_dataloader: DataLoader, + eval_dataloader: Optional[DataLoader] = None, + logger: Optional[DistributedLogger] = None, + use_wandb: bool = False): + """ + Args: + train_dataloader: the dataloader to use for training + eval_dataloader: the dataloader to use for evaluation + """ + self.train_dataloader = train_dataloader + self.eval_dataloader = eval_dataloader + + self.logger = logger + self.use_wandb = use_wandb if use_wandb: wandb.init(project="Coati", name=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) wandb.watch(self.model) - total_loss = 0 - # epoch_bar = tqdm(range(self.epochs), desc='Epochs', disable=not is_rank_0()) - step_bar = tqdm(range(len(self.train_dataloader) // self.accumulation_steps * self.max_epochs), - desc=f'steps', - disable=not is_rank_0()) - for epoch in range(self.max_epochs): - - # process_bar = tqdm(range(len(self.train_dataloader)), desc=f'Train process for{epoch}', disable=not is_rank_0()) - # train - self.model.train() - for batch_id, batch in enumerate(self.train_dataloader): - - batch = to_device(batch, torch.cuda.current_device()) - outputs = self.model(batch["input_ids"], attention_mask=batch["attention_mask"], labels=batch["labels"]) - - loss = outputs.loss - - if loss >= 2.5 and is_rank_0(): - logger.warning(f"batch_id:{batch_id}, abnormal loss: {loss}") - - loss = loss / self.accumulation_steps - - self.strategy.backward(loss, self.model, self.optimizer) - - total_loss += loss.item() - - # gradient accumulation - if (batch_id + 1) % self.accumulation_steps == 0: - self.strategy.optimizer_step(self.optimizer) - self.optimizer.zero_grad() - self.scheduler.step() - if is_rank_0() and use_wandb: - wandb.log({ - "loss": total_loss / self.accumulation_steps, - "lr": self.scheduler.get_last_lr()[0], - "epoch": epoch, - "batch_id": batch_id - }) - total_loss = 0 - step_bar.update() - - # if batch_id % log_interval == 0: - # logger.info(f'Train Epoch {epoch}/{self.epochs} Batch {batch_id} Rank {dist.get_rank()} loss {loss.item()}') - # wandb.log({"loss": loss.item()}) - - # process_bar.update() - - # eval - if self.eval_dataloader is not None: - self.model.eval() - with torch.no_grad(): - loss_sum = 0 - num_seen = 0 - for batch in self.eval_dataloader: - batch = to_device(batch, torch.cuda.current_device()) - outputs = self.model(batch["input_ids"], - attention_mask=batch["attention_mask"], - labels=batch["labels"]) - loss = outputs.loss - - loss_sum += loss.item() - num_seen += batch["input_ids"].size(0) - - loss_mean = loss_sum / num_seen - if dist.get_rank() == 0: - logger.info(f'Eval Epoch {epoch}/{self.max_epochs} loss {loss_mean}') - - # epoch_bar.update() + + self.total_loss = 0 + self.no_epoch_bar = True + self.step_bar = tqdm.trange( + len(self.train_dataloader) // self.accumulation_steps * self.max_epochs, + desc=f'steps', + disable=not is_rank_0() + ) diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index f31551f22318..e5a69f3351cb 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -1,4 +1,3 @@ -import functools import warnings from typing import Optional @@ -103,7 +102,7 @@ def __init__( # NOTE: dist should be initialized before calling get_current_device() if stage == 3: plugin_initializer = lambda: GeminiPlugin( - # gemini_config + # gemini_config device=get_current_device(), placement_policy=placement_policy, precision=precision, @@ -113,20 +112,20 @@ def __init__( search_range_m=search_range_m, hidden_dim=hidden_dim, min_chunk_size_m=min_chunk_size_m, - # zero_optim_config + # zero_optim_config gpu_margin_mem_ratio=gpu_margin_mem_ratio, - # optim_config + # optim_config **optim_kwargs) else: plugin_initializer = lambda: LowLevelZeroPlugin( - # zero_config + # zero_config stage=stage, precision=precision, - # zero_optim_config + # zero_optim_config reduce_bucket_size_in_m=reduce_bucket_size, overlap_communication=overlap_communication, cpu_offload=(placement_policy == 'cpu'), - # optim_config + # optim_config **optim_kwargs) super().__init__(seed, plugin_initializer) diff --git a/applications/Chat/coati/trainer/utils.py b/applications/Chat/coati/trainer/utils.py index 9cccb5c92603..c9fc8d0fe19f 100644 --- a/applications/Chat/coati/trainer/utils.py +++ b/applications/Chat/coati/trainer/utils.py @@ -3,6 +3,33 @@ import torch import torch.distributed as dist from torch.utils._pytree import tree_map +from torch.utils.data import DataLoader + + +class CycledDataLoader: + """ + Why do we need this class? + In version 4da324cd60, "prompts = next(iter(self.prompt_dataloader))" is used to sample a batch of prompts/pretrain. + However, this may be inefficient due to frequent re-initialization of the dataloader. (re-initialize workers...) + NOTE: next(iter(dataloader)) is not equivalent to for batch in dataloader: break, it causes slightly different behavior. + """ + + def __init__(self, + dataloader: DataLoader, + ) -> None: + self.dataloader = dataloader + + self.count = 0 + self.dataloader_iter = iter(dataloader) + + def next(self): + self.count += 1 + try: + return next(self.dataloader_iter) + except StopIteration: + self.count = 0 + self.dataloader_iter = iter(self.dataloader) + return next(self.dataloader_iter) def is_rank_0() -> bool: diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 72810738d017..3e9d9c4325d8 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -171,9 +171,8 @@ Pretrain dataset: the pretrain dataset including the instruction and correspondi - --pretrain_dataset: path of the ptx dataset, type=str, default=None - --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False - --num_episodes: num of episodes for training, type=int, default=10 -- --max_epochs: max epochs for training in one episode, type=int, default=5 -- --max_timesteps: max episodes in one batch, type=int, default=10 -- --update_timesteps: timesteps to update, type=int, default=10 +- --num_update_steps: number of steps to update policy per episode, type=int +- --num_collect_steps: number of steps to collect experience per episode, type=int - --train_batch_size: batch size while training, type=int, default=8 - --ptx_batch_size: batch size to compute ptx loss, type=int, default=1 - --experience_batch_size: batch size to make experience, type=int, default=8 diff --git a/applications/Chat/examples/community/peft/train_peft_prompts.py b/applications/Chat/examples/community/peft/train_peft_prompts.py index ba8470f38fad..00ed7aa36257 100644 --- a/applications/Chat/examples/community/peft/train_peft_prompts.py +++ b/applications/Chat/examples/community/peft/train_peft_prompts.py @@ -171,7 +171,6 @@ def tokenize_fn(texts): critic_optim, kl_coef=args.kl_coef, ptx_coef=args.ptx_coef, - max_epochs=args.max_epochs, train_batch_size=args.train_batch_size, experience_batch_size=args.experience_batch_size, tokenizer=tokenize_fn, @@ -186,8 +185,8 @@ def tokenize_fn(texts): trainer.fit(prompt_dataloader=prompt_dataloader, pretrain_dataloader=pretrain_dataloader, num_episodes=args.num_episodes, - max_timesteps=args.max_timesteps, - update_timesteps=args.update_timesteps) + num_update_steps=args.num_update_steps, + num_collect_steps=args.num_collect_steps) # save model checkpoint after fitting trainer.save_model(args.save_path, only_rank0=True, tokenizer=tokenizer) @@ -215,9 +214,8 @@ def tokenize_fn(texts): parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts') parser.add_argument('--need_optim_ckpt', type=bool, default=False) parser.add_argument('--num_episodes', type=int, default=10) - parser.add_argument('--max_timesteps', type=int, default=10) - parser.add_argument('--update_timesteps', type=int, default=10) - parser.add_argument('--max_epochs', type=int, default=5) + parser.add_argument('--num_collect_steps', type=int, default=10) + parser.add_argument('--num_update_steps', type=int, default=5) parser.add_argument('--train_batch_size', type=int, default=2) parser.add_argument('--ptx_batch_size', type=int, default=1) parser.add_argument('--experience_batch_size', type=int, default=8) diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index 85728e95820c..4bf5524afb01 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -63,8 +63,8 @@ for model in 'gpt2' 'bloom' 'opt' 'llama' 'roberta'; do torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ --strategy $strategy --model $model \ - --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2 + --num_episodes 1 --num_collect_steps 2 --num_update_steps 1 \ + --train_batch_size 2 done done @@ -149,8 +149,8 @@ rm -rf ${BASE}/rm_ckpt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ + --strategy colossalai_zero2 --num_episodes 1 \ + --num_collect_steps 2 --num_update_steps 1 --train_batch_size 2 \ --pretrain 'facebook/opt-350m' --model opt \ --rm_pretrain 'facebook/opt-350m' \ --rm_path ${BASE}/rm_ckpt_opt.pt \ @@ -159,8 +159,8 @@ rm -rf ${BASE}/rm_ckpt_opt.pt torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ - --strategy colossalai_zero2 --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ + --strategy colossalai_zero2 --num_episodes 1 \ + --num_collect_steps 2 --num_update_steps 1 --train_batch_size 2 \ --pretrain 'gpt2' --model gpt2 \ --rm_pretrain 'gpt2' \ --rm_path ${BASE}/rm_ckpt_gpt.pt \ @@ -168,8 +168,8 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ - --strategy colossalai_gemini --num_episodes 1 --max_timesteps 2 \ - --update_timesteps 2 --max_epochs 1 --train_batch_size 2 \ + --strategy colossalai_gemini --num_episodes 1 \ + --num_collect_steps 2 --num_update_steps 1 --train_batch_size 2 \ --pretrain 'gpt2' --model gpt2 \ --rm_pretrain 'gpt2' \ --rm_path ${BASE}/rm_ckpt_gpt.pt \ diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index 2a47dda637bb..a9bc0e532e5d 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -177,7 +177,6 @@ def main(args): critic_optim, kl_coef=args.kl_coef, ptx_coef=args.ptx_coef, - max_epochs=args.max_epochs, train_batch_size=args.train_batch_size, max_length=args.max_seq_len, use_cache=True, @@ -192,8 +191,8 @@ def main(args): trainer.fit(prompt_dataloader=prompt_dataloader, pretrain_dataloader=pretrain_dataloader, num_episodes=args.num_episodes, - max_timesteps=args.max_timesteps, - update_timesteps=args.update_timesteps) + num_collect_steps=args.num_collect_steps, + num_update_steps=args.num_update_steps) # save model checkpoint after fitting strategy.save_model(actor, args.save_path, only_rank0=True) @@ -220,9 +219,8 @@ def main(args): parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts') parser.add_argument('--need_optim_ckpt', type=bool, default=False) parser.add_argument('--num_episodes', type=int, default=10) - parser.add_argument('--max_timesteps', type=int, default=10) - parser.add_argument('--update_timesteps', type=int, default=10) - parser.add_argument('--max_epochs', type=int, default=5) + parser.add_argument('--num_collect_steps', type=int, default=10) + parser.add_argument('--num_update_steps', type=int, default=5) parser.add_argument('--train_batch_size', type=int, default=8) parser.add_argument('--ptx_batch_size', type=int, default=1) parser.add_argument('--experience_batch_size', type=int, default=8) diff --git a/applications/Chat/examples/train_prompts.sh b/applications/Chat/examples/train_prompts.sh index 7f3b2636ca32..d04c416015b1 100755 --- a/applications/Chat/examples/train_prompts.sh +++ b/applications/Chat/examples/train_prompts.sh @@ -1,13 +1,13 @@ set_n_least_used_CUDA_VISIBLE_DEVICES() { local n=${1:-"9999"} echo "GPU Memory Usage:" - local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \ - | tail -n +2 \ - | nl -v 0 \ - | tee /dev/tty \ - | sort -g -k 2 \ - | awk '{print $1}' \ - | head -n $n) + local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv | + tail -n +2 | + nl -v 0 | + tee /dev/tty | + sort -g -k 2 | + awk '{print $1}' | + head -n $n) export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') echo "Now CUDA_VISIBLE_DEVICES is set to:" echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" @@ -17,4 +17,9 @@ set_n_least_used_CUDA_VISIBLE_DEVICES 2 # torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai_zero2 -torchrun --standalone --nproc_per_node=2 train_prompts.py --prompt_dataset /path/to/data.json --strategy colossalai_zero2 +torchrun --standalone --nproc_per_node=2 train_prompts.py \ + --pretrain_dataset /path/to/data.json \ + --prompt_dataset /path/to/data.json \ + --strategy colossalai_zero2 \ + --num_episodes 1 --num_collect_steps 2 --num_update_steps 1 \ + --train_batch_size 2 diff --git a/applications/Chat/examples/train_reward_model.py b/applications/Chat/examples/train_reward_model.py index 2df3bc391b9b..4a6851ab5b24 100644 --- a/applications/Chat/examples/train_reward_model.py +++ b/applications/Chat/examples/train_reward_model.py @@ -178,12 +178,11 @@ def train(args): optim=optim, lr_scheduler=lr_scheduler, loss_fn=loss_fn, - train_dataloader=train_dataloader, - valid_dataloader=valid_dataloader, - eval_dataloader=eval_dataloader, max_epochs=args.max_epochs) - trainer.fit() + trainer.fit(train_dataloader=train_dataloader, + valid_dataloader=valid_dataloader, + eval_dataloader=eval_dataloader) # save model checkpoint after fitting on only rank0 strategy.save_model(model, args.save_path, only_rank0=True) # save optimizer checkpoint on all ranks diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index 717eb95311fb..967b7c277c6a 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -170,12 +170,13 @@ def train(args): strategy=strategy, optim=optim, lr_scheduler=lr_scheduler, - train_dataloader=train_dataloader, - eval_dataloader=eval_dataloader, max_epochs=args.max_epochs, accumulation_steps=args.accumulation_steps) - trainer.fit(logger=logger, use_wandb=args.use_wandb) + trainer.fit(train_dataloader=train_dataloader, + eval_dataloader=eval_dataloader, + logger=logger, + use_wandb=args.use_wandb) # save model checkpoint after fitting on only rank0 strategy.save_pretrained(model, path=args.save_path, only_rank0=True, tokenizer=tokenizer) From edd75a59eada232a7d093b070e4ec7bfd81f31c3 Mon Sep 17 00:00:00 2001 From: Wenhao Chen Date: Thu, 29 Jun 2023 18:11:00 +0800 Subject: [PATCH 342/413] [chat] remove naive strategy and split colossalai strategy (#4094) * feat: remove on_learn_epoch fn as not used * revert: add _on_learn_epoch fn * to: remove the use of NaiveStrategy * test: remove NaiveStrategy tests * feat: remove NaiveStrategy * style: modify comments and params * feat: split ColossalAIStrategy into LowLevelZeroStrategy and GeminiStrategy * fix: remove naive * fix: align with modified colossal strategy * fix: fix ddp _try_init_dist arg --- applications/Chat/README.md | 2 +- .../benchmarks/benchmark_opt_lora_dummy.py | 20 +- .../Chat/benchmarks/ray/1mmt_dummy.py | 8 +- .../Chat/benchmarks/ray/mmmt_dummy.py | 8 +- .../Chat/coati/ray/detached_trainer_ppo.py | 4 +- applications/Chat/coati/ray/utils.py | 16 +- .../trainer/callbacks/save_checkpoint.py | 4 +- applications/Chat/coati/trainer/ppo.py | 7 +- applications/Chat/coati/trainer/sft.py | 7 +- .../Chat/coati/trainer/strategies/__init__.py | 8 +- .../coati/trainer/strategies/colossalai.py | 259 ++++++++++-------- .../Chat/coati/trainer/strategies/ddp.py | 68 ++++- .../Chat/coati/trainer/strategies/naive.py | 103 ------- applications/Chat/examples/README.md | 6 +- .../community/peft/train_peft_prompts.py | 14 +- .../examples/community/peft/train_peft_sft.py | 24 +- .../community/ray/train_prompts_on_ray.py | 16 +- applications/Chat/examples/ray/1mmt_prompt.py | 8 +- applications/Chat/examples/ray/mmmt_prompt.py | 8 +- applications/Chat/examples/test_ci.sh | 14 +- applications/Chat/examples/train_prompts.py | 12 +- .../Chat/examples/train_reward_model.py | 12 +- applications/Chat/examples/train_sft.py | 16 +- applications/Chat/tests/test_checkpoint.py | 6 +- applications/Chat/tests/test_data.py | 4 +- 25 files changed, 314 insertions(+), 340 deletions(-) delete mode 100644 applications/Chat/coati/trainer/strategies/naive.py diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 082cbb22b587..016272ed8c89 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -287,7 +287,7 @@ If you only have a single 24G GPU, you can use the following script. `batch_size torchrun --standalone --nproc_per_node=1 train_sft.py \ --pretrain "/path/to/LLaMa-7B/" \ --model 'llama' \ - --strategy naive \ + --strategy ddp \ --log_interval 10 \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ diff --git a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py index 39f2f28eca16..90471ed727b0 100644 --- a/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py +++ b/applications/Chat/benchmarks/benchmark_opt_lora_dummy.py @@ -8,7 +8,7 @@ from coati.models.opt import OPTActor, OPTCritic from coati.trainer import PPOTrainer from coati.trainer.callbacks import PerformanceEvaluator -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, Strategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy, Strategy from torch.optim import Adam from torch.utils.data import DataLoader from transformers import AutoTokenizer @@ -19,10 +19,8 @@ def get_model_numel(model: nn.Module, strategy: Strategy) -> int: numel = sum(p.numel() for p in model.parameters()) - if isinstance(strategy, ColossalAIStrategy): - from colossalai.booster.plugin import GeminiPlugin - if isinstance(strategy.plugin, GeminiPlugin) and strategy.shard_init: - numel *= dist.get_world_size() + if isinstance(strategy, GeminiStrategy) and strategy.shard_init: + numel *= dist.get_world_size() return numel @@ -78,17 +76,17 @@ def main(args): if args.strategy == 'ddp': strategy = DDPStrategy() elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + strategy = GeminiStrategy(placement_policy='cuda', initial_scale=2**5) elif args.strategy == 'colossalai_gemini_cpu': - strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) + strategy = GeminiStrategy(placement_policy='cpu', initial_scale=2**5) elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + strategy = LowLevelZeroStrategy(stage=2, placement_policy='cuda') elif args.strategy == 'colossalai_zero2_cpu': - strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') + strategy = LowLevelZeroStrategy(stage=2, placement_policy='cpu') elif args.strategy == 'colossalai_zero1': - strategy = ColossalAIStrategy(stage=1, placement_policy='cuda') + strategy = LowLevelZeroStrategy(stage=1, placement_policy='cuda') elif args.strategy == 'colossalai_zero1_cpu': - strategy = ColossalAIStrategy(stage=1, placement_policy='cpu') + strategy = LowLevelZeroStrategy(stage=1, placement_policy='cpu') else: raise ValueError(f'Unsupported strategy "{args.strategy}"') diff --git a/applications/Chat/benchmarks/ray/1mmt_dummy.py b/applications/Chat/benchmarks/ray/1mmt_dummy.py index 9e8f36cefc4f..7fc990448805 100644 --- a/applications/Chat/benchmarks/ray/1mmt_dummy.py +++ b/applications/Chat/benchmarks/ray/1mmt_dummy.py @@ -83,8 +83,8 @@ def model_fn(): env_info=env_info_maker, kl_coef=0.1, debug=args.debug, - # sync_models_from_trainers=True, - # generation kwargs: + # sync_models_from_trainers=True, + # generation kwargs: max_length=512, do_sample=True, temperature=1.0, @@ -153,10 +153,10 @@ def build_dataloader(size): parser.add_argument('--num_trainers', type=int, default=1) parser.add_argument('--trainer_strategy', choices=[ - 'naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', + 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', 'colossalai_zero2_cpu' ], - default='naive') + default='ddp') parser.add_argument('--maker_strategy', choices=['naive'], default='naive') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) diff --git a/applications/Chat/benchmarks/ray/mmmt_dummy.py b/applications/Chat/benchmarks/ray/mmmt_dummy.py index 46a0062893b8..ca1df22070fc 100644 --- a/applications/Chat/benchmarks/ray/mmmt_dummy.py +++ b/applications/Chat/benchmarks/ray/mmmt_dummy.py @@ -87,8 +87,8 @@ def model_fn(): env_info=env_info_maker, kl_coef=0.1, debug=args.debug, - # sync_models_from_trainers=True, - # generation kwargs: + # sync_models_from_trainers=True, + # generation kwargs: max_length=512, do_sample=True, temperature=1.0, @@ -164,10 +164,10 @@ def build_dataloader(size): parser.add_argument('--num_trainers', type=int, default=1) parser.add_argument('--trainer_strategy', choices=[ - 'naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', + 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', 'colossalai_zero2_cpu' ], - default='naive') + default='ddp') parser.add_argument('--maker_strategy', choices=['naive'], default='naive') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) diff --git a/applications/Chat/coati/ray/detached_trainer_ppo.py b/applications/Chat/coati/ray/detached_trainer_ppo.py index 5f0032716f93..2f2aa0e29579 100644 --- a/applications/Chat/coati/ray/detached_trainer_ppo.py +++ b/applications/Chat/coati/ray/detached_trainer_ppo.py @@ -6,7 +6,7 @@ from coati.models.base import Actor, Critic from coati.models.loss import PolicyLoss, ValueLoss from coati.trainer.callbacks import Callback -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy, Strategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy, Strategy from torch.optim import Adam from colossalai.nn.optimizer import HybridAdam @@ -85,7 +85,7 @@ def __init__( evaluator = TrainerPerformanceEvaluator(actor_numel, critic_numel) callbacks = callbacks + [evaluator] - if isinstance(self.strategy, ColossalAIStrategy): + if isinstance(self.strategy, (LowLevelZeroStrategy, GeminiStrategy)): self.actor_optim = HybridAdam(self.actor.parameters(), lr=1e-7) self.critic_optim = HybridAdam(self.critic.parameters(), lr=1e-7) else: diff --git a/applications/Chat/coati/ray/utils.py b/applications/Chat/coati/ray/utils.py index 4361ee236771..4f8e0b8a87e9 100644 --- a/applications/Chat/coati/ray/utils.py +++ b/applications/Chat/coati/ray/utils.py @@ -1,6 +1,6 @@ import os -from typing import Any, Callable, Dict, List, Optional from collections import OrderedDict +from typing import Any, Callable, Dict, List, Optional import torch import torch.distributed as dist @@ -10,7 +10,7 @@ from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM from coati.models.opt import OPTRM, OPTActor, OPTCritic from coati.models.roberta import RoBERTaActor, RoBERTaCritic, RoBERTaRM -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy from coati.utils import prepare_llama_tokenizer_and_embedding from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer, RobertaTokenizer @@ -76,18 +76,16 @@ def get_reward_model_from_args(model: str, pretrained: str = None, config=None): def get_strategy_from_args(strategy: str): - if strategy == 'naive': - strategy_ = NaiveStrategy() - elif strategy == 'ddp': + if strategy == 'ddp': strategy_ = DDPStrategy() elif strategy == 'colossalai_gemini': - strategy_ = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + strategy_ = GeminiStrategy(placement_policy='cuda', initial_scale=2**5) elif strategy == 'colossalai_zero2': - strategy_ = ColossalAIStrategy(stage=2, placement_policy='cuda') + strategy_ = LowLevelZeroStrategy(stage=2, placement_policy='cuda') elif strategy == 'colossalai_gemini_cpu': - strategy_ = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) + strategy_ = GeminiStrategy(placement_policy='cpu', initial_scale=2**5) elif strategy == 'colossalai_zero2_cpu': - strategy_ = ColossalAIStrategy(stage=2, placement_policy='cpu') + strategy_ = LowLevelZeroStrategy(stage=2, placement_policy='cpu') else: raise ValueError(f'Unsupported strategy "{strategy}"') return strategy_ diff --git a/applications/Chat/coati/trainer/callbacks/save_checkpoint.py b/applications/Chat/coati/trainer/callbacks/save_checkpoint.py index d2dcc0dd4c65..f0d77a191a88 100644 --- a/applications/Chat/coati/trainer/callbacks/save_checkpoint.py +++ b/applications/Chat/coati/trainer/callbacks/save_checkpoint.py @@ -1,7 +1,7 @@ import os import torch.distributed as dist -from coati.trainer.strategies import ColossalAIStrategy, Strategy +from coati.trainer.strategies import GeminiStrategy, LowLevelZeroStrategy, Strategy from coati.trainer.utils import is_rank_0 from torch import nn from torch.optim import Optimizer @@ -69,7 +69,7 @@ def on_episode_end(self, episode: int) -> None: # save optimizer if self.model_dict[model][1] is None: continue - only_rank0 = not isinstance(self.strategy, ColossalAIStrategy) + only_rank0 = not isinstance(self.strategy, (LowLevelZeroStrategy, GeminiStrategy)) rank = 0 if is_rank_0() else dist.get_rank() optim_path = os.path.join(base_path, f'{model}-optim-rank-{rank}.pt') self.strategy.save_optimizer(optimizer=self.model_dict[model][1], path=optim_path, only_rank0=only_rank0) diff --git a/applications/Chat/coati/trainer/ppo.py b/applications/Chat/coati/trainer/ppo.py index 451abe2a7438..4c4a1002e96d 100644 --- a/applications/Chat/coati/trainer/ppo.py +++ b/applications/Chat/coati/trainer/ppo.py @@ -15,7 +15,7 @@ from .base import OnPolicyTrainer from .callbacks import Callback -from .strategies import ColossalAIStrategy, Strategy +from .strategies import GeminiStrategy, Strategy from .utils import is_rank_0, to_device @@ -82,9 +82,8 @@ def __init__(self, callbacks: List[Callback] = [], **generate_kwargs ) -> None: - if isinstance(strategy, ColossalAIStrategy): - from colossalai.booster.plugin import GeminiPlugin - assert not (isinstance(strategy.plugin, GeminiPlugin) and offload_inference_models), \ + if isinstance(strategy, GeminiStrategy): + assert not offload_inference_models, \ "GeminiPlugin is not compatible with manual model.to('cpu')" buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload) diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index 12c51d7a80c3..0812ba165286 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -12,7 +12,7 @@ from colossalai.logging import DistributedLogger from .base import SLTrainer -from .strategies import ColossalAIStrategy, Strategy +from .strategies import GeminiStrategy, Strategy from .utils import is_rank_0, to_device @@ -38,9 +38,8 @@ def __init__( max_epochs: int = 2, accumulation_steps: int = 8, ) -> None: - if accumulation_steps > 1 and isinstance(strategy, ColossalAIStrategy): - from colossalai.booster.plugin import GeminiPlugin - assert not isinstance(strategy.plugin, GeminiPlugin), \ + if accumulation_steps > 1: + assert not isinstance(strategy, GeminiStrategy), \ "Accumulation steps are not supported in stage 3 of ColossalAI" super().__init__(strategy, max_epochs, model, optim) diff --git a/applications/Chat/coati/trainer/strategies/__init__.py b/applications/Chat/coati/trainer/strategies/__init__.py index f258c9b8a873..b49a2c742db3 100644 --- a/applications/Chat/coati/trainer/strategies/__init__.py +++ b/applications/Chat/coati/trainer/strategies/__init__.py @@ -1,6 +1,8 @@ from .base import Strategy -from .colossalai import ColossalAIStrategy +from .colossalai import GeminiStrategy, LowLevelZeroStrategy from .ddp import DDPStrategy -from .naive import NaiveStrategy -__all__ = ['Strategy', 'NaiveStrategy', 'DDPStrategy', 'ColossalAIStrategy'] +__all__ = [ + 'Strategy', 'DDPStrategy', + 'LowLevelZeroStrategy', 'GeminiStrategy' +] diff --git a/applications/Chat/coati/trainer/strategies/colossalai.py b/applications/Chat/coati/trainer/strategies/colossalai.py index e5a69f3351cb..1b59d704eec3 100644 --- a/applications/Chat/coati/trainer/strategies/colossalai.py +++ b/applications/Chat/coati/trainer/strategies/colossalai.py @@ -18,13 +18,96 @@ from .ddp import DDPStrategy -class ColossalAIStrategy(DDPStrategy): +class LowLevelZeroStrategy(DDPStrategy): + """ + The strategy for training with ColossalAI. + + Args: + stage(int): The stage to use in ZeRO. Choose in (1, 2) + precision(str): The precision to use. Choose in ('fp32', 'fp16'). + seed(int): The seed for the random number generator. + placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda') + If it is “cpu”, parameters, gradients and optimizer states will be offloaded to CPU, + If it is “cuda”, they will not be offloaded, which means max CUDA memory will be used. It is the fastest. + reduce_bucket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2. + overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2. + initial_scale(float): The initial scale for the optimizer. + growth_factor(float): The growth factor for the optimizer. + backoff_factor(float): The backoff factor for the optimizer. + growth_interval(int): The growth interval for the optimizer. + hysteresis(int): The hysteresis for the optimizer. + min_scale(float): The minimum scale for the optimizer. + max_scale(float): The maximum scale for the optimizer. + max_norm(float): The maximum norm for the optimizer. + norm_type(float): The norm type for the optimizer. + + """ + + def __init__(self, + stage: int = 3, + precision: str = 'fp16', + seed: int = 42, + placement_policy: str = 'cuda', + reduce_bucket_size: int = 12 * 1024**2, # only for stage 1&2 + overlap_communication: bool = True, # only for stage 1&2 + initial_scale: float = 2**16, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + min_scale: float = 1, + max_scale: float = 2**32, + max_norm: float = 0.0, + norm_type: float = 2.0 + ) -> None: + + assert stage in (1, 2), f'Unsupported stage "{stage}"' + assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy "{placement_policy}"' + assert precision in ('fp32', 'fp16'), f'Unsupported precision "{precision}"' + + plugin_initializer = lambda: LowLevelZeroPlugin( + # zero_config + stage=stage, + precision=precision, + # zero_optim_config + reduce_bucket_size_in_m=reduce_bucket_size, + overlap_communication=overlap_communication, + cpu_offload=(placement_policy == 'cpu'), + # optim_config + initial_scale=initial_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + min_scale=min_scale, + max_scale=max_scale, + max_norm=max_norm, + norm_type=norm_type + ) + + super().__init__(seed, plugin_initializer) + + def _post_init(self) -> None: + assert isinstance(self.plugin, LowLevelZeroPlugin), \ + f'{type(self).__name__}\'s plugin is not initialized properly.' + + def setup_distributed(self) -> None: + colossalai.launch_from_torch({}, seed=self.seed) + + def unwrap_model(self, model: nn.Module) -> nn.Module: + assert isinstance(model, LowLevelZeroModel) + return model.module + + def get_model_state_dict_shard(self, model: nn.Module, **config): + assert isinstance(model, LowLevelZeroModel) + yield from model.state_dict_shard(max_shard_size=1024, only_rank_0=False) + + +class GeminiStrategy(DDPStrategy): """ The strategy for training with ColossalAI. Args: - stage(int): The stage to use in ZeRO. Choose in (1, 2, 3) - precision(str): The precision to use. Choose in ('fp32', 'fp16'). Stage 3 only supports fp16. seed(int): The seed for the random number generator. shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3. This is not compatible with `from_pretrained()`. We temporarily disable this and will support it in the future. @@ -37,8 +120,6 @@ class ColossalAIStrategy(DDPStrategy): hidden_dim(optional, int): The hidden dimension for the gemini. Only for ZeRO-3. min_chunk_size_m(float): The minimum chunk size divided by 2^20. Only for ZeRO-3. gpu_margin_mem_ratio(float): The margin memory ratio for the GPU. Only for ZeRO-3. - reduce_bucket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2. - overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2. initial_scale(float): The initial scale for the optimizer. growth_factor(float): The growth factor for the optimizer. backoff_factor(float): The backoff factor for the optimizer. @@ -51,132 +132,96 @@ class ColossalAIStrategy(DDPStrategy): """ - def __init__( - self, - stage: int = 3, - precision: str = 'fp16', - seed: int = 42, - shard_init: bool = False, # only for stage 3 - placement_policy: str = 'cuda', - pin_memory: bool = True, # only for stage 3 - force_outputs_fp32: bool = False, # only for stage 3 - search_range_m: int = 32, # only for stage 3 - hidden_dim: Optional[int] = None, # only for stage 3 - min_chunk_size_m: float = 32, # only for stage 3 - gpu_margin_mem_ratio: float = 0.0, # only for stage 3 - reduce_bucket_size: int = 12 * 1024**2, # only for stage 1&2 - overlap_communication: bool = True, # only for stage 1&2 - initial_scale: float = 2**16, - growth_factor: float = 2, - backoff_factor: float = 0.5, - growth_interval: int = 1000, - hysteresis: int = 2, - min_scale: float = 1, - max_scale: float = 2**32, - max_norm: float = 0.0, - norm_type: float = 2.0) -> None: - - assert stage in (1, 2, 3), f'Unsupported stage "{stage}"' + def __init__(self, + seed: int = 42, + shard_init: bool = False, # only for stage 3 + placement_policy: str = 'cuda', + pin_memory: bool = True, # only for stage 3 + force_outputs_fp32: bool = False, # only for stage 3 + search_range_m: int = 32, # only for stage 3 + hidden_dim: Optional[int] = None, # only for stage 3 + min_chunk_size_m: float = 32, # only for stage 3 + gpu_margin_mem_ratio: float = 0.0, # only for stage 3 + initial_scale: float = 2**16, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + min_scale: float = 1, + max_scale: float = 2**32, + max_norm: float = 0.0, + norm_type: float = 2.0 + ) -> None: + assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy "{placement_policy}"' - assert precision in ('fp32', 'fp16'), f'Unsupported precision "{precision}"' # TODO(ver217): support shard_init when using from_pretrained() if shard_init: - warnings.warn(f'Shard init is not supported model.from_pretrained() yet. ' - 'Please load weights after strategy.prepare()') - if stage == 3 and precision == 'fp32': - warnings.warn(f'Stage 3 only supports fp16. Precision is set to fp16.') - precision = 'fp16' - self.precision = precision + warnings.warn( + f'Shard init is not supported model.from_pretrained() yet. ' + 'Please load weights after strategy.prepare()' + ) self.shard_init = shard_init - optim_kwargs = dict(initial_scale=initial_scale, - growth_factor=growth_factor, - backoff_factor=backoff_factor, - growth_interval=growth_interval, - hysteresis=hysteresis, - min_scale=min_scale, - max_scale=max_scale, - max_norm=max_norm, - norm_type=norm_type) + warnings.warn(f'Stage 3 only supports fp16. Precision is set to fp16.') + # NOTE: dist should be initialized before calling get_current_device() - if stage == 3: - plugin_initializer = lambda: GeminiPlugin( - # gemini_config - device=get_current_device(), - placement_policy=placement_policy, - precision=precision, - pin_memory=pin_memory, - force_outputs_fp32=force_outputs_fp32, - strict_ddp_mode=shard_init, - search_range_m=search_range_m, - hidden_dim=hidden_dim, - min_chunk_size_m=min_chunk_size_m, - # zero_optim_config - gpu_margin_mem_ratio=gpu_margin_mem_ratio, - # optim_config - **optim_kwargs) - else: - plugin_initializer = lambda: LowLevelZeroPlugin( - # zero_config - stage=stage, - precision=precision, - # zero_optim_config - reduce_bucket_size_in_m=reduce_bucket_size, - overlap_communication=overlap_communication, - cpu_offload=(placement_policy == 'cpu'), - # optim_config - **optim_kwargs) + plugin_initializer = lambda: GeminiPlugin( + # gemini_config + device=get_current_device(), + placement_policy=placement_policy, + precision='fp16', + pin_memory=pin_memory, + force_outputs_fp32=force_outputs_fp32, + strict_ddp_mode=shard_init, + search_range_m=search_range_m, + hidden_dim=hidden_dim, + min_chunk_size_m=min_chunk_size_m, + # zero_optim_config + gpu_margin_mem_ratio=gpu_margin_mem_ratio, + # optim_config + initial_scale=initial_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + min_scale=min_scale, + max_scale=max_scale, + max_norm=max_norm, + norm_type=norm_type + ) super().__init__(seed, plugin_initializer) def _post_init(self) -> None: - assert isinstance(self.plugin, (LowLevelZeroPlugin, GeminiPlugin)), \ + assert isinstance(self.plugin, GeminiPlugin), \ f'{type(self).__name__}\'s plugin is not initialized properly.' def setup_distributed(self) -> None: colossalai.launch_from_torch({}, seed=self.seed) def model_init_context(self): - if isinstance(self.plugin, GeminiPlugin): - world_size = dist.get_world_size() - shard_pg = ProcessGroup(tp_degree=world_size) if self.shard_init else None - default_dist_spec = ShardSpec([-1], [world_size]) if self.shard_init else None - return ColoInitContext(device=get_current_device(), - dtype=torch.half, - default_pg=shard_pg, - default_dist_spec=default_dist_spec) - return super().model_init_context() + world_size = dist.get_world_size() + shard_pg = ProcessGroup(tp_degree=world_size) if self.shard_init else None + default_dist_spec = ShardSpec([-1], [world_size]) if self.shard_init else None + return ColoInitContext(device=get_current_device(), + dtype=torch.half, + default_pg=shard_pg, + default_dist_spec=default_dist_spec) def unwrap_model(self, model: nn.Module) -> nn.Module: - if isinstance(self.plugin, GeminiPlugin): - assert isinstance(model, GeminiModel) - ddp_model = model.unwrap() - assert isinstance(ddp_model, GeminiDDP) - return ddp_model.module - elif isinstance(self.plugin, LowLevelZeroPlugin): - assert isinstance(model, LowLevelZeroModel) - return model.module - else: - raise RuntimeError(f'Unsupported plugin {type(self.plugin)}') + assert isinstance(model, GeminiModel) + ddp_model = model.unwrap() + assert isinstance(ddp_model, GeminiDDP) + return ddp_model.module def save_pretrained(self, model: nn.Module, path: str, only_rank0: bool = True, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - if isinstance(self.plugin, GeminiPlugin): - raise RuntimeError('ColossalAI strategy with stage-3 does not support save_pretrained() now') - super().save_pretrained(model, path, only_rank0, tokenizer) + raise RuntimeError('ColossalAI strategy with stage-3 does not support save_pretrained() now') def get_model_state_dict_shard(self, model: nn.Module, **config): - if not isinstance(self.plugin, GeminiPlugin): - yield from super().get_model_state_dict_shard(model, **config) - else: - # unwrapped_model = self._unwrap_model(model) - # for module in unwrapped_model.modules(): - # if isinstance(module, LoraLinear): - # module.merge_weights = True - # module.eval() - assert isinstance(model, LowLevelZeroModel) - yield from model.state_dict_shard(max_shard_size=1024, only_rank_0=False) + assert isinstance(self.plugin, GeminiPlugin) + yield from super().get_model_state_dict_shard(model, **config) diff --git a/applications/Chat/coati/trainer/strategies/ddp.py b/applications/Chat/coati/trainer/strategies/ddp.py index 42867645290c..e1c1bbf19f35 100644 --- a/applications/Chat/coati/trainer/strategies/ddp.py +++ b/applications/Chat/coati/trainer/strategies/ddp.py @@ -1,4 +1,6 @@ +import os import random +from collections import OrderedDict from typing import Callable, Optional import numpy as np @@ -6,18 +8,27 @@ import torch.distributed as dist import torch.nn as nn from coati.replay_buffer import ReplayBuffer -from torch.optim import Optimizer from torch.utils.data import DataLoader +from transformers.modeling_utils import PreTrainedModel from transformers.tokenization_utils_base import PreTrainedTokenizerBase from colossalai.booster.plugin import TorchDDPPlugin from colossalai.booster.plugin.torch_ddp_plugin import TorchDDPModel -from .naive import NaiveStrategy +from .base import Strategy from .sampler import DistributedSampler -class DDPStrategy(NaiveStrategy): +# TODO Move this to a util.py (Moving to ray.util introduces ringed import) +def get_grad_required_state_dict(model: nn.Module): + state_dict = OrderedDict() + for name, parameter in model.named_parameters(): + if parameter.requires_grad: + state_dict[name] = parameter.detach() + return state_dict + + +class DDPStrategy(Strategy): """ Strategy for distributed training using torch.distributed. """ @@ -29,6 +40,24 @@ def __init__(self, self.seed = seed super().__init__(plugin_initializer) + def _try_init_dist(self, force: bool = False) -> None: + try: + rank = int(os.environ['RANK']) + local_rank = int(os.environ['LOCAL_RANK']) + world_size = int(os.environ['WORLD_SIZE']) + host = os.environ['MASTER_ADDR'] + port = int(os.environ['MASTER_PORT']) + dist.init_process_group('nccl', init_method=f'tcp://[{host}]:{port}', world_size=world_size, rank=rank) + torch.cuda.set_device(local_rank) + except KeyError as e: + if force: + raise RuntimeError( + f"Could not find {e} in the torch environment, visit https://www.colossalai.org/ for more information on launching with torch" + ) + except Exception as e: + if force: + raise e + def _post_init(self) -> None: assert isinstance(self.plugin, TorchDDPPlugin), \ f'{type(self).__name__}\'s plugin is not initialized properly.' @@ -42,9 +71,6 @@ def set_seed(self, seed: int) -> None: np.random.seed(seed) torch.manual_seed(seed) - def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: Optimizer, **kwargs) -> None: - self.booster.backward(loss, optimizer) - def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: return self.plugin.prepare_dataloader(replay_buffer, batch_size=replay_buffer.sample_batch_size, @@ -68,4 +94,32 @@ def save_pretrained(self, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: if only_rank0 and dist.get_rank() != 0: return - super().save_pretrained(model, path, only_rank0, tokenizer) + unwrapped_model = self.unwrap_model(model) + assert isinstance(unwrapped_model, PreTrainedModel) + unwrapped_model.save_pretrained(path) + if tokenizer is not None: + tokenizer.save_pretrained(path) + + def get_model_state_dict_shard(self, model: nn.Module, **config): + # TODO: implement sharding on naive strategy + model = self.unwrap_model(model) + if 'requires_grad_only' in config and config['requires_grad_only'] == True: + state_dict = get_grad_required_state_dict(model) + else: + state_dict = model.state_dict() + + if 'shard_size' in config: + shard_size = config['shard_size'] + accumulate_size = 0 + state_dict_shard = OrderedDict() + for name, param in state_dict.items(): + state_dict_shard[name] = param + accumulate_size += param.numel() * param.element_size() + if accumulate_size >= shard_size: + accumulate_size = 0 + yield state_dict_shard + state_dict_shard = OrderedDict() + if accumulate_size > 0: + yield state_dict_shard + else: + yield state_dict diff --git a/applications/Chat/coati/trainer/strategies/naive.py b/applications/Chat/coati/trainer/strategies/naive.py deleted file mode 100644 index d121237a68ea..000000000000 --- a/applications/Chat/coati/trainer/strategies/naive.py +++ /dev/null @@ -1,103 +0,0 @@ -import os -from collections import OrderedDict -from typing import Optional - -import torch -import torch.distributed as dist -import torch.nn as nn -from coati.replay_buffer import ReplayBuffer -from torch.optim import Optimizer -from torch.utils.data import DataLoader -from transformers.modeling_utils import PreTrainedModel -from transformers.tokenization_utils_base import PreTrainedTokenizerBase - -from .base import Strategy - - -# TODO Move this to a util.py (Moving to ray.util introduces ringed import) -def get_grad_required_state_dict(model: nn.Module): - state_dict = OrderedDict() - for name, parameter in model.named_parameters(): - if parameter.requires_grad: - state_dict[name] = parameter.detach() - return state_dict - - -class NaiveStrategy(Strategy): - """ - Strategy for single GPU. No parallelism is used. - """ - - def _post_init(self) -> None: - assert self.plugin is None, \ - f'{type(self).__name__}\'s plugin is not initialized properly.' - - def setup_distributed(self) -> None: - self._try_init_dist(force=False) - - def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: Optimizer, **kwargs) -> None: - # HACK: self.booster.backward(loss, optimizer) can't work if plugin is None, - # it would run `optimizer.backward(loss)`, which is not compatible with torch.optim.Optimizer - assert self.plugin is None, "DO NOT call this method if plugin is not None" - loss.backward() - - def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader: - return DataLoader(replay_buffer, - batch_size=replay_buffer.sample_batch_size, - shuffle=True, - drop_last=True, - pin_memory=pin_memory, - collate_fn=replay_buffer.collate_fn) - - def save_pretrained(self, - model: nn.Module, - path: str, - only_rank0: bool = True, - tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None: - unwrapped_model = self.unwrap_model(model) - assert isinstance(unwrapped_model, PreTrainedModel) - unwrapped_model.save_pretrained(path) - if tokenizer is not None: - tokenizer.save_pretrained(path) - - def get_model_state_dict_shard(self, model: nn.Module, **config): - # TODO: implement sharding on naive strategy - model = self.unwrap_model(model) - if 'requires_grad_only' in config and config['requires_grad_only'] == True: - state_dict = get_grad_required_state_dict(model) - else: - state_dict = model.state_dict() - - if 'shard_size' in config: - shard_size = config['shard_size'] - accumulate_size = 0 - state_dict_shard = OrderedDict() - for name, param in state_dict.items(): - state_dict_shard[name] = param - accumulate_size += param.numel() * param.element_size() - if accumulate_size >= shard_size: - accumulate_size = 0 - yield state_dict_shard - state_dict_shard = OrderedDict() - if accumulate_size > 0: - yield state_dict_shard - else: - yield state_dict - - def _try_init_dist(self, force: bool = False) -> None: - try: - rank = int(os.environ['RANK']) - local_rank = int(os.environ['LOCAL_RANK']) - world_size = int(os.environ['WORLD_SIZE']) - host = os.environ['MASTER_ADDR'] - port = int(os.environ['MASTER_PORT']) - dist.init_process_group('nccl', init_method=f'tcp://[{host}]:{port}', world_size=world_size, rank=rank) - torch.cuda.set_device(local_rank) - except KeyError as e: - if force: - raise RuntimeError( - f"Could not find {e} in the torch environment, visit https://www.colossalai.org/ for more information on launching with torch" - ) - except Exception as e: - if force: - raise e diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index 3e9d9c4325d8..56e4cc992c17 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -69,7 +69,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --grad_checkpoint ``` ### Arg List -- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' +- --strategy: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' - --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' - --pretrain: pretrain model, type=str, default=None - --max_datasets_size: the max size of dataset, type=int, default=None @@ -118,7 +118,7 @@ Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862):
    We also train the reward model based on LLaMA-7B, which reaches the ACC of 72.06% after 1 epoch, performing almost the same as Anthropic's best RM. ### Arg List -- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' +- --strategy: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' - --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' - --pretrain: pretrain model, type=str, default=None - --model_path: the path of rm model(if continue to train), type=str, default=None @@ -160,7 +160,7 @@ Prompt dataset: the instruction dataset mentioned in the above figure which incl Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning. ### Arg List -- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' +- --strategy: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' - --model: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' - --pretrain: pretrain model, type=str, default=None - --rm_model: reward model type, type=str, choices=['gpt2', 'bloom', 'opt', 'llama'], default=None diff --git a/applications/Chat/examples/community/peft/train_peft_prompts.py b/applications/Chat/examples/community/peft/train_peft_prompts.py index 00ed7aa36257..9d8dbb38ae5d 100644 --- a/applications/Chat/examples/community/peft/train_peft_prompts.py +++ b/applications/Chat/examples/community/peft/train_peft_prompts.py @@ -9,7 +9,7 @@ from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM from coati.models.opt import OPTRM, OPTActor, OPTCritic from coati.trainer import PPOTrainer -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy from coati.utils import prepare_llama_tokenizer_and_embedding from easy_dataset import EasyPromptsDataset, EasySupervisedDataset from easy_models import BLOOMActor @@ -24,14 +24,12 @@ def main(args): # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': + if args.strategy == 'ddp': strategy = DDPStrategy() elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5) + strategy = GeminiStrategy(placement_policy='cpu', initial_scale=2**5) elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') + strategy = LowLevelZeroStrategy(stage=2, placement_policy='cpu') else: raise ValueError(f'Unsupported strategy "{args.strategy}"') @@ -202,8 +200,8 @@ def tokenize_fn(texts): parser.add_argument('--prompt_path', type=str, default=None, help='path to the prompt dataset') parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset') parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive', + choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='ddp', help='strategy to use') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--pretrain', type=str, default=None) diff --git a/applications/Chat/examples/community/peft/train_peft_sft.py b/applications/Chat/examples/community/peft/train_peft_sft.py index d2b08b72ca95..54fe0ad55049 100644 --- a/applications/Chat/examples/community/peft/train_peft_sft.py +++ b/applications/Chat/examples/community/peft/train_peft_sft.py @@ -11,7 +11,7 @@ from coati.models.llama import LlamaLM from coati.models.opt import OPTLM from coati.trainer import SFTTrainer -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from easy_dataset import EasyDataset @@ -30,14 +30,12 @@ def train(args): # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': + if args.strategy == 'ddp': strategy = DDPStrategy() elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') + strategy = GeminiStrategy(placement_policy='cuda') elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + strategy = LowLevelZeroStrategy(stage=2, placement_policy='cuda') else: raise ValueError(f'Unsupported strategy "{args.strategy}"') @@ -45,15 +43,15 @@ def train(args): with strategy.model_init_context(): print('Warning: currently only bloom is tested, gpt2,llama and opt are not tested') model = AutoModelForCausalLM.from_pretrained(args.pretrain).to(torch.cuda.current_device()) - #if the args.save_path exists and args.save_path+'/adapter_config.json' exists, we'll load the adapter_config.json - if os.path.exists(args.save_path) and os.path.exists(args.save_path+'/adapter_config.json') \ - and os.path.exists(args.save_path+'/adapter_model.bin'): + # if the args.save_path exists and args.save_path+'/adapter_config.json' exists, we'll load the adapter_config.json + if os.path.exists(args.save_path) and os.path.exists(args.save_path + '/adapter_config.json') \ + and os.path.exists(args.save_path + '/adapter_model.bin'): print("loading from saved peft model ", args.save_path) model = PeftModel.from_pretrained(model, args.save_path) else: - #we'll use peft lora library to do the lora + # we'll use peft lora library to do the lora lora_rank = args.lora_rank if args.lora_rank > 0 else 32 - #config lora with rank of lora_rank + # config lora with rank of lora_rank lora_config = LoraConfig(task_type=TaskType.CAUSAL_LM, inference_mode=False, r=lora_rank, @@ -170,8 +168,8 @@ def train(args): if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') + choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='ddp') parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--dataset', type=str, default=None) diff --git a/applications/Chat/examples/community/ray/train_prompts_on_ray.py b/applications/Chat/examples/community/ray/train_prompts_on_ray.py index 289330ad8415..1bba9ad66fbc 100644 --- a/applications/Chat/examples/community/ray/train_prompts_on_ray.py +++ b/applications/Chat/examples/community/ray/train_prompts_on_ray.py @@ -15,7 +15,7 @@ from coati.models.loss import PolicyLoss, ValueLoss from coati.models.opt import OPTActor, OPTCritic from coati.models.utils import compute_reward -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy from ray.util.placement_group import placement_group from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy from torch.optim import Adam @@ -99,19 +99,17 @@ def make_experience(self, experience_computation_ref: ExperienceCompositionRefs) def _init_strategy(self, strategy: str): # configure strategy - if strategy == 'naive': - self._strategy = NaiveStrategy() - elif strategy == 'ddp': + if strategy == 'ddp': self._strategy = DDPStrategy() elif strategy == 'colossalai_gemini': - self._strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + self._strategy = GeminiStrategy(placement_policy='cuda', initial_scale=2**5) elif strategy == 'colossalai_zero2': - self._strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + self._strategy = LowLevelZeroStrategy(stage=2, placement_policy='cuda') else: raise ValueError(f'Unsupported strategy "{strategy}"') def _init_optimizer(self): - if isinstance(self._strategy, ColossalAIStrategy): + if isinstance(self._strategy, (GeminiStrategy, LowLevelZeroStrategy)): self._optimizer = HybridAdam(self._model.parameters(), lr=5e-6) else: self._optimizer = Adam(self._model.parameters(), lr=5e-6) @@ -534,8 +532,8 @@ def main(args): parser = argparse.ArgumentParser() parser.add_argument('--prompt_csv_url', type=str) parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], - default='naive') + choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], + default='ddp') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) parser.add_argument('--pretrain', type=str, default='gpt2') parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts.pt') diff --git a/applications/Chat/examples/ray/1mmt_prompt.py b/applications/Chat/examples/ray/1mmt_prompt.py index afdd6a922cc7..5dd52f1790e6 100644 --- a/applications/Chat/examples/ray/1mmt_prompt.py +++ b/applications/Chat/examples/ray/1mmt_prompt.py @@ -103,8 +103,8 @@ def model_fn(): kl_coef=0.1, debug=args.debug, update_lora_weights=not (args.lora_rank == 0), - # sync_models_from_trainers=True, - # generation kwargs: + # sync_models_from_trainers=True, + # generation kwargs: max_length=512, do_sample=True, temperature=1.0, @@ -150,10 +150,10 @@ def tokenize_fn(texts): parser.add_argument('--num_trainers', type=int, default=1) parser.add_argument('--trainer_strategy', choices=[ - 'naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', + 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', 'colossalai_zero2_cpu' ], - default='naive') + default='ddp') parser.add_argument('--maker_strategy', choices=['naive'], default='naive') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) diff --git a/applications/Chat/examples/ray/mmmt_prompt.py b/applications/Chat/examples/ray/mmmt_prompt.py index fa7b2bd7edfd..60f049bd5b70 100644 --- a/applications/Chat/examples/ray/mmmt_prompt.py +++ b/applications/Chat/examples/ray/mmmt_prompt.py @@ -87,8 +87,8 @@ def model_fn(): kl_coef=0.1, debug=args.debug, update_lora_weights=not (args.lora_rank == 0), - # sync_models_from_trainers=True, - # generation kwargs: + # sync_models_from_trainers=True, + # generation kwargs: max_length=512, do_sample=True, temperature=1.0, @@ -163,10 +163,10 @@ def tokenize_fn(texts): parser.add_argument('--num_trainers', type=int, default=1) parser.add_argument('--trainer_strategy', choices=[ - 'naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', + 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', 'colossalai_zero2_cpu' ], - default='naive') + default='ddp') parser.add_argument('--maker_strategy', choices=['naive'], default='naive') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index 4bf5524afb01..dec1f7c036c8 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -49,13 +49,13 @@ wandb init -m offline # - roberta-*: RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)` SKIPPED_TESTS=( "gpt2-ddp" - "llama-naive" "llama-ddp" "llama-colossalai_gemini" "llama-colossalai_zero2" - "roberta-naive" "roberta-ddp" "roberta-colossalai_gemini" "roberta-colossalai_zero2" + "llama-ddp" "llama-colossalai_gemini" "llama-colossalai_zero2" + "roberta-ddp" "roberta-colossalai_gemini" "roberta-colossalai_zero2" ) # These tests are quick and do not have any dependencies for model in 'gpt2' 'bloom' 'opt' 'llama' 'roberta'; do - for strategy in 'naive' 'ddp' 'colossalai_gemini' 'colossalai_zero2'; do + for strategy in 'ddp' 'colossalai_gemini' 'colossalai_zero2'; do if [[ " ${SKIPPED_TESTS[*]} " =~ " ${model}-${strategy} " ]]; then echo "[Test]: Skipped $model-$strategy" continue @@ -91,12 +91,6 @@ torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'gpt2' --model 'gpt2' --strategy ddp --lora_rank 4 \ --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ --save_path ${BASE}/output - -# torchrun --standalone --nproc_per_node=4 ${BASE}/train_sft.py --pretrain 'facebook/opt-350m' \ -# --model 'opt' --strategy naive \ -# --dataset $SFT_DATASET --max_datasets_size 512 --max_epochs 1 \ -# --save_path ${BASE}/output - rm -rf ${BASE}/output # train rm @@ -144,9 +138,9 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ --test True --lora_rank 4 \ --save_path ${BASE}/rm_ckpt.pt - rm -rf ${BASE}/rm_ckpt.pt +# train rl torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ --strategy colossalai_zero2 --num_episodes 1 \ diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index a9bc0e532e5d..c748eeb21065 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -9,7 +9,7 @@ from coati.models.opt import OPTRM, OPTActor, OPTCritic from coati.models.roberta import RoBERTaActor, RoBERTaCritic, RoBERTaRM from coati.trainer import PPOTrainer -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy from coati.utils import prepare_llama_tokenizer_and_embedding from torch.optim import Adam from torch.utils.data import DataLoader @@ -21,14 +21,12 @@ def main(args): # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': + if args.strategy == 'ddp': strategy = DDPStrategy() elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + strategy = GeminiStrategy(placement_policy='cuda', initial_scale=2**5) elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + strategy = LowLevelZeroStrategy(stage=2, placement_policy='cuda') else: raise ValueError(f'Unsupported strategy "{args.strategy}"') @@ -208,7 +206,7 @@ def main(args): parser.add_argument('--prompt_dataset', type=str, default=None, help='path to the prompt dataset') parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset') parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2', help='strategy to use') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama', 'roberta']) diff --git a/applications/Chat/examples/train_reward_model.py b/applications/Chat/examples/train_reward_model.py index 4a6851ab5b24..e9618e0c1d5e 100644 --- a/applications/Chat/examples/train_reward_model.py +++ b/applications/Chat/examples/train_reward_model.py @@ -14,7 +14,7 @@ from coati.models.opt import OPTRM from coati.models.roberta import RoBERTaRM from coati.trainer import RewardModelTrainer -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from torch.optim import Adam @@ -29,14 +29,12 @@ def train(args): # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': + if args.strategy == 'ddp': strategy = DDPStrategy() elif args.strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') + strategy = GeminiStrategy(placement_policy='cuda') elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + strategy = LowLevelZeroStrategy(stage=2, placement_policy='cuda') else: raise ValueError(f'Unsupported strategy "{args.strategy}"') @@ -195,7 +193,7 @@ def train(args): if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], + choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2') parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta', 'llama', 'roberta'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index 967b7c277c6a..30becd8a68a1 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -8,7 +8,7 @@ from coati.dataset import DataCollatorForSupervisedDataset, SFTDataset, SupervisedDataset from coati.models import convert_to_lora_module from coati.trainer import SFTTrainer -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from torch.optim import Adam @@ -29,18 +29,16 @@ def train(args): # configure strategy - if args.strategy == 'naive': - strategy = NaiveStrategy() - elif args.strategy == 'ddp': + if args.strategy == 'ddp': strategy = DDPStrategy() elif args.strategy == 'colossalai_gemini': raise NotImplementedError( 'Gemini is not supported .from_pretrained() yet. We will update this after checkpoint io is ready.') - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda') + strategy = GeminiStrategy(placement_policy='cuda') elif args.strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + strategy = LowLevelZeroStrategy(stage=2, placement_policy='cuda') elif args.strategy == 'colossalai_zero2_cpu': - strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') + strategy = LowLevelZeroStrategy(stage=2, placement_policy='cpu') else: raise ValueError(f'Unsupported strategy "{args.strategy}"') @@ -66,7 +64,7 @@ def train(args): tokenizer = GPT2Tokenizer.from_pretrained('gpt2') tokenizer.pad_token = tokenizer.eos_token elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m') tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") @@ -190,7 +188,7 @@ def train(args): if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--strategy', - choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_zero2_cpu'], + choices=['ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_zero2_cpu'], default='colossalai_zero2') parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) diff --git a/applications/Chat/tests/test_checkpoint.py b/applications/Chat/tests/test_checkpoint.py index cfa39e44b476..19338da437ab 100644 --- a/applications/Chat/tests/test_checkpoint.py +++ b/applications/Chat/tests/test_checkpoint.py @@ -7,7 +7,7 @@ import torch.distributed as dist from coati.models.gpt import GPTActor from coati.models.utils import calc_action_log_probs -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy from transformers.models.gpt2.configuration_gpt2 import GPT2Config from colossalai.nn.optimizer import HybridAdam @@ -28,9 +28,9 @@ def run_test_checkpoint(strategy): if strategy == 'ddp': strategy = DDPStrategy() elif strategy == 'colossalai_gemini': - strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5) + strategy = GeminiStrategy(placement_policy='cuda', initial_scale=2**5) elif strategy == 'colossalai_zero2': - strategy = ColossalAIStrategy(stage=2, placement_policy='cuda') + strategy = LowLevelZeroStrategy(stage=2, placement_policy='cuda') else: raise ValueError(f'Unsupported strategy "{strategy}"') diff --git a/applications/Chat/tests/test_data.py b/applications/Chat/tests/test_data.py index 67016f6ed286..db641a6218b1 100644 --- a/applications/Chat/tests/test_data.py +++ b/applications/Chat/tests/test_data.py @@ -8,7 +8,7 @@ from coati.models.base import RewardModel from coati.models.gpt import GPTActor, GPTCritic from coati.replay_buffer import NaiveReplayBuffer -from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy +from coati.trainer.strategies import DDPStrategy, GeminiStrategy from transformers.models.gpt2.configuration_gpt2 import GPT2Config from colossalai.testing import rerun_if_address_is_in_use, spawn @@ -39,7 +39,7 @@ def run_test_data(strategy): if strategy == 'ddp': strategy = DDPStrategy() elif strategy == 'colossalai': - strategy = ColossalAIStrategy(placement_policy='cuda') + strategy = GeminiStrategy(placement_policy='cuda') else: raise ValueError(f'Unsupported strategy "{strategy}"') From 09fe9dc704dd388f08e3b19dca65d3d0be64f106 Mon Sep 17 00:00:00 2001 From: digger yu Date: Fri, 30 Jun 2023 17:23:22 +0800 Subject: [PATCH 343/413] [nfc]fix ColossalaiOptimizer is not defined (#4122) --- colossalai/engine/_base_engine.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/colossalai/engine/_base_engine.py b/colossalai/engine/_base_engine.py index ff8979d82401..db27ad0e8abe 100644 --- a/colossalai/engine/_base_engine.py +++ b/colossalai/engine/_base_engine.py @@ -12,7 +12,7 @@ from colossalai.engine.schedule import BaseSchedule, InterleavedPipelineSchedule, NonPipelineSchedule, PipelineSchedule from colossalai.logging import get_dist_logger from colossalai.zero.legacy.gemini import BaseOpHook, register_ophooks_recursively - +from colossalai.nn.optimizer import ColossalaiOptimizer class Engine: """Basic engine class for training and evaluation. It runs a specific process method From 7e46bc87b6e68b52e05c7eee0c9a4d4bd04a9a8f Mon Sep 17 00:00:00 2001 From: digger yu Date: Mon, 3 Jul 2023 17:09:06 +0800 Subject: [PATCH 344/413] fix CheckpointIndexFile is not defined (#4109) --- colossalai/checkpoint_io/utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 3dada00cd9b5..f28b3e82cc9e 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -11,6 +11,7 @@ from torch.optim import Optimizer from colossalai.tensor.d_tensor.d_tensor import DTensor +from .index_file import CheckpointIndexFile SAFE_WEIGHTS_NAME = "model.safetensors" WEIGHTS_NAME = "pytorch_model.bin" From 8abc87798f3ea728c9e03bef6b5ceea4b5c74ff8 Mon Sep 17 00:00:00 2001 From: digger yu Date: Mon, 3 Jul 2023 17:10:18 +0800 Subject: [PATCH 345/413] fix Tensor is not defined (#4129) --- colossalai/kernel/jit/bias_dropout_add.py | 1 + 1 file changed, 1 insertion(+) diff --git a/colossalai/kernel/jit/bias_dropout_add.py b/colossalai/kernel/jit/bias_dropout_add.py index 3687dde79a08..32965c1ebd69 100644 --- a/colossalai/kernel/jit/bias_dropout_add.py +++ b/colossalai/kernel/jit/bias_dropout_add.py @@ -1,4 +1,5 @@ import torch +from torch import Tensor def bias_dropout_add(x, bias, residual, prob, training): From 1350ece492f47f769ccc871fc79bc780b333b351 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Mon, 3 Jul 2023 22:14:37 +0800 Subject: [PATCH 346/413] [hotfix] fix import bug in checkpoint_io (#4142) --- colossalai/checkpoint_io/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index f28b3e82cc9e..3dada00cd9b5 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -11,7 +11,6 @@ from torch.optim import Optimizer from colossalai.tensor.d_tensor.d_tensor import DTensor -from .index_file import CheckpointIndexFile SAFE_WEIGHTS_NAME = "model.safetensors" WEIGHTS_NAME = "pytorch_model.bin" From 3d8d5d0d586b08c567304826b79b0281f0aaf2e4 Mon Sep 17 00:00:00 2001 From: Wenhao Chen Date: Tue, 4 Jul 2023 13:49:09 +0800 Subject: [PATCH 347/413] [chat] use official transformers and fix some issues (#4117) * feat: remove on_learn_epoch fn as not used * revert: add _on_learn_epoch fn * feat: remove NaiveStrategy * test: update train_prompts tests * fix: remove prepare_llama_tokenizer_and_embedding * test: add lora arg * feat: remove roberta support in train_prompts due to runtime errs * feat: remove deberta & roberta in rm as not used * test: remove deberta and roberta tests * feat: remove deberta and roberta models as not used * fix: remove calls to roberta * fix: remove prepare_llama_tokenizer_and_embedding * chore: update transformers version * docs: update transformers version * fix: fix actor inference * fix: fix ci * feat: change llama pad token to unk * revert: revert ddp setup_distributed * fix: change llama pad token to unk * revert: undo unnecessary changes * fix: use pip to install transformers --- .github/workflows/run_chatgpt_examples.yml | 14 ++-- applications/Chat/README.md | 5 +- .../Chat/coati/models/deberta/__init__.py | 4 - .../coati/models/deberta/deberta_critic.py | 36 --------- .../Chat/coati/models/deberta/deberta_rm.py | 37 ---------- .../Chat/coati/models/roberta/__init__.py | 5 -- .../coati/models/roberta/roberta_actor.py | 35 --------- .../coati/models/roberta/roberta_critic.py | 38 ---------- .../Chat/coati/models/roberta/roberta_rm.py | 39 ---------- applications/Chat/coati/ray/utils.py | 12 +-- applications/Chat/coati/utils/__init__.py | 3 - .../Chat/coati/utils/tokenizer_utils.py | 73 ------------------- .../community/peft/train_peft_prompts.py | 10 +-- .../examples/community/peft/train_peft_sft.py | 30 ++++---- applications/Chat/examples/inference.py | 25 +++---- applications/Chat/examples/test_ci.sh | 30 ++------ applications/Chat/examples/train_prompts.py | 27 ++----- .../Chat/examples/train_reward_model.py | 39 +++------- applications/Chat/examples/train_sft.py | 35 ++++----- 19 files changed, 76 insertions(+), 421 deletions(-) delete mode 100644 applications/Chat/coati/models/deberta/__init__.py delete mode 100644 applications/Chat/coati/models/deberta/deberta_critic.py delete mode 100644 applications/Chat/coati/models/deberta/deberta_rm.py delete mode 100644 applications/Chat/coati/models/roberta/__init__.py delete mode 100644 applications/Chat/coati/models/roberta/roberta_actor.py delete mode 100644 applications/Chat/coati/models/roberta/roberta_critic.py delete mode 100644 applications/Chat/coati/models/roberta/roberta_rm.py delete mode 100644 applications/Chat/coati/utils/__init__.py delete mode 100644 applications/Chat/coati/utils/tokenizer_utils.py diff --git a/.github/workflows/run_chatgpt_examples.yml b/.github/workflows/run_chatgpt_examples.yml index 129bf7ed3270..510f6b6f0985 100644 --- a/.github/workflows/run_chatgpt_examples.yml +++ b/.github/workflows/run_chatgpt_examples.yml @@ -4,11 +4,10 @@ on: pull_request: types: [synchronize, opened, reopened] paths: - - 'applications/Chat/coati/**' - - 'applications/Chat/requirements.txt' - - 'applications/Chat/setup.py' - - 'applications/Chat/examples/**' - + - "applications/Chat/coati/**" + - "applications/Chat/requirements.txt" + - "applications/Chat/setup.py" + - "applications/Chat/examples/**" jobs: tests: @@ -38,10 +37,7 @@ jobs: - name: Install Transformers run: | - cd applications/Chat - git clone https://github.com/hpcaitech/transformers - cd transformers - pip install -v . + pip install transformers==4.30.2 - name: Execute Examples run: | diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 016272ed8c89..162528cee414 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -98,12 +98,9 @@ pip install . ``` ### Install the Transformers -Given Hugging Face hasn't officially supported the LLaMA models, We fork a branch of Transformers that can be compatible with our code ```shell -git clone https://github.com/hpcaitech/transformers -cd transformers -pip install . +pip install transformers==4.30.2 ``` ## How to use? diff --git a/applications/Chat/coati/models/deberta/__init__.py b/applications/Chat/coati/models/deberta/__init__.py deleted file mode 100644 index b66888f34fd0..000000000000 --- a/applications/Chat/coati/models/deberta/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .deberta_critic import DebertaCritic -from .deberta_rm import DebertaRM - -__all__ = ['DebertaCritic', 'DebertaRM'] diff --git a/applications/Chat/coati/models/deberta/deberta_critic.py b/applications/Chat/coati/models/deberta/deberta_critic.py deleted file mode 100644 index e84c1dbd8380..000000000000 --- a/applications/Chat/coati/models/deberta/deberta_critic.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers import DebertaV2Config, DebertaV2Model - -from ..base import Critic - - -class DebertaCritic(Critic): - """ - Deberta Critic model. - - Args: - pretrained (str): Pretrained model name or path. - config (DebertaV2Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the LO-RA decomposition. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[DebertaV2Config] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = DebertaV2Model.from_pretrained(pretrained) - elif config is not None: - model = DebertaV2Model(config) - else: - model = DebertaV2Model(DebertaV2Config()) - if checkpoint: - model.gradient_checkpointing_enable() - value_head = nn.Linear(model.config.hidden_size, 1) - super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/deberta/deberta_rm.py b/applications/Chat/coati/models/deberta/deberta_rm.py deleted file mode 100644 index 2448c879ec85..000000000000 --- a/applications/Chat/coati/models/deberta/deberta_rm.py +++ /dev/null @@ -1,37 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers import DebertaV2Config, DebertaV2Model - -from ..base import RewardModel - - -class DebertaRM(RewardModel): - """ - Deberta Reward model. - - Args: - pretrained (str): Pretrained model name or path. - config (DebertaV2Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the LO-RA decomposition. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: str = None, - config: Optional[DebertaV2Config] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = DebertaV2Model.from_pretrained(pretrained) - elif config is not None: - model = DebertaV2Model(config) - else: - model = DebertaV2Model(DebertaV2Config()) - if checkpoint: - model.gradient_checkpointing_enable() - value_head = nn.Linear(model.config.hidden_size, 1) - value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1)) - super().__init__(model, value_head, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/roberta/__init__.py b/applications/Chat/coati/models/roberta/__init__.py deleted file mode 100644 index 0f4a8de067b1..000000000000 --- a/applications/Chat/coati/models/roberta/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .roberta_actor import RoBERTaActor -from .roberta_critic import RoBERTaCritic -from .roberta_rm import RoBERTaRM - -__all__ = ['RoBERTaActor', 'RoBERTaCritic', 'RoBERTaRM'] \ No newline at end of file diff --git a/applications/Chat/coati/models/roberta/roberta_actor.py b/applications/Chat/coati/models/roberta/roberta_actor.py deleted file mode 100644 index e35fa6eb19a8..000000000000 --- a/applications/Chat/coati/models/roberta/roberta_actor.py +++ /dev/null @@ -1,35 +0,0 @@ -from typing import Optional - -from transformers.models.roberta.configuration_roberta import RobertaConfig -from transformers.models.roberta.modeling_roberta import RobertaForCausalLM - -from ..base import Actor - -class RoBERTaActor(Actor): - """ - RoBERTa Actor model. - - Args: - pretrained (str): Pretrained model name or path. - config (RoBERTaConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the low-rank approximation. - lora_train_bias (str): LoRA bias training mode. - """ - - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[RobertaConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = RobertaForCausalLM.from_pretrained(pretrained) - elif config is not None: - model = RobertaForCausalLM(config) - else: - model = RobertaForCausalLM(RobertaConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - super().__init__(model, lora_rank, lora_train_bias) diff --git a/applications/Chat/coati/models/roberta/roberta_critic.py b/applications/Chat/coati/models/roberta/roberta_critic.py deleted file mode 100644 index c8dc0d9e14f2..000000000000 --- a/applications/Chat/coati/models/roberta/roberta_critic.py +++ /dev/null @@ -1,38 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers.models.roberta.configuration_roberta import RobertaConfig -from transformers.models.roberta.modeling_roberta import RobertaModel - -from ..base import Critic - - -class RoBERTaCritic(Critic): - """ - RoBERTa Critic model. - - Args: - pretrained (str): Pretrained model name or path. - config (RoBERTa Config): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the low-rank approximation. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[RobertaConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none', - **kwargs) -> None: - if pretrained is not None: - model = RobertaModel.from_pretrained(pretrained, add_pooling_layer=False) - elif config is not None: - model = RobertaModel(config) - else: - model = RobertaModel(RobertaConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - value_head = nn.Linear(model.config.hidden_size, 1) - super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs) diff --git a/applications/Chat/coati/models/roberta/roberta_rm.py b/applications/Chat/coati/models/roberta/roberta_rm.py deleted file mode 100644 index 77075052978b..000000000000 --- a/applications/Chat/coati/models/roberta/roberta_rm.py +++ /dev/null @@ -1,39 +0,0 @@ -from typing import Optional - -import torch.nn as nn -from transformers import RobertaConfig, RobertaModel - - -from ..base import RewardModel - - -class RoBERTaRM(RewardModel): - """ - RoBERTa Reward model. - - Args: - pretrained (str): Pretrained model name or path. - config (RoBERTaConfig): Model config. - checkpoint (bool): Enable gradient checkpointing. - lora_rank (int): Rank of the low-rank approximation. - lora_train_bias (str): LoRA bias training mode. - """ - - def __init__(self, - pretrained: Optional[str] = None, - config: Optional[RobertaConfig] = None, - checkpoint: bool = False, - lora_rank: int = 0, - lora_train_bias: str = 'none') -> None: - if pretrained is not None: - model = RobertaModel.from_pretrained(pretrained, add_pooling_layer=False) - elif config is not None: - model = RobertaModel(config) - else: - model = RobertaModel(RobertaConfig()) - if checkpoint: - model.gradient_checkpointing_enable() - - value_head = nn.Linear(model.config.hidden_size, 1) - value_head.weight.data.normal_(mean=0.0, std=1/(model.config.hidden_size + 1)) - super().__init__(model, value_head, lora_rank, lora_train_bias) \ No newline at end of file diff --git a/applications/Chat/coati/ray/utils.py b/applications/Chat/coati/ray/utils.py index 4f8e0b8a87e9..761186b95ee5 100644 --- a/applications/Chat/coati/ray/utils.py +++ b/applications/Chat/coati/ray/utils.py @@ -9,10 +9,8 @@ from coati.models.gpt import GPTRM, GPTActor, GPTCritic from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM from coati.models.opt import OPTRM, OPTActor, OPTCritic -from coati.models.roberta import RoBERTaActor, RoBERTaCritic, RoBERTaRM from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy -from coati.utils import prepare_llama_tokenizer_and_embedding -from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer, RobertaTokenizer +from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer def is_rank_0() -> bool: @@ -36,8 +34,6 @@ def get_actor_from_args(model: str, pretrained: str = None, config=None, lora_ra actor = OPTActor(pretrained=pretrained, config=config, lora_rank=lora_rank) elif model == 'llama': actor = LlamaActor(pretrained=pretrained, config=config, lora_rank=lora_rank) - elif model == 'roberta': - actor = RoBERTaActor(pretrained=pretrained, config=config, lora_rank=lora_rank) else: raise ValueError(f'Unsupported actor model "{model}"') return actor @@ -52,8 +48,6 @@ def get_critic_from_args(model: str, pretrained: str = None, config=None, lora_r critic = OPTCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True) elif model == 'llama': critic = LlamaCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True) - elif model == 'roberta': - critic = RoBERTaCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True) else: raise ValueError(f'Unsupported reward model "{model}"') return critic @@ -68,8 +62,6 @@ def get_reward_model_from_args(model: str, pretrained: str = None, config=None): reward_model = OPTRM(pretrained=pretrained, config=config) elif model == 'llama': reward_model = LlamaRM(pretrained=pretrained, config=config) - elif model == 'roberta': - reward_model = RoBERTaRM(pretrained=pretrained, config=config) else: raise ValueError(f'Unsupported reward model "{model}"') return reward_model @@ -101,8 +93,6 @@ def get_tokenizer_from_args(model: str, **kwargs): elif model == 'llama': pretrain_path = kwargs["pretrain"] tokenizer = AutoTokenizer.from_pretrained(pretrain_path) - elif model == 'roberta': - tokenizer = RobertaTokenizer.from_pretrained("roberta-base") else: raise ValueError(f'Unsupported model "{model}"') diff --git a/applications/Chat/coati/utils/__init__.py b/applications/Chat/coati/utils/__init__.py deleted file mode 100644 index 112b82b97064..000000000000 --- a/applications/Chat/coati/utils/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .tokenizer_utils import prepare_llama_tokenizer_and_embedding, smart_tokenizer_and_embedding_resize - -__all__ = ['smart_tokenizer_and_embedding_resize', 'prepare_llama_tokenizer_and_embedding'] \ No newline at end of file diff --git a/applications/Chat/coati/utils/tokenizer_utils.py b/applications/Chat/coati/utils/tokenizer_utils.py deleted file mode 100644 index e0d96cfc8be2..000000000000 --- a/applications/Chat/coati/utils/tokenizer_utils.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Dict - -import transformers - -DEFAULT_PAD_TOKEN = "[PAD]" -DEFAULT_EOS_TOKEN = "" -DEFAULT_BOS_TOKEN = "" -DEFAULT_UNK_TOKEN = "" - - -def prepare_llama_tokenizer_and_embedding( - tokenizer: transformers.PreTrainedTokenizer, - model: transformers.PreTrainedModel, - special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN), -): - """prepare llama tokenizer and embedding. - - """ - - if tokenizer.pad_token is None: - smart_tokenizer_and_embedding_resize( - special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN), - tokenizer=tokenizer, - model=model, - ) - - tokenizer.add_special_tokens({ - "eos_token": DEFAULT_EOS_TOKEN, - "bos_token": DEFAULT_BOS_TOKEN, - "unk_token": DEFAULT_UNK_TOKEN, - }) - - return tokenizer - - -def smart_tokenizer_and_embedding_resize( - tokenizer: transformers.PreTrainedTokenizer, - model: transformers.PreTrainedModel, - special_tokens_dict: Dict = dict(pad_token=DEFAULT_PAD_TOKEN), -): - """Resize tokenizer and embedding. - - Note: This is the unoptimized version that may make your embedding size not be divisible by 64. - """ - - if tokenizer.pad_token is None: - num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) - - model.resize_token_embeddings(len(tokenizer)) - - if num_new_tokens > 0: - input_embeddings = model.get_input_embeddings().weight.data - output_embeddings = model.get_output_embeddings().weight.data - - input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) - output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) - - input_embeddings[-num_new_tokens:] = input_embeddings_avg - output_embeddings[-num_new_tokens:] = output_embeddings_avg diff --git a/applications/Chat/examples/community/peft/train_peft_prompts.py b/applications/Chat/examples/community/peft/train_peft_prompts.py index 9d8dbb38ae5d..9385e457d852 100644 --- a/applications/Chat/examples/community/peft/train_peft_prompts.py +++ b/applications/Chat/examples/community/peft/train_peft_prompts.py @@ -10,7 +10,6 @@ from coati.models.opt import OPTRM, OPTActor, OPTCritic from coati.trainer import PPOTrainer from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy -from coati.utils import prepare_llama_tokenizer_and_embedding from easy_dataset import EasyPromptsDataset, EasySupervisedDataset from easy_models import BLOOMActor from peft import PeftModel @@ -112,21 +111,20 @@ def main(args): # configure tokenizer if args.model == 'gpt2': tokenizer = GPT2Tokenizer.from_pretrained(args.rm_pretrain) + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'bloom': tokenizer = BloomTokenizerFast.from_pretrained(args.rm_pretrain) + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained(args.rm_pretrain) + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'llama': tokenizer = LlamaTokenizer.from_pretrained(args.pretrain) tokenizer.eos_token = '<\s>' + tokenizer.pad_token = tokenizer.unk_token else: raise ValueError(f'Unsupported model "{args.model}"') - if args.model == 'llama': - tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, actor) - else: - tokenizer.pad_token = tokenizer.eos_token - data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) prompt_dataset = EasyPromptsDataset(args.prompt_path, tokenizer) diff --git a/applications/Chat/examples/community/peft/train_peft_sft.py b/applications/Chat/examples/community/peft/train_peft_sft.py index 54fe0ad55049..4af08e6d0141 100644 --- a/applications/Chat/examples/community/peft/train_peft_sft.py +++ b/applications/Chat/examples/community/peft/train_peft_sft.py @@ -12,7 +12,6 @@ from coati.models.opt import OPTLM from coati.trainer import SFTTrainer from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy -from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from easy_dataset import EasyDataset from peft import LoraConfig, PeftModel, TaskType, get_peft_model @@ -65,10 +64,11 @@ def train(args): tokenizer = GPT2Tokenizer.from_pretrained('gpt2') tokenizer.pad_token = tokenizer.eos_token elif args.model == 'bloom': - tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain) + tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom-560m") tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'llama': tokenizer = AutoTokenizer.from_pretrained( args.pretrain, @@ -76,23 +76,19 @@ def train(args): use_fast=False, ) tokenizer.eos_token = '<\s>' + tokenizer.pad_token = tokenizer.unk_token else: raise ValueError(f'Unsupported model "{args.model}"') - tokenizer.pad_token = tokenizer.eos_token - if args.model == 'llama': - tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model) - - if args.strategy == 'colossalai_gemini': - # this is a hack to deal with the resized embedding - # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatibility - for name, param in model.named_parameters(): - if not isinstance(param, ColoParameter): - sub_module_name = '.'.join(name.split('.')[:-1]) - weight_name = name.split('.')[-1] - sub_module = model.get_submodule(sub_module_name) - setattr(sub_module, weight_name, ColoParameter(param)) - else: - tokenizer.pad_token = tokenizer.eos_token + + if args.model == 'llama' and args.strategy == 'colossalai_gemini': + # this is a hack to deal with the resized embedding + # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatibility + for name, param in model.named_parameters(): + if not isinstance(param, ColoParameter): + sub_module_name = '.'.join(name.split('.')[:-1]) + weight_name = name.split('.')[-1] + sub_module = model.get_submodule(sub_module_name) + setattr(sub_module, weight_name, ColoParameter(param)) # configure optimizer if args.strategy.startswith('colossalai'): diff --git a/applications/Chat/examples/inference.py b/applications/Chat/examples/inference.py index ae59d91c1822..4b49e76088bc 100644 --- a/applications/Chat/examples/inference.py +++ b/applications/Chat/examples/inference.py @@ -2,10 +2,10 @@ import torch from coati.models.bloom import BLOOMActor +from coati.models.generation import generate from coati.models.gpt import GPTActor from coati.models.opt import OPTActor -from coati.models.roberta import RoBERTaActor -from transformers import AutoTokenizer, RobertaTokenizer +from transformers import AutoTokenizer from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer @@ -17,13 +17,11 @@ def eval(args): actor = BLOOMActor(pretrained=args.pretrain).to(torch.cuda.current_device()) elif args.model == 'opt': actor = OPTActor(pretrained=args.pretrain).to(torch.cuda.current_device()) - elif args.model == 'roberta': - actor = RoBERTaActor(pretrained=args.pretrain).to(torch.cuda.current_device()) else: raise ValueError(f'Unsupported model "{args.model}"') state_dict = torch.load(args.model_path) - actor.model.load_state_dict(state_dict) + actor.load_state_dict(state_dict) # configure tokenizer if args.model == 'gpt2': @@ -34,27 +32,26 @@ def eval(args): tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m') - elif args.model == 'roberta': - tokenizer = RobertaTokenizer.from_pretrained("roberta-base") else: raise ValueError(f'Unsupported model "{args.model}"') actor.eval() input = args.input input_ids = tokenizer.encode(input, return_tensors='pt').to(torch.cuda.current_device()) - outputs = actor.generate(input_ids, - max_length=args.max_length, - do_sample=True, - top_k=50, - top_p=0.95, - num_return_sequences=1) + outputs = generate(actor, + input_ids, + max_length=args.max_length, + do_sample=True, + top_k=50, + top_p=0.95, + num_return_sequences=1) output = tokenizer.batch_decode(outputs[0], skip_special_tokens=True) print(output) if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'roberta']) + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt']) # We suggest to use the pretrained model from HuggingFace, use pretrain to configure model parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--model_path', type=str, default=None) diff --git a/applications/Chat/examples/test_ci.sh b/applications/Chat/examples/test_ci.sh index dec1f7c036c8..fe2af471017e 100755 --- a/applications/Chat/examples/test_ci.sh +++ b/applications/Chat/examples/test_ci.sh @@ -43,18 +43,18 @@ pip install -r ${BASE}/requirements.txt wandb init -m offline -# FIXME: This is a hack to skip tests that are not working (tested at commit b3ab7fbabf) +# FIXME: This is a hack to skip tests that are not working # - gpt2-ddp: RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation -# - llama-*: Repository Not Found for url: https://huggingface.co/{...}/resolve/main/tokenizer.model. -# - roberta-*: RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)` +# - llama-*: These tests can be passed locally, skipped for long execution time SKIPPED_TESTS=( "gpt2-ddp" - "llama-ddp" "llama-colossalai_gemini" "llama-colossalai_zero2" - "roberta-ddp" "roberta-colossalai_gemini" "roberta-colossalai_zero2" + "llama-ddp" + "llama-colossalai_gemini" + "llama-colossalai_zero2" ) # These tests are quick and do not have any dependencies -for model in 'gpt2' 'bloom' 'opt' 'llama' 'roberta'; do +for model in 'gpt2' 'bloom' 'opt' 'llama'; do for strategy in 'ddp' 'colossalai_gemini' 'colossalai_zero2'; do if [[ " ${SKIPPED_TESTS[*]} " =~ " ${model}-${strategy} " ]]; then echo "[Test]: Skipped $model-$strategy" @@ -64,7 +64,7 @@ for model in 'gpt2' 'bloom' 'opt' 'llama' 'roberta'; do --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ --strategy $strategy --model $model \ --num_episodes 1 --num_collect_steps 2 --num_update_steps 1 \ - --train_batch_size 2 + --train_batch_size 2 --lora_rank 4 done done @@ -124,22 +124,6 @@ torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ --save_path ${BASE}/rm_ckpt.pt rm -rf ${BASE}/rm_ckpt.pt -torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'microsoft/deberta-v3-large' --model 'deberta' \ - --strategy colossalai_zero2 --loss_fn 'log_sig' \ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ - --test True --lora_rank 4 \ - --save_path ${BASE}/rm_ckpt.pt -rm -rf ${BASE}/rm_ckpt.pt - -torchrun --standalone --nproc_per_node=2 ${BASE}/train_reward_model.py \ - --pretrain 'roberta-base' --model 'roberta' \ - --strategy colossalai_zero2 --loss_fn 'log_exp' \ - --dataset 'Anthropic/hh-rlhf' --subset 'harmless-base' \ - --test True --lora_rank 4 \ - --save_path ${BASE}/rm_ckpt.pt -rm -rf ${BASE}/rm_ckpt.pt - # train rl torchrun --standalone --nproc_per_node=2 ${BASE}/train_prompts.py \ --prompt_dataset $PROMPT_PATH --pretrain_dataset $PRETRAIN_DATASET \ diff --git a/applications/Chat/examples/train_prompts.py b/applications/Chat/examples/train_prompts.py index c748eeb21065..7338a6d51142 100644 --- a/applications/Chat/examples/train_prompts.py +++ b/applications/Chat/examples/train_prompts.py @@ -7,14 +7,12 @@ from coati.models.gpt import GPTRM, GPTActor, GPTCritic from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM from coati.models.opt import OPTRM, OPTActor, OPTCritic -from coati.models.roberta import RoBERTaActor, RoBERTaCritic, RoBERTaRM from coati.trainer import PPOTrainer from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy -from coati.utils import prepare_llama_tokenizer_and_embedding from torch.optim import Adam from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler -from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer, RobertaTokenizer +from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer from colossalai.nn.optimizer import HybridAdam @@ -43,8 +41,6 @@ def main(args): initial_model = OPTActor(pretrained=args.pretrain) elif args.model == 'llama': initial_model = LlamaActor(pretrained=args.pretrain) - elif args.model == 'roberta': - initial_model = RoBERTaActor(pretrained=args.pretrain) else: raise ValueError(f'Unsupported actor model "{args.model}"') @@ -61,8 +57,6 @@ def main(args): reward_model = OPTRM(pretrained=args.rm_pretrain) elif rm_model_name == 'llama': reward_model = LlamaRM(pretrained=args.rm_pretrain) - elif rm_model_name == 'roberta': - reward_model = RoBERTaRM(pretrained=args.rm_pretrain) else: raise ValueError(f'Unsupported reward model "{rm_model_name}"') @@ -80,8 +74,6 @@ def main(args): actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) elif args.model == 'llama': actor = LlamaActor(pretrained=args.pretrain, lora_rank=args.lora_rank) - elif args.model == 'roberta': - actor = RoBERTaActor(pretrained=args.pretrain, lora_rank=args.lora_rank) else: raise ValueError(f'Unsupported actor model "{args.model}"') @@ -93,8 +85,6 @@ def main(args): critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) elif rm_model_name == 'llama': critic = LlamaCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) - elif rm_model_name == 'roberta': - critic = RoBERTaCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) else: raise ValueError(f'Unsupported reward model "{rm_model_name}"') @@ -117,23 +107,20 @@ def main(args): # configure tokenizer if args.model == 'gpt2': tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'bloom': tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m') + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'llama': tokenizer = LlamaTokenizer.from_pretrained(args.pretrain) tokenizer.eos_token = '<\s>' - elif args.model == 'roberta': - tokenizer = RobertaTokenizer.from_pretrained("roberta-base") + tokenizer.pad_token = tokenizer.unk_token else: raise ValueError(f'Unsupported model "{args.model}"') - if args.model == 'llama': - tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, actor) - else: - tokenizer.pad_token = tokenizer.eos_token - data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_dataset, max_datasets_size=16384) @@ -209,9 +196,9 @@ def main(args): choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2', help='strategy to use') - parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama', 'roberta']) + parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--pretrain', type=str, default=None) - parser.add_argument('--rm_model', default=None, choices=['gpt2', 'bloom', 'opt', 'llama', 'roberta']) + parser.add_argument('--rm_model', default=None, choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--rm_path', type=str, default=None) parser.add_argument('--rm_pretrain', type=str, default=None) parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts') diff --git a/applications/Chat/examples/train_reward_model.py b/applications/Chat/examples/train_reward_model.py index e9618e0c1d5e..5b1b8d3d16b2 100644 --- a/applications/Chat/examples/train_reward_model.py +++ b/applications/Chat/examples/train_reward_model.py @@ -1,27 +1,22 @@ import argparse from random import randint -import loralib as lora import torch import torch.distributed as dist from coati.dataset import HhRlhfDataset, RmStaticDataset from coati.models import LogExpLoss, LogSigLoss -from coati.models.base import RewardModel from coati.models.bloom import BLOOMRM -from coati.models.deberta import DebertaRM from coati.models.gpt import GPTRM from coati.models.llama import LlamaRM from coati.models.opt import OPTRM -from coati.models.roberta import RoBERTaRM from coati.trainer import RewardModelTrainer from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy -from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from torch.optim import Adam from torch.optim.lr_scheduler import CosineAnnealingLR from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler -from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer, LlamaTokenizer, RobertaTokenizer +from transformers import AutoTokenizer, BloomTokenizerFast, LlamaTokenizer from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer from colossalai.nn.optimizer import HybridAdam @@ -46,12 +41,8 @@ def train(args): model = OPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) elif args.model == 'gpt2': model = GPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'deberta': - model = DebertaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) elif args.model == 'llama': model = LlamaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) - elif args.model == 'roberta': - model = RoBERTaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device()) else: raise ValueError(f'Unsupported model "{args.model}"') @@ -64,24 +55,18 @@ def train(args): # configure tokenizer if args.model == 'gpt2': tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'bloom': tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m') + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - elif args.model == 'deberta': - tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v3-large') + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'llama': tokenizer = LlamaTokenizer.from_pretrained(args.pretrain) - elif args.model == 'roberta': - tokenizer = RobertaTokenizer.from_pretrained("roberta-base") + tokenizer.pad_token = tokenizer.unk_token else: raise ValueError(f'Unsupported model "{args.model}"') - max_len = args.max_len - - if args.model == 'llama': - tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model) - else: - tokenizer.pad_token = tokenizer.eos_token # configure optimizer if args.strategy.startswith('colossalai'): @@ -112,13 +97,13 @@ def train(args): valid_data = data['test'].select((randint(0, len(eval_data) - 1) for _ in range(len(eval_data) // 5))) if args.dataset == 'Dahoas/rm-static': - train_dataset = RmStaticDataset(train_data, tokenizer, max_len) - valid_dataset = RmStaticDataset(valid_data, tokenizer, max_len) - eval_dataset = RmStaticDataset(eval_data, tokenizer, max_len) + train_dataset = RmStaticDataset(train_data, tokenizer, args.max_len) + valid_dataset = RmStaticDataset(valid_data, tokenizer, args.max_len) + eval_dataset = RmStaticDataset(eval_data, tokenizer, args.max_len) elif args.dataset == 'Anthropic/hh-rlhf': - train_dataset = HhRlhfDataset(train_data, tokenizer, max_len) - valid_dataset = HhRlhfDataset(valid_data, tokenizer, max_len) - eval_dataset = HhRlhfDataset(eval_data, tokenizer, max_len) + train_dataset = HhRlhfDataset(train_data, tokenizer, args.max_len) + valid_dataset = HhRlhfDataset(valid_data, tokenizer, args.max_len) + eval_dataset = HhRlhfDataset(eval_data, tokenizer, args.max_len) else: raise ValueError(f'Unsupported dataset "{args.dataset}"') @@ -195,7 +180,7 @@ def train(args): parser.add_argument('--strategy', choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2') - parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta', 'llama', 'roberta'], default='bloom') + parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom') parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--model_path', type=str, default=None) parser.add_argument('--need_optim_ckpt', type=bool, default=False) diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index 30becd8a68a1..cb3eb649d76c 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -9,7 +9,6 @@ from coati.models import convert_to_lora_module from coati.trainer import SFTTrainer from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy -from coati.utils import prepare_llama_tokenizer_and_embedding from datasets import load_dataset from torch.optim import Adam from torch.utils.data import DataLoader @@ -68,6 +67,7 @@ def train(args): tokenizer.pad_token = tokenizer.eos_token elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + tokenizer.pad_token = tokenizer.eos_token elif args.model == 'llama': tokenizer = AutoTokenizer.from_pretrained( args.pretrain, @@ -75,24 +75,19 @@ def train(args): use_fast=False, ) tokenizer.eos_token = '<\s>' + tokenizer.pad_token = tokenizer.unk_token else: raise ValueError(f'Unsupported model "{args.model}"') - tokenizer.pad_token = tokenizer.eos_token - max_len = args.max_len - if args.model == 'llama': - tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model) - - if args.strategy == 'colossalai_gemini': - # this is a hack to deal with the resized embedding - # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatibility - for name, param in model.named_parameters(): - if not isinstance(param, ColoParameter): - sub_module_name = '.'.join(name.split('.')[:-1]) - weight_name = name.split('.')[-1] - sub_module = model.get_submodule(sub_module_name) - setattr(sub_module, weight_name, ColoParameter(param)) - else: - tokenizer.pad_token = tokenizer.eos_token + + if args.model == 'llama' and args.strategy == 'colossalai_gemini': + # this is a hack to deal with the resized embedding + # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatibility + for name, param in model.named_parameters(): + if not isinstance(param, ColoParameter): + sub_module_name = '.'.join(name.split('.')[:-1]) + weight_name = name.split('.')[-1] + sub_module = model.get_submodule(sub_module_name) + setattr(sub_module, weight_name, ColoParameter(param)) # configure optimizer if args.strategy.startswith('colossalai'): @@ -107,14 +102,14 @@ def train(args): train_data = load_dataset(args.dataset, 'super_natural_instructions', split='train') eval_data = load_dataset(args.dataset, 'super_natural_instructions', split='test') - train_dataset = SFTDataset(train_data, tokenizer, max_len) - eval_dataset = SFTDataset(eval_data, tokenizer, max_len) + train_dataset = SFTDataset(train_data, tokenizer, args.max_len) + eval_dataset = SFTDataset(eval_data, tokenizer, args.max_len) else: train_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.dataset, max_datasets_size=args.max_datasets_size, - max_length=max_len) + max_length=args.max_len) eval_dataset = None data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) From 8d68de767d156012924cdbcba318ac8d85bd72a7 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Mon, 22 May 2023 15:02:17 +0800 Subject: [PATCH 348/413] [shardformer] init shardformer code structure (#3731) * init shardformer code structure * add implement of sharder (inject and replace) * add implement of replace layer to colossal layer * separate different layer policy, add some notion * implement 1d and 2d slicer, can tell col or row * fix bug when slicing and inject model * fix some bug; add inference test example --- colossalai/shardformer/__init__.py | 0 colossalai/shardformer/model/__init__.py | 0 colossalai/shardformer/model/modeling_bert.py | 63 +++++ colossalai/shardformer/policies/__init__.py | 0 colossalai/shardformer/policies/autopolicy.py | 41 +++ colossalai/shardformer/policies/basepolicy.py | 182 ++++++++++++++ colossalai/shardformer/policies/bert.py | 168 +++++++++++++ colossalai/shardformer/shard/__init__.py | 0 colossalai/shardformer/shard/shardconfig.py | 18 ++ colossalai/shardformer/shard/sharder.py | 238 ++++++++++++++++++ colossalai/shardformer/shard/shardmodel.py | 58 +++++ colossalai/shardformer/shard/slicer.py | 167 ++++++++++++ colossalai/shardformer/test/config.py | 5 + colossalai/shardformer/test/test.py | 37 +++ colossalai/shardformer/utils/__init__.py | 0 colossalai/shardformer/utils/utils.py | 56 +++++ 16 files changed, 1033 insertions(+) create mode 100644 colossalai/shardformer/__init__.py create mode 100644 colossalai/shardformer/model/__init__.py create mode 100644 colossalai/shardformer/model/modeling_bert.py create mode 100644 colossalai/shardformer/policies/__init__.py create mode 100644 colossalai/shardformer/policies/autopolicy.py create mode 100644 colossalai/shardformer/policies/basepolicy.py create mode 100644 colossalai/shardformer/policies/bert.py create mode 100644 colossalai/shardformer/shard/__init__.py create mode 100644 colossalai/shardformer/shard/shardconfig.py create mode 100644 colossalai/shardformer/shard/sharder.py create mode 100644 colossalai/shardformer/shard/shardmodel.py create mode 100644 colossalai/shardformer/shard/slicer.py create mode 100644 colossalai/shardformer/test/config.py create mode 100644 colossalai/shardformer/test/test.py create mode 100644 colossalai/shardformer/utils/__init__.py create mode 100644 colossalai/shardformer/utils/utils.py diff --git a/colossalai/shardformer/__init__.py b/colossalai/shardformer/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/model/__init__.py b/colossalai/shardformer/model/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/model/modeling_bert.py b/colossalai/shardformer/model/modeling_bert.py new file mode 100644 index 000000000000..87ed8ac308a5 --- /dev/null +++ b/colossalai/shardformer/model/modeling_bert.py @@ -0,0 +1,63 @@ +import torch +import torch.nn as nn +from torch.nn import CrossEntropyLoss +from typing import Any, Dict, List, Type + + +from transformers import BertForMaskedLM +from transformers.models.bert.modeling_bert import MaskedLMOutput +class BertForMaskedLM_(BertForMaskedLM): + def forward( + self, + input_ids=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + print("[Inject OK] Injected forward method") + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + + # if input_ids is not None: + # masked_lm_loss = applyDistCrossEntropy(prediction_scores, input_ids, self.config.vocab_size) + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) \ No newline at end of file diff --git a/colossalai/shardformer/policies/__init__.py b/colossalai/shardformer/policies/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py new file mode 100644 index 000000000000..9142e0dae22e --- /dev/null +++ b/colossalai/shardformer/policies/autopolicy.py @@ -0,0 +1,41 @@ +import torch.nn as nn + +def build_policies(): + """ + Build the policies for the model + + Return: + The dict for the policies + """ + auto_policy_dict = {} + + from transformers.models.bert.modeling_bert import BertForMaskedLM + from .bert import BertForMaskedLMPolicy + auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy + + from transformers.models.bert.modeling_bert import BertForSequenceClassification + from .bert import BertForSequenceClassificationPolicy + auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy + + return auto_policy_dict + +def get_autopolicy(model:nn.Module): + """ + Return the auto policy for the model + + Args: + model: The model to be used + + Return: + The auto policy for the model + """ + auto_policy_dict = build_policies() + policy = auto_policy_dict.get(model.__class__, None) + if policy is None: + raise NotImplementedError(f"Auto policy for {model.__class__.__qualname__} is not implemented\n Supported models are {[i.__qualname__ for i in auto_policy_dict.keys()]}") + return policy + +# from transformers.models.bert.modeling_bert import BertForMaskedLM, BertForPreTraining +# model = BertForPreTraining +# policy = get_autopolicy(model) +# print(policy) diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py new file mode 100644 index 000000000000..d444aeb53bf8 --- /dev/null +++ b/colossalai/shardformer/policies/basepolicy.py @@ -0,0 +1,182 @@ +# part of code modified from https://github.com/tunib-ai/parallelformers + +import torch +import torch.nn as nn +import colossalai.nn as col_nn +from typing import Any, Dict, List, Type, Tuple, Callable +from transformers import AutoConfig +from dataclasses import dataclass, field + +@dataclass +class Argument: + attr_dict : Dict[str, Any] + param_funcs : List[Callable] + binding_layers : List[nn.Module] = field(default_factory=list) + +@dataclass +class Layer: + """ + The layer object for the policy + + Args: + weight: The weight name of the layer + bias: The bias name of the layer + replace_layer: The layer to replace the original layer + ignore: Whether to ignore this layer if it is not in the model + """ + weight: str = None + bias: str = None + replace_layer: Any = None + ignore: bool = False + + +@dataclass +class Col_Layer(Layer): + """ + Class for col shard layer in MegatronLM + """ + gather_output: bool = False + + +@dataclass +class Row_Layer(Layer): + """ + Class for col shard layer in MegatronLM + """ + pass + + +class Policy(): + """ + The base class for all the policies + For each different model, it should have a different policy class, like BertPolicy for Bert Model + or OPTPolicy for OPT model. + AutoPolicy: + shardformer already defined some policies for huggingface model, just set custom_policy = None + to use the auto policy. In shardformer autopolicy, we define a base policy for one type model, + like BertPolicy, and for each different Bert modle in huggingface like, BertForMaskedLM, + BertForSequenceClassification, etc., for each different Bert model we difine different policy class + and overwrite the method inject_policy + + CustomPolicy: + """ + @staticmethod + def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument]: + """ + Return a dict, the key is layer will be modified and the value is the Argument class with param setting and param functions + + Args: + model_config: The config of transformer model + shard_setting: The config of distributed model + + Return: + Dict for the modify policy, + { + origin layer class1 (nn.Module): Argument( + attr_dict = { + argument1: value1, + argument2: value2, + ... + }, + param_funcs = [ + staticmethod1, + staticmethod2, + ... + ] + ), + origin layer class2 (nn.Module): Argument( + attr_dict = { + argument1: value1, + argument2: value2, + ... + }, + param_funcs = [ + staticmethod1, + staticmethod2, + ... + ] + ), + ... + } + + """ + raise NotImplementedError + + + @staticmethod + def inject_policy() -> Tuple[nn.Module, nn.Module]: + """ + Return the dict for the inject model + + Return: + The injected model, key is the original model and value is the new shardmodel + """ + return () + + + @staticmethod + def attn_in() -> List: + """ + Attention qkv layer + + Returns: + List[Layer]: List of layer object, each layer is the new + """ + return NotImplementedError + + + @staticmethod + def attn_out() -> List: + """ + Attention output projection layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + + @staticmethod + def mlp_in() -> List: + """ + h -> 4h mlp layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + + @staticmethod + def mlp_out() -> List: + """ + 4h -> h mlp layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + + @staticmethod + def embedding()->List: + """ + Partially slice the embedding layer + vocab_size->vocab_size//gpu_nums + + Return: + List[Layer]: List of layer object + """ + return NotImplementedError + + + @staticmethod + def unembedding()->List: + """ + Partially slice the embedding layer + vocab_size->vocab_size//gpu_nums + + Return: + List[Layer]: List of layer object + """ + return NotImplementedError diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py new file mode 100644 index 000000000000..24b95e827347 --- /dev/null +++ b/colossalai/shardformer/policies/bert.py @@ -0,0 +1,168 @@ +from typing import Dict, List, Tuple, Type, Any, Callable +import torch.nn as nn +from .basepolicy import Policy, Layer, Argument, Col_Layer, Row_Layer +import colossalai.nn as col_nn +from transformers.models.bert.modeling_bert import BertLayer, BertEmbeddings, BertLMPredictionHead +from dataclasses import dataclass + + +class BertPolicy(Policy): + @staticmethod + def argument_policy(config, world_size: int) -> Dict[nn.Module,Argument]: + return { + BertLayer: Argument( + attr_dict = { + # 1. shard hidden size + "attention.self.all_head_size": config.hidden_size // world_size, + "crossattention.self.all_head_size": config.hidden_size // world_size, + # 2. shard number of heads + "attention.self.num_attention_heads": config.num_attention_heads // world_size, + "crossattention.self.num_attention_heads": config.num_attention_heads // world_size, + + }, + param_funcs = [ + BertPolicy.attn_in, + BertPolicy.attn_out, + BertPolicy.mlp_in, + BertPolicy.mlp_out + ] + ), + BertEmbeddings: Argument( + attr_dict = { + # 1. shard vocab size + # "word_embeddings.num_embeddings": config.vocab_size // world_size, + # 2. add the size of the sliced embedding layer excluding the last slice + "word_embeddings.dim_size": (config.vocab_size+world_size-1) // world_size, + }, + param_funcs = [ + BertPolicy.embedding, + ], + binding_layers = [ + BertLMPredictionHead, + ] + ), + BertLMPredictionHead: Argument( + attr_dict = { + # 1. shard vocab size + # "word_embeddings.num_embeddings": config.vocab_size // world_size, + # 2. add the size of the sliced embedding layer excluding the last slice + }, + param_funcs = [ + BertPolicy.unembedding, + ] + ) + } + + @staticmethod + def attn_in() -> List: + return [ + Col_Layer( + weight="attention.self.query.weight", + bias="attention.self.query.bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + weight="attention.self.key.weight", + bias="attention.self.key.bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + weight="attention.self.value.weight", + bias="attention.self.value.bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + weight="crossattention.self.query.weight", + bias="crossattention.self.query.bias", + replace_layer=col_nn.Linear1D_Col, + ignore=True, + ), + Col_Layer( + weight="crossattention.self.key.weight", + bias="crossattention.self.key.bias", + replace_layer=col_nn.Linear1D_Col, + ignore=True, + ), + Col_Layer( + weight="crossattention.self.value.weight", + bias="crossattention.self.value.bias", + replace_layer=col_nn.Linear1D_Col, + ignore=True, + ), + + ] + + @staticmethod + def attn_out() -> List: + return [ + Row_Layer( + weight="attention.output.dense.weight", + bias="attention.output.dense.bias", + replace_layer=col_nn.Linear1D_Row, + ), + Row_Layer( + weight="crossattention.output.dense.weight", + bias="crossattention.output.dense.bias", + replace_layer=col_nn.Linear1D_Row, + ignore=True, + ), + ] + + @staticmethod + def mlp_in() -> List: + return [ + Col_Layer( + weight="intermediate.dense.weight", + bias="intermediate.dense.bias", + replace_layer=col_nn.Linear1D_Col, + ), + ] + + @staticmethod + def mlp_out() -> List: + return [ + Row_Layer( + weight="output.dense.weight", + bias="output.dense.bias", + replace_layer=col_nn.Linear1D_Row, + ), + ] + + @staticmethod + def embedding() -> List: + return [ + Col_Layer( + weight="word_embeddings.weight", + replace_layer=col_nn.VocabParallelEmbedding1D, + ) + ] + + @staticmethod + def unembedding() -> List: + return [ + Col_Layer( + weight="decoder.weight", + bias="decoder.bias", + replace_layer=col_nn.Linear1D_Col, + gather_output=True, + ) + ] + +from transformers import BertForMaskedLM +from colossalai.shardformer.model.modeling_bert import BertForMaskedLM_ +class BertForMaskedLMPolicy(BertPolicy): + @staticmethod + def inject_policy() -> Tuple[nn.Module, nn.Module]: + return (BertForMaskedLM, BertForMaskedLM_) + + + +class BertForSequenceClassificationPolicy(BertPolicy): + @staticmethod + def inject_policy() -> Dict: + return {} + + +# model = BertForMaskedLM.from_pretrained("bert-base-uncased") +# _ = BertForMaskedLMPolicy(model) +# print(isinstance(model,list(_.inject_policy().keys())[0])) \ No newline at end of file diff --git a/colossalai/shardformer/shard/__init__.py b/colossalai/shardformer/shard/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/shard/shardconfig.py b/colossalai/shardformer/shard/shardconfig.py new file mode 100644 index 000000000000..be265ff0c8c1 --- /dev/null +++ b/colossalai/shardformer/shard/shardconfig.py @@ -0,0 +1,18 @@ +from dataclasses import dataclass + + +@dataclass +class ShardConfig: + """ + The config for sharding the huggingface model for test + """ + rank: int + fp16: bool = True + num_gpus: int = 2 + world_size: int = 2 + backend="nccl" + verbose: str = 'simple' + seed: int = None + require_grad: bool = False + master_addr: str = "127.0.0.1" + master_port: int = 29500 \ No newline at end of file diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py new file mode 100644 index 000000000000..ef785cfee9da --- /dev/null +++ b/colossalai/shardformer/shard/sharder.py @@ -0,0 +1,238 @@ +import torch +import torch.nn as nn +from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union, Callable +from .shardconfig import ShardConfig +from dataclasses import dataclass +from ..policies.basepolicy import Policy, Layer +from ..policies.autopolicy import get_autopolicy +from .slicer import Slicer +from ..utils.utils import hasattr_, setattr_, getattr_ +import colossalai.nn as col_nn +from colossalai.logging import get_dist_logger +import os + + +logger = get_dist_logger() + +class ModelSharder(object): + """ + Shard the original huggingface model according to the policy + + Args: + policy: The policy to shard the model + model: The model to shard + dist_setting: The setting of distributed model + """ + def __init__( + self, + model: nn.Module, + policy: Policy, + shard_config: ShardConfig = None, # TODO + ) -> None: + self.model = model + self.policy = get_autopolicy(self.model) if policy is None else policy + self.slicer = Slicer(shard_config) + self.shard_config = shard_config + self.model_config = self.model.config + self.binding_map = {} + + + def shard(self) -> None: + self.inject_model(self.model) + self.replace_layer(self.model) + + + def inject_model( + self, + model: nn.Module, + ) -> None: + """ + Replace the model to policy defined model + Mainly modify the forward and backward to fit distributed model + + e.g. + BertForMaskedLM.forward -> BertForMaskedLM_.forward + """ + inject_policy = self.policy.inject_policy() + + org_model_cls = inject_policy[0] + shard_model_cls = inject_policy[1] + + if model.__class__ == org_model_cls: + for key in shard_model_cls.__dict__.keys(): + if hasattr(model.__class__, key): + setattr( + model.__class__, + key, + getattr(shard_model_cls,key), + ) + else: + raise NotImplementedError(f"{model.__class__} is not implemented so far") + + + def replace_layer( + self, + model: nn.Module, + ) -> None: + """ + Replace the layer according to the policy, and replace the layer one by one + + Args: + layer: The layer to shard + """ + argument_policies = self.policy.argument_policy(self.model_config, self.shard_config.world_size) + for argument_policy in argument_policies.items(): + origin_layer_cls = argument_policy[0] + attr_dict = argument_policy[1].attr_dict + param_funcs = argument_policy[1].param_funcs + binding_layers = argument_policy[1].binding_layers + # if binding_layer is not None: + # self.binding_map[origin_layer_cls] = binding_layer + self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs, binding_layers) + + + def reverse_replace_layer( + self, + layer: nn.Module, + origin_cls: nn.Module, + attr_dict: Dict[str, Any], + param_funcs: List[Callable], + binding_layers: List[nn.Module] + ) -> None: + """ + Reverse the replace layer operation + + Args: + layer: The object of layer to shard + origin_cls: The origin layer class + attr_dict: The attribute dict to modify + policy_cls: The policy class + """ + for name, child in layer.named_children(): + if child.__class__ == origin_cls: + # replac_layer = child + for k, v in attr_dict.items(): + setattr_(child, k, v, ignore=True) + # print(f"Sharding {name} layer", replac_layer.attention.self.__dict__) + # setattr_(layer, name, self.shard_one_layer(child, policy_cls)) + self.shard_one_layer(child, param_funcs, binding_layers) + continue + + self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs, binding_layers) + return layer + + + def shard_one_layer( + self, + org_layer: nn.Module, + param_funcs: List[Callable], + binding_layers: List[nn.Module] + ) -> None: + """ + Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict + + Args: + org_layer: The origin layer object to shard + param_funcs: The function list to get shard information in policy class + + """ + # print(org_layer) + for func in param_funcs: + policy_layers = func() + for policy_layer in policy_layers: + weight = None + bias = None + weight_attr = policy_layer.weight + bias_attr = policy_layer.bias + replace_layer_cls = policy_layer.replace_layer + ignore = policy_layer.ignore + if policy_layer.__class__.__name__ == "Col_Layer": + gather_output = policy_layer.gather_output + print(gather_output) + + if weight_attr is not None: + if hasattr_(org_layer, weight_attr): + weight = getattr_(org_layer, weight_attr) + elif not ignore: + raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {weight_attr}") + + if bias_attr is not None: + if hasattr_(org_layer, bias_attr): + bias = getattr_(org_layer, bias_attr) + elif not ignore: + raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {bias_attr}") + + # dont have the attribute in policy, and ignore is true + if weight is None and bias is None and ignore: + continue + + # set the sliced weight and bias to the new nn_col layer + assert weight is not None or bias is not None + layer_attr = (lambda x: x[:x.rfind(".")])(weight_attr or bias_attr) + + # slice weight and bias + weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__) + print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None) + # save the binding information + for binding_layer in binding_layers: + self.binding_map[binding_layer] = dict(weight=weight, bias=bias) + + # create new object to replace the origin layer + if replace_layer_cls is not None: + # print(f"RANK {os.environ['RANK']}: replace {getattr_(org_layer, layer_attr).__class__} to {replace_layer_cls}, shape is {weight.shape}") + if isinstance(getattr_(org_layer, layer_attr), nn.Linear): + if replace_layer_cls.__name__ == "Linear1D_Row": + replace_layer = replace_layer_cls(weight.shape[1], weight.shape[0], bias=False if bias is None else True) + elif replace_layer_cls.__name__ == "Linear1D_Col": + replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], bias=False if bias is None else True, gather_output=gather_output) + setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) + self.set_param(replace_layer, weight, bias) + elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding): + replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True)) + setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) + self.set_param(replace_layer, weight, bias) + else: + raise NotImplementedError(f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far") + # do not replace the layer object, just replace the weight and bias + else: + self.set_param(org_layer, layer_attr, weight, bias) + + + def set_param( + self, + layer: Any, + layer_attr: str = "", + weight: torch.Tensor = None, + bias: torch.Tensor = None + ) -> None: + """ + Reset the weight and bias of the layer object + + Args: + layer: The layer object + layer_attr: The attribute name of the layer + weight: The weight of the layer + bias: The bias of the layer + """ + assert weight is not None or bias is not None + if weight is not None: + setattr_(layer, "weight" if layer_attr == "" else layer_attr+".weight", nn.Parameter(weight)) + self.set_layer_size(layer, layer_attr, weight.shape) + if bias is not None: + setattr_(layer, "bias" if layer_attr == "" else layer_attr+".bias", nn.Parameter(bias)) + + + def set_layer_size(self, layer: nn.Module, layer_attr: str, size: torch.Size) -> None: + """ + Set the layer attribute + + Args: + layer: The layer object + layer_attr: The attribute name of the layer + size: Torch.size + """ + # Tensor.shape[0] -> out_features, Tensor.shape[1] -> in_features + attrs = ["out_features", "in_features"] + for i, attr in enumerate(attrs): + if hasattr_(layer, f"{layer_attr}.{attr}"): + setattr_(layer, f"{layer_attr}.{attr}", size[i]) diff --git a/colossalai/shardformer/shard/shardmodel.py b/colossalai/shardformer/shard/shardmodel.py new file mode 100644 index 000000000000..54d7b5ba02d9 --- /dev/null +++ b/colossalai/shardformer/shard/shardmodel.py @@ -0,0 +1,58 @@ +import os +import torch +import torch.nn as nn +import transformers +import torch.distributed as dist +from dataclasses import dataclass +from contextlib import suppress + +from colossalai.tensor.d_tensor.layout import Layout +from ..policies.basepolicy import Policy +from .sharder import ModelSharder +from .shardconfig import ShardConfig + + +class ShardModel(object): + """ + The class for sharding the huggingface model, self.model is the sharded model + Just creat a new ShardModel object to shard huggingface model + + Args: + model: the origin huggingface model + dist_config: the config for distribute information + custom_policy: the custom policy for sharding + """ + def __init__( + self, + model: nn.Module, + shard_config: ShardConfig = None, # TODO + custom_policy: Policy = None, + ) -> None: + self.model = model + self.shard_config = shard_config + self.policy = custom_policy + # self.layout=, # TODO + + sharder=ModelSharder( + model=self.model, + policy=self.policy, + shard_config=self.shard_config, + ) + sharder.shard() + + + def set_environ(self) -> None: + os.environ["TOKENIZERS_PARALLELISM"] = "true" + os.environ["MKL_SERVICE_FORCE_INTEL"] = "GNU" + os.environ["MASTER_ADDR"] = str(self.dist_config.master_addr) + os.environ["MASTER_PORT"] = str(self.dist_config.master_port) + os.environ["WORLD_SIZE"] = str(self.dist_config.num_gpus) + os.environ["RANK"] = str(self.dist_config.rank) + os.environ["LOCAL_RANK"] = str(self.dist_config.rank) + if not dist.is_initialized(): + dist.init_process_group(backend=self.dist_config.backend) + + torch.cuda.set_device(int(os.getenv("LOCAL_RANK", "0"))) + + def back_to_org() -> None: + pass \ No newline at end of file diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py new file mode 100644 index 000000000000..1849cdc99c72 --- /dev/null +++ b/colossalai/shardformer/shard/slicer.py @@ -0,0 +1,167 @@ +import os +from typing import Dict, Tuple +from dataclasses import dataclass + +import torch +import torch.distributed as dist +from ..policies.basepolicy import Layer, Col_Layer, Row_Layer +from .shardconfig import ShardConfig + + +dim_mapping = {Col_Layer: 1, Row_Layer: 0} + +class Slicer(): + + def __init__( + self, + shardconfig: ShardConfig #TODO + ) -> None: + self.shardconfig = shardconfig + + + def slice_weight_bias( + self, + weight: torch.Tensor, + bias: torch.Tensor, + policy_layer_cls: Layer, + ): + """ + Slice the weight and bias according to policy layer cls + Layer -> do nothing + Col_Layer -> slice the weight and bias along dim 1 + Row_Layer -> slice the weight along dim 0 and do not slice bias + + Args: + weight: The weight of the layer + bias: The bias of the layer + policy_layer_class: The class represent how to slice the tensor + """ + if policy_layer_cls == Layer: + return weight, bias + elif policy_layer_cls == Col_Layer: + weight = self.slice_tensor(weight, 1, False) + bias = self.slice_tensor(bias, 0, True) + elif policy_layer_cls == Row_Layer: + weight = self.slice_tensor(weight, 0, False) + else: + raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported") + return weight, bias + + + def slice_weight( + self, + weight: torch.Tensor, + policy_layer_cls: Layer, + ) -> torch.Tensor: + """ + Slice the weight and bias according to the shardconfig + + Args: + weight: The weight of the layer + bias: The bias of the layer + policy_layer_class: The class represent how to slice the tensor + """ + if weight is not None: + dim = dim_mapping[policy_layer_cls] + weight = self.slice_tensor(weight, dim, False) + return weight + + + def slice_bias( + self, + bias: torch.Tensor, + ) -> torch.Tensor: + """ + Slice the bias according to the shardconfig + + Args: + bias: The bias of the layer + """ + assert bias is not None, "The bias is None" + if bias is not None: + bias = self.slice_tensor(bias, 1, True) + return bias + + + def slice_tensor( + self, + tensor_in: torch.Tensor, + dim: int, + is_bias: bool, + ) -> torch.Tensor: + """ + Slice tensor according to the config + """ + if tensor_in is None: + return None + if not is_bias: + return self.slice_2d(tensor_in, dim) + else: + return self.slice_1d(tensor_in) + + + def slice_2d( + self, + tensor: torch.Tensor, + dim: int, + ) -> torch.Tensor: + """ + Slice the 2D tensor + + Args: + tensor: The tensor to slice + """ + assert dim in [0,1], f"Only support 2D tensor, but got {dim}D tensor" + if dim == 0: + return self.slice_row(tensor) + elif dim == 1: + return self.slice_col(tensor) + + + def slice_1d( + self, + tensor: torch.Tensor, + dim: int = None, + ) -> torch.Tensor: + """ + Slice the 1D tensor + + Args: + tensor: The tensor to slice + """ + delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size + down_idx = self.shardconfig.rank * delta + up_idx = down_idx + delta + return tensor[down_idx:up_idx] + + def slice_col( + self, + tensor: torch.Tensor, + ) -> torch.Tensor: + """ + Slice the tensor in column + + Args: + tensor: The tensor to slice + """ + delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size + down_idx = self.shardconfig.rank * delta + up_idx = down_idx + delta + return tensor[down_idx:up_idx,:] + + + def slice_row( + self, + tensor: torch.Tensor, + ) -> torch.Tensor: + """ + Slice the tensor in column + + Args: + tensor: The tensor to slice + """ + delta = (tensor.shape[1] + self.shardconfig.world_size - 1) // self.shardconfig.world_size + down_idx = self.shardconfig.rank * delta + up_idx = down_idx + delta + return tensor[:,down_idx:up_idx] + \ No newline at end of file diff --git a/colossalai/shardformer/test/config.py b/colossalai/shardformer/test/config.py new file mode 100644 index 000000000000..295529429237 --- /dev/null +++ b/colossalai/shardformer/test/config.py @@ -0,0 +1,5 @@ +parallel = dict( + data=1, + pipeline=1, + tensor=dict(size=2, mode='1d') +) \ No newline at end of file diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py new file mode 100644 index 000000000000..c2a9053ca2f6 --- /dev/null +++ b/colossalai/shardformer/test/test.py @@ -0,0 +1,37 @@ +from transformers import AutoTokenizer +from transformers import BertForMaskedLM +import colossalai +from colossalai.shardformer.shard.shardmodel import ShardModel +from colossalai.utils import get_current_device, print_rank_0 +from colossalai.logging import get_dist_logger +from colossalai.shardformer.shard.shardconfig import ShardConfig +import inspect +import argparse +import torch.nn as nn +import os + +tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + +def get_args(): + parser = colossalai.get_default_parser() + return parser.parse_args() + +def inference(model: nn.Module): + # print(model) + token = "Hello, my dog is cute" + inputs = tokenizer(token, return_tensors="pt") + inputs.to("cuda") + model.to("cuda") + outputs = model(**inputs) + print(outputs) + +if __name__ == "__main__": + args = get_args() + colossalai.launch_from_torch(config=args.config) + model = BertForMaskedLM.from_pretrained("bert-base-uncased") + shard_config = ShardConfig( + rank = int(str(get_current_device()).split(':')[-1]), + world_size= int(os.environ['WORLD_SIZE']), + ) + shardmodel = ShardModel(model, shard_config) + inference(shardmodel.model) diff --git a/colossalai/shardformer/utils/__init__.py b/colossalai/shardformer/utils/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/utils/utils.py b/colossalai/shardformer/utils/utils.py new file mode 100644 index 000000000000..5eba87f6fe09 --- /dev/null +++ b/colossalai/shardformer/utils/utils.py @@ -0,0 +1,56 @@ +def hasattr_(obj, attr: str): + """ + Check whether the object has the multi sublevel attr + + Args: + obj: The object to check + attr: The multi level attr to check + """ + attrs = attr.split('.') + for a in attrs: + try: + obj = getattr(obj, a) + except AttributeError: + return False + return True + +def setattr_(obj, attr: str, value, ignore: bool=False): + """ + Set the object's multi sublevel attr to value, if ignore, ignore when it doesn't exist + + Args: + obj: The object to set + attr: The multi level attr to set + value: The value to set + ignore: Whether to ignore when the attr doesn't exist + """ + + attrs = attr.split('.') + for a in attrs[:-1]: + try: + obj = getattr(obj, a) + except AttributeError: + if ignore: + return + raise AttributeError(f"Object {obj} has no attribute {attr}") + setattr(obj, attrs[-1], value) + +def getattr_(obj, attr: str, ignore: bool=None): + """ + Get the object's multi sublevel attr + + Args: + obj: The object to set + attr: The multi level attr to set + ignore: Whether to ignore when the attr doesn't exist + """ + + attrs = attr.split('.') + for a in attrs: + try: + obj = getattr(obj, a) + except AttributeError: + if ignore: + return None + raise AttributeError(f"Object {obj} has no attribute {attr}") + return obj \ No newline at end of file From 8cc11235c0bc8decea3c840c5afb35e40888ad57 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Wed, 24 May 2023 10:26:46 +0800 Subject: [PATCH 349/413] [shardformer]: Feature/shardformer, add some docstring and readme (#3816) * init shardformer code structure * add implement of sharder (inject and replace) * add implement of replace layer to colossal layer * separate different layer policy, add some notion * implement 1d and 2d slicer, can tell col or row * fix bug when slicing and inject model * fix some bug; add inference test example * add share weight and train example * add train * add docstring and readme * add docstring for other files * pre-commit --- colossalai/nn/layer/parallel_1d/_operation.py | 2 + colossalai/nn/layer/parallel_1d/layers.py | 9 +- colossalai/shardformer/README.md | 177 +++++++++++++++++ colossalai/shardformer/model/modeling_bert.py | 16 +- colossalai/shardformer/policies/autopolicy.py | 25 ++- colossalai/shardformer/policies/basepolicy.py | 128 +++++++----- colossalai/shardformer/policies/bert.py | 125 ++++++------ colossalai/shardformer/shard/shardconfig.py | 4 +- colossalai/shardformer/shard/sharder.py | 187 +++++++++--------- colossalai/shardformer/shard/shardmodel.py | 36 ++-- colossalai/shardformer/shard/slicer.py | 113 +++++------ colossalai/shardformer/test/config.py | 6 +- colossalai/shardformer/test/test.py | 87 ++++++-- colossalai/shardformer/utils/utils.py | 36 ++-- 14 files changed, 612 insertions(+), 339 deletions(-) create mode 100644 colossalai/shardformer/README.md diff --git a/colossalai/nn/layer/parallel_1d/_operation.py b/colossalai/nn/layer/parallel_1d/_operation.py index 394334558275..c5e33fd497cd 100644 --- a/colossalai/nn/layer/parallel_1d/_operation.py +++ b/colossalai/nn/layer/parallel_1d/_operation.py @@ -1,5 +1,6 @@ import torch import torch.distributed as dist + from colossalai.core import global_context as gpc try: @@ -72,6 +73,7 @@ def backward(ctx, grad_output): total_input = input grad_input = grad_output.matmul(weight) + grad_output = grad_output.contiguous() # Convert the tensor shapes to 2D for execution compatibility grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2]) total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2]) diff --git a/colossalai/nn/layer/parallel_1d/layers.py b/colossalai/nn/layer/parallel_1d/layers.py index 406173a18c60..0ee3b4fcb502 100644 --- a/colossalai/nn/layer/parallel_1d/layers.py +++ b/colossalai/nn/layer/parallel_1d/layers.py @@ -469,7 +469,8 @@ def __init__(self, if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') - self.out_features_per_partition = divide(out_features, gpc.tensor_parallel_size) + # self.out_features_per_partition = divide(out_features*2, gpc.tensor_parallel_size) + self.out_features_per_partition = out_features # Parameters. # Initialize weight. @@ -612,7 +613,8 @@ def __init__(self, raise ValueError('cannot skip bias addition if bias is None') # Divide the weight matrix along the last dimension. - self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size) + # self.input_size_per_partition = divide(in_features*2, gpc.tensor_parallel_size) + self.input_size_per_partition = in_features # Parameters. # Initialize weight. @@ -884,7 +886,8 @@ def __init__(self, tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) - self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) + # self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) + self.num_embeddings_per_partition = num_embeddings self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md new file mode 100644 index 000000000000..a47e280f2be4 --- /dev/null +++ b/colossalai/shardformer/README.md @@ -0,0 +1,177 @@ +## ShardFormer + +### Intro +Make the model in huggingface.co can be paralleled and can be used with colossalai according to custom policy. + +### Quick start +1. Usage +- Use +``` python +from colossalai.shardformer.shard.shardmodel import ShardModel +from transformers import BertForMaskedLM + +# create huggingface model as normal +model = BertForMaskedLM.from_pretrained("bert-base-uncased") + +# make the huggingface model paralleled to ShardModel +# auto policy: +shardmodel = ShardModel(model).model + +# custom policy: +from xxx import +shardmodel = ShardModel(model, ).model + + +# do angthing as normal +... +``` +- Policy + +If you wanna parallel the model in custom way, just overwrite the policy class for the huggingface model. + +You should do: + +1. Inherit Policy class +2. Overwrite argument_policy method + - In this method you need to list which layers class you wanna modify and the attributes and parameters in those layers. +3. Overwrite inject_policy method [Optional] + - If you need to modify the forward or backward progress. +4. Overwrite or add the param recording functions + - These function use suffix to record the path of weight or bias for the layer. +5. Overwrite binding + +More details can be found in shardformer/policies/basepolicy.py +``` python +from colossalai.shardformer.policies.basepolicy import Policy, Layer, Col_Layer, Row_Layer, Argument + +CustomPolicy(Policy): + @staticmethod + def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument]: + """ + Return a dict, the key is layer will be modified and the value is the Argument class with param setting and param functions + + Args: + model_config: The config of transformer model + shard_setting: The config of distributed model + + Return: + Dict for the modify policy, + { + origin layer class1 (nn.Module): Argument( + attr_dict = { + argument1: value1, + argument2: value2, + ... + }, + param_funcs = [ + staticmethod1, + staticmethod2, + ... + ] + ), + origin layer class2 (nn.Module): Argument( + attr_dict = { + argument1: value1, + argument2: value2, + ... + }, + param_funcs = [ + staticmethod1, + staticmethod2, + ... + ] + ), + ... + } + + """ + raise NotImplementedError + + @staticmethod + def inject_policy() -> Tuple[nn.Module, nn.Module]: + """ + Return the dict for the inject model + + Return: + The injected model, key is the original model and value is the new shardmodel + """ + return () + + @staticmethod + def binding_policy() -> Dict: + """ + Return the dict for the binding model + """ + return NotImplementedError + + @staticmethod + def attn_in() -> List: + """ + Attention qkv layer + + Returns: + List[Layer]: List of layer object, each layer is the new + """ + return NotImplementedError + + @staticmethod + def attn_out() -> List: + """ + Attention output projection layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + @staticmethod + def mlp_in() -> List: + """ + h -> 4h mlp layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + @staticmethod + def mlp_out() -> List: + """ + 4h -> h mlp layer + + Returns: + List[Layer]: List of layer object + """ + return NotImplementedError + + @staticmethod + def embedding() -> List: + """ + Partially slice the embedding layer + vocab_size->vocab_size//gpu_nums + + Return: + List[Layer]: List of layer object + """ + return NotImplementedError + + @staticmethod + def unembedding() -> List: + """ + Partially slice the embedding layer + vocab_size->vocab_size//gpu_nums + + Return: + List[Layer]: List of layer object + """ + return NotImplementedError + +``` + +2. Simple example +``` shell +# inference +colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode inference +# train +colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode train +``` diff --git a/colossalai/shardformer/model/modeling_bert.py b/colossalai/shardformer/model/modeling_bert.py index 87ed8ac308a5..6741ae866991 100644 --- a/colossalai/shardformer/model/modeling_bert.py +++ b/colossalai/shardformer/model/modeling_bert.py @@ -1,12 +1,14 @@ +from typing import Any, Dict, List, Type + import torch import torch.nn as nn from torch.nn import CrossEntropyLoss -from typing import Any, Dict, List, Type - - from transformers import BertForMaskedLM from transformers.models.bert.modeling_bert import MaskedLMOutput + + class BertForMaskedLM_(BertForMaskedLM): + def forward( self, input_ids=None, @@ -23,7 +25,7 @@ def forward( return_dict=None, **kwargs, ): - print("[Inject OK] Injected forward method") + # print("[Inject OK] Injected forward method") return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.bert( @@ -46,9 +48,9 @@ def forward( masked_lm_loss = None # if input_ids is not None: - # masked_lm_loss = applyDistCrossEntropy(prediction_scores, input_ids, self.config.vocab_size) + # masked_lm_loss = applyDistCrossEntropy(prediction_scores, input_ids, self.config.vocab_size) if labels is not None: - loss_fct = CrossEntropyLoss() # -100 index = padding token + loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: @@ -60,4 +62,4 @@ def forward( logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, - ) \ No newline at end of file + ) diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 9142e0dae22e..e096c2b13a59 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -1,40 +1,47 @@ import torch.nn as nn + def build_policies(): - """ + r""" Build the policies for the model - + Return: The dict for the policies """ auto_policy_dict = {} from transformers.models.bert.modeling_bert import BertForMaskedLM + from .bert import BertForMaskedLMPolicy auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy from transformers.models.bert.modeling_bert import BertForSequenceClassification + from .bert import BertForSequenceClassificationPolicy auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy - + return auto_policy_dict -def get_autopolicy(model:nn.Module): - """ + +def get_autopolicy(model: nn.Module): + r""" Return the auto policy for the model Args: - model: The model to be used + model (:class:`nn.Module`): The model to get the auto policy Return: - The auto policy for the model + :class:`Policy`: The auto policy for the model """ auto_policy_dict = build_policies() policy = auto_policy_dict.get(model.__class__, None) - if policy is None: - raise NotImplementedError(f"Auto policy for {model.__class__.__qualname__} is not implemented\n Supported models are {[i.__qualname__ for i in auto_policy_dict.keys()]}") + if policy is None: + raise NotImplementedError( + f"Auto policy for {model.__class__.__qualname__} is not implemented\n Supported models are {[i.__qualname__ for i in auto_policy_dict.keys()]}" + ) return policy + # from transformers.models.bert.modeling_bert import BertForMaskedLM, BertForPreTraining # model = BertForPreTraining # policy = get_autopolicy(model) diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index d444aeb53bf8..a5cc0bc68df6 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -1,28 +1,38 @@ # part of code modified from https://github.com/tunib-ai/parallelformers +from dataclasses import dataclass, field +from typing import Any, Callable, Dict, List, Tuple, Type + import torch import torch.nn as nn -import colossalai.nn as col_nn -from typing import Any, Dict, List, Type, Tuple, Callable from transformers import AutoConfig -from dataclasses import dataclass, field + +import colossalai.nn as col_nn + @dataclass class Argument: - attr_dict : Dict[str, Any] - param_funcs : List[Callable] - binding_layers : List[nn.Module] = field(default_factory=list) + r""" + The argument class for the policy + + Args: + attr_dict (Dict[str, Any]): The dict for the param setting + param_funcs (:class:`List[Callable]`): The list for the param functions + """ + attr_dict: Dict[str, Any] + param_funcs: List[Callable] + @dataclass class Layer: - """ + r""" The layer object for the policy Args: - weight: The weight name of the layer - bias: The bias name of the layer - replace_layer: The layer to replace the original layer - ignore: Whether to ignore this layer if it is not in the model + weight (str): The weight suffix of the layer + bias (str): The bias suffix of the layer + replace_layer (:class:`colosalai.nn`): The layer to replace the original layer + ignore (bool): Whether to ignore this layer if it is not in the model """ weight: str = None bias: str = None @@ -32,45 +42,55 @@ class Layer: @dataclass class Col_Layer(Layer): - """ + r""" Class for col shard layer in MegatronLM + + Args: + gather_output (bool): Whether to gather the output of the layer """ gather_output: bool = False @dataclass class Row_Layer(Layer): - """ + r""" Class for col shard layer in MegatronLM """ pass class Policy(): - """ + r""" The base class for all the policies - For each different model, it should have a different policy class, like BertPolicy for Bert Model - or OPTPolicy for OPT model. + For each different model, it should have a different policy class, like BertPolicy for Bert Model + or OPTPolicy for OPT model. AutoPolicy: - shardformer already defined some policies for huggingface model, just set custom_policy = None + Shardformer already defined some policies for huggingface model, just set ``custom_policy`` = None to use the auto policy. In shardformer autopolicy, we define a base policy for one type model, - like BertPolicy, and for each different Bert modle in huggingface like, BertForMaskedLM, + like BertPolicy, and for each different Bert modle in huggingface like, BertForMaskedLM, BertForSequenceClassification, etc., for each different Bert model we difine different policy class - and overwrite the method inject_policy - + and overwrite the method like ``inject_policy`` to modify the forward and backward process. + CustomPolicy: + If you want to define your own policy, you can set ``custom_policy`` = CustomPolicy, and overwrite + all the methods in ``Policy`` class. You can refer to any policy we defined like the ``BertPolicy`` + class for the example. + """ + @staticmethod - def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument]: - """ - Return a dict, the key is layer will be modified and the value is the Argument class with param setting and param functions + def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]: + r""" + Return the dict for the modify policy, the key is the original layer class and the value is the + argument for the modify layer Args: - model_config: The config of transformer model - shard_setting: The config of distributed model - + model_config (:class:`tansformer.Config`): The config of transformer model + shard_config (:class:`ShardConfig`): The config for sharding model + Return: Dict for the modify policy, + :: { origin layer class1 (nn.Module): Argument( attr_dict = { @@ -101,33 +121,51 @@ def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument] """ raise NotImplementedError - @staticmethod def inject_policy() -> Tuple[nn.Module, nn.Module]: - """ - Return the dict for the inject model + r""" + Return the dict for the inject model Return: The injected model, key is the original model and value is the new shardmodel + :: + (OrignModel, CustomModel) + in `CustomModel`, we can overwrite the forward and backward process """ return () - @staticmethod - def attn_in() -> List: + def binding_policy() -> Dict: + r""" + Return the dict for the binding model + + Return: + This method should return the binding relationship for some layers share the weight or bias, + the key and value is the suffix of the weight or bias of the model + :: + return { + "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", + } """ + return NotImplementedError + + @staticmethod + def attn_in() -> List: + r""" Attention qkv layer + In this kind of method, we should return the list of ``Layer`` object, each ``Layer`` object should be + ``Layer`` for no slicing, ``Col_Layer`` for col slicing, ``Row_Layer`` for row slicing. And the parameters + in ``Layer`` object can refer to the ``Layer`` class. Returns: - List[Layer]: List of layer object, each layer is the new + List[Layer]: List of layer object, each layer is the new """ return NotImplementedError - @staticmethod def attn_out() -> List: - """ + r""" Attention output projection layer Returns: @@ -135,46 +173,40 @@ def attn_out() -> List: """ return NotImplementedError - @staticmethod def mlp_in() -> List: - """ + r""" h -> 4h mlp layer Returns: List[Layer]: List of layer object """ return NotImplementedError - @staticmethod def mlp_out() -> List: - """ + r""" 4h -> h mlp layer Returns: List[Layer]: List of layer object """ return NotImplementedError - - + @staticmethod - def embedding()->List: - """ + def embedding() -> List: + r""" Partially slice the embedding layer - vocab_size->vocab_size//gpu_nums Return: List[Layer]: List of layer object """ return NotImplementedError - - + @staticmethod - def unembedding()->List: - """ + def unembedding() -> List: + r""" Partially slice the embedding layer - vocab_size->vocab_size//gpu_nums Return: List[Layer]: List of layer object diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 24b95e827347..5d91d8ddc766 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,56 +1,57 @@ -from typing import Dict, List, Tuple, Type, Any, Callable +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Tuple, Type + import torch.nn as nn -from .basepolicy import Policy, Layer, Argument, Col_Layer, Row_Layer +from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead + import colossalai.nn as col_nn -from transformers.models.bert.modeling_bert import BertLayer, BertEmbeddings, BertLMPredictionHead -from dataclasses import dataclass + +from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer class BertPolicy(Policy): + @staticmethod - def argument_policy(config, world_size: int) -> Dict[nn.Module,Argument]: + def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: return { - BertLayer: Argument( - attr_dict = { - # 1. shard hidden size - "attention.self.all_head_size": config.hidden_size // world_size, - "crossattention.self.all_head_size": config.hidden_size // world_size, - # 2. shard number of heads - "attention.self.num_attention_heads": config.num_attention_heads // world_size, - "crossattention.self.num_attention_heads": config.num_attention_heads // world_size, - - }, - param_funcs = [ - BertPolicy.attn_in, - BertPolicy.attn_out, - BertPolicy.mlp_in, - BertPolicy.mlp_out - ] - ), - BertEmbeddings: Argument( - attr_dict = { - # 1. shard vocab size - # "word_embeddings.num_embeddings": config.vocab_size // world_size, - # 2. add the size of the sliced embedding layer excluding the last slice - "word_embeddings.dim_size": (config.vocab_size+world_size-1) // world_size, - }, - param_funcs = [ - BertPolicy.embedding, - ], - binding_layers = [ - BertLMPredictionHead, - ] - ), - BertLMPredictionHead: Argument( - attr_dict = { - # 1. shard vocab size - # "word_embeddings.num_embeddings": config.vocab_size // world_size, - # 2. add the size of the sliced embedding layer excluding the last slice - }, - param_funcs = [ - BertPolicy.unembedding, - ] - ) + BertLayer: + Argument( + attr_dict={ + # 1. shard hidden size + "attention.self.all_head_size": config.hidden_size // world_size, + "crossattention.self.all_head_size": config.hidden_size // world_size, + # 2. shard number of heads + "attention.self.num_attention_heads": config.num_attention_heads // world_size, + "crossattention.self.num_attention_heads": config.num_attention_heads // world_size, + }, + param_funcs=[BertPolicy.attn_in, BertPolicy.attn_out, BertPolicy.mlp_in, BertPolicy.mlp_out]), + BertEmbeddings: + Argument( + attr_dict={ + # 1. shard vocab size + # "word_embeddings.num_embeddings": config.vocab_size // world_size, + # 2. add the size of the sliced embedding layer excluding the last slice + "word_embeddings.dim_size": (config.vocab_size + world_size - 1) // world_size, + }, + param_funcs=[ + BertPolicy.embedding, + ]), + BertLMPredictionHead: + Argument( + attr_dict={ + # 1. shard vocab size + # "word_embeddings.num_embeddings": config.vocab_size // world_size, + # 2. add the size of the sliced embedding layer excluding the last slice + }, + param_funcs=[ + BertPolicy.unembedding, + ]) + } + + @staticmethod + def binding_policy() -> Dict: + return { + "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", } @staticmethod @@ -89,9 +90,8 @@ def attn_in() -> List: replace_layer=col_nn.Linear1D_Col, ignore=True, ), - ] - + @staticmethod def attn_out() -> List: return [ @@ -107,17 +107,17 @@ def attn_out() -> List: ignore=True, ), ] - + @staticmethod def mlp_in() -> List: return [ - Col_Layer( + Col_Layer( weight="intermediate.dense.weight", bias="intermediate.dense.bias", replace_layer=col_nn.Linear1D_Col, ), ] - + @staticmethod def mlp_out() -> List: return [ @@ -130,13 +130,11 @@ def mlp_out() -> List: @staticmethod def embedding() -> List: - return [ - Col_Layer( - weight="word_embeddings.weight", - replace_layer=col_nn.VocabParallelEmbedding1D, - ) - ] - + return [Col_Layer( + weight="word_embeddings.weight", + replace_layer=col_nn.VocabParallelEmbedding1D, + )] + @staticmethod def unembedding() -> List: return [ @@ -148,16 +146,21 @@ def unembedding() -> List: ) ] + from transformers import BertForMaskedLM + from colossalai.shardformer.model.modeling_bert import BertForMaskedLM_ + + class BertForMaskedLMPolicy(BertPolicy): + @staticmethod def inject_policy() -> Tuple[nn.Module, nn.Module]: return (BertForMaskedLM, BertForMaskedLM_) - - + class BertForSequenceClassificationPolicy(BertPolicy): + @staticmethod def inject_policy() -> Dict: return {} @@ -165,4 +168,4 @@ def inject_policy() -> Dict: # model = BertForMaskedLM.from_pretrained("bert-base-uncased") # _ = BertForMaskedLMPolicy(model) -# print(isinstance(model,list(_.inject_policy().keys())[0])) \ No newline at end of file +# print(isinstance(model,list(_.inject_policy().keys())[0])) diff --git a/colossalai/shardformer/shard/shardconfig.py b/colossalai/shardformer/shard/shardconfig.py index be265ff0c8c1..c6a2513a6eff 100644 --- a/colossalai/shardformer/shard/shardconfig.py +++ b/colossalai/shardformer/shard/shardconfig.py @@ -10,9 +10,9 @@ class ShardConfig: fp16: bool = True num_gpus: int = 2 world_size: int = 2 - backend="nccl" + backend = "nccl" verbose: str = 'simple' seed: int = None require_grad: bool = False master_addr: str = "127.0.0.1" - master_port: int = 29500 \ No newline at end of file + master_port: int = 29500 diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index ef785cfee9da..2f6bb4265a11 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -1,56 +1,59 @@ +import os +from dataclasses import dataclass +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union + import torch import torch.nn as nn -from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union, Callable -from .shardconfig import ShardConfig -from dataclasses import dataclass -from ..policies.basepolicy import Policy, Layer -from ..policies.autopolicy import get_autopolicy -from .slicer import Slicer -from ..utils.utils import hasattr_, setattr_, getattr_ + import colossalai.nn as col_nn from colossalai.logging import get_dist_logger -import os +from ..policies.autopolicy import get_autopolicy +from ..policies.basepolicy import Layer, Policy +from ..utils.utils import getattr_, hasattr_, setattr_ +from .shardconfig import ShardConfig +from .slicer import Slicer logger = get_dist_logger() + class ModelSharder(object): - """ + r""" Shard the original huggingface model according to the policy Args: - policy: The policy to shard the model - model: The model to shard - dist_setting: The setting of distributed model + policy (:class:`Policy`): The policy to shard the model + model (:class:`torch.Module`): The model to shard + shard_config: The setting of distributed model """ + def __init__( self, model: nn.Module, policy: Policy, - shard_config: ShardConfig = None, # TODO - ) -> None: + shard_config: ShardConfig = None, # TODO + ) -> None: self.model = model self.policy = get_autopolicy(self.model) if policy is None else policy self.slicer = Slicer(shard_config) self.shard_config = shard_config self.model_config = self.model.config - self.binding_map = {} - def shard(self) -> None: self.inject_model(self.model) self.replace_layer(self.model) - - + self.bind_layer(self.model) + def inject_model( - self, - model: nn.Module, - ) -> None: - """ + self, + model: nn.Module, + ) -> None: + r""" Replace the model to policy defined model Mainly modify the forward and backward to fit distributed model - + e.g. + :: BertForMaskedLM.forward -> BertForMaskedLM_.forward """ inject_policy = self.policy.inject_policy() @@ -64,49 +67,43 @@ def inject_model( setattr( model.__class__, key, - getattr(shard_model_cls,key), + getattr(shard_model_cls, key), ) else: raise NotImplementedError(f"{model.__class__} is not implemented so far") - def replace_layer( - self, - model: nn.Module, - ) -> None: - """ + self, + model: nn.Module, + ) -> None: + r""" Replace the layer according to the policy, and replace the layer one by one Args: - layer: The layer to shard + model (:class:`torch.nn.Module`): The layer to shard """ argument_policies = self.policy.argument_policy(self.model_config, self.shard_config.world_size) for argument_policy in argument_policies.items(): origin_layer_cls = argument_policy[0] attr_dict = argument_policy[1].attr_dict param_funcs = argument_policy[1].param_funcs - binding_layers = argument_policy[1].binding_layers - # if binding_layer is not None: - # self.binding_map[origin_layer_cls] = binding_layer - self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs, binding_layers) - + self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs) def reverse_replace_layer( - self, - layer: nn.Module, - origin_cls: nn.Module, - attr_dict: Dict[str, Any], - param_funcs: List[Callable], - binding_layers: List[nn.Module] - ) -> None: - """ + self, + layer: nn.Module, + origin_cls: nn.Module, + attr_dict: Dict[str, Any], + param_funcs: List[Callable], + ) -> None: + r""" Reverse the replace layer operation Args: - layer: The object of layer to shard - origin_cls: The origin layer class - attr_dict: The attribute dict to modify - policy_cls: The policy class + layer (:class:`torch.nn.Module`): The object of layer to shard + origin_cls (:class:`transformers.model`): The origin layer class + attr_dict (Dict): The attribute dict to modify + policy_cls (:class:`Policy`): The policy class """ for name, child in layer.named_children(): if child.__class__ == origin_cls: @@ -115,25 +112,23 @@ def reverse_replace_layer( setattr_(child, k, v, ignore=True) # print(f"Sharding {name} layer", replac_layer.attention.self.__dict__) # setattr_(layer, name, self.shard_one_layer(child, policy_cls)) - self.shard_one_layer(child, param_funcs, binding_layers) + self.shard_one_layer(child, param_funcs) continue - self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs, binding_layers) + self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs) return layer - def shard_one_layer( - self, - org_layer: nn.Module, - param_funcs: List[Callable], - binding_layers: List[nn.Module] - ) -> None: - """ + self, + org_layer: nn.Module, + param_funcs: List[Callable], + ) -> None: + r""" Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict Args: - org_layer: The origin layer object to shard - param_funcs: The function list to get shard information in policy class + org_layer (:class:`torch.nn.Module`): The origin layer object to shard + param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class """ # print(org_layer) @@ -148,7 +143,7 @@ def shard_one_layer( ignore = policy_layer.ignore if policy_layer.__class__.__name__ == "Col_Layer": gather_output = policy_layer.gather_output - print(gather_output) + # print(gather_output) if weight_attr is not None: if hasattr_(org_layer, weight_attr): @@ -172,67 +167,81 @@ def shard_one_layer( # slice weight and bias weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__) - print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None) - # save the binding information - for binding_layer in binding_layers: - self.binding_map[binding_layer] = dict(weight=weight, bias=bias) + # print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None) # create new object to replace the origin layer if replace_layer_cls is not None: # print(f"RANK {os.environ['RANK']}: replace {getattr_(org_layer, layer_attr).__class__} to {replace_layer_cls}, shape is {weight.shape}") if isinstance(getattr_(org_layer, layer_attr), nn.Linear): if replace_layer_cls.__name__ == "Linear1D_Row": - replace_layer = replace_layer_cls(weight.shape[1], weight.shape[0], bias=False if bias is None else True) + replace_layer = replace_layer_cls(weight.shape[1], + weight.shape[0], + bias=False if bias is None else True) elif replace_layer_cls.__name__ == "Linear1D_Col": - replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], bias=False if bias is None else True, gather_output=gather_output) + replace_layer = replace_layer_cls(weight.shape[0], + weight.shape[1], + bias=False if bias is None else True, + gather_output=gather_output) setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) self.set_param(replace_layer, weight, bias) - elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding): - replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True)) + elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding): + replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], + getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True)) setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) self.set_param(replace_layer, weight, bias) else: - raise NotImplementedError(f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far") + raise NotImplementedError( + f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far") # do not replace the layer object, just replace the weight and bias else: self.set_param(org_layer, layer_attr, weight, bias) - - def set_param( - self, - layer: Any, - layer_attr: str = "", - weight: torch.Tensor = None, - bias: torch.Tensor = None - ) -> None: - """ + def set_param(self, + layer: Any, + weight: torch.Tensor = None, + bias: torch.Tensor = None, + layer_attr: str = "") -> None: + r""" Reset the weight and bias of the layer object Args: - layer: The layer object - layer_attr: The attribute name of the layer - weight: The weight of the layer - bias: The bias of the layer + layer (:class:`torch.nn.Module`): The layer object + layer_attr (str): The attribute name of the layer + weight (:class:`torch.Tensor`): The weight of the layer + bias (:class:`torch.Tensor`): The bias of the layer """ assert weight is not None or bias is not None if weight is not None: - setattr_(layer, "weight" if layer_attr == "" else layer_attr+".weight", nn.Parameter(weight)) + setattr_(layer, "weight" if layer_attr == "" else layer_attr + ".weight", nn.Parameter(weight.contiguous())) self.set_layer_size(layer, layer_attr, weight.shape) if bias is not None: - setattr_(layer, "bias" if layer_attr == "" else layer_attr+".bias", nn.Parameter(bias)) - + setattr_(layer, "bias" if layer_attr == "" else layer_attr + ".bias", nn.Parameter(bias.contiguous())) def set_layer_size(self, layer: nn.Module, layer_attr: str, size: torch.Size) -> None: - """ + r""" Set the layer attribute Args: - layer: The layer object - layer_attr: The attribute name of the layer - size: Torch.size + layer (:class:`torch.nn.Module`): The layer object + layer_attr (str): The attribute name of the layer + size (:class:`torch.Size`): The size of the tensor """ # Tensor.shape[0] -> out_features, Tensor.shape[1] -> in_features attrs = ["out_features", "in_features"] for i, attr in enumerate(attrs): if hasattr_(layer, f"{layer_attr}.{attr}"): - setattr_(layer, f"{layer_attr}.{attr}", size[i]) + setattr_(layer, f"{layer_attr}.{attr}", size[i]) + + def bind_layer(self, model: nn.Module) -> None: + r""" + Bind the layer according to the binding policy + + Args: + model (:class:`torch.nn.Module`): The shard model + """ + binding_map = self.policy.binding_policy() + for k, v in binding_map.items(): + param = getattr_(model, k) + param = nn.Parameter(param) + setattr_(model, k, param) + setattr_(model, v, param) diff --git a/colossalai/shardformer/shard/shardmodel.py b/colossalai/shardformer/shard/shardmodel.py index 54d7b5ba02d9..7e7d1576afd6 100644 --- a/colossalai/shardformer/shard/shardmodel.py +++ b/colossalai/shardformer/shard/shardmodel.py @@ -1,46 +1,48 @@ import os +from contextlib import suppress +from dataclasses import dataclass + import torch +import torch.distributed as dist import torch.nn as nn import transformers -import torch.distributed as dist -from dataclasses import dataclass -from contextlib import suppress from colossalai.tensor.d_tensor.layout import Layout + from ..policies.basepolicy import Policy -from .sharder import ModelSharder from .shardconfig import ShardConfig +from .sharder import ModelSharder class ShardModel(object): - """ - The class for sharding the huggingface model, self.model is the sharded model + r""" + The class for sharding the huggingface model, ''self.model'' is the sharded model Just creat a new ShardModel object to shard huggingface model Args: - model: the origin huggingface model - dist_config: the config for distribute information - custom_policy: the custom policy for sharding + model (:class:`torch.nn.Model`): the origin huggingface model + dist_config (:class:`ShardConfig`): the config for distribute information + custom_policy (:class:`Policy`): the custom policy for sharding """ + def __init__( - self, - model: nn.Module, - shard_config: ShardConfig = None, # TODO - custom_policy: Policy = None, - ) -> None: + self, + model: nn.Module, + shard_config: ShardConfig = None, # TODO + custom_policy: Policy = None, + ) -> None: self.model = model self.shard_config = shard_config self.policy = custom_policy # self.layout=, # TODO - sharder=ModelSharder( + sharder = ModelSharder( model=self.model, policy=self.policy, shard_config=self.shard_config, ) sharder.shard() - def set_environ(self) -> None: os.environ["TOKENIZERS_PARALLELISM"] = "true" os.environ["MKL_SERVICE_FORCE_INTEL"] = "GNU" @@ -55,4 +57,4 @@ def set_environ(self) -> None: torch.cuda.set_device(int(os.getenv("LOCAL_RANK", "0"))) def back_to_org() -> None: - pass \ No newline at end of file + pass diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 1849cdc99c72..096f5db95f49 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -1,40 +1,40 @@ import os -from typing import Dict, Tuple from dataclasses import dataclass +from typing import Dict, Tuple import torch import torch.distributed as dist -from ..policies.basepolicy import Layer, Col_Layer, Row_Layer -from .shardconfig import ShardConfig +from ..policies.basepolicy import Col_Layer, Layer, Row_Layer +from .shardconfig import ShardConfig dim_mapping = {Col_Layer: 1, Row_Layer: 0} + class Slicer(): def __init__( - self, - shardconfig: ShardConfig #TODO + self, + shardconfig: ShardConfig #TODO ) -> None: self.shardconfig = shardconfig - def slice_weight_bias( self, weight: torch.Tensor, bias: torch.Tensor, policy_layer_cls: Layer, ): - """ + r""" Slice the weight and bias according to policy layer cls - Layer -> do nothing - Col_Layer -> slice the weight and bias along dim 1 - Row_Layer -> slice the weight along dim 0 and do not slice bias + ``Layer`` -> do nothing + ``Col_Layer`` -> slice the weight and bias along dim 1 + ``Row_Layer`` -> slice the weight along dim 0 and do not slice bias Args: - weight: The weight of the layer - bias: The bias of the layer - policy_layer_class: The class represent how to slice the tensor + weight (:class:`torch.nn.Module`): The weight of the layer + bias: (:class:`torch.nn.Module`): The bias of the layer + policy_layer_class (:class:`Policy`): The class represent how to slice the tensor """ if policy_layer_cls == Layer: return weight, bias @@ -46,42 +46,6 @@ def slice_weight_bias( else: raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported") return weight, bias - - - def slice_weight( - self, - weight: torch.Tensor, - policy_layer_cls: Layer, - ) -> torch.Tensor: - """ - Slice the weight and bias according to the shardconfig - - Args: - weight: The weight of the layer - bias: The bias of the layer - policy_layer_class: The class represent how to slice the tensor - """ - if weight is not None: - dim = dim_mapping[policy_layer_cls] - weight = self.slice_tensor(weight, dim, False) - return weight - - - def slice_bias( - self, - bias: torch.Tensor, - ) -> torch.Tensor: - """ - Slice the bias according to the shardconfig - - Args: - bias: The bias of the layer - """ - assert bias is not None, "The bias is None" - if bias is not None: - bias = self.slice_tensor(bias, 1, True) - return bias - def slice_tensor( self, @@ -89,8 +53,13 @@ def slice_tensor( dim: int, is_bias: bool, ) -> torch.Tensor: - """ + r""" Slice tensor according to the config + + Args: + tensor_in (:class:`torch.Tensor`): The tensor to slice + dim (int): The dimension to slice + is_bias (bool): Whether the tensor is bias """ if tensor_in is None: return None @@ -99,69 +68,75 @@ def slice_tensor( else: return self.slice_1d(tensor_in) - def slice_2d( self, tensor: torch.Tensor, dim: int, ) -> torch.Tensor: - """ - Slice the 2D tensor + r""" + Slice the 2D tensor Args: - tensor: The tensor to slice + tensor (:class:`torch.Tensor`): The tensor to slice + dim (int): The dimension to slice """ - assert dim in [0,1], f"Only support 2D tensor, but got {dim}D tensor" + assert dim in [0, 1], f"Only support 2D tensor, but got {dim}D tensor" if dim == 0: return self.slice_row(tensor) elif dim == 1: return self.slice_col(tensor) - def slice_1d( self, tensor: torch.Tensor, - dim: int = None, ) -> torch.Tensor: - """ - Slice the 1D tensor + r""" + Slice the 1D tensor Args: - tensor: The tensor to slice + tensor (:class:`torch.Tensor`): The tensor to slice + + Returns: + :class:`torch.Tensor`: The sliced tensor """ delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size down_idx = self.shardconfig.rank * delta up_idx = down_idx + delta - return tensor[down_idx:up_idx] + return tensor[down_idx:up_idx].contiguous() def slice_col( self, tensor: torch.Tensor, ) -> torch.Tensor: - """ + r""" Slice the tensor in column Args: - tensor: The tensor to slice + tensor (:class:`torch.Tensor`): The tensor to slice + + Returns: + :class:`torch.Tensor`: The sliced tensor + """ delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size down_idx = self.shardconfig.rank * delta up_idx = down_idx + delta - return tensor[down_idx:up_idx,:] - + return tensor[down_idx:up_idx, :].contiguous() def slice_row( self, tensor: torch.Tensor, ) -> torch.Tensor: - """ + r""" Slice the tensor in column Args: - tensor: The tensor to slice + tensor (:class:`torch.Tensor`): The tensor to slice + + Returns: + :class:`torch.Tensor`: The sliced tensor """ delta = (tensor.shape[1] + self.shardconfig.world_size - 1) // self.shardconfig.world_size down_idx = self.shardconfig.rank * delta up_idx = down_idx + delta - return tensor[:,down_idx:up_idx] - \ No newline at end of file + return tensor[:, down_idx:up_idx].contiguous() diff --git a/colossalai/shardformer/test/config.py b/colossalai/shardformer/test/config.py index 295529429237..2b80d8b3ca12 100644 --- a/colossalai/shardformer/test/config.py +++ b/colossalai/shardformer/test/config.py @@ -1,5 +1 @@ -parallel = dict( - data=1, - pipeline=1, - tensor=dict(size=2, mode='1d') -) \ No newline at end of file +parallel = dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')) diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py index c2a9053ca2f6..0cdc6ef38fd2 100644 --- a/colossalai/shardformer/test/test.py +++ b/colossalai/shardformer/test/test.py @@ -1,23 +1,51 @@ -from transformers import AutoTokenizer -from transformers import BertForMaskedLM +import argparse +import inspect +import os + +import torch +import torch.nn as nn +from datasets import load_dataset +from torch.utils.data import DataLoader +from tqdm.auto import tqdm +from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments + import colossalai -from colossalai.shardformer.shard.shardmodel import ShardModel -from colossalai.utils import get_current_device, print_rank_0 from colossalai.logging import get_dist_logger from colossalai.shardformer.shard.shardconfig import ShardConfig -import inspect -import argparse -import torch.nn as nn -import os +from colossalai.shardformer.shard.shardmodel import ShardModel +from colossalai.utils import get_current_device, print_rank_0 +os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + def get_args(): parser = colossalai.get_default_parser() + parser.add_argument("--mode", type=str, default='inference') return parser.parse_args() + +def load_data(): + datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') + # datasets=load_dataset("yelp_review_full") + tokenized_datasets = datasets.map( + lambda examples: tokenizer(examples["text"], truncation=True, padding="max_length"), batched=True) + tokenized_datasets = tokenized_datasets.remove_columns(["text"]) + # tokenized_datasets=tokenized_datasets.rename_column("label","labels") + tokenized_datasets.set_format("torch") + + train_dataset = tokenized_datasets["train"].select(range(500)) + test_dataset = tokenized_datasets["test"].select(range(100)) + + datacollector = DataCollatorForLanguageModeling(tokenizer, mlm=True, mlm_probability=0.15, return_tensors="pt") + train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True, collate_fn=datacollector) + eval_dataloader = DataLoader(test_dataset, batch_size=8, shuffle=True, collate_fn=datacollector) + return train_dataloader, eval_dataloader + + def inference(model: nn.Module): - # print(model) + print(model) + tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") token = "Hello, my dog is cute" inputs = tokenizer(token, return_tensors="pt") inputs.to("cuda") @@ -25,13 +53,48 @@ def inference(model: nn.Module): outputs = model(**inputs) print(outputs) + +def train(model: nn.Module, num_epoch: int = 2): + train_dataloader, eval_dataloader = load_data() + optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5) + progress_bar = tqdm(range((num_epoch) * len(train_dataloader))) + criterion = nn.CrossEntropyLoss() + model.to("cuda") + model.train() + for epoch in range(num_epoch): + progress_bar.set_description(f"Rank {get_current_device()} epoch {epoch}") + + for batch in train_dataloader: + optimizer.zero_grad() + batch = {k: v.to('cuda') for k, v in batch.items()} + outputs = model(**batch) + loss = outputs.loss + loss.backward() + optimizer.step() + progress_bar.update(1) + train_loss = loss + + loss = 0.0 + for batch in eval_dataloader: + batch = {k: v.to('cuda') for k, v in batch.items()} + outputs = model(**batch) + # loss = outputs.loss + loss += outputs.loss.item() + # loss = criterion(outputs.logits, batch["input_ids"]) + test_loss = loss / len(eval_dataloader) + print_rank_0(f"Train Loss: {train_loss:.4f} Test Loss:{test_loss:.4f}") + + if __name__ == "__main__": args = get_args() colossalai.launch_from_torch(config=args.config) model = BertForMaskedLM.from_pretrained("bert-base-uncased") shard_config = ShardConfig( - rank = int(str(get_current_device()).split(':')[-1]), - world_size= int(os.environ['WORLD_SIZE']), + rank=int(str(get_current_device()).split(':')[-1]), + world_size=int(os.environ['WORLD_SIZE']), ) shardmodel = ShardModel(model, shard_config) - inference(shardmodel.model) + if args.mode == "train": + train(shardmodel.model) + elif args.mode == "inference": + inference(shardmodel.model) diff --git a/colossalai/shardformer/utils/utils.py b/colossalai/shardformer/utils/utils.py index 5eba87f6fe09..eb84edd88404 100644 --- a/colossalai/shardformer/utils/utils.py +++ b/colossalai/shardformer/utils/utils.py @@ -1,10 +1,10 @@ def hasattr_(obj, attr: str): - """ + r""" Check whether the object has the multi sublevel attr Args: - obj: The object to check - attr: The multi level attr to check + obj (object): The object to check + attr (str): The multi level attr to check """ attrs = attr.split('.') for a in attrs: @@ -14,15 +14,16 @@ def hasattr_(obj, attr: str): return False return True -def setattr_(obj, attr: str, value, ignore: bool=False): - """ + +def setattr_(obj, attr: str, value, ignore: bool = False): + r""" Set the object's multi sublevel attr to value, if ignore, ignore when it doesn't exist Args: - obj: The object to set - attr: The multi level attr to set - value: The value to set - ignore: Whether to ignore when the attr doesn't exist + obj (object): The object to set + attr (str): The multi level attr to set + value (Any): The value to set + ignore (bool): Whether to ignore when the attr doesn't exist """ attrs = attr.split('.') @@ -31,18 +32,19 @@ def setattr_(obj, attr: str, value, ignore: bool=False): obj = getattr(obj, a) except AttributeError: if ignore: - return + return raise AttributeError(f"Object {obj} has no attribute {attr}") setattr(obj, attrs[-1], value) -def getattr_(obj, attr: str, ignore: bool=None): - """ + +def getattr_(obj, attr: str, ignore: bool = None): + r""" Get the object's multi sublevel attr - + Args: - obj: The object to set - attr: The multi level attr to set - ignore: Whether to ignore when the attr doesn't exist + obj (object): The object to set + attr (str): The multi level attr to set + ignore (bool): Whether to ignore when the attr doesn't exist """ attrs = attr.split('.') @@ -53,4 +55,4 @@ def getattr_(obj, attr: str, ignore: bool=None): if ignore: return None raise AttributeError(f"Object {obj} has no attribute {attr}") - return obj \ No newline at end of file + return obj From 235792f1702f6aaaa2eb06429f081e8b58c6a82c Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 24 May 2023 11:51:48 +0800 Subject: [PATCH 350/413] [shardformer] updated readme (#3827) --- colossalai/shardformer/README.md | 53 ++++++++++++++++++++------------ 1 file changed, 33 insertions(+), 20 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index a47e280f2be4..f76cbac8d7b8 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -1,11 +1,22 @@ -## ShardFormer +# ⚡️ ShardFormer -### Intro -Make the model in huggingface.co can be paralleled and can be used with colossalai according to custom policy. +## 📚 Table of Contents + +- [⚡️ ShardFormer](#️-shardformer) + - [📚 Table of Contents](#-table-of-contents) + - [🔗 Introduction](#-introduction) + - [🔨 Usage](#-usage) + - [🔮 Simple example](#-simple-example) + - [💡 Policy](#-policy) + +## 🔗 Introduction + +**Shardformer** is a module that automatically parallelizes the mainstream models in libraries such as HuggingFace and TIMM. This module aims to make parallelization hassle-free for users who are not from the system background. + +## 🔨 Usage + +The sample API usage is given below: -### Quick start -1. Usage -- Use ``` python from colossalai.shardformer.shard.shardmodel import ShardModel from transformers import BertForMaskedLM @@ -21,23 +32,33 @@ shardmodel = ShardModel(model).model from xxx import shardmodel = ShardModel(model, ).model - # do angthing as normal ... ``` -- Policy -If you wanna parallel the model in custom way, just overwrite the policy class for the huggingface model. +## 🔮 Simple example + +``` shell +# inference +colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode inference +# train +colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode train +``` + + +## 💡 Policy + +If you wanna parallel the model in a custom way, just overwrite the policy class for the Hugging Face model. You should do: 1. Inherit Policy class 2. Overwrite argument_policy method - - In this method you need to list which layers class you wanna modify and the attributes and parameters in those layers. -3. Overwrite inject_policy method [Optional] + - In this method, you need to list which layers class you wanna modify and the attributes and parameters in those layers. +3. Overwrite inject_policy method (Optional) - If you need to modify the forward or backward progress. 4. Overwrite or add the param recording functions - - These function use suffix to record the path of weight or bias for the layer. + - These functions use a suffix to record the path of weight or bias for the layer. 5. Overwrite binding More details can be found in shardformer/policies/basepolicy.py @@ -167,11 +188,3 @@ CustomPolicy(Policy): return NotImplementedError ``` - -2. Simple example -``` shell -# inference -colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode inference -# train -colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode train -``` From 4972e1f40e7e7fe9f0aa59ee510e4befaae4739e Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 24 May 2023 16:01:26 +0800 Subject: [PATCH 351/413] [shardformer] refactored the user api (#3828) * [shardformer] refactored the user api * polish code --- colossalai/shardformer/README.md | 6 +- colossalai/shardformer/shard/__init__.py | 5 ++ .../shard/{shardconfig.py => shard_config.py} | 2 + colossalai/shardformer/shard/sharder.py | 27 ++++++--- colossalai/shardformer/shard/shardmodel.py | 60 ------------------- colossalai/shardformer/shard/slicer.py | 7 +-- colossalai/shardformer/test/test.py | 15 ++--- 7 files changed, 35 insertions(+), 87 deletions(-) rename colossalai/shardformer/shard/{shardconfig.py => shard_config.py} (93%) delete mode 100644 colossalai/shardformer/shard/shardmodel.py diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index f76cbac8d7b8..10fd1809b287 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -18,7 +18,7 @@ The sample API usage is given below: ``` python -from colossalai.shardformer.shard.shardmodel import ShardModel +from colossalai.shardformer import shard_model from transformers import BertForMaskedLM # create huggingface model as normal @@ -26,11 +26,11 @@ model = BertForMaskedLM.from_pretrained("bert-base-uncased") # make the huggingface model paralleled to ShardModel # auto policy: -shardmodel = ShardModel(model).model +sharded_model = shard_model(model) # custom policy: from xxx import -shardmodel = ShardModel(model, ).model +sharded_model = shard_model(model, ) # do angthing as normal ... diff --git a/colossalai/shardformer/shard/__init__.py b/colossalai/shardformer/shard/__init__.py index e69de29bb2d1..d5f70163ad57 100644 --- a/colossalai/shardformer/shard/__init__.py +++ b/colossalai/shardformer/shard/__init__.py @@ -0,0 +1,5 @@ +from .shard_config import ShardConfig +from .sharder import ModelSharder, shard_model +from .slicer import Slicer + +__all__ = ['ShardConfig', 'ModelSharder', 'shard_model', 'Slicer'] diff --git a/colossalai/shardformer/shard/shardconfig.py b/colossalai/shardformer/shard/shard_config.py similarity index 93% rename from colossalai/shardformer/shard/shardconfig.py rename to colossalai/shardformer/shard/shard_config.py index c6a2513a6eff..4cf9162b9548 100644 --- a/colossalai/shardformer/shard/shardconfig.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -1,5 +1,7 @@ from dataclasses import dataclass +__all__ = ['ShardConfig'] + @dataclass class ShardConfig: diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 2f6bb4265a11..2218661889f8 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -1,20 +1,15 @@ -import os -from dataclasses import dataclass -from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union +from typing import Any, Callable, Dict, List import torch import torch.nn as nn -import colossalai.nn as col_nn -from colossalai.logging import get_dist_logger - from ..policies.autopolicy import get_autopolicy -from ..policies.basepolicy import Layer, Policy +from ..policies.basepolicy import Policy from ..utils.utils import getattr_, hasattr_, setattr_ -from .shardconfig import ShardConfig +from .shard_config import ShardConfig from .slicer import Slicer -logger = get_dist_logger() +__all__ = ['ModelSharder', 'shard_model'] class ModelSharder(object): @@ -245,3 +240,17 @@ def bind_layer(self, model: nn.Module) -> None: param = nn.Parameter(param) setattr_(model, k, param) setattr_(model, v, param) + + +def shard_model(model: nn.Module, shard_config: ShardConfig = None, policy: Policy = None): + r""" + The function is used to shard the PyTorch model. + + Args: + model (`torch.nn.Model`): the origin huggingface model + shard_config (`ShardConfig`): the config for distribute information + policy (`Policy`): the custom policy for sharding + """ + sharder = ModelSharder(model=model, shard_config=shard_config, policy=policy) + sharder.shard() + return model diff --git a/colossalai/shardformer/shard/shardmodel.py b/colossalai/shardformer/shard/shardmodel.py deleted file mode 100644 index 7e7d1576afd6..000000000000 --- a/colossalai/shardformer/shard/shardmodel.py +++ /dev/null @@ -1,60 +0,0 @@ -import os -from contextlib import suppress -from dataclasses import dataclass - -import torch -import torch.distributed as dist -import torch.nn as nn -import transformers - -from colossalai.tensor.d_tensor.layout import Layout - -from ..policies.basepolicy import Policy -from .shardconfig import ShardConfig -from .sharder import ModelSharder - - -class ShardModel(object): - r""" - The class for sharding the huggingface model, ''self.model'' is the sharded model - Just creat a new ShardModel object to shard huggingface model - - Args: - model (:class:`torch.nn.Model`): the origin huggingface model - dist_config (:class:`ShardConfig`): the config for distribute information - custom_policy (:class:`Policy`): the custom policy for sharding - """ - - def __init__( - self, - model: nn.Module, - shard_config: ShardConfig = None, # TODO - custom_policy: Policy = None, - ) -> None: - self.model = model - self.shard_config = shard_config - self.policy = custom_policy - # self.layout=, # TODO - - sharder = ModelSharder( - model=self.model, - policy=self.policy, - shard_config=self.shard_config, - ) - sharder.shard() - - def set_environ(self) -> None: - os.environ["TOKENIZERS_PARALLELISM"] = "true" - os.environ["MKL_SERVICE_FORCE_INTEL"] = "GNU" - os.environ["MASTER_ADDR"] = str(self.dist_config.master_addr) - os.environ["MASTER_PORT"] = str(self.dist_config.master_port) - os.environ["WORLD_SIZE"] = str(self.dist_config.num_gpus) - os.environ["RANK"] = str(self.dist_config.rank) - os.environ["LOCAL_RANK"] = str(self.dist_config.rank) - if not dist.is_initialized(): - dist.init_process_group(backend=self.dist_config.backend) - - torch.cuda.set_device(int(os.getenv("LOCAL_RANK", "0"))) - - def back_to_org() -> None: - pass diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 096f5db95f49..957ce1f85814 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -1,12 +1,7 @@ -import os -from dataclasses import dataclass -from typing import Dict, Tuple - import torch -import torch.distributed as dist from ..policies.basepolicy import Col_Layer, Layer, Row_Layer -from .shardconfig import ShardConfig +from .shard_config import ShardConfig dim_mapping = {Col_Layer: 1, Row_Layer: 0} diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py index 0cdc6ef38fd2..202208123ced 100644 --- a/colossalai/shardformer/test/test.py +++ b/colossalai/shardformer/test/test.py @@ -1,5 +1,3 @@ -import argparse -import inspect import os import torch @@ -7,12 +5,10 @@ from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm -from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments +from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling import colossalai -from colossalai.logging import get_dist_logger -from colossalai.shardformer.shard.shardconfig import ShardConfig -from colossalai.shardformer.shard.shardmodel import ShardModel +from colossalai.shardformer.shard import ShardConfig, shard_model from colossalai.utils import get_current_device, print_rank_0 os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' @@ -93,8 +89,9 @@ def train(model: nn.Module, num_epoch: int = 2): rank=int(str(get_current_device()).split(':')[-1]), world_size=int(os.environ['WORLD_SIZE']), ) - shardmodel = ShardModel(model, shard_config) + sharded_model = shard_model(model, shard_config) + if args.mode == "train": - train(shardmodel.model) + train(sharded_model) elif args.mode == "inference": - inference(shardmodel.model) + inference(sharded_model) From c594dc2f1c8dd1ac02280d01726602177d53abbc Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Wed, 24 May 2023 18:02:54 +0800 Subject: [PATCH 352/413] [shardformer] update readme with modules implement doc (#3834) * update readme with modules content * remove img --- colossalai/shardformer/README.md | 69 ++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 10fd1809b287..55b6aa75ef84 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -8,6 +8,8 @@ - [🔨 Usage](#-usage) - [🔮 Simple example](#-simple-example) - [💡 Policy](#-policy) + - [😊 Module](#-module) + ## 🔗 Introduction @@ -188,3 +190,70 @@ CustomPolicy(Policy): return NotImplementedError ``` + + +## 😊 Module + + 1. Flowchart + +

    + +

    + + 2. Important Modules + + - CLASS `shard_model`: + + This is the user api to use shardformer, just create a model from transformers and define a custom policy or use shardformer autopolicy to make a shard model. + + - CLASS `Layer`: + + Parameters: + - weight (str): The weight suffix of the layer + - bias (str): The bias suffix of the layer + - replace_layer (:class:`colosalai.nn`): The layer to replace the original layer + - ignore (bool): Whether to ignore this layer if it is not in the model + + This class is used to specify the replacement policy for a particular layer. If `replace_layer` is None, only parameter partitioning will be performed without replacing the layer class. + + CLASS `Col_Layer(Layer)`: + - gather_output (bool): Whether to gather the output of the layer + + This class inherited from `Layer`, representing the layer will be sliced along column. + + CLASS `Row_Layer(Layer)`: + + This class inherited from `Layer`, representing the layer will be sliced along row. + + - CLASS `Policy`: + + In Shardformer, this class holds significant importance as it defines the model partitioning methods, required parameter modifications, and model injection techniques all within a single Policy class. + - `Policy.attn_in()/attn_out()/mlp_in()/mlp_out()/embedding()/unembedding()`...... + + These functions define the partitioning methods of the parameters at different locations in the model. Each function returns a list of objects of Layer class that specify the replacement approach for these parameters. Shardformer also supports user-defined functions for modifying their models, in addition to the listed functions. + - `Policy.argument_policy()` + + In this function, the user should use multiple dict to define which class of layers will require replacement. This includes the attributes and parameters that need to be modified or replaced. Attributes are stored in the form of a "suffix-string: value" dict, while parameters are stored via multiple static methods that return the replacement approach. + - `Policy.inject_policy()` + + This function will return the injected model to replace the original model. The new model should be a nn.Module class which includes modified forward or backward functions or anything else. + - `Policy.binding_policy()` + + This function will return the weight sharing information in the model in some dict. The key and value are both the suffixes of the shared parameters. + + - CLASS `ModelSharder(model, policy)`: + + This class helps shard the model, the parameter is the created transformers model and the custom policy. If custom policy is None, shardformer will automatically get already defined policy for the model. + - `ModelShard.inject_model()` + + This function is used to inject the model to modify the forward and backward progress. + - `ModelShard.replace_layer()` + + This function is used to replace the original layers with colossalai layer to make them paralleled and can do distributed communication. + - `ModelShard.bind_layer()` + + This function is used to help different layers share weight or bias. + + - CLASS `Slicer`: + + This class is used to slice tensor according to policy. From ab8a47f830c89debfa263c445ea6d0b75d83e6b4 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Thu, 1 Jun 2023 16:21:02 +0800 Subject: [PATCH 353/413] [shardformer] add Dropout layer support different dropout pattern (#3856) * add dropout layer, add dropout test * modify seed manager as context manager * add a copy of col_nn.layer * add dist_crossentropy loss; separate module test * polish the code * fix dist crossentropy loss --- colossalai/nn/layer/parallel_1d/_operation.py | 1 - colossalai/nn/layer/parallel_1d/layers.py | 9 +- colossalai/shardformer/README.md | 19 + colossalai/shardformer/layer/__init__.py | 0 colossalai/shardformer/layer/_operation.py | 97 ++ .../shardformer/layer/dist_crossentropy.py | 105 ++ colossalai/shardformer/layer/dropout.py | 58 + colossalai/shardformer/layer/layers.py | 1043 +++++++++++++++++ colossalai/shardformer/model/modeling_bert.py | 10 +- colossalai/shardformer/policies/basepolicy.py | 2 - colossalai/shardformer/policies/bert.py | 4 +- colossalai/shardformer/shard/slicer.py | 15 +- colossalai/shardformer/test/module_test.py | 50 + colossalai/shardformer/test/test.py | 41 +- 14 files changed, 1413 insertions(+), 41 deletions(-) create mode 100644 colossalai/shardformer/layer/__init__.py create mode 100644 colossalai/shardformer/layer/_operation.py create mode 100644 colossalai/shardformer/layer/dist_crossentropy.py create mode 100644 colossalai/shardformer/layer/dropout.py create mode 100644 colossalai/shardformer/layer/layers.py create mode 100644 colossalai/shardformer/test/module_test.py diff --git a/colossalai/nn/layer/parallel_1d/_operation.py b/colossalai/nn/layer/parallel_1d/_operation.py index c5e33fd497cd..300baf9c12ba 100644 --- a/colossalai/nn/layer/parallel_1d/_operation.py +++ b/colossalai/nn/layer/parallel_1d/_operation.py @@ -73,7 +73,6 @@ def backward(ctx, grad_output): total_input = input grad_input = grad_output.matmul(weight) - grad_output = grad_output.contiguous() # Convert the tensor shapes to 2D for execution compatibility grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2]) total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2]) diff --git a/colossalai/nn/layer/parallel_1d/layers.py b/colossalai/nn/layer/parallel_1d/layers.py index 0ee3b4fcb502..406173a18c60 100644 --- a/colossalai/nn/layer/parallel_1d/layers.py +++ b/colossalai/nn/layer/parallel_1d/layers.py @@ -469,8 +469,7 @@ def __init__(self, if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') - # self.out_features_per_partition = divide(out_features*2, gpc.tensor_parallel_size) - self.out_features_per_partition = out_features + self.out_features_per_partition = divide(out_features, gpc.tensor_parallel_size) # Parameters. # Initialize weight. @@ -613,8 +612,7 @@ def __init__(self, raise ValueError('cannot skip bias addition if bias is None') # Divide the weight matrix along the last dimension. - # self.input_size_per_partition = divide(in_features*2, gpc.tensor_parallel_size) - self.input_size_per_partition = in_features + self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size) # Parameters. # Initialize weight. @@ -886,8 +884,7 @@ def __init__(self, tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) - # self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) - self.num_embeddings_per_partition = num_embeddings + self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 55b6aa75ef84..3394e9457da3 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -257,3 +257,22 @@ CustomPolicy(Policy): - CLASS `Slicer`: This class is used to slice tensor according to policy. + + + 3. DistCrossEntropy Loss + - Overview + + In order to reduce the communication size, caculate the crossentropy before all gather, refer to [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), reduce the communication size from [batch_size * seq_length * vocab_size] to [batch_size * seq_length]. The origin loss function is: + $$ loss = -\log(\frac{\exp(x[class])}{\sum_i\exp(x[i])})$$ + + alse can be represented as: + + $$ loss = \log(\sum_i\exp(x[i])) - x[class]$$ + + - Step + + - First get the maximum logits across all the devices, make all the logist minus the maximun value to scale the value less than zero to avoid the value of exp being too large + + - Get a mask to mask the logits not in the local device + + - Caculate the loss according to the second formula diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/colossalai/shardformer/layer/_operation.py b/colossalai/shardformer/layer/_operation.py new file mode 100644 index 000000000000..e817ea3ebbee --- /dev/null +++ b/colossalai/shardformer/layer/_operation.py @@ -0,0 +1,97 @@ +import torch +import torch.distributed as dist + +from colossalai.core import global_context as gpc + +try: + import fused_mix_prec_layer_norm_cuda +except: + fused_mix_prec_layer_norm_cuda = None + + +class FusedLayerNormAffineFunction1D(torch.autograd.Function): + r"""Layernorm + + Args: + input: input matrix. + weight: weight matrix. + bias: bias matrix. + normalized_shape: input shape from an expected input of size. + :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]` + If a single integer is used, it is treated as a singleton list, and this module will + normalize over the last dimension which is expected to be of that specific size. + eps: a value added to the denominator for numerical stability + """ + + @staticmethod + def forward(ctx, input, weight, bias, normalized_shape, eps): + ctx.normalized_shape = normalized_shape + ctx.eps = eps + input_ = input.contiguous() + weight_ = weight.contiguous() + bias_ = bias.contiguous() + output, mean, invvar = fused_mix_prec_layer_norm_cuda.forward_affine(input_, ctx.normalized_shape, weight_, + bias_, ctx.eps) + ctx.save_for_backward(input_, weight_, bias_, mean, invvar) + return output + + @staticmethod + def backward(ctx, grad_output): + input_, weight_, bias_, mean, invvar = ctx.saved_tensors + grad_input = grad_weight = grad_bias = None + grad_input, grad_weight, grad_bias \ + = fused_mix_prec_layer_norm_cuda.backward_affine( + grad_output.contiguous(), mean, invvar, + input_, ctx.normalized_shape, + weight_, bias_, ctx.eps) + + return grad_input, grad_weight, grad_bias, None, None + + +class LinearWithAsyncCommunication(torch.autograd.Function): + """ + Linear layer execution with asynchronous communication in backprop. + """ + + @staticmethod + def forward(ctx, input_, weight, bias, parallel_mode, async_grad_allreduce): + ctx.save_for_backward(input_, weight) + ctx.use_bias = bias is not None + ctx.parallel_mode = parallel_mode + ctx.async_grad_allreduce = async_grad_allreduce + + output = torch.matmul(input_, weight.t()) + if bias is not None: + output = output + bias + return output + + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + use_bias = ctx.use_bias + + total_input = input + grad_input = grad_output.matmul(weight) + grad_output = grad_output.contiguous() + # Convert the tensor shapes to 2D for execution compatibility + grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2]) + total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2]) + + if ctx.async_grad_allreduce: + # Asynchronous all-reduce + handle = dist.all_reduce(grad_input, group=gpc.get_group(ctx.parallel_mode), async_op=True) + # Delay the start of weight gradient computation shortly (3us) to have + # all-reduce scheduled first and have GPU resources allocated + _ = torch.empty(1, device=grad_output.device) + 1 + + grad_weight = grad_output.t().matmul(total_input) + grad_bias = grad_output.sum(dim=0) if use_bias else None + + if ctx.async_grad_allreduce: + handle.wait() + + return grad_input, grad_weight, grad_bias, None, None, None + + +def linear_with_async_comm(input_, weight, bias, parallel_mode, async_grad_allreduce): + return LinearWithAsyncCommunication.apply(input_, weight, bias, parallel_mode, async_grad_allreduce) diff --git a/colossalai/shardformer/layer/dist_crossentropy.py b/colossalai/shardformer/layer/dist_crossentropy.py new file mode 100644 index 000000000000..1869594670ce --- /dev/null +++ b/colossalai/shardformer/layer/dist_crossentropy.py @@ -0,0 +1,105 @@ +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Function + + +class DistCrossEntropy(Function): + r""" + Overwrite the forward and backward function to calculate the cross entropy loss before gather + + Args: + Function (:class:`torch.autograd.Function`): default + """ + + @staticmethod + def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor): + r""" + Calculate the cross entropy loss before gather, the origin loss function is as follows: + loss = -log(exp(x[class])/sum(exp(x[i])) + and can be rewrite as: + loss = log(sum(exp(x[i])) - x[class] + + To avoid the `nan` of log(sim(exp(x[i]))), we minus the max of x[i] + + Args: + vocab_logits (:class:`torch.Tensor`): The logits of the vocabulary, shape is + [batch_size, seq_len, vocab_size] + labels (:class:`torch.Tensor`): The labels of the vocabulary, shape is + [batch_size, seq_len] + + Returns: + :class:`torch.Tensor`: The cross entropy loss + """ + # get the max + logits_max = torch.max(vocab_logits, dim=-1)[0] + dist.all_reduce(logits_max, op=dist.ReduceOp.MAX) + + # minus the max to avoid the result of sum of exp is too large and the log is nan + vocab_logits = vocab_logits - logits_max.unsqueeze(dim=-1) + + # mask the target in the local device + partition_vocab_size = vocab_logits.size()[-1] + rank = dist.get_rank() + world_size = dist.get_world_size() + global_vocab_size = partition_vocab_size * world_size + + # [down, up) => false, other device and -100 => true + delta = (global_vocab_size + world_size - 1) // world_size + down_shreshold = rank * delta + up_shreshold = down_shreshold + delta + mask = (target < down_shreshold) | (target >= up_shreshold) + masked_target = target.clone() - down_shreshold + masked_target[mask] = 0 + + # reshape the logist and target + # reshape the vocab_logits to [bath_size * seq_len, vocab_size] + # reshape the labels to [bath_size * seq_len] + logits_2d = vocab_logits.view(-1, partition_vocab_size) + masked_target_1d = masked_target.view(-1) + + # extract the x[class] and set the x[other device] to zero + pred_logits_1d = logits_2d[torch.arange(start=0, end=logits_2d.shape[0], device=logits_2d.device), + masked_target_1d] + pred_logits_1d = pred_logits_1d.clone().contiguous() + pred_logits = pred_logits_1d.view_as(target) + pred_logits[mask] = 0.0 + + # allreduce the get all x(i,y) + dist.all_reduce(pred_logits, op=dist.ReduceOp.SUM) + exp_logits = vocab_logits + torch.exp(vocab_logits, out=exp_logits) + sum_exp_logits = torch.sum(exp_logits, dim=-1) + dist.all_reduce(sum_exp_logits, op=dist.ReduceOp.SUM) + + # calculate the loss + # loss = log(sum(exp(x[i]))) - x[class] + loss = torch.log(sum_exp_logits) - pred_logits + loss = torch.sum(loss).div_(loss.numel()) + + # caculate the softmax + exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1)) + ctx.save_for_backward(exp_logits, mask, masked_target_1d) + + return loss + + @staticmethod + def backward(ctx, grad_output): + # retrieve the saved tensors + exp_logits, mask, masked_target_1d = ctx.saved_tensors + + # use exp logits as the input grad + grad_logits = exp_logits + partion_vocab_size = grad_logits.shape[-1] + grad_logits_2d = grad_logits.view(-1, partion_vocab_size) + + update = 1.0 - mask.view(-1).float() + grad_logits_2d[torch.arange(0, grad_logits_2d.shape[0]), masked_target_1d] -= update + + grad_logits.mul_(grad_output.unsqueeze(dim=-1)) + return grad_logits, None, None + + +def applyDistCrossEntropy(vocab_logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + return DistCrossEntropy.apply(vocab_logits, labels) diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py new file mode 100644 index 000000000000..acc114029ac1 --- /dev/null +++ b/colossalai/shardformer/layer/dropout.py @@ -0,0 +1,58 @@ +import os +import time +from contextlib import contextmanager + +import torch +import torch.nn as nn + + +class SeedManager: + """ + This class is a random state manager to change random state for different random seed. + + """ + + def __init__(self): + original_state = torch.cuda.get_rng_state() + seed = int(f"{int(time.time())}{os.environ['RANK']}") + torch.cuda.manual_seed(int(seed)) + self.dropout_state = torch.cuda.get_rng_state() + torch.cuda.set_rng_state(original_state) + + def set_mode(self, rng_state): + torch.cuda.set_rng_state(rng_state) + + def get_current_mode(self): + current_state = torch.cuda.get_rng_state() + return current_state + + @contextmanager + def dropout_mode(self): + """ + This is a context manager to change the dropout state and recover the original state. + + Usage: + :: + >>> with _seed_manager.dropout_mode(): + >>> input = super().forward(input) + """ + try: + current_mode = self.get_current_mode() + yield self.set_mode(self.dropout_state) + finally: + self.dropout_state = self.get_current_mode() + self.set_mode(current_mode) + + +_seed_manager = SeedManager() + + +class Dropout1D(nn.Dropout): + + def __init__(self, p=0.5, inplace=False): + super().__init__(p, inplace) + + def forward(self, input): + with _seed_manager.dropout_mode(): + input = super().forward(input) + return input diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py new file mode 100644 index 000000000000..f5123885bbe4 --- /dev/null +++ b/colossalai/shardformer/layer/layers.py @@ -0,0 +1,1043 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +import math +from collections import OrderedDict +from typing import Callable, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.nn.parameter import Parameter + +from colossalai.communication import broadcast +from colossalai.context import ParallelMode, seed +from colossalai.core import global_context as gpc +from colossalai.global_variables import tensor_parallel_env as env +from colossalai.kernel import LayerNorm +from colossalai.nn import init as init +from colossalai.nn.layer.base_layer import ParallelLayer +from colossalai.nn.layer.colossalai_layer._utils import ColossalaiModule +from colossalai.nn.layer.parallel_1d._utils import ( + gather_forward_split_backward, + get_parallel_input, + reduce_grad, + reduce_input, + set_parallel_input, + split_forward_gather_backward, +) +from colossalai.nn.layer.utils import divide, set_tensor_parallel_attribute_by_partition +from colossalai.nn.layer.vanilla import VanillaLayerNorm, VanillaPatchEmbedding +from colossalai.registry import LAYERS +from colossalai.utils.checkpointing import ( + broadcast_state_dict, + gather_tensor_parallel_state_dict, + partition_tensor_parallel_state_dict, +) +from colossalai.utils.cuda import get_current_device + +from ._operation import linear_with_async_comm + +Fast_LN = None +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm + Fast_LN = FastLayerNorm +except ImportError: + pass + + +# @LAYERS.register_module +class Linear1D(ColossalaiModule): + r"""Linear layer for 1D parallelism. + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + gather_output (bool, optional): Whether to call all-gather on output, defaults to False. + skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + gather_output: bool = False, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + parallel_input = get_parallel_input() + if not parallel_input and not gather_output: + layer = Linear1D_Col(in_features, + out_features, + bias=bias, + dtype=dtype, + skip_bias_add=skip_bias_add, + weight_initializer=weight_initializer, + bias_initializer=bias_initializer) + else: + layer = Linear1D_Row(in_features, + out_features, + bias=bias, + dtype=dtype, + parallel_input=parallel_input, + skip_bias_add=skip_bias_add, + weight_initializer=weight_initializer, + bias_initializer=bias_initializer) + super().__init__(layer) + + +# @LAYERS.register_module +class LayerNorm1D(ColossalaiModule): + r""" + Layer Normalization for colossalai + + Args: + normalized_shape (int): input shape from an expected input of size. + :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] + \times \ldots \times \text{normalized_shape}[-1]]` + If a single integer is used, it is treated as a singleton list, and this module will + normalize over the last dimension which is expected to be of that specific size. + eps (float): a value added to the denominator for numerical stability, defaults to 1e-05. + bias (bool, optional): Whether to add a bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + """ + + _fast_ln_supported_sizes = [ + 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, + 24576, 25600, 30720, 32768, 40960, 49152, 65536 + ] + + def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None): + if Fast_LN is not None and normalized_shape in self._fast_ln_supported_sizes: + norm = Fast_LN(normalized_shape, eps=eps).to(dtype) + else: + norm = None + try: + from apex.normalization import FusedLayerNorm + norm = FusedLayerNorm(normalized_shape, eps=eps).to(dtype) + except ImportError: + norm = LayerNorm(normalized_shape, eps=eps).to(dtype) + super().__init__(norm) + + def _load_from_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) + super()._load_from_state_dict(local_state, prefix, *args) + + def _save_to_state_dict(self, destination, prefix, keep_vars): + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + super()._save_to_state_dict(destination, prefix, keep_vars) + + +# @LAYERS.register_module +class Classifier1D(ParallelLayer): + r"""RowLinear with given weight. Classifier of 1D parallelism. + + Args: + in_features (int): size of each input sample. + num_classes (int): number of classes. + weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + num_classes: int, + weight: Parameter = None, + bias: bool = True, + dtype: torch.dtype = None, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + super().__init__() + self.in_features = in_features + self.num_classes = num_classes + self.parallel_input = get_parallel_input() + + # Divide the weight matrix along the last dimension. + self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size) + + # Parameters. + # Initialize weight. + factory_kwargs = {'device': get_current_device(), 'dtype': dtype} + if weight is not None: + self.weight = weight + self.has_weight = False + else: + self.weight = Parameter(torch.empty(self.num_classes, self.input_size_per_partition, **factory_kwargs)) + self.has_weight = True + if bias: + self.bias = Parameter(torch.empty(self.num_classes, **factory_kwargs)) + else: + self.bias = None + with seed(ParallelMode.TENSOR): + self.reset_parameters(weight_initializer, bias_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + env.vocab_parallel = False + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.num_classes + if self.has_weight: + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D) + + def _set_tensor_parallel_attributes(self): + if self.has_weight: + num_partition = gpc.get_world_size(ParallelMode.TENSOR) + set_tensor_parallel_attribute_by_partition(self.weight, num_partition) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + if self.has_weight: + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + if self.bias is not None: + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: -1, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: False + }) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + local_state = OrderedDict() + if self.has_weight: + local_state[weight_key] = self.weight + if self.bias is not None: + local_state[bias_key] = self.bias + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: -1, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: False + }, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + # Set up backprop all-reduce. + if self.parallel_input: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + input_ = input_ + else: + assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \ + 'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size) + input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1) + + output_parallel = F.linear(input_, self.weight) + output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) + if self.bias is not None: + output = output + self.bias + return output + + +# @LAYERS.register_module +class VocabParallelClassifier1D(ParallelLayer): + r"""ColLinear with given weight. Classifier of 1D parallelism. + + Args: + in_features (int): size of each input sample. + num_classes (int): number of classes. + weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + num_classes: int, + weight: Parameter = None, + bias: bool = True, + dtype: torch.dtype = None, + gather_output: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + super().__init__() + self.in_features = in_features + self.num_classes = num_classes + self.gather_output = gather_output + self.parallel_input = get_parallel_input() + + # Divide the weight matrix along the last dimension. + self.num_classes_per_partition = divide(num_classes, gpc.tensor_parallel_size) + + # Parameters. + # Initialize weight. + factory_kwargs = {'device': get_current_device(), 'dtype': dtype} + if weight is not None: + self.weight = weight + self.has_weight = False + else: + self.weight = Parameter(torch.empty(self.num_classes_per_partition, self.in_features, **factory_kwargs)) + self.has_weight = True + if bias: + self.bias = Parameter(torch.empty(self.num_classes_per_partition, **factory_kwargs)) + else: + self.bias = None + with seed(ParallelMode.TENSOR): + self.reset_parameters(weight_initializer, bias_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + env.vocab_parallel = True + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.num_classes + if self.has_weight: + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + + def _set_tensor_parallel_attributes(self): + num_partition = gpc.get_world_size(ParallelMode.TENSOR) + if self.has_weight: + set_tensor_parallel_attribute_by_partition(self.weight, num_partition) + if self.bias is not None: + set_tensor_parallel_attribute_by_partition(self.bias, num_partition) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + if self.has_weight: + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + if self.bias is not None: + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: 0, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: True + }) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + local_state = OrderedDict() + if self.has_weight: + local_state[weight_key] = self.weight + if self.bias is not None: + local_state[bias_key] = self.bias + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: 0, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: True + }, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in VocabParallelClassifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + # Set up backprop all-reduce. + input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) + # Matrix multiply. + output_parallel = F.linear(input_parallel, self.weight, self.bias) + if self.gather_output: + # All-gather across the partitions. + output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) + else: + output = output_parallel + return output + + +# @LAYERS.register_module +class Linear1D_Col(ParallelLayer): + r"""Linear layer with column parallelism. + + The linear layer is defined as :math:`Y = XA + b`. A is parallelized along + its second dimension as :math:`A = [A_1, ..., A_p]`. + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + gather_output (bool, optional): If true, call all-gather on output and make Y available + to all GPUs, otherwise, every GPU will have its output + which is :math:`Y_i = XA_i`, defaults to False + skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + gather_output: bool = False, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + super().__init__() + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.gather_output = gather_output + self.skip_bias_add = skip_bias_add + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + # self.out_features_per_partition = divide(out_features*2, gpc.tensor_parallel_size) + self.out_features_per_partition = out_features + + # Parameters. + # Initialize weight. + factory_kwargs = {'device': get_current_device(), 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) + + if bias: + self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) + else: + self.bias = None + with seed(ParallelMode.TENSOR): + self.reset_parameters(weight_initializer, bias_initializer) + self._set_tensor_parallel_attributes() + is_parallel_output = not self.gather_output + set_parallel_input(is_parallel_output) + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + + def _set_tensor_parallel_attributes(self): + num_partition = gpc.get_world_size(ParallelMode.TENSOR) + set_tensor_parallel_attribute_by_partition(self.weight, num_partition) + if self.bias is not None: + set_tensor_parallel_attribute_by_partition(self.bias, num_partition) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + if self.bias is not None: + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: 0, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: True + }) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + local_state = OrderedDict({weight_key: self.weight}) + if self.bias is not None: + local_state[bias_key] = self.bias + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: 0, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: True + }, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + # Set up backprop all-reduce. + # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) + input_parallel = input_ + # Matrix multiply. + bias = self.bias if not self.skip_bias_add else None + # output_parallel = F.linear(input_parallel, self.weight, bias) + output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, ParallelMode.PARALLEL_1D, True) + if self.gather_output: + # All-gather across the partitions. + output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) + else: + output = output_parallel + + if self.skip_bias_add: + return output, self.bias + else: + return output + + +# @LAYERS.register_module +class Linear1D_Row(ParallelLayer): + r""" Linear layer with row parallelism + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + parallel_input (bool, optional): If set to ``True``, it's assumed that the input is split, defaults to False. + skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + parallel_input: bool = True, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), + stream_chunk_num: int = 1): + super().__init__() + + self.stream_chunk_num = stream_chunk_num + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.parallel_input = parallel_input + self.skip_bias_add = skip_bias_add + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + # Divide the weight matrix along the last dimension. + # self.input_size_per_partition = divide(in_features*2, gpc.tensor_parallel_size) + self.input_size_per_partition = in_features + + # Parameters. + # Initialize weight. + factory_kwargs = {'device': get_current_device(), 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) + + if self.stream_chunk_num > 1: + # TODO() work for inference only + self.chunk_weight() + if bias: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + else: + self.bias = None + with seed(ParallelMode.TENSOR): + self.reset_parameters(weight_initializer, bias_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + + def chunk_weight(self): + self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D) + + def _set_tensor_parallel_attributes(self): + num_partition = gpc.get_world_size(ParallelMode.TENSOR) + set_tensor_parallel_attribute_by_partition(self.weight, num_partition) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + if self.bias is not None: + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: -1, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: False + }) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + local_state = OrderedDict({weight_key: self.weight}) + if self.bias is not None: + local_state[bias_key] = self.bias + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={ + weight_key: -1, + bias_key: 0 + }, + partition_states={ + weight_key: True, + bias_key: False + }, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + # Set up backprop all-reduce. + if self.parallel_input: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + input_ = input_ + else: + assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size) + input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1) + + if self.stream_chunk_num > 1: + if self.training: + raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!") + with torch.no_grad(): + output_parallel_list = [None for i in range(self.stream_chunk_num)] + handle_list = [] + for i in range(self.stream_chunk_num): + output_parallel_list[i] = F.linear(input_, self.weight_list[i]) + handle = torch.distributed.all_reduce(output_parallel_list[i], + group=gpc.get_group(ParallelMode.PARALLEL_1D), + async_op=True) + handle_list.append(handle) + # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D) + for handle in handle_list: + handle.wait() + output = torch.cat(output_parallel_list, dim=-1) + else: + output_parallel = F.linear(input_, self.weight) + # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) + output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) + if not self.skip_bias_add: + if self.bias is not None: + output = output + self.bias + return output + else: + return output, self.bias + + +# @LAYERS.register_module +class Embedding1D(ParallelLayer): + r"""Embedding for 1D parallelism. + + Args: + num_embeddings (int): number of embeddings. + embedding_dim (int): dimension of embedding. + padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; + therefore, the embedding vector at padding_idx is not updated during training, + i.e. it remains as a fixed “pad”, defaults to None. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + he initializer of weight, defaults to normal initializer. + + The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain: + :: + + max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is + renormalized to have norm max_norm. Note: this will modify weight in-place. + norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. + scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse + of frequency of the words in the mini-batch. Default False. + sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. + + More details about ``args`` and ``kwargs`` could be found in + `Embedding `_. + + More details about ``initializer`` please refer to + `init `_ + """ + + def __init__(self, + num_embeddings: int, + embedding_dim: int, + padding_idx: int = None, + dtype: torch.dtype = None, + weight_initializer: Callable = init.normal_(), + *args, + **kwargs): + super().__init__() + + self.num_embeddings = num_embeddings + self.embed_dim = embedding_dim + embed_dim_per_partition = divide(embedding_dim, gpc.tensor_parallel_size) + + self.padding_idx = padding_idx + self.embed_args = args + self.embed_kwargs = kwargs + + self.weight = Parameter( + torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype)) + + self.reset_parameters(weight_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + + def _set_tensor_parallel_attributes(self): + set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size) + + def reset_parameters(self, weight_initializer) -> None: + with seed(ParallelMode.TENSOR): + fan_in, fan_out = self.num_embeddings, self.embed_dim + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + self._fill_padding_idx_with_zero() + + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None: + with torch.no_grad(): + self.weight[self.padding_idx].fill_(0) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={weight_key: -1}, + partition_states={weight_key: True}) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + local_state = OrderedDict({weight_key: self.weight}) + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={weight_key: -1}, + partition_states={weight_key: True}, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + + output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) + + output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) + + return output + + +# @LAYERS.register_module +class VocabParallelEmbedding1D(ParallelLayer): + r"""Embedding parallelized in the vocabulary dimension. + + Args: + num_embeddings (int): number of embeddings. + embedding_dim (int): dimension of embedding. + padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; + therefore, the embedding vector at padding_idx is not updated during training, + i.e. it remains as a fixed “pad”, defaults to None. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + he initializer of weight, defaults to normal initializer. + + The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain: + :: + + max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is + renormalized to have norm max_norm. Note: this will modify weight in-place. + norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. + scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse + of frequency of the words in the mini-batch. Default False. + sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. + + More details about ``args`` and ``kwargs`` could be found in + `Embedding `_. + + More details about initializer please refer to + `init `_. + """ + + def __init__(self, + num_embeddings: int, + embedding_dim: int, + padding_idx: int = None, + dtype: torch.dtype = None, + weight_initializer: Callable = init.normal_(), + *args, + **kwargs): + super().__init__() + self.num_embeddings = num_embeddings + self.embed_dim = embedding_dim + self.padding_idx = padding_idx + self.embed_args = args + self.embed_kwargs = kwargs + + tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) + tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) + # self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) + self.num_embeddings_per_partition = num_embeddings + self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition + self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition + + self.weight = Parameter( + torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=get_current_device(), dtype=dtype)) + + self.reset_parameters(weight_initializer) + self._set_tensor_parallel_attributes() + set_parallel_input(False) + env.vocab_parallel = True + + def _set_tensor_parallel_attributes(self): + set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size) + + def reset_parameters(self, weight_initializer) -> None: + with seed(ParallelMode.TENSOR): + fan_in, fan_out = self.num_embeddings, self.embed_dim + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + self._fill_padding_idx_with_zero() + + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None and \ + self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index: + with torch.no_grad(): + self.weight[self.padding_idx - self.vocab_start_index].fill_(0) + + def _load_from_global_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + + local_state = partition_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={weight_key: 0}, + partition_states={weight_key: True}) + super()._load_from_global_state_dict(local_state, prefix, *args) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + local_state = OrderedDict({weight_key: self.weight}) + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={weight_key: 0}, + partition_states={weight_key: True}, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + # Build the mask. + input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index) + # Mask the input. + masked_input = input_.clone() - self.vocab_start_index + masked_input[input_mask] = 0 + + output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args, + **self.embed_kwargs) + + # Mask the output embedding. + output_parallel[input_mask, :] = 0. + # Reduce across all the model parallel GPUs. + output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) + return output + + +# @LAYERS.register_module +class Dropout1D(ParallelLayer): + """Dropout layer of 1D parallelism. + + Args: + p (float, optional): probability of an element to be zeroed, defaults 0.5. + inplace (bool, optional): whether to do dropout in-place, default to be False. + """ + + def __init__(self, p: float = 0.5, inplace: bool = False): + super().__init__() + self.parallel_input = get_parallel_input() + self.p = p + self.inplace = inplace + + def forward(self, input_: Tensor) -> Tensor: + if self.parallel_input: + with seed(ParallelMode.TENSOR): + output = F.dropout(input_, self.p, self.training, self.inplace) + else: + output = F.dropout(input_, self.p, self.training, self.inplace) + return output + + +# @LAYERS.register_module +class PatchEmbedding1D(ColossalaiModule): + """ + 2D Image to Patch Embedding + + :param img_size: image size + :type img_size: int + :param patch_size: patch size + :type patch_size: int + :param in_chans: number of channels of input image + :type in_chans: int + :param embed_size: size of embedding + :type embed_size: int + :param dtype: The dtype of parameters, defaults to None + :type dtype: torch.dtype, optional + :param flatten: whether to flatten output tensor, defaults to True + :type flatten: bool, optional + :param weight_initializer: The initializer of weight, defaults to kaiming uniform initializer + :type weight_initializer: typing.Callable, optional + :param bias_initializer: The initializer of bias, defaults to xavier uniform initializer + :type bias_initializer: typing.Callable, optional + :param position_embed_initializer: The initializer of position embedding, defaults to zero + :type position_embed_initializer: typing.Callable, optional + """ + + def __init__(self, + img_size: int, + patch_size: int, + in_chans: int, + embed_size: int, + dtype: torch.dtype = None, + flatten: bool = True, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), + position_embed_initializer: Callable = init.zeros_()): + embed = VanillaPatchEmbedding(img_size, + patch_size, + in_chans, + embed_size, + dtype=dtype, + flatten=flatten, + weight_initializer=weight_initializer, + bias_initializer=bias_initializer, + position_embed_initializer=position_embed_initializer) + super().__init__(embed) + + def _load_from_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + param_keys = [prefix + 'weight', prefix + 'bias', prefix + 'cls_token', prefix + 'pos_embed'] + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + for key in param_keys: + param = state_dict.pop(key, None) + if param is not None: + local_state[key] = param + + local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) + super()._load_from_state_dict(local_state, prefix, *args) + + def _save_to_state_dict(self, destination, prefix, keep_vars): + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + super()._save_to_state_dict(destination, prefix, keep_vars) diff --git a/colossalai/shardformer/model/modeling_bert.py b/colossalai/shardformer/model/modeling_bert.py index 6741ae866991..bd07ab80c00d 100644 --- a/colossalai/shardformer/model/modeling_bert.py +++ b/colossalai/shardformer/model/modeling_bert.py @@ -6,6 +6,8 @@ from transformers import BertForMaskedLM from transformers.models.bert.modeling_bert import MaskedLMOutput +from ..layer.dist_crossentropy import applyDistCrossEntropy + class BertForMaskedLM_(BertForMaskedLM): @@ -47,11 +49,11 @@ def forward( masked_lm_loss = None - # if input_ids is not None: - # masked_lm_loss = applyDistCrossEntropy(prediction_scores, input_ids, self.config.vocab_size) if labels is not None: - loss_fct = CrossEntropyLoss() # -100 index = padding token - masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + masked_lm_loss = applyDistCrossEntropy(prediction_scores, labels) + # if labels is not None: + # loss_fct = CrossEntropyLoss() # -100 index = padding token + # masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index a5cc0bc68df6..2eb7eb29e1a4 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -7,8 +7,6 @@ import torch.nn as nn from transformers import AutoConfig -import colossalai.nn as col_nn - @dataclass class Argument: diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 5d91d8ddc766..ab77b29f71f4 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -4,7 +4,7 @@ import torch.nn as nn from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead -import colossalai.nn as col_nn +import colossalai.shardformer.layer.layers as col_nn from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer @@ -142,7 +142,7 @@ def unembedding() -> List: weight="decoder.weight", bias="decoder.bias", replace_layer=col_nn.Linear1D_Col, - gather_output=True, + # gather_output=True, ) ] diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 957ce1f85814..26053b9f7408 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -94,10 +94,7 @@ def slice_1d( Returns: :class:`torch.Tensor`: The sliced tensor """ - delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size - down_idx = self.shardconfig.rank * delta - up_idx = down_idx + delta - return tensor[down_idx:up_idx].contiguous() + return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() def slice_col( self, @@ -113,10 +110,7 @@ def slice_col( :class:`torch.Tensor`: The sliced tensor """ - delta = (tensor.shape[0] + self.shardconfig.world_size - 1) // self.shardconfig.world_size - down_idx = self.shardconfig.rank * delta - up_idx = down_idx + delta - return tensor[down_idx:up_idx, :].contiguous() + return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() def slice_row( self, @@ -131,7 +125,4 @@ def slice_row( Returns: :class:`torch.Tensor`: The sliced tensor """ - delta = (tensor.shape[1] + self.shardconfig.world_size - 1) // self.shardconfig.world_size - down_idx = self.shardconfig.rank * delta - up_idx = down_idx + delta - return tensor[:, down_idx:up_idx].contiguous() + return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() diff --git a/colossalai/shardformer/test/module_test.py b/colossalai/shardformer/test/module_test.py new file mode 100644 index 000000000000..83dc7ec6cf4a --- /dev/null +++ b/colossalai/shardformer/test/module_test.py @@ -0,0 +1,50 @@ +import os + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import colossalai +from colossalai.shardformer.layer.dist_crossentropy import applyDistCrossEntropy +from colossalai.shardformer.layer.dropout import Dropout1D + + +def get_args(): + parser = colossalai.get_default_parser() + parser.add_argument("--module", type=str, default='distloss') + return parser.parse_args() + + +def test_dist_crossentropy(): + pred = torch.randn(2, 4, 8, requires_grad=True) + labels = torch.randint(8, (1, 4)).repeat(2, 1) + + pred_ = pred.view(-1, 8) + labels_ = labels.view(-1) + loss = F.cross_entropy(pred_, labels_) + loss.backward() + print(f"normal loss:{loss}") + + pred = pred.chunk(int(os.environ['WORLD_SIZE']), -1)[int(os.environ['RANK'])] + loss = applyDistCrossEntropy(pred.to('cuda'), labels.to('cuda')) + loss.backward() + print(f"dist loss:{loss}") + + +def test_dropout(): + input = torch.randn(5, 4).to("cuda") + m = Dropout1D(p=0.2).to("cuda") + for i in range(2): + print(f"Output: {m(input)}") + print(torch.randn(1)) + + +if __name__ == '__main__': + args = get_args() + colossalai.launch_from_torch(config={}) + if args.module == 'distloss': + test_dist_crossentropy() + elif args.module == 'dropout': + test_dropout() + else: + print("not implemented yet") diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py index 202208123ced..b896fd4a4020 100644 --- a/colossalai/shardformer/test/test.py +++ b/colossalai/shardformer/test/test.py @@ -1,11 +1,12 @@ import os +import random import torch import torch.nn as nn from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm -from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling +from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, get_scheduler import colossalai from colossalai.shardformer.shard import ShardConfig, shard_model @@ -18,6 +19,7 @@ def get_args(): parser = colossalai.get_default_parser() parser.add_argument("--mode", type=str, default='inference') + parser.add_argument("--save_model", action='store_true') return parser.parse_args() @@ -30,36 +32,40 @@ def load_data(): # tokenized_datasets=tokenized_datasets.rename_column("label","labels") tokenized_datasets.set_format("torch") - train_dataset = tokenized_datasets["train"].select(range(500)) - test_dataset = tokenized_datasets["test"].select(range(100)) + train_dataset = tokenized_datasets["train"] + test_dataset = tokenized_datasets["test"] datacollector = DataCollatorForLanguageModeling(tokenizer, mlm=True, mlm_probability=0.15, return_tensors="pt") - train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True, collate_fn=datacollector) - eval_dataloader = DataLoader(test_dataset, batch_size=8, shuffle=True, collate_fn=datacollector) + train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=True, collate_fn=datacollector) + eval_dataloader = DataLoader(test_dataset, batch_size=16, shuffle=True, collate_fn=datacollector) return train_dataloader, eval_dataloader -def inference(model: nn.Module): - print(model) +def inference(model: nn.Module, args): tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") token = "Hello, my dog is cute" inputs = tokenizer(token, return_tensors="pt") inputs.to("cuda") + model.eval() model.to("cuda") outputs = model(**inputs) print(outputs) -def train(model: nn.Module, num_epoch: int = 2): +def train(model: nn.Module, args, num_epoch: int = 3): train_dataloader, eval_dataloader = load_data() optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5) - progress_bar = tqdm(range((num_epoch) * len(train_dataloader))) - criterion = nn.CrossEntropyLoss() + num_training = num_epoch * len(train_dataloader) + progress_bar = tqdm(range(num_training)) + lr_scheduler = get_scheduler(name="linear", + optimizer=optimizer, + num_warmup_steps=0, + num_training_steps=num_training) + best_test_loss = float("inf") model.to("cuda") model.train() for epoch in range(num_epoch): progress_bar.set_description(f"Rank {get_current_device()} epoch {epoch}") - for batch in train_dataloader: optimizer.zero_grad() batch = {k: v.to('cuda') for k, v in batch.items()} @@ -67,6 +73,7 @@ def train(model: nn.Module, num_epoch: int = 2): loss = outputs.loss loss.backward() optimizer.step() + lr_scheduler.step() progress_bar.update(1) train_loss = loss @@ -75,16 +82,20 @@ def train(model: nn.Module, num_epoch: int = 2): batch = {k: v.to('cuda') for k, v in batch.items()} outputs = model(**batch) # loss = outputs.loss + assert not torch.isnan(outputs.loss), f"{batch}" loss += outputs.loss.item() # loss = criterion(outputs.logits, batch["input_ids"]) test_loss = loss / len(eval_dataloader) print_rank_0(f"Train Loss: {train_loss:.4f} Test Loss:{test_loss:.4f}") + if args.save_model and test_loss < best_test_loss: + best_test_loss = test_loss + torch.save(model.state_dict(), "./checkpoints/best_model.pth") if __name__ == "__main__": args = get_args() - colossalai.launch_from_torch(config=args.config) model = BertForMaskedLM.from_pretrained("bert-base-uncased") + colossalai.launch_from_torch(config=args.config) shard_config = ShardConfig( rank=int(str(get_current_device()).split(':')[-1]), world_size=int(os.environ['WORLD_SIZE']), @@ -92,6 +103,8 @@ def train(model: nn.Module, num_epoch: int = 2): sharded_model = shard_model(model, shard_config) if args.mode == "train": - train(sharded_model) + train(sharded_model, args) elif args.mode == "inference": - inference(sharded_model) + inference(sharded_model, args) + else: + raise NotImplementedError From 70173e31233ef634846f5ce7a43710f41557d01f Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Tue, 6 Jun 2023 15:31:52 +0800 Subject: [PATCH 354/413] update README (#3909) --- colossalai/shardformer/README.md | 46 ++++++++++++++++++++++---------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 3394e9457da3..93a4f1e578e4 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -55,30 +55,37 @@ If you wanna parallel the model in a custom way, just overwrite the policy class You should do: 1. Inherit Policy class -2. Overwrite argument_policy method - - In this method, you need to list which layers class you wanna modify and the attributes and parameters in those layers. -3. Overwrite inject_policy method (Optional) - - If you need to modify the forward or backward progress. -4. Overwrite or add the param recording functions +2. Overwrite `argument_policy` method + - In this method, you need to list which layers class you wanna modify and the attributes and parameters in those layers. Shardformer will replace all the layer belonging to the class you specified. + - `attr_dict` is dict contains all the attributes need to be modified in this layer. + - `param_funcs` is a list contains some functions which will return the path of the weight and bias from the layer. +3. Overwrite `inject_policy` method (Optional) + - Shardformer will inject the model according to this method. If you need to modify the forward or backward progress (like distributed corssentropy loss in Bert) you need to overwrite this method. +4. Overwrite or add the param functions - These functions use a suffix to record the path of weight or bias for the layer. -5. Overwrite binding + - The return is a list contains some `Col_Layer` or `Row_Layer` objects, which means slice along col and row respectively. +5. Overwrite `binding_policy` (Optional) + - Overwrite to specify Shardformer will bind some weight between layers, like embedding and unembedding layers. + - This function will return a dict, the key and value are the suffix of weight need to be binded. More details can be found in shardformer/policies/basepolicy.py ``` python from colossalai.shardformer.policies.basepolicy import Policy, Layer, Col_Layer, Row_Layer, Argument CustomPolicy(Policy): - @staticmethod - def argument_policy(model_config, shard_config: int) -> Dict[nn.Module,Argument]: - """ - Return a dict, the key is layer will be modified and the value is the Argument class with param setting and param functions +@staticmethod + def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]: + r""" + Return the dict for the modify policy, the key is the original layer class and the value is the + argument for the modify layer Args: - model_config: The config of transformer model - shard_setting: The config of distributed model + model_config (:class:`tansformer.Config`): The config of transformer model + shard_config (:class:`ShardConfig`): The config for sharding model Return: Dict for the modify policy, + :: { origin layer class1 (nn.Module): Argument( attr_dict = { @@ -112,18 +119,29 @@ CustomPolicy(Policy): @staticmethod def inject_policy() -> Tuple[nn.Module, nn.Module]: - """ + r""" Return the dict for the inject model Return: The injected model, key is the original model and value is the new shardmodel + :: + (OrignModel, CustomModel) + in `CustomModel`, we can overwrite the forward and backward process """ return () @staticmethod def binding_policy() -> Dict: - """ + r""" Return the dict for the binding model + + Return: + This method should return the binding relationship for some layers share the weight or bias, + the key and value is the suffix of the weight or bias of the model + :: + return { + "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", + } """ return NotImplementedError From 79f8d5d54b321e4c627a119202ec84881f2d6f70 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Wed, 7 Jun 2023 16:09:40 +0800 Subject: [PATCH 355/413] [shardformer] add gpt2 policy and modify shard and slicer to support (#3883) * add gpt2 policy and modify shard and slicer to support * remove unused code * polish code --- colossalai/shardformer/policies/autopolicy.py | 14 ++- colossalai/shardformer/policies/basepolicy.py | 17 ++- colossalai/shardformer/policies/bert.py | 1 - colossalai/shardformer/policies/gpt2.py | 118 ++++++++++++++++++ colossalai/shardformer/shard/sharder.py | 46 ++++--- colossalai/shardformer/shard/slicer.py | 53 ++++++-- colossalai/shardformer/test/test.py | 28 +++-- 7 files changed, 233 insertions(+), 44 deletions(-) create mode 100644 colossalai/shardformer/policies/gpt2.py diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index e096c2b13a59..54cc63ba124f 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -10,16 +10,26 @@ def build_policies(): """ auto_policy_dict = {} - from transformers.models.bert.modeling_bert import BertForMaskedLM + from transformers import BertForMaskedLM from .bert import BertForMaskedLMPolicy auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy - from transformers.models.bert.modeling_bert import BertForSequenceClassification + from transformers import BertForSequenceClassification from .bert import BertForSequenceClassificationPolicy auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy + from transformers import GPT2Model + + from .gpt2 import GPT2Policy + auto_policy_dict[GPT2Model] = GPT2Policy + + from transformers import GPT2LMHeadModel + + from .gpt2 import GPT2LMHeadModelPolicy + auto_policy_dict[GPT2LMHeadModel] = GPT2LMHeadModelPolicy + return auto_policy_dict diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 2eb7eb29e1a4..644d115a270e 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -1,11 +1,9 @@ # part of code modified from https://github.com/tunib-ai/parallelformers -from dataclasses import dataclass, field +from dataclasses import dataclass from typing import Any, Callable, Dict, List, Tuple, Type -import torch import torch.nn as nn -from transformers import AutoConfig @dataclass @@ -31,11 +29,18 @@ class Layer: bias (str): The bias suffix of the layer replace_layer (:class:`colosalai.nn`): The layer to replace the original layer ignore (bool): Whether to ignore this layer if it is not in the model + reversed (bool): Whether the weight in layer is reversed, commonly the weight in `torch.nn.Linear` is [out, in], + but in GPT2 `Conv1D` layer is [in, out] which is reversed. + n_cast (int): The number of weight will cast to, like q, k, v in attention layer, n_cast should be 3. commonly in TP, we just chunk the weight with the number of devices, + but in multi-head attention, we need to chunk the weight with the number of devices * n_head, and + each device should have a part of Q, K and V weight. """ weight: str = None bias: str = None replace_layer: Any = None ignore: bool = False + reversed: bool = False + n_cast: int = None @dataclass @@ -131,7 +136,7 @@ def inject_policy() -> Tuple[nn.Module, nn.Module]: (OrignModel, CustomModel) in `CustomModel`, we can overwrite the forward and backward process """ - return () + return None @staticmethod def binding_policy() -> Dict: @@ -146,7 +151,7 @@ def binding_policy() -> Dict: "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", } """ - return NotImplementedError + return None @staticmethod def attn_in() -> List: @@ -209,4 +214,4 @@ def unembedding() -> List: Return: List[Layer]: List of layer object """ - return NotImplementedError + return None diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index ab77b29f71f4..89b32f065c27 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,4 +1,3 @@ -from dataclasses import dataclass from typing import Any, Callable, Dict, List, Tuple, Type import torch.nn as nn diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py new file mode 100644 index 000000000000..44dc9c72f986 --- /dev/null +++ b/colossalai/shardformer/policies/gpt2.py @@ -0,0 +1,118 @@ +from typing import Any, Callable, Dict, List, Tuple, Type + +import torch.nn as nn +from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model + +import colossalai.shardformer.layer.layers as col_nn + +from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer + + +class GPT2Policy(Policy): + + @staticmethod + def argument_policy(config, world_size): + return { + GPT2Model: + Argument(attr_dict={}, param_funcs=[ + GPT2Policy.embedding, + ]), + GPT2Block: + Argument( + attr_dict={ + # 1. reduce hidden size + "attn.embed_dim": config.hidden_size // world_size, + "attn.split_size": config.hidden_size // world_size, + "crossattention.embed_dim": config.hidden_size // world_size, + "crossattention.split_size": config.hidden_size // world_size, + # 2. reduce number of heads + "attn.num_heads": config.num_attention_heads // world_size, + "crossattention.num_heads": config.num_attention_heads // world_size, + }, + param_funcs=[ + GPT2Policy.attn_in, + GPT2Policy.attn_out, + GPT2Policy.mlp_in, + GPT2Policy.mlp_out, + ]), + } + + @staticmethod + def attn_in() -> List: + return [ + Col_Layer(weight="attn.c_attn.weight", + bias="attn.c_attn.bias", + n_cast=3, + reversed=True, + replace_layer=col_nn.Linear1D_Col), + Col_Layer(weight="crossattention.c_attn.weight", + bias="crossattention.c_attn.bias", + n_cast=2, + reversed=True, + ignore=True, + replace_layer=col_nn.Linear1D_Col), + Col_Layer(weight="crossattention.q_attn.weight", + bias="crossattention.q_attn.bias", + reversed=True, + ignore=True, + replace_layer=col_nn.Linear1D_Col) + ] + + @staticmethod + def attn_out() -> List: + return [ + Row_Layer(weight="attn.c_proj.weight", + bias="attn.c_proj.bias", + reversed=True, + replace_layer=col_nn.Linear1D_Row), + Row_Layer(weight="crossattention.c_proj.weight", + bias="crossattention.c_proj.bias", + reversed=True, + ignore=True, + replace_layer=col_nn.Linear1D_Row) + ] + + @staticmethod + def mlp_in() -> List: + return [ + Col_Layer(weight="mlp.c_fc.weight", bias="mlp.c_fc.bias", reversed=True, replace_layer=col_nn.Linear1D_Col), + ] + + @staticmethod + def mlp_out() -> List: + return [ + Row_Layer(weight="mlp.c_proj.weight", + bias="mlp.c_proj.bias", + reversed=True, + replace_layer=col_nn.Linear1D_Row) + ] + + @staticmethod + def embedding() -> List: + return [Col_Layer(weight="wte.weight", replace_layer=col_nn.VocabParallelEmbedding1D)] + + +from transformers import GPT2LMHeadModel + + +class GPT2LMHeadModelPolicy(GPT2Policy): + + @staticmethod + def argument_policy(config, world_size): + base_argument = GPT2Policy.argument_policy(config, world_size) + argument = { + GPT2LMHeadModel: Argument(attr_dict={}, param_funcs=[ + GPT2LMHeadModelPolicy.unembedding, + ]), + } + argument.update(base_argument) + return argument + + @staticmethod + def unembedding() -> List: + return [ + Col_Layer(weight="lm_head.weight", + bias="lm_head.bias", + replace_layer=col_nn.Linear1D_Col, + gather_output=True) + ] diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 2218661889f8..1ada75e06b67 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -2,6 +2,7 @@ import torch import torch.nn as nn +from transformers.pytorch_utils import Conv1D from ..policies.autopolicy import get_autopolicy from ..policies.basepolicy import Policy @@ -35,10 +36,22 @@ def __init__( self.model_config = self.model.config def shard(self) -> None: + self.reshape_embedding() self.inject_model(self.model) self.replace_layer(self.model) self.bind_layer(self.model) + def reshape_embedding(self,) -> None: + r""" + Reshape the Embedding layer to make the embedding dimension divisible by world_size + """ + vocab_size = self.model_config.vocab_size + world_size = self.shard_config.world_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + self.model_config = self.model.config + def inject_model( self, model: nn.Module, @@ -53,6 +66,8 @@ def inject_model( """ inject_policy = self.policy.inject_policy() + if inject_policy is None: + return org_model_cls = inject_policy[0] shard_model_cls = inject_policy[1] @@ -82,9 +97,9 @@ def replace_layer( origin_layer_cls = argument_policy[0] attr_dict = argument_policy[1].attr_dict param_funcs = argument_policy[1].param_funcs - self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs) + self.traverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs) - def reverse_replace_layer( + def traverse_replace_layer( self, layer: nn.Module, origin_cls: nn.Module, @@ -100,17 +115,12 @@ def reverse_replace_layer( attr_dict (Dict): The attribute dict to modify policy_cls (:class:`Policy`): The policy class """ + if layer.__class__ == origin_cls: + for k, v in attr_dict.items(): + setattr_(layer, k, v, ignore=True) + self.shard_one_layer(layer, param_funcs) for name, child in layer.named_children(): - if child.__class__ == origin_cls: - # replac_layer = child - for k, v in attr_dict.items(): - setattr_(child, k, v, ignore=True) - # print(f"Sharding {name} layer", replac_layer.attention.self.__dict__) - # setattr_(layer, name, self.shard_one_layer(child, policy_cls)) - self.shard_one_layer(child, param_funcs) - continue - - self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs) + self.traverse_replace_layer(child, origin_cls, attr_dict, param_funcs) return layer def shard_one_layer( @@ -126,7 +136,6 @@ def shard_one_layer( param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class """ - # print(org_layer) for func in param_funcs: policy_layers = func() for policy_layer in policy_layers: @@ -136,9 +145,10 @@ def shard_one_layer( bias_attr = policy_layer.bias replace_layer_cls = policy_layer.replace_layer ignore = policy_layer.ignore + n_cast = policy_layer.n_cast + reversed = policy_layer.reversed if policy_layer.__class__.__name__ == "Col_Layer": gather_output = policy_layer.gather_output - # print(gather_output) if weight_attr is not None: if hasattr_(org_layer, weight_attr): @@ -161,13 +171,11 @@ def shard_one_layer( layer_attr = (lambda x: x[:x.rfind(".")])(weight_attr or bias_attr) # slice weight and bias - weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__) - # print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None) + weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__, n_cast, reversed) # create new object to replace the origin layer if replace_layer_cls is not None: - # print(f"RANK {os.environ['RANK']}: replace {getattr_(org_layer, layer_attr).__class__} to {replace_layer_cls}, shape is {weight.shape}") - if isinstance(getattr_(org_layer, layer_attr), nn.Linear): + if isinstance(getattr_(org_layer, layer_attr), (nn.Linear, Conv1D)): if replace_layer_cls.__name__ == "Linear1D_Row": replace_layer = replace_layer_cls(weight.shape[1], weight.shape[0], @@ -235,6 +243,8 @@ def bind_layer(self, model: nn.Module) -> None: model (:class:`torch.nn.Module`): The shard model """ binding_map = self.policy.binding_policy() + if binding_map is None: + return for k, v in binding_map.items(): param = getattr_(model, k) param = nn.Parameter(param) diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 26053b9f7408..6d35bd193fed 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -19,6 +19,8 @@ def slice_weight_bias( weight: torch.Tensor, bias: torch.Tensor, policy_layer_cls: Layer, + n_cast: int = None, + reversed: bool = False, ): r""" Slice the weight and bias according to policy layer cls @@ -33,13 +35,18 @@ def slice_weight_bias( """ if policy_layer_cls == Layer: return weight, bias - elif policy_layer_cls == Col_Layer: - weight = self.slice_tensor(weight, 1, False) + + dim = dim_mapping[policy_layer_cls] if not reversed else (1 - dim_mapping[policy_layer_cls]) + # print(weight.shape, dim) + if policy_layer_cls == Col_Layer: + weight = self.slice_tensor(weight, dim, False, n_cast) bias = self.slice_tensor(bias, 0, True) elif policy_layer_cls == Row_Layer: - weight = self.slice_tensor(weight, 0, False) + weight = self.slice_tensor(weight, dim, False, n_cast) else: raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported") + if reversed: + weight = weight.transpose(0, 1).contiguous() return weight, bias def slice_tensor( @@ -47,6 +54,7 @@ def slice_tensor( tensor_in: torch.Tensor, dim: int, is_bias: bool, + n_cast: int = None, ) -> torch.Tensor: r""" Slice tensor according to the config @@ -59,14 +67,15 @@ def slice_tensor( if tensor_in is None: return None if not is_bias: - return self.slice_2d(tensor_in, dim) + return self.slice_2d(tensor_in, dim, n_cast) else: - return self.slice_1d(tensor_in) + return self.slice_1d(tensor_in, n_cast) def slice_2d( self, tensor: torch.Tensor, dim: int, + n_cast: int = None, ) -> torch.Tensor: r""" Slice the 2D tensor @@ -77,13 +86,14 @@ def slice_2d( """ assert dim in [0, 1], f"Only support 2D tensor, but got {dim}D tensor" if dim == 0: - return self.slice_row(tensor) + return self.slice_row(tensor, n_cast) elif dim == 1: - return self.slice_col(tensor) + return self.slice_col(tensor, n_cast) def slice_1d( self, tensor: torch.Tensor, + n_cast: int = None, ) -> torch.Tensor: r""" Slice the 1D tensor @@ -94,11 +104,19 @@ def slice_1d( Returns: :class:`torch.Tensor`: The sliced tensor """ - return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() + if n_cast is None: + return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() + else: + tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) + chunk_list = [ + tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) + ] + return torch.cat(chunk_list, dim=0).contiguous() def slice_col( self, tensor: torch.Tensor, + n_cast: int = None, ) -> torch.Tensor: r""" Slice the tensor in column @@ -110,11 +128,19 @@ def slice_col( :class:`torch.Tensor`: The sliced tensor """ - return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() + if n_cast is None: + return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() + else: + tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) + chunk_list = [ + tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) + ] + return torch.cat(chunk_list, dim=0).contiguous() def slice_row( self, tensor: torch.Tensor, + n_cast: int = None, ) -> torch.Tensor: r""" Slice the tensor in column @@ -125,4 +151,11 @@ def slice_row( Returns: :class:`torch.Tensor`: The sliced tensor """ - return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() + if n_cast is None: + return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() + else: + tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=1) + chunk_list = [ + tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) + ] + return torch.cat(chunk_list, dim=1).contiguous() diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py index b896fd4a4020..e2d5a94c782a 100644 --- a/colossalai/shardformer/test/test.py +++ b/colossalai/shardformer/test/test.py @@ -6,24 +6,28 @@ from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm -from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, get_scheduler +from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, GPT2LMHeadModel, get_scheduler import colossalai from colossalai.shardformer.shard import ShardConfig, shard_model from colossalai.utils import get_current_device, print_rank_0 os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' -tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") def get_args(): parser = colossalai.get_default_parser() parser.add_argument("--mode", type=str, default='inference') parser.add_argument("--save_model", action='store_true') + parser.add_argument("--model", type=str, default='bert-base-uncased') return parser.parse_args() -def load_data(): +def load_data(args): + tokenizer = AutoTokenizer.from_pretrained(args.model) + if tokenizer.pad_token is None: + tokenizer.add_special_tokens({"pad_token": "[PAD]"}) + # tokenizer.pad_token_id = 0 datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') # datasets=load_dataset("yelp_review_full") tokenized_datasets = datasets.map( @@ -42,18 +46,23 @@ def load_data(): def inference(model: nn.Module, args): - tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + print(model) + # print(model.wte.weight.shape) + tokenizer = AutoTokenizer.from_pretrained(args.model) + if tokenizer.pad_token is None: + tokenizer.add_special_tokens({"pad_token": "[PAD]"}) + tokenizer.pad_token_id = 0 token = "Hello, my dog is cute" inputs = tokenizer(token, return_tensors="pt") inputs.to("cuda") model.eval() model.to("cuda") outputs = model(**inputs) - print(outputs) + print(outputs[0]) def train(model: nn.Module, args, num_epoch: int = 3): - train_dataloader, eval_dataloader = load_data() + train_dataloader, eval_dataloader = load_data(args) optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5) num_training = num_epoch * len(train_dataloader) progress_bar = tqdm(range(num_training)) @@ -94,8 +103,13 @@ def train(model: nn.Module, args, num_epoch: int = 3): if __name__ == "__main__": args = get_args() - model = BertForMaskedLM.from_pretrained("bert-base-uncased") colossalai.launch_from_torch(config=args.config) + if args.model == 'bert-base-uncased': + model = BertForMaskedLM.from_pretrained("bert-base-uncased") + elif args.model == 'gpt2': + model = GPT2LMHeadModel.from_pretrained("gpt2") + else: + raise AttributeError("model not supported") shard_config = ShardConfig( rank=int(str(get_current_device()).split(':')[-1]), world_size=int(os.environ['WORLD_SIZE']), From f1cb5ac6bf8fd57d2a70a3755f9798b11506c622 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Fri, 9 Jun 2023 14:36:54 +0800 Subject: [PATCH 356/413] [shardformer] Align bert value (#3907) * add bert align test, fix dist loss bug * forward and backward align * add ignore index * add shardformer CI * add gather_output optional for user in shardconfig * update readme with optional gather_ouput * add dist crossentropy loss test, remove unused files * remove unused file * remove unused file * rename the file * polish code --- colossalai/shardformer/README.md | 13 +- colossalai/shardformer/__init__.py | 1 + .../shardformer/layer/dist_crossentropy.py | 10 +- colossalai/shardformer/policies/bert.py | 5 +- colossalai/shardformer/shard/shard_config.py | 18 ++- colossalai/shardformer/shard/sharder.py | 4 +- colossalai/shardformer/test/config.py | 1 - colossalai/shardformer/test/module_test.py | 50 ------- colossalai/shardformer/test/test.py | 124 ------------------ .../test_model/test_shard_bert.py | 103 +++++++++++++++ .../test_module/test_distcrossentropy.py | 42 ++++++ 11 files changed, 174 insertions(+), 197 deletions(-) delete mode 100644 colossalai/shardformer/test/config.py delete mode 100644 colossalai/shardformer/test/module_test.py delete mode 100644 colossalai/shardformer/test/test.py create mode 100644 tests/test_shardformer/test_model/test_shard_bert.py create mode 100644 tests/test_shardformer/test_module/test_distcrossentropy.py diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 93a4f1e578e4..222626db3e9d 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -20,7 +20,7 @@ The sample API usage is given below: ``` python -from colossalai.shardformer import shard_model +from colossalai.shardformer import ShardConfig, shard_model from transformers import BertForMaskedLM # create huggingface model as normal @@ -28,7 +28,12 @@ model = BertForMaskedLM.from_pretrained("bert-base-uncased") # make the huggingface model paralleled to ShardModel # auto policy: -sharded_model = shard_model(model) +shardconfig = ShardConfig( + rank=rank, + world_size=world_size, + gather_output=True, +) +sharded_model = shard_model(model, config=shardconfig) # custom policy: from xxx import @@ -72,7 +77,7 @@ More details can be found in shardformer/policies/basepolicy.py ``` python from colossalai.shardformer.policies.basepolicy import Policy, Layer, Col_Layer, Row_Layer, Argument -CustomPolicy(Policy): +class CustomPolicy(Policy): @staticmethod def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]: r""" @@ -235,7 +240,7 @@ CustomPolicy(Policy): This class is used to specify the replacement policy for a particular layer. If `replace_layer` is None, only parameter partitioning will be performed without replacing the layer class. CLASS `Col_Layer(Layer)`: - - gather_output (bool): Whether to gather the output of the layer + - gather_output (bool): Whether the output of this layer can be gathered, like the last layer can be gathered, but most of the time, the intermediate layers of the model do not need to be gathered. This class inherited from `Layer`, representing the layer will be sliced along column. diff --git a/colossalai/shardformer/__init__.py b/colossalai/shardformer/__init__.py index e69de29bb2d1..50c92738077a 100644 --- a/colossalai/shardformer/__init__.py +++ b/colossalai/shardformer/__init__.py @@ -0,0 +1 @@ +from .shard import ShardConfig, shard_model diff --git a/colossalai/shardformer/layer/dist_crossentropy.py b/colossalai/shardformer/layer/dist_crossentropy.py index 1869594670ce..05c04bb545c1 100644 --- a/colossalai/shardformer/layer/dist_crossentropy.py +++ b/colossalai/shardformer/layer/dist_crossentropy.py @@ -14,7 +14,7 @@ class DistCrossEntropy(Function): """ @staticmethod - def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor): + def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor, ignore_index: int): r""" Calculate the cross entropy loss before gather, the origin loss function is as follows: loss = -log(exp(x[class])/sum(exp(x[i])) @@ -75,8 +75,8 @@ def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor): # calculate the loss # loss = log(sum(exp(x[i]))) - x[class] - loss = torch.log(sum_exp_logits) - pred_logits - loss = torch.sum(loss).div_(loss.numel()) + loss = torch.where(target == ignore_index, 0.0, torch.log(sum_exp_logits) - pred_logits) + loss = torch.sum(loss).div_(torch.sum(loss != 0.0)) # caculate the softmax exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1)) @@ -101,5 +101,5 @@ def backward(ctx, grad_output): return grad_logits, None, None -def applyDistCrossEntropy(vocab_logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: - return DistCrossEntropy.apply(vocab_logits, labels) +def applyDistCrossEntropy(vocab_logits: torch.Tensor, labels: torch.Tensor, ignore_index: int = -100) -> torch.Tensor: + return DistCrossEntropy.apply(vocab_logits, labels, ignore_index) diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 89b32f065c27..5d489f41986c 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -141,7 +141,7 @@ def unembedding() -> List: weight="decoder.weight", bias="decoder.bias", replace_layer=col_nn.Linear1D_Col, - # gather_output=True, + gather_output=True, ) ] @@ -155,7 +155,8 @@ class BertForMaskedLMPolicy(BertPolicy): @staticmethod def inject_policy() -> Tuple[nn.Module, nn.Module]: - return (BertForMaskedLM, BertForMaskedLM_) + # return (BertForMaskedLM, BertForMaskedLM_) + return None class BertForSequenceClassificationPolicy(BertPolicy): diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 4cf9162b9548..e8d6f3408c76 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -5,16 +5,14 @@ @dataclass class ShardConfig: - """ - The config for sharding the huggingface model for test + r""" + The config for sharding the huggingface model + + Args: + rank (int): The rank of local process + world_size (int): The world size of the distributed process + gather_output (bool): Whether to gather the output of the model of the last layer """ rank: int - fp16: bool = True - num_gpus: int = 2 world_size: int = 2 - backend = "nccl" - verbose: str = 'simple' - seed: int = None - require_grad: bool = False - master_addr: str = "127.0.0.1" - master_port: int = 29500 + gather_output: bool = True diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 1ada75e06b67..159bebccd02d 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -65,6 +65,8 @@ def inject_model( BertForMaskedLM.forward -> BertForMaskedLM_.forward """ inject_policy = self.policy.inject_policy() + if inject_policy is None: + return if inject_policy is None: return @@ -148,7 +150,7 @@ def shard_one_layer( n_cast = policy_layer.n_cast reversed = policy_layer.reversed if policy_layer.__class__.__name__ == "Col_Layer": - gather_output = policy_layer.gather_output + gather_output = policy_layer.gather_output and self.shard_config.gather_output if weight_attr is not None: if hasattr_(org_layer, weight_attr): diff --git a/colossalai/shardformer/test/config.py b/colossalai/shardformer/test/config.py deleted file mode 100644 index 2b80d8b3ca12..000000000000 --- a/colossalai/shardformer/test/config.py +++ /dev/null @@ -1 +0,0 @@ -parallel = dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')) diff --git a/colossalai/shardformer/test/module_test.py b/colossalai/shardformer/test/module_test.py deleted file mode 100644 index 83dc7ec6cf4a..000000000000 --- a/colossalai/shardformer/test/module_test.py +++ /dev/null @@ -1,50 +0,0 @@ -import os - -import torch -import torch.nn as nn -import torch.nn.functional as F - -import colossalai -from colossalai.shardformer.layer.dist_crossentropy import applyDistCrossEntropy -from colossalai.shardformer.layer.dropout import Dropout1D - - -def get_args(): - parser = colossalai.get_default_parser() - parser.add_argument("--module", type=str, default='distloss') - return parser.parse_args() - - -def test_dist_crossentropy(): - pred = torch.randn(2, 4, 8, requires_grad=True) - labels = torch.randint(8, (1, 4)).repeat(2, 1) - - pred_ = pred.view(-1, 8) - labels_ = labels.view(-1) - loss = F.cross_entropy(pred_, labels_) - loss.backward() - print(f"normal loss:{loss}") - - pred = pred.chunk(int(os.environ['WORLD_SIZE']), -1)[int(os.environ['RANK'])] - loss = applyDistCrossEntropy(pred.to('cuda'), labels.to('cuda')) - loss.backward() - print(f"dist loss:{loss}") - - -def test_dropout(): - input = torch.randn(5, 4).to("cuda") - m = Dropout1D(p=0.2).to("cuda") - for i in range(2): - print(f"Output: {m(input)}") - print(torch.randn(1)) - - -if __name__ == '__main__': - args = get_args() - colossalai.launch_from_torch(config={}) - if args.module == 'distloss': - test_dist_crossentropy() - elif args.module == 'dropout': - test_dropout() - else: - print("not implemented yet") diff --git a/colossalai/shardformer/test/test.py b/colossalai/shardformer/test/test.py deleted file mode 100644 index e2d5a94c782a..000000000000 --- a/colossalai/shardformer/test/test.py +++ /dev/null @@ -1,124 +0,0 @@ -import os -import random - -import torch -import torch.nn as nn -from datasets import load_dataset -from torch.utils.data import DataLoader -from tqdm.auto import tqdm -from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, GPT2LMHeadModel, get_scheduler - -import colossalai -from colossalai.shardformer.shard import ShardConfig, shard_model -from colossalai.utils import get_current_device, print_rank_0 - -os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' - - -def get_args(): - parser = colossalai.get_default_parser() - parser.add_argument("--mode", type=str, default='inference') - parser.add_argument("--save_model", action='store_true') - parser.add_argument("--model", type=str, default='bert-base-uncased') - return parser.parse_args() - - -def load_data(args): - tokenizer = AutoTokenizer.from_pretrained(args.model) - if tokenizer.pad_token is None: - tokenizer.add_special_tokens({"pad_token": "[PAD]"}) - # tokenizer.pad_token_id = 0 - datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') - # datasets=load_dataset("yelp_review_full") - tokenized_datasets = datasets.map( - lambda examples: tokenizer(examples["text"], truncation=True, padding="max_length"), batched=True) - tokenized_datasets = tokenized_datasets.remove_columns(["text"]) - # tokenized_datasets=tokenized_datasets.rename_column("label","labels") - tokenized_datasets.set_format("torch") - - train_dataset = tokenized_datasets["train"] - test_dataset = tokenized_datasets["test"] - - datacollector = DataCollatorForLanguageModeling(tokenizer, mlm=True, mlm_probability=0.15, return_tensors="pt") - train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=True, collate_fn=datacollector) - eval_dataloader = DataLoader(test_dataset, batch_size=16, shuffle=True, collate_fn=datacollector) - return train_dataloader, eval_dataloader - - -def inference(model: nn.Module, args): - print(model) - # print(model.wte.weight.shape) - tokenizer = AutoTokenizer.from_pretrained(args.model) - if tokenizer.pad_token is None: - tokenizer.add_special_tokens({"pad_token": "[PAD]"}) - tokenizer.pad_token_id = 0 - token = "Hello, my dog is cute" - inputs = tokenizer(token, return_tensors="pt") - inputs.to("cuda") - model.eval() - model.to("cuda") - outputs = model(**inputs) - print(outputs[0]) - - -def train(model: nn.Module, args, num_epoch: int = 3): - train_dataloader, eval_dataloader = load_data(args) - optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5) - num_training = num_epoch * len(train_dataloader) - progress_bar = tqdm(range(num_training)) - lr_scheduler = get_scheduler(name="linear", - optimizer=optimizer, - num_warmup_steps=0, - num_training_steps=num_training) - best_test_loss = float("inf") - model.to("cuda") - model.train() - for epoch in range(num_epoch): - progress_bar.set_description(f"Rank {get_current_device()} epoch {epoch}") - for batch in train_dataloader: - optimizer.zero_grad() - batch = {k: v.to('cuda') for k, v in batch.items()} - outputs = model(**batch) - loss = outputs.loss - loss.backward() - optimizer.step() - lr_scheduler.step() - progress_bar.update(1) - train_loss = loss - - loss = 0.0 - for batch in eval_dataloader: - batch = {k: v.to('cuda') for k, v in batch.items()} - outputs = model(**batch) - # loss = outputs.loss - assert not torch.isnan(outputs.loss), f"{batch}" - loss += outputs.loss.item() - # loss = criterion(outputs.logits, batch["input_ids"]) - test_loss = loss / len(eval_dataloader) - print_rank_0(f"Train Loss: {train_loss:.4f} Test Loss:{test_loss:.4f}") - if args.save_model and test_loss < best_test_loss: - best_test_loss = test_loss - torch.save(model.state_dict(), "./checkpoints/best_model.pth") - - -if __name__ == "__main__": - args = get_args() - colossalai.launch_from_torch(config=args.config) - if args.model == 'bert-base-uncased': - model = BertForMaskedLM.from_pretrained("bert-base-uncased") - elif args.model == 'gpt2': - model = GPT2LMHeadModel.from_pretrained("gpt2") - else: - raise AttributeError("model not supported") - shard_config = ShardConfig( - rank=int(str(get_current_device()).split(':')[-1]), - world_size=int(os.environ['WORLD_SIZE']), - ) - sharded_model = shard_model(model, shard_config) - - if args.mode == "train": - train(sharded_model, args) - elif args.mode == "inference": - inference(sharded_model, args) - else: - raise NotImplementedError diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py new file mode 100644 index 000000000000..55b78d040505 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -0,0 +1,103 @@ +import os +import random + +import pytest +import torch +from transformers import AutoTokenizer, BertConfig, BertForMaskedLM + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.shardformer.shard import ShardConfig, shard_model +from colossalai.testing import rerun_if_address_is_in_use, spawn + +os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' +CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) +tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + + +def build_model(rank, world_size): + config = BertConfig.from_pretrained('bert-base-uncased') + config.hidden_dropout_prob = 0 + config.attention_probs_dropout_prob = 0 + + org_model = BertForMaskedLM.from_pretrained('bert-base-uncased', config=config).to('cuda') + + shardconfig = ShardConfig( + rank=rank, + world_size=world_size, + gather_output=True, + ) + sharded_model = shard_model(BertForMaskedLM.from_pretrained('bert-base-uncased', config=config), + shardconfig).to('cuda') + + return org_model, sharded_model + + +def check_forward(org_model, sharded_model): + input = 'Hello, my dog is cute' + tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') + + #orgin model + org_model.eval() + org_out = org_model(**tokenized_input) + + #shard model + sharded_model.eval() + shard_out = sharded_model(**tokenized_input) + + assert torch.allclose( + org_out[0], shard_out[0], + atol=1e-5), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}" + + +def check_backward(org_model, sharded_model): + # prepare input + input = 'Hello, my dog is cute' + tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') + labels = tokenized_input['input_ids'].clone() + labels[labels == tokenizer.pad_token_id] = -100 + tokenized_input['labels'] = labels + + #orgin model + org_model.train() + org_out = org_model(**tokenized_input) + org_loss = org_out.loss + org_loss.backward() + org_grad = org_model.bert.encoder.layer[0].attention.self.query.weight.grad + + #shard model + sharded_model.train() + shard_out = sharded_model(**tokenized_input) + shard_loss = shard_out.loss + shard_loss.backward() + shard_grad = sharded_model.bert.encoder.layer[0].attention.self.query.weight.grad + + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + + +def check_bert(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + org_model, sharded_model = build_model(rank, world_size) + check_forward(org_model, sharded_model) + check_backward(org_model, sharded_model) + + torch.cuda.empty_cache() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_bert(): + spawn(check_bert, 2) + + +if __name__ == "__main__": + test_bert() diff --git a/tests/test_shardformer/test_module/test_distcrossentropy.py b/tests/test_shardformer/test_module/test_distcrossentropy.py new file mode 100644 index 000000000000..9a19ec57821d --- /dev/null +++ b/tests/test_shardformer/test_module/test_distcrossentropy.py @@ -0,0 +1,42 @@ +import pytest +import torch +import torch.nn.functional as F + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.shardformer.layer.dist_crossentropy import applyDistCrossEntropy +from colossalai.testing import rerun_if_address_is_in_use, spawn + +CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) + + +def check_dist_crossentropy(rank, world_size, port, ignore_index): + disable_existing_loggers() + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, port=port, host='localhost', backend='nccl') + + # prepare data + pred = torch.randn(2, 4, 8, requires_grad=True) + labels = torch.randint(8, (2, 4)) + # set some label to -100 to test the ignore index + labels[0, -1] = ignore_index + + org_pred = pred.view(-1, 8) + org_labels = labels.view(-1) + org_loss = F.cross_entropy(org_pred, org_labels) + + dist_pred = pred.chunk(world_size, -1)[rank] + dist_loss = applyDistCrossEntropy(dist_pred.to('cuda'), labels.to('cuda'), ignore_index=ignore_index) + + assert torch.allclose(org_loss, dist_loss, + atol=1e-5), f"dist cross entropy loss is not equal to orgin loss\n{org_loss}\n{dist_loss}" + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_dist_crossentropy(): + ignore_index = -100 + spawn(check_dist_crossentropy, 2, ignore_index=ignore_index) + + +if __name__ == '__main__': + test_dist_crossentropy() From a73130482df257e5efd7bdc88435bad0578cb5e4 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Mon, 12 Jun 2023 13:56:09 +0800 Subject: [PATCH 357/413] [shardformer] Unit test (#3928) * fix bug in slicer, add slicer unit test * add dropout test * use pid as dropout seed * updata dropout test with local pattern * ad todo --- colossalai/shardformer/layer/dropout.py | 4 +- colossalai/shardformer/shard/slicer.py | 16 ++-- .../test_module/test_dropout.py | 51 ++++++++++++ .../test_module/test_slicer.py | 78 +++++++++++++++++++ 4 files changed, 139 insertions(+), 10 deletions(-) create mode 100644 tests/test_shardformer/test_module/test_dropout.py create mode 100644 tests/test_shardformer/test_module/test_slicer.py diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py index acc114029ac1..0f653a9be780 100644 --- a/colossalai/shardformer/layer/dropout.py +++ b/colossalai/shardformer/layer/dropout.py @@ -1,5 +1,4 @@ import os -import time from contextlib import contextmanager import torch @@ -14,7 +13,8 @@ class SeedManager: def __init__(self): original_state = torch.cuda.get_rng_state() - seed = int(f"{int(time.time())}{os.environ['RANK']}") + # TODO: unify this seed manager with the colossalai.context.random + seed = os.getpid() torch.cuda.manual_seed(int(seed)) self.dropout_state = torch.cuda.get_rng_state() torch.cuda.set_rng_state(original_state) diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 6d35bd193fed..09e3219f87a2 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -3,7 +3,7 @@ from ..policies.basepolicy import Col_Layer, Layer, Row_Layer from .shard_config import ShardConfig -dim_mapping = {Col_Layer: 1, Row_Layer: 0} +dim_mapping = {Col_Layer: 0, Row_Layer: 1} class Slicer(): @@ -40,7 +40,7 @@ def slice_weight_bias( # print(weight.shape, dim) if policy_layer_cls == Col_Layer: weight = self.slice_tensor(weight, dim, False, n_cast) - bias = self.slice_tensor(bias, 0, True) + bias = self.slice_tensor(bias, 0, True, n_cast) elif policy_layer_cls == Row_Layer: weight = self.slice_tensor(weight, dim, False, n_cast) else: @@ -129,13 +129,13 @@ def slice_col( """ if n_cast is None: - return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() + return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() else: - tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) + tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=1) chunk_list = [ tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) ] - return torch.cat(chunk_list, dim=0).contiguous() + return torch.cat(chunk_list, dim=1).contiguous() def slice_row( self, @@ -152,10 +152,10 @@ def slice_row( :class:`torch.Tensor`: The sliced tensor """ if n_cast is None: - return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() + return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() else: - tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=1) + tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) chunk_list = [ tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) ] - return torch.cat(chunk_list, dim=1).contiguous() + return torch.cat(chunk_list, dim=0).contiguous() diff --git a/tests/test_shardformer/test_module/test_dropout.py b/tests/test_shardformer/test_module/test_dropout.py new file mode 100644 index 000000000000..4a13eb61c1fc --- /dev/null +++ b/tests/test_shardformer/test_module/test_dropout.py @@ -0,0 +1,51 @@ +import pytest +import torch +import torch.nn.functional as F + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.shardformer.layer.dropout import Dropout1D +from colossalai.testing import rerun_if_address_is_in_use, spawn + +CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) + + +def check_dropout(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, port=port, host='localhost', backend='nccl') + + # prepare data + input = torch.randn(5, 4).to('cuda') + dropout = Dropout1D(p=0.4).to('cuda') + output_list = [] + # compare the dropout pattern in each device + for i in range(2): + output = dropout(input) + output_list.append(output) + dist_output_list = [torch.zeros(*output.shape).to('cuda') for _ in range(world_size)] + torch.distributed.all_gather(dist_output_list, output) + for j in range(world_size): + for k in range(world_size): + if j != k: + mask = torch.eq(dist_output_list[j], 0.0) == torch.eq(dist_output_list[k], 0.0) + assert torch.all( + mask + ) == False, f"The dropout pattern in each device is not unique\n{dist_output_list[j]}\n{dist_output_list[k]}" + # compare the dropout pattern in loacl device + for i in range(len(output_list)): + for j in range(len(output_list)): + if i != j: + mask = torch.eq(output_list[i], 0.0) == torch.eq(output_list[j], 0.0) + assert torch.all( + mask + ) == False, f"The dropout pattern in one device is not unique\n{output_list[i]}\n{output_list[j]}" + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_dropout(): + spawn(check_dropout, 2) + + +if __name__ == '__main__': + test_dropout() diff --git a/tests/test_shardformer/test_module/test_slicer.py b/tests/test_shardformer/test_module/test_slicer.py new file mode 100644 index 000000000000..c72a0357573b --- /dev/null +++ b/tests/test_shardformer/test_module/test_slicer.py @@ -0,0 +1,78 @@ +import pytest +import torch +import torch.nn.functional as F + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.shardformer.policies.basepolicy import Col_Layer, Layer, Row_Layer +from colossalai.shardformer.shard.shard_config import ShardConfig +from colossalai.shardformer.shard.slicer import Slicer +from colossalai.testing import rerun_if_address_is_in_use, spawn + +CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) + + +def check_slicer(rank, world_size, port, in_feature, out_feature): + disable_existing_loggers() + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, port=port, host='localhost', backend='nccl') + # initialize slicer + shardconfig = ShardConfig(rank=rank, world_size=world_size) + slicer = Slicer(shardconfig) + # initialize test data + weight = torch.randn(in_feature, out_feature) + bias = torch.randn(out_feature) + policy_layer_cls_list = [Layer, Col_Layer, Row_Layer] + n_cast_list = [None, 2, 3, 4] + # weight and bias + for n_cast in n_cast_list: + sliced_weight, sliced_bias = slicer.slice_weight_bias(weight, bias, policy_layer_cls=Layer, n_cast=n_cast) + expected_sliced_weight = weight + expected_sliced_bias = bias + assert torch.equal( + sliced_weight, expected_sliced_weight + ), f"In Layer case, weight: sliced_weight is not equal to expected_sliced_weight\norg:{weight}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" + assert torch.equal( + sliced_bias, expected_sliced_bias + ), f"In Layer case, bias: sliced_bias is not equal to expected_sliced_bias\norg:{bias}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" + + sliced_weight, sliced_bias = slicer.slice_weight_bias(weight, bias, policy_layer_cls=Col_Layer, n_cast=n_cast) + if (n_cast is None): + expected_sliced_weight = weight.chunk(world_size, dim=0)[rank] + expected_sliced_bias = bias.chunk(world_size)[rank] + else: + chunks = weight.chunk(world_size * n_cast, dim=0) + expected_sliced_weight = torch.cat([chunks[i] for i in range(rank, n_cast * world_size, world_size)], dim=0) + chunks = bias.chunk(world_size * n_cast, dim=0) + expected_sliced_bias = torch.cat([chunks[i] for i in range(rank, n_cast * world_size, world_size)]) + assert torch.equal( + sliced_weight, expected_sliced_weight + ), f"In Col_Layer {n_cast} cast case, weight: sliced_weight is not equal to expected_sliced_weight\norg:{weight}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" + assert torch.equal( + sliced_bias, expected_sliced_bias + ), f"In Col_Layer {n_cast} cast case, bias: sliced_bias is not equal to expected_sliced_bias\norg:{bias}\nsliced:{sliced_bias}\nexpected:{expected_sliced_bias}" + + sliced_weight, sliced_bias = slicer.slice_weight_bias(weight, bias, policy_layer_cls=Row_Layer, n_cast=n_cast) + if (n_cast is None): + expected_sliced_weight = weight.chunk(world_size, dim=1)[rank] + expected_sliced_bias = bias + else: + chunks = weight.chunk(world_size * n_cast, dim=1) + expected_sliced_weight = torch.cat([chunks[i] for i in range(rank, n_cast * world_size, world_size)], dim=1) + expected_sliced_bias = bias + assert torch.equal( + sliced_weight, expected_sliced_weight + ), f"In Row_Layer {n_cast} cast case, weight: sliced_weight is not equal to expected_sliced_weight\norg:{weight}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" + assert torch.equal( + sliced_bias, expected_sliced_bias + ), f"In Row_Layer {n_cast} cast case, bias: sliced_bias is not equal to expected_sliced_bias\norg:{bias}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_slicer(): + args = dict(in_feature=24, out_feature=48) + spawn(check_slicer, nprocs=2, in_feature=args['in_feature'], out_feature=args['out_feature']) + + +if __name__ == '__main__': + test_slicer() From 45927d552746c1e02c9a860ca5375a5eb3facda4 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Mon, 12 Jun 2023 16:52:18 +0800 Subject: [PATCH 358/413] [shardformer] Add dropout layer in shard model and refactor policy api (#3949) * add dist dropout in model * update docstring and bert policy with dropout * refactor basepolicy and sharded, update bert * update format * update gpt2 policy * update bert policy * remove unused code * update readme for new policy usage --- colossalai/shardformer/README.md | 80 +++++----- colossalai/shardformer/policies/basepolicy.py | 68 +++++---- colossalai/shardformer/policies/bert.py | 139 ++++++++++-------- colossalai/shardformer/policies/gpt2.py | 40 +++-- colossalai/shardformer/shard/sharder.py | 108 +++++++------- colossalai/shardformer/shard/slicer.py | 4 +- colossalai/shardformer/utils/utils.py | 2 +- 7 files changed, 255 insertions(+), 186 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 222626db3e9d..b8357c203939 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -55,7 +55,7 @@ colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py ## 💡 Policy -If you wanna parallel the model in a custom way, just overwrite the policy class for the Hugging Face model. +If you wanna parallel the model in a custom way, just overwrite the policy class for the Hugging Face model. Please refer to any policy that we have pre-established, like [bert policy](./policies/bert.py) or [gpt2 policy](./policies/gpt2.py). You should do: @@ -68,7 +68,7 @@ You should do: - Shardformer will inject the model according to this method. If you need to modify the forward or backward progress (like distributed corssentropy loss in Bert) you need to overwrite this method. 4. Overwrite or add the param functions - These functions use a suffix to record the path of weight or bias for the layer. - - The return is a list contains some `Col_Layer` or `Row_Layer` objects, which means slice along col and row respectively. + - The return is a list contains some `Col_Layer`, `Row_Layer` or `Dropout_Layer` objects, which means slice along col and row respectively or as dropout layer, refer to CLASS `Layer` for more details. 5. Overwrite `binding_policy` (Optional) - Overwrite to specify Shardformer will bind some weight between layers, like embedding and unembedding layers. - This function will return a dict, the key and value are the suffix of weight need to be binded. @@ -123,7 +123,7 @@ class CustomPolicy(Policy): raise NotImplementedError @staticmethod - def inject_policy() -> Tuple[nn.Module, nn.Module]: + def inject_policy() -> Union[Tuple[nn.Module, nn.Module], None]: r""" Return the dict for the inject model @@ -133,12 +133,12 @@ class CustomPolicy(Policy): (OrignModel, CustomModel) in `CustomModel`, we can overwrite the forward and backward process """ - return () + return None @staticmethod - def binding_policy() -> Dict: + def binding_policy() -> Union[Dict[str, str], None]: r""" - Return the dict for the binding model + Return the dict for the binding model, None means no need to bind Return: This method should return the binding relationship for some layers share the weight or bias, @@ -148,69 +148,70 @@ class CustomPolicy(Policy): "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", } """ - return NotImplementedError + return None @staticmethod - def attn_in() -> List: - """ + def attn_in() -> Union[List, None]: + r""" Attention qkv layer + In this kind of method, we should return the list of ``Layer`` object, each ``Layer`` object should be + ``Layer`` for no slicing, ``Col_Layer`` for col slicing, ``Row_Layer`` for row slicing. And the parameters + in ``Layer`` object can refer to the ``Layer`` class. Returns: List[Layer]: List of layer object, each layer is the new """ - return NotImplementedError + return None @staticmethod - def attn_out() -> List: - """ + def attn_out() -> Union[List, None]: + r""" Attention output projection layer Returns: List[Layer]: List of layer object """ - return NotImplementedError + return None @staticmethod - def mlp_in() -> List: - """ + def mlp_in() -> Union[List, None]: + r""" h -> 4h mlp layer Returns: List[Layer]: List of layer object """ - return NotImplementedError + return None @staticmethod - def mlp_out() -> List: - """ + def mlp_out() -> Union[List, None]: + r""" 4h -> h mlp layer Returns: List[Layer]: List of layer object """ - return NotImplementedError + return None @staticmethod - def embedding() -> List: - """ + def embedding() -> Union[List, None]: + r""" Partially slice the embedding layer - vocab_size->vocab_size//gpu_nums Return: List[Layer]: List of layer object """ - return NotImplementedError + return None @staticmethod - def unembedding() -> List: - """ - Partially slice the embedding layer - vocab_size->vocab_size//gpu_nums + def unembedding() -> Union[List, None]: + r""" + Partially slice the embedding layer, None means there is no unembedding layer Return: List[Layer]: List of layer object """ - return NotImplementedError + return None ``` @@ -232,21 +233,26 @@ class CustomPolicy(Policy): - CLASS `Layer`: Parameters: - - weight (str): The weight suffix of the layer - - bias (str): The bias suffix of the layer + - suffix: (str): the suffix of the layer to indicate the attribute of the layer. - replace_layer (:class:`colosalai.nn`): The layer to replace the original layer - ignore (bool): Whether to ignore this layer if it is not in the model + - reversed (bool): Whether the weight in layer is reversed, commonly the weight in `torch.nn.Linear` is [out, in], but in GPT2 `Conv1D` layer is [in, out] which is reversed. + - n_cast (int): The number of weight will cast to, like q, k, v in attention layer, n_cast should be 3. commonly in TP, we just chunk the weight with the number of devices, but in multi-head attention, we need to chunk the weight with the number of $ devices * n\_head $, and each device should have a part of Q, K and V weight. - This class is used to specify the replacement policy for a particular layer. If `replace_layer` is None, only parameter partitioning will be performed without replacing the layer class. + This class is a base class used to specify the replacement policy and the suffix the layer for a particular layer. CLASS `Col_Layer(Layer)`: + - weight (str): The weight suffix of the layer + - bias (str): The bias suffix of the layer - gather_output (bool): Whether the output of this layer can be gathered, like the last layer can be gathered, but most of the time, the intermediate layers of the model do not need to be gathered. - This class inherited from `Layer`, representing the layer will be sliced along column. + This class inherited from `Layer`, representing the layer will be sliced along colum and indicate the attributes of weight and bias. Setting `bias` to `None` means ignoring bias, regardless of whether or not it originally exists. CLASS `Row_Layer(Layer)`: + - weight (str): The weight suffix of the layer + - bias (str): The bias suffix of the layer - This class inherited from `Layer`, representing the layer will be sliced along row. + This class inherited from `Layer`, representing the layer will be sliced along row. Just like `Col_Layer` but in tensor parrallel, there is no need to gather the output of layer sliced by row. - CLASS `Policy`: @@ -254,29 +260,37 @@ class CustomPolicy(Policy): - `Policy.attn_in()/attn_out()/mlp_in()/mlp_out()/embedding()/unembedding()`...... These functions define the partitioning methods of the parameters at different locations in the model. Each function returns a list of objects of Layer class that specify the replacement approach for these parameters. Shardformer also supports user-defined functions for modifying their models, in addition to the listed functions. + - `Policy.argument_policy()` In this function, the user should use multiple dict to define which class of layers will require replacement. This includes the attributes and parameters that need to be modified or replaced. Attributes are stored in the form of a "suffix-string: value" dict, while parameters are stored via multiple static methods that return the replacement approach. + - `Policy.inject_policy()` This function will return the injected model to replace the original model. The new model should be a nn.Module class which includes modified forward or backward functions or anything else. + - `Policy.binding_policy()` This function will return the weight sharing information in the model in some dict. The key and value are both the suffixes of the shared parameters. + - CLASS `ModelSharder(model, policy)`: This class helps shard the model, the parameter is the created transformers model and the custom policy. If custom policy is None, shardformer will automatically get already defined policy for the model. + - `ModelShard.inject_model()` This function is used to inject the model to modify the forward and backward progress. + - `ModelShard.replace_layer()` This function is used to replace the original layers with colossalai layer to make them paralleled and can do distributed communication. + - `ModelShard.bind_layer()` This function is used to help different layers share weight or bias. + - CLASS `Slicer`: This class is used to slice tensor according to policy. diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 644d115a270e..d55df59fdc8b 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -1,7 +1,7 @@ # part of code modified from https://github.com/tunib-ai/parallelformers from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Tuple, Type +from typing import Any, Callable, Dict, List, Tuple, Union import torch.nn as nn @@ -25,8 +25,7 @@ class Layer: The layer object for the policy Args: - weight (str): The weight suffix of the layer - bias (str): The bias suffix of the layer + suffix: (str): the suffix of the layer. replace_layer (:class:`colosalai.nn`): The layer to replace the original layer ignore (bool): Whether to ignore this layer if it is not in the model reversed (bool): Whether the weight in layer is reversed, commonly the weight in `torch.nn.Linear` is [out, in], @@ -35,8 +34,7 @@ class Layer: but in multi-head attention, we need to chunk the weight with the number of devices * n_head, and each device should have a part of Q, K and V weight. """ - weight: str = None - bias: str = None + suffix: str = None replace_layer: Any = None ignore: bool = False reversed: bool = False @@ -46,20 +44,40 @@ class Layer: @dataclass class Col_Layer(Layer): r""" - Class for col shard layer in MegatronLM + Class for col shard layer in tensor parrallel Args: + weight (str): The weight suffix of the layer + bias (str): The bias suffix of the layer gather_output (bool): Whether to gather the output of the layer """ + weight: str = None + bias: str = None gather_output: bool = False @dataclass class Row_Layer(Layer): r""" - Class for col shard layer in MegatronLM + Class for col shard layer in tensor parrallel + + Args: + weight (str): The weight suffix of the layer + bias (str): The bias suffix of the layer """ - pass + weight: str = None + bias: str = None + + +@dataclass +class Dropout_Layer(Layer): + r""" + Class for dropout layer in tensor parrallel + + Args: + p (str): The dropout rate suffix of the layer + """ + p: str = None class Policy(): @@ -82,14 +100,14 @@ class for the example. """ @staticmethod - def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]: + def argument_policy(model_config, world_size: int) -> Dict[nn.Module, Argument]: r""" Return the dict for the modify policy, the key is the original layer class and the value is the argument for the modify layer Args: model_config (:class:`tansformer.Config`): The config of transformer model - shard_config (:class:`ShardConfig`): The config for sharding model + world_size (int)): The world size of sharding model Return: Dict for the modify policy, @@ -126,7 +144,7 @@ def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument raise NotImplementedError @staticmethod - def inject_policy() -> Tuple[nn.Module, nn.Module]: + def inject_policy() -> Union[Tuple[nn.Module, nn.Module], None]: r""" Return the dict for the inject model @@ -139,9 +157,9 @@ def inject_policy() -> Tuple[nn.Module, nn.Module]: return None @staticmethod - def binding_policy() -> Dict: + def binding_policy() -> Union[Dict[str, str], None]: r""" - Return the dict for the binding model + Return the dict for the binding model, None means no need to bind Return: This method should return the binding relationship for some layers share the weight or bias, @@ -154,7 +172,7 @@ def binding_policy() -> Dict: return None @staticmethod - def attn_in() -> List: + def attn_in() -> Union[List, None]: r""" Attention qkv layer In this kind of method, we should return the list of ``Layer`` object, each ``Layer`` object should be @@ -164,52 +182,52 @@ def attn_in() -> List: Returns: List[Layer]: List of layer object, each layer is the new """ - return NotImplementedError + return None @staticmethod - def attn_out() -> List: + def attn_out() -> Union[List, None]: r""" Attention output projection layer Returns: List[Layer]: List of layer object """ - return NotImplementedError + return None @staticmethod - def mlp_in() -> List: + def mlp_in() -> Union[List, None]: r""" h -> 4h mlp layer Returns: List[Layer]: List of layer object """ - return NotImplementedError + return None @staticmethod - def mlp_out() -> List: + def mlp_out() -> Union[List, None]: r""" 4h -> h mlp layer Returns: List[Layer]: List of layer object """ - return NotImplementedError + return None @staticmethod - def embedding() -> List: + def embedding() -> Union[List, None]: r""" Partially slice the embedding layer Return: List[Layer]: List of layer object """ - return NotImplementedError + return None @staticmethod - def unembedding() -> List: + def unembedding() -> Union[List, None]: r""" - Partially slice the embedding layer + Partially slice the embedding layer, None means there is no unembedding layer Return: List[Layer]: List of layer object diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 5d489f41986c..67e910d521e9 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -5,7 +5,7 @@ import colossalai.shardformer.layer.layers as col_nn -from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer +from .basepolicy import Argument, Col_Layer, Dropout_Layer, Policy, Row_Layer class BertPolicy(Policy): @@ -28,123 +28,126 @@ def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: Argument( attr_dict={ # 1. shard vocab size - # "word_embeddings.num_embeddings": config.vocab_size // world_size, - # 2. add the size of the sliced embedding layer excluding the last slice "word_embeddings.dim_size": (config.vocab_size + world_size - 1) // world_size, }, param_funcs=[ BertPolicy.embedding, ]), - BertLMPredictionHead: - Argument( - attr_dict={ - # 1. shard vocab size - # "word_embeddings.num_embeddings": config.vocab_size // world_size, - # 2. add the size of the sliced embedding layer excluding the last slice - }, - param_funcs=[ - BertPolicy.unembedding, - ]) } @staticmethod - def binding_policy() -> Dict: + def binding_policy(): return { "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", } @staticmethod - def attn_in() -> List: + def attn_in(): return [ Col_Layer( - weight="attention.self.query.weight", - bias="attention.self.query.bias", + suffix="attention.self.query", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Col, ), Col_Layer( - weight="attention.self.key.weight", - bias="attention.self.key.bias", + suffix="attention.self.key", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Col, ), Col_Layer( - weight="attention.self.value.weight", - bias="attention.self.value.bias", + suffix="attention.self.value", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Col, ), + Dropout_Layer( + suffix="attention.self.dropout", + p="p", + replace_layer=col_nn.Dropout1D, + ), Col_Layer( - weight="crossattention.self.query.weight", - bias="crossattention.self.query.bias", + suffix="crossattention.self.query", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Col, ignore=True, ), Col_Layer( - weight="crossattention.self.key.weight", - bias="crossattention.self.key.bias", + suffix="crossattention.self.key", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Col, ignore=True, ), Col_Layer( - weight="crossattention.self.value.weight", - bias="crossattention.self.value.bias", + suffix="crossattention.self.value", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Col, ignore=True, ), ] @staticmethod - def attn_out() -> List: + def attn_out(): return [ Row_Layer( - weight="attention.output.dense.weight", - bias="attention.output.dense.bias", + suffix="attention.output.dense", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Row, ), + Dropout_Layer( + suffix="attention.output.dropout", + p="p", + replace_layer=col_nn.Dropout1D, + ), Row_Layer( - weight="crossattention.output.dense.weight", - bias="crossattention.output.dense.bias", + suffix="crossattention.output.dense", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Row, ignore=True, ), ] @staticmethod - def mlp_in() -> List: + def mlp_in(): return [ Col_Layer( - weight="intermediate.dense.weight", - bias="intermediate.dense.bias", + suffix="intermediate.dense", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Col, ), ] @staticmethod - def mlp_out() -> List: + def mlp_out(): return [ Row_Layer( - weight="output.dense.weight", - bias="output.dense.bias", + suffix="output.dense", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Row, ), + Dropout_Layer( + suffix="output.dropout", + p="p", + replace_layer=col_nn.Dropout1D, + ) ] @staticmethod - def embedding() -> List: + def embedding(): return [Col_Layer( - weight="word_embeddings.weight", + suffix="word_embeddings", + weight="weight", replace_layer=col_nn.VocabParallelEmbedding1D, )] - @staticmethod - def unembedding() -> List: - return [ - Col_Layer( - weight="decoder.weight", - bias="decoder.bias", - replace_layer=col_nn.Linear1D_Col, - gather_output=True, - ) - ] - from transformers import BertForMaskedLM @@ -154,18 +157,36 @@ def unembedding() -> List: class BertForMaskedLMPolicy(BertPolicy): @staticmethod - def inject_policy() -> Tuple[nn.Module, nn.Module]: + def argument_policy(config, world_size): + base_argument = BertPolicy.argument_policy(config, world_size) + argument = { + BertLMPredictionHead: Argument(attr_dict={}, param_funcs=[ + BertForMaskedLMPolicy.unembedding, + ]), + } + argument.update(base_argument) + return argument + + @staticmethod + def inject_policy(): # return (BertForMaskedLM, BertForMaskedLM_) return None + @staticmethod + def unembedding(): + return [ + Col_Layer( + suffix="decoder", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + gather_output=True, + ) + ] + class BertForSequenceClassificationPolicy(BertPolicy): @staticmethod - def inject_policy() -> Dict: - return {} - - -# model = BertForMaskedLM.from_pretrained("bert-base-uncased") -# _ = BertForMaskedLMPolicy(model) -# print(isinstance(model,list(_.inject_policy().keys())[0])) + def inject_policy(): + return None diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 44dc9c72f986..0d4342e75783 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -40,19 +40,22 @@ def argument_policy(config, world_size): @staticmethod def attn_in() -> List: return [ - Col_Layer(weight="attn.c_attn.weight", - bias="attn.c_attn.bias", + Col_Layer(suffix="attn.c_attn", + weight="weight", + bias="bias", n_cast=3, reversed=True, replace_layer=col_nn.Linear1D_Col), - Col_Layer(weight="crossattention.c_attn.weight", - bias="crossattention.c_attn.bias", + Col_Layer(suffix="crossattention.c_attn", + weight="weight", + bias="bias", n_cast=2, reversed=True, ignore=True, replace_layer=col_nn.Linear1D_Col), - Col_Layer(weight="crossattention.q_attn.weight", - bias="crossattention.q_attn.bias", + Col_Layer(suffix="crossattention.q_attn", + weight="weight", + bias="bias", reversed=True, ignore=True, replace_layer=col_nn.Linear1D_Col) @@ -61,12 +64,14 @@ def attn_in() -> List: @staticmethod def attn_out() -> List: return [ - Row_Layer(weight="attn.c_proj.weight", - bias="attn.c_proj.bias", + Row_Layer(suffix="attn.c_proj", + weight="weight", + bias="bias", reversed=True, replace_layer=col_nn.Linear1D_Row), - Row_Layer(weight="crossattention.c_proj.weight", - bias="crossattention.c_proj.bias", + Row_Layer(suffix="crossattention.c_proj", + weight="weight", + bias="bias", reversed=True, ignore=True, replace_layer=col_nn.Linear1D_Row) @@ -75,21 +80,23 @@ def attn_out() -> List: @staticmethod def mlp_in() -> List: return [ - Col_Layer(weight="mlp.c_fc.weight", bias="mlp.c_fc.bias", reversed=True, replace_layer=col_nn.Linear1D_Col), + Col_Layer(suffix="mlp.c_fc", weight="weight", bias="bias", reversed=True, + replace_layer=col_nn.Linear1D_Col), ] @staticmethod def mlp_out() -> List: return [ - Row_Layer(weight="mlp.c_proj.weight", - bias="mlp.c_proj.bias", + Row_Layer(suffix="mlp.c_proj", + weight="weight", + bias="bias", reversed=True, replace_layer=col_nn.Linear1D_Row) ] @staticmethod def embedding() -> List: - return [Col_Layer(weight="wte.weight", replace_layer=col_nn.VocabParallelEmbedding1D)] + return [Col_Layer(suffix="wte", weight="weight", replace_layer=col_nn.VocabParallelEmbedding1D)] from transformers import GPT2LMHeadModel @@ -111,8 +118,9 @@ def argument_policy(config, world_size): @staticmethod def unembedding() -> List: return [ - Col_Layer(weight="lm_head.weight", - bias="lm_head.bias", + Col_Layer(suffix="lm_head", + weight="weight", + bias="bias", replace_layer=col_nn.Linear1D_Col, gather_output=True) ] diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 159bebccd02d..95184cfe6929 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -5,7 +5,7 @@ from transformers.pytorch_utils import Conv1D from ..policies.autopolicy import get_autopolicy -from ..policies.basepolicy import Policy +from ..policies.basepolicy import Col_Layer, Dropout_Layer, Policy, Row_Layer from ..utils.utils import getattr_, hasattr_, setattr_ from .shard_config import ShardConfig from .slicer import Slicer @@ -141,65 +141,73 @@ def shard_one_layer( for func in param_funcs: policy_layers = func() for policy_layer in policy_layers: - weight = None - bias = None - weight_attr = policy_layer.weight - bias_attr = policy_layer.bias + suffix = policy_layer.suffix replace_layer_cls = policy_layer.replace_layer ignore = policy_layer.ignore - n_cast = policy_layer.n_cast reversed = policy_layer.reversed - if policy_layer.__class__.__name__ == "Col_Layer": - gather_output = policy_layer.gather_output and self.shard_config.gather_output - - if weight_attr is not None: - if hasattr_(org_layer, weight_attr): - weight = getattr_(org_layer, weight_attr) - elif not ignore: - raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {weight_attr}") - - if bias_attr is not None: - if hasattr_(org_layer, bias_attr): - bias = getattr_(org_layer, bias_attr) - elif not ignore: - raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {bias_attr}") - - # dont have the attribute in policy, and ignore is true - if weight is None and bias is None and ignore: - continue - - # set the sliced weight and bias to the new nn_col layer - assert weight is not None or bias is not None - layer_attr = (lambda x: x[:x.rfind(".")])(weight_attr or bias_attr) + n_cast = policy_layer.n_cast - # slice weight and bias - weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__, n_cast, reversed) + assert replace_layer_cls is not None, 'replace_layer should not be None' # create new object to replace the origin layer - if replace_layer_cls is not None: - if isinstance(getattr_(org_layer, layer_attr), (nn.Linear, Conv1D)): - if replace_layer_cls.__name__ == "Linear1D_Row": - replace_layer = replace_layer_cls(weight.shape[1], - weight.shape[0], - bias=False if bias is None else True) - elif replace_layer_cls.__name__ == "Linear1D_Col": - replace_layer = replace_layer_cls(weight.shape[0], - weight.shape[1], - bias=False if bias is None else True, - gather_output=gather_output) - setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) - self.set_param(replace_layer, weight, bias) - elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding): + # Linear + suffix_layer = getattr_(org_layer, suffix, ignore=True) + assert suffix_layer is not None or ignore, f"Layer {org_layer.__class__.__qualname__} has no attribute {suffix}" + if suffix_layer is None and ignore: + continue + if isinstance(policy_layer, (Col_Layer, Row_Layer)): + weight = None + bias = None + weight_attr = suffix + '.' + policy_layer.weight if policy_layer.weight is not None else None + bias_attr = suffix + '.' + policy_layer.bias if policy_layer.bias is not None else None + + if weight_attr is not None: + if hasattr_(org_layer, weight_attr): + weight = getattr_(org_layer, weight_attr) + else: + raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {weight_attr}") + + if bias_attr is not None: + if hasattr_(org_layer, bias_attr): + bias = getattr_(org_layer, bias_attr) + else: + raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {bias_attr}") + + # set the sliced weight and bias to the new nn_col layer + assert weight is not None or bias is not None + + # slice weight and bias + weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__, n_cast, reversed) + + if replace_layer_cls.__name__ == "Linear1D_Row": + replace_layer = replace_layer_cls(weight.shape[1], + weight.shape[0], + bias=False if bias is None else True) + elif replace_layer_cls.__name__ == "Linear1D_Col": + gather_output = policy_layer.gather_output and self.shard_config.gather_output + replace_layer = replace_layer_cls(weight.shape[0], + weight.shape[1], + bias=False if bias is None else True, + gather_output=gather_output) + elif replace_layer_cls.__name__ == "VocabParallelEmbedding1D": replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], - getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True)) - setattr_(org_layer, layer_attr, replace_layer, ignore=ignore) - self.set_param(replace_layer, weight, bias) + getattr_(org_layer, f"{suffix}.padding_idx", ignore=True)) + # setattr_(org_layer, suffix, replace_layer, ignore=ignore) + # self.set_param(replace_layer, weight, bias) else: raise NotImplementedError( - f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far") - # do not replace the layer object, just replace the weight and bias + f"Replacing to {replace_layer_cls.__name__} is not implemented so far") + setattr_(org_layer, suffix, replace_layer, ignore=ignore) + self.set_param(replace_layer, weight, bias) + # dropout + elif isinstance(policy_layer, Dropout_Layer): + p_attr = suffix + '.' + policy_layer.p + p = getattr_(org_layer, p_attr, ignore=True) + replace_layer = replace_layer_cls(p) + setattr_(org_layer, suffix, replace_layer, ignore=ignore) else: - self.set_param(org_layer, layer_attr, weight, bias) + raise NotImplementedError( + f"Replacing {getattr_(org_layer, suffix).__class__} is not implemented so far") def set_param(self, layer: Any, diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 09e3219f87a2..0bf8f58b8544 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -1,6 +1,6 @@ import torch -from ..policies.basepolicy import Col_Layer, Layer, Row_Layer +from ..policies.basepolicy import Col_Layer, Dropout_Layer, Layer, Row_Layer from .shard_config import ShardConfig dim_mapping = {Col_Layer: 0, Row_Layer: 1} @@ -33,7 +33,7 @@ def slice_weight_bias( bias: (:class:`torch.nn.Module`): The bias of the layer policy_layer_class (:class:`Policy`): The class represent how to slice the tensor """ - if policy_layer_cls == Layer: + if policy_layer_cls in [Layer, Dropout_Layer]: return weight, bias dim = dim_mapping[policy_layer_cls] if not reversed else (1 - dim_mapping[policy_layer_cls]) diff --git a/colossalai/shardformer/utils/utils.py b/colossalai/shardformer/utils/utils.py index eb84edd88404..2c02b6f69a3e 100644 --- a/colossalai/shardformer/utils/utils.py +++ b/colossalai/shardformer/utils/utils.py @@ -37,7 +37,7 @@ def setattr_(obj, attr: str, value, ignore: bool = False): setattr(obj, attrs[-1], value) -def getattr_(obj, attr: str, ignore: bool = None): +def getattr_(obj, attr: str, ignore: bool = False): r""" Get the object's multi sublevel attr From 6b30dfb7ce002be3acc0668d3fa44c4d4ebb4108 Mon Sep 17 00:00:00 2001 From: wukong1992 Date: Tue, 13 Jun 2023 14:44:40 +0800 Subject: [PATCH 359/413] [shardformer] support llama model using shardformer (#3969) adjust layer attr --- .../shardformer/layer/dist_crossentropy.py | 2 +- colossalai/shardformer/policies/autopolicy.py | 14 ++ colossalai/shardformer/policies/llama.py | 122 ++++++++++++++++++ .../test_model/test_shard_llama.py | 106 +++++++++++++++ 4 files changed, 243 insertions(+), 1 deletion(-) create mode 100644 colossalai/shardformer/policies/llama.py create mode 100644 tests/test_shardformer/test_model/test_shard_llama.py diff --git a/colossalai/shardformer/layer/dist_crossentropy.py b/colossalai/shardformer/layer/dist_crossentropy.py index 05c04bb545c1..ff05209fefe8 100644 --- a/colossalai/shardformer/layer/dist_crossentropy.py +++ b/colossalai/shardformer/layer/dist_crossentropy.py @@ -21,7 +21,7 @@ def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor, ignore_index: and can be rewrite as: loss = log(sum(exp(x[i])) - x[class] - To avoid the `nan` of log(sim(exp(x[i]))), we minus the max of x[i] + To avoid the `nan` of log(sum(exp(x[i]))), we minus the max of x[i] Args: vocab_logits (:class:`torch.Tensor`): The logits of the vocabulary, shape is diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 54cc63ba124f..27fd09b4561b 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -19,6 +19,20 @@ def build_policies(): from .bert import BertForSequenceClassificationPolicy auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy + from transformers.models.llama.modeling_llama import LlamaModel + + from .llama import LlamaPolicy + auto_policy_dict[LlamaModel] = LlamaPolicy + + from transformers import LlamaForSequenceClassification + + from .llama import LlamaForSequenceClassificationPolicy + auto_policy_dict[LlamaForSequenceClassification] = LlamaForSequenceClassificationPolicy + + from transformers import LlamaForCausalLM + + from .llama import LlamaForCausalLMPolicy + auto_policy_dict[LlamaForCausalLM] = LlamaForCausalLMPolicy from transformers import GPT2Model diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py new file mode 100644 index 000000000000..fac6765cdcb5 --- /dev/null +++ b/colossalai/shardformer/policies/llama.py @@ -0,0 +1,122 @@ +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Tuple, Type + +import torch.nn as nn +from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel + +import colossalai.shardformer.layer.layers as col_nn + +from .basepolicy import Argument, Col_Layer, Policy, Row_Layer + + +class LlamaPolicy(Policy): + + @staticmethod + def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: + return { + LlamaDecoderLayer: + Argument(attr_dict={ + "self_attn.hidden_size": config.hidden_size // world_size, + "self_attn.num_heads": config.num_attention_heads // world_size, + }, + param_funcs=[LlamaPolicy.attn_layer, LlamaPolicy.mlp_layer]), + LlamaModel: + Argument(attr_dict={}, param_funcs=[LlamaPolicy.embeddings]) + } + + @staticmethod + def attn_layer() -> List: + return [ + Col_Layer( + suffix="self_attn.q_proj", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + suffix="self_attn.k_proj", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + suffix="self_attn.v_proj", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + ), + Row_Layer( + suffix="self_attn.o_proj", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Row, + ) + ] + + @staticmethod + def mlp_layer() -> List: + return [ + Col_Layer( + suffix="mlp.gate_proj", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + gather_output=True, + ), + Col_Layer( + suffix="mlp.up_proj", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Row, + gather_output=True, + ), + Col_Layer( + suffix="mlp.down_proj", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + gather_output=True, + ), + ] + + @staticmethod + def embeddings() -> List: + return [Col_Layer( + suffix="embed_tokens", + weight="weight", + replace_layer=col_nn.VocabParallelEmbedding1D, + )] + +from transformers import LlamaForCausalLM + + +class LlamaForCausalLMPolicy(LlamaPolicy): + + @staticmethod + def argument(config, world_size): + llamapolicy = LlamaPolicy.argument_policy(config, world_size) + argument = {LlamaForCausalLM: Argument(attr_dict={}, param_funcs=[LlamaForCausalLMPolicy.lm_head])} + argument.update(llamapolicy) + + @staticmethod + def lm_head() -> List: + return [Col_Layer(suffix="lm_head", weight="weight", replace_layer=col_nn.Linear1D_Col, gather_output=True)] + + +from transformers import LlamaForSequenceClassification + + +class LlamaForSequenceClassificationPolicy(LlamaPolicy): + + @staticmethod + def argument(config, world_size): + llamapolicy = LlamaPolicy.argument_policy(config, world_size) + argument = { + LlamaForSequenceClassification: + Argument(attr_dict={}, param_funcs=[LlamaForSequenceClassificationPolicy.score]) + } + argument.update(llamapolicy) + + @staticmethod + def score() -> List: + return [Col_Layer(suffix="score", weight="weight", replace_layer=col_nn.Linear1D_Col, gather_output=True)] diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py new file mode 100644 index 000000000000..689898bbbad2 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -0,0 +1,106 @@ +import copy +import os +import random + +import pytest +import torch +from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM, LlamaModel, LlamaTokenizerFast + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.shardformer.shard import ShardConfig, shard_model +from colossalai.testing import rerun_if_address_is_in_use, spawn + +os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' +CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=4, mode='1d')),) +tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer") + + +def build_model(rank, world_size): + cfg = LlamaConfig(num_hidden_layers=16) + org_model = LlamaForCausalLM(cfg) + + shardconfig = ShardConfig( + rank=rank, + world_size=world_size, + gather_output=True, + ) + org_model = org_model.to('cuda') + + org_model_forshard = copy.deepcopy(org_model) + sharded_model = shard_model(org_model_forshard, shardconfig).to('cuda') + + return org_model, sharded_model + + +def check_forward(org_model, sharded_model): + input = 'Hello, my dog is cute' + inputs = tokenizer(input, return_tensors='pt').to('cuda') + del inputs["token_type_ids"] + del inputs["attention_mask"] + #orgin model + org_model.eval() + org_out = org_model(**inputs) + + #shard model + sharded_model.eval() + shard_out = sharded_model(**inputs) + + assert torch.allclose( + org_out[0], shard_out[0], + atol=1e-4), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}" + + +def check_backward(org_model, sharded_model): + # prepare input + input = 'Hello, my dog is cute' + tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') + del tokenized_input["token_type_ids"] + del tokenized_input["attention_mask"] + labels = tokenized_input['input_ids'].clone() + labels[labels == tokenizer.pad_token_id] = -100 + tokenized_input['labels'] = labels + + #orgin model + org_model.train() + org_out = org_model(**tokenized_input) + org_loss = org_out.loss + org_loss.backward() + org_grad = org_model.model.layers[0].self_attn.q_proj.weight.grad + + torch.cuda.empty_cache() + #shard model + sharded_model.train() + shard_out = sharded_model(**tokenized_input) + shard_loss = shard_out.loss + shard_loss.backward() + shard_grad = sharded_model.model.layers[0].self_attn.q_proj.weight.grad + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + + +def check_llama(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + org_model, sharded_model = build_model(rank, world_size) + check_forward(org_model, sharded_model) + check_backward(org_model, sharded_model) + + torch.cuda.empty_cache() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_llama(): + spawn(check_llama, 4) + + +if __name__ == "__main__": + test_llama() From c1c672d0f0fcba484f294ad8550df59ee5448fdd Mon Sep 17 00:00:00 2001 From: wukong1992 Date: Thu, 15 Jun 2023 16:50:08 +0800 Subject: [PATCH 360/413] [shardformer] shardformer support t5 model (#3994) test t5 --- applications/Chat/coati/trainer/.sft.py.swp | Bin 0 -> 20480 bytes colossalai/shardformer/layer/layers.py | 8 +- colossalai/shardformer/policies/autopolicy.py | 9 + colossalai/shardformer/policies/basepolicy.py | 12 ++ colossalai/shardformer/policies/t5.py | 159 ++++++++++++++++++ colossalai/shardformer/shard/sharder.py | 11 +- colossalai/shardformer/shard/slicer.py | 6 +- colossalai/shardformer/utils/utils.py | 25 ++- requirements/requirements-test.txt | 1 + .../test_model/test_shard_t5.py | 99 +++++++++++ 10 files changed, 320 insertions(+), 10 deletions(-) create mode 100644 applications/Chat/coati/trainer/.sft.py.swp create mode 100644 colossalai/shardformer/policies/t5.py create mode 100644 tests/test_shardformer/test_model/test_shard_t5.py diff --git a/applications/Chat/coati/trainer/.sft.py.swp b/applications/Chat/coati/trainer/.sft.py.swp new file mode 100644 index 0000000000000000000000000000000000000000..302cf2a775338fb4fcd6b9b12c1a8e80f3969a01 GIT binary patch literal 20480 zcmeHOU5q4E6)psY74VP5@FaJ89H$z3t7mp0aohBo-E~(9yMp_JSthfc>h9aq#Z*@{ zRn;uJ!vHZRh#HOZ#2YFJFB0QVSObsxWI{~Lk{BNh;)6b@tf-4W;QF2WQ+2DldsbdF zA-9unZdKiL?>+as=box_s;0Yq@0~~4UT@jK?*oo=(;FxI-#GnI=k?bd=l&oa`tc}; zsBW8IF4yNr58UI0{+tGnwG2>WBO9Z2IGI81(!sh@-T5aOh{vUW~J5 zmiR%$lV&=|yz$v>bg)&H0n5OZ7`V~dvwKBs^@Z9_cGHJWUa5htw+vVYECZGS%YbFT zGGH073|I!UFsSOzQumI2Ga|A7I&?>KiO*ng4|0C@keHBLF9S~i4+E<} z3%D7$h(y4zfBv-t%5*a`ZrO?|Z0V174>laU{aAzEycM%t>?or)eT z7)T}dwb^d(4(3OF7NniTk2X5XZoM_Fg3>&mWaCMedUQH1q-rrkF||?L=bgLq$?QqGObFGlw zsur=BXQ9tuXN* 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device=get_current_device(), dtype=dtype)) @@ -832,8 +834,10 @@ def _save_to_global_state_dict(self, destination, prefix, keep_vars): def forward(self, input_: Tensor) -> Tensor: output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) - - output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) + if self.gather_output: + output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) + else: + output = output_parallel return output diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 27fd09b4561b..d4425497bd8e 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -43,6 +43,15 @@ def build_policies(): from .gpt2 import GPT2LMHeadModelPolicy auto_policy_dict[GPT2LMHeadModel] = GPT2LMHeadModelPolicy + + from .t5 import T5ForConditionalGenerationPolicy, T5EncoderModelPolicy, T5ModelPolicy + from transformers import T5ForConditionalGeneration, T5EncoderModel, T5Model + t5 = { + T5ForConditionalGeneration: T5ForConditionalGenerationPolicy, + T5EncoderModel: T5EncoderModelPolicy, + T5Model: T5ModelPolicy, + } + auto_policy_dict.update(t5) return auto_policy_dict diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index d55df59fdc8b..ba3a97f1bbcd 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -80,6 +80,18 @@ class Dropout_Layer(Layer): p: str = None +@dataclass +class Embedding_Layer(Layer): + r""" + Class for col shard layer in tensor parrallel + + Args: + weight (str): The weight suffix of the layer + """ + weight: str = None + gather_output: bool = True + + class Policy(): r""" The base class for all the policies diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py new file mode 100644 index 000000000000..7b013a37845a --- /dev/null +++ b/colossalai/shardformer/policies/t5.py @@ -0,0 +1,159 @@ +from typing import Dict + +import torch.nn as nn +from torch.nn import Embedding +from transformers.models.t5.modeling_t5 import ( + T5Attention, + T5Block, + T5DenseActDense, + T5DenseGatedActDense, + T5LayerCrossAttention, + T5LayerFF, + T5LayerSelfAttention, + T5Model, + T5Stack, +) + +import colossalai.shardformer.layer.layers as col_nn + +from .basepolicy import Argument, Col_Layer, Dropout_Layer, Embedding_Layer, Policy, Row_Layer + + +class T5ModelPolicy(Policy): + + @staticmethod + def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: + print('config heads', config.num_heads) + return { + T5Stack: + Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout, T5ModelPolicy.embedding]), + T5Block: + Argument(attr_dict={}, param_funcs=[]), + T5LayerSelfAttention: + Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout]), + T5LayerCrossAttention: + Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout]), + T5Attention: + Argument(attr_dict={ + "d_model": config.d_model // world_size, + "n_heads": config.num_heads // world_size, + "inner_dim": config.num_heads * config.d_kv // world_size, + }, + param_funcs=[T5ModelPolicy.attn_layer]), + T5LayerFF: + Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout]), + T5DenseGatedActDense: + Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout, T5ModelPolicy.dense_gated_layer]), + T5DenseActDense: + Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout, T5ModelPolicy.dense_act_layer]), + } + + @staticmethod + def dense_gated_layer(): + return [ + Col_Layer( + suffix="wi_0", + weight="weight", + replace_layer=col_nn.Linear1D_Col, + ), + Row_Layer( + suffix="wi_1", + weight="weight", + replace_layer=col_nn.Linear1D_Row, + ), + Col_Layer(suffix="wo", weight="weight", replace_layer=col_nn.Linear1D_Col, gather_output=True) + ] + + @staticmethod + def dense_act_layer(): + return [ + Col_Layer( + suffix="wi", + weight="weight", + replace_layer=col_nn.Linear1D_Col, + ), + Row_Layer( + suffix="wo", + weight="weight", + replace_layer=col_nn.Linear1D_Row, + ) + ] + + @staticmethod + def attn_layer(): + return [ + Col_Layer( + suffix="q", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + suffix="k", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + ), + Col_Layer( + suffix="v", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + ), + Row_Layer( + suffix="o", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Row, + ), + ] + + @staticmethod + def dropout(): + return [Dropout_Layer( + suffix="dropout", + p="p", + replace_layer=col_nn.Dropout1D, + )] + + @staticmethod + def embedding(): + return [ + Embedding_Layer( + suffix="block[0].layer[0].SelfAttention.relative_attention_bias", + weight="weight", + replace_layer=col_nn.Embedding1D, + gather_output=False, + ) + ] + + +from transformers import T5ForConditionalGeneration + + +class T5ForConditionalGenerationPolicy(T5ModelPolicy): + + @staticmethod + def argument_policy(config, world_size): + base_argument = T5ModelPolicy.argument_policy(config, world_size) + argument = { + T5ForConditionalGeneration: Argument(attr_dict={}, param_funcs=[T5ForConditionalGenerationPolicy.lm_head]) + } + argument.update(base_argument) + return argument + + @staticmethod + def lm_head(): + return [Col_Layer( + suffix="lm_head", + weight="weight", + replace_layer=col_nn.Linear1D_Col, + gather_output=True, + )] + + +from transformers import T5EncoderModel + + +class T5EncoderModelPolicy(T5ModelPolicy): + pass diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 95184cfe6929..8f6514cb4f5f 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -5,7 +5,7 @@ from transformers.pytorch_utils import Conv1D from ..policies.autopolicy import get_autopolicy -from ..policies.basepolicy import Col_Layer, Dropout_Layer, Policy, Row_Layer +from ..policies.basepolicy import Col_Layer, Dropout_Layer, Policy, Row_Layer, Embedding_Layer from ..utils.utils import getattr_, hasattr_, setattr_ from .shard_config import ShardConfig from .slicer import Slicer @@ -155,11 +155,11 @@ def shard_one_layer( assert suffix_layer is not None or ignore, f"Layer {org_layer.__class__.__qualname__} has no attribute {suffix}" if suffix_layer is None and ignore: continue - if isinstance(policy_layer, (Col_Layer, Row_Layer)): + if isinstance(policy_layer, (Col_Layer, Row_Layer, Embedding_Layer)): weight = None bias = None weight_attr = suffix + '.' + policy_layer.weight if policy_layer.weight is not None else None - bias_attr = suffix + '.' + policy_layer.bias if policy_layer.bias is not None else None + bias_attr = suffix + '.' + policy_layer.bias if hasattr(policy_layer, 'bias') and policy_layer.bias is not None else None if weight_attr is not None: if hasattr_(org_layer, weight_attr): @@ -189,6 +189,11 @@ def shard_one_layer( weight.shape[1], bias=False if bias is None else True, gather_output=gather_output) + elif replace_layer_cls.__name__ == "Embedding1D": + gather_output = policy_layer.gather_output + replace_layer = replace_layer_cls(weight.shape[0], + weight.shape[1], + gather_output=gather_output) elif replace_layer_cls.__name__ == "VocabParallelEmbedding1D": replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], getattr_(org_layer, f"{suffix}.padding_idx", ignore=True)) diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py index 0bf8f58b8544..860533dca50d 100644 --- a/colossalai/shardformer/shard/slicer.py +++ b/colossalai/shardformer/shard/slicer.py @@ -1,9 +1,9 @@ import torch -from ..policies.basepolicy import Col_Layer, Dropout_Layer, Layer, Row_Layer +from ..policies.basepolicy import Col_Layer, Dropout_Layer, Layer, Row_Layer, Embedding_Layer from .shard_config import ShardConfig -dim_mapping = {Col_Layer: 0, Row_Layer: 1} +dim_mapping = {Col_Layer: 0, Row_Layer: 1, Embedding_Layer: 1} class Slicer(): @@ -43,6 +43,8 @@ def slice_weight_bias( bias = self.slice_tensor(bias, 0, True, n_cast) elif policy_layer_cls == Row_Layer: weight = self.slice_tensor(weight, dim, False, n_cast) + elif policy_layer_cls == Embedding_Layer: + weight = self.slice_tensor(weight, dim, False, n_cast) else: raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported") if reversed: diff --git a/colossalai/shardformer/utils/utils.py b/colossalai/shardformer/utils/utils.py index 2c02b6f69a3e..05a6a3ae6c30 100644 --- a/colossalai/shardformer/utils/utils.py +++ b/colossalai/shardformer/utils/utils.py @@ -1,3 +1,22 @@ +import re + + +def get_obj_list_element(obj, a): + re_pattern = r'\[\d+\]' + prog = re.compile(re_pattern) + result = prog.search(a) + if result: + matched_brackets = result.group() + matched_index = matched_brackets.replace('[', '') + matched_index = matched_index.replace(']', '') + a_ = a.replace(matched_brackets, '') + container_obj = getattr(obj, a_) + obj = container_obj[int(matched_index)] + else: + obj = getattr(obj, a) + return obj + + def hasattr_(obj, attr: str): r""" Check whether the object has the multi sublevel attr @@ -9,7 +28,7 @@ def hasattr_(obj, attr: str): attrs = attr.split('.') for a in attrs: try: - obj = getattr(obj, a) + obj = get_obj_list_element(obj, a) except AttributeError: return False return True @@ -29,7 +48,7 @@ def setattr_(obj, attr: str, value, ignore: bool = False): attrs = attr.split('.') for a in attrs[:-1]: try: - obj = getattr(obj, a) + obj = get_obj_list_element(obj, a) except AttributeError: if ignore: return @@ -50,7 +69,7 @@ def getattr_(obj, attr: str, ignore: bool = False): attrs = attr.split('.') for a in attrs: try: - obj = getattr(obj, a) + obj = get_obj_list_element(obj, a) except AttributeError: if ignore: return None diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index 6895113bc637..50121a9283f2 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -15,3 +15,4 @@ einops triton==2.0.0.dev20221202 git+https://github.com/HazyResearch/flash-attention.git@c422fee3776eb3ea24e011ef641fd5fbeb212623#egg=flash_attn requests==2.27.1 # downgrade to avoid huggingface error https://github.com/huggingface/transformers/issues/17611 +SentencePiece diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py new file mode 100644 index 000000000000..ca44f0b00a74 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -0,0 +1,99 @@ +import copy +import os +import random + +import pytest +import torch +from transformers import AutoTokenizer, BertConfig, BertForMaskedLM, T5Config, T5ForConditionalGeneration, T5Tokenizer + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.shardformer.shard import ShardConfig, shard_model +from colossalai.testing import rerun_if_address_is_in_use, spawn + +os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' +CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) +tokenizer = T5Tokenizer.from_pretrained("t5-small") + + +def build_model(rank, world_size): + config = T5Config.from_pretrained("t5-small") + config.dropout_rate = 0 + org_model = T5ForConditionalGeneration.from_pretrained("t5-small", config=config).to('cuda') + + shardconfig = ShardConfig( + rank=rank, + world_size=world_size, + gather_output=True, + ) + + org_model_for_shard = copy.deepcopy(org_model) + + sharded_model = shard_model(org_model_for_shard, shardconfig).to('cuda') + + return org_model, sharded_model + + +def check_forward(org_model, sharded_model): + + input_ids = tokenizer("translate English to German: The house is wonderful.", + return_tensors="pt").input_ids.to('cuda') + #orgin model + org_model.eval() + org_output = org_model.generate(input_ids) + + #shard model + sharded_model.eval() + shard_output = sharded_model.generate(input_ids) + assert torch.allclose( + org_output[0], shard_output[0], + atol=1e-5), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}" + + +def check_backward(org_model, sharded_model): + # prepare input + input_ids = tokenizer("translate English to German: The house is wonderful.", + return_tensors="pt").input_ids.to('cuda') + labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids.to('cuda') + + #orgin model + org_model.train() + org_loss = org_model(input_ids=input_ids, labels=labels).loss + org_loss.backward() + org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad + + #shard model + sharded_model.train() + shard_loss = sharded_model(input_ids=input_ids, labels=labels).loss + shard_loss.backward() + shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad + + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + + +def check_t5(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + org_model, sharded_model = build_model(rank, world_size) + check_forward(org_model, sharded_model) + check_backward(org_model, sharded_model) + + torch.cuda.empty_cache() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_t5(): + spawn(check_t5, 2) + + +if __name__ == "__main__": + test_t5() From f7774ec0f36c6d04fc6639622eafe9ce75272d3f Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Thu, 15 Jun 2023 17:56:51 +0800 Subject: [PATCH 361/413] [Shardformer] Downstream bert (#3979) * add dist dropout in model * update docstring and bert policy with dropout * refactor basepolicy and sharded, update bert * update format * update gpt2 policy * update bert policy * remove unused code * update readme for new policy usage * add downstream model of bert * remove unused code --- colossalai/shardformer/policies/autopolicy.py | 25 ++++ colossalai/shardformer/policies/bert.py | 112 +++++++++++++++--- colossalai/shardformer/shard/shard_config.py | 4 +- colossalai/shardformer/shard/sharder.py | 1 + .../test_model/test_shard_bert.py | 42 +++++-- 5 files changed, 151 insertions(+), 33 deletions(-) diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index d4425497bd8e..e864719ac1ff 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -10,11 +10,31 @@ def build_policies(): """ auto_policy_dict = {} + from transformers import BertModel + + from .bert import BertModelPolicy + auto_policy_dict[BertModel] = BertModelPolicy + + from transformers import BertForPreTraining + + from .bert import BertForPretrainingPolicy + auto_policy_dict[BertForPreTraining] = BertForPretrainingPolicy + + from transformers import BertLMHeadModel + + from .bert import BertLMHeadModelPolicy + auto_policy_dict[BertLMHeadModel] = BertLMHeadModelPolicy + from transformers import BertForMaskedLM from .bert import BertForMaskedLMPolicy auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy + from transformers import BertForNextSentencePrediction + + from .bert import BertForNextSentencePredictionPolicy + auto_policy_dict[BertForNextSentencePrediction] = BertForNextSentencePredictionPolicy + from transformers import BertForSequenceClassification from .bert import BertForSequenceClassificationPolicy @@ -34,6 +54,11 @@ def build_policies(): from .llama import LlamaForCausalLMPolicy auto_policy_dict[LlamaForCausalLM] = LlamaForCausalLMPolicy + from transformers import BertForMultipleChoice + + from .bert import BertForMultipleChoicePolicy + auto_policy_dict[BertForMultipleChoice] = BertForMultipleChoicePolicy + from transformers import GPT2Model from .gpt2 import GPT2Policy diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 67e910d521e9..ba2266353e3e 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -35,12 +35,6 @@ def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: ]), } - @staticmethod - def binding_policy(): - return { - "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", - } - @staticmethod def attn_in(): return [ @@ -148,9 +142,53 @@ def embedding(): replace_layer=col_nn.VocabParallelEmbedding1D, )] + @staticmethod + def unembedding(): + return [ + Col_Layer( + suffix="decoder", + weight="weight", + bias="bias", + replace_layer=col_nn.Linear1D_Col, + gather_output=True, + ) + ] + + +# BertModel +class BertModelPolicy(BertPolicy): + + @staticmethod + def argument_policy(config, world_size): + return BertPolicy.argument_policy(config, world_size) + -from transformers import BertForMaskedLM +# BertForPretraining +class BertForPretrainingPolicy(BertPolicy): + @staticmethod + def argument_policy(config, world_size): + base_argument = BertPolicy.argument_policy(config, world_size) + argument = { + BertLMPredictionHead: Argument(attr_dict={}, param_funcs=[ + BertPolicy.unembedding, + ]), + } + argument.update(base_argument) + return argument + + @staticmethod + def inject_policy(): + return None + + @staticmethod + def binding_policy(): + return { + "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", + } + + +# BertForMaskedLM from colossalai.shardformer.model.modeling_bert import BertForMaskedLM_ @@ -161,7 +199,7 @@ def argument_policy(config, world_size): base_argument = BertPolicy.argument_policy(config, world_size) argument = { BertLMPredictionHead: Argument(attr_dict={}, param_funcs=[ - BertForMaskedLMPolicy.unembedding, + BertPolicy.unembedding, ]), } argument.update(base_argument) @@ -173,20 +211,56 @@ def inject_policy(): return None @staticmethod - def unembedding(): - return [ - Col_Layer( - suffix="decoder", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - gather_output=True, - ) - ] + def binding_policy(): + return { + "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", + } -class BertForSequenceClassificationPolicy(BertPolicy): +# BertLMHeadModel +class BertLMHeadModelPolicy(BertPolicy): + + @staticmethod + def argument_policy(config, world_size): + base_argument = BertPolicy.argument_policy(config, world_size) + argument = { + BertLMPredictionHead: Argument(attr_dict={}, param_funcs=[ + BertPolicy.unembedding, + ]), + } + argument.update(base_argument) + return argument @staticmethod def inject_policy(): return None + + @staticmethod + def binding_policy(): + return { + "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", + } + + +# BertForNextSentencePrediction +class BertForNextSentencePredictionPolicy(BertPolicy): + + @staticmethod + def argument_policy(config, world_size): + return BertPolicy.argument_policy(config, world_size) + + +# BertForSequenceClassification +class BertForSequenceClassificationPolicy(BertPolicy): + + @staticmethod + def argument_policy(config, world_size): + return BertPolicy.argument_policy(config, world_size) + + +# BertForMultipleChoice +class BertForMultipleChoicePolicy(BertPolicy): + + @staticmethod + def argument_policy(config, world_size): + return BertPolicy.argument_policy(config, world_size) diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index e8d6f3408c76..96c287577ddc 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -13,6 +13,6 @@ class ShardConfig: world_size (int): The world size of the distributed process gather_output (bool): Whether to gather the output of the model of the last layer """ - rank: int - world_size: int = 2 + rank: int = None + world_size: int = None gather_output: bool = True diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 8f6514cb4f5f..7ef0c37a4040 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -276,6 +276,7 @@ def shard_model(model: nn.Module, shard_config: ShardConfig = None, policy: Poli shard_config (`ShardConfig`): the config for distribute information policy (`Policy`): the custom policy for sharding """ + # TODO: init shard_config automatically sharder = ModelSharder(model=model, shard_config=shard_config, policy=policy) sharder.shard() return model diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 55b78d040505..9b29111eadb2 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -1,9 +1,19 @@ +import copy import os -import random import pytest import torch -from transformers import AutoTokenizer, BertConfig, BertForMaskedLM +from transformers import ( + AutoTokenizer, + BertConfig, + BertForMaskedLM, + BertForMultipleChoice, + BertForNextSentencePrediction, + BertForPreTraining, + BertForSequenceClassification, + BertLMHeadModel, + BertModel, +) import colossalai from colossalai.logging import disable_existing_loggers @@ -15,20 +25,21 @@ tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") -def build_model(rank, world_size): +def build_model(rank, world_size, model): config = BertConfig.from_pretrained('bert-base-uncased') config.hidden_dropout_prob = 0 config.attention_probs_dropout_prob = 0 - org_model = BertForMaskedLM.from_pretrained('bert-base-uncased', config=config).to('cuda') + org_model = model(config=config) + org_model_forshard = copy.deepcopy(org_model) + org_model = org_model.to('cuda') shardconfig = ShardConfig( rank=rank, world_size=world_size, gather_output=True, ) - sharded_model = shard_model(BertForMaskedLM.from_pretrained('bert-base-uncased', config=config), - shardconfig).to('cuda') + sharded_model = shard_model(org_model_forshard, shardconfig).to('cuda') return org_model, sharded_model @@ -85,12 +96,19 @@ def check_backward(org_model, sharded_model): def check_bert(rank, world_size, port): disable_existing_loggers() colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - - org_model, sharded_model = build_model(rank, world_size) - check_forward(org_model, sharded_model) - check_backward(org_model, sharded_model) - - torch.cuda.empty_cache() + forward_list = [ + BertModel, BertForPreTraining, BertForMaskedLM, BertLMHeadModel, BertForNextSentencePrediction, + BertForSequenceClassification + ] + backward_lsit = [BertForMaskedLM, BertLMHeadModel] + + for model in forward_list: + org_model, sharded_model = build_model(rank, world_size, model) + check_forward(org_model, sharded_model) + if model in backward_lsit: + check_backward(org_model, sharded_model) + + torch.cuda.empty_cache() @pytest.mark.dist From a2f9af810d8fd1f39e644bdb2b1e18429e088d49 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Thu, 15 Jun 2023 10:14:08 +0800 Subject: [PATCH 362/413] [shardformer] fix an error in readme (#3988) * fix an error in readme * simplify code --- colossalai/tensor/d_tensor/RAEDME.md | 103 +++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 colossalai/tensor/d_tensor/RAEDME.md diff --git a/colossalai/tensor/d_tensor/RAEDME.md b/colossalai/tensor/d_tensor/RAEDME.md new file mode 100644 index 000000000000..3d862dddbf20 --- /dev/null +++ b/colossalai/tensor/d_tensor/RAEDME.md @@ -0,0 +1,103 @@ +# 🔢 Distributed Tensor + +## 📚 Table of Contents + +- [🔢 Distributed Tensor](#-distributed-tensor) + - [📚 Table of Contents](#-table-of-contents) + - [🔗 Introduction](#-introduction) + - [📝 Design](#-design) + - [🔨 Usage](#-usage) + - [🎈 Progress Log](#-progress-log) + +## 🔗 Introduction + +Distributed tensor is a type of tensor that is distributed across multiple devices. It is a wrapper of PyTorch tensor, and it is used to support distributed training. +It can represent the device topology and tensor placement over the devices in the topology. It also provides a set of APIs to manipulate the distributed tensor. + +## 📝 Design + +Our implementation is inspired by the work [Alpa](https://arxiv.org/abs/2201.12023), which unifies data parallelism and tensor parallelism as intra-op parallelism. It uses notations `S` to represent the sharded dimension and `R` to represent the replicated dimension. For example, given a 2D matrix, `[S, R]` represents the tensor is sharded over the first dimension. + +Each sharded dimension will have a subscript to represent its placement over the devices. Assuming we have 4 GPUs and the GPUs are arranged in a 2 x 2 manner. Let's say we have a 2D matrix like below: + + +```text + [1, 2, 3, 4 ] +A = [4, 5, 6, 7 ] + [8, 9, 10, 11] + [12, 13, 14, 15] +``` + +`[S0, R]` would mean that the first dimension is sharded over the rows in the device topology. + +```text +| --------------------—————————————————————-| +| | | +| [1, 2, 3, 4 ] | [1, 2, 3, 4 ] | +| [4, 5, 6, 7 ] | [4, 5, 6, 7 ] | +| | | +| --------------------——————————————————----- +| | | +| [8, 9, 10, 11] | [8, 9, 10, 11] | +| [12, 13, 14, 15] | [12, 13, 14, 15] | +| | | +| --------------------——————————————————----- +``` + +`[S01, R]` would mean that the first dimension is sharded over both the row and column in the device topology. + +```text +| --------------------—————————————————————-| +| | | +| [1, 2, 3, 4 ] | [4, 5, 6, 7 ] | +| | | +| --------------------——————————————————----- +| | | +| [8, 9, 10, 11] | [12, 13, 14, 15] | +| | | +| --------------------——————————————————----- +``` + +## 🔨 Usage + +A sample API usage is given below. + +```python +import torch + +import colossalai +from colossalai.device.device_mesh import DeviceMesh +from colossalai.tensor.d_tensor import DTensor, ShardingSpec + +colossalai.launch_from_torch(config={}) + +# define your device mesh +# assume you have 4 GPUs +physical_mesh_id = torch.arange(0, 4) +mesh_shape = (2, 2) +device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) + +# define a tensor +a = torch.rand(16, 32).cuda() + +# create sharding spec for the tensor +# assume the sharding spec is [S0, R] +dim_partition_dict = {0: [0]} +sharding_spec = ShardingSpec(a.dim(), dim_partition_dict) + +# create a distributed tensor +d_tensor = DTensor(a, device_mesh, sharding_spec) +print(d_tensor) + +global_tensor = d_tensor.to_global() +print(global_tensor) +``` + + +## 🎈 Progress Log + +- [x] Support layout conversion +- [x] Support sharding on 2D device mesh +- [ ] Support sharding on 3D device mesh +- [ ] Support sharding 4D device mesh +- [ ] Support sharding info saving and offline tensor merge (we can save tensor as dtensor and gather the tensors back to the global tensor based on the sharding info in a single process in CPU, useful for distributed training checkpoint load and save.) From 611971248cb1056d110b90ec768a36fb137c56af Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 15 Jun 2023 11:49:16 +0800 Subject: [PATCH 363/413] [device] support init device mesh from process group (#3990) --- colossalai/device/device_mesh.py | 553 +++++++++++++++++++------- tests/test_device/test_device_mesh.py | 69 ++++ 2 files changed, 474 insertions(+), 148 deletions(-) diff --git a/colossalai/device/device_mesh.py b/colossalai/device/device_mesh.py index 2a5f747fbc23..3e96310e1890 100644 --- a/colossalai/device/device_mesh.py +++ b/colossalai/device/device_mesh.py @@ -3,11 +3,19 @@ with some changes. """ import operator +from dataclasses import dataclass from functools import reduce -from typing import List, Tuple +from typing import Dict, List, Union import torch import torch.distributed as dist +from torch.distributed import ProcessGroup + + +@dataclass +class ProcessGroupContainer: + process_group: ProcessGroup + ranks: List[int] # modified from alpa LogicalDeviceMesh(https://github.com/alpa-projects/alpa/blob/main/alpa/shard_parallel/auto_sharding.py) @@ -27,9 +35,11 @@ class DeviceMesh: during initializing the DeviceMesh instance if the init_process_group set to True. Otherwise, users need to call create_process_groups_for_logical_mesh manually to init logical process group. (default: False) - need_flatten(bool, optional): initialize flatten_device_mesh during initializing the DeviceMesh instance if the need_flatten set to True. + device (str): the device for the process groups used by the DeviceMesh instance. (default: 'cuda') """ + _DIST_BACKEND = {"cuda": "nccl", "cpu": "gloo"} + def __init__(self, physical_mesh_id: torch.Tensor, mesh_shape: torch.Size = None, @@ -37,160 +47,442 @@ def __init__(self, mesh_alpha: List[float] = None, mesh_beta: List[float] = None, init_process_group: bool = False, - need_flatten: bool = True): - self.physical_mesh_id = physical_mesh_id + device: str = 'cuda'): + # ============================ + # Physical & Logical Mesh IDs + # ============================ + self._physical_mesh_id = physical_mesh_id + assert physical_mesh_id.dim() == 1, "physical_mesh_id should be a 1D tensor." + + # logical mesh ids can be obtained via two ways + # 1. provide physical mesh id and provide mesh shape + # 2. directly supply the logical mesh id + assert mesh_shape is None or logical_mesh_id is None, \ + "Only one of mesh_shape and logical_mesh_id can be specified." \ + "Logical mesh IDs are obtained from either mesh_shape + phyiscal_mesh_id or directly from the user-supplied logical_mesh_id" + if logical_mesh_id is None: - self.mesh_shape = mesh_shape - self._logical_mesh_id = self.physical_mesh_id.reshape(self.mesh_shape) + self._mesh_shape = mesh_shape + self._logical_mesh_id = self._physical_mesh_id.reshape(self._mesh_shape) else: self._logical_mesh_id = logical_mesh_id - self.mesh_shape = self._logical_mesh_id.shape + self._mesh_shape = self._logical_mesh_id.shape + + # ensure two things: + # 1. logical and physical mesh IDs should contain the same elements + # 2. there is no duplicate IDs in each mesh, e.g. [2, 2] is not allowed + assert torch.equal(torch.unique(self._physical_mesh_id), torch.unique(self.logical_mesh_id)), \ + "physical and logical mesh IDs should contain the same elements, please check if you have consistent physical_mesh_id and logical_mesh_id." + assert torch.unique(self._physical_mesh_id).numel() == self._physical_mesh_id.numel(), \ + "Found duplicate IDs in the phyiscal_mesh_id and this is not allowed, please check your physical_mesh_id again." + assert torch.unique(self.logical_mesh_id).numel() == self.logical_mesh_id.numel(), \ + "Found duplicate IDs in the logical_mesh_id and this is not allowed, please check your logical_mesh_id again." - # map global rank into logical rank - self.convert_map = {} - self._global_rank_to_logical_rank_map(self._logical_mesh_id, []) + # =============================================== # coefficient for alpha-beta communication model + # alpha is latency and beta is bandwidth + # =============================================== + # if the values are not provided, we assume they are 1 for simplicity if mesh_alpha is None: - mesh_alpha = [1] * len(self.mesh_shape) + mesh_alpha = [1] * len(self._mesh_shape) if mesh_beta is None: - mesh_beta = [1] * len(self.mesh_shape) + mesh_beta = [1] * len(self._mesh_shape) + self.mesh_alpha = tuple(mesh_alpha) self.mesh_beta = tuple(mesh_beta) - self.init_process_group = init_process_group - self.need_flatten = need_flatten - if self.init_process_group: - self.process_groups_dict = self.create_process_groups_for_logical_mesh() - if self.need_flatten and self._logical_mesh_id.dim() > 1: - self.flatten_device_mesh = self.flatten() - # Create a new member `flatten_device_meshes` to distinguish from original flatten methods (Because I'm not sure if there are functions that rely on the self.flatten()) - # self.flatten_device_meshes = FlattenDeviceMesh(self.physical_mesh_id, self.mesh_shape, self.mesh_alpha, - # self.mesh_beta) + + # ensure the alpha and beta have the same shape + assert len(self.mesh_alpha) == len(self.mesh_beta), \ + "mesh_alpha and mesh_beta should have the same length, please check your mesh_alpha and mesh_beta again." + + # ========================= + # Device for Process Group + # ========================= + self._device = device + self._dist_backend = self._DIST_BACKEND[device] + + # ========================= + # Process Group Management + # ========================= + # the _global_to_local_rank_mapping is structured as follows + # { + # : [ , , , ...] + # } + self._global_to_local_rank_mapping = dict() + self._init_global_to_logical_rank_mapping(mapping=self._global_to_local_rank_mapping, + tensor=self.logical_mesh_id) + + # create process group + self._process_group_dict = {} + self._ranks_in_the_process_group = {} + self._global_rank_of_current_process = None + self._is_initialized = False + + # attribute used to inidicate whether this objectd + # is created using DeviceMesh.from_process_group + # this attribute can be used to do some check in methods + # such get_process_group as no global rank information + # is known if created with from_process_group + self._is_init_from_process_group = False + + # initialize process group if specified + self._init_ranks_in_the_same_group() + self._init_process_group = init_process_group + if init_process_group: + self.init_logical_process_group() @property - def shape(self): - return self.mesh_shape + def shape(self) -> torch.Size: + """ + Return the shape of the logical mesh. + """ + return self._mesh_shape @property - def num_devices(self): - return reduce(operator.mul, self.physical_mesh_id.shape, 1) + def num_devices(self) -> int: + """ + Return the number of devices contained in the device mesh. + """ + return reduce(operator.mul, self._physical_mesh_id.shape, 1) @property - def logical_mesh_id(self): + def logical_mesh_id(self) -> torch.Tensor: + """ + Return the logical mesh id. + """ return self._logical_mesh_id - def __deepcopy__(self, memo): + @property + def is_initialized(self) -> bool: + """ + Return whether the process group is initialized. + """ + return self._is_initialized + + @staticmethod + def from_process_group(process_group: Union[ProcessGroup, List[ProcessGroup]]) -> "DeviceMesh": + """ + Create a DeviceMesh instance from the current process group. Please note that the DeviceMesh object created with this method + will not have information about the physical mesh id, and thus will not be able to query for other ranks and perform alpha-beta communication. + + Args: + process_group (Union[ProcessGroup, List[ProcessGroup]]): the process group or a list of process groups for the device mesh. + If the input is a ProcessGroup object, a 1D DeviceMesh object will be created. If the input is a list of ProcessGroup objects, + the ProcessGroup at the ith index will correspond to the process group in the ith axis of the device mesh. + + Returns: + DeviceMesh: the device mesh instance. + """ + + def _get_device_by_backend(process_group): + """ + Get the device type given a process group's backend. + """ + backend = dist.get_backend(process_group) + for _device, _backend in DeviceMesh._DIST_BACKEND.items(): + if _backend == backend: + return _device + return None + + if isinstance(process_group, ProcessGroup): + process_group = [process_group] + + # get mesh shape + mesh_shape = [dist.get_world_size(pg) for pg in process_group] + + # get device + device_list = [_get_device_by_backend(pg) for pg in process_group] + + # make sure all devices are the same + assert all([device == device_list[0] for device in device_list]), \ + "All devices should be the same, please check your input process groups are created with the same distributed backend." + + # create a fake physical mesh id + # as we only get the process group associated with the current process, + # we cannot get the global ranks for all processes in the mesh + # therefore, we only use this fake physical mesh id to create the device mesh + # and will remove this fake physical mesh id later + fake_physical_mesh_id = torch.arange(reduce(operator.mul, mesh_shape, 1)) + + # create the device mesh + device_mesh = DeviceMesh(physical_mesh_id=fake_physical_mesh_id, mesh_shape=mesh_shape, device=device_list[0]) + + # hack the device attribute + device_mesh._physical_mesh_id = None + device_mesh._logical_mesh_id = None + device_mesh._global_rank_of_current_process = dist.get_rank() + device_mesh._is_initialized = False + device_mesh._process_group_dict = { + device_mesh._global_rank_of_current_process: {axis: pg for axis, pg in enumerate(process_group)} + } + + return device_mesh + + def get_process_group(self, axis: int, global_rank: int = None) -> ProcessGroup: + """ + Return the process group on the specified axis. + + Args: + axis (int): the axis of the process group. + global_rank (int, optional): the global rank of the process group. If not specified, the current process is used. (default: None) + """ + if global_rank is None: + global_rank = self._global_rank_of_current_process + elif self._is_init_from_process_group: + raise RuntimeError( + "The logical device mesh is create with DeviceMesh.from_process_group, this method is not supported for this creation method as no global rank information is known." + ) + return self._process_group_dict[global_rank][axis] + + def get_process_group_for_all_axes(self, global_rank: int = None) -> Dict[int, ProcessGroup]: + """ + Return the process groups for all axes. + + Args: + global_rank (int, optional): the global rank of the process + """ + if global_rank is None: + global_rank = self._global_rank_of_current_process + elif self._is_init_from_process_group: + raise RuntimeError( + "The logical device mesh is create with DeviceMesh.from_process_group, this method is not supported for this creation method as no global rank information is known." + ) + return self._process_group_dict[global_rank] + + def get_ranks_in_process_group(self, axis: int, global_rank: int = None) -> List[int]: + """ + Return the ranks in the process group on the specified axis. + + Args: + axis (int): the axis of the process group. + global_rank (int, optional): the global rank of the process + """ + if global_rank is None: + global_rank = self._global_rank_of_current_process + elif self._is_init_from_process_group: + raise RuntimeError( + "The logical device mesh is create with DeviceMesh.from_process_group, this method is not supported for this creation method as no global rank information is known." + ) + return self._ranks_in_the_process_group[global_rank][axis] + + def __deepcopy__(self, memo) -> "DeviceMesh": cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result for k, v in self.__dict__.items(): - if k != 'process_groups_dict': + if k != '_process_group_dict': setattr(result, k, __import__("copy").deepcopy(v, memo)) else: + # process group cannot be copied + # thus, we share them directly setattr(result, k, v) - return result - def flatten(self): - """ - Flatten the logical mesh into an effective 1d logical mesh, + def _init_global_to_logical_rank_mapping(self, + mapping: Dict, + tensor: torch.Tensor, + index_list: List[int] = []) -> Dict[int, List[int]]: """ - flatten_mesh_shape_size = len(self.mesh_shape) - flatten_mesh_shape = [self.num_devices] - return DeviceMesh(self.physical_mesh_id, - tuple(flatten_mesh_shape), - mesh_alpha=[max(self.mesh_alpha)] * (flatten_mesh_shape_size - 1), - mesh_beta=[max(self.mesh_beta)] * (flatten_mesh_shape_size - 1), - init_process_group=self.init_process_group, - need_flatten=False) + Build a global rank to local rank mapping for each process group in different axis in the logical device mesh. - def _global_rank_to_logical_rank_map(self, tensor, index_list): - ''' - This method is a helper function to build convert_map recursively. - ''' + Args: + mapping (Dict): a dictionary that maps the global rank to the local rank in the logical device mesh. + tensor (torch.Tensor): the tensor that contains the logical mesh ids. + index_list (List[int]) + + Returns: + mapping (Dict): a dictionary that maps the global rank to the local rank in the logical device mesh. + The value is a list of integers and each integer represents the local rank in the indexed axis. + """ for index, inner_tensor in enumerate(tensor): + # index means the local rank in the current axis + # inner_tensor refers to the processes with the same local rank + if inner_tensor.numel() == 1: - self.convert_map[int(inner_tensor)] = index_list + [index] + # if the inner_tensor only has one element, it means that + # it already reaches the last axis + # we append its local_rank in the last axis to the index_list + # and assign to the mapping + # the value of the mapping is the the local rank at the indexed axis of the device mesh + mapping[int(inner_tensor)] = index_list + [index] else: - self._global_rank_to_logical_rank_map(inner_tensor, index_list + [index]) + # we recursively go into the function until we reach the last axis + # meanwhile, we should add the local rank in the current axis in the index_list + self._init_global_to_logical_rank_mapping(mapping, inner_tensor, index_list + [index]) - def create_process_groups_for_logical_mesh(self): + def init_logical_process_group(self): ''' This method is used to initialize the logical process groups which will be used in communications among logical device mesh. Note: if init_process_group set to False, you have to call this method manually. Otherwise, the communication related function, such as ShapeConsistencyManager.apply will raise errors. ''' - process_groups_dict = {} - check_duplicate_list = [] - global_rank_flatten_list = self.physical_mesh_id.view(-1).tolist() + # sanity check + assert dist.is_initialized, "The torch.distributed should be initialized before calling init_logical_process_group" + assert not self._is_initialized, "The logical process group has been initialized, do not call init_logical_process_group twice" + + # update the global rank of the current process + self._global_rank_of_current_process = dist.get_rank() + duplicate_check_list = [] + + # flatten the global ranks to 1D list + global_rank_flatten_list = self._physical_mesh_id.view(-1).tolist() + for global_rank in global_rank_flatten_list: - process_groups = self.global_rank_to_process_groups_with_global_rank(global_rank) - for axis, process_group in process_groups.items(): - if axis not in process_groups_dict: - process_groups_dict[axis] = [] - if process_group not in check_duplicate_list: - check_duplicate_list.append(process_group) - process_group_handler = dist.new_group(process_group) - process_groups_dict[axis].append((process_group, process_group_handler)) + # find the other ranks which are in the same process group as global_rank + ranks_in_same_group_by_axis = self._collate_global_ranks_in_same_process_group(global_rank) - return process_groups_dict + for axis, ranks_in_same_group in ranks_in_same_group_by_axis.items(): + # skip duplicated process group creation + if ranks_in_same_group in duplicate_check_list: + continue - def global_rank_to_logical_rank(self, rank): - return self.convert_map[rank] + # create the process group + pg_handler = dist.new_group(ranks=ranks_in_same_group, backend=self._dist_backend) - def global_rank_to_process_groups_with_logical_rank(self, rank): - ''' - Give a global rank and return all logical process groups of this rank. - for example: - physical_mesh_id = torch.arange(0, 16).reshape(2, 8) - mesh_shape = (4, 4) - # [[0, 1, 2, 3], - # [4, 5, 6, 7], - # [8, 9, 10,11], - # [12,13,14,15]] - device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - print(device_mesh.global_rank_to_process_groups_with_logical_rank(0)) - output: - # key is axis name - # value is a list of logical ranks in same axis with rank 0 - {0: [[0, 0], [1, 0], [2, 0], [3, 0]], 1: [[0, 0], [0, 1], [0, 2], [0, 3]]} - ''' - process_groups = {} - for d in range(self.logical_mesh_id.dim()): - for replacer in range(self.logical_mesh_id.shape[d]): - if d not in process_groups: - process_groups[d] = [] - process_group_member = self.convert_map[rank].copy() - process_group_member[d] = replacer - process_groups[d].append(process_group_member) - return process_groups - - def global_rank_to_process_groups_with_global_rank(self, rank): + # keep this process group in the process_groups_dict + for rank in ranks_in_same_group: + if rank not in self._process_group_dict: + self._process_group_dict[rank] = dict() + self._process_group_dict[rank][axis] = pg_handler + + # update the init flag + # we only allow init for once + self._is_initialized = True + + def _init_ranks_in_the_same_group(self): + """ + This method is used to initialize the ranks_in_the_same_group dictionary. + """ + # flatten the global ranks to 1D list + global_rank_flatten_list = self._physical_mesh_id.view(-1).tolist() + + for global_rank in global_rank_flatten_list: + # find the other ranks which are in the same process group as global_rank + ranks_in_same_group_by_axis = self._collate_global_ranks_in_same_process_group(global_rank) + + for axis, ranks_in_same_group in ranks_in_same_group_by_axis.items(): + # create dict for each rank + if global_rank not in self._process_group_dict: + self._ranks_in_the_process_group[global_rank] = dict() + + # keep this process group in the process_groups_dict + self._ranks_in_the_process_group[global_rank][axis] = ranks_in_same_group + + def global_rank_to_local_rank(self, rank: int, axis: int = None) -> Union[List[int], int]: + """ + Return the local rank of the given global rank in the logical device mesh. + + Args: + rank (int): the global rank in the logical device mesh. + axis (int): the axis of the logical device mesh. + """ + if self._is_init_from_process_group: + raise RuntimeError( + "The logical device mesh is create with DeviceMesh.from_process_group, this method is not supported for this creation method as no global rank information is known." + ) + + local_ranks = self._global_to_local_rank_mapping[rank] + if axis: + return local_ranks[axis] + else: + return local_ranks + + def _collate_global_ranks_in_same_process_group(self, global_rank): ''' - Give a global rank and return all process groups of this rank. - for example: - physical_mesh_id = torch.arange(0, 16).reshape(2, 8) - mesh_shape = (4, 4) - # [[0, 1, 2, 3], - # [4, 5, 6, 7], - # [8, 9, 10,11], - # [12,13,14,15]] - device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - print(device_mesh.global_rank_to_process_groups_with_global_rank(0)) - output: - # key is axis name - # value is a list of global ranks in same axis with rank 0 - {0: [0, 4, 8, 12], 1: [0, 1, 2, 3]} + Give a global rank and return all global ranks involved in its associated process group in each axis. + + Example: + + ```python + sphysical_mesh_id = torch.arange(0, 16) + mesh_shape = (4, 4) + + # logical mesh will look like + # [[0, 1, 2, 3], + # [4, 5, 6, 7], + # [8, 9, 10,11], + # [12,13,14,15]] + + device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) + print(device_mesh.collate_global_ranks_in_same_process_group(0)) + + # key is axis name + # value is a list of global ranks in same axis with rank 0 + # output will look like + # { + 0: [0, 4, 8, 12], + 1: [0, 1, 2, 3] + # } ''' - logical_process_groups = self.global_rank_to_process_groups_with_logical_rank(rank) - process_groups = {} - for dim, logical_ranks in logical_process_groups.items(): - process_groups[dim] = [] - for logical_rank in logical_ranks: - for g_rank, l_rank in self.convert_map.items(): - if l_rank == logical_rank: - process_groups[dim].append(g_rank) - return process_groups + # We have init the global rank to local rank by calling _init_global_to_logical_rank_mapping + # for self._global_to_local_rank_mapping + # the key is the global rank + # the value is the list of local ranks corresponding to the global rank with respect of different axes + # we can see the list of local ranks as the process coordinates for simplicity + # the key and value are all unique, therefore, + # we can also to use the coordinates to find the global rank + + # ========================================================================= + # Step 1 + # find all the process_coordinates for processes in the same process group + # as the given global rank + # ========================================================================= + + # each + processes_in_the_same_process_group = {} + + for dim in range(self.logical_mesh_id.dim()): + # iterate over the dimension size so that we can include all processes + # in the same process group in the given axis + # the _local_rank refers to the local rank of the current process + for _local_rank in range(self.logical_mesh_id.shape[dim]): + + # if this dimension is not initailized yet, + # initialize it with an empty array + if dim not in processes_in_the_same_process_group: + processes_in_the_same_process_group[dim] = [] + + # get the local rank corresponding to the global rank + process_coordinates = self._global_to_local_rank_mapping[global_rank].copy() + + # replace the local rank in the given dimension with the + # lcoal rank of the current process iterated + process_coordinates[dim] = _local_rank + processes_in_the_same_process_group[dim].append(process_coordinates) + + # ================================================================= + # Step 2 + # Use local rank combination to find its corresponding global rank + # ================================================================= + # the key of the dict is the axis + # the value is the list of global ranks which are in the same process group as the given global rank + global_pg_ranks = {} + for dim, coordinates_of_all_processes in processes_in_the_same_process_group.items(): + global_pg_ranks[dim] = [] + for process_coordinates in coordinates_of_all_processes: + # find the global rank by local rank combination + for _global_rank, _process_coordinates in self._global_to_local_rank_mapping.items(): + if process_coordinates == _process_coordinates: + global_pg_ranks[dim].append(_global_rank) + return global_pg_ranks + + def flatten(self): + """ + Flatten the logical mesh into an effective 1d logical mesh, + """ + if self._is_init_from_process_group: + raise RuntimeError( + "The logical device mesh is create with DeviceMesh.from_process_group, this method is not supported for this creation method as no global rank information is known." + ) + + flatten_mesh_shape_size = len(self._mesh_shape) + flatten_mesh_shape = [self.num_devices] + return DeviceMesh(self._physical_mesh_id, + tuple(flatten_mesh_shape), + mesh_alpha=[max(self.mesh_alpha)] * (flatten_mesh_shape_size - 1), + mesh_beta=[max(self.mesh_beta)] * (flatten_mesh_shape_size - 1), + init_process_group=self._init_process_group) def all_gather_cost(self, num_bytes, mesh_dim): num_devices = self.logical_mesh_id.shape[mesh_dim] @@ -211,39 +503,4 @@ def all_to_all_cost(self, num_bytes, mesh_dim): num_devices = self.logical_mesh_id.shape[mesh_dim] penalty_factor = num_devices / 2.0 return (self.mesh_alpha[mesh_dim] + self.mesh_beta[mesh_dim] * - (num_devices - 1) / num_devices / num_devices * num_bytes * penalty_factor + 0.001) - - -class FlattenDeviceMesh(DeviceMesh): - - def __init__(self, physical_mesh_id, mesh_shape, mesh_alpha=None, mesh_beta=None): - super().__init__(physical_mesh_id, - mesh_shape, - mesh_alpha, - mesh_beta, - init_process_group=False, - need_flatten=False) - # Different from flatten(), mesh_shape leaves unchanged, mesh_alpha and mesh_beta are scalars - self.mesh_alpha = max(self.mesh_alpha) - self.mesh_beta = min(self.mesh_beta) - # Different from original process_groups_dict, rank_list is not stored - self.process_number_dict = self.create_process_numbers_for_logical_mesh() - - def create_process_numbers_for_logical_mesh(self): - ''' - Build 1d DeviceMesh in column-major(0) and row-major(1) - for example: - mesh_shape = (2,4) - # [[0, 1, 2, 3], - # [4, 5, 6, 7]] - # return {0: [0, 4, 1, 5, 2, 6, 3, 7], 1: [0, 1, 2, 3, 4, 5, 6, 7]} - ''' - num_devices = reduce(operator.mul, self.mesh_shape, 1) - process_numbers_dict = {} - process_numbers_dict[0] = torch.arange(num_devices).reshape(self.mesh_shape).transpose(1, 0).flatten().tolist() - process_numbers_dict[1] = torch.arange(num_devices).reshape(self.mesh_shape).flatten().tolist() - return process_numbers_dict - - def mix_gather_cost(self, num_bytes): - num_devices = reduce(operator.mul, self.mesh_shape, 1) - return (self.mesh_alpha + self.mesh_beta * (num_devices - 1) / num_devices * num_bytes + 0.1) + (num_devices - 1) / num_devices / num_devices * num_bytes * penalty_factor + 0.001) \ No newline at end of file diff --git a/tests/test_device/test_device_mesh.py b/tests/test_device/test_device_mesh.py index 789ce8ab35b8..e9f0f9477e4a 100644 --- a/tests/test_device/test_device_mesh.py +++ b/tests/test_device/test_device_mesh.py @@ -1,6 +1,10 @@ +import pytest import torch +import torch.distributed as dist +import colossalai from colossalai.device.device_mesh import DeviceMesh +from colossalai.testing import rerun_if_address_is_in_use, spawn def test_device_mesh(): @@ -18,5 +22,70 @@ def test_device_mesh(): assert device_mesh.global_rank_to_process_groups_with_global_rank(2)[1] == [0, 1, 2, 3] +def check_1d_device_mesh(): + # check for 1D device mesh + process_group = dist.GroupMember.WORLD + device_mesh = DeviceMesh.from_process_group(process_group) + + # checks + assert device_mesh.shape == [4] + assert len(device_mesh.get_process_group_for_all_axes().keys()) == 1, 'Expected 1 axis for the process group dict' + assert device_mesh.get_process_group(axis=0) == process_group, 'Expected world process group' + assert device_mesh.is_initialized + assert device_mesh.num_devices == 4 + assert device_mesh.is_initialized + assert device_mesh.logical_mesh_id is None + assert device_mesh._is_init_from_process_group + + +def check_2d_device_mesh(): + # create process group for 2D device mesh + first_row_ranks = [0, 1] + second_row_ranks = [2, 3] + first_col_ranks = [0, 2] + second_col_ranks = [1, 3] + + first_row_pg = dist.new_group(first_row_ranks, backend='nccl') + second_row_pg = dist.new_group(second_row_ranks, backend='nccl') + first_col_pg = dist.new_group(first_col_ranks, backend='nccl') + second_col_pg = dist.new_group(second_col_ranks, backend='nccl') + + # check for + current_rank = dist.get_rank() + + if current_rank in first_row_ranks: + row_pg = first_row_pg + else: + row_pg = second_row_pg + + if current_rank in first_col_ranks: + col_pg = first_col_pg + else: + col_pg = second_col_pg + + device_mesh = DeviceMesh.from_process_group([col_pg, row_pg]) + + # checks + assert device_mesh.shape == [2, 2] + assert len(device_mesh.get_process_group_for_all_axes().keys()) == 2, 'Expected 2 axes for the process group dict' + assert device_mesh.get_process_group(axis=0) == col_pg, 'Expected column process group' + assert device_mesh.get_process_group(axis=1) == row_pg, 'Expected row process group' + assert device_mesh.num_devices == 4 + assert device_mesh.is_initialized + assert device_mesh.logical_mesh_id is None + assert device_mesh._is_init_from_process_group + + +def check_init_from_process_group(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_device_mesh_from_process_group(): + spawn(check_init_from_process_group, 4) + + if __name__ == '__main__': test_device_mesh() + test_device_mesh_from_process_group() From d3bc5308498b0382280e99cfd13094d8ba801436 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Thu, 15 Jun 2023 17:55:42 +0800 Subject: [PATCH 364/413] [shardformer] Refactor shardformer api (#4001) * fix an error in readme * simplify code * refactor shardformer * add todo * remove slicer * resolve code review --- colossalai/shardformer/__init__.py | 2 +- colossalai/shardformer/policies/autopolicy.py | 56 ++-- colossalai/shardformer/policies/basepolicy.py | 257 +++++----------- colossalai/shardformer/policies/bert.py | 282 ++++------------- colossalai/shardformer/shard/__init__.py | 6 +- colossalai/shardformer/shard/shard_config.py | 17 +- colossalai/shardformer/shard/sharder.py | 291 ++++++------------ colossalai/shardformer/shard/shardformer.py | 77 +++++ colossalai/shardformer/shard/slicer.py | 163 ---------- colossalai/shardformer/utils/__init__.py | 1 + 10 files changed, 342 insertions(+), 810 deletions(-) create mode 100644 colossalai/shardformer/shard/shardformer.py delete mode 100644 colossalai/shardformer/shard/slicer.py diff --git a/colossalai/shardformer/__init__.py b/colossalai/shardformer/__init__.py index 50c92738077a..77c2af8d18f7 100644 --- a/colossalai/shardformer/__init__.py +++ b/colossalai/shardformer/__init__.py @@ -1 +1 @@ -from .shard import ShardConfig, shard_model +from .shard import ShardConfig, ShardFormer diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index e864719ac1ff..6239397b7cbe 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -1,5 +1,7 @@ import torch.nn as nn +from .basepolicy import Policy + def build_policies(): r""" @@ -41,47 +43,25 @@ def build_policies(): auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy from transformers.models.llama.modeling_llama import LlamaModel - from .llama import LlamaPolicy - auto_policy_dict[LlamaModel] = LlamaPolicy - - from transformers import LlamaForSequenceClassification - - from .llama import LlamaForSequenceClassificationPolicy - auto_policy_dict[LlamaForSequenceClassification] = LlamaForSequenceClassificationPolicy - - from transformers import LlamaForCausalLM - - from .llama import LlamaForCausalLMPolicy - auto_policy_dict[LlamaForCausalLM] = LlamaForCausalLMPolicy - - from transformers import BertForMultipleChoice - - from .bert import BertForMultipleChoicePolicy - auto_policy_dict[BertForMultipleChoice] = BertForMultipleChoicePolicy - - from transformers import GPT2Model - - from .gpt2 import GPT2Policy - auto_policy_dict[GPT2Model] = GPT2Policy - - from transformers import GPT2LMHeadModel - - from .gpt2 import GPT2LMHeadModelPolicy - auto_policy_dict[GPT2LMHeadModel] = GPT2LMHeadModelPolicy - - from .t5 import T5ForConditionalGenerationPolicy, T5EncoderModelPolicy, T5ModelPolicy - from transformers import T5ForConditionalGeneration, T5EncoderModel, T5Model - t5 = { - T5ForConditionalGeneration: T5ForConditionalGenerationPolicy, - T5EncoderModel: T5EncoderModelPolicy, - T5Model: T5ModelPolicy, - } - auto_policy_dict.update(t5) + # from .llama import LlamaPolicy + # auto_policy_dict[LlamaModel] = LlamaPolicy + # from transformers import LlamaForSequenceClassification + # from .llama import LlamaForSequenceClassificationPolicy + # auto_policy_dict[LlamaForSequenceClassification] = LlamaForSequenceClassificationPolicy + # from transformers import LlamaForCausalLM + # from .llama import LlamaForCausalLMPolicy + # auto_policy_dict[LlamaForCausalLM] = LlamaForCausalLMPolicy + # from transformers import GPT2Model + # from .gpt2 import GPT2Policy + # auto_policy_dict[GPT2Model] = GPT2Policy + # from transformers import GPT2LMHeadModel + # from .gpt2 import GPT2LMHeadModelPolicy + # auto_policy_dict[GPT2LMHeadModel] = GPT2LMHeadModelPolicy return auto_policy_dict -def get_autopolicy(model: nn.Module): +def get_autopolicy(model: nn.Module) -> Policy: r""" Return the auto policy for the model @@ -97,7 +77,7 @@ def get_autopolicy(model: nn.Module): raise NotImplementedError( f"Auto policy for {model.__class__.__qualname__} is not implemented\n Supported models are {[i.__qualname__ for i in auto_policy_dict.keys()]}" ) - return policy + return policy() # from transformers.models.bert.modeling_bert import BertForMaskedLM, BertForPreTraining diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index ba3a97f1bbcd..80ea7a252131 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -1,102 +1,65 @@ # part of code modified from https://github.com/tunib-ai/parallelformers +from abc import ABC, abstractmethod from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Tuple, Union +from typing import Any, Callable, Dict, List, Tuple, Type, Union import torch.nn as nn +from ..shard.shard_config import ShardConfig -@dataclass -class Argument: - r""" - The argument class for the policy - Args: - attr_dict (Dict[str, Any]): The dict for the param setting - param_funcs (:class:`List[Callable]`): The list for the param functions - """ - attr_dict: Dict[str, Any] - param_funcs: List[Callable] +class ParallelModule(): - -@dataclass -class Layer: - r""" - The layer object for the policy - - Args: - suffix: (str): the suffix of the layer. - replace_layer (:class:`colosalai.nn`): The layer to replace the original layer - ignore (bool): Whether to ignore this layer if it is not in the model - reversed (bool): Whether the weight in layer is reversed, commonly the weight in `torch.nn.Linear` is [out, in], - but in GPT2 `Conv1D` layer is [in, out] which is reversed. - n_cast (int): The number of weight will cast to, like q, k, v in attention layer, n_cast should be 3. commonly in TP, we just chunk the weight with the number of devices, - but in multi-head attention, we need to chunk the weight with the number of devices * n_head, and - each device should have a part of Q, K and V weight. - """ - suffix: str = None - replace_layer: Any = None - ignore: bool = False - reversed: bool = False - n_cast: int = None + def __init__(self): + pass @dataclass -class Col_Layer(Layer): +class SubModuleReplacementDescription: r""" - Class for col shard layer in tensor parrallel + Describe how a submodule will be replaced - Args: - weight (str): The weight suffix of the layer - bias (str): The bias suffix of the layer - gather_output (bool): Whether to gather the output of the layer + suffix (str): used to get the submodule object + target_module (ParallelModule): specifies the module class used to replace to submodule + kwargs (Dict[str, Any]): the dictionary used to pass extra arguments to the `ParallelModule.from_native_module` method. """ - weight: str = None - bias: str = None - gather_output: bool = False + suffix: str + target_module: ParallelModule + kwargs: Dict[str, Any] = None @dataclass -class Row_Layer(Layer): +class ModulePolicyDescription: r""" - Class for col shard layer in tensor parrallel - - Args: - weight (str): The weight suffix of the layer - bias (str): The bias suffix of the layer - """ - weight: str = None - bias: str = None + Describe how the attributes and parameters will be transformed in a policy + attribute_replacement (Dict[str, Any]): key is the attribute name, value is the attribute value after sharding + param_replacement (List[Callable]): a list of functions to perform in-place param replacement. The function + must receive two arguments: module, process_group. One example is -@dataclass -class Dropout_Layer(Layer): - r""" - Class for dropout layer in tensor parrallel - - Args: - p (str): The dropout rate suffix of the layer - """ - p: str = None - - -@dataclass -class Embedding_Layer(Layer): - r""" - Class for col shard layer in tensor parrallel + ```python + def example_replace_weight(module: torch.nn.Module, process_group): + weight = module.weight + new_weight = shard_rowwise(weight, process_group) + module.weight = torch.nn.Parameter(new_weight) + ``` - Args: - weight (str): The weight suffix of the layer + sub_module_replacement: each element in the list is a ParamReplacementDescription object which specifies + the module to be replaced and the target module used to replacement """ - weight: str = None - gather_output: bool = True + attribute_replacement: Dict[str, Any] + param_replacement: List[Callable] + sub_module_replacement: List[SubModuleReplacementDescription] -class Policy(): +class Policy(ABC): r""" The base class for all the policies + For each different model, it should have a different policy class, like BertPolicy for Bert Model or OPTPolicy for OPT model. + AutoPolicy: Shardformer already defined some policies for huggingface model, just set ``custom_policy`` = None to use the auto policy. In shardformer autopolicy, we define a base policy for one type model, @@ -111,137 +74,75 @@ class for the example. """ - @staticmethod - def argument_policy(model_config, world_size: int) -> Dict[nn.Module, Argument]: + def __init__(self) -> None: + self.model = None + + def set_model(self, model: nn.Module) -> None: r""" - Return the dict for the modify policy, the key is the original layer class and the value is the - argument for the modify layer + Set model as an attribute of the Policy object so that we can access the model's attributes. Args: - model_config (:class:`tansformer.Config`): The config of transformer model - world_size (int)): The world size of sharding model + model (:class:`nn.Module`): The model to be perform + """ + self.model = model + + @abstractmethod + def preprocess(self, shard_config: ShardConfig = None) -> nn.Module: + r""" + Perform some preprocessing of the model, like reshaping the embedding layer + """ + + @abstractmethod + def module_policy(self, shard_config: ShardConfig = None) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: + r""" + Return the dict for the modify policy, the key is the original layer class and the value is the + argument for the modify layer Return: Dict for the modify policy, :: { - origin layer class1 (nn.Module): Argument( - attr_dict = { - argument1: value1, - argument2: value2, + origin layer class1 (nn.Module): ModulePolicyDescription( + attribute_replacement = { + "attribute1": value1, + "attribute2": value2, ... }, - param_funcs = [ - staticmethod1, - staticmethod2, + param_replacement = [ + function1, + function2, ... - ] - ), - origin layer class2 (nn.Module): Argument( - attr_dict = { - argument1: value1, - argument2: value2, - ... - }, - param_funcs = [ - staticmethod1, - staticmethod2, + ], + sub_module_replacement = [ + `SubModuleReplacementDescription` description1, + `SubModuleReplacementDescription` description2, ... ] ), + origin layer class2 (nn.Module): ModulePolicyDescription( + ... + ), ... } - - """ - raise NotImplementedError - - @staticmethod - def inject_policy() -> Union[Tuple[nn.Module, nn.Module], None]: - r""" - Return the dict for the inject model - - Return: - The injected model, key is the original model and value is the new shardmodel - :: - (OrignModel, CustomModel) - in `CustomModel`, we can overwrite the forward and backward process """ - return None - @staticmethod - def binding_policy() -> Union[Dict[str, str], None]: + @abstractmethod + def new_model_class(self) -> Union[Type[nn.Module], None]: r""" - Return the dict for the binding model, None means no need to bind + Return the new model class for the new model, None means no need to modify the model class Return: - This method should return the binding relationship for some layers share the weight or bias, - the key and value is the suffix of the weight or bias of the model - :: - return { - "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", - } - """ - return None - - @staticmethod - def attn_in() -> Union[List, None]: - r""" - Attention qkv layer - In this kind of method, we should return the list of ``Layer`` object, each ``Layer`` object should be - ``Layer`` for no slicing, ``Col_Layer`` for col slicing, ``Row_Layer`` for row slicing. And the parameters - in ``Layer`` object can refer to the ``Layer`` class. - - Returns: - List[Layer]: List of layer object, each layer is the new - """ - return None - - @staticmethod - def attn_out() -> Union[List, None]: - r""" - Attention output projection layer - - Returns: - List[Layer]: List of layer object - """ - return None - - @staticmethod - def mlp_in() -> Union[List, None]: - r""" - h -> 4h mlp layer - - Returns: - List[Layer]: List of layer object - """ - return None - - @staticmethod - def mlp_out() -> Union[List, None]: - r""" - 4h -> h mlp layer - - Returns: - List[Layer]: List of layer object - """ - return None - - @staticmethod - def embedding() -> Union[List, None]: - r""" - Partially slice the embedding layer + New model class - Return: - List[Layer]: List of layer object + E.g. + ``` + return BertModel_ + ``` """ - return None - @staticmethod - def unembedding() -> Union[List, None]: + @abstractmethod + def postprocess(self) -> nn.Module: r""" - Partially slice the embedding layer, None means there is no unembedding layer - - Return: - List[Layer]: List of layer object + Perform some postprocessing of the model, like binding the weight of embedding layer with + the classifier layer """ - return None diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index ba2266353e3e..f3431c386fe4 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,220 +1,77 @@ -from typing import Any, Callable, Dict, List, Tuple, Type - import torch.nn as nn from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead import colossalai.shardformer.layer.layers as col_nn -from .basepolicy import Argument, Col_Layer, Dropout_Layer, Policy, Row_Layer +from ..shard.shard_config import ShardConfig +from ..utils import getattr_, setattr_ +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + + +class ParallelModule(): + + def __init__(self): + pass class BertPolicy(Policy): - @staticmethod - def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: + def preprocess(self, shard_config: ShardConfig = None): + # reshape the embedding layer + r""" + Reshape the Embedding layer to make the embedding dimension divisible by world_size + """ + # TODO: + vocab_size = self.model.config.vocab_size + world_size = shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + return self.model + + def module_policy(self, shard_config: ShardConfig = None): return { BertLayer: - Argument( - attr_dict={ + ModulePolicyDescription( + attribute_replacement={ # 1. shard hidden size - "attention.self.all_head_size": config.hidden_size // world_size, - "crossattention.self.all_head_size": config.hidden_size // world_size, + "attention.self.all_head_size": + self.model.config.hidden_size // shard_config.tensor_parallel_size, + "crossattention.self.all_head_size": + self.model.config.hidden_size // shard_config.tensor_parallel_size, # 2. shard number of heads - "attention.self.num_attention_heads": config.num_attention_heads // world_size, - "crossattention.self.num_attention_heads": config.num_attention_heads // world_size, + "attention.self.num_attention_heads": + self.model.config.num_attention_heads // shard_config.tensor_parallel_size, + "crossattention.self.num_attention_heads": + self.model.config.num_attention_heads // shard_config.tensor_parallel_size, }, - param_funcs=[BertPolicy.attn_in, BertPolicy.attn_out, BertPolicy.mlp_in, BertPolicy.mlp_out]), - BertEmbeddings: - Argument( - attr_dict={ - # 1. shard vocab size - "word_embeddings.dim_size": (config.vocab_size + world_size - 1) // world_size, - }, - param_funcs=[ - BertPolicy.embedding, - ]), - } - - @staticmethod - def attn_in(): - return [ - Col_Layer( - suffix="attention.self.query", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - Col_Layer( - suffix="attention.self.key", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - Col_Layer( - suffix="attention.self.value", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - Dropout_Layer( - suffix="attention.self.dropout", - p="p", - replace_layer=col_nn.Dropout1D, - ), - Col_Layer( - suffix="crossattention.self.query", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ignore=True, - ), - Col_Layer( - suffix="crossattention.self.key", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ignore=True, - ), - Col_Layer( - suffix="crossattention.self.value", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ignore=True, - ), - ] - - @staticmethod - def attn_out(): - return [ - Row_Layer( - suffix="attention.output.dense", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Row, - ), - Dropout_Layer( - suffix="attention.output.dropout", - p="p", - replace_layer=col_nn.Dropout1D, - ), - Row_Layer( - suffix="crossattention.output.dense", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Row, - ignore=True, - ), - ] - - @staticmethod - def mlp_in(): - return [ - Col_Layer( - suffix="intermediate.dense", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - ] - - @staticmethod - def mlp_out(): - return [ - Row_Layer( - suffix="output.dense", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Row, - ), - Dropout_Layer( - suffix="output.dropout", - p="p", - replace_layer=col_nn.Dropout1D, - ) - ] - - @staticmethod - def embedding(): - return [Col_Layer( - suffix="word_embeddings", - weight="weight", - replace_layer=col_nn.VocabParallelEmbedding1D, - )] - - @staticmethod - def unembedding(): - return [ - Col_Layer( - suffix="decoder", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - gather_output=True, - ) - ] - - -# BertModel -class BertModelPolicy(BertPolicy): - - @staticmethod - def argument_policy(config, world_size): - return BertPolicy.argument_policy(config, world_size) - - -# BertForPretraining -class BertForPretrainingPolicy(BertPolicy): - - @staticmethod - def argument_policy(config, world_size): - base_argument = BertPolicy.argument_policy(config, world_size) - argument = { - BertLMPredictionHead: Argument(attr_dict={}, param_funcs=[ - BertPolicy.unembedding, - ]), + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attention.self.query", + target_module=ParallelModule, + ), + ]) } - argument.update(base_argument) - return argument - @staticmethod - def inject_policy(): + def new_model_class(self): + # do nothing return None - @staticmethod - def binding_policy(): - return { - "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", - } - - -# BertForMaskedLM -from colossalai.shardformer.model.modeling_bert import BertForMaskedLM_ + def postprocess(self): + binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + param = nn.Parameter(param) + setattr_(self.model, k, param) + setattr_(self.model, v, param) + return self.model class BertForMaskedLMPolicy(BertPolicy): - @staticmethod - def argument_policy(config, world_size): - base_argument = BertPolicy.argument_policy(config, world_size) - argument = { - BertLMPredictionHead: Argument(attr_dict={}, param_funcs=[ - BertPolicy.unembedding, - ]), - } - argument.update(base_argument) - return argument - - @staticmethod - def inject_policy(): - # return (BertForMaskedLM, BertForMaskedLM_) - return None - - @staticmethod - def binding_policy(): - return { - "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", - } + def __init__(self) -> None: + super().__init__() # BertLMHeadModel @@ -231,36 +88,5 @@ def argument_policy(config, world_size): argument.update(base_argument) return argument - @staticmethod - def inject_policy(): - return None - - @staticmethod - def binding_policy(): - return { - "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", - } - - -# BertForNextSentencePrediction -class BertForNextSentencePredictionPolicy(BertPolicy): - - @staticmethod - def argument_policy(config, world_size): - return BertPolicy.argument_policy(config, world_size) - - -# BertForSequenceClassification -class BertForSequenceClassificationPolicy(BertPolicy): - - @staticmethod - def argument_policy(config, world_size): - return BertPolicy.argument_policy(config, world_size) - - -# BertForMultipleChoice -class BertForMultipleChoicePolicy(BertPolicy): - - @staticmethod - def argument_policy(config, world_size): - return BertPolicy.argument_policy(config, world_size) + def __init__(self) -> None: + super().__init__() diff --git a/colossalai/shardformer/shard/__init__.py b/colossalai/shardformer/shard/__init__.py index d5f70163ad57..7abdd45ec7c5 100644 --- a/colossalai/shardformer/shard/__init__.py +++ b/colossalai/shardformer/shard/__init__.py @@ -1,5 +1,5 @@ from .shard_config import ShardConfig -from .sharder import ModelSharder, shard_model -from .slicer import Slicer +from .sharder import ModelSharder +from .shardformer import ShardFormer -__all__ = ['ShardConfig', 'ModelSharder', 'shard_model', 'Slicer'] +__all__ = ['ShardConfig', 'ModelSharder', 'ShardFormer'] diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 96c287577ddc..53999529d277 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -1,4 +1,5 @@ from dataclasses import dataclass +from typing import List, Literal __all__ = ['ShardConfig'] @@ -9,10 +10,18 @@ class ShardConfig: The config for sharding the huggingface model Args: - rank (int): The rank of local process - world_size (int): The world size of the distributed process + data_parallel_size (int): The size of data parallel + tensor_parallel_size (int): The size of tensor parallel + pipeline_parallel_size (int): The size of pipeline parallel + tensor_parallel_mode (List): The mode of tensor parallel, choose from `['1d','2d','2.5d','3d'] + inference_only (bool): Whether to use the inference only mode, when setting to `True`, the model + will not calculate the loss and just return the output. gather_output (bool): Whether to gather the output of the model of the last layer """ - rank: int = None - world_size: int = None + data_parallel_size: int + tensor_parallel_size: int + + pipeline_parallel_size: int + tensor_parallel_mode: Literal['1d', '2d', '2.5d', '3d'] + inference_only: bool = True gather_output: bool = True diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 7ef0c37a4040..8eee3c6a3b7e 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -4,11 +4,12 @@ import torch.nn as nn from transformers.pytorch_utils import Conv1D +from colossalai.cluster.process_group_manager import ProcessGroupManager + from ..policies.autopolicy import get_autopolicy -from ..policies.basepolicy import Col_Layer, Dropout_Layer, Policy, Row_Layer, Embedding_Layer -from ..utils.utils import getattr_, hasattr_, setattr_ +from ..policies.basepolicy import Policy +from ..utils.utils import setattr_ from .shard_config import ShardConfig -from .slicer import Slicer __all__ = ['ModelSharder', 'shard_model'] @@ -28,20 +29,23 @@ def __init__( model: nn.Module, policy: Policy, shard_config: ShardConfig = None, # TODO - ) -> None: + pg_manager: ProcessGroupManager = None) -> None: self.model = model self.policy = get_autopolicy(self.model) if policy is None else policy - self.slicer = Slicer(shard_config) self.shard_config = shard_config - self.model_config = self.model.config + self.pg_manager = pg_manager def shard(self) -> None: - self.reshape_embedding() - self.inject_model(self.model) - self.replace_layer(self.model) - self.bind_layer(self.model) + r""" + Shard the model according to the policy + """ + self.policy.set_model(self.model) + self.preprocess() + self.replace_model_class() + self.replace_module() + self.postprocess() - def reshape_embedding(self,) -> None: + def reshape_embedding(self) -> None: r""" Reshape the Embedding layer to make the embedding dimension divisible by world_size """ @@ -52,10 +56,13 @@ def reshape_embedding(self,) -> None: self.model.resize_token_embeddings(new_vocab_size) self.model_config = self.model.config - def inject_model( - self, - model: nn.Module, - ) -> None: + def preprocess(self) -> None: + self.model = self.policy.preprocess(self.shard_config) + + def postprocess(self) -> None: + self.model = self.policy.postprocess() + + def replace_model_class(self,) -> None: r""" Replace the model to policy defined model Mainly modify the forward and backward to fit distributed model @@ -64,49 +71,43 @@ def inject_model( :: BertForMaskedLM.forward -> BertForMaskedLM_.forward """ - inject_policy = self.policy.inject_policy() - if inject_policy is None: - return - - if inject_policy is None: + new_model_class = self.policy.new_model_class() + if new_model_class is None: return - org_model_cls = inject_policy[0] - shard_model_cls = inject_policy[1] - if model.__class__ == org_model_cls: - for key in shard_model_cls.__dict__.keys(): - if hasattr(model.__class__, key): - setattr( - model.__class__, - key, - getattr(shard_model_cls, key), - ) - else: - raise NotImplementedError(f"{model.__class__} is not implemented so far") + for key in new_model_class.__dict__.keys(): + if hasattr(self.model.__class__, key): + setattr( + self.model.__class__, + key, + getattr(new_model_class, key), + ) - def replace_layer( - self, - model: nn.Module, - ) -> None: + def replace_module(self,) -> None: r""" - Replace the layer according to the policy, and replace the layer one by one + Replace the module according to the policy, and replace the module one by one Args: - model (:class:`torch.nn.Module`): The layer to shard + model (:class:`torch.nn.Module`): The model to shard """ - argument_policies = self.policy.argument_policy(self.model_config, self.shard_config.world_size) - for argument_policy in argument_policies.items(): - origin_layer_cls = argument_policy[0] - attr_dict = argument_policy[1].attr_dict - param_funcs = argument_policy[1].param_funcs - self.traverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs) - - def traverse_replace_layer( + print(self.policy) + module_descriptions = self.policy.module_policy(self.shard_config) + print(f"*******{module_descriptions}") + for module_description in module_descriptions.items(): + origin_layer_cls = module_description[0] + attr_replacement = module_description[1].attribute_replacement + param_replacement = module_description[1].param_replacement + sub_module_replacement = module_description[1].sub_module_replacement + self._recursive_replace_layer(self.model, origin_layer_cls, attr_replacement, param_replacement, + sub_module_replacement) + + def _recursive_replace_layer( self, - layer: nn.Module, + module: nn.Module, origin_cls: nn.Module, - attr_dict: Dict[str, Any], - param_funcs: List[Callable], + attr_replacement: Dict[str, Any], + param_replacement: List[Callable], + sub_module_replacement: List[Callable], ) -> None: r""" Reverse the replace layer operation @@ -114,169 +115,69 @@ def traverse_replace_layer( Args: layer (:class:`torch.nn.Module`): The object of layer to shard origin_cls (:class:`transformers.model`): The origin layer class - attr_dict (Dict): The attribute dict to modify - policy_cls (:class:`Policy`): The policy class + attr_replacement (Dict): The attribute dict to modify + param_replacement (List[Callable]): The function list to get parameter shard information in polic + sub_module_replacement (List[Callable]): The function list to get sub module shard information in policy """ - if layer.__class__ == origin_cls: - for k, v in attr_dict.items(): - setattr_(layer, k, v, ignore=True) - self.shard_one_layer(layer, param_funcs) - for name, child in layer.named_children(): - self.traverse_replace_layer(child, origin_cls, attr_dict, param_funcs) - return layer - - def shard_one_layer( + if module.__class__ == origin_cls: + self._replace_attr(module, attr_replacement) + self._replace_param(module, param_replacement) + self._replace_sub_module(module, sub_module_replacement) + for name, child in module.named_children(): + self._recursive_replace_layer(child, origin_cls, attr_replacement, param_replacement, + sub_module_replacement) + + def _replace_attr( self, - org_layer: nn.Module, - param_funcs: List[Callable], + module: nn.Module, + attr_replacement: Dict[str, Any], ) -> None: r""" - Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict + Replace the attribute of the layer Args: - org_layer (:class:`torch.nn.Module`): The origin layer object to shard - param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class - + layer (:class:`torch.nn.Module`): The object of layer to shard + attr_replacement (Dict): The attribute dict to modify """ - for func in param_funcs: - policy_layers = func() - for policy_layer in policy_layers: - suffix = policy_layer.suffix - replace_layer_cls = policy_layer.replace_layer - ignore = policy_layer.ignore - reversed = policy_layer.reversed - n_cast = policy_layer.n_cast - - assert replace_layer_cls is not None, 'replace_layer should not be None' - - # create new object to replace the origin layer - # Linear - suffix_layer = getattr_(org_layer, suffix, ignore=True) - assert suffix_layer is not None or ignore, f"Layer {org_layer.__class__.__qualname__} has no attribute {suffix}" - if suffix_layer is None and ignore: - continue - if isinstance(policy_layer, (Col_Layer, Row_Layer, Embedding_Layer)): - weight = None - bias = None - weight_attr = suffix + '.' + policy_layer.weight if policy_layer.weight is not None else None - bias_attr = suffix + '.' + policy_layer.bias if hasattr(policy_layer, 'bias') and policy_layer.bias is not None else None - - if weight_attr is not None: - if hasattr_(org_layer, weight_attr): - weight = getattr_(org_layer, weight_attr) - else: - raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {weight_attr}") + for k, v in attr_replacement.items(): + setattr_(module, k, v, ignore=True) - if bias_attr is not None: - if hasattr_(org_layer, bias_attr): - bias = getattr_(org_layer, bias_attr) - else: - raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {bias_attr}") - - # set the sliced weight and bias to the new nn_col layer - assert weight is not None or bias is not None - - # slice weight and bias - weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__, n_cast, reversed) - - if replace_layer_cls.__name__ == "Linear1D_Row": - replace_layer = replace_layer_cls(weight.shape[1], - weight.shape[0], - bias=False if bias is None else True) - elif replace_layer_cls.__name__ == "Linear1D_Col": - gather_output = policy_layer.gather_output and self.shard_config.gather_output - replace_layer = replace_layer_cls(weight.shape[0], - weight.shape[1], - bias=False if bias is None else True, - gather_output=gather_output) - elif replace_layer_cls.__name__ == "Embedding1D": - gather_output = policy_layer.gather_output - replace_layer = replace_layer_cls(weight.shape[0], - weight.shape[1], - gather_output=gather_output) - elif replace_layer_cls.__name__ == "VocabParallelEmbedding1D": - replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], - getattr_(org_layer, f"{suffix}.padding_idx", ignore=True)) - # setattr_(org_layer, suffix, replace_layer, ignore=ignore) - # self.set_param(replace_layer, weight, bias) - else: - raise NotImplementedError( - f"Replacing to {replace_layer_cls.__name__} is not implemented so far") - setattr_(org_layer, suffix, replace_layer, ignore=ignore) - self.set_param(replace_layer, weight, bias) - # dropout - elif isinstance(policy_layer, Dropout_Layer): - p_attr = suffix + '.' + policy_layer.p - p = getattr_(org_layer, p_attr, ignore=True) - replace_layer = replace_layer_cls(p) - setattr_(org_layer, suffix, replace_layer, ignore=ignore) - else: - raise NotImplementedError( - f"Replacing {getattr_(org_layer, suffix).__class__} is not implemented so far") - - def set_param(self, - layer: Any, - weight: torch.Tensor = None, - bias: torch.Tensor = None, - layer_attr: str = "") -> None: + def _replace_param( + self, + module: nn.Module, + param_replacement: List[Callable], + ) -> None: r""" - Reset the weight and bias of the layer object + Replace the parameter of the layer Args: - layer (:class:`torch.nn.Module`): The layer object - layer_attr (str): The attribute name of the layer - weight (:class:`torch.Tensor`): The weight of the layer - bias (:class:`torch.Tensor`): The bias of the layer + layer (:class:`torch.nn.Module`): The object of layer to shard + param_replacement (List[Callable]): The function list to get parameter shard information in policy """ - assert weight is not None or bias is not None - if weight is not None: - setattr_(layer, "weight" if layer_attr == "" else layer_attr + ".weight", nn.Parameter(weight.contiguous())) - self.set_layer_size(layer, layer_attr, weight.shape) - if bias is not None: - setattr_(layer, "bias" if layer_attr == "" else layer_attr + ".bias", nn.Parameter(bias.contiguous())) + # TODO: support parameter shard + pass - def set_layer_size(self, layer: nn.Module, layer_attr: str, size: torch.Size) -> None: + def _replace_sub_module( + self, + org_layer: nn.Module, + sub_module_replacement: List[Callable], + ) -> None: r""" - Set the layer attribute + Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict Args: - layer (:class:`torch.nn.Module`): The layer object - layer_attr (str): The attribute name of the layer - size (:class:`torch.Size`): The size of the tensor - """ - # Tensor.shape[0] -> out_features, Tensor.shape[1] -> in_features - attrs = ["out_features", "in_features"] - for i, attr in enumerate(attrs): - if hasattr_(layer, f"{layer_attr}.{attr}"): - setattr_(layer, f"{layer_attr}.{attr}", size[i]) - - def bind_layer(self, model: nn.Module) -> None: - r""" - Bind the layer according to the binding policy + org_layer (:class:`torch.nn.Module`): The origin layer object to shard + param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class - Args: - model (:class:`torch.nn.Module`): The shard model """ - binding_map = self.policy.binding_policy() - if binding_map is None: - return - for k, v in binding_map.items(): - param = getattr_(model, k) - param = nn.Parameter(param) - setattr_(model, k, param) - setattr_(model, v, param) - + for description in sub_module_replacement: + suffix = description.suffix + target_module = description.target_module + kwargs = description.kwargs -def shard_model(model: nn.Module, shard_config: ShardConfig = None, policy: Policy = None): - r""" - The function is used to shard the PyTorch model. + assert target_module is not None, 'target_module should not be None' - Args: - model (`torch.nn.Model`): the origin huggingface model - shard_config (`ShardConfig`): the config for distribute information - policy (`Policy`): the custom policy for sharding - """ - # TODO: init shard_config automatically - sharder = ModelSharder(model=model, shard_config=shard_config, policy=policy) - sharder.shard() - return model + # TODO: integrate with new layer + # replace_layer = target_module.from_native_layer(org_layer, self.pg_manager) + replace_layer = None + setattr_(org_layer, suffix, replace_layer) diff --git a/colossalai/shardformer/shard/shardformer.py b/colossalai/shardformer/shard/shardformer.py new file mode 100644 index 000000000000..5313dfecb37e --- /dev/null +++ b/colossalai/shardformer/shard/shardformer.py @@ -0,0 +1,77 @@ +import torch.nn as nn +from torch.utils.data import Dataset + +from colossalai.cluster import DistCoordinator, ProcessGroupManager + +from ..policies.basepolicy import Policy +from .shard_config import ShardConfig +from .sharder import ModelSharder + + +class ShardFormer: + """ + Parallelize model based on the given config and policy + + Example: + + ```python + from colossalai.shardformer import ShardFormer, ShardConfig + from transformers import BertForMaskedLM + import colossalai + import torch + + colossalai.launch_from_torch(config={}) + + org_model = BertForMaskedLM.from_pretrained('bert-base-uncased') + shard_config = ShardConfig( + tensor_parallel_size=2, + data_parallel_size=1, + pipeline_parallel_size=1, + tensor_parallel_mode='1d', + inference_only=True, + gather_output=True + ) + shard_former = ShardFormer(shard_config=shard_config) + shard_former.init_distributed() + model = shard_former.shard_model(org_model) + ``` + """ + + def __init__(self, shard_config: ShardConfig): + """ + Do two things: + 1. Create a colossalai.cluster.process_group_manager to manage process groups for dp, tp and pp + 2. serve as a store for + """ + self.coordinator = DistCoordinator() + self.shard_config = shard_config + self.pg_manager = None + + def init_distributed(self) -> ProcessGroupManager: + """ + Initialize the distributed process group according to the + """ + pg_manager = ProcessGroupManager() + if (self.shard_config.tensor_parallel_mode == '1d'): + pg_manager.create_process_group(name='tp1d', ranks=range(self.coordinator.world_size)) + self.pg_manager = pg_manager + return pg_manager + + def shard_model(self, model: nn.Module, policy: Policy = None): + r""" + The function is used to shard the PyTorch model. + + Args: + model (`torch.nn.Model`): the origin huggingface model + shard_config (`ShardConfig`): the config for distribute information + policy (`Policy`): the custom policy for sharding + """ + sharder = ModelSharder(model=model, shard_config=self.shard_config, policy=policy, pg_manager=self.pg_manager) + sharder.shard() + return model + + def shard_dataset(self, dataset: Dataset): + """ + Shard dataset for DP + """ + pass diff --git a/colossalai/shardformer/shard/slicer.py b/colossalai/shardformer/shard/slicer.py deleted file mode 100644 index 860533dca50d..000000000000 --- a/colossalai/shardformer/shard/slicer.py +++ /dev/null @@ -1,163 +0,0 @@ -import torch - -from ..policies.basepolicy import Col_Layer, Dropout_Layer, Layer, Row_Layer, Embedding_Layer -from .shard_config import ShardConfig - -dim_mapping = {Col_Layer: 0, Row_Layer: 1, Embedding_Layer: 1} - - -class Slicer(): - - def __init__( - self, - shardconfig: ShardConfig #TODO - ) -> None: - self.shardconfig = shardconfig - - def slice_weight_bias( - self, - weight: torch.Tensor, - bias: torch.Tensor, - policy_layer_cls: Layer, - n_cast: int = None, - reversed: bool = False, - ): - r""" - Slice the weight and bias according to policy layer cls - ``Layer`` -> do nothing - ``Col_Layer`` -> slice the weight and bias along dim 1 - ``Row_Layer`` -> slice the weight along dim 0 and do not slice bias - - Args: - weight (:class:`torch.nn.Module`): The weight of the layer - bias: (:class:`torch.nn.Module`): The bias of the layer - policy_layer_class (:class:`Policy`): The class represent how to slice the tensor - """ - if policy_layer_cls in [Layer, Dropout_Layer]: - return weight, bias - - dim = dim_mapping[policy_layer_cls] if not reversed else (1 - dim_mapping[policy_layer_cls]) - # print(weight.shape, dim) - if policy_layer_cls == Col_Layer: - weight = self.slice_tensor(weight, dim, False, n_cast) - bias = self.slice_tensor(bias, 0, True, n_cast) - elif policy_layer_cls == Row_Layer: - weight = self.slice_tensor(weight, dim, False, n_cast) - elif policy_layer_cls == Embedding_Layer: - weight = self.slice_tensor(weight, dim, False, n_cast) - else: - raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported") - if reversed: - weight = weight.transpose(0, 1).contiguous() - return weight, bias - - def slice_tensor( - self, - tensor_in: torch.Tensor, - dim: int, - is_bias: bool, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice tensor according to the config - - Args: - tensor_in (:class:`torch.Tensor`): The tensor to slice - dim (int): The dimension to slice - is_bias (bool): Whether the tensor is bias - """ - if tensor_in is None: - return None - if not is_bias: - return self.slice_2d(tensor_in, dim, n_cast) - else: - return self.slice_1d(tensor_in, n_cast) - - def slice_2d( - self, - tensor: torch.Tensor, - dim: int, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice the 2D tensor - - Args: - tensor (:class:`torch.Tensor`): The tensor to slice - dim (int): The dimension to slice - """ - assert dim in [0, 1], f"Only support 2D tensor, but got {dim}D tensor" - if dim == 0: - return self.slice_row(tensor, n_cast) - elif dim == 1: - return self.slice_col(tensor, n_cast) - - def slice_1d( - self, - tensor: torch.Tensor, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice the 1D tensor - - Args: - tensor (:class:`torch.Tensor`): The tensor to slice - - Returns: - :class:`torch.Tensor`: The sliced tensor - """ - if n_cast is None: - return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() - else: - tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) - chunk_list = [ - tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) - ] - return torch.cat(chunk_list, dim=0).contiguous() - - def slice_col( - self, - tensor: torch.Tensor, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice the tensor in column - - Args: - tensor (:class:`torch.Tensor`): The tensor to slice - - Returns: - :class:`torch.Tensor`: The sliced tensor - - """ - if n_cast is None: - return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous() - else: - tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=1) - chunk_list = [ - tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) - ] - return torch.cat(chunk_list, dim=1).contiguous() - - def slice_row( - self, - tensor: torch.Tensor, - n_cast: int = None, - ) -> torch.Tensor: - r""" - Slice the tensor in column - - Args: - tensor (:class:`torch.Tensor`): The tensor to slice - - Returns: - :class:`torch.Tensor`: The sliced tensor - """ - if n_cast is None: - return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous() - else: - tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0) - chunk_list = [ - tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size) - ] - return torch.cat(chunk_list, dim=0).contiguous() diff --git a/colossalai/shardformer/utils/__init__.py b/colossalai/shardformer/utils/__init__.py index e69de29bb2d1..b50e7b2f6d80 100644 --- a/colossalai/shardformer/utils/__init__.py +++ b/colossalai/shardformer/utils/__init__.py @@ -0,0 +1 @@ +from .utils import getattr_, hasattr_, setattr_ From 015af592f805d84b1713a06f5324ae40b05c3e84 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 15 Jun 2023 18:03:38 +0800 Subject: [PATCH 365/413] [shardformer] integrated linear 1D with dtensor (#3996) * [shardformer] integrated linear 1D with dtensor * polish code --- colossalai/nn/layer/base_layer.py | 1 + colossalai/shardformer/layer/_operation.py | 133 +++- colossalai/shardformer/layer/dropout.py | 54 +- colossalai/shardformer/layer/layers.py | 653 +++++++++--------- colossalai/shardformer/layer/utils.py | 138 ++++ colossalai/tensor/d_tensor/api.py | 44 ++ colossalai/tensor/d_tensor/layout.py | 21 +- .../tensor/d_tensor/layout_converter.py | 2 +- .../test_layer/test_linear_1d.py | 67 ++ 9 files changed, 706 insertions(+), 407 deletions(-) create mode 100644 colossalai/shardformer/layer/utils.py create mode 100644 colossalai/tensor/d_tensor/api.py create mode 100644 tests/test_shardformer/test_layer/test_linear_1d.py diff --git a/colossalai/nn/layer/base_layer.py b/colossalai/nn/layer/base_layer.py index 5234b6b1a1b5..4a06bdcb7629 100644 --- a/colossalai/nn/layer/base_layer.py +++ b/colossalai/nn/layer/base_layer.py @@ -10,6 +10,7 @@ class ParallelLayer(nn.Module): + global_state_dict: bool = True def __init__(self): diff --git a/colossalai/shardformer/layer/_operation.py b/colossalai/shardformer/layer/_operation.py index e817ea3ebbee..208a391c33e2 100644 --- a/colossalai/shardformer/layer/_operation.py +++ b/colossalai/shardformer/layer/_operation.py @@ -54,10 +54,10 @@ class LinearWithAsyncCommunication(torch.autograd.Function): """ @staticmethod - def forward(ctx, input_, weight, bias, parallel_mode, async_grad_allreduce): + def forward(ctx, input_, weight, bias, process_group, async_grad_allreduce): ctx.save_for_backward(input_, weight) ctx.use_bias = bias is not None - ctx.parallel_mode = parallel_mode + ctx.process_group = process_group ctx.async_grad_allreduce = async_grad_allreduce output = torch.matmul(input_, weight.t()) @@ -74,12 +74,13 @@ def backward(ctx, grad_output): grad_input = grad_output.matmul(weight) grad_output = grad_output.contiguous() # Convert the tensor shapes to 2D for execution compatibility - grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2]) - total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2]) + if len(grad_output.shape) > 2: + grad_output = grad_output.view(-1, grad_output.shape[-1]) + total_input = total_input.view(-1, total_input.shape[-1]) if ctx.async_grad_allreduce: # Asynchronous all-reduce - handle = dist.all_reduce(grad_input, group=gpc.get_group(ctx.parallel_mode), async_op=True) + handle = dist.all_reduce(grad_input, group=ctx.process_group, async_op=True) # Delay the start of weight gradient computation shortly (3us) to have # all-reduce scheduled first and have GPU resources allocated _ = torch.empty(1, device=grad_output.device) + 1 @@ -93,5 +94,123 @@ def backward(ctx, grad_output): return grad_input, grad_weight, grad_bias, None, None, None -def linear_with_async_comm(input_, weight, bias, parallel_mode, async_grad_allreduce): - return LinearWithAsyncCommunication.apply(input_, weight, bias, parallel_mode, async_grad_allreduce) +class _SplitForwardGatherBackward(torch.autograd.Function): + """ + Split the input and keep only the corresponding chuck to the rank. + + Args: + input_ (`torch.Tensor`): input matrix. + dim (int): the dimension to perform split and gather + process_group (`torch.distributed.ProcessGroup`): the process group used for collective communication + + """ + + @staticmethod + def forward(ctx, input_, dim, process_group): + ctx.process_group = process_group + ctx.dim = dim + return _split(input_, dim, process_group) + + @staticmethod + def backward(ctx, grad_output): + return _gather(grad_output, ctx.dim, ctx.process_group), None, None + + +class _ReduceInput(torch.autograd.Function): + """ + All-reduce the input from the model parallel region. + + Args: + input_: input matrix. + parallel_mode: parallel mode. + """ + + @staticmethod + def forward(ctx, input_, process_group): + return _reduce(input_, process_group) + + @staticmethod + def backward(ctx, grad_output): + return grad_output, None + + +def _reduce(input_, process_group): + # skip if only one rank involved + if dist.get_world_size(process_group) == 1: + return input_ + else: + dist.all_reduce(input_, group=process_group) + return input_ + + +def _split(input_, dim=-1, process_group=None): + # skip if only one rank involved + world_size = dist.get_world_size(process_group) + if world_size == 1: + return input_ + + # Split along last dimension. + dim_size = input_.size(dim) + assert dim_size % world_size == 0, \ + f'The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), ' \ + f'cannot split tensor evenly' + + tensor_list = torch.split(input_, dim_size // world_size, dim=dim) + rank = dist.get_rank(process_group) + output = tensor_list[rank].contiguous() + + return output + + +def _gather(input_, dim=-1, process_group=None): + # skip if only one rank involved + world_size = dist.get_world_size(process_group) + if world_size == 1: + return input_ + + # all gather + rank = dist.get_rank(process_group) + tensor_list = [torch.empty_like(input_) for _ in range(world_size)] + tensor_list[rank] = input_ + torch.distributed.all_gather(tensor_list, input_, group=process_group) + + # concat + output = torch.cat(tensor_list, dim=dim).contiguous() + + return output + + +class _GatherForwardSplitBackward(torch.autograd.Function): + """Gather the input from model parallel region and concatenate. + + Args: + input_: input matrix. + parallel_mode: parallel mode. + dim: dimension + """ + + @staticmethod + def forward(ctx, input_, dim, process_group): + ctx.process_group = process_group + ctx.dim = dim + return _gather(input_, dim, process_group) + + @staticmethod + def backward(ctx, grad_output): + return _split(grad_output, ctx.dim, ctx.process_group), None, None + + +def linear_with_async_comm(input_, weight, bias, process_group, async_grad_allreduce): + return LinearWithAsyncCommunication.apply(input_, weight, bias, process_group, async_grad_allreduce) + + +def gather_forward_split_backward(input_, dim, process_group): + return _GatherForwardSplitBackward.apply(input_, dim, process_group) + + +def split_forward_gather_backward(input_, dim, process_group): + return _SplitForwardGatherBackward.apply(input_, dim, process_group) + + +def reduce_input(input_, process_group): + return _ReduceInput.apply(input_, process_group) diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py index 0f653a9be780..5d295be6bd83 100644 --- a/colossalai/shardformer/layer/dropout.py +++ b/colossalai/shardformer/layer/dropout.py @@ -1,58 +1,20 @@ -import os -from contextlib import contextmanager - import torch +import torch.distributed as dist import torch.nn as nn - -class SeedManager: - """ - This class is a random state manager to change random state for different random seed. - - """ - - def __init__(self): - original_state = torch.cuda.get_rng_state() - # TODO: unify this seed manager with the colossalai.context.random - seed = os.getpid() - torch.cuda.manual_seed(int(seed)) - self.dropout_state = torch.cuda.get_rng_state() - torch.cuda.set_rng_state(original_state) - - def set_mode(self, rng_state): - torch.cuda.set_rng_state(rng_state) - - def get_current_mode(self): - current_state = torch.cuda.get_rng_state() - return current_state - - @contextmanager - def dropout_mode(self): - """ - This is a context manager to change the dropout state and recover the original state. - - Usage: - :: - >>> with _seed_manager.dropout_mode(): - >>> input = super().forward(input) - """ - try: - current_mode = self.get_current_mode() - yield self.set_mode(self.dropout_state) - finally: - self.dropout_state = self.get_current_mode() - self.set_mode(current_mode) - - -_seed_manager = SeedManager() +from .utils import create_randomizer_with_offset class Dropout1D(nn.Dropout): - def __init__(self, p=0.5, inplace=False): + def __init__(self, p=0.5, inplace=False, process_group=None): super().__init__(p, inplace) + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=process_group) + def forward(self, input): - with _seed_manager.dropout_mode(): + with self.randomizer.fork_rng(): input = super().forward(input) return input diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py index a9f3cf5ad14c..2ad6523c9a86 100644 --- a/colossalai/shardformer/layer/layers.py +++ b/colossalai/shardformer/layer/layers.py @@ -2,12 +2,16 @@ # -*- encoding: utf-8 -*- import math +from abc import ABC, abstractmethod from collections import OrderedDict -from typing import Callable, Tuple +from typing import Callable, List, Tuple, Union import torch +import torch.distributed as dist +import torch.nn as nn import torch.nn.functional as F from torch import Tensor +from torch.distributed import ProcessGroup from torch.nn.parameter import Parameter from colossalai.communication import broadcast @@ -22,13 +26,11 @@ gather_forward_split_backward, get_parallel_input, reduce_grad, - reduce_input, set_parallel_input, - split_forward_gather_backward, ) from colossalai.nn.layer.utils import divide, set_tensor_parallel_attribute_by_partition from colossalai.nn.layer.vanilla import VanillaLayerNorm, VanillaPatchEmbedding -from colossalai.registry import LAYERS +from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise from colossalai.utils.checkpointing import ( broadcast_state_dict, gather_tensor_parallel_state_dict, @@ -36,7 +38,13 @@ ) from colossalai.utils.cuda import get_current_device -from ._operation import linear_with_async_comm +from ._operation import ( + gather_forward_split_backward, + linear_with_async_comm, + reduce_input, + split_forward_gather_backward, +) +from .utils import create_randomizer_with_offset Fast_LN = None try: @@ -46,21 +54,44 @@ pass -# @LAYERS.register_module -class Linear1D(ColossalaiModule): - r"""Linear layer for 1D parallelism. +class ParallelModule(nn.Module, ABC): + + @abstractmethod + def from_native_module(module: nn.Module, + process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "ParallelModule": + """ + Convert a native PyTorch module to a parallelized module. + + Args: + module (nn.Module): the module to be converted. + process_group (ProcessGroup or list[ProcessGroup]): the process group(s) to be used for communication. + If this is a list, the process group at the ith index of the list will correspond to the process group + in the ith axis of the device mesh. Defaults to None, which means the global process group. + """ + pass + + +class Linear1D_Col(ParallelModule): + r"""Linear layer with column parallelism. + + The linear layer is defined as :math:`Y = XA + b`. A is parallelized along + its second dimension as :math:`A = [A_1, ..., A_p]`. Args: in_features (int): size of each input sample. out_features (int): size of each output sample. bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - gather_output (bool, optional): Whether to call all-gather on output, defaults to False. - skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, + dtype (`torch.dtype`): The dtype of parameters, defaults to None. + device (`torch.device`): The device of parameters, defaults to None. + process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None. + gather_output (bool, optional): If true, call all-gather on output and make Y available + to all GPUs, otherwise, every GPU will have its output + which is :math:`Y_i = XA_i`, defaults to False + skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion, defaults to False - weight_initializer (:class:`typing.Callable`, optional): + weight_initializer (`typing.Callable`): The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): + bias_initializer (`typing.Callable`): The initializer of bias, defaults to xavier uniform initializer. More details about ``initializer`` please refer to @@ -72,32 +103,281 @@ def __init__(self, out_features: int, bias: bool = True, dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, gather_output: bool = False, skip_bias_add: bool = False, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): - parallel_input = get_parallel_input() - if not parallel_input and not gather_output: - layer = Linear1D_Col(in_features, - out_features, + super().__init__() + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.gather_output = gather_output + self.skip_bias_add = skip_bias_add + self.device = device + self.process_group = process_group + self.num_partitions = dist.get_world_size(self.process_group) + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + self.out_features_per_partition = divide(out_features, self.num_partitions) + + # Parameters. + # Initialize weight. + if device is None: + device = get_current_device() + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) + + if bias: + self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) + else: + self.bias = None + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer, bias_initializer) + + def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, + **kwargs) -> ParallelModule: + r""" + Convert a native PyTorch linear layer to a parallelized linear layer. + """ + # get the attributes + in_features = module.in_features + out_features = module.out_features + bias = module.bias is not None + device = module.weight.device + + # ensure only one process group is passed + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + linear_1d = Linear1D_Col(in_features=in_features, + out_features=out_features, bias=bias, - dtype=dtype, - skip_bias_add=skip_bias_add, - weight_initializer=weight_initializer, - bias_initializer=bias_initializer) + device=device, + process_group=process_group, + *args, + **kwargs) + + # TODO: copy the sharded weights + with torch.no_grad(): + # the weigh to the linear layer is a transpose + # thus shard on row is equal to shard on column + sharded_weight = shard_rowwise(module.weight.data, process_group) + linear_1d.weight.data.copy_(sharded_weight) + if bias: + sharded_bias = shard_colwise(module.bias.data, process_group) + linear_1d.bias.copy_(sharded_bias) + + return linear_1d + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + + def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + # Set up backprop all-reduce. + # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) + input_parallel = input_ + # Matrix multiply. + bias = self.bias if not self.skip_bias_add else None + output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True) + + if self.gather_output: + # All-gather across the partitions. + output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) + else: + output = output_parallel + + if self.skip_bias_add: + return output, self.bias + else: + return output + + +class Linear1D_Row(ParallelModule): + r""" Linear layer with row parallelism + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (`torch.dtype`): The dtype of parameters, defaults to None. + parallel_input (bool): If set to ``True``, it's assumed that the input is split, defaults to False. + skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, + parallel_input: bool = True, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), + stream_chunk_num: int = 1): + super().__init__() + + self.stream_chunk_num = stream_chunk_num + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.parallel_input = parallel_input + self.skip_bias_add = skip_bias_add + self.process_group = process_group + self.num_partitions = dist.get_world_size(self.process_group) + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + # Divide the weight matrix along the last dimension. + self.input_size_per_partition = divide(in_features, self.num_partitions) + + # Parameters. + # Initialize weight. + if device is None: + device = get_current_device() + + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) + + if self.stream_chunk_num > 1: + # TODO() work for inference only + self.chunk_weight() + if bias: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) else: - layer = Linear1D_Row(in_features, - out_features, + self.bias = None + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer, bias_initializer) + + @staticmethod + def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, + **kwargs) -> ParallelModule: + r""" + Convert a native PyTorch linear layer to a parallelized linear layer. + """ + # get the attributes + in_features = module.in_features + out_features = module.out_features + bias = module.bias is not None + device = module.weight.device + + # ensure only one process group is passed + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + linear_1d = Linear1D_Row(in_features=in_features, + out_features=out_features, bias=bias, - dtype=dtype, - parallel_input=parallel_input, - skip_bias_add=skip_bias_add, - weight_initializer=weight_initializer, - bias_initializer=bias_initializer) - super().__init__(layer) + device=device, + process_group=process_group, + *args, + **kwargs) + + # TODO: copy the sharded weights + with torch.no_grad(): + # the weigh to the linear layer is a transpose + # thus shard on col is equal to shard on row + sharded_weight = shard_colwise(module.weight.data, process_group) + linear_1d.weight.data.copy_(sharded_weight) + + if bias: + linear_1d.bias.copy_(module.bias.data) + + return linear_1d + + def chunk_weight(self): + self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + if self.process_group is None: + src_rank = 0 + else: + src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) + dist.broadcast(self.bias, src=src_rank, group=self.process_group) + + def forward(self, input_: Tensor) -> Tensor: + # Set up backprop all-reduce. + if self.parallel_input: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + input_ = input_ + else: + assert divide(input_.shape[-1], self.num_partitions) == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1] * self.num_partitions) + input_ = split_forward_gather_backward(input_, dim=-1, process_group=self.process_group) + + if self.stream_chunk_num > 1: + if self.training: + raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!") + with torch.no_grad(): + output_parallel_list = [None for i in range(self.stream_chunk_num)] + handle_list = [] + for i in range(self.stream_chunk_num): + output_parallel_list[i] = F.linear(input_, self.weight_list[i]) + handle = torch.distributed.all_reduce(output_parallel_list[i], + group=self.process_group, + async_op=True) + handle_list.append(handle) + # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D) + for handle in handle_list: + handle.wait() + output = torch.cat(output_parallel_list, dim=-1) + else: + output_parallel = F.linear(input_, self.weight) + # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) + output = reduce_input(output_parallel, self.process_group) + + if not self.skip_bias_add: + if self.bias is not None: + output = output + self.bias + return output + else: + return output, self.bias -# @LAYERS.register_module class LayerNorm1D(ColossalaiModule): r""" Layer Normalization for colossalai @@ -152,7 +432,6 @@ def _save_to_state_dict(self, destination, prefix, keep_vars): super()._save_to_state_dict(destination, prefix, keep_vars) -# @LAYERS.register_module class Classifier1D(ParallelLayer): r"""RowLinear with given weight. Classifier of 1D parallelism. @@ -288,7 +567,6 @@ def forward(self, input_: Tensor) -> Tensor: return output -# @LAYERS.register_module class VocabParallelClassifier1D(ParallelLayer): r"""ColLinear with given weight. Classifier of 1D parallelism. @@ -424,317 +702,8 @@ def forward(self, input_: Tensor) -> Tensor: # @LAYERS.register_module -class Linear1D_Col(ParallelLayer): - r"""Linear layer with column parallelism. - - The linear layer is defined as :math:`Y = XA + b`. A is parallelized along - its second dimension as :math:`A = [A_1, ..., A_p]`. - - Args: - in_features (int): size of each input sample. - out_features (int): size of each output sample. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - gather_output (bool, optional): If true, call all-gather on output and make Y available - to all GPUs, otherwise, every GPU will have its output - which is :math:`Y_i = XA_i`, defaults to False - skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, - which is preserved for kernel fusion, defaults to False - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - More details about ``initializer`` please refer to - `init `_. - """ - def __init__(self, - in_features: int, - out_features: int, - bias: bool = True, - dtype: torch.dtype = None, - gather_output: bool = False, - skip_bias_add: bool = False, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): - super().__init__() - - # Keep input parameters - self.in_features = in_features - self.out_features = out_features - self.gather_output = gather_output - self.skip_bias_add = skip_bias_add - - if skip_bias_add and not bias: - raise ValueError('cannot skip bias addition if bias is None') - - # self.out_features_per_partition = divide(out_features*2, gpc.tensor_parallel_size) - self.out_features_per_partition = out_features - - # Parameters. - # Initialize weight. - factory_kwargs = {'device': get_current_device(), 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) - - if bias: - self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) - else: - self.bias = None - with seed(ParallelMode.TENSOR): - self.reset_parameters(weight_initializer, bias_initializer) - self._set_tensor_parallel_attributes() - is_parallel_output = not self.gather_output - set_parallel_input(is_parallel_output) - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.out_features - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - - def _set_tensor_parallel_attributes(self): - num_partition = gpc.get_world_size(ParallelMode.TENSOR) - set_tensor_parallel_attribute_by_partition(self.weight, num_partition) - if self.bias is not None: - set_tensor_parallel_attribute_by_partition(self.bias, num_partition) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - if self.bias is not None: - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: 0, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: True - }) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - local_state = OrderedDict({weight_key: self.weight}) - if self.bias is not None: - local_state[bias_key] = self.bias - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: 0, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: True - }, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - # Set up backprop all-reduce. - # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) - input_parallel = input_ - # Matrix multiply. - bias = self.bias if not self.skip_bias_add else None - # output_parallel = F.linear(input_parallel, self.weight, bias) - output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, ParallelMode.PARALLEL_1D, True) - if self.gather_output: - # All-gather across the partitions. - output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) - else: - output = output_parallel - - if self.skip_bias_add: - return output, self.bias - else: - return output - - -# @LAYERS.register_module -class Linear1D_Row(ParallelLayer): - r""" Linear layer with row parallelism - - Args: - in_features (int): size of each input sample. - out_features (int): size of each output sample. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - parallel_input (bool, optional): If set to ``True``, it's assumed that the input is split, defaults to False. - skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer, - which is preserved for kernel fusion, defaults to False - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - out_features: int, - bias: bool = True, - dtype: torch.dtype = None, - parallel_input: bool = True, - skip_bias_add: bool = False, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), - stream_chunk_num: int = 1): - super().__init__() - - self.stream_chunk_num = stream_chunk_num - - # Keep input parameters - self.in_features = in_features - self.out_features = out_features - self.parallel_input = parallel_input - self.skip_bias_add = skip_bias_add - - if skip_bias_add and not bias: - raise ValueError('cannot skip bias addition if bias is None') - - # Divide the weight matrix along the last dimension. - # self.input_size_per_partition = divide(in_features*2, gpc.tensor_parallel_size) - self.input_size_per_partition = in_features - - # Parameters. - # Initialize weight. - factory_kwargs = {'device': get_current_device(), 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) - - if self.stream_chunk_num > 1: - # TODO() work for inference only - self.chunk_weight() - if bias: - self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) - else: - self.bias = None - with seed(ParallelMode.TENSOR): - self.reset_parameters(weight_initializer, bias_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) - - def chunk_weight(self): - self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.out_features - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D) - - def _set_tensor_parallel_attributes(self): - num_partition = gpc.get_world_size(ParallelMode.TENSOR) - set_tensor_parallel_attribute_by_partition(self.weight, num_partition) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - if self.bias is not None: - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: -1, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: False - }) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - local_state = OrderedDict({weight_key: self.weight}) - if self.bias is not None: - local_state[bias_key] = self.bias - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: -1, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: False - }, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tensor: - # Set up backprop all-reduce. - if self.parallel_input: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - input_ = input_ - else: - assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \ - 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size) - input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1) - - if self.stream_chunk_num > 1: - if self.training: - raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!") - with torch.no_grad(): - output_parallel_list = [None for i in range(self.stream_chunk_num)] - handle_list = [] - for i in range(self.stream_chunk_num): - output_parallel_list[i] = F.linear(input_, self.weight_list[i]) - handle = torch.distributed.all_reduce(output_parallel_list[i], - group=gpc.get_group(ParallelMode.PARALLEL_1D), - async_op=True) - handle_list.append(handle) - # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D) - for handle in handle_list: - handle.wait() - output = torch.cat(output_parallel_list, dim=-1) - else: - output_parallel = F.linear(input_, self.weight) - # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) - output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) - if not self.skip_bias_add: - if self.bias is not None: - output = output + self.bias - return output - else: - return output, self.bias - - -# @LAYERS.register_module class Embedding1D(ParallelLayer): r"""Embedding for 1D parallelism. @@ -842,7 +811,6 @@ def forward(self, input_: Tensor) -> Tensor: return output -# @LAYERS.register_module class VocabParallelEmbedding1D(ParallelLayer): r"""Embedding parallelized in the vocabulary dimension. @@ -960,7 +928,6 @@ def forward(self, input_: Tensor) -> Tensor: return output -# @LAYERS.register_module class Dropout1D(ParallelLayer): """Dropout layer of 1D parallelism. diff --git a/colossalai/shardformer/layer/utils.py b/colossalai/shardformer/layer/utils.py new file mode 100644 index 000000000000..c3d6ab57e3e9 --- /dev/null +++ b/colossalai/shardformer/layer/utils.py @@ -0,0 +1,138 @@ +from contextlib import contextmanager + +import torch +import torch.distributed as dist +from torch.distributed import ProcessGroup + + +class Randomizer: + """ + Randomizer enables the program to be executed under a different seed within the context. + + Example: + + ```python + randomizer = Randomizer(seed=1024) + + with randomizer.fork(): + # do something here with seed 1024 + do_something() + ``` + + Args: + seed (int): The random seed to set. + enable_cpu (bool): fork the CPU RNG state as well. + with_index (bool): whether to use the index of the randomizer. + """ + + _INDEX = 0 + + def __init__(self, seed: int): + # TODO: remove colossalai.context.random + + self.seed = seed + + # Handle CUDA rng state + # 1. get the current rng state + # 2. set the seed and store the rng state + # 3. recover the original rng state + cuda_original_rng_state = torch.cuda.get_rng_state() + torch.cuda.manual_seed(seed) + self.cuda_rng_state = torch.cuda.get_rng_state() + torch.cuda.set_rng_state(cuda_original_rng_state) + + # to the same for cpu rng state + cpu_original_rng_state = torch.get_rng_state() + torch.manual_seed(seed) + self.cpu_rng_state = torch.get_rng_state() + torch.set_rng_state(cpu_original_rng_state) + + def _set_cuda_rng_state(self, rng_state): + torch.cuda.set_rng_state(rng_state) + + def _get_cuda_rng_state(self): + current_state = torch.cuda.get_rng_state() + return current_state + + def _set_cpu_rng_state(self, rng_state): + torch.set_rng_state(rng_state) + + def _get_cpu_rng_state(self): + current_state = torch.get_rng_state() + return current_state + + @contextmanager + def fork_rng(self, enable_cpu: bool = False): + """ + This is a context manager to change the dropout state and recover the original state. + + Usage: + :: + >>> with _seed_manager.dropout_mode(): + >>> input = super().forward(input) + """ + try: + current_cuda_rng_state = self._get_cuda_rng_state() + self._set_cuda_rng_state(self.cuda_rng_state) + + if enable_cpu: + current_cpu_rng_state = self._get_cpu_rng_state() + self._set_cpu_rng_state(self.cpu_rng_state) + yield + finally: + self.cuda_rng_state = self._get_cuda_rng_state() + self._set_cuda_rng_state(current_cuda_rng_state) + + if enable_cpu: + self.cpu_rng_state = self._get_cpu_rng_state() + self._set_cpu_rng_state(current_cpu_rng_state) + + @staticmethod + def index(): + """ + Return the index of the randomizer. The index is useful when the user wants + to introduce some randomness in the program. + + Note: + The index will increment by one each time this method is called. + + Example: + + ```python + # assume we need a randomizer to init the weight of different layers + # we can use the index of the randomizer to do so that + # each layer has its own randomizer with a different seed + base_seed = torch.random.initial_seed() + seed = base_seed + Randomizer.index() + randomizer = Randomizer(seed) + + with randomizer.fork(): + init_weights() + ``` + + """ + idx = Randomizer._INDEX + Randomizer._INDEX += 1 + return idx + + +def create_randomizer_with_offset(seed: int, process_group: ProcessGroup = None): + """ + Create a randomizer with an offset. The offset is equal to the rank of the process and the index of the randomizer. + + Args: + seed (int): The base random seed to set. + enable_cpu (bool): fork the CPU RNG state as well. + process_group (ProcessGroup): the process group to get the rank from. + + Returns: + Randomizer: the randomizer with offset. + """ + offset = Randomizer.index() + + if dist.is_initialized(): + rank = dist.get_rank(process_group) + offset += rank + + seed += offset + return Randomizer(seed=seed) diff --git a/colossalai/tensor/d_tensor/api.py b/colossalai/tensor/d_tensor/api.py new file mode 100644 index 000000000000..afb1fc003e02 --- /dev/null +++ b/colossalai/tensor/d_tensor/api.py @@ -0,0 +1,44 @@ +from typing import Union + +import torch +import torch.distributed as dist +from torch.distributed import ProcessGroup + +from colossalai.device.device_mesh import DeviceMesh + +from .d_tensor import DTensor +from .sharding_spec import ShardingSpec + + +def shard_rowwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None) -> DTensor: + """ + Shard the first dim of the given tensor + """ + # if the group_or_device_mesh is None, we shard the tensor with respect to the global process group + if group_or_device_mesh is None: + group_or_device_mesh = dist.GroupMember.WORLD + + if isinstance(group_or_device_mesh, ProcessGroup): + device_mesh = DeviceMesh.from_process_group(group_or_device_mesh) + else: + assert len(group_or_device_mesh.shape) == 1, 'Only 1D DeviceMesh is accepted for row-wise sharding.' + device_mesh = group_or_device_mesh + sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={0: [0]}) + return DTensor(tensor, device_mesh, sharding_spec) + + +def shard_colwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None) -> DTensor: + """ + Shard the first dim of the given tensor + """ + # if the group_or_device_mesh is None, we shard the tensor with respect to the global process group + if group_or_device_mesh is None: + group_or_device_mesh = dist.GroupMember.WORLD + + if isinstance(group_or_device_mesh, ProcessGroup): + device_mesh = DeviceMesh.from_process_group(group_or_device_mesh) + else: + assert len(group_or_device_mesh.shape) == 1, 'Only 1D DeviceMesh is accepted for row-wise sharding.' + device_mesh = group_or_device_mesh + sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={-1: [0]}) + return DTensor(tensor, device_mesh, sharding_spec) diff --git a/colossalai/tensor/d_tensor/layout.py b/colossalai/tensor/d_tensor/layout.py index ee7ef74a99ae..f15956ea3d52 100644 --- a/colossalai/tensor/d_tensor/layout.py +++ b/colossalai/tensor/d_tensor/layout.py @@ -34,7 +34,7 @@ def __hash__(self) -> int: def get_sharded_shape_per_device(self): sharded_shape = list(self.entire_shape) for dim, shard_list in self.sharding_spec.dim_partition_dict.items(): - mesh_list = [self.device_mesh.mesh_shape[mesh_dim] for mesh_dim in shard_list] + mesh_list = [self.device_mesh.shape[mesh_dim] for mesh_dim in shard_list] shard_partitions = reduce(operator.mul, mesh_list, 1) assert sharded_shape[ dim] % shard_partitions == 0, f'Cannot shard dimension {dim} into {shard_partitions} partitions.' @@ -45,14 +45,15 @@ def _sanity_check(self): sharding_spec = self.sharding_spec # make sure all axes in logical device mesh only be used once - dim_check_list = list(range(self.device_mesh.logical_mesh_id.dim())) - for dim, shard_list in sharding_spec.dim_partition_dict.items(): - for element in shard_list: - if element in dim_check_list: - dim_check_list.remove(element) - else: - raise DuplicatedShardingDimensionError( - f"find an invalid sharding axis {element} in dim_partition_dict in tensor dimension {dim}.") + if self.device_mesh.logical_mesh_id is not None: + dim_check_list = list(range(self.device_mesh.logical_mesh_id.dim())) + for dim, shard_list in sharding_spec.dim_partition_dict.items(): + for element in shard_list: + if element in dim_check_list: + dim_check_list.remove(element) + else: + raise DuplicatedShardingDimensionError( + f"find an invalid sharding axis {element} in dim_partition_dict in tensor dimension {dim}.") # make sure that the sharding for a dimension is divisible by the number of devices for dim, shard_list in sharding_spec.dim_partition_dict.items(): @@ -60,7 +61,7 @@ def _sanity_check(self): num_devices = 1 for element in shard_list: - num_devices *= self.device_mesh.mesh_shape[element] + num_devices *= self.device_mesh.shape[element] if tensor_dim_size % num_devices != 0: raise ShardingNotDivisibleError( diff --git a/colossalai/tensor/d_tensor/layout_converter.py b/colossalai/tensor/d_tensor/layout_converter.py index cf02aac309f4..abc70e19a126 100644 --- a/colossalai/tensor/d_tensor/layout_converter.py +++ b/colossalai/tensor/d_tensor/layout_converter.py @@ -304,7 +304,7 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec process_groups_dict = source_layout.device_mesh.process_groups_dict # legal sharding dims means the mesh_id is still available to use. - legal_sharding_dims = [i for i in range(len(source_layout.device_mesh.mesh_shape))] + legal_sharding_dims = [i for i in range(len(source_layout.device_mesh.shape))] for dim, shard_list in source_spec.dim_partition_dict.items(): for element in shard_list: legal_sharding_dims.remove(element) diff --git a/tests/test_shardformer/test_layer/test_linear_1d.py b/tests/test_shardformer/test_layer/test_linear_1d.py new file mode 100644 index 000000000000..449522c64129 --- /dev/null +++ b/tests/test_shardformer/test_layer/test_linear_1d.py @@ -0,0 +1,67 @@ +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.testing import assert_close + +import colossalai +from colossalai.shardformer.layer.layers import Linear1D_Col, Linear1D_Row +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn + + +def check_linear_1d_col(): + linear = nn.Linear(32, 128).cuda() + linear_col = Linear1D_Col.from_native_module(linear, process_group=None, gather_output=True) + + assert linear_col.weight.shape == torch.Size([64, 32]) + assert linear_col.bias.shape == torch.Size([64]) + + # check computation correctness + x = torch.rand(4, 32).cuda() + out = linear(x) + gather_out = linear_col(x) + assert_close(out, gather_out) + + # check backward correctness + out.sum().backward() + gather_out.sum().backward() + + rank = dist.get_rank() + target_grad = torch.chunk(linear.weight.grad, 2, dim=0)[rank] + assert_close(target_grad, linear_col.weight.grad) + + +def check_linear_1d_row(): + linear = nn.Linear(32, 128).cuda() + linear_row = Linear1D_Row.from_native_module(linear, process_group=None, parallel_input=False) + + assert linear_row.weight.shape == torch.Size([128, 16]) + assert linear_row.bias.shape == torch.Size([128]) + + # check computation correctness + x = torch.rand(4, 32).cuda() + out = linear(x) + gather_out = linear_row(x) + assert_close(out, gather_out) + + # check backward correctness + out.sum().backward() + gather_out.sum().backward() + + rank = dist.get_rank() + target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank] + assert_close(target_grad, linear_row.weight.grad) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + check_linear_1d_col() + check_linear_1d_row() + + +@rerun_if_address_is_in_use() +def test_linear(): + spawn(run_dist, nprocs=2) + + +if __name__ == '__main__': + test_linear() From dfca9678fa3fd35ef185370289a3aabffb8dbf85 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Fri, 16 Jun 2023 11:23:30 +0800 Subject: [PATCH 366/413] integrate with dist layer (#4011) --- colossalai/shardformer/policies/bert.py | 28 ++++++++++++++----- colossalai/shardformer/shard/sharder.py | 15 +++++----- .../test_model/test_shard_bert.py | 23 +++++++++------ 3 files changed, 42 insertions(+), 24 deletions(-) diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index f3431c386fe4..fc3e8447337d 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -8,12 +8,6 @@ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription -class ParallelModule(): - - def __init__(self): - pass - - class BertPolicy(Policy): def preprocess(self, shard_config: ShardConfig = None): @@ -49,7 +43,27 @@ def module_policy(self, shard_config: ShardConfig = None): sub_module_replacement=[ SubModuleReplacementDescription( suffix="attention.self.query", - target_module=ParallelModule, + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.self.key", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.self.value", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.output.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="intermediate.dense", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="output.dense", + target_module=col_nn.Linear1D_Row, ), ]) } diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 8eee3c6a3b7e..eb8300d5998e 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -7,8 +7,8 @@ from colossalai.cluster.process_group_manager import ProcessGroupManager from ..policies.autopolicy import get_autopolicy -from ..policies.basepolicy import Policy -from ..utils.utils import setattr_ +from ..policies.basepolicy import Policy, SubModuleReplacementDescription +from ..utils.utils import getattr_, setattr_ from .shard_config import ShardConfig __all__ = ['ModelSharder', 'shard_model'] @@ -90,9 +90,7 @@ def replace_module(self,) -> None: Args: model (:class:`torch.nn.Module`): The model to shard """ - print(self.policy) module_descriptions = self.policy.module_policy(self.shard_config) - print(f"*******{module_descriptions}") for module_description in module_descriptions.items(): origin_layer_cls = module_description[0] attr_replacement = module_description[1].attribute_replacement @@ -160,7 +158,7 @@ def _replace_param( def _replace_sub_module( self, org_layer: nn.Module, - sub_module_replacement: List[Callable], + sub_module_replacement: List[SubModuleReplacementDescription], ) -> None: r""" Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict @@ -177,7 +175,8 @@ def _replace_sub_module( assert target_module is not None, 'target_module should not be None' - # TODO: integrate with new layer - # replace_layer = target_module.from_native_layer(org_layer, self.pg_manager) - replace_layer = None + # TODO: support different parallel mode + native_sub_module = getattr_(org_layer, suffix) + replace_layer = target_module.from_native_module(native_sub_module, self.pg_manager.pg_store['tp1d']) + setattr_(org_layer, suffix, replace_layer) diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 9b29111eadb2..05d03343632f 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -17,7 +17,7 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.shardformer.shard import ShardConfig, shard_model +from colossalai.shardformer import ShardConfig, ShardFormer from colossalai.testing import rerun_if_address_is_in_use, spawn os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' @@ -30,16 +30,21 @@ def build_model(rank, world_size, model): config.hidden_dropout_prob = 0 config.attention_probs_dropout_prob = 0 - org_model = model(config=config) + org_model = BertForMaskedLM.from_pretrained('bert-base-uncased', config=config) org_model_forshard = copy.deepcopy(org_model) - org_model = org_model.to('cuda') - shardconfig = ShardConfig( - rank=rank, - world_size=world_size, - gather_output=True, - ) - sharded_model = shard_model(org_model_forshard, shardconfig).to('cuda') + org_model.to('cuda') + # TODO: no need to transfer to cuda + org_model_forshard.to('cuda') + shard_config = ShardConfig(tensor_parallel_size=2, + data_parallel_size=1, + pipeline_parallel_size=1, + tensor_parallel_mode='1d', + inference_only=True, + gather_output=True) + shard_former = ShardFormer(shard_config=shard_config) + shard_former.init_distributed() + sharded_model = shard_former.shard_model(org_model_forshard).to('cuda') return org_model, sharded_model From 3893fa1a8d0cdcc3df3c6e4eb11956e688541cbd Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 16 Jun 2023 15:00:26 +0800 Subject: [PATCH 367/413] [shardformer] refactored embedding and dropout to parallel module (#4013) * [shardformer] refactored embedding and dropout to parallel module * polish code --- .../shardformer/layer/dist_crossentropy.py | 20 +- colossalai/shardformer/layer/dropout.py | 32 +- colossalai/shardformer/layer/layers.py | 467 +++--------------- .../test_layer/test_dropout.py | 53 ++ .../test_layer/test_embedding.py | 43 ++ .../test_layer/test_linear_1d.py | 2 +- 6 files changed, 196 insertions(+), 421 deletions(-) create mode 100644 tests/test_shardformer/test_layer/test_dropout.py create mode 100644 tests/test_shardformer/test_layer/test_embedding.py diff --git a/colossalai/shardformer/layer/dist_crossentropy.py b/colossalai/shardformer/layer/dist_crossentropy.py index ff05209fefe8..7840c2f2e5da 100644 --- a/colossalai/shardformer/layer/dist_crossentropy.py +++ b/colossalai/shardformer/layer/dist_crossentropy.py @@ -3,6 +3,7 @@ import torch.nn as nn import torch.nn.functional as F from torch.autograd import Function +from torch.distributed import ProcessGroup class DistCrossEntropy(Function): @@ -14,7 +15,7 @@ class DistCrossEntropy(Function): """ @staticmethod - def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor, ignore_index: int): + def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor, ignore_index: int, process_group: ProcessGroup): r""" Calculate the cross entropy loss before gather, the origin loss function is as follows: loss = -log(exp(x[class])/sum(exp(x[i])) @@ -34,15 +35,15 @@ def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor, ignore_index: """ # get the max logits_max = torch.max(vocab_logits, dim=-1)[0] - dist.all_reduce(logits_max, op=dist.ReduceOp.MAX) + dist.all_reduce(logits_max, op=dist.ReduceOp.MAX, group=process_group) # minus the max to avoid the result of sum of exp is too large and the log is nan vocab_logits = vocab_logits - logits_max.unsqueeze(dim=-1) # mask the target in the local device partition_vocab_size = vocab_logits.size()[-1] - rank = dist.get_rank() - world_size = dist.get_world_size() + rank = dist.get_rank(group=process_group) + world_size = dist.get_world_size(group=process_group) global_vocab_size = partition_vocab_size * world_size # [down, up) => false, other device and -100 => true @@ -67,11 +68,11 @@ def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor, ignore_index: pred_logits[mask] = 0.0 # allreduce the get all x(i,y) - dist.all_reduce(pred_logits, op=dist.ReduceOp.SUM) + dist.all_reduce(pred_logits, op=dist.ReduceOp.SUM, group=process_group) exp_logits = vocab_logits torch.exp(vocab_logits, out=exp_logits) sum_exp_logits = torch.sum(exp_logits, dim=-1) - dist.all_reduce(sum_exp_logits, op=dist.ReduceOp.SUM) + dist.all_reduce(sum_exp_logits, op=dist.ReduceOp.SUM, group=process_group) # calculate the loss # loss = log(sum(exp(x[i]))) - x[class] @@ -101,5 +102,8 @@ def backward(ctx, grad_output): return grad_logits, None, None -def applyDistCrossEntropy(vocab_logits: torch.Tensor, labels: torch.Tensor, ignore_index: int = -100) -> torch.Tensor: - return DistCrossEntropy.apply(vocab_logits, labels, ignore_index) +def cross_entropy_1d(vocab_logits: torch.Tensor, + labels: torch.Tensor, + ignore_index: int = -100, + process_group: ProcessGroup = None) -> torch.Tensor: + return DistCrossEntropy.apply(vocab_logits, labels, ignore_index, process_group) diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py index 5d295be6bd83..ec08d072f338 100644 --- a/colossalai/shardformer/layer/dropout.py +++ b/colossalai/shardformer/layer/dropout.py @@ -1,19 +1,43 @@ +from typing import List, Union + import torch -import torch.distributed as dist import torch.nn as nn +from torch.distributed import ProcessGroup +from .layers import ParallelModule from .utils import create_randomizer_with_offset -class Dropout1D(nn.Dropout): +class Dropout1D(ParallelModule, nn.Dropout): + """ + The Dropout Layer will apply dropout mask to the input tensor. The dropout mask is generated with + randomness on different ranks of the given process group. This can avoid the same dropout mask is generated + and applied on the same position of different ranks, leading to poor convergence performance. + + Args: + p (float): probability of an element to be zeroed. Defaults to 0.5. + inplace (bool): If set to True, will do this operation in-place. Defaults to False. + process_group (ProcessGroup): the process group to be used for generating randomness. Defaults to None. + """ - def __init__(self, p=0.5, inplace=False, process_group=None): - super().__init__(p, inplace) + def __init__(self, p: float = 0.5, inplace: bool = False, process_group: ProcessGroup = None): + # init with nn.Dropout + super(nn.Dropout, self).__init__(p=p, inplace=inplace) # offset the seed with randomizer index and rank seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=process_group) + @staticmethod + def from_native_module(module: nn.Dropout, + process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "Dropout1D": + """ + Create a Dropout1D layer from a native dropout layer. + """ + p = module.p + inplace = module.inplace + return Dropout1D(p=p, inplace=inplace, process_group=process_group) + def forward(self, input): with self.randomizer.fork_rng(): input = super().forward(input) diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py index 2ad6523c9a86..87d24f18e178 100644 --- a/colossalai/shardformer/layer/layers.py +++ b/colossalai/shardformer/layer/layers.py @@ -22,12 +22,7 @@ from colossalai.nn import init as init from colossalai.nn.layer.base_layer import ParallelLayer from colossalai.nn.layer.colossalai_layer._utils import ColossalaiModule -from colossalai.nn.layer.parallel_1d._utils import ( - gather_forward_split_backward, - get_parallel_input, - reduce_grad, - set_parallel_input, -) +from colossalai.nn.layer.parallel_1d._utils import get_parallel_input, reduce_grad, set_parallel_input from colossalai.nn.layer.utils import divide, set_tensor_parallel_attribute_by_partition from colossalai.nn.layer.vanilla import VanillaLayerNorm, VanillaPatchEmbedding from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise @@ -432,279 +427,7 @@ def _save_to_state_dict(self, destination, prefix, keep_vars): super()._save_to_state_dict(destination, prefix, keep_vars) -class Classifier1D(ParallelLayer): - r"""RowLinear with given weight. Classifier of 1D parallelism. - - Args: - in_features (int): size of each input sample. - num_classes (int): number of classes. - weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - num_classes: int, - weight: Parameter = None, - bias: bool = True, - dtype: torch.dtype = None, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): - super().__init__() - self.in_features = in_features - self.num_classes = num_classes - self.parallel_input = get_parallel_input() - - # Divide the weight matrix along the last dimension. - self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size) - - # Parameters. - # Initialize weight. - factory_kwargs = {'device': get_current_device(), 'dtype': dtype} - if weight is not None: - self.weight = weight - self.has_weight = False - else: - self.weight = Parameter(torch.empty(self.num_classes, self.input_size_per_partition, **factory_kwargs)) - self.has_weight = True - if bias: - self.bias = Parameter(torch.empty(self.num_classes, **factory_kwargs)) - else: - self.bias = None - with seed(ParallelMode.TENSOR): - self.reset_parameters(weight_initializer, bias_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) - env.vocab_parallel = False - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.num_classes - if self.has_weight: - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D) - - def _set_tensor_parallel_attributes(self): - if self.has_weight: - num_partition = gpc.get_world_size(ParallelMode.TENSOR) - set_tensor_parallel_attribute_by_partition(self.weight, num_partition) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - if self.has_weight: - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - if self.bias is not None: - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: -1, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: False - }) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - local_state = OrderedDict() - if self.has_weight: - local_state[weight_key] = self.weight - if self.bias is not None: - local_state[bias_key] = self.bias - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: -1, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: False - }, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tensor: - # Set up backprop all-reduce. - if self.parallel_input: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - input_ = input_ - else: - assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \ - 'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size) - input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1) - - output_parallel = F.linear(input_, self.weight) - output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) - if self.bias is not None: - output = output + self.bias - return output - - -class VocabParallelClassifier1D(ParallelLayer): - r"""ColLinear with given weight. Classifier of 1D parallelism. - - Args: - in_features (int): size of each input sample. - num_classes (int): number of classes. - weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - num_classes: int, - weight: Parameter = None, - bias: bool = True, - dtype: torch.dtype = None, - gather_output: bool = False, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): - super().__init__() - self.in_features = in_features - self.num_classes = num_classes - self.gather_output = gather_output - self.parallel_input = get_parallel_input() - - # Divide the weight matrix along the last dimension. - self.num_classes_per_partition = divide(num_classes, gpc.tensor_parallel_size) - - # Parameters. - # Initialize weight. - factory_kwargs = {'device': get_current_device(), 'dtype': dtype} - if weight is not None: - self.weight = weight - self.has_weight = False - else: - self.weight = Parameter(torch.empty(self.num_classes_per_partition, self.in_features, **factory_kwargs)) - self.has_weight = True - if bias: - self.bias = Parameter(torch.empty(self.num_classes_per_partition, **factory_kwargs)) - else: - self.bias = None - with seed(ParallelMode.TENSOR): - self.reset_parameters(weight_initializer, bias_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) - env.vocab_parallel = True - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.num_classes - if self.has_weight: - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - - def _set_tensor_parallel_attributes(self): - num_partition = gpc.get_world_size(ParallelMode.TENSOR) - if self.has_weight: - set_tensor_parallel_attribute_by_partition(self.weight, num_partition) - if self.bias is not None: - set_tensor_parallel_attribute_by_partition(self.bias, num_partition) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - if self.has_weight: - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - if self.bias is not None: - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: 0, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: True - }) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - local_state = OrderedDict() - if self.has_weight: - local_state[weight_key] = self.weight - if self.bias is not None: - local_state[bias_key] = self.bias - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={ - weight_key: 0, - bias_key: 0 - }, - partition_states={ - weight_key: True, - bias_key: True - }, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tensor: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in VocabParallelClassifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - # Set up backprop all-reduce. - input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) - # Matrix multiply. - output_parallel = F.linear(input_parallel, self.weight, self.bias) - if self.gather_output: - # All-gather across the partitions. - output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) - else: - output = output_parallel - return output - - -# @LAYERS.register_module - - -class Embedding1D(ParallelLayer): +class Embedding1D(ParallelModule): r"""Embedding for 1D parallelism. Args: @@ -739,7 +462,8 @@ def __init__(self, embedding_dim: int, padding_idx: int = None, dtype: torch.dtype = None, - gather_output: bool = True, + device: torch.device = None, + process_group: ProcessGroup = None, weight_initializer: Callable = init.normal_(), *args, **kwargs): @@ -747,66 +471,79 @@ def __init__(self, self.num_embeddings = num_embeddings self.embed_dim = embedding_dim - embed_dim_per_partition = divide(embedding_dim, gpc.tensor_parallel_size) + self.process_group = process_group + self.num_partitions = dist.get_world_size(process_group) + self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions) self.padding_idx = padding_idx self.embed_args = args self.embed_kwargs = kwargs self.gather_output = gather_output - self.weight = Parameter( - torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype)) + if device is None: + device = get_current_device() - self.reset_parameters(weight_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) + self.weight = Parameter(torch.empty((num_embeddings, self.embed_dim_per_partition), device=device, dtype=dtype)) - def _set_tensor_parallel_attributes(self): - set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size) + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer) + + @staticmethod + def from_native_module(module: nn.Embedding, + process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "Embedding1D": + r""" + Build a 1D parallelized Embedding from a native nn.Embedding module. + """ + # get the attributes + num_embedding = module.num_embeddings + embedding_dim = module.embedding_dim + padding_idx = module.padding_idx + max_norm = module.max_norm + norm_type = module.norm_type + scale_grad_by_freq = module.scale_grad_by_freq + sparse = module.sparse + dtype = module.weight.dtype + device = module.weight.device + + # sparse is not support yet + if sparse: + raise NotImplementedError("The Embedding1D module does not support sparse embedding yet.") + + embedding = Embedding1D(num_embeddings=num_embedding, + embedding_dim=embedding_dim, + padding_idx=padding_idx, + process_group=process_group, + dtype=dtype, + device=device, + max_norm=max_norm, + norm_type=norm_type, + scale_grad_by_freq=scale_grad_by_freq, + sparse=sparse) + + # copy the weight + with torch.no_grad(): + sharded_weight = shard_colwise(module.weight.data, process_group) + embedding.weight.copy_(sharded_weight) + + return embedding def reset_parameters(self, weight_initializer) -> None: - with seed(ParallelMode.TENSOR): - fan_in, fan_out = self.num_embeddings, self.embed_dim - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - self._fill_padding_idx_with_zero() + fan_in, fan_out = self.num_embeddings, self.embed_dim + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + self._fill_padding_idx_with_zero() def _fill_padding_idx_with_zero(self) -> None: if self.padding_idx is not None: with torch.no_grad(): self.weight[self.padding_idx].fill_(0) - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={weight_key: -1}, - partition_states={weight_key: True}) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - local_state = OrderedDict({weight_key: self.weight}) - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={weight_key: -1}, - partition_states={weight_key: True}, - keep_vars=keep_vars) - destination.update(local_state) - def forward(self, input_: Tensor) -> Tensor: - output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) - if self.gather_output: - output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1) - else: - output = output_parallel + output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) return output @@ -926,89 +663,3 @@ def forward(self, input_: Tensor) -> Tensor: # Reduce across all the model parallel GPUs. output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) return output - - -class Dropout1D(ParallelLayer): - """Dropout layer of 1D parallelism. - - Args: - p (float, optional): probability of an element to be zeroed, defaults 0.5. - inplace (bool, optional): whether to do dropout in-place, default to be False. - """ - - def __init__(self, p: float = 0.5, inplace: bool = False): - super().__init__() - self.parallel_input = get_parallel_input() - self.p = p - self.inplace = inplace - - def forward(self, input_: Tensor) -> Tensor: - if self.parallel_input: - with seed(ParallelMode.TENSOR): - output = F.dropout(input_, self.p, self.training, self.inplace) - else: - output = F.dropout(input_, self.p, self.training, self.inplace) - return output - - -# @LAYERS.register_module -class PatchEmbedding1D(ColossalaiModule): - """ - 2D Image to Patch Embedding - - :param img_size: image size - :type img_size: int - :param patch_size: patch size - :type patch_size: int - :param in_chans: number of channels of input image - :type in_chans: int - :param embed_size: size of embedding - :type embed_size: int - :param dtype: The dtype of parameters, defaults to None - :type dtype: torch.dtype, optional - :param flatten: whether to flatten output tensor, defaults to True - :type flatten: bool, optional - :param weight_initializer: The initializer of weight, defaults to kaiming uniform initializer - :type weight_initializer: typing.Callable, optional - :param bias_initializer: The initializer of bias, defaults to xavier uniform initializer - :type bias_initializer: typing.Callable, optional - :param position_embed_initializer: The initializer of position embedding, defaults to zero - :type position_embed_initializer: typing.Callable, optional - """ - - def __init__(self, - img_size: int, - patch_size: int, - in_chans: int, - embed_size: int, - dtype: torch.dtype = None, - flatten: bool = True, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), - position_embed_initializer: Callable = init.zeros_()): - embed = VanillaPatchEmbedding(img_size, - patch_size, - in_chans, - embed_size, - dtype=dtype, - flatten=flatten, - weight_initializer=weight_initializer, - bias_initializer=bias_initializer, - position_embed_initializer=position_embed_initializer) - super().__init__(embed) - - def _load_from_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - param_keys = [prefix + 'weight', prefix + 'bias', prefix + 'cls_token', prefix + 'pos_embed'] - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - for key in param_keys: - param = state_dict.pop(key, None) - if param is not None: - local_state[key] = param - - local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) - super()._load_from_state_dict(local_state, prefix, *args) - - def _save_to_state_dict(self, destination, prefix, keep_vars): - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - super()._save_to_state_dict(destination, prefix, keep_vars) diff --git a/tests/test_shardformer/test_layer/test_dropout.py b/tests/test_shardformer/test_layer/test_dropout.py new file mode 100644 index 000000000000..c48c11b36d91 --- /dev/null +++ b/tests/test_shardformer/test_layer/test_dropout.py @@ -0,0 +1,53 @@ +import torch +import torch.distributed as dist +import torch.nn as nn + +import colossalai +from colossalai.shardformer.layer.dropout import Dropout1D +from colossalai.testing import assert_equal, assert_not_equal, rerun_if_address_is_in_use, spawn + + +def check_dropout(): + dropout = nn.Dropout().cuda() + dropout_1d = Dropout1D.from_native_module(dropout, process_group=None) + + # check computation correctness + x = torch.rand(4, 128).cuda() + + # we set seed so that dropout will generate the same mask + torch.cuda.manual_seed(1024) + out = dropout(x) + + # we set seed to simulate the same scenario + # but expect the dropout mask to be different + # due to the internal randomness control + torch.cuda.manual_seed(1024) + out_1d = dropout_1d(x) + + # ensure out is the same across all ranks + world_size = dist.get_world_size() + out_all = [torch.empty_like(out) for _ in range(world_size)] + dist.all_gather(out_all, out) + + for i in range(world_size): + assert_equal(out_all[i], out_all[0]) + + # ensure out_1d is different across ranks + out_1d_all = [torch.zeros_like(out_1d) for _ in range(world_size)] + dist.all_gather(out_1d_all, out_1d) + for i in range(1, world_size): + assert_not_equal(out_1d_all[i], out_1d_all[0]) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + check_dropout() + + +@rerun_if_address_is_in_use() +def test_dropout(): + spawn(run_dist, nprocs=2) + + +if __name__ == '__main__': + test_dropout() diff --git a/tests/test_shardformer/test_layer/test_embedding.py b/tests/test_shardformer/test_layer/test_embedding.py new file mode 100644 index 000000000000..462349ecb93b --- /dev/null +++ b/tests/test_shardformer/test_layer/test_embedding.py @@ -0,0 +1,43 @@ +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.testing import assert_close + +import colossalai +from colossalai.shardformer.layer.layers import Embedding1D +from colossalai.testing import rerun_if_address_is_in_use, spawn + + +def check_embedding_1d(): + embedding = nn.Embedding(32, 128).cuda() + embedding_1d = Embedding1D.from_native_module(embedding, process_group=None) + + assert embedding_1d.weight.shape == torch.Size([32, 64]) + + # check computation correctness + x = torch.randint(low=0, high=32, size=(4, 32)).cuda() + out = embedding(x) + gather_out = embedding_1d(x) + assert_close(out, gather_out) + + # check backward correctness + out.sum().backward() + gather_out.sum().backward() + + rank = dist.get_rank() + target_grad = torch.chunk(embedding.weight.grad, 2, dim=1)[rank] + assert_close(target_grad, embedding_1d.weight.grad) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + check_embedding_1d() + + +@rerun_if_address_is_in_use() +def test_embedding_1d(): + spawn(run_dist, nprocs=2) + + +if __name__ == '__main__': + test_embedding_1d() diff --git a/tests/test_shardformer/test_layer/test_linear_1d.py b/tests/test_shardformer/test_layer/test_linear_1d.py index 449522c64129..2a3ce99384cb 100644 --- a/tests/test_shardformer/test_layer/test_linear_1d.py +++ b/tests/test_shardformer/test_layer/test_linear_1d.py @@ -5,7 +5,7 @@ import colossalai from colossalai.shardformer.layer.layers import Linear1D_Col, Linear1D_Row -from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from colossalai.testing import rerun_if_address_is_in_use, spawn def check_linear_1d_col(): From 45d93843460613161e81331168c67a8b4cc831d2 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 16 Jun 2023 15:58:27 +0800 Subject: [PATCH 368/413] [shardformer] removed inplace tensor sharding (#4018) --- colossalai/shardformer/layer/layers.py | 4 +++ colossalai/tensor/d_tensor/api.py | 40 +++++++++++++++++++++++--- 2 files changed, 40 insertions(+), 4 deletions(-) diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py index 87d24f18e178..586aec124b86 100644 --- a/colossalai/shardformer/layer/layers.py +++ b/colossalai/shardformer/layer/layers.py @@ -329,7 +329,11 @@ def reset_parameters(self, weight_initializer, bias_initializer) -> None: src_rank = 0 else: src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) + + origin_device = self.bias.device + self.bias = self.bias.cuda() dist.broadcast(self.bias, src=src_rank, group=self.process_group) + self.bias = self.bias.to(origin_device) def forward(self, input_: Tensor) -> Tensor: # Set up backprop all-reduce. diff --git a/colossalai/tensor/d_tensor/api.py b/colossalai/tensor/d_tensor/api.py index afb1fc003e02..b58edadfef20 100644 --- a/colossalai/tensor/d_tensor/api.py +++ b/colossalai/tensor/d_tensor/api.py @@ -10,9 +10,21 @@ from .sharding_spec import ShardingSpec -def shard_rowwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None) -> DTensor: +def shard_rowwise(tensor: torch.Tensor, + group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None, + inplace: bool = False) -> DTensor: """ - Shard the first dim of the given tensor + Shard the first dim of the given tensor. + + Args: + tensor (torch.Tensor): The tensor to be sharded. + group_or_device_mesh (Union[ProcessGroup, DeviceMesh], optional): The group or device mesh to shard the tensor. + If None, the tensor will be sharded with respect to the global process group. + Defaults to None. + inplace (bool, optional): Whether to shard the tensor in-place. Defaults to False. + + Returns: + DTensor: The sharded tensor. """ # if the group_or_device_mesh is None, we shard the tensor with respect to the global process group if group_or_device_mesh is None: @@ -24,12 +36,28 @@ def shard_rowwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup assert len(group_or_device_mesh.shape) == 1, 'Only 1D DeviceMesh is accepted for row-wise sharding.' device_mesh = group_or_device_mesh sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={0: [0]}) + + if not inplace: + tensor = tensor.detach().clone() + return DTensor(tensor, device_mesh, sharding_spec) -def shard_colwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None) -> DTensor: +def shard_colwise(tensor: torch.Tensor, + group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None, + inplace: bool = False) -> DTensor: """ - Shard the first dim of the given tensor + Shard the first dim of the given tensor. + + Args: + tensor (torch.Tensor): The tensor to be sharded. + group_or_device_mesh (Union[ProcessGroup, DeviceMesh], optional): The group or device mesh to shard the tensor. + If None, the tensor will be sharded with respect to the global process group. + Defaults to None. + inplace (bool, optional): Whether to shard the tensor in-place. Defaults to False. + + Returns: + DTensor: The sharded tensor. """ # if the group_or_device_mesh is None, we shard the tensor with respect to the global process group if group_or_device_mesh is None: @@ -41,4 +69,8 @@ def shard_colwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup assert len(group_or_device_mesh.shape) == 1, 'Only 1D DeviceMesh is accepted for row-wise sharding.' device_mesh = group_or_device_mesh sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={-1: [0]}) + + if not inplace: + tensor = tensor.detach().clone() + return DTensor(tensor, device_mesh, sharding_spec) From 507c0ad368dd8016f3faa4147ff5ce0b7e3ae0c6 Mon Sep 17 00:00:00 2001 From: FoolPlayer <498107402@qq.com> Date: Fri, 16 Jun 2023 15:04:07 +0800 Subject: [PATCH 369/413] add vocabembedding layer --- colossalai/shardformer/layer/layers.py | 65 ++++++++++++++++--- .../test_vocab_parallel_embedding_1d.py | 45 +++++++++++++ 2 files changed, 100 insertions(+), 10 deletions(-) create mode 100644 tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py index 586aec124b86..ad6e1896aa5e 100644 --- a/colossalai/shardformer/layer/layers.py +++ b/colossalai/shardformer/layer/layers.py @@ -139,6 +139,7 @@ def __init__(self, with self.randomizer.fork_rng(enable_cpu=True): self.reset_parameters(weight_initializer, bias_initializer) + @staticmethod def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs) -> ParallelModule: r""" @@ -587,6 +588,8 @@ def __init__(self, embedding_dim: int, padding_idx: int = None, dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, weight_initializer: Callable = init.normal_(), *args, **kwargs): @@ -596,21 +599,63 @@ def __init__(self, self.padding_idx = padding_idx self.embed_args = args self.embed_kwargs = kwargs + self.process_group = process_group - tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D) - tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D) - # self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) - self.num_embeddings_per_partition = num_embeddings + tensor_parallel_size = dist.get_world_size(group=process_group) + tensor_parallel_rank = dist.get_rank(group=process_group) + + self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) + self.num_embeddings = self.num_embeddings_per_partition self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition self.weight = Parameter( - torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=get_current_device(), dtype=dtype)) + torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=device, dtype=dtype)) + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer) + # self.reset_parameters(weight_initializer) + # self._set_tensor_parallel_attributes() + # set_parallel_input(False) + # env.vocab_parallel = True + + @staticmethod + def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, + **kwargs) -> ParallelModule: + r""" + Convert a native pytorch embedding module to a parallel module. + """ + # get the origin attributes + num_embeddings = module.num_embeddings + embedding_dim = module.embedding_dim + padding_idx = module.padding_idx + device = module.weight.device + + # ensure only one process group is used + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + # create the parallel module + vocab_embedding_1d = VocabParallelEmbedding1D(num_embeddings=num_embeddings, + embedding_dim=embedding_dim, + padding_idx=padding_idx, + device=device, + process_group=process_group, + *args, + **kwargs) + with torch.no_grad(): + # shard and slice the weight along the vocabulary(num_embeddings) dimension + # the shape of the weight is (num_embeddings, embedding_dim) + shard_weight = shard_rowwise(module.weight.data, process_group) + vocab_embedding_1d.weight.data.copy_(shard_weight) - self.reset_parameters(weight_initializer) - self._set_tensor_parallel_attributes() - set_parallel_input(False) - env.vocab_parallel = True + return vocab_embedding_1d def _set_tensor_parallel_attributes(self): set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size) @@ -665,5 +710,5 @@ def forward(self, input_: Tensor) -> Tensor: # Mask the output embedding. output_parallel[input_mask, :] = 0. # Reduce across all the model parallel GPUs. - output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D) + output = reduce_input(output_parallel, self.process_group) return output diff --git a/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py new file mode 100644 index 000000000000..3df53e8a8458 --- /dev/null +++ b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py @@ -0,0 +1,45 @@ +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.testing import assert_close + +import colossalai +from colossalai.shardformer.layer.layers import VocabParallelEmbedding1D +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn + + +def check_vocab_embedding_1d(): + embedding = nn.Embedding(128, 32).to('cuda') + dist_embedding_1d = VocabParallelEmbedding1D.from_native_module(embedding, process_group=None) + + assert dist_embedding_1d.weight.shape == torch.Size([64, 32]) + assert dist_embedding_1d.num_embeddings == 64 + assert dist_embedding_1d.embed_dim == 32 + + # check embedding correctness + x = torch.randint(0, 128, (4, 32)).to('cuda') + org_out = embedding(x) + dist_out = dist_embedding_1d(x) + assert_close(org_out, dist_out) + + # check backward correctness + org_out.sum().backward() + dist_out.sum().backward() + + rank = dist.get_rank() + target_grad = torch.chunk(embedding.weight.grad, 2, dim=0)[rank] + assert_close(target_grad, dist_embedding_1d.weight.grad) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + check_vocab_embedding_1d() + + +@rerun_if_address_is_in_use() +def test_vocab_embedding(): + spawn(run_dist, nprocs=2) + + +if __name__ == '__main__': + test_vocab_embedding() From df018fc305c1401c26d00e4e03e0e11b24649a21 Mon Sep 17 00:00:00 2001 From: FoolPlayer <498107402@qq.com> Date: Fri, 16 Jun 2023 16:12:27 +0800 Subject: [PATCH 370/413] support bert with new api --- colossalai/shardformer/policies/bert.py | 35 ++++++++++++++++++++++++- colossalai/shardformer/shard/sharder.py | 5 ++-- 2 files changed, 37 insertions(+), 3 deletions(-) diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index fc3e8447337d..fe74f83ca745 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -2,6 +2,7 @@ from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead import colossalai.shardformer.layer.layers as col_nn +from colossalai.shardformer.layer.dropout import Dropout1D from ..shard.shard_config import ShardConfig from ..utils import getattr_, setattr_ @@ -65,7 +66,24 @@ def module_policy(self, shard_config: ShardConfig = None): suffix="output.dense", target_module=col_nn.Linear1D_Row, ), - ]) + SubModuleReplacementDescription( + suffix="attention.self.dropout", + target_module=Dropout1D, + ), + SubModuleReplacementDescription( + suffix="attention.output.dropout", + target_module=Dropout1D, + ) + ]), + BertEmbeddings: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="word_embeddings", + target_module=col_nn.VocabParallelEmbedding1D, + ) + ]) } def new_model_class(self): @@ -87,6 +105,21 @@ class BertForMaskedLMPolicy(BertPolicy): def __init__(self) -> None: super().__init__() + def module_policy(self, shard_config: ShardConfig = None): + module_policy = super().module_policy(shard_config) + addon_module = { + BertLMPredictionHead: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="decoder", + target_module=col_nn.Linear1D_Col, + kwargs={"gather_output": True}) + ]) + } + module_policy.update(addon_module) + return module_policy + # BertLMHeadModel class BertLMHeadModelPolicy(BertPolicy): diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index eb8300d5998e..5c8584595c0c 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -171,12 +171,13 @@ def _replace_sub_module( for description in sub_module_replacement: suffix = description.suffix target_module = description.target_module - kwargs = description.kwargs + kwargs = {} if description.kwargs is None else description.kwargs assert target_module is not None, 'target_module should not be None' # TODO: support different parallel mode native_sub_module = getattr_(org_layer, suffix) - replace_layer = target_module.from_native_module(native_sub_module, self.pg_manager.pg_store['tp1d']) + replace_layer = target_module.from_native_module(native_sub_module, self.pg_manager.pg_store['tp1d'], + **kwargs) setattr_(org_layer, suffix, replace_layer) From e253a07007ac486c5b601a8c21fd02bdacdabedc Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 16 Jun 2023 16:15:10 +0800 Subject: [PATCH 371/413] [shardformer] updated doc (#4016) --- colossalai/shardformer/README.md | 504 ++++++++++++++++--------------- 1 file changed, 257 insertions(+), 247 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index b8357c203939..dc2946ec937f 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -6,9 +6,15 @@ - [📚 Table of Contents](#-table-of-contents) - [🔗 Introduction](#-introduction) - [🔨 Usage](#-usage) - - [🔮 Simple example](#-simple-example) - - [💡 Policy](#-policy) - - [😊 Module](#-module) + - [Quick Start](#quick-start) + - [Write your own policy](#write-your-own-policy) + - [🗺 Roadmap](#-roadmap) + - [💡 API Design](#-api-design) + - [Distributed Modules](#distributed-modules) + - [Shard Config](#shard-config) + - [Policy](#policy) + - [Model Sharder](#model-sharder) + - [User-facing API](#user-facing-api) ## 🔗 Introduction @@ -17,299 +23,303 @@ ## 🔨 Usage +### Quick Start + The sample API usage is given below: ``` python -from colossalai.shardformer import ShardConfig, shard_model +from colossalai.shardformer import ShardConfig, Shard from transformers import BertForMaskedLM -# create huggingface model as normal -model = BertForMaskedLM.from_pretrained("bert-base-uncased") - -# make the huggingface model paralleled to ShardModel -# auto policy: -shardconfig = ShardConfig( - rank=rank, - world_size=world_size, - gather_output=True, -) -sharded_model = shard_model(model, config=shardconfig) - -# custom policy: -from xxx import -sharded_model = shard_model(model, ) - -# do angthing as normal -... -``` +# launch colossalai +colossalai.launch_from_torch() -## 🔮 Simple example +# create model +config = BertConfig.from_pretrained('bert-base-uncased') +model = BertForMaskedLM.from_pretrained('bert-base-uncased', config=config) -``` shell -# inference -colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode inference -# train -colossalai run --nproc_per_node 2 --master_port 29500 test.py --config config.py --mode train +# create huggingface model as normal +shard_config = ShardConfig(tensor_parallel_size=2, + data_parallel_size=1, + gather_output=True) +shard_former = ShardFormer(shard_config=shard_config) +shard_former.init_distributed() +sharded_model = shard_former.shard_model(model).to('cuda') + +# do everything like normal +... ``` +### Write your own policy -## 💡 Policy - -If you wanna parallel the model in a custom way, just overwrite the policy class for the Hugging Face model. Please refer to any policy that we have pre-established, like [bert policy](./policies/bert.py) or [gpt2 policy](./policies/gpt2.py). - -You should do: - -1. Inherit Policy class -2. Overwrite `argument_policy` method - - In this method, you need to list which layers class you wanna modify and the attributes and parameters in those layers. Shardformer will replace all the layer belonging to the class you specified. - - `attr_dict` is dict contains all the attributes need to be modified in this layer. - - `param_funcs` is a list contains some functions which will return the path of the weight and bias from the layer. -3. Overwrite `inject_policy` method (Optional) - - Shardformer will inject the model according to this method. If you need to modify the forward or backward progress (like distributed corssentropy loss in Bert) you need to overwrite this method. -4. Overwrite or add the param functions - - These functions use a suffix to record the path of weight or bias for the layer. - - The return is a list contains some `Col_Layer`, `Row_Layer` or `Dropout_Layer` objects, which means slice along col and row respectively or as dropout layer, refer to CLASS `Layer` for more details. -5. Overwrite `binding_policy` (Optional) - - Overwrite to specify Shardformer will bind some weight between layers, like embedding and unembedding layers. - - This function will return a dict, the key and value are the suffix of weight need to be binded. - -More details can be found in shardformer/policies/basepolicy.py -``` python -from colossalai.shardformer.policies.basepolicy import Policy, Layer, Col_Layer, Row_Layer, Argument - -class CustomPolicy(Policy): -@staticmethod - def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]: - r""" - Return the dict for the modify policy, the key is the original layer class and the value is the - argument for the modify layer - - Args: - model_config (:class:`tansformer.Config`): The config of transformer model - shard_config (:class:`ShardConfig`): The config for sharding model - - Return: - Dict for the modify policy, - :: - { - origin layer class1 (nn.Module): Argument( - attr_dict = { - argument1: value1, - argument2: value2, - ... - }, - param_funcs = [ - staticmethod1, - staticmethod2, - ... - ] - ), - origin layer class2 (nn.Module): Argument( - attr_dict = { - argument1: value1, - argument2: value2, - ... - }, - param_funcs = [ - staticmethod1, - staticmethod2, - ... - ] - ), - ... - } - - """ - raise NotImplementedError - - @staticmethod - def inject_policy() -> Union[Tuple[nn.Module, nn.Module], None]: - r""" - Return the dict for the inject model - - Return: - The injected model, key is the original model and value is the new shardmodel - :: - (OrignModel, CustomModel) - in `CustomModel`, we can overwrite the forward and backward process - """ - return None - - @staticmethod - def binding_policy() -> Union[Dict[str, str], None]: - r""" - Return the dict for the binding model, None means no need to bind - - Return: - This method should return the binding relationship for some layers share the weight or bias, - the key and value is the suffix of the weight or bias of the model - :: - return { - "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight", - } - """ - return None - - @staticmethod - def attn_in() -> Union[List, None]: - r""" - Attention qkv layer - In this kind of method, we should return the list of ``Layer`` object, each ``Layer`` object should be - ``Layer`` for no slicing, ``Col_Layer`` for col slicing, ``Row_Layer`` for row slicing. And the parameters - in ``Layer`` object can refer to the ``Layer`` class. - - Returns: - List[Layer]: List of layer object, each layer is the new - """ - return None - - @staticmethod - def attn_out() -> Union[List, None]: - r""" - Attention output projection layer - - Returns: - List[Layer]: List of layer object - """ - return None +If you have a custom model, you can also use Shardformer to parallelize it by writing your own sharding policy. More information about the sharding policy can be found in [API Design](#-api-design). - @staticmethod - def mlp_in() -> Union[List, None]: - r""" - h -> 4h mlp layer +```python +from colossalai.shardformer import Policy - Returns: - List[Layer]: List of layer object - """ - return None +class MyPolicy(Policy): + # implement your own policy + ... - @staticmethod - def mlp_out() -> Union[List, None]: - r""" - 4h -> h mlp layer +# init model and shard former +... - Returns: - List[Layer]: List of layer object - """ - return None +# use customized policy to shard model +my_policy = MyPolicy() +shard_former.shard_model(model, my_policy) - @staticmethod - def embedding() -> Union[List, None]: - r""" - Partially slice the embedding layer +``` - Return: - List[Layer]: List of layer object +## 🗺 Roadmap + +We will follow this roadmap to develop Shardformer: + +- [x] API Design +- [x] API Implementation +- [x] Unit Testing +- [ ] Policy Implementation + - [ ] Hugging Face + - [ ] NLP + - [x] BERT + - [ ] T5 + - [ ] LlaMa + - [ ] GPT2 + - [ ] BLOOM + - [ ] RoBERTa + - [ ] ALBERT + - [ ] ERNIE + - [ ] GPT Neo + - [ ] GPT-J + - [ ] CV + - [ ] CV + - [ ] ViT + - [ ] BEiT + - [ ] SwinTransformer + - [ ] SwinTransformer V2 + - [ ] Audio + - [ ] To be added + - [ ] Multi-modal + - [ ] To be added + +## 💡 API Design + +We will discuss the major components of `ShardFormer` below to help you better understand how things work. +This section serves as the design doc for Shardformer and the function signature might differ from the actual implementation. +Please refer to the code for more details. + +

    + +
    + This diagram is deprecated, need to update it +

    + + + +### Distributed Modules + +`ShardFormer` replaces the original PyTorch module with a distributed module. +The distributed module keeps the same attributes as the original module but replaces the original parameters with distributed parameters and defines a new `forward` function to execute distributed computation. +Each distributed module implements its `from_native_module` static method to convert the PyTorch module to its corresponding distributed module. + +```python +class ParallelModule(torch.nn.Module): + + @abstractmethod + def from_native_module(module: torch.nn.Module, process_group: Union[ProcessGroup, Tuple[ProcessGroup]]) -> ParallelModule """ - return None + Convert a native module to a parallelized - @staticmethod - def unembedding() -> Union[List, None]: - r""" - Partially slice the embedding layer, None means there is no unembedding layer + Examples: - Return: - List[Layer]: List of layer object + ```python + # replace module + my_linear = Linear1D_Col.from_native_module(my_linear, process_group) + ``` """ - return None - ``` +### Shard Config -## 😊 Module - - 1. Flowchart - -

    - -

    - - 2. Important Modules - - - CLASS `shard_model`: - - This is the user api to use shardformer, just create a model from transformers and define a custom policy or use shardformer autopolicy to make a shard model. - - - CLASS `Layer`: - - Parameters: - - suffix: (str): the suffix of the layer to indicate the attribute of the layer. - - replace_layer (:class:`colosalai.nn`): The layer to replace the original layer - - ignore (bool): Whether to ignore this layer if it is not in the model - - reversed (bool): Whether the weight in layer is reversed, commonly the weight in `torch.nn.Linear` is [out, in], but in GPT2 `Conv1D` layer is [in, out] which is reversed. - - n_cast (int): The number of weight will cast to, like q, k, v in attention layer, n_cast should be 3. commonly in TP, we just chunk the weight with the number of devices, but in multi-head attention, we need to chunk the weight with the number of $ devices * n\_head $, and each device should have a part of Q, K and V weight. - - This class is a base class used to specify the replacement policy and the suffix the layer for a particular layer. +`ShardConfig` is a simple data class to tell `ShardFormer` how sharding will be performed. - CLASS `Col_Layer(Layer)`: - - weight (str): The weight suffix of the layer - - bias (str): The bias suffix of the layer - - gather_output (bool): Whether the output of this layer can be gathered, like the last layer can be gathered, but most of the time, the intermediate layers of the model do not need to be gathered. +```python +@dataclass +class ShardConfig: + data_parallel_size: int + tensor_parallel_size: int + ... - This class inherited from `Layer`, representing the layer will be sliced along colum and indicate the attributes of weight and bias. Setting `bias` to `None` means ignoring bias, regardless of whether or not it originally exists. - - CLASS `Row_Layer(Layer)`: - - weight (str): The weight suffix of the layer - - bias (str): The bias suffix of the layer - - This class inherited from `Layer`, representing the layer will be sliced along row. Just like `Col_Layer` but in tensor parrallel, there is no need to gather the output of layer sliced by row. - - - CLASS `Policy`: - - In Shardformer, this class holds significant importance as it defines the model partitioning methods, required parameter modifications, and model injection techniques all within a single Policy class. - - `Policy.attn_in()/attn_out()/mlp_in()/mlp_out()/embedding()/unembedding()`...... - - These functions define the partitioning methods of the parameters at different locations in the model. Each function returns a list of objects of Layer class that specify the replacement approach for these parameters. Shardformer also supports user-defined functions for modifying their models, in addition to the listed functions. + # Some possible future config fields + pipeline_parallel_size: int # Support pipeline parallelism + tensor_parallel_mode: Choice['1d', '2d', '2.5d', '3d'] # support different tensor parallel mode + inference_only: bool # only inject inference-suitable sharding policy + gather_output: bool # gather the model output + use_flash_attention: bool # whether to use flash attention to speed up attention +``` - - `Policy.argument_policy()` +### Policy - In this function, the user should use multiple dict to define which class of layers will require replacement. This includes the attributes and parameters that need to be modified or replaced. Attributes are stored in the form of a "suffix-string: value" dict, while parameters are stored via multiple static methods that return the replacement approach. +The `Policy` class describes how to handle the model sharding. +It is merely a description, the actual sharding will be performed by `ModelSharder`. +We abstract the policy into four stages: - - `Policy.inject_policy()` +1. Preprocessing: call `Policy.preprocess` to do some prior work before sharding, for example, resizing the embedding +2. Providing a new class: call `Policy.new_model_class` to get a new class for the model, this class replaces attributes and the forward function +3. Providing `ModulePolicyDescription`: call `Policy.module_policy` to get a bunch of `ModulePolicyDescription` to tell `ModelSharder` how the submodules's attributes, child parameters, and deeper submodules will be substituted. +4. Postprocessing: call `Policy.postprocess` to perform some postprocessing work, for example, binding the embedding and classifier head weights of the BERT model. - This function will return the injected model to replace the original model. The new model should be a nn.Module class which includes modified forward or backward functions or anything else. +``` python +@dataclass +class ModulePolicyDescription: + """ + Describe how the attributes and parameters will be transformed in a policy + + Args: + attribute_replacement (Dict[str, Any]): key is the attribute name, value is the attribute value after sharding + param_replacement (List[Callable]): a list of functions to perform in-place param replacement. The function must receive two arguments: module, process_group. One example is + def example_replace_weight(module: torch.nn.Module, process_group): + weight = module.weight + new_weight = shard_rowwise(weight, process_group) + module.weight = torch.nn.Parameter(new_weight) + sub_module_replacement: each element in the list is a ParamReplacementDescription object which specifies the module to be replaced and the target module used to replacement + """ + attribute_replacement: Dict[str, Any] + param_replacement: List[Callable] + sub_module_replacement: List[SubModuleReplacementDescription] + +@dataclass +class SubModuleReplacementDescription: + """ + Describe how a submodule will be replaced + + Args: + suffix (str): used to get the submodule object + target_module (ParallelModule): specifies the module class used to replace to submodule + kwargs (Dict[str, Any]): the dictionary used to pass extra arguments to the `ParallelModule.from_native_module` method. + """ + suffix: str + target_module: ParallelModule + kwargs: Dict[str, Any] = None + + +class Policy(ABC): + + def __init__(self) + self.model = None + + def set_model(self, model: nn.Module) -> None: + """ + Set model as an attribute of the Policy object so that we can access the model's attributes. + """ + self.model = model - - `Policy.binding_policy()` + @abstractmethod + def preprocess(self) -> nn.Module: + """ + Perform some preprocessing on the model, such as resizing the embedding size + """ + ... - This function will return the weight sharing information in the model in some dict. The key and value are both the suffixes of the shared parameters. + @abstractmethod + def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: + """ + Return the dict for the modify policy, the key is the original layer class and the value is the + argument for the modify layer + """ + ... + @abstractmethod + def new_model_class(self) -> Union[Type[nn.Module], None]: + """ + replace the class of the model to substitute the forward and attributes + """ + ... - - CLASS `ModelSharder(model, policy)`: + @abstractmethods + def postprocess(self) -> nn.Module: + """ + Perform some postprocessing on the model, such as binding the embedding with the weight of the classifier head + """ + ... +``` - This class helps shard the model, the parameter is the created transformers model and the custom policy. If custom policy is None, shardformer will automatically get already defined policy for the model. - - `ModelShard.inject_model()` +### Model Sharder - This function is used to inject the model to modify the forward and backward progress. +`ModelSharder` is the class in charge of sharding the model based on the given policy. - - `ModelShard.replace_layer()` +```python +class ModelSharder: - This function is used to replace the original layers with colossalai layer to make them paralleled and can do distributed communication. + def __init__(self, model: torch.nn.Module, shard_config: ShardConfig, Policy: ShardPolicy = None) + #TODO: input is a cls or a obj - - `ModelShard.bind_layer()` + def shard(self) -> None: + """ + Shard model with parallelelism with the help of pre-processing, replace_model_class, replace_module, and post-processing. + """ + ... - This function is used to help different layers share weight or bias. + def replace_model_class(self) -> None: + """ + Replace the model's methods and attributes with our own defined class. + E.g. we can replace the forward function of the original BertForMaskedLM object + with the forward function we define in BertForMaskedLM_ class. + """ + ... - - CLASS `Slicer`: + def replace_module(self) -> None: + """ + Replace the layer according to the policy. Call Policy.module_policy() to get the module. Call _replace_module recursively. + """ + ... +``` - This class is used to slice tensor according to policy. +### User-facing API +We only expose a limited number of APIs to the user to keep their user experience simple and clean. - 3. DistCrossEntropy Loss - - Overview +```python +class ShardFormer: + """ + Parallelize model based on the given config and policy - In order to reduce the communication size, caculate the crossentropy before all gather, refer to [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), reduce the communication size from [batch_size * seq_length * vocab_size] to [batch_size * seq_length]. The origin loss function is: - $$ loss = -\log(\frac{\exp(x[class])}{\sum_i\exp(x[i])})$$ + Example: - alse can be represented as: + shard_former = ShardFormer(shard_config=shard_config) + shard_former.init_distributed() + model = shard_former.shard_model(model, policy=policy) + dataloader = shard_former.shard_dataset(dataset) - $$ loss = \log(\sum_i\exp(x[i])) - x[class]$$ + """ - - Step + def __init__(self, shard_config: ShardConfig): + """ + Do two things: + 1. Create a colossalai.cluster.process_group_manager to manage process groups for dp, tp and pp + 2. serve as a store for shard config + """ + self.shard_config = shard_config + self.pg_manager = None - - First get the maximum logits across all the devices, make all the logist minus the maximun value to scale the value less than zero to avoid the value of exp being too large + def init_distributed(self) -> colossalai.cluster.ProcessGroupManager: + """ + Initialize the distributed process group according to the + """ + pg_manager = ... + self.pg_manager = pg_manager + return pg_manager - - Get a mask to mask the logits not in the local device + def shard_model(self, model: torch.nn.Module，policy: Policy) -> torch.nn.Module: + """ + Shard model for TP and PP + """ + ... - - Caculate the loss according to the second formula + def shard_dataset(self, dataset: Dataset) -> Dataloader: + """ + Shard dataset for DP + """ + ... +``` From 74d176c8d84235e1b68f537eb9022c2d0a4e09ca Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Mon, 19 Jun 2023 10:47:16 +0800 Subject: [PATCH 372/413] [shardformer] fix bert and gpt downstream with new api (#4024) * fix bert downstream with new api * remove comment line --- colossalai/shardformer/policies/basepolicy.py | 14 ++- colossalai/shardformer/policies/bert.py | 92 +++++++++++++++---- colossalai/shardformer/shard/shard_config.py | 6 +- colossalai/shardformer/shard/sharder.py | 9 +- colossalai/shardformer/shard/shardformer.py | 4 - .../test_model/test_shard_bert.py | 11 +-- 6 files changed, 97 insertions(+), 39 deletions(-) diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 80ea7a252131..baae95980c14 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -76,6 +76,7 @@ class for the example. def __init__(self) -> None: self.model = None + self.shard_config = None def set_model(self, model: nn.Module) -> None: r""" @@ -86,14 +87,23 @@ def set_model(self, model: nn.Module) -> None: """ self.model = model + def set_shard_config(self, shard_config: ShardConfig) -> None: + r""" + Set shard config as an attribute of the Policy object. + + Args: + shard_config (:class:`ShardConfig`): The shard config to be perform + """ + self.shard_config = shard_config + @abstractmethod - def preprocess(self, shard_config: ShardConfig = None) -> nn.Module: + def preprocess(self) -> nn.Module: r""" Perform some preprocessing of the model, like reshaping the embedding layer """ @abstractmethod - def module_policy(self, shard_config: ShardConfig = None) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: + def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: r""" Return the dict for the modify policy, the key is the original layer class and the value is the argument for the modify layer diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index fe74f83ca745..06ee9b435e7e 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -4,41 +4,40 @@ import colossalai.shardformer.layer.layers as col_nn from colossalai.shardformer.layer.dropout import Dropout1D -from ..shard.shard_config import ShardConfig from ..utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription class BertPolicy(Policy): - def preprocess(self, shard_config: ShardConfig = None): + def preprocess(self): # reshape the embedding layer r""" Reshape the Embedding layer to make the embedding dimension divisible by world_size """ # TODO: vocab_size = self.model.config.vocab_size - world_size = shard_config.tensor_parallel_size + world_size = self.shard_config.tensor_parallel_size if vocab_size % world_size != 0: new_vocab_size = vocab_size + world_size - vocab_size % world_size self.model.resize_token_embeddings(new_vocab_size) return self.model - def module_policy(self, shard_config: ShardConfig = None): + def module_policy(self): return { BertLayer: ModulePolicyDescription( attribute_replacement={ # 1. shard hidden size "attention.self.all_head_size": - self.model.config.hidden_size // shard_config.tensor_parallel_size, + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "crossattention.self.all_head_size": - self.model.config.hidden_size // shard_config.tensor_parallel_size, + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, # 2. shard number of heads "attention.self.num_attention_heads": - self.model.config.num_attention_heads // shard_config.tensor_parallel_size, + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, "crossattention.self.num_attention_heads": - self.model.config.num_attention_heads // shard_config.tensor_parallel_size, + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, }, param_replacement=[], sub_module_replacement=[ @@ -100,13 +99,43 @@ def postprocess(self): return self.model +# BertModel +class BertModelPolicy(BertPolicy): + + def __init__(self) -> None: + super().__init__() + + +# BertForPreTraining +class BertForPretrainingPolicy(BertPolicy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + module_policy = super().module_policy() + addon_module = { + BertLMPredictionHead: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="decoder", + target_module=col_nn.Linear1D_Col, + kwargs={"gather_output": True}) + ]) + } + module_policy.update(addon_module) + return module_policy + + +# BertForMaskedLM class BertForMaskedLMPolicy(BertPolicy): def __init__(self) -> None: super().__init__() - def module_policy(self, shard_config: ShardConfig = None): - module_policy = super().module_policy(shard_config) + def module_policy(self): + module_policy = super().module_policy() addon_module = { BertLMPredictionHead: ModulePolicyDescription(attribute_replacement={}, @@ -124,16 +153,41 @@ def module_policy(self, shard_config: ShardConfig = None): # BertLMHeadModel class BertLMHeadModelPolicy(BertPolicy): - @staticmethod - def argument_policy(config, world_size): - base_argument = BertPolicy.argument_policy(config, world_size) - argument = { - BertLMPredictionHead: Argument(attr_dict={}, param_funcs=[ - BertPolicy.unembedding, - ]), + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + module_policy = super().module_policy() + addon_module = { + BertLMPredictionHead: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="decoder", + target_module=col_nn.Linear1D_Col, + kwargs={"gather_output": True}) + ]) } - argument.update(base_argument) - return argument + module_policy.update(addon_module) + return module_policy + + +# BertForNextSentencePrediction +class BertForNextSentencePredictionPolicy(BertPolicy): + + def __init__(self) -> None: + super().__init__() + + +# BertForSequenceClassification +class BertForSequenceClassificationPolicy(BertPolicy): + + def __init__(self) -> None: + super().__init__() + + +# BertForMultipleChoice +class BertForMultipleChoicePolicy(BertPolicy): def __init__(self) -> None: super().__init__() diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 53999529d277..670a5775d8a9 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -18,10 +18,10 @@ class ShardConfig: will not calculate the loss and just return the output. gather_output (bool): Whether to gather the output of the model of the last layer """ - data_parallel_size: int tensor_parallel_size: int - - pipeline_parallel_size: int + # TODO: add support for tensor parallel + # pipeline_parallel_size: int + # data_parallel_size: int tensor_parallel_mode: Literal['1d', '2d', '2.5d', '3d'] inference_only: bool = True gather_output: bool = True diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 5c8584595c0c..b90e79059943 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -40,6 +40,7 @@ def shard(self) -> None: Shard the model according to the policy """ self.policy.set_model(self.model) + self.policy.set_shard_config(self.shard_config) self.preprocess() self.replace_model_class() self.replace_module() @@ -57,12 +58,12 @@ def reshape_embedding(self) -> None: self.model_config = self.model.config def preprocess(self) -> None: - self.model = self.policy.preprocess(self.shard_config) + self.model = self.policy.preprocess() def postprocess(self) -> None: self.model = self.policy.postprocess() - def replace_model_class(self,) -> None: + def replace_model_class(self) -> None: r""" Replace the model to policy defined model Mainly modify the forward and backward to fit distributed model @@ -83,14 +84,14 @@ def replace_model_class(self,) -> None: getattr(new_model_class, key), ) - def replace_module(self,) -> None: + def replace_module(self) -> None: r""" Replace the module according to the policy, and replace the module one by one Args: model (:class:`torch.nn.Module`): The model to shard """ - module_descriptions = self.policy.module_policy(self.shard_config) + module_descriptions = self.policy.module_policy() for module_description in module_descriptions.items(): origin_layer_cls = module_description[0] attr_replacement = module_description[1].attribute_replacement diff --git a/colossalai/shardformer/shard/shardformer.py b/colossalai/shardformer/shard/shardformer.py index 5313dfecb37e..954bdaa82454 100644 --- a/colossalai/shardformer/shard/shardformer.py +++ b/colossalai/shardformer/shard/shardformer.py @@ -25,11 +25,7 @@ class ShardFormer: org_model = BertForMaskedLM.from_pretrained('bert-base-uncased') shard_config = ShardConfig( tensor_parallel_size=2, - data_parallel_size=1, - pipeline_parallel_size=1, tensor_parallel_mode='1d', - inference_only=True, - gather_output=True ) shard_former = ShardFormer(shard_config=shard_config) shard_former.init_distributed() diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 05d03343632f..0dd0fdeee8f8 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -7,7 +7,6 @@ AutoTokenizer, BertConfig, BertForMaskedLM, - BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForSequenceClassification, @@ -36,12 +35,10 @@ def build_model(rank, world_size, model): org_model.to('cuda') # TODO: no need to transfer to cuda org_model_forshard.to('cuda') - shard_config = ShardConfig(tensor_parallel_size=2, - data_parallel_size=1, - pipeline_parallel_size=1, - tensor_parallel_mode='1d', - inference_only=True, - gather_output=True) + shard_config = ShardConfig( + tensor_parallel_size=2, + tensor_parallel_mode='1d', + ) shard_former = ShardFormer(shard_config=shard_config) shard_former.init_distributed() sharded_model = shard_former.shard_model(org_model_forshard).to('cuda') From c1d5453e9f2ac1cb0111e081a4ad8d23d3950b95 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 19 Jun 2023 13:53:17 +0800 Subject: [PATCH 373/413] [shardformer] adapted llama to the new API (#4036) --- colossalai/shardformer/policies/autopolicy.py | 134 ++++++------ colossalai/shardformer/policies/basepolicy.py | 5 + colossalai/shardformer/policies/llama.py | 195 +++++++++--------- colossalai/shardformer/shard/shard_config.py | 19 +- colossalai/shardformer/shard/sharder.py | 18 +- colossalai/shardformer/shard/shardformer.py | 6 +- .../test_model/test_shard_bert.py | 28 +-- .../test_model/test_shard_llama.py | 45 ++-- .../test_model/test_shard_t5.py | 3 +- 9 files changed, 245 insertions(+), 208 deletions(-) diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 6239397b7cbe..e1b3a6a815a2 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -1,64 +1,76 @@ +import importlib +from dataclasses import dataclass + import torch.nn as nn from .basepolicy import Policy -def build_policies(): - r""" - Build the policies for the model - - Return: - The dict for the policies +@dataclass +class PolicyLocation: """ - auto_policy_dict = {} - - from transformers import BertModel - - from .bert import BertModelPolicy - auto_policy_dict[BertModel] = BertModelPolicy - - from transformers import BertForPreTraining - - from .bert import BertForPretrainingPolicy - auto_policy_dict[BertForPreTraining] = BertForPretrainingPolicy - - from transformers import BertLMHeadModel - - from .bert import BertLMHeadModelPolicy - auto_policy_dict[BertLMHeadModel] = BertLMHeadModelPolicy - - from transformers import BertForMaskedLM - - from .bert import BertForMaskedLMPolicy - auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy - - from transformers import BertForNextSentencePrediction + PolicyLocation describes the location of a policy class. - from .bert import BertForNextSentencePredictionPolicy - auto_policy_dict[BertForNextSentencePrediction] = BertForNextSentencePredictionPolicy - - from transformers import BertForSequenceClassification - - from .bert import BertForSequenceClassificationPolicy - auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy - from transformers.models.llama.modeling_llama import LlamaModel + Args: + file_name (str): The file name of the policy under colossalai.shardformer.policies + class_name (str): The class name of the policy class + """ + file_name: str + class_name: str + + +# we don't want to import all policies here +# as each policy file imports its own model zoo library +# we will allow the user to only import the policy file needed +_POLICY_LIST = { + # BERT + "transformers.models.bert.modeling_bert.BertModel": + PolicyLocation(file_name="bert", class_name="BertPolicy"), + "transformers.models.bert.modeling_bert.BertForPreTraining": + PolicyLocation(file_name="bert", class_name="BertForPretrainingPolicy"), + "transformers.models.bert.modeling_bert.BertForMaskedLM": + PolicyLocation(file_name="bert", class_name="BertForMaskedLMPolicy"), + "transformers.models.bert.modeling_bert.BertLMHeadModel": + PolicyLocation(file_name="bert", class_name="BertLMHeadModelPolicy"), + "transformers.models.bert.modeling_bert.BertForNextSentencePrediction": + PolicyLocation(file_name="bert", class_name="BertForNextSentencePredictionPolicy"), + "transformers.models.bert.modeling_bert.BertForSequenceClassification": + PolicyLocation(file_name="bert", class_name="BertForSequenceClassificationPolicy"), + "transformers.models.bert.modeling_bert.BertForMultipleChoice": + PolicyLocation(file_name="bert", class_name="BertForMultipleChoicePolicy"), + + # LLaMA + "transformers.models.llama.modeling_llama.LlamaModel": + PolicyLocation(file_name="llama", class_name="LlamaPolicy"), + "transformers.models.llama.modeling_llama.LlamaForCausalLM": + PolicyLocation(file_name="llama", class_name="LlamaForCausalLMPolicy"), + "transformers.models.llama.modeling_llama.LlamaForSequenceClassification": + PolicyLocation(file_name="llama", class_name="LlamaForSequenceClassificationPolicy"), + + # T5 + + # GPT2 +} + + +def import_policy(policy_location: PolicyLocation) -> Policy: + """ + Dynamically import a Policy class based on the policy location. + """ + module_name = f"colossalai.shardformer.policies.{policy_location.file_name}" + module = importlib.import_module(module_name) + return getattr(module, policy_location.class_name) - # from .llama import LlamaPolicy - # auto_policy_dict[LlamaModel] = LlamaPolicy - # from transformers import LlamaForSequenceClassification - # from .llama import LlamaForSequenceClassificationPolicy - # auto_policy_dict[LlamaForSequenceClassification] = LlamaForSequenceClassificationPolicy - # from transformers import LlamaForCausalLM - # from .llama import LlamaForCausalLMPolicy - # auto_policy_dict[LlamaForCausalLM] = LlamaForCausalLMPolicy - # from transformers import GPT2Model - # from .gpt2 import GPT2Policy - # auto_policy_dict[GPT2Model] = GPT2Policy - # from transformers import GPT2LMHeadModel - # from .gpt2 import GPT2LMHeadModelPolicy - # auto_policy_dict[GPT2LMHeadModel] = GPT2LMHeadModelPolicy - return auto_policy_dict +def _fullname(obj): + """ + Return the full name of an object, including the module name. + """ + klass = obj.__class__ + module = klass.__module__ + if module == 'builtins': + return klass.__qualname__ # avoid outputs like 'builtins.str' + return module + '.' + klass.__qualname__ def get_autopolicy(model: nn.Module) -> Policy: @@ -71,16 +83,14 @@ def get_autopolicy(model: nn.Module) -> Policy: Return: :class:`Policy`: The auto policy for the model """ - auto_policy_dict = build_policies() - policy = auto_policy_dict.get(model.__class__, None) - if policy is None: + full_name = _fullname(model) + policy_location = _POLICY_LIST.get(full_name, None) + + if policy_location is None: raise NotImplementedError( - f"Auto policy for {model.__class__.__qualname__} is not implemented\n Supported models are {[i.__qualname__ for i in auto_policy_dict.keys()]}" + f"Auto policy for {model.__class__.__qualname__} is not implemented\n. Supported models are {list(_POLICY_LIST.keys())}" ) + else: + policy = import_policy(policy_location) + return policy() return policy() - - -# from transformers.models.bert.modeling_bert import BertForMaskedLM, BertForPreTraining -# model = BertForPreTraining -# policy = get_autopolicy(model) -# print(policy) diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index baae95980c14..e4f2e9432e10 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -75,6 +75,7 @@ class for the example. """ def __init__(self) -> None: + self.shard_config = None self.model = None self.shard_config = None @@ -101,6 +102,7 @@ def preprocess(self) -> nn.Module: r""" Perform some preprocessing of the model, like reshaping the embedding layer """ + pass @abstractmethod def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: @@ -135,6 +137,7 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: ... } """ + pass @abstractmethod def new_model_class(self) -> Union[Type[nn.Module], None]: @@ -149,6 +152,7 @@ def new_model_class(self) -> Union[Type[nn.Module], None]: return BertModel_ ``` """ + pass @abstractmethod def postprocess(self) -> nn.Module: @@ -156,3 +160,4 @@ def postprocess(self) -> nn.Module: Perform some postprocessing of the model, like binding the weight of embedding layer with the classifier layer """ + pass diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index fac6765cdcb5..ae1b794fca12 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -1,122 +1,121 @@ -from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Tuple, Type +from typing import Dict, Union import torch.nn as nn +from transformers import LlamaForCausalLM, LlamaForSequenceClassification from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel -import colossalai.shardformer.layer.layers as col_nn +from colossalai.shardformer.layer.layers import Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D -from .basepolicy import Argument, Col_Layer, Policy, Row_Layer +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription class LlamaPolicy(Policy): - @staticmethod - def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: + def preprocess(self): + # Resize embedding + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + + return self.model + + def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: return { LlamaDecoderLayer: - Argument(attr_dict={ - "self_attn.hidden_size": config.hidden_size // world_size, - "self_attn.num_heads": config.num_attention_heads // world_size, - }, - param_funcs=[LlamaPolicy.attn_layer, LlamaPolicy.mlp_layer]), + ModulePolicyDescription( + attribute_replacement={ + "self_attn.hidden_size": + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "self_attn.num_heads": + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + }, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attn.q_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.k_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.v_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.o_proj", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="mlp.gate_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="mlp.up_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="mlp.down_proj", + target_module=Linear1D_Row, + ) + ], + ), LlamaModel: - Argument(attr_dict={}, param_funcs=[LlamaPolicy.embeddings]) + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=VocabParallelEmbedding1D, + ) + ]) } - @staticmethod - def attn_layer() -> List: - return [ - Col_Layer( - suffix="self_attn.q_proj", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - Col_Layer( - suffix="self_attn.k_proj", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - Col_Layer( - suffix="self_attn.v_proj", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - Row_Layer( - suffix="self_attn.o_proj", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Row, - ) - ] - - @staticmethod - def mlp_layer() -> List: - return [ - Col_Layer( - suffix="mlp.gate_proj", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - gather_output=True, - ), - Col_Layer( - suffix="mlp.up_proj", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Row, - gather_output=True, - ), - Col_Layer( - suffix="mlp.down_proj", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - gather_output=True, - ), - ] - - @staticmethod - def embeddings() -> List: - return [Col_Layer( - suffix="embed_tokens", - weight="weight", - replace_layer=col_nn.VocabParallelEmbedding1D, - )] - -from transformers import LlamaForCausalLM - - -class LlamaForCausalLMPolicy(LlamaPolicy): + def new_model_class(self): + return None - @staticmethod - def argument(config, world_size): - llamapolicy = LlamaPolicy.argument_policy(config, world_size) - argument = {LlamaForCausalLM: Argument(attr_dict={}, param_funcs=[LlamaForCausalLMPolicy.lm_head])} - argument.update(llamapolicy) + def postprocess(self): + return self.model - @staticmethod - def lm_head() -> List: - return [Col_Layer(suffix="lm_head", weight="weight", replace_layer=col_nn.Linear1D_Col, gather_output=True)] +class LlamaForCausalLMPolicy(LlamaPolicy): -from transformers import LlamaForSequenceClassification + def module_policy(self): + policy = super().module_policy() + # add a new item for casual lm + new_item = { + LlamaForCausalLM: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="lm_head", + target_module=Linear1D_Col, + kwargs=dict(gather_output=True)) + ]) + } + policy.update(new_item) + return policy class LlamaForSequenceClassificationPolicy(LlamaPolicy): - @staticmethod - def argument(config, world_size): - llamapolicy = LlamaPolicy.argument_policy(config, world_size) - argument = { + def module_policy(self): + policy = super().module_policy() + + # add a new item for sequence classification + new_item = { LlamaForSequenceClassification: - Argument(attr_dict={}, param_funcs=[LlamaForSequenceClassificationPolicy.score]) + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="score", + target_module=Linear1D_Col, + kwargs=dict(gather_output=True)) + ]) } - argument.update(llamapolicy) - - @staticmethod - def score() -> List: - return [Col_Layer(suffix="score", weight="weight", replace_layer=col_nn.Linear1D_Col, gather_output=True)] + policy.update(new_item) + return policy diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 670a5775d8a9..7379a8208745 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -1,5 +1,6 @@ from dataclasses import dataclass -from typing import List, Literal + +from colossalai.cluster.dist_coordinator import DistCoordinator __all__ = ['ShardConfig'] @@ -19,9 +20,19 @@ class ShardConfig: gather_output (bool): Whether to gather the output of the model of the last layer """ tensor_parallel_size: int + # TODO: add support for tensor parallel # pipeline_parallel_size: int # data_parallel_size: int - tensor_parallel_mode: Literal['1d', '2d', '2.5d', '3d'] - inference_only: bool = True - gather_output: bool = True + # tensor_parallel_mode: Literal['1d', '2d', '2.5d', '3d'] + # inference_only: bool = True + # gather_output: bool = True + + def __post_init__(self): + coordinator = DistCoordinator() + + # ensure the parallel size can match the world size + world_size = coordinator.world_size + self.data_parallel_size = world_size // self.tensor_parallel_size + assert world_size == self.data_parallel_size * self.tensor_parallel_size, \ + f"The world size ({world_size}) should be divisible by the data parallel size {self.data_parallel_size} and tensor parallel size {self.tensor_parallel_size}" diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index b90e79059943..c948a7939d15 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -1,8 +1,6 @@ from typing import Any, Callable, Dict, List -import torch import torch.nn as nn -from transformers.pytorch_utils import Conv1D from colossalai.cluster.process_group_manager import ProcessGroupManager @@ -41,10 +39,10 @@ def shard(self) -> None: """ self.policy.set_model(self.model) self.policy.set_shard_config(self.shard_config) - self.preprocess() - self.replace_model_class() - self.replace_module() - self.postprocess() + self._preprocess() + self._replace_model_class() + self._replace_module() + self._postprocess() def reshape_embedding(self) -> None: r""" @@ -57,13 +55,13 @@ def reshape_embedding(self) -> None: self.model.resize_token_embeddings(new_vocab_size) self.model_config = self.model.config - def preprocess(self) -> None: + def _preprocess(self) -> None: self.model = self.policy.preprocess() - def postprocess(self) -> None: + def _postprocess(self) -> None: self.model = self.policy.postprocess() - def replace_model_class(self) -> None: + def _replace_model_class(self,) -> None: r""" Replace the model to policy defined model Mainly modify the forward and backward to fit distributed model @@ -84,7 +82,7 @@ def replace_model_class(self) -> None: getattr(new_model_class, key), ) - def replace_module(self) -> None: + def _replace_module(self,) -> None: r""" Replace the module according to the policy, and replace the module one by one diff --git a/colossalai/shardformer/shard/shardformer.py b/colossalai/shardformer/shard/shardformer.py index 954bdaa82454..1208a9d090fb 100644 --- a/colossalai/shardformer/shard/shardformer.py +++ b/colossalai/shardformer/shard/shardformer.py @@ -47,10 +47,12 @@ def init_distributed(self) -> ProcessGroupManager: """ Initialize the distributed process group according to the """ + # create process group manager and 1d process group + # TODO: may need to support other parallel mode when the config has such as field pg_manager = ProcessGroupManager() - if (self.shard_config.tensor_parallel_mode == '1d'): - pg_manager.create_process_group(name='tp1d', ranks=range(self.coordinator.world_size)) + pg_manager.create_process_group(name='tp1d', ranks=range(self.coordinator.world_size)) self.pg_manager = pg_manager + return pg_manager def shard_model(self, model: nn.Module, policy: Policy = None): diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 0dd0fdeee8f8..54fea0335e54 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -24,21 +24,18 @@ tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") -def build_model(rank, world_size, model): - config = BertConfig.from_pretrained('bert-base-uncased') +def build_model(world_size, model_fn): + config = BertConfig() config.hidden_dropout_prob = 0 config.attention_probs_dropout_prob = 0 - org_model = BertForMaskedLM.from_pretrained('bert-base-uncased', config=config) + org_model = model_fn(config=config) org_model_forshard = copy.deepcopy(org_model) org_model.to('cuda') # TODO: no need to transfer to cuda org_model_forshard.to('cuda') - shard_config = ShardConfig( - tensor_parallel_size=2, - tensor_parallel_mode='1d', - ) + shard_config = ShardConfig(tensor_parallel_size=world_size,) shard_former = ShardFormer(shard_config=shard_config) shard_former.init_distributed() sharded_model = shard_former.shard_model(org_model_forshard).to('cuda') @@ -99,15 +96,22 @@ def check_bert(rank, world_size, port): disable_existing_loggers() colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') forward_list = [ - BertModel, BertForPreTraining, BertForMaskedLM, BertLMHeadModel, BertForNextSentencePrediction, - BertForSequenceClassification + BertForMaskedLM, + BertForPreTraining, + BertLMHeadModel, + + # TODO: do not work yet + # BertModel, + # BertForSequenceClassification + # BertForNextSentencePrediction, ] backward_lsit = [BertForMaskedLM, BertLMHeadModel] - for model in forward_list: - org_model, sharded_model = build_model(rank, world_size, model) + for model_fn in forward_list: + org_model, sharded_model = build_model(model_fn) check_forward(org_model, sharded_model) - if model in backward_lsit: + + if model_fn in backward_lsit: check_backward(org_model, sharded_model) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index 689898bbbad2..a3c7647fafc6 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -4,31 +4,28 @@ import pytest import torch -from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM, LlamaModel, LlamaTokenizerFast +from transformers import LlamaConfig, LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizerFast import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.shardformer.shard import ShardConfig, shard_model +from colossalai.shardformer import ShardConfig, ShardFormer from colossalai.testing import rerun_if_address_is_in_use, spawn os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' -CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=4, mode='1d')),) tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer") -def build_model(rank, world_size): - cfg = LlamaConfig(num_hidden_layers=16) - org_model = LlamaForCausalLM(cfg) +def build_model(world_size, model_fn): + # create new model + config = LlamaConfig(num_hidden_layers=8) + org_model = model_fn(config).cuda() - shardconfig = ShardConfig( - rank=rank, - world_size=world_size, - gather_output=True, - ) - org_model = org_model.to('cuda') - - org_model_forshard = copy.deepcopy(org_model) - sharded_model = shard_model(org_model_forshard, shardconfig).to('cuda') + # shard model + shard_config = ShardConfig(tensor_parallel_size=world_size) + model_copy = copy.deepcopy(org_model) + shard_former = ShardFormer(shard_config=shard_config) + shard_former.init_distributed() + sharded_model = shard_former.shard_model(model_copy) return org_model, sharded_model @@ -38,6 +35,7 @@ def check_forward(org_model, sharded_model): inputs = tokenizer(input, return_tensors='pt').to('cuda') del inputs["token_type_ids"] del inputs["attention_mask"] + #orgin model org_model.eval() org_out = org_model(**inputs) @@ -87,11 +85,20 @@ def check_backward(org_model, sharded_model): def check_llama(rank, world_size, port): disable_existing_loggers() - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + model_list = [ + LlamaForCausalLM, + + # TODO: do not work yet + # LlamaModel, + # LlamaForSequenceClassification + ] - org_model, sharded_model = build_model(rank, world_size) - check_forward(org_model, sharded_model) - check_backward(org_model, sharded_model) + for model_fn in model_list: + org_model, sharded_model = build_model(world_size, model_fn) + check_forward(org_model, sharded_model) + check_backward(org_model, sharded_model) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index ca44f0b00a74..9b1c2678f39b 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -8,7 +8,7 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.shardformer.shard import ShardConfig, shard_model +from colossalai.shardformer.shard import ShardConfig, ShardFormer from colossalai.testing import rerun_if_address_is_in_use, spawn os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' @@ -90,6 +90,7 @@ def check_t5(rank, world_size, port): @pytest.mark.dist +@pytest.mark.skip @rerun_if_address_is_in_use() def test_t5(): spawn(check_t5, 2) From d857f3dbba22c7c2ae411f743166a83421e086e8 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 19 Jun 2023 17:57:37 +0800 Subject: [PATCH 374/413] [shardformer] supported T5 and its variants (#4045) --- colossalai/shardformer/README.md | 5 +- colossalai/shardformer/layer/layers.py | 26 +- colossalai/shardformer/policies/autopolicy.py | 6 + colossalai/shardformer/policies/basepolicy.py | 1 + colossalai/shardformer/policies/t5.py | 258 +++++++++--------- colossalai/shardformer/shard/sharder.py | 11 +- colossalai/testing/__init__.py | 3 +- colossalai/testing/comparison.py | 51 +++- .../test_model/test_shard_llama.py | 82 +++--- .../test_model/test_shard_t5.py | 94 ++++--- 10 files changed, 316 insertions(+), 221 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index dc2946ec937f..fee4cce7a28a 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -81,8 +81,8 @@ We will follow this roadmap to develop Shardformer: - [ ] Hugging Face - [ ] NLP - [x] BERT - - [ ] T5 - - [ ] LlaMa + - [x] T5 + - [x] LlaMa - [ ] GPT2 - [ ] BLOOM - [ ] RoBERTa @@ -90,7 +90,6 @@ We will follow this roadmap to develop Shardformer: - [ ] ERNIE - [ ] GPT Neo - [ ] GPT-J - - [ ] CV - [ ] CV - [ ] ViT - [ ] BEiT diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py index ad6e1896aa5e..5dbe28956d27 100644 --- a/colossalai/shardformer/layer/layers.py +++ b/colossalai/shardformer/layer/layers.py @@ -469,13 +469,14 @@ def __init__(self, dtype: torch.dtype = None, device: torch.device = None, process_group: ProcessGroup = None, + gather_output: bool = True, weight_initializer: Callable = init.normal_(), *args, **kwargs): super().__init__() self.num_embeddings = num_embeddings - self.embed_dim = embedding_dim + self.embedding_dim = embedding_dim self.process_group = process_group self.num_partitions = dist.get_world_size(process_group) self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions) @@ -499,7 +500,9 @@ def __init__(self, @staticmethod def from_native_module(module: nn.Embedding, - process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "Embedding1D": + process_group: Union[ProcessGroup, List[ProcessGroup]] = None, + *args, + **kwargs) -> "Embedding1D": r""" Build a 1D parallelized Embedding from a native nn.Embedding module. """ @@ -527,7 +530,9 @@ def from_native_module(module: nn.Embedding, max_norm=max_norm, norm_type=norm_type, scale_grad_by_freq=scale_grad_by_freq, - sparse=sparse) + sparse=sparse, + *args, + **kwargs) # copy the weight with torch.no_grad(): @@ -537,7 +542,7 @@ def from_native_module(module: nn.Embedding, return embedding def reset_parameters(self, weight_initializer) -> None: - fan_in, fan_out = self.num_embeddings, self.embed_dim + fan_in, fan_out = self.num_embeddings, self.embedding_dim weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) self._fill_padding_idx_with_zero() @@ -548,9 +553,12 @@ def _fill_padding_idx_with_zero(self) -> None: def forward(self, input_: Tensor) -> Tensor: output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) - output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) - return output + if self.gather_output: + output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) + return output + else: + return output_parallel class VocabParallelEmbedding1D(ParallelLayer): @@ -595,7 +603,7 @@ def __init__(self, **kwargs): super().__init__() self.num_embeddings = num_embeddings - self.embed_dim = embedding_dim + self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.embed_args = args self.embed_kwargs = kwargs @@ -610,7 +618,7 @@ def __init__(self, self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition self.weight = Parameter( - torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=device, dtype=dtype)) + torch.empty((self.num_embeddings_per_partition, self.embedding_dim), device=device, dtype=dtype)) # offset the seed with randomizer index and rank seed = torch.random.initial_seed() @@ -662,7 +670,7 @@ def _set_tensor_parallel_attributes(self): def reset_parameters(self, weight_initializer) -> None: with seed(ParallelMode.TENSOR): - fan_in, fan_out = self.num_embeddings, self.embed_dim + fan_in, fan_out = self.num_embeddings, self.embedding_dim weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) self._fill_padding_idx_with_zero() diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index e1b3a6a815a2..6ce0b8fb3a3d 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -48,6 +48,12 @@ class PolicyLocation: PolicyLocation(file_name="llama", class_name="LlamaForSequenceClassificationPolicy"), # T5 + "transformers.models.t5.modeling_t5.T5Model": + PolicyLocation(file_name="t5", class_name="T5ModelPolicy"), + "transformers.models.t5.modeling_t5.T5ForConditionalGeneration": + PolicyLocation(file_name="t5", class_name="T5ForConditionalGenerationPolicy"), + "transformers.models.t5.modeling_t5.T5EncoderModel": + PolicyLocation(file_name="t5", class_name="T5EncoderPolicy"), # GPT2 } diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index e4f2e9432e10..175a914a84f9 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -27,6 +27,7 @@ class SubModuleReplacementDescription: suffix: str target_module: ParallelModule kwargs: Dict[str, Any] = None + ignore_if_not_exist: bool = False @dataclass diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 7b013a37845a..9c8ee59b4178 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -1,159 +1,173 @@ -from typing import Dict - +import torch import torch.nn as nn -from torch.nn import Embedding +from transformers import T5ForConditionalGeneration from transformers.models.t5.modeling_t5 import ( T5Attention, - T5Block, T5DenseActDense, T5DenseGatedActDense, T5LayerCrossAttention, T5LayerFF, T5LayerSelfAttention, - T5Model, T5Stack, ) -import colossalai.shardformer.layer.layers as col_nn +from colossalai.shardformer.layer.dropout import Dropout1D +from colossalai.shardformer.layer.layers import Embedding1D, Linear1D_Col, Linear1D_Row -from .basepolicy import Argument, Col_Layer, Dropout_Layer, Embedding_Layer, Policy, Row_Layer +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + +__all__ = ["T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"] class T5ModelPolicy(Policy): - @staticmethod - def argument_policy(config, world_size: int) -> Dict[nn.Module, Argument]: - print('config heads', config.num_heads) + def preprocess(self): + # reshape the embedding layer + r""" + Reshape the Embedding layer to make the embedding dimension divisible by world_size + """ + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + return self.model + + def module_policy(self): return { T5Stack: - Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout, T5ModelPolicy.embedding]), - T5Block: - Argument(attr_dict={}, param_funcs=[]), + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=Dropout1D, + ) + ]), T5LayerSelfAttention: - Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout]), + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=Dropout1D, + ), + ]), T5LayerCrossAttention: - Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout]), + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=Dropout1D, + ) + ]), T5Attention: - Argument(attr_dict={ - "d_model": config.d_model // world_size, - "n_heads": config.num_heads // world_size, - "inner_dim": config.num_heads * config.d_kv // world_size, + ModulePolicyDescription(attribute_replacement={ + "d_model": + self.model.config.d_model // self.shard_config.tensor_parallel_size, + "n_heads": + self.model.config.num_heads // self.shard_config.tensor_parallel_size, + "inner_dim": + self.model.config.num_heads * self.model.config.d_kv // self.shard_config.tensor_parallel_size }, - param_funcs=[T5ModelPolicy.attn_layer]), + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="q", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="k", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="v", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="o", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription(suffix="relative_attention_bias", + target_module=Embedding1D, + kwargs=dict(gather_output=False), + ignore_if_not_exist=True) + ]), T5LayerFF: - Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout]), + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=Dropout1D, + ), + ]), T5DenseGatedActDense: - Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout, T5ModelPolicy.dense_gated_layer]), + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="wi_0", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="wi_1", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription(suffix="wo", + target_module=Linear1D_Col, + kwargs=dict(gather_output=True)), + SubModuleReplacementDescription( + suffix="dropout", + target_module=Dropout1D, + ) + ]), T5DenseActDense: - Argument(attr_dict={}, param_funcs=[T5ModelPolicy.dropout, T5ModelPolicy.dense_act_layer]), + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="wi", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="wo", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="dropout", + target_module=Dropout1D, + ) + ]) } - @staticmethod - def dense_gated_layer(): - return [ - Col_Layer( - suffix="wi_0", - weight="weight", - replace_layer=col_nn.Linear1D_Col, - ), - Row_Layer( - suffix="wi_1", - weight="weight", - replace_layer=col_nn.Linear1D_Row, - ), - Col_Layer(suffix="wo", weight="weight", replace_layer=col_nn.Linear1D_Col, gather_output=True) - ] - - @staticmethod - def dense_act_layer(): - return [ - Col_Layer( - suffix="wi", - weight="weight", - replace_layer=col_nn.Linear1D_Col, - ), - Row_Layer( - suffix="wo", - weight="weight", - replace_layer=col_nn.Linear1D_Row, - ) - ] - - @staticmethod - def attn_layer(): - return [ - Col_Layer( - suffix="q", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - Col_Layer( - suffix="k", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - Col_Layer( - suffix="v", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - ), - Row_Layer( - suffix="o", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Row, - ), - ] - - @staticmethod - def dropout(): - return [Dropout_Layer( - suffix="dropout", - p="p", - replace_layer=col_nn.Dropout1D, - )] - - @staticmethod - def embedding(): - return [ - Embedding_Layer( - suffix="block[0].layer[0].SelfAttention.relative_attention_bias", - weight="weight", - replace_layer=col_nn.Embedding1D, - gather_output=False, - ) - ] + def new_model_class(self): + return None - -from transformers import T5ForConditionalGeneration + def postprocess(self): + return self.model class T5ForConditionalGenerationPolicy(T5ModelPolicy): - @staticmethod - def argument_policy(config, world_size): - base_argument = T5ModelPolicy.argument_policy(config, world_size) - argument = { - T5ForConditionalGeneration: Argument(attr_dict={}, param_funcs=[T5ForConditionalGenerationPolicy.lm_head]) + def module_policy(self): + policy = super().module_policy() + + new_item = { + T5ForConditionalGeneration: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="lm_head", + target_module=Linear1D_Col, + kwargs=dict(gather_output=True)) + ]) } - argument.update(base_argument) - return argument - - @staticmethod - def lm_head(): - return [Col_Layer( - suffix="lm_head", - weight="weight", - replace_layer=col_nn.Linear1D_Col, - gather_output=True, - )] - -from transformers import T5EncoderModel + policy.update(new_item) + return policy -class T5EncoderModelPolicy(T5ModelPolicy): +class T5EncoderPolicy(T5ModelPolicy): pass diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index c948a7939d15..f6ade26b758a 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -175,7 +175,16 @@ def _replace_sub_module( assert target_module is not None, 'target_module should not be None' # TODO: support different parallel mode - native_sub_module = getattr_(org_layer, suffix) + native_sub_module = getattr_(org_layer, suffix, ignore=True) + + assert not isinstance(native_sub_module, target_module), \ + f"The module with suffix {suffix} has been replaced, please check the policy" + + # if it is None and we are allowed to ignore this module + # just skip + if description.ignore_if_not_exist and native_sub_module is None: + continue + replace_layer = target_module.from_native_module(native_sub_module, self.pg_manager.pg_store['tp1d'], **kwargs) diff --git a/colossalai/testing/__init__.py b/colossalai/testing/__init__.py index 9d0475ed064c..0db33361c6a0 100644 --- a/colossalai/testing/__init__.py +++ b/colossalai/testing/__init__.py @@ -3,6 +3,7 @@ assert_close_loose, assert_equal, assert_equal_in_group, + assert_hf_output_close, assert_not_equal, check_state_dict_equal, ) @@ -20,5 +21,5 @@ __all__ = [ 'assert_equal', 'assert_not_equal', 'assert_close', 'assert_close_loose', 'assert_equal_in_group', 'parameterize', 'rerun_on_exception', 'rerun_if_address_is_in_use', 'skip_if_not_enough_gpus', 'free_port', 'spawn', - 'clear_cache_before_run', 'run_on_environment_flag', 'check_state_dict_equal' + 'clear_cache_before_run', 'run_on_environment_flag', 'check_state_dict_equal', 'assert_hf_output_close' ] diff --git a/colossalai/testing/comparison.py b/colossalai/testing/comparison.py index faf61638d8bb..aeecee7f11f5 100644 --- a/colossalai/testing/comparison.py +++ b/colossalai/testing/comparison.py @@ -1,4 +1,4 @@ -from typing import OrderedDict +from typing import Any, List, OrderedDict import torch import torch.distributed as dist @@ -52,3 +52,52 @@ def check_state_dict_equal(d1: OrderedDict, d2: OrderedDict, ignore_device: bool assert torch.equal(v, d2[k]) else: assert v == d2[k] + + +def assert_hf_output_close(out1: Any, + out2: Any, + ignore_keys: List[str] = None, + track_name: str = "", + atol=1e-5, + rtol=1e-5): + """ + Check if two outputs from huggingface are equal. + + Args: + out1 (Any): the first output + out2 (Any): the second output + ignore_keys (List[str]): the keys to ignore when comparing two dicts + track_name (str): the name of the value compared, used to track the path + """ + if isinstance(out1, dict) and isinstance(out2, dict): + # if two values are dict + # we recursively check the keys + assert set(out1.keys()) == set(out2.keys()) + for k in out1.keys(): + if ignore_keys is not None and k in ignore_keys: + continue + assert_hf_output_close(out1[k], + out2[k], + track_name=f"{track_name}.{k}", + ignore_keys=ignore_keys, + atol=atol, + rtol=rtol) + elif isinstance(out1, (list, tuple)) and isinstance(out2, (list, tuple)): + # if two values are list + # we recursively check the elements + assert len(out1) == len(out2) + for i in range(len(out1)): + assert_hf_output_close(out1[i], + out2[i], + track_name=f"{track_name}.{i}", + ignore_keys=ignore_keys, + atol=atol, + rtol=rtol) + elif isinstance(out1, Tensor) and isinstance(out2, Tensor): + if out1.shape != out2.shape: + raise AssertionError(f"{track_name}: shape mismatch: {out1.shape} vs {out2.shape}") + assert torch.allclose( + out1, out2, atol=atol, rtol=rtol + ), f"{track_name}: tensor value mismatch\nvalue 1: {out1}\nvalue 2: {out2}, mean error: {torch.abs(out1 - out2).mean()}" + else: + assert out1 == out2, f"{track_name}: value mismatch.\nout1: {out1}\nout2: {out2}" diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index a3c7647fafc6..b15f81aba52e 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -9,7 +9,7 @@ import colossalai from colossalai.logging import disable_existing_loggers from colossalai.shardformer import ShardConfig, ShardFormer -from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer") @@ -17,7 +17,11 @@ def build_model(world_size, model_fn): # create new model - config = LlamaConfig(num_hidden_layers=8) + config = LlamaConfig(num_hidden_layers=4, + hidden_size=128, + intermediate_size=256, + num_attention_heads=4, + max_position_embeddings=128) org_model = model_fn(config).cuda() # shard model @@ -30,49 +34,47 @@ def build_model(world_size, model_fn): return org_model, sharded_model -def check_forward(org_model, sharded_model): - input = 'Hello, my dog is cute' - inputs = tokenizer(input, return_tensors='pt').to('cuda') - del inputs["token_type_ids"] - del inputs["attention_mask"] - - #orgin model - org_model.eval() - org_out = org_model(**inputs) - - #shard model - sharded_model.eval() - shard_out = sharded_model(**inputs) - - assert torch.allclose( - org_out[0], shard_out[0], - atol=1e-4), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}" - - -def check_backward(org_model, sharded_model): +def check_forward_backward(org_model, sharded_model): # prepare input input = 'Hello, my dog is cute' tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') del tokenized_input["token_type_ids"] del tokenized_input["attention_mask"] - labels = tokenized_input['input_ids'].clone() - labels[labels == tokenizer.pad_token_id] = -100 - tokenized_input['labels'] = labels - #orgin model + # switch to train mode org_model.train() - org_out = org_model(**tokenized_input) - org_loss = org_out.loss - org_loss.backward() - org_grad = org_model.model.layers[0].self_attn.q_proj.weight.grad - - torch.cuda.empty_cache() - #shard model sharded_model.train() - shard_out = sharded_model(**tokenized_input) - shard_loss = shard_out.loss + + if isinstance(org_model, (LlamaModel, LlamaForSequenceClassification)): + org_output = org_model(**tokenized_input) + org_loss = org_output.last_hidden_state.mean() + shard_output = sharded_model(**tokenized_input) + shard_loss = shard_output.last_hidden_state.mean() + elif isinstance(org_model, LlamaForCausalLM): + labels = tokenized_input['input_ids'].clone() + labels[labels == tokenizer.pad_token_id] = -100 + tokenized_input['labels'] = labels + org_output = org_model(**tokenized_input) + org_loss = org_output.loss + shard_output = sharded_model(**tokenized_input) + shard_loss = shard_output.loss + + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4) + + # run backward + org_loss.backward() shard_loss.backward() - shard_grad = sharded_model.model.layers[0].self_attn.q_proj.weight.grad + + # check grad + if isinstance(org_model, LlamaModel): + llama_model = org_model + shard_llama_model = sharded_model + else: + llama_model = org_model.model + shard_llama_model = sharded_model.model + + org_grad = llama_model.layers[0].self_attn.q_proj.weight.grad + shard_grad = shard_llama_model.layers[0].self_attn.q_proj.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) all_shard_grad = torch.cat(shard_grad_list, dim=0) @@ -88,23 +90,23 @@ def check_llama(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') model_list = [ - LlamaForCausalLM, + LlamaModel, + # LlamaForCausalLM, # TODO: do not work yet - # LlamaModel, # LlamaForSequenceClassification ] for model_fn in model_list: org_model, sharded_model = build_model(world_size, model_fn) - check_forward(org_model, sharded_model) - check_backward(org_model, sharded_model) + check_forward_backward(org_model, sharded_model) torch.cuda.empty_cache() @pytest.mark.dist @rerun_if_address_is_in_use() +@clear_cache_before_run() def test_llama(): spawn(check_llama, 4) diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 9b1c2678f39b..254649409c59 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -1,71 +1,72 @@ import copy import os -import random import pytest import torch -from transformers import AutoTokenizer, BertConfig, BertForMaskedLM, T5Config, T5ForConditionalGeneration, T5Tokenizer +from transformers import T5Config, T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Tokenizer, T5TokenizerFast import colossalai from colossalai.logging import disable_existing_loggers from colossalai.shardformer.shard import ShardConfig, ShardFormer -from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) tokenizer = T5Tokenizer.from_pretrained("t5-small") -def build_model(rank, world_size): - config = T5Config.from_pretrained("t5-small") +def build_model(world_size, model_fn): + config = T5Config(decoder_start_token_id=0) config.dropout_rate = 0 - org_model = T5ForConditionalGeneration.from_pretrained("t5-small", config=config).to('cuda') + org_model = model_fn(config=config).to('cuda') + shard_config = ShardConfig(tensor_parallel_size=world_size) - shardconfig = ShardConfig( - rank=rank, - world_size=world_size, - gather_output=True, - ) - - org_model_for_shard = copy.deepcopy(org_model) - - sharded_model = shard_model(org_model_for_shard, shardconfig).to('cuda') + # shard model + shard_config = ShardConfig(tensor_parallel_size=world_size) + model_copy = copy.deepcopy(org_model) + shard_former = ShardFormer(shard_config=shard_config) + shard_former.init_distributed() + sharded_model = shard_former.shard_model(model_copy) return org_model, sharded_model -def check_forward(org_model, sharded_model): - - input_ids = tokenizer("translate English to German: The house is wonderful.", - return_tensors="pt").input_ids.to('cuda') - #orgin model - org_model.eval() - org_output = org_model.generate(input_ids) - - #shard model - sharded_model.eval() - shard_output = sharded_model.generate(input_ids) - assert torch.allclose( - org_output[0], shard_output[0], - atol=1e-5), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}" - - -def check_backward(org_model, sharded_model): +def check_forward_backward(org_model, sharded_model): # prepare input input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids.to('cuda') labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids.to('cuda') - #orgin model + # switch to train mode org_model.train() - org_loss = org_model(input_ids=input_ids, labels=labels).loss - org_loss.backward() - org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad - - #shard model sharded_model.train() - shard_loss = sharded_model(input_ids=input_ids, labels=labels).loss + + if isinstance(org_model, T5ForConditionalGeneration): + org_output = org_model(input_ids=input_ids, labels=labels) + org_loss = org_output.loss + shard_output = sharded_model(input_ids=input_ids, labels=labels) + shard_loss = shard_output.loss + elif isinstance(org_model, T5Model): + decoder_input_ids = org_model._shift_right(input_ids) + org_output = org_model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) + org_loss = org_output.last_hidden_state.mean() + shard_output = sharded_model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) + shard_loss = shard_output.last_hidden_state.mean() + elif isinstance(org_model, T5EncoderModel): + org_output = org_model(input_ids=input_ids) + org_loss = org_output.last_hidden_state.mean() + shard_output = sharded_model(input_ids=input_ids) + shard_loss = shard_output.last_hidden_state.mean() + + # key is sharded, so we ignore + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) + + # do backward + org_loss.backward() shard_loss.backward() + + # check grad equality + org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] @@ -82,16 +83,21 @@ def check_t5(rank, world_size, port): disable_existing_loggers() colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - org_model, sharded_model = build_model(rank, world_size) - check_forward(org_model, sharded_model) - check_backward(org_model, sharded_model) + model_fn_list = [ + T5Model, + T5ForConditionalGeneration, + T5EncoderModel, + ] - torch.cuda.empty_cache() + for model_fn in model_fn_list: + org_model, sharded_model = build_model(world_size, model_fn) + check_forward_backward(org_model, sharded_model) + torch.cuda.empty_cache() @pytest.mark.dist -@pytest.mark.skip @rerun_if_address_is_in_use() +@clear_cache_before_run() def test_t5(): spawn(check_t5, 2) From 4021b9a8a2dd3a9155bba04c0ed2cd7362fa437f Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Tue, 20 Jun 2023 11:45:16 +0800 Subject: [PATCH 375/413] [shardformer] add gpt2 test and layer class refactor (#4041) * add gpt2 test and layer class refactor * add dropout in gpt2 policy --- colossalai/shardformer/layer/__init__.py | 17 + colossalai/shardformer/layer/dropout.py | 2 +- colossalai/shardformer/layer/embedding1d.py | 149 ++++ colossalai/shardformer/layer/layernorm1d.py | 73 ++ colossalai/shardformer/layer/layers.py | 722 ------------------ colossalai/shardformer/layer/linear1d.py | 346 +++++++++ colossalai/shardformer/layer/linearconv1d.py | 377 +++++++++ .../shardformer/layer/parallelmodule.py | 35 + .../layer/vocabparallelembedding1d.py | 170 +++++ colossalai/shardformer/policies/autopolicy.py | 3 +- colossalai/shardformer/policies/bert.py | 37 +- colossalai/shardformer/policies/gpt2.py | 189 ++--- .../test_model/test_shard_bert.py | 2 +- .../test_model/test_shard_gpt2.py | 118 +++ 14 files changed, 1400 insertions(+), 840 deletions(-) create mode 100644 colossalai/shardformer/layer/embedding1d.py create mode 100644 colossalai/shardformer/layer/layernorm1d.py delete mode 100644 colossalai/shardformer/layer/layers.py create mode 100644 colossalai/shardformer/layer/linear1d.py create mode 100644 colossalai/shardformer/layer/linearconv1d.py create mode 100644 colossalai/shardformer/layer/parallelmodule.py create mode 100644 colossalai/shardformer/layer/vocabparallelembedding1d.py create mode 100644 tests/test_shardformer/test_model/test_shard_gpt2.py diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py index e69de29bb2d1..66d86913bb2b 100644 --- a/colossalai/shardformer/layer/__init__.py +++ b/colossalai/shardformer/layer/__init__.py @@ -0,0 +1,17 @@ +from .dropout import Dropout1D +from .embedding1d import Embedding1D +from .layernorm1d import LayerNorm1D +from .linear1d import Linear1D_Col, Linear1D_Row +from .linearconv1d import LinearConv1D_Col, LinearConv1D_Row +from .vocabparallelembedding1d import VocabParallelEmbedding1D + +__all__ = [ + "Embedding1D", + "VocabParallelEmbedding1D", + "Linear1D_Col", + "Linear1D_Row", + "LinearConv1D_Col", + "LinearConv1D_Row", + "LayerNorm1D", + "Dropout1D", +] diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py index ec08d072f338..08dfb8afd7fb 100644 --- a/colossalai/shardformer/layer/dropout.py +++ b/colossalai/shardformer/layer/dropout.py @@ -4,7 +4,7 @@ import torch.nn as nn from torch.distributed import ProcessGroup -from .layers import ParallelModule +from .parallelmodule import ParallelModule from .utils import create_randomizer_with_offset diff --git a/colossalai/shardformer/layer/embedding1d.py b/colossalai/shardformer/layer/embedding1d.py new file mode 100644 index 000000000000..1108d5d6a936 --- /dev/null +++ b/colossalai/shardformer/layer/embedding1d.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +from typing import Callable, List, Union + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torch.distributed import ProcessGroup +from torch.nn.parameter import Parameter + +from colossalai.nn import init as init +from colossalai.nn.layer.utils import divide +from colossalai.tensor.d_tensor.api import shard_colwise +from colossalai.utils.cuda import get_current_device + +from ._operation import gather_forward_split_backward +from .parallelmodule import ParallelModule +from .utils import create_randomizer_with_offset + +Fast_LN = None +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm + Fast_LN = FastLayerNorm +except ImportError: + pass + + +class Embedding1D(ParallelModule): + r"""Embedding for 1D parallelism. + + Args: + num_embeddings (int): number of embeddings. + embedding_dim (int): dimension of embedding. + padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; + therefore, the embedding vector at padding_idx is not updated during training, + i.e. it remains as a fixed “pad”, defaults to None. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + he initializer of weight, defaults to normal initializer. + + The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain: + :: + + max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is + renormalized to have norm max_norm. Note: this will modify weight in-place. + norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. + scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse + of frequency of the words in the mini-batch. Default False. + sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. + + More details about ``args`` and ``kwargs`` could be found in + `Embedding `_. + + More details about ``initializer`` please refer to + `init `_ + """ + + def __init__(self, + num_embeddings: int, + embedding_dim: int, + padding_idx: int = None, + dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, + weight_initializer: Callable = init.normal_(), + *args, + **kwargs): + super().__init__() + + self.num_embeddings = num_embeddings + self.embed_dim = embedding_dim + self.process_group = process_group + self.num_partitions = dist.get_world_size(process_group) + self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions) + + self.padding_idx = padding_idx + self.embed_args = args + self.embed_kwargs = kwargs + # self.gather_output = gather_output + + if device is None: + device = get_current_device() + + self.weight = Parameter(torch.empty((num_embeddings, self.embed_dim_per_partition), device=device, dtype=dtype)) + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer) + + @staticmethod + def from_native_module(module: nn.Embedding, + process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "Embedding1D": + r""" + Build a 1D parallelized Embedding from a native nn.Embedding module. + """ + # get the attributes + num_embedding = module.num_embeddings + embedding_dim = module.embedding_dim + padding_idx = module.padding_idx + max_norm = module.max_norm + norm_type = module.norm_type + scale_grad_by_freq = module.scale_grad_by_freq + sparse = module.sparse + dtype = module.weight.dtype + device = module.weight.device + + # sparse is not support yet + if sparse: + raise NotImplementedError("The Embedding1D module does not support sparse embedding yet.") + + embedding = Embedding1D(num_embeddings=num_embedding, + embedding_dim=embedding_dim, + padding_idx=padding_idx, + process_group=process_group, + dtype=dtype, + device=device, + max_norm=max_norm, + norm_type=norm_type, + scale_grad_by_freq=scale_grad_by_freq, + sparse=sparse) + + # copy the weight + with torch.no_grad(): + sharded_weight = shard_colwise(module.weight.data, process_group) + embedding.weight.copy_(sharded_weight) + + return embedding + + def reset_parameters(self, weight_initializer) -> None: + fan_in, fan_out = self.num_embeddings, self.embed_dim + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + self._fill_padding_idx_with_zero() + + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None: + with torch.no_grad(): + self.weight[self.padding_idx].fill_(0) + + def forward(self, input_: Tensor) -> Tensor: + output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) + output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) + + return output diff --git a/colossalai/shardformer/layer/layernorm1d.py b/colossalai/shardformer/layer/layernorm1d.py new file mode 100644 index 000000000000..78bd64cfb504 --- /dev/null +++ b/colossalai/shardformer/layer/layernorm1d.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +from collections import OrderedDict + +from colossalai.context import ParallelMode, seed +from colossalai.core import global_context as gpc +from colossalai.global_variables import tensor_parallel_env as env +from colossalai.kernel import LayerNorm +from colossalai.nn import init as init +from colossalai.nn.layer.colossalai_layer._utils import ColossalaiModule +from colossalai.utils.checkpointing import broadcast_state_dict + +Fast_LN = None +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm + Fast_LN = FastLayerNorm +except ImportError: + pass + + +class LayerNorm1D(ColossalaiModule): + r""" + Layer Normalization for colossalai + + Args: + normalized_shape (int): input shape from an expected input of size. + :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] + \times \ldots \times \text{normalized_shape}[-1]]` + If a single integer is used, it is treated as a singleton list, and this module will + normalize over the last dimension which is expected to be of that specific size. + eps (float): a value added to the denominator for numerical stability, defaults to 1e-05. + bias (bool, optional): Whether to add a bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + """ + + _fast_ln_supported_sizes = [ + 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, + 24576, 25600, 30720, 32768, 40960, 49152, 65536 + ] + + def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None): + if Fast_LN is not None and normalized_shape in self._fast_ln_supported_sizes: + norm = Fast_LN(normalized_shape, eps=eps).to(dtype) + else: + norm = None + try: + from apex.normalization import FusedLayerNorm + norm = FusedLayerNorm(normalized_shape, eps=eps).to(dtype) + except ImportError: + norm = LayerNorm(normalized_shape, eps=eps).to(dtype) + super().__init__(norm) + + def _load_from_state_dict(self, state_dict, prefix, *args): + local_state = OrderedDict() + weight_key = prefix + 'weight' + bias_key = prefix + 'bias' + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + # weight + weight = state_dict.pop(weight_key, None) + if weight is not None: + local_state[weight_key] = weight + # bias + bias = state_dict.pop(bias_key, None) + if bias is not None: + local_state[bias_key] = bias + + local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) + super()._load_from_state_dict(local_state, prefix, *args) + + def _save_to_state_dict(self, destination, prefix, keep_vars): + if gpc.get_local_rank(ParallelMode.TENSOR) == 0: + super()._save_to_state_dict(destination, prefix, keep_vars) diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py deleted file mode 100644 index 5dbe28956d27..000000000000 --- a/colossalai/shardformer/layer/layers.py +++ /dev/null @@ -1,722 +0,0 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -import math -from abc import ABC, abstractmethod -from collections import OrderedDict -from typing import Callable, List, Tuple, Union - -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F -from torch import Tensor -from torch.distributed import ProcessGroup -from torch.nn.parameter import Parameter - -from colossalai.communication import broadcast -from colossalai.context import ParallelMode, seed -from colossalai.core import global_context as gpc -from colossalai.global_variables import tensor_parallel_env as env -from colossalai.kernel import LayerNorm -from colossalai.nn import init as init -from colossalai.nn.layer.base_layer import ParallelLayer -from colossalai.nn.layer.colossalai_layer._utils import ColossalaiModule -from colossalai.nn.layer.parallel_1d._utils import get_parallel_input, reduce_grad, set_parallel_input -from colossalai.nn.layer.utils import divide, set_tensor_parallel_attribute_by_partition -from colossalai.nn.layer.vanilla import VanillaLayerNorm, VanillaPatchEmbedding -from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise -from colossalai.utils.checkpointing import ( - broadcast_state_dict, - gather_tensor_parallel_state_dict, - partition_tensor_parallel_state_dict, -) -from colossalai.utils.cuda import get_current_device - -from ._operation import ( - gather_forward_split_backward, - linear_with_async_comm, - reduce_input, - split_forward_gather_backward, -) -from .utils import create_randomizer_with_offset - -Fast_LN = None -try: - from apex.contrib.layer_norm.layer_norm import FastLayerNorm - Fast_LN = FastLayerNorm -except ImportError: - pass - - -class ParallelModule(nn.Module, ABC): - - @abstractmethod - def from_native_module(module: nn.Module, - process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "ParallelModule": - """ - Convert a native PyTorch module to a parallelized module. - - Args: - module (nn.Module): the module to be converted. - process_group (ProcessGroup or list[ProcessGroup]): the process group(s) to be used for communication. - If this is a list, the process group at the ith index of the list will correspond to the process group - in the ith axis of the device mesh. Defaults to None, which means the global process group. - """ - pass - - -class Linear1D_Col(ParallelModule): - r"""Linear layer with column parallelism. - - The linear layer is defined as :math:`Y = XA + b`. A is parallelized along - its second dimension as :math:`A = [A_1, ..., A_p]`. - - Args: - in_features (int): size of each input sample. - out_features (int): size of each output sample. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (`torch.dtype`): The dtype of parameters, defaults to None. - device (`torch.device`): The device of parameters, defaults to None. - process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None. - gather_output (bool, optional): If true, call all-gather on output and make Y available - to all GPUs, otherwise, every GPU will have its output - which is :math:`Y_i = XA_i`, defaults to False - skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, - which is preserved for kernel fusion, defaults to False - weight_initializer (`typing.Callable`): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (`typing.Callable`): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - out_features: int, - bias: bool = True, - dtype: torch.dtype = None, - device: torch.device = None, - process_group: ProcessGroup = None, - gather_output: bool = False, - skip_bias_add: bool = False, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): - super().__init__() - - # Keep input parameters - self.in_features = in_features - self.out_features = out_features - self.gather_output = gather_output - self.skip_bias_add = skip_bias_add - self.device = device - self.process_group = process_group - self.num_partitions = dist.get_world_size(self.process_group) - - if skip_bias_add and not bias: - raise ValueError('cannot skip bias addition if bias is None') - - self.out_features_per_partition = divide(out_features, self.num_partitions) - - # Parameters. - # Initialize weight. - if device is None: - device = get_current_device() - factory_kwargs = {'device': device, 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) - - if bias: - self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) - else: - self.bias = None - - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer, bias_initializer) - - @staticmethod - def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, - **kwargs) -> ParallelModule: - r""" - Convert a native PyTorch linear layer to a parallelized linear layer. - """ - # get the attributes - in_features = module.in_features - out_features = module.out_features - bias = module.bias is not None - device = module.weight.device - - # ensure only one process group is passed - if isinstance(process_group, (list, tuple)): - assert len(process_group) == 1, \ - f'Expected only one process group, got {len(process_group)}.' - process_group = process_group[0] - - linear_1d = Linear1D_Col(in_features=in_features, - out_features=out_features, - bias=bias, - device=device, - process_group=process_group, - *args, - **kwargs) - - # TODO: copy the sharded weights - with torch.no_grad(): - # the weigh to the linear layer is a transpose - # thus shard on row is equal to shard on column - sharded_weight = shard_rowwise(module.weight.data, process_group) - linear_1d.weight.data.copy_(sharded_weight) - if bias: - sharded_bias = shard_colwise(module.bias.data, process_group) - linear_1d.bias.copy_(sharded_bias) - - return linear_1d - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.out_features - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - - def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - # Set up backprop all-reduce. - # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) - input_parallel = input_ - # Matrix multiply. - bias = self.bias if not self.skip_bias_add else None - output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True) - - if self.gather_output: - # All-gather across the partitions. - output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) - else: - output = output_parallel - - if self.skip_bias_add: - return output, self.bias - else: - return output - - -class Linear1D_Row(ParallelModule): - r""" Linear layer with row parallelism - - Args: - in_features (int): size of each input sample. - out_features (int): size of each output sample. - bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. - dtype (`torch.dtype`): The dtype of parameters, defaults to None. - parallel_input (bool): If set to ``True``, it's assumed that the input is split, defaults to False. - skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, - which is preserved for kernel fusion, defaults to False - weight_initializer (:class:`typing.Callable`, optional): - The initializer of weight, defaults to kaiming uniform initializer. - bias_initializer (:class:`typing.Callable`, optional): - The initializer of bias, defaults to xavier uniform initializer. - - More details about ``initializer`` please refer to - `init `_. - """ - - def __init__(self, - in_features: int, - out_features: int, - bias: bool = True, - dtype: torch.dtype = None, - device: torch.device = None, - process_group: ProcessGroup = None, - parallel_input: bool = True, - skip_bias_add: bool = False, - weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), - bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), - stream_chunk_num: int = 1): - super().__init__() - - self.stream_chunk_num = stream_chunk_num - - # Keep input parameters - self.in_features = in_features - self.out_features = out_features - self.parallel_input = parallel_input - self.skip_bias_add = skip_bias_add - self.process_group = process_group - self.num_partitions = dist.get_world_size(self.process_group) - - if skip_bias_add and not bias: - raise ValueError('cannot skip bias addition if bias is None') - - # Divide the weight matrix along the last dimension. - self.input_size_per_partition = divide(in_features, self.num_partitions) - - # Parameters. - # Initialize weight. - if device is None: - device = get_current_device() - - factory_kwargs = {'device': device, 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) - - if self.stream_chunk_num > 1: - # TODO() work for inference only - self.chunk_weight() - if bias: - self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) - else: - self.bias = None - - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer, bias_initializer) - - @staticmethod - def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, - **kwargs) -> ParallelModule: - r""" - Convert a native PyTorch linear layer to a parallelized linear layer. - """ - # get the attributes - in_features = module.in_features - out_features = module.out_features - bias = module.bias is not None - device = module.weight.device - - # ensure only one process group is passed - if isinstance(process_group, (list, tuple)): - assert len(process_group) == 1, \ - f'Expected only one process group, got {len(process_group)}.' - process_group = process_group[0] - - linear_1d = Linear1D_Row(in_features=in_features, - out_features=out_features, - bias=bias, - device=device, - process_group=process_group, - *args, - **kwargs) - - # TODO: copy the sharded weights - with torch.no_grad(): - # the weigh to the linear layer is a transpose - # thus shard on col is equal to shard on row - sharded_weight = shard_colwise(module.weight.data, process_group) - linear_1d.weight.data.copy_(sharded_weight) - - if bias: - linear_1d.bias.copy_(module.bias.data) - - return linear_1d - - def chunk_weight(self): - self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) - - def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.out_features - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - if self.process_group is None: - src_rank = 0 - else: - src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) - - origin_device = self.bias.device - self.bias = self.bias.cuda() - dist.broadcast(self.bias, src=src_rank, group=self.process_group) - self.bias = self.bias.to(origin_device) - - def forward(self, input_: Tensor) -> Tensor: - # Set up backprop all-reduce. - if self.parallel_input: - assert input_.shape[-1] == self.weight.shape[-1], \ - 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) - input_ = input_ - else: - assert divide(input_.shape[-1], self.num_partitions) == self.weight.shape[-1], \ - 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1] * self.num_partitions) - input_ = split_forward_gather_backward(input_, dim=-1, process_group=self.process_group) - - if self.stream_chunk_num > 1: - if self.training: - raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!") - with torch.no_grad(): - output_parallel_list = [None for i in range(self.stream_chunk_num)] - handle_list = [] - for i in range(self.stream_chunk_num): - output_parallel_list[i] = F.linear(input_, self.weight_list[i]) - handle = torch.distributed.all_reduce(output_parallel_list[i], - group=self.process_group, - async_op=True) - handle_list.append(handle) - # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D) - for handle in handle_list: - handle.wait() - output = torch.cat(output_parallel_list, dim=-1) - else: - output_parallel = F.linear(input_, self.weight) - # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) - output = reduce_input(output_parallel, self.process_group) - - if not self.skip_bias_add: - if self.bias is not None: - output = output + self.bias - return output - else: - return output, self.bias - - -class LayerNorm1D(ColossalaiModule): - r""" - Layer Normalization for colossalai - - Args: - normalized_shape (int): input shape from an expected input of size. - :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] - \times \ldots \times \text{normalized_shape}[-1]]` - If a single integer is used, it is treated as a singleton list, and this module will - normalize over the last dimension which is expected to be of that specific size. - eps (float): a value added to the denominator for numerical stability, defaults to 1e-05. - bias (bool, optional): Whether to add a bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - """ - - _fast_ln_supported_sizes = [ - 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, - 24576, 25600, 30720, 32768, 40960, 49152, 65536 - ] - - def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None): - if Fast_LN is not None and normalized_shape in self._fast_ln_supported_sizes: - norm = Fast_LN(normalized_shape, eps=eps).to(dtype) - else: - norm = None - try: - from apex.normalization import FusedLayerNorm - norm = FusedLayerNorm(normalized_shape, eps=eps).to(dtype) - except ImportError: - norm = LayerNorm(normalized_shape, eps=eps).to(dtype) - super().__init__(norm) - - def _load_from_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) - super()._load_from_state_dict(local_state, prefix, *args) - - def _save_to_state_dict(self, destination, prefix, keep_vars): - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - super()._save_to_state_dict(destination, prefix, keep_vars) - - -class Embedding1D(ParallelModule): - r"""Embedding for 1D parallelism. - - Args: - num_embeddings (int): number of embeddings. - embedding_dim (int): dimension of embedding. - padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; - therefore, the embedding vector at padding_idx is not updated during training, - i.e. it remains as a fixed “pad”, defaults to None. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - weight_initializer (:class:`typing.Callable`, optional): - he initializer of weight, defaults to normal initializer. - - The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain: - :: - - max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is - renormalized to have norm max_norm. Note: this will modify weight in-place. - norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. - scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse - of frequency of the words in the mini-batch. Default False. - sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. - - More details about ``args`` and ``kwargs`` could be found in - `Embedding `_. - - More details about ``initializer`` please refer to - `init `_ - """ - - def __init__(self, - num_embeddings: int, - embedding_dim: int, - padding_idx: int = None, - dtype: torch.dtype = None, - device: torch.device = None, - process_group: ProcessGroup = None, - gather_output: bool = True, - weight_initializer: Callable = init.normal_(), - *args, - **kwargs): - super().__init__() - - self.num_embeddings = num_embeddings - self.embedding_dim = embedding_dim - self.process_group = process_group - self.num_partitions = dist.get_world_size(process_group) - self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions) - - self.padding_idx = padding_idx - self.embed_args = args - self.embed_kwargs = kwargs - self.gather_output = gather_output - - if device is None: - device = get_current_device() - - self.weight = Parameter(torch.empty((num_embeddings, self.embed_dim_per_partition), device=device, dtype=dtype)) - - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer) - - @staticmethod - def from_native_module(module: nn.Embedding, - process_group: Union[ProcessGroup, List[ProcessGroup]] = None, - *args, - **kwargs) -> "Embedding1D": - r""" - Build a 1D parallelized Embedding from a native nn.Embedding module. - """ - # get the attributes - num_embedding = module.num_embeddings - embedding_dim = module.embedding_dim - padding_idx = module.padding_idx - max_norm = module.max_norm - norm_type = module.norm_type - scale_grad_by_freq = module.scale_grad_by_freq - sparse = module.sparse - dtype = module.weight.dtype - device = module.weight.device - - # sparse is not support yet - if sparse: - raise NotImplementedError("The Embedding1D module does not support sparse embedding yet.") - - embedding = Embedding1D(num_embeddings=num_embedding, - embedding_dim=embedding_dim, - padding_idx=padding_idx, - process_group=process_group, - dtype=dtype, - device=device, - max_norm=max_norm, - norm_type=norm_type, - scale_grad_by_freq=scale_grad_by_freq, - sparse=sparse, - *args, - **kwargs) - - # copy the weight - with torch.no_grad(): - sharded_weight = shard_colwise(module.weight.data, process_group) - embedding.weight.copy_(sharded_weight) - - return embedding - - def reset_parameters(self, weight_initializer) -> None: - fan_in, fan_out = self.num_embeddings, self.embedding_dim - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - self._fill_padding_idx_with_zero() - - def _fill_padding_idx_with_zero(self) -> None: - if self.padding_idx is not None: - with torch.no_grad(): - self.weight[self.padding_idx].fill_(0) - - def forward(self, input_: Tensor) -> Tensor: - output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) - - if self.gather_output: - output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) - return output - else: - return output_parallel - - -class VocabParallelEmbedding1D(ParallelLayer): - r"""Embedding parallelized in the vocabulary dimension. - - Args: - num_embeddings (int): number of embeddings. - embedding_dim (int): dimension of embedding. - padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; - therefore, the embedding vector at padding_idx is not updated during training, - i.e. it remains as a fixed “pad”, defaults to None. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - weight_initializer (:class:`typing.Callable`, optional): - he initializer of weight, defaults to normal initializer. - - The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain: - :: - - max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is - renormalized to have norm max_norm. Note: this will modify weight in-place. - norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. - scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse - of frequency of the words in the mini-batch. Default False. - sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. - - More details about ``args`` and ``kwargs`` could be found in - `Embedding `_. - - More details about initializer please refer to - `init `_. - """ - - def __init__(self, - num_embeddings: int, - embedding_dim: int, - padding_idx: int = None, - dtype: torch.dtype = None, - device: torch.device = None, - process_group: ProcessGroup = None, - weight_initializer: Callable = init.normal_(), - *args, - **kwargs): - super().__init__() - self.num_embeddings = num_embeddings - self.embedding_dim = embedding_dim - self.padding_idx = padding_idx - self.embed_args = args - self.embed_kwargs = kwargs - self.process_group = process_group - - tensor_parallel_size = dist.get_world_size(group=process_group) - tensor_parallel_rank = dist.get_rank(group=process_group) - - self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) - self.num_embeddings = self.num_embeddings_per_partition - self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition - self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition - - self.weight = Parameter( - torch.empty((self.num_embeddings_per_partition, self.embedding_dim), device=device, dtype=dtype)) - - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer) - # self.reset_parameters(weight_initializer) - # self._set_tensor_parallel_attributes() - # set_parallel_input(False) - # env.vocab_parallel = True - - @staticmethod - def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, - **kwargs) -> ParallelModule: - r""" - Convert a native pytorch embedding module to a parallel module. - """ - # get the origin attributes - num_embeddings = module.num_embeddings - embedding_dim = module.embedding_dim - padding_idx = module.padding_idx - device = module.weight.device - - # ensure only one process group is used - if isinstance(process_group, (list, tuple)): - assert len(process_group) == 1, \ - f'Expected only one process group, got {len(process_group)}.' - process_group = process_group[0] - - # create the parallel module - vocab_embedding_1d = VocabParallelEmbedding1D(num_embeddings=num_embeddings, - embedding_dim=embedding_dim, - padding_idx=padding_idx, - device=device, - process_group=process_group, - *args, - **kwargs) - with torch.no_grad(): - # shard and slice the weight along the vocabulary(num_embeddings) dimension - # the shape of the weight is (num_embeddings, embedding_dim) - shard_weight = shard_rowwise(module.weight.data, process_group) - vocab_embedding_1d.weight.data.copy_(shard_weight) - - return vocab_embedding_1d - - def _set_tensor_parallel_attributes(self): - set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size) - - def reset_parameters(self, weight_initializer) -> None: - with seed(ParallelMode.TENSOR): - fan_in, fan_out = self.num_embeddings, self.embedding_dim - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - self._fill_padding_idx_with_zero() - - def _fill_padding_idx_with_zero(self) -> None: - if self.padding_idx is not None and \ - self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index: - with torch.no_grad(): - self.weight[self.padding_idx - self.vocab_start_index].fill_(0) - - def _load_from_global_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - - local_state = partition_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={weight_key: 0}, - partition_states={weight_key: True}) - super()._load_from_global_state_dict(local_state, prefix, *args) - - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - local_state = OrderedDict({weight_key: self.weight}) - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={weight_key: 0}, - partition_states={weight_key: True}, - keep_vars=keep_vars) - destination.update(local_state) - - def forward(self, input_: Tensor) -> Tensor: - # Build the mask. - input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index) - # Mask the input. - masked_input = input_.clone() - self.vocab_start_index - masked_input[input_mask] = 0 - - output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args, - **self.embed_kwargs) - - # Mask the output embedding. - output_parallel[input_mask, :] = 0. - # Reduce across all the model parallel GPUs. - output = reduce_input(output_parallel, self.process_group) - return output diff --git a/colossalai/shardformer/layer/linear1d.py b/colossalai/shardformer/layer/linear1d.py new file mode 100644 index 000000000000..d59d32df824e --- /dev/null +++ b/colossalai/shardformer/layer/linear1d.py @@ -0,0 +1,346 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +import math +from typing import Callable, List, Tuple, Union + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torch.distributed import ProcessGroup +from torch.nn.parameter import Parameter + +from colossalai.nn import init as init +from colossalai.nn.layer.utils import divide +from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise +from colossalai.utils.cuda import get_current_device + +from ._operation import ( + gather_forward_split_backward, + linear_with_async_comm, + reduce_input, + split_forward_gather_backward, +) +from .parallelmodule import ParallelModule +from .utils import create_randomizer_with_offset + +Fast_LN = None +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm + Fast_LN = FastLayerNorm +except ImportError: + pass + + +class Linear1D_Col(ParallelModule): + r"""Linear layer with column parallelism. + + The linear layer is defined as :math:`Y = XA + b`. A is parallelized along + its second dimension as :math:`A = [A_1, ..., A_p]`. + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (`torch.dtype`): The dtype of parameters, defaults to None. + device (`torch.device`): The device of parameters, defaults to None. + process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None. + gather_output (bool, optional): If true, call all-gather on output and make Y available + to all GPUs, otherwise, every GPU will have its output + which is :math:`Y_i = XA_i`, defaults to False + skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (`typing.Callable`): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (`typing.Callable`): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, + gather_output: bool = False, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + super().__init__() + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.gather_output = gather_output + self.skip_bias_add = skip_bias_add + self.device = device + self.process_group = process_group + self.num_partitions = dist.get_world_size(self.process_group) + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + self.out_features_per_partition = divide(out_features, self.num_partitions) + + # Parameters. + # Initialize weight. + if device is None: + device = get_current_device() + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) + + if bias: + self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) + else: + self.bias = None + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer, bias_initializer) + + @staticmethod + def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, + **kwargs) -> ParallelModule: + r""" + Convert a native PyTorch linear layer to a parallelized linear layer. + """ + # get the attributes + in_features = module.in_features + out_features = module.out_features + bias = module.bias is not None + device = module.weight.device + + # ensure only one process group is passed + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + linear_1d = Linear1D_Col(in_features=in_features, + out_features=out_features, + bias=bias, + device=device, + process_group=process_group, + *args, + **kwargs) + + # TODO: copy the sharded weights + with torch.no_grad(): + # the weigh to the linear layer is a transpose + # thus shard on row is equal to shard on column + sharded_weight = shard_rowwise(module.weight.data, process_group) + linear_1d.weight.data.copy_(sharded_weight) + if bias: + sharded_bias = shard_colwise(module.bias.data, process_group) + linear_1d.bias.copy_(sharded_bias) + + return linear_1d + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + + def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + # Set up backprop all-reduce. + # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) + input_parallel = input_ + # Matrix multiply. + bias = self.bias if not self.skip_bias_add else None + output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True) + + if self.gather_output: + # All-gather across the partitions. + output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) + else: + output = output_parallel + + if self.skip_bias_add: + return output, self.bias + else: + return output + + +class Linear1D_Row(ParallelModule): + r""" Linear layer with row parallelism + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (`torch.dtype`): The dtype of parameters, defaults to None. + parallel_input (bool): If set to ``True``, it's assumed that the input is split, defaults to False. + skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, + parallel_input: bool = True, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), + stream_chunk_num: int = 1): + super().__init__() + + self.stream_chunk_num = stream_chunk_num + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.parallel_input = parallel_input + self.skip_bias_add = skip_bias_add + self.process_group = process_group + self.num_partitions = dist.get_world_size(self.process_group) + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + # Divide the weight matrix along the last dimension. + self.input_size_per_partition = divide(in_features, self.num_partitions) + + # Parameters. + # Initialize weight. + if device is None: + device = get_current_device() + + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) + + if self.stream_chunk_num > 1: + # TODO() work for inference only + self.chunk_weight() + if bias: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + else: + self.bias = None + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer, bias_initializer) + + @staticmethod + def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, + **kwargs) -> ParallelModule: + r""" + Convert a native PyTorch linear layer to a parallelized linear layer. + """ + # get the attributes + in_features = module.in_features + out_features = module.out_features + bias = module.bias is not None + device = module.weight.device + + # ensure only one process group is passed + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + linear_1d = Linear1D_Row(in_features=in_features, + out_features=out_features, + bias=bias, + device=device, + process_group=process_group, + *args, + **kwargs) + + # TODO: copy the sharded weights + with torch.no_grad(): + # the weigh to the linear layer is a transpose + # thus shard on col is equal to shard on row + sharded_weight = shard_colwise(module.weight.data, process_group) + linear_1d.weight.data.copy_(sharded_weight) + + if bias: + linear_1d.bias.copy_(module.bias.data) + + return linear_1d + + def chunk_weight(self): + self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + if self.process_group is None: + src_rank = 0 + else: + src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) + + origin_device = self.bias.device + self.bias = self.bias.cuda() + dist.broadcast(self.bias, src=src_rank, group=self.process_group) + self.bias = self.bias.to(origin_device) + + def forward(self, input_: Tensor) -> Tensor: + # Set up backprop all-reduce. + if self.parallel_input: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + input_ = input_ + else: + assert divide(input_.shape[-1], self.num_partitions) == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1] * self.num_partitions) + input_ = split_forward_gather_backward(input_, dim=-1, process_group=self.process_group) + + if self.stream_chunk_num > 1: + if self.training: + raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!") + with torch.no_grad(): + output_parallel_list = [None for i in range(self.stream_chunk_num)] + handle_list = [] + for i in range(self.stream_chunk_num): + output_parallel_list[i] = F.linear(input_, self.weight_list[i]) + handle = torch.distributed.all_reduce(output_parallel_list[i], + group=self.process_group, + async_op=True) + handle_list.append(handle) + # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D) + for handle in handle_list: + handle.wait() + output = torch.cat(output_parallel_list, dim=-1) + else: + output_parallel = F.linear(input_, self.weight) + # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) + output = reduce_input(output_parallel, self.process_group) + + if not self.skip_bias_add: + if self.bias is not None: + output = output + self.bias + return output + else: + return output, self.bias diff --git a/colossalai/shardformer/layer/linearconv1d.py b/colossalai/shardformer/layer/linearconv1d.py new file mode 100644 index 000000000000..4a5cb0707900 --- /dev/null +++ b/colossalai/shardformer/layer/linearconv1d.py @@ -0,0 +1,377 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +import math +from typing import Callable, List, Tuple, Union + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torch.distributed import ProcessGroup +from torch.nn.parameter import Parameter + +from colossalai.nn import init as init +from colossalai.nn.layer.utils import divide +from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise +from colossalai.utils.cuda import get_current_device + +from ._operation import ( + gather_forward_split_backward, + linear_with_async_comm, + reduce_input, + split_forward_gather_backward, +) +from .parallelmodule import ParallelModule +from .utils import create_randomizer_with_offset + +Fast_LN = None +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm + Fast_LN = FastLayerNorm +except ImportError: + pass + + +class LinearConv1D_Col(ParallelModule): + r"""Linear layer with column parallelism. + + The linear layer is defined as :math:`Y = XA + b`. A is parallelized along + its second dimension as :math:`A = [A_1, ..., A_p]`. This layer is used to fit `Conv1D` layer in gpt2 of huggingface. + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (`torch.dtype`): The dtype of parameters, defaults to None. + device (`torch.device`): The device of parameters, defaults to None. + process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None. + gather_output (bool, optional): If true, call all-gather on output and make Y available + to all GPUs, otherwise, every GPU will have its output + which is :math:`Y_i = XA_i`, defaults to False + skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (`typing.Callable`): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (`typing.Callable`): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, + gather_output: bool = False, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + super().__init__() + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.gather_output = gather_output + self.skip_bias_add = skip_bias_add + self.device = device + self.process_group = process_group + self.num_partitions = dist.get_world_size(self.process_group) + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + self.out_features_per_partition = divide(out_features, self.num_partitions) + + # Parameters. + # Initialize weight. + if device is None: + device = get_current_device() + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) + + if bias: + self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) + else: + self.bias = None + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer, bias_initializer) + + @staticmethod + def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], n_cast: int, + *args, **kwargs) -> ParallelModule: + r""" + Convert a huggingface layer `Conv1D` in gpt2 to a parallelized linear layer. + """ + # get the attributes + in_features = module.weight.shape[0] + out_features = module.weight.shape[1] + bias = module.bias is not None + device = module.weight.device + + # ensure only one process group is passed + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + linear_1d = LinearConv1D_Col(in_features=in_features, + out_features=out_features, + bias=bias, + device=device, + process_group=process_group, + *args, + **kwargs) + + # TODO: copy the sharded weights + with torch.no_grad(): + # the weigh to the linear layer is a transpose + # thus shard on row is equal to shard on column + + # first rearange the order of weight and bias + world_size = dist.get_world_size(group=process_group) + order = torch.arange(world_size * n_cast) + new_order = [] + for i in range(world_size): + new_order.append(order[i::world_size]) + new_order = torch.cat(new_order) + + weight_chunks = torch.chunk(module.weight.data, world_size * n_cast, dim=1) + rearanged_weight_chunks = [weight_chunks[i] for i in new_order] + rearanged_weight = torch.cat(rearanged_weight_chunks, dim=1) + sharded_weight = shard_colwise(rearanged_weight, process_group) + linear_1d.weight.data.copy_(sharded_weight.T.contiguous()) + + if bias: + bias_chunks = torch.chunk(module.bias.data, world_size * n_cast, dim=0) + rearanged_bias_chunks = [bias_chunks[i] for i in new_order] + rearanged_bias = torch.cat(rearanged_bias_chunks, dim=0) + sharded_bias = shard_colwise(rearanged_bias, process_group) + linear_1d.bias.copy_(sharded_bias.contiguous()) + + return linear_1d + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + + def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + # Set up backprop all-reduce. + # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) + input_parallel = input_ + # Matrix multiply. + bias = self.bias if not self.skip_bias_add else None + output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True) + + if self.gather_output: + # All-gather across the partitions. + output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) + else: + output = output_parallel + + if self.skip_bias_add: + return output, self.bias + else: + return output + + +class LinearConv1D_Row(ParallelModule): + r""" Linear layer with row parallelism + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (`torch.dtype`): The dtype of parameters, defaults to None. + parallel_input (bool): If set to ``True``, it's assumed that the input is split, defaults to False. + skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (:class:`typing.Callable`, optional): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (:class:`typing.Callable`, optional): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, + parallel_input: bool = True, + skip_bias_add: bool = False, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), + stream_chunk_num: int = 1): + super().__init__() + + self.stream_chunk_num = stream_chunk_num + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.parallel_input = parallel_input + self.skip_bias_add = skip_bias_add + self.process_group = process_group + self.num_partitions = dist.get_world_size(self.process_group) + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + # Divide the weight matrix along the last dimension. + self.input_size_per_partition = divide(in_features, self.num_partitions) + + # Parameters. + # Initialize weight. + if device is None: + device = get_current_device() + + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) + + if self.stream_chunk_num > 1: + # TODO() work for inference only + self.chunk_weight() + if bias: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + else: + self.bias = None + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer, bias_initializer) + + @staticmethod + def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], n_cast: int, + *args, **kwargs) -> ParallelModule: + r""" + Convert a native PyTorch linear layer to a parallelized linear layer. + """ + # get the attributes + in_features = module.weight.shape[0] + out_features = module.weight.shape[1] + bias = module.bias is not None + device = module.weight.device + + # ensure only one process group is passed + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + linear_1d = LinearConv1D_Row(in_features=in_features, + out_features=out_features, + bias=bias, + device=device, + process_group=process_group, + *args, + **kwargs) + + # TODO: copy the sharded weights + with torch.no_grad(): + # the weigh to the linear layer is a transpose + # thus shard on col is equal to shard on row + + # first rearange the order of weight and bias + world_size = dist.get_world_size(group=process_group) + order = torch.arange(world_size * n_cast) + new_order = [] + for i in range(world_size): + new_order.append(order[i::world_size]) + new_order = torch.cat(new_order) + + weight_chunks = torch.chunk(module.weight.data, world_size * n_cast, dim=0) + rearanged_weight_chunks = [weight_chunks[i] for i in new_order] + rearanged_weight = torch.cat(rearanged_weight_chunks, dim=0) + sharded_weight = shard_rowwise(rearanged_weight, process_group) + linear_1d.weight.data.copy_(sharded_weight.T.contiguous()) + + if bias: + bias_chunks = torch.chunk(module.bias.data, world_size * n_cast, dim=0) + rearanged_bias_chunks = [bias_chunks[i] for i in new_order] + rearanged_bias = torch.cat(rearanged_bias_chunks, dim=0) + linear_1d.bias.copy_(rearanged_bias.contiguous()) + + return linear_1d + + def chunk_weight(self): + self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + if self.process_group is None: + src_rank = 0 + else: + src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) + + origin_device = self.bias.device + self.bias = self.bias.cuda() + dist.broadcast(self.bias, src=src_rank, group=self.process_group) + self.bias = self.bias.to(origin_device) + + def forward(self, input_: Tensor) -> Tensor: + # Set up backprop all-reduce. + if self.parallel_input: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + input_ = input_ + else: + assert divide(input_.shape[-1], self.num_partitions) == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1] * self.num_partitions) + input_ = split_forward_gather_backward(input_, dim=-1, process_group=self.process_group) + + if self.stream_chunk_num > 1: + if self.training: + raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!") + with torch.no_grad(): + output_parallel_list = [None for i in range(self.stream_chunk_num)] + handle_list = [] + for i in range(self.stream_chunk_num): + output_parallel_list[i] = F.linear(input_, self.weight_list[i]) + handle = torch.distributed.all_reduce(output_parallel_list[i], + group=self.process_group, + async_op=True) + handle_list.append(handle) + # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D) + for handle in handle_list: + handle.wait() + output = torch.cat(output_parallel_list, dim=-1) + else: + output_parallel = F.linear(input_, self.weight) + # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) + output = reduce_input(output_parallel, self.process_group) + + if not self.skip_bias_add: + if self.bias is not None: + output = output + self.bias + return output + else: + return output, self.bias diff --git a/colossalai/shardformer/layer/parallelmodule.py b/colossalai/shardformer/layer/parallelmodule.py new file mode 100644 index 000000000000..3d19bbea7e47 --- /dev/null +++ b/colossalai/shardformer/layer/parallelmodule.py @@ -0,0 +1,35 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +from abc import ABC, abstractmethod +from typing import List, Union + +import torch.nn as nn +from torch.distributed import ProcessGroup + +from colossalai.global_variables import tensor_parallel_env as env +from colossalai.nn import init as init + +Fast_LN = None +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm + Fast_LN = FastLayerNorm +except ImportError: + pass + + +class ParallelModule(nn.Module, ABC): + + @abstractmethod + def from_native_module(module: nn.Module, + process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "ParallelModule": + """ + Convert a native PyTorch module to a parallelized module. + + Args: + module (nn.Module): the module to be converted. + process_group (ProcessGroup or list[ProcessGroup]): the process group(s) to be used for communication. + If this is a list, the process group at the ith index of the list will correspond to the process group + in the ith axis of the device mesh. Defaults to None, which means the global process group. + """ + pass diff --git a/colossalai/shardformer/layer/vocabparallelembedding1d.py b/colossalai/shardformer/layer/vocabparallelembedding1d.py new file mode 100644 index 000000000000..4c325c68421b --- /dev/null +++ b/colossalai/shardformer/layer/vocabparallelembedding1d.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +from collections import OrderedDict +from typing import Callable, List, Union + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torch.distributed import ProcessGroup +from torch.nn.parameter import Parameter + +from colossalai.context import ParallelMode, seed +from colossalai.nn import init as init +from colossalai.nn.layer.base_layer import ParallelLayer +from colossalai.nn.layer.utils import divide +from colossalai.tensor.d_tensor.api import shard_rowwise +from colossalai.utils.checkpointing import gather_tensor_parallel_state_dict + +from ._operation import reduce_input +from .parallelmodule import ParallelModule +from .utils import create_randomizer_with_offset + +Fast_LN = None +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm + Fast_LN = FastLayerNorm +except ImportError: + pass + + +class VocabParallelEmbedding1D(ParallelLayer): + r"""Embedding parallelized in the vocabulary dimension. + + Args: + num_embeddings (int): number of embeddings. + embedding_dim (int): dimension of embedding. + padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; + therefore, the embedding vector at padding_idx is not updated during training, + i.e. it remains as a fixed “pad”, defaults to None. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + he initializer of weight, defaults to normal initializer. + + The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain: + :: + + max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is + renormalized to have norm max_norm. Note: this will modify weight in-place. + norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. + scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse + of frequency of the words in the mini-batch. Default False. + sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. + + More details about ``args`` and ``kwargs`` could be found in + `Embedding `_. + + More details about initializer please refer to + `init `_. + """ + + def __init__(self, + num_embeddings: int, + embedding_dim: int, + padding_idx: int = None, + dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, + weight_initializer: Callable = init.normal_(), + *args, + **kwargs): + super().__init__() + self.num_embeddings = num_embeddings + self.embed_dim = embedding_dim + self.padding_idx = padding_idx + self.embed_args = args + self.embed_kwargs = kwargs + self.process_group = process_group + + tensor_parallel_size = dist.get_world_size(group=process_group) + tensor_parallel_rank = dist.get_rank(group=process_group) + + self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size) + self.num_embeddings = self.num_embeddings_per_partition + self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition + self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition + + self.weight = Parameter( + torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=device, dtype=dtype)) + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer) + + @staticmethod + def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, + **kwargs) -> ParallelModule: + r""" + Convert a native pytorch embedding module to a parallel module. + """ + # get the origin attributes + num_embeddings = module.num_embeddings + embedding_dim = module.embedding_dim + padding_idx = module.padding_idx + device = module.weight.device + + # ensure only one process group is used + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + # create the parallel module + vocab_embedding_1d = VocabParallelEmbedding1D(num_embeddings=num_embeddings, + embedding_dim=embedding_dim, + padding_idx=padding_idx, + device=device, + process_group=process_group, + *args, + **kwargs) + with torch.no_grad(): + # shard and slice the weight along the vocabulary(num_embeddings) dimension + # the shape of the weight is (num_embeddings, embedding_dim) + shard_weight = shard_rowwise(module.weight.data, process_group) + vocab_embedding_1d.weight.data.copy_(shard_weight) + + return vocab_embedding_1d + + def reset_parameters(self, weight_initializer) -> None: + with seed(ParallelMode.TENSOR): + fan_in, fan_out = self.num_embeddings, self.embed_dim + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + self._fill_padding_idx_with_zero() + + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None and \ + self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index: + with torch.no_grad(): + self.weight[self.padding_idx - self.vocab_start_index].fill_(0) + + def _save_to_global_state_dict(self, destination, prefix, keep_vars): + weight_key = prefix + 'weight' + local_state = OrderedDict({weight_key: self.weight}) + local_state = gather_tensor_parallel_state_dict(local_state, + ParallelMode.PARALLEL_1D, + dims={weight_key: 0}, + partition_states={weight_key: True}, + keep_vars=keep_vars) + destination.update(local_state) + + def forward(self, input_: Tensor) -> Tensor: + # Build the mask. + input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index) + # Mask the input. + masked_input = input_.clone() - self.vocab_start_index + masked_input[input_mask] = 0 + + output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args, + **self.embed_kwargs) + + # Mask the output embedding. + output_parallel[input_mask, :] = 0. + # Reduce across all the model parallel GPUs. + output = reduce_input(output_parallel, self.process_group) + return output diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 6ce0b8fb3a3d..5e7a285e3285 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -56,6 +56,8 @@ class PolicyLocation: PolicyLocation(file_name="t5", class_name="T5EncoderPolicy"), # GPT2 + "transformers.models.gpt2.modeling_gpt2.GPT2Model": + PolicyLocation(file_name="gpt2", class_name="GPT2ModelPolicy"), } @@ -99,4 +101,3 @@ def get_autopolicy(model: nn.Module) -> Policy: else: policy = import_policy(policy_location) return policy() - return policy() diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 06ee9b435e7e..2a204f0defe4 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,7 +1,7 @@ import torch.nn as nn from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead -import colossalai.shardformer.layer.layers as col_nn +import colossalai.shardformer.layer as col_nn from colossalai.shardformer.layer.dropout import Dropout1D from ..utils import getattr_, setattr_ @@ -87,15 +87,9 @@ def module_policy(self): def new_model_class(self): # do nothing - return None + return self.model def postprocess(self): - binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - param = nn.Parameter(param) - setattr_(self.model, k, param) - setattr_(self.model, v, param) return self.model @@ -127,6 +121,15 @@ def module_policy(self): module_policy.update(addon_module) return module_policy + def postprocess(self): + binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + param = nn.Parameter(param) + setattr_(self.model, k, param) + setattr_(self.model, v, param) + return self.model + # BertForMaskedLM class BertForMaskedLMPolicy(BertPolicy): @@ -149,6 +152,15 @@ def module_policy(self): module_policy.update(addon_module) return module_policy + def postprocess(self): + binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + param = nn.Parameter(param) + setattr_(self.model, k, param) + setattr_(self.model, v, param) + return self.model + # BertLMHeadModel class BertLMHeadModelPolicy(BertPolicy): @@ -171,6 +183,15 @@ def module_policy(self): module_policy.update(addon_module) return module_policy + def postprocess(self): + binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + param = nn.Parameter(param) + setattr_(self.model, k, param) + setattr_(self.model, v, param) + return self.model + # BertForNextSentencePrediction class BertForNextSentencePredictionPolicy(BertPolicy): diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 0d4342e75783..d255325b2084 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -1,126 +1,101 @@ -from typing import Any, Callable, Dict, List, Tuple, Type +from typing import Type, Union import torch.nn as nn from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model -import colossalai.shardformer.layer.layers as col_nn +import colossalai.shardformer.layer as col_nn +from colossalai.shardformer.layer.dropout import Dropout1D -from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer +from ..utils import getattr_, setattr_ +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription class GPT2Policy(Policy): - @staticmethod - def argument_policy(config, world_size): + def preprocess(self): + # reshape the embedding layer + r""" + Reshape the Embedding layer to make the embedding dimension divisible by world_size + """ + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + return self.model + + def module_policy(self): return { GPT2Model: - Argument(attr_dict={}, param_funcs=[ - GPT2Policy.embedding, - ]), + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="wte", + target_module=col_nn.VocabParallelEmbedding1D, + ), + ]), GPT2Block: - Argument( - attr_dict={ - # 1. reduce hidden size - "attn.embed_dim": config.hidden_size // world_size, - "attn.split_size": config.hidden_size // world_size, - "crossattention.embed_dim": config.hidden_size // world_size, - "crossattention.split_size": config.hidden_size // world_size, - # 2. reduce number of heads - "attn.num_heads": config.num_attention_heads // world_size, - "crossattention.num_heads": config.num_attention_heads // world_size, - }, - param_funcs=[ - GPT2Policy.attn_in, - GPT2Policy.attn_out, - GPT2Policy.mlp_in, - GPT2Policy.mlp_out, - ]), + ModulePolicyDescription(attribute_replacement={ + "attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + }, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attn.c_attn", + target_module=col_nn.LinearConv1D_Col, + kwargs={ + "n_cast": 3, + }, + ), + SubModuleReplacementDescription( + suffix="attn.c_proj", + target_module=col_nn.LinearConv1D_Row, + kwargs={ + "n_cast": 1, + }, + ), + SubModuleReplacementDescription( + suffix="mlp.c_fc", + target_module=col_nn.LinearConv1D_Col, + kwargs={ + "n_cast": 1, + }, + ), + SubModuleReplacementDescription( + suffix="mlp.c_proj", + target_module=col_nn.LinearConv1D_Row, + kwargs={ + "n_cast": 1, + }, + ), + SubModuleReplacementDescription( + suffix="attn.attn_dropout", + target_module=col_nn.Dropout1D, + ), + SubModuleReplacementDescription( + suffix="attn.resid_dropout", + target_module=col_nn.Dropout1D, + ), + SubModuleReplacementDescription( + suffix="mlp.dropout", + target_module=col_nn.Dropout1D, + ), + ]) } - @staticmethod - def attn_in() -> List: - return [ - Col_Layer(suffix="attn.c_attn", - weight="weight", - bias="bias", - n_cast=3, - reversed=True, - replace_layer=col_nn.Linear1D_Col), - Col_Layer(suffix="crossattention.c_attn", - weight="weight", - bias="bias", - n_cast=2, - reversed=True, - ignore=True, - replace_layer=col_nn.Linear1D_Col), - Col_Layer(suffix="crossattention.q_attn", - weight="weight", - bias="bias", - reversed=True, - ignore=True, - replace_layer=col_nn.Linear1D_Col) - ] + def new_model_class(self): - @staticmethod - def attn_out() -> List: - return [ - Row_Layer(suffix="attn.c_proj", - weight="weight", - bias="bias", - reversed=True, - replace_layer=col_nn.Linear1D_Row), - Row_Layer(suffix="crossattention.c_proj", - weight="weight", - bias="bias", - reversed=True, - ignore=True, - replace_layer=col_nn.Linear1D_Row) - ] + return self.model - @staticmethod - def mlp_in() -> List: - return [ - Col_Layer(suffix="mlp.c_fc", weight="weight", bias="bias", reversed=True, - replace_layer=col_nn.Linear1D_Col), - ] + def postprocess(self): + return self.model - @staticmethod - def mlp_out() -> List: - return [ - Row_Layer(suffix="mlp.c_proj", - weight="weight", - bias="bias", - reversed=True, - replace_layer=col_nn.Linear1D_Row) - ] - @staticmethod - def embedding() -> List: - return [Col_Layer(suffix="wte", weight="weight", replace_layer=col_nn.VocabParallelEmbedding1D)] +# GPT2Model +class GPT2ModelPolicy(GPT2Policy): - -from transformers import GPT2LMHeadModel - - -class GPT2LMHeadModelPolicy(GPT2Policy): - - @staticmethod - def argument_policy(config, world_size): - base_argument = GPT2Policy.argument_policy(config, world_size) - argument = { - GPT2LMHeadModel: Argument(attr_dict={}, param_funcs=[ - GPT2LMHeadModelPolicy.unembedding, - ]), - } - argument.update(base_argument) - return argument - - @staticmethod - def unembedding() -> List: - return [ - Col_Layer(suffix="lm_head", - weight="weight", - bias="bias", - replace_layer=col_nn.Linear1D_Col, - gather_output=True) - ] + def __init__(self) -> None: + super().__init__() diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 54fea0335e54..043ed1a74a27 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -108,7 +108,7 @@ def check_bert(rank, world_size, port): backward_lsit = [BertForMaskedLM, BertLMHeadModel] for model_fn in forward_list: - org_model, sharded_model = build_model(model_fn) + org_model, sharded_model = build_model(world_size, model_fn) check_forward(org_model, sharded_model) if model_fn in backward_lsit: diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py new file mode 100644 index 000000000000..2f679b83f99b --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -0,0 +1,118 @@ +import copy +import os + +import pytest +import torch +from transformers import AutoTokenizer, GPT2Config, GPT2Model + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.shardformer import ShardConfig, ShardFormer +from colossalai.testing import rerun_if_address_is_in_use, spawn + +os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' +CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) +tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + + +def build_model(world_size, model_fn): + config = GPT2Config() + config.attn_pdrop = 0 + config.embd_pdrop = 0 + config.resid_pdrop = 0 + config.summary_first_dropout + + org_model = model_fn(config=config) + org_model_forshard = copy.deepcopy(org_model) + + org_model.to('cuda') + # TODO: no need to transfer to cuda + org_model_forshard.to('cuda') + shard_config = ShardConfig(tensor_parallel_size=world_size,) + shard_former = ShardFormer(shard_config=shard_config) + shard_former.init_distributed() + sharded_model = shard_former.shard_model(org_model_forshard).to('cuda') + + return org_model, sharded_model + + +def check_forward(org_model, sharded_model): + input = 'Hello, my dog is cute' + tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') + + #orgin model + org_model.eval() + org_out = org_model(**tokenized_input) + + #shard model + sharded_model.eval() + shard_out = sharded_model(**tokenized_input) + + assert torch.allclose( + org_out[0], shard_out[0], + atol=1e-5), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}" + + +def check_backward(org_model, sharded_model): + # prepare input + input = 'Hello, my dog is cute' + tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') + labels = tokenized_input['input_ids'].clone() + labels[labels == tokenizer.pad_token_id] = -100 + # tokenized_input['labels'] = labels + + #orgin model + org_model.train() + org_out = org_model(**tokenized_input) + org_loss = org_out.loss + org_loss.backward() + org_grad = org_model.h[0].attn.c_attn.weight.grad + + #shard model + sharded_model.train() + shard_out = sharded_model(**tokenized_input) + shard_loss = shard_out.loss + shard_loss.backward() + shard_grad = sharded_model.h[0].attn.c_attn.weight.grad + + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + + +def check_bert(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + forward_list = [ + GPT2Model, + + # TODO: do not work yet + # BertModel, + # BertForSequenceClassification + # BertForNextSentencePrediction, + ] + backward_lsit = [] + + for model_fn in forward_list: + org_model, sharded_model = build_model(world_size, model_fn) + check_forward(org_model, sharded_model) + + if model_fn in backward_lsit: + check_backward(org_model, sharded_model) + + torch.cuda.empty_cache() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_gpt2(): + spawn(check_bert, 2) + + +if __name__ == "__main__": + test_gpt2() From 58df720570e18d168e523a9b481b5fb9648ea934 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 21 Jun 2023 09:32:46 +0800 Subject: [PATCH 376/413] [shardformer] adapted T5 and LLaMa test to use kit (#4049) * [shardformer] adapted T5 and LLaMa test to use kit * polish code --- colossalai/shardformer/layer/embedding1d.py | 22 ++++-- colossalai/shardformer/policies/llama.py | 2 +- colossalai/shardformer/policies/t5.py | 3 +- colossalai/shardformer/shard/sharder.py | 11 ++- colossalai/testing/comparison.py | 2 +- tests/kit/model_zoo/registry.py | 30 ++++--- tests/kit/model_zoo/transformers/__init__.py | 1 + tests/kit/model_zoo/transformers/llama.py | 76 ++++++++++++++++++ tests/kit/model_zoo/transformers/t5.py | 53 ++++++++++--- .../test_mixed_precision/test_fp16_torch.py | 2 +- .../test_plugin/test_gemini_plugin.py | 2 +- .../test_plugin/test_low_level_zero_plugin.py | 2 +- .../test_plugin/test_torch_ddp_plugin.py | 2 +- .../test_plugin/test_torch_fsdp_plugin.py | 2 +- .../test_hf_model/test_hf_diffuser.py | 4 +- .../test_timm_model/test_timm_model.py | 2 +- .../test_torchaudio_model.py | 2 +- tests/test_lazy/lazy_init_utils.py | 2 +- tests/test_lazy/test_distribute.py | 2 +- tests/test_shardformer/__init__.py | 0 tests/test_shardformer/test_model/__init__.py | 0 tests/test_shardformer/test_model/_utils.py | 38 +++++++++ .../test_model/test_shard_llama.py | 78 ++++--------------- .../test_model/test_shard_t5.py | 75 ++++-------------- 24 files changed, 242 insertions(+), 171 deletions(-) create mode 100644 tests/kit/model_zoo/transformers/llama.py create mode 100644 tests/test_shardformer/__init__.py create mode 100644 tests/test_shardformer/test_model/__init__.py create mode 100644 tests/test_shardformer/test_model/_utils.py diff --git a/colossalai/shardformer/layer/embedding1d.py b/colossalai/shardformer/layer/embedding1d.py index 1108d5d6a936..ace7deb3ad0c 100644 --- a/colossalai/shardformer/layer/embedding1d.py +++ b/colossalai/shardformer/layer/embedding1d.py @@ -65,13 +65,14 @@ def __init__(self, dtype: torch.dtype = None, device: torch.device = None, process_group: ProcessGroup = None, + gather_output: bool = True, weight_initializer: Callable = init.normal_(), *args, **kwargs): super().__init__() self.num_embeddings = num_embeddings - self.embed_dim = embedding_dim + self.embedding_dim = embedding_dim self.process_group = process_group self.num_partitions = dist.get_world_size(process_group) self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions) @@ -79,7 +80,7 @@ def __init__(self, self.padding_idx = padding_idx self.embed_args = args self.embed_kwargs = kwargs - # self.gather_output = gather_output + self.gather_output = gather_output if device is None: device = get_current_device() @@ -95,7 +96,9 @@ def __init__(self, @staticmethod def from_native_module(module: nn.Embedding, - process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "Embedding1D": + process_group: Union[ProcessGroup, List[ProcessGroup]] = None, + *args, + **kwargs) -> "Embedding1D": r""" Build a 1D parallelized Embedding from a native nn.Embedding module. """ @@ -123,7 +126,9 @@ def from_native_module(module: nn.Embedding, max_norm=max_norm, norm_type=norm_type, scale_grad_by_freq=scale_grad_by_freq, - sparse=sparse) + sparse=sparse, + *args, + **kwargs) # copy the weight with torch.no_grad(): @@ -133,7 +138,7 @@ def from_native_module(module: nn.Embedding, return embedding def reset_parameters(self, weight_initializer) -> None: - fan_in, fan_out = self.num_embeddings, self.embed_dim + fan_in, fan_out = self.num_embeddings, self.embedding_dim weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) self._fill_padding_idx_with_zero() @@ -144,6 +149,9 @@ def _fill_padding_idx_with_zero(self) -> None: def forward(self, input_: Tensor) -> Tensor: output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) - output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) - return output + if self.gather_output: + output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) + return output + else: + return output_parallel diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index ae1b794fca12..a13f5f087da4 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -4,7 +4,7 @@ from transformers import LlamaForCausalLM, LlamaForSequenceClassification from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel -from colossalai.shardformer.layer.layers import Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D +from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 9c8ee59b4178..9e0c8604969c 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -11,8 +11,7 @@ T5Stack, ) -from colossalai.shardformer.layer.dropout import Dropout1D -from colossalai.shardformer.layer.layers import Embedding1D, Linear1D_Col, Linear1D_Row +from colossalai.shardformer.layer import Dropout1D, Embedding1D, Linear1D_Col, Linear1D_Row from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index f6ade26b758a..66934b09b3ac 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -185,7 +185,14 @@ def _replace_sub_module( if description.ignore_if_not_exist and native_sub_module is None: continue - replace_layer = target_module.from_native_module(native_sub_module, self.pg_manager.pg_store['tp1d'], - **kwargs) + try: + replace_layer = target_module.from_native_module(native_sub_module, self.pg_manager.pg_store['tp1d'], + **kwargs) + except Exception as e: + raise RuntimeError( + f"Failed to replace {suffix} of type {native_sub_module.__class__.__qualname__}" + f" with {target_module.__qualname__} with the exception: {e}. " + "Please check your model configuration or sharding policy, you can set up an issue for us to help you as well." + ) setattr_(org_layer, suffix, replace_layer) diff --git a/colossalai/testing/comparison.py b/colossalai/testing/comparison.py index aeecee7f11f5..5cbfb936b144 100644 --- a/colossalai/testing/comparison.py +++ b/colossalai/testing/comparison.py @@ -98,6 +98,6 @@ def assert_hf_output_close(out1: Any, raise AssertionError(f"{track_name}: shape mismatch: {out1.shape} vs {out2.shape}") assert torch.allclose( out1, out2, atol=atol, rtol=rtol - ), f"{track_name}: tensor value mismatch\nvalue 1: {out1}\nvalue 2: {out2}, mean error: {torch.abs(out1 - out2).mean()}" + ), f"{track_name}: tensor value mismatch\nvalue 1: {out1}\nvalue 2: {out2}, \nmean error: {torch.abs(out1 - out2).mean()}" else: assert out1 == out2, f"{track_name}: value mismatch.\nout1: {out1}\nout2: {out2}" diff --git a/tests/kit/model_zoo/registry.py b/tests/kit/model_zoo/registry.py index 6cc4c8ef370d..efbf3a4d37b1 100644 --- a/tests/kit/model_zoo/registry.py +++ b/tests/kit/model_zoo/registry.py @@ -28,27 +28,35 @@ def register(self, model_fn: Callable, data_gen_fn: Callable, output_transform_fn: Callable, + loss_fn: Callable = None, model_attribute: ModelAttribute = None): """ Register a model and data generation function. Examples: - >>> # Register - >>> model_zoo = ModelZooRegistry() - >>> model_zoo.register('resnet18', resnet18, resnet18_data_gen) - >>> # Run the model - >>> data = resnet18_data_gen() # do not input any argument - >>> model = resnet18() # do not input any argument - >>> out = model(**data) + + ```python + # normal forward workflow + model = resnet18() + data = resnet18_data_gen() + output = model(**data) + transformed_output = output_transform_fn(output) + loss = loss_fn(transformed_output) + + # Register + model_zoo = ModelZooRegistry() + model_zoo.register('resnet18', resnet18, resnet18_data_gen, output_transform_fn, loss_fn) + ``` Args: name (str): Name of the model. - model_fn (callable): A function that returns a model. **It must not contain any arguments.** - output_transform_fn (callable): A function that transforms the output of the model into Dict. - data_gen_fn (callable): A function that returns a data sample in the form of Dict. **It must not contain any arguments.** + model_fn (Callable): A function that returns a model. **It must not contain any arguments.** + data_gen_fn (Callable): A function that returns a data sample in the form of Dict. **It must not contain any arguments.** + output_transform_fn (Callable): A function that transforms the output of the model into Dict. + loss_fn (Callable): a function to compute the loss from the given output. Defaults to None model_attribute (ModelAttribute): Attributes of the model. Defaults to None. """ - self[name] = (model_fn, data_gen_fn, output_transform_fn, model_attribute) + self[name] = (model_fn, data_gen_fn, output_transform_fn, loss_fn, model_attribute) def get_sub_registry(self, keyword: str): """ diff --git a/tests/kit/model_zoo/transformers/__init__.py b/tests/kit/model_zoo/transformers/__init__.py index f56ff7ad84eb..ffaf4c566df9 100644 --- a/tests/kit/model_zoo/transformers/__init__.py +++ b/tests/kit/model_zoo/transformers/__init__.py @@ -1,5 +1,6 @@ from .albert import * from .bert import * from .gpt import * +from .llama import * from .opt import * from .t5 import * diff --git a/tests/kit/model_zoo/transformers/llama.py b/tests/kit/model_zoo/transformers/llama.py new file mode 100644 index 000000000000..705bbc7364ba --- /dev/null +++ b/tests/kit/model_zoo/transformers/llama.py @@ -0,0 +1,76 @@ +import torch +import transformers + +from ..registry import ModelAttribute, model_zoo + +try: + from transformers import LlamaConfig, LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel + HAS_LLAMA = True +except ImportError: + HAS_LLAMA = False + +if HAS_LLAMA: + # =============================== + # Register LLaMA + # =============================== + + def data_gen(): + # the input ids are corresponding to the sentence + # 'Hello, my dog is cute' + # + # the code is give below: + # ----------------------------------- + # from transformers import LlamaTokenizerFast + # tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer") + # input = 'Hello, my dog is cute' + # tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') + # ----------------------------------- + + input_ids = torch.Tensor([[1, 15043, 29892, 590, 11203, 338, 274, 1082]]).long() + attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1]]).long() + return dict(input_ids=input_ids, attention_mask=attention_mask) + + # label is needed for casual lm + def data_gen_for_casual_lm(): + data = data_gen() + labels = data['input_ids'].clone() + data['labels'] = labels + return data + + # transform the output to a dict + output_transform_fn = lambda x: x + + # function to get the loss + loss_fn = lambda output: output.last_hidden_state.mean() + loss_fn_for_casual_lm = lambda output: output.loss + loss_fn_for_seq_classification = lambda output: output.logits.mean() + + config = LlamaConfig(num_hidden_layers=4, + hidden_size=128, + intermediate_size=256, + num_attention_heads=4, + max_position_embeddings=128, + num_labels=16) + + # register the following models + # transformers.LlamaModel, + # transformers.LlamaForCausalLM, + # transformers.LlamaForSequenceClassification, + model_zoo.register(name='transformers_llama', + model_fn=lambda: transformers.LlamaModel(config), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn, + model_attribute=ModelAttribute(has_control_flow=True)) + model_zoo.register(name='transformers_llama_for_casual_lm', + model_fn=lambda: transformers.LlamaForCausalLM(config), + data_gen_fn=data_gen_for_casual_lm, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_casual_lm, + model_attribute=ModelAttribute(has_control_flow=True)) + model_zoo.register(name='transformers_llama_for_sequence_classification', + model_fn=lambda: transformers.LlamaForSequenceClassification(config), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_seq_classification, + model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/kit/model_zoo/transformers/t5.py b/tests/kit/model_zoo/transformers/t5.py index b81bcad90db8..689db2c40abb 100644 --- a/tests/kit/model_zoo/transformers/t5.py +++ b/tests/kit/model_zoo/transformers/t5.py @@ -6,24 +6,50 @@ # =============================== # Register single-sentence T5 # =============================== -BATCH_SIZE = 2 -SEQ_LENGTH = 16 - - -def data_gen(): - input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) - decoder_input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) - return dict(input_ids=input_ids, decoder_input_ids=decoder_input_ids) +# define data gen function def data_gen_for_encoder_only(): - input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) + # Generated from following code snippet + # + # from transformers import T5Config, T5Tokenizer + # config = T5Config(decoder_start_token_id=0) + # tokenizer = T5Tokenizer.from_pretrained("t5-small") + # input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids + input_ids = torch.Tensor([[13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 1]]).long() return dict(input_ids=input_ids) +def data_gen_for_conditional_generation(): + # labels is generated with the following code + # + # labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids + data = data_gen_for_encoder_only() + labels = torch.Tensor([[644, 4598, 229, 19250, 5, 1]]).long() + data['labels'] = labels + return data + + +def data_gen_for_t5_model(): + # decoder_inputs_ids is obtained with the following code + # + # decoder_input_ids = model._shift_right(input_ids) + data = data_gen_for_encoder_only() + decoder_input_ids = torch.Tensor([[0, 13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5]]).long() + data['decoder_input_ids'] = decoder_input_ids + return data + + +# output transform function output_transform_fn = lambda x: x -config = transformers.T5Config(d_model=128, num_layers=2) +# define loss funciton +loss_fn_for_t5_model = lambda x: x.last_hidden_state.mean() +loss_fn_for_encoder_only = lambda x: x.last_hidden_state.mean() +loss_fn_for_conditional_generation = lambda x: x.loss + +# define model config +config = transformers.T5Config(d_model=128, num_layers=2, dropout_rate=0, decoder_start_token_id=0) # register the following models # transformers.T5Model, @@ -31,16 +57,19 @@ def data_gen_for_encoder_only(): # transformers.T5EncoderModel, model_zoo.register(name='transformers_t5', model_fn=lambda: transformers.T5Model(config), - data_gen_fn=data_gen, + data_gen_fn=data_gen_for_t5_model, output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_t5_model, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_t5_for_conditional_generation', model_fn=lambda: transformers.T5ForConditionalGeneration(config), - data_gen_fn=data_gen, + data_gen_fn=data_gen_for_conditional_generation, output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_conditional_generation, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_t5_encoder_model', model_fn=lambda: transformers.T5EncoderModel(config), data_gen_fn=data_gen_for_encoder_only, output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_encoder_only, model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_booster/test_mixed_precision/test_fp16_torch.py b/tests/test_booster/test_mixed_precision/test_fp16_torch.py index 963387da262b..26ce00e94869 100644 --- a/tests/test_booster/test_mixed_precision/test_fp16_torch.py +++ b/tests/test_booster/test_mixed_precision/test_fp16_torch.py @@ -11,7 +11,7 @@ def run_torch_amp(rank, world_size, port): # init dist env colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') sub_model_zoo = model_zoo.get_sub_registry('timm') - for name, (model_fn, data_gen_fn, output_transform_fn, _) in sub_model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, _) in sub_model_zoo.items(): # dlrm_interactionarch has not parameters, so skip if name == 'dlrm_interactionarch': continue diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index d606d6d89bd4..d29c92926066 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -71,7 +71,7 @@ def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): passed_models = [] failed_info = {} # (model_name, error) pair - for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, _) in model_zoo.items(): # These models lead to CUDA error if name in ('diffusers_auto_encoder_kl', 'diffusers_vq_model', 'diffusers_unet2d_model', 'timm_resmlp', 'timm_gmixer_12_224', 'timm_gmlp_b16_224', 'timm_mixer_b16_224', 'timm_convnext'): diff --git a/tests/test_booster/test_plugin/test_low_level_zero_plugin.py b/tests/test_booster/test_plugin/test_low_level_zero_plugin.py index f70f27be2aa7..eedd8c59a3a8 100644 --- a/tests/test_booster/test_plugin/test_low_level_zero_plugin.py +++ b/tests/test_booster/test_plugin/test_low_level_zero_plugin.py @@ -61,7 +61,7 @@ def check_low_level_zero_plugin(stage: int, early_stop: bool = True): ignore_models = _AMP_ERR_MODELS + _LOW_LEVEL_ZERO_ERR_MODELS + _STUCK_MODELS skipped_models = [] - for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, _) in model_zoo.items(): # FIXME(ver217): fix these models if name in ignore_models: skipped_models.append(name) diff --git a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py index fbe44e5ce6fb..1484273973ae 100644 --- a/tests/test_booster/test_plugin/test_torch_ddp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_ddp_plugin.py @@ -40,7 +40,7 @@ def run_fn(model_fn, data_gen_fn, output_transform_fn): def check_torch_ddp_plugin(): - for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, _) in model_zoo.items(): if name == 'dlrm_interactionarch': continue run_fn(model_fn, data_gen_fn, output_transform_fn) diff --git a/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py index 44767f051fdd..cbd5d57800db 100644 --- a/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py +++ b/tests/test_booster/test_plugin/test_torch_fsdp_plugin.py @@ -42,7 +42,7 @@ def run_fn(model_fn, data_gen_fn, output_transform_fn): def check_torch_fsdp_plugin(): - for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, _) in model_zoo.items(): if any(element in name for element in [ 'diffusers', 'deepfm_sparsearch', 'dlrm_interactionarch', 'torchvision_googlenet', 'torchvision_inception_v3' diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py index 0cbea82e083a..ccbe2da58bf2 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py @@ -47,7 +47,7 @@ def test_diffusers(): sub_model_zoo = model_zoo.get_sub_registry('diffusers') - for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in sub_model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, attribute) in sub_model_zoo.items(): data = data_gen_fn() trace_and_compare(model_fn, data, output_transform_fn) torch.cuda.synchronize() @@ -60,7 +60,7 @@ def test_torch_diffusers(): sub_model_zoo = model_zoo.get_sub_registry('diffusers') - for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in sub_model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, attribute) in sub_model_zoo.items(): data = data_gen_fn() model = model_fn() output = model(**data) diff --git a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py index 11302e8f36b0..117c70c84aa8 100644 --- a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py +++ b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py @@ -56,7 +56,7 @@ def test_timm_models(): sub_model_zoo = model_zoo.get_sub_registry('timm') - for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in sub_model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, attribute) in sub_model_zoo.items(): data = data_gen_fn() if attribute is not None and attribute.has_control_flow: meta_args = {k: v.to('meta') for k, v in data.items()} diff --git a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py index eafcaca10b1d..f73c5bb9a590 100644 --- a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py +++ b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py @@ -16,7 +16,7 @@ def test_torchaudio_models(): sub_model_zoo = model_zoo.get_sub_registry('torchaudio') - for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in sub_model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, attribute) in sub_model_zoo.items(): model = model_fn() trace_and_compare(model, data_gen_fn, diff --git a/tests/test_lazy/lazy_init_utils.py b/tests/test_lazy/lazy_init_utils.py index 85bfd0e27801..2dd8d1ca3216 100644 --- a/tests/test_lazy/lazy_init_utils.py +++ b/tests/test_lazy/lazy_init_utils.py @@ -60,7 +60,7 @@ def assert_forward_equal(m1: torch.nn.Module, m2: torch.nn.Module, data_gen_fn: def check_lazy_init(entry: TestingEntry, seed: int = 42, verbose: bool = False, check_forward: bool = False) -> None: - model_fn, data_gen_fn, output_transform_fn, model_attr = entry + model_fn, data_gen_fn, output_transform_fn, _, model_attr = entry _MyTensor._pre_op_fn = lambda *args: set_seed(seed) LazyTensor._pre_op_fn = lambda *args: set_seed(seed) ctx = LazyInitContext(tensor_cls=_MyTensor) diff --git a/tests/test_lazy/test_distribute.py b/tests/test_lazy/test_distribute.py index d515b175a9ea..f33c037e3de6 100644 --- a/tests/test_lazy/test_distribute.py +++ b/tests/test_lazy/test_distribute.py @@ -78,7 +78,7 @@ def run_dist_lazy_init(subset, seed: int = 42): if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base'): continue print_rank_0(name) - model_fn, data_gen_fn, output_transform_fn, model_attr = entry + model_fn, data_gen_fn, output_transform_fn, _, model_attr = entry ctx = LazyInitContext(tensor_cls=_MyTensor) with ctx: model = model_fn() diff --git a/tests/test_shardformer/__init__.py b/tests/test_shardformer/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/test_shardformer/test_model/__init__.py b/tests/test_shardformer/test_model/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py new file mode 100644 index 000000000000..52ca7fce895b --- /dev/null +++ b/tests/test_shardformer/test_model/_utils.py @@ -0,0 +1,38 @@ +import copy + +from colossalai.shardformer import ShardConfig, ShardFormer + + +def build_model(world_size, model_fn): + # create new model + org_model = model_fn().cuda() + + # shard model + shard_config = ShardConfig(tensor_parallel_size=world_size) + model_copy = copy.deepcopy(org_model) + shard_former = ShardFormer(shard_config=shard_config) + shard_former.init_distributed() + sharded_model = shard_former.shard_model(model_copy) + + return org_model, sharded_model + + +def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # prepare input + data = data_gen_fn() + data = {k: v.cuda() for k, v in data.items()} + + # switch to train mode + original_model.train() + sharded_model.train() + + # run forward + org_output = original_model(**data) + org_output = output_transform_fn(org_output) + org_loss = loss_fn(org_output) + + shard_output = sharded_model(**data) + shard_output = output_transform_fn(shard_output) + shard_loss = loss_fn(shard_output) + + return org_output, org_loss, shard_output, shard_loss diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index b15f81aba52e..8b672af500bd 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -1,64 +1,22 @@ -import copy import os -import random import pytest import torch -from transformers import LlamaConfig, LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizerFast import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.shardformer import ShardConfig, ShardFormer from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' -tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer") - - -def build_model(world_size, model_fn): - # create new model - config = LlamaConfig(num_hidden_layers=4, - hidden_size=128, - intermediate_size=256, - num_attention_heads=4, - max_position_embeddings=128) - org_model = model_fn(config).cuda() - - # shard model - shard_config = ShardConfig(tensor_parallel_size=world_size) - model_copy = copy.deepcopy(org_model) - shard_former = ShardFormer(shard_config=shard_config) - shard_former.init_distributed() - sharded_model = shard_former.shard_model(model_copy) - - return org_model, sharded_model - - -def check_forward_backward(org_model, sharded_model): - # prepare input - input = 'Hello, my dog is cute' - tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') - del tokenized_input["token_type_ids"] - del tokenized_input["attention_mask"] - - # switch to train mode - org_model.train() - sharded_model.train() - - if isinstance(org_model, (LlamaModel, LlamaForSequenceClassification)): - org_output = org_model(**tokenized_input) - org_loss = org_output.last_hidden_state.mean() - shard_output = sharded_model(**tokenized_input) - shard_loss = shard_output.last_hidden_state.mean() - elif isinstance(org_model, LlamaForCausalLM): - labels = tokenized_input['input_ids'].clone() - labels[labels == tokenizer.pad_token_id] = -100 - tokenized_input['labels'] = labels - org_output = org_model(**tokenized_input) - org_loss = org_output.loss - shard_output = sharded_model(**tokenized_input) - shard_loss = shard_output.loss + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + + # forward check assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4) # run backward @@ -66,12 +24,12 @@ def check_forward_backward(org_model, sharded_model): shard_loss.backward() # check grad - if isinstance(org_model, LlamaModel): - llama_model = org_model - shard_llama_model = sharded_model - else: + if hasattr(org_model, 'model'): llama_model = org_model.model shard_llama_model = sharded_model.model + else: + llama_model = org_model + shard_llama_model = sharded_model org_grad = llama_model.layers[0].self_attn.q_proj.weight.grad shard_grad = shard_llama_model.layers[0].self_attn.q_proj.weight.grad @@ -89,17 +47,11 @@ def check_llama(rank, world_size, port): disable_existing_loggers() colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - model_list = [ - LlamaModel, - # LlamaForCausalLM, - - # TODO: do not work yet - # LlamaForSequenceClassification - ] + sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') - for model_fn in model_list: + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(world_size, model_fn) - check_forward_backward(org_model, sharded_model) + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 254649409c59..2698d7675c8e 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -1,64 +1,20 @@ -import copy import os import pytest import torch -from transformers import T5Config, T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Tokenizer, T5TokenizerFast import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.shardformer.shard import ShardConfig, ShardFormer from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward -os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' -CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) -tokenizer = T5Tokenizer.from_pretrained("t5-small") - - -def build_model(world_size, model_fn): - config = T5Config(decoder_start_token_id=0) - config.dropout_rate = 0 - org_model = model_fn(config=config).to('cuda') - shard_config = ShardConfig(tensor_parallel_size=world_size) - - # shard model - shard_config = ShardConfig(tensor_parallel_size=world_size) - model_copy = copy.deepcopy(org_model) - shard_former = ShardFormer(shard_config=shard_config) - shard_former.init_distributed() - sharded_model = shard_former.shard_model(model_copy) - - return org_model, sharded_model - - -def check_forward_backward(org_model, sharded_model): - # prepare input - input_ids = tokenizer("translate English to German: The house is wonderful.", - return_tensors="pt").input_ids.to('cuda') - labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids.to('cuda') - - # switch to train mode - org_model.train() - sharded_model.train() - - if isinstance(org_model, T5ForConditionalGeneration): - org_output = org_model(input_ids=input_ids, labels=labels) - org_loss = org_output.loss - shard_output = sharded_model(input_ids=input_ids, labels=labels) - shard_loss = shard_output.loss - elif isinstance(org_model, T5Model): - decoder_input_ids = org_model._shift_right(input_ids) - org_output = org_model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) - org_loss = org_output.last_hidden_state.mean() - shard_output = sharded_model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) - shard_loss = shard_output.last_hidden_state.mean() - elif isinstance(org_model, T5EncoderModel): - org_output = org_model(input_ids=input_ids) - org_loss = org_output.last_hidden_state.mean() - shard_output = sharded_model(input_ids=input_ids) - shard_loss = shard_output.last_hidden_state.mean() - - # key is sharded, so we ignore + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + # the value "past_key_values" is sharded, so we ignore + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) # do backward @@ -81,18 +37,15 @@ def check_forward_backward(org_model, sharded_model): def check_t5(rank, world_size, port): disable_existing_loggers() - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - model_fn_list = [ - T5Model, - T5ForConditionalGeneration, - T5EncoderModel, - ] + sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') - for model_fn in model_fn_list: + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(world_size, model_fn) - check_forward_backward(org_model, sharded_model) - torch.cuda.empty_cache() + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + + torch.cuda.empty_cache() @pytest.mark.dist From f22ddacef03c00ac19ac20ca7d5274f9ab4c9ff1 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 21 Jun 2023 14:30:06 +0800 Subject: [PATCH 377/413] [shardformer] refactored the shardformer layer structure (#4053) --- .../shardformer/{utils/utils.py => _utils.py} | 3 + colossalai/shardformer/layer/__init__.py | 19 +- colossalai/shardformer/layer/_operation.py | 2 - colossalai/shardformer/layer/dropout.py | 4 +- ...cabparallelembedding1d.py => embedding.py} | 165 +++++++++++++++--- colossalai/shardformer/layer/embedding1d.py | 157 ----------------- colossalai/shardformer/layer/layernorm1d.py | 73 -------- .../layer/{linear1d.py => linear.py} | 22 +-- .../layer/{linearconv1d.py => linear_conv.py} | 57 +++--- .../layer/{dist_crossentropy.py => loss.py} | 4 +- .../{parallelmodule.py => parallel_module.py} | 10 +- colossalai/shardformer/policies/basepolicy.py | 2 +- colossalai/shardformer/policies/bert.py | 2 +- colossalai/shardformer/policies/gpt2.py | 5 - colossalai/shardformer/policies/t5.py | 2 - colossalai/shardformer/shard/sharder.py | 2 +- colossalai/shardformer/utils/__init__.py | 1 - .../test_dist_crossentropy.py} | 4 +- .../test_layer/test_dropout.py | 2 +- .../test_layer/test_embedding.py | 2 +- .../test_layer/test_linear_1d.py | 2 +- .../test_vocab_parallel_embedding_1d.py | 2 +- .../test_module/test_dropout.py | 51 ------ .../test_module/test_slicer.py | 78 --------- 24 files changed, 198 insertions(+), 473 deletions(-) rename colossalai/shardformer/{utils/utils.py => _utils.py} (97%) rename colossalai/shardformer/layer/{vocabparallelembedding1d.py => embedding.py} (52%) delete mode 100644 colossalai/shardformer/layer/embedding1d.py delete mode 100644 colossalai/shardformer/layer/layernorm1d.py rename colossalai/shardformer/layer/{linear1d.py => linear.py} (96%) rename colossalai/shardformer/layer/{linearconv1d.py => linear_conv.py} (92%) rename colossalai/shardformer/layer/{dist_crossentropy.py => loss.py} (98%) rename colossalai/shardformer/layer/{parallelmodule.py => parallel_module.py} (78%) delete mode 100644 colossalai/shardformer/utils/__init__.py rename tests/test_shardformer/{test_module/test_distcrossentropy.py => test_layer/test_dist_crossentropy.py} (87%) delete mode 100644 tests/test_shardformer/test_module/test_dropout.py delete mode 100644 tests/test_shardformer/test_module/test_slicer.py diff --git a/colossalai/shardformer/utils/utils.py b/colossalai/shardformer/_utils.py similarity index 97% rename from colossalai/shardformer/utils/utils.py rename to colossalai/shardformer/_utils.py index 05a6a3ae6c30..a1c7203a929f 100644 --- a/colossalai/shardformer/utils/utils.py +++ b/colossalai/shardformer/_utils.py @@ -2,6 +2,9 @@ def get_obj_list_element(obj, a): + r""" + Get the element of the list in the object + """ re_pattern = r'\[\d+\]' prog = re.compile(re_pattern) result = prog.search(a) diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py index 66d86913bb2b..808ebbc12aeb 100644 --- a/colossalai/shardformer/layer/__init__.py +++ b/colossalai/shardformer/layer/__init__.py @@ -1,17 +1,10 @@ from .dropout import Dropout1D -from .embedding1d import Embedding1D -from .layernorm1d import LayerNorm1D -from .linear1d import Linear1D_Col, Linear1D_Row -from .linearconv1d import LinearConv1D_Col, LinearConv1D_Row -from .vocabparallelembedding1d import VocabParallelEmbedding1D +from .embedding import Embedding1D, VocabParallelEmbedding1D +from .linear import Linear1D_Col, Linear1D_Row +from .linear_conv import LinearConv1D_Col, LinearConv1D_Row +from .loss import cross_entropy_1d __all__ = [ - "Embedding1D", - "VocabParallelEmbedding1D", - "Linear1D_Col", - "Linear1D_Row", - "LinearConv1D_Col", - "LinearConv1D_Row", - "LayerNorm1D", - "Dropout1D", + "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", "LinearConv1D_Col", "LinearConv1D_Row", + "Dropout1D", "cross_entropy_1d" ] diff --git a/colossalai/shardformer/layer/_operation.py b/colossalai/shardformer/layer/_operation.py index 208a391c33e2..280d5526342b 100644 --- a/colossalai/shardformer/layer/_operation.py +++ b/colossalai/shardformer/layer/_operation.py @@ -1,8 +1,6 @@ import torch import torch.distributed as dist -from colossalai.core import global_context as gpc - try: import fused_mix_prec_layer_norm_cuda except: diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py index 08dfb8afd7fb..2c49b49faad6 100644 --- a/colossalai/shardformer/layer/dropout.py +++ b/colossalai/shardformer/layer/dropout.py @@ -4,9 +4,11 @@ import torch.nn as nn from torch.distributed import ProcessGroup -from .parallelmodule import ParallelModule +from .parallel_module import ParallelModule from .utils import create_randomizer_with_offset +__all__ = ['Dropout1D'] + class Dropout1D(ParallelModule, nn.Dropout): """ diff --git a/colossalai/shardformer/layer/vocabparallelembedding1d.py b/colossalai/shardformer/layer/embedding.py similarity index 52% rename from colossalai/shardformer/layer/vocabparallelembedding1d.py rename to colossalai/shardformer/layer/embedding.py index 4c325c68421b..8b9fb03ec798 100644 --- a/colossalai/shardformer/layer/vocabparallelembedding1d.py +++ b/colossalai/shardformer/layer/embedding.py @@ -1,7 +1,6 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from collections import OrderedDict from typing import Callable, List, Union import torch @@ -12,26 +11,148 @@ from torch.distributed import ProcessGroup from torch.nn.parameter import Parameter -from colossalai.context import ParallelMode, seed from colossalai.nn import init as init -from colossalai.nn.layer.base_layer import ParallelLayer from colossalai.nn.layer.utils import divide -from colossalai.tensor.d_tensor.api import shard_rowwise -from colossalai.utils.checkpointing import gather_tensor_parallel_state_dict +from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise +from colossalai.utils.cuda import get_current_device -from ._operation import reduce_input -from .parallelmodule import ParallelModule +from ._operation import gather_forward_split_backward, reduce_input +from .parallel_module import ParallelModule from .utils import create_randomizer_with_offset -Fast_LN = None -try: - from apex.contrib.layer_norm.layer_norm import FastLayerNorm - Fast_LN = FastLayerNorm -except ImportError: - pass +__all__ = ['Embedding1D', 'VocabParallelEmbedding1D'] -class VocabParallelEmbedding1D(ParallelLayer): +class Embedding1D(ParallelModule): + r"""Embedding for 1D parallelism. + + Args: + num_embeddings (int): number of embeddings. + embedding_dim (int): dimension of embedding. + padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; + therefore, the embedding vector at padding_idx is not updated during training, + i.e. it remains as a fixed “pad”, defaults to None. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + weight_initializer (:class:`typing.Callable`, optional): + he initializer of weight, defaults to normal initializer. + + The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain: + :: + + max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is + renormalized to have norm max_norm. Note: this will modify weight in-place. + norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. + scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse + of frequency of the words in the mini-batch. Default False. + sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. + + More details about ``args`` and ``kwargs`` could be found in + `Embedding `_. + + More details about ``initializer`` please refer to + `init `_ + """ + + def __init__(self, + num_embeddings: int, + embedding_dim: int, + padding_idx: int = None, + dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, + gather_output: bool = True, + weight_initializer: Callable = init.normal_(), + *args, + **kwargs): + super().__init__() + + self.num_embeddings = num_embeddings + self.embedding_dim = embedding_dim + self.process_group = process_group + self.num_partitions = dist.get_world_size(process_group) + self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions) + + self.padding_idx = padding_idx + self.embed_args = args + self.embed_kwargs = kwargs + self.gather_output = gather_output + + if device is None: + device = get_current_device() + + self.weight = Parameter(torch.empty((num_embeddings, self.embed_dim_per_partition), device=device, dtype=dtype)) + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer) + + @staticmethod + def from_native_module(module: nn.Embedding, + process_group: Union[ProcessGroup, List[ProcessGroup]] = None, + *args, + **kwargs) -> "Embedding1D": + r""" + Build a 1D parallelized Embedding from a native nn.Embedding module. + """ + # get the attributes + num_embedding = module.num_embeddings + embedding_dim = module.embedding_dim + padding_idx = module.padding_idx + max_norm = module.max_norm + norm_type = module.norm_type + scale_grad_by_freq = module.scale_grad_by_freq + sparse = module.sparse + dtype = module.weight.dtype + device = module.weight.device + + # sparse is not support yet + if sparse: + raise NotImplementedError("The Embedding1D module does not support sparse embedding yet.") + + embedding = Embedding1D(num_embeddings=num_embedding, + embedding_dim=embedding_dim, + padding_idx=padding_idx, + process_group=process_group, + dtype=dtype, + device=device, + max_norm=max_norm, + norm_type=norm_type, + scale_grad_by_freq=scale_grad_by_freq, + sparse=sparse, + *args, + **kwargs) + + # copy the weight + with torch.no_grad(): + sharded_weight = shard_colwise(module.weight.data, process_group) + embedding.weight.copy_(sharded_weight) + + return embedding + + def reset_parameters(self, weight_initializer) -> None: + fan_in, fan_out = self.num_embeddings, self.embedding_dim + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + self._fill_padding_idx_with_zero() + + def _fill_padding_idx_with_zero(self) -> None: + if self.padding_idx is not None: + with torch.no_grad(): + self.weight[self.padding_idx].fill_(0) + + def forward(self, input_: Tensor) -> Tensor: + output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) + + if self.gather_output: + output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) + return output + else: + return output_parallel + + +class VocabParallelEmbedding1D(ParallelModule): r"""Embedding parallelized in the vocabulary dimension. Args: @@ -93,9 +214,7 @@ def __init__(self, # offset the seed with randomizer index and rank seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer) + self.reset_parameters(weight_initializer) @staticmethod def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, @@ -132,7 +251,7 @@ def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, return vocab_embedding_1d def reset_parameters(self, weight_initializer) -> None: - with seed(ParallelMode.TENSOR): + with self.randomizer.fork_rng(enable_cpu=True): fan_in, fan_out = self.num_embeddings, self.embed_dim weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) self._fill_padding_idx_with_zero() @@ -143,16 +262,6 @@ def _fill_padding_idx_with_zero(self) -> None: with torch.no_grad(): self.weight[self.padding_idx - self.vocab_start_index].fill_(0) - def _save_to_global_state_dict(self, destination, prefix, keep_vars): - weight_key = prefix + 'weight' - local_state = OrderedDict({weight_key: self.weight}) - local_state = gather_tensor_parallel_state_dict(local_state, - ParallelMode.PARALLEL_1D, - dims={weight_key: 0}, - partition_states={weight_key: True}, - keep_vars=keep_vars) - destination.update(local_state) - def forward(self, input_: Tensor) -> Tensor: # Build the mask. input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index) diff --git a/colossalai/shardformer/layer/embedding1d.py b/colossalai/shardformer/layer/embedding1d.py deleted file mode 100644 index ace7deb3ad0c..000000000000 --- a/colossalai/shardformer/layer/embedding1d.py +++ /dev/null @@ -1,157 +0,0 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -from typing import Callable, List, Union - -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F -from torch import Tensor -from torch.distributed import ProcessGroup -from torch.nn.parameter import Parameter - -from colossalai.nn import init as init -from colossalai.nn.layer.utils import divide -from colossalai.tensor.d_tensor.api import shard_colwise -from colossalai.utils.cuda import get_current_device - -from ._operation import gather_forward_split_backward -from .parallelmodule import ParallelModule -from .utils import create_randomizer_with_offset - -Fast_LN = None -try: - from apex.contrib.layer_norm.layer_norm import FastLayerNorm - Fast_LN = FastLayerNorm -except ImportError: - pass - - -class Embedding1D(ParallelModule): - r"""Embedding for 1D parallelism. - - Args: - num_embeddings (int): number of embeddings. - embedding_dim (int): dimension of embedding. - padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient; - therefore, the embedding vector at padding_idx is not updated during training, - i.e. it remains as a fixed “pad”, defaults to None. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - weight_initializer (:class:`typing.Callable`, optional): - he initializer of weight, defaults to normal initializer. - - The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain: - :: - - max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is - renormalized to have norm max_norm. Note: this will modify weight in-place. - norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2. - scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse - of frequency of the words in the mini-batch. Default False. - sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False. - - More details about ``args`` and ``kwargs`` could be found in - `Embedding `_. - - More details about ``initializer`` please refer to - `init `_ - """ - - def __init__(self, - num_embeddings: int, - embedding_dim: int, - padding_idx: int = None, - dtype: torch.dtype = None, - device: torch.device = None, - process_group: ProcessGroup = None, - gather_output: bool = True, - weight_initializer: Callable = init.normal_(), - *args, - **kwargs): - super().__init__() - - self.num_embeddings = num_embeddings - self.embedding_dim = embedding_dim - self.process_group = process_group - self.num_partitions = dist.get_world_size(process_group) - self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions) - - self.padding_idx = padding_idx - self.embed_args = args - self.embed_kwargs = kwargs - self.gather_output = gather_output - - if device is None: - device = get_current_device() - - self.weight = Parameter(torch.empty((num_embeddings, self.embed_dim_per_partition), device=device, dtype=dtype)) - - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer) - - @staticmethod - def from_native_module(module: nn.Embedding, - process_group: Union[ProcessGroup, List[ProcessGroup]] = None, - *args, - **kwargs) -> "Embedding1D": - r""" - Build a 1D parallelized Embedding from a native nn.Embedding module. - """ - # get the attributes - num_embedding = module.num_embeddings - embedding_dim = module.embedding_dim - padding_idx = module.padding_idx - max_norm = module.max_norm - norm_type = module.norm_type - scale_grad_by_freq = module.scale_grad_by_freq - sparse = module.sparse - dtype = module.weight.dtype - device = module.weight.device - - # sparse is not support yet - if sparse: - raise NotImplementedError("The Embedding1D module does not support sparse embedding yet.") - - embedding = Embedding1D(num_embeddings=num_embedding, - embedding_dim=embedding_dim, - padding_idx=padding_idx, - process_group=process_group, - dtype=dtype, - device=device, - max_norm=max_norm, - norm_type=norm_type, - scale_grad_by_freq=scale_grad_by_freq, - sparse=sparse, - *args, - **kwargs) - - # copy the weight - with torch.no_grad(): - sharded_weight = shard_colwise(module.weight.data, process_group) - embedding.weight.copy_(sharded_weight) - - return embedding - - def reset_parameters(self, weight_initializer) -> None: - fan_in, fan_out = self.num_embeddings, self.embedding_dim - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - self._fill_padding_idx_with_zero() - - def _fill_padding_idx_with_zero(self) -> None: - if self.padding_idx is not None: - with torch.no_grad(): - self.weight[self.padding_idx].fill_(0) - - def forward(self, input_: Tensor) -> Tensor: - output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) - - if self.gather_output: - output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) - return output - else: - return output_parallel diff --git a/colossalai/shardformer/layer/layernorm1d.py b/colossalai/shardformer/layer/layernorm1d.py deleted file mode 100644 index 78bd64cfb504..000000000000 --- a/colossalai/shardformer/layer/layernorm1d.py +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -from collections import OrderedDict - -from colossalai.context import ParallelMode, seed -from colossalai.core import global_context as gpc -from colossalai.global_variables import tensor_parallel_env as env -from colossalai.kernel import LayerNorm -from colossalai.nn import init as init -from colossalai.nn.layer.colossalai_layer._utils import ColossalaiModule -from colossalai.utils.checkpointing import broadcast_state_dict - -Fast_LN = None -try: - from apex.contrib.layer_norm.layer_norm import FastLayerNorm - Fast_LN = FastLayerNorm -except ImportError: - pass - - -class LayerNorm1D(ColossalaiModule): - r""" - Layer Normalization for colossalai - - Args: - normalized_shape (int): input shape from an expected input of size. - :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] - \times \ldots \times \text{normalized_shape}[-1]]` - If a single integer is used, it is treated as a singleton list, and this module will - normalize over the last dimension which is expected to be of that specific size. - eps (float): a value added to the denominator for numerical stability, defaults to 1e-05. - bias (bool, optional): Whether to add a bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. - """ - - _fast_ln_supported_sizes = [ - 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, - 24576, 25600, 30720, 32768, 40960, 49152, 65536 - ] - - def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None): - if Fast_LN is not None and normalized_shape in self._fast_ln_supported_sizes: - norm = Fast_LN(normalized_shape, eps=eps).to(dtype) - else: - norm = None - try: - from apex.normalization import FusedLayerNorm - norm = FusedLayerNorm(normalized_shape, eps=eps).to(dtype) - except ImportError: - norm = LayerNorm(normalized_shape, eps=eps).to(dtype) - super().__init__(norm) - - def _load_from_state_dict(self, state_dict, prefix, *args): - local_state = OrderedDict() - weight_key = prefix + 'weight' - bias_key = prefix + 'bias' - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - # weight - weight = state_dict.pop(weight_key, None) - if weight is not None: - local_state[weight_key] = weight - # bias - bias = state_dict.pop(bias_key, None) - if bias is not None: - local_state[bias_key] = bias - - local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D) - super()._load_from_state_dict(local_state, prefix, *args) - - def _save_to_state_dict(self, destination, prefix, keep_vars): - if gpc.get_local_rank(ParallelMode.TENSOR) == 0: - super()._save_to_state_dict(destination, prefix, keep_vars) diff --git a/colossalai/shardformer/layer/linear1d.py b/colossalai/shardformer/layer/linear.py similarity index 96% rename from colossalai/shardformer/layer/linear1d.py rename to colossalai/shardformer/layer/linear.py index d59d32df824e..b87981c6db42 100644 --- a/colossalai/shardformer/layer/linear1d.py +++ b/colossalai/shardformer/layer/linear.py @@ -23,15 +23,10 @@ reduce_input, split_forward_gather_backward, ) -from .parallelmodule import ParallelModule +from .parallel_module import ParallelModule from .utils import create_randomizer_with_offset -Fast_LN = None -try: - from apex.contrib.layer_norm.layer_norm import FastLayerNorm - Fast_LN = FastLayerNorm -except ImportError: - pass +__all__ = ['Linear1D_Col', 'Linear1D_Row'] class Linear1D_Col(ParallelModule): @@ -104,8 +99,8 @@ def __init__(self, seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer, bias_initializer) + # init weights + self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, @@ -146,10 +141,11 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis return linear_1d def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.out_features - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) + with self.randomizer.fork_rng(enable_cpu=True): + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: assert input_.shape[-1] == self.weight.shape[-1], \ diff --git a/colossalai/shardformer/layer/linearconv1d.py b/colossalai/shardformer/layer/linear_conv.py similarity index 92% rename from colossalai/shardformer/layer/linearconv1d.py rename to colossalai/shardformer/layer/linear_conv.py index 4a5cb0707900..b4599f48942d 100644 --- a/colossalai/shardformer/layer/linearconv1d.py +++ b/colossalai/shardformer/layer/linear_conv.py @@ -23,19 +23,15 @@ reduce_input, split_forward_gather_backward, ) -from .parallelmodule import ParallelModule +from .parallel_module import ParallelModule from .utils import create_randomizer_with_offset -Fast_LN = None -try: - from apex.contrib.layer_norm.layer_norm import FastLayerNorm - Fast_LN = FastLayerNorm -except ImportError: - pass +__all__ = ['LinearConv1D_Col', 'LinearConv1D_Row'] class LinearConv1D_Col(ParallelModule): r"""Linear layer with column parallelism. + Specially created for HuggingFace's GPT2 model. The linear layer is defined as :math:`Y = XA + b`. A is parallelized along its second dimension as :math:`A = [A_1, ..., A_p]`. This layer is used to fit `Conv1D` layer in gpt2 of huggingface. @@ -104,8 +100,8 @@ def __init__(self, seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer, bias_initializer) + # init weights + self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], n_cast: int, @@ -162,10 +158,11 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis return linear_1d def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.out_features - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) + with self.randomizer.fork_rng(enable_cpu=True): + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: assert input_.shape[-1] == self.weight.shape[-1], \ @@ -192,6 +189,7 @@ def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: class LinearConv1D_Row(ParallelModule): r""" Linear layer with row parallelism + Specially created for HuggingFace's GPT2 model. Args: in_features (int): size of each input sample. @@ -260,8 +258,8 @@ def __init__(self, seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer, bias_initializer) + # init weights + self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], n_cast: int, @@ -320,20 +318,21 @@ def chunk_weight(self): self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) def reset_parameters(self, weight_initializer, bias_initializer) -> None: - fan_in, fan_out = self.in_features, self.out_features - weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) - - if self.bias is not None: - bias_initializer(self.bias, fan_in=fan_in) - if self.process_group is None: - src_rank = 0 - else: - src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) - - origin_device = self.bias.device - self.bias = self.bias.cuda() - dist.broadcast(self.bias, src=src_rank, group=self.process_group) - self.bias = self.bias.to(origin_device) + with self.randomizer.fork_rng(enable_cpu=True): + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + if self.process_group is None: + src_rank = 0 + else: + src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) + + origin_device = self.bias.device + self.bias = self.bias.cuda() + dist.broadcast(self.bias, src=src_rank, group=self.process_group) + self.bias = self.bias.to(origin_device) def forward(self, input_: Tensor) -> Tensor: # Set up backprop all-reduce. diff --git a/colossalai/shardformer/layer/dist_crossentropy.py b/colossalai/shardformer/layer/loss.py similarity index 98% rename from colossalai/shardformer/layer/dist_crossentropy.py rename to colossalai/shardformer/layer/loss.py index 7840c2f2e5da..38a5395a0f57 100644 --- a/colossalai/shardformer/layer/dist_crossentropy.py +++ b/colossalai/shardformer/layer/loss.py @@ -1,10 +1,10 @@ import torch import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F from torch.autograd import Function from torch.distributed import ProcessGroup +__all__ = ['DistCrossEntropy', 'cross_entropy_1d'] + class DistCrossEntropy(Function): r""" diff --git a/colossalai/shardformer/layer/parallelmodule.py b/colossalai/shardformer/layer/parallel_module.py similarity index 78% rename from colossalai/shardformer/layer/parallelmodule.py rename to colossalai/shardformer/layer/parallel_module.py index 3d19bbea7e47..c68cd57786ab 100644 --- a/colossalai/shardformer/layer/parallelmodule.py +++ b/colossalai/shardformer/layer/parallel_module.py @@ -7,15 +7,7 @@ import torch.nn as nn from torch.distributed import ProcessGroup -from colossalai.global_variables import tensor_parallel_env as env -from colossalai.nn import init as init - -Fast_LN = None -try: - from apex.contrib.layer_norm.layer_norm import FastLayerNorm - Fast_LN = FastLayerNorm -except ImportError: - pass +__all__ = ['ParallelModule'] class ParallelModule(nn.Module, ABC): diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 175a914a84f9..b5d9cdbd7289 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -2,7 +2,7 @@ from abc import ABC, abstractmethod from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Tuple, Type, Union +from typing import Any, Callable, Dict, List, Type, Union import torch.nn as nn diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 2a204f0defe4..d5e8e01cf154 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -4,7 +4,7 @@ import colossalai.shardformer.layer as col_nn from colossalai.shardformer.layer.dropout import Dropout1D -from ..utils import getattr_, setattr_ +from .._utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index d255325b2084..da9e6b7bd32d 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -1,12 +1,7 @@ -from typing import Type, Union - -import torch.nn as nn from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model import colossalai.shardformer.layer as col_nn -from colossalai.shardformer.layer.dropout import Dropout1D -from ..utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 9e0c8604969c..30433f751088 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -1,5 +1,3 @@ -import torch -import torch.nn as nn from transformers import T5ForConditionalGeneration from transformers.models.t5.modeling_t5 import ( T5Attention, diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 66934b09b3ac..22f5f1c12d26 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -4,9 +4,9 @@ from colossalai.cluster.process_group_manager import ProcessGroupManager +from .._utils import getattr_, setattr_ from ..policies.autopolicy import get_autopolicy from ..policies.basepolicy import Policy, SubModuleReplacementDescription -from ..utils.utils import getattr_, setattr_ from .shard_config import ShardConfig __all__ = ['ModelSharder', 'shard_model'] diff --git a/colossalai/shardformer/utils/__init__.py b/colossalai/shardformer/utils/__init__.py deleted file mode 100644 index b50e7b2f6d80..000000000000 --- a/colossalai/shardformer/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .utils import getattr_, hasattr_, setattr_ diff --git a/tests/test_shardformer/test_module/test_distcrossentropy.py b/tests/test_shardformer/test_layer/test_dist_crossentropy.py similarity index 87% rename from tests/test_shardformer/test_module/test_distcrossentropy.py rename to tests/test_shardformer/test_layer/test_dist_crossentropy.py index 9a19ec57821d..72e6e5cf26ed 100644 --- a/tests/test_shardformer/test_module/test_distcrossentropy.py +++ b/tests/test_shardformer/test_layer/test_dist_crossentropy.py @@ -4,7 +4,7 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.shardformer.layer.dist_crossentropy import applyDistCrossEntropy +from colossalai.shardformer.layer import cross_entropy_1d from colossalai.testing import rerun_if_address_is_in_use, spawn CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) @@ -25,7 +25,7 @@ def check_dist_crossentropy(rank, world_size, port, ignore_index): org_loss = F.cross_entropy(org_pred, org_labels) dist_pred = pred.chunk(world_size, -1)[rank] - dist_loss = applyDistCrossEntropy(dist_pred.to('cuda'), labels.to('cuda'), ignore_index=ignore_index) + dist_loss = cross_entropy_1d(dist_pred.to('cuda'), labels.to('cuda'), ignore_index=ignore_index) assert torch.allclose(org_loss, dist_loss, atol=1e-5), f"dist cross entropy loss is not equal to orgin loss\n{org_loss}\n{dist_loss}" diff --git a/tests/test_shardformer/test_layer/test_dropout.py b/tests/test_shardformer/test_layer/test_dropout.py index c48c11b36d91..c62d25d94aa4 100644 --- a/tests/test_shardformer/test_layer/test_dropout.py +++ b/tests/test_shardformer/test_layer/test_dropout.py @@ -3,7 +3,7 @@ import torch.nn as nn import colossalai -from colossalai.shardformer.layer.dropout import Dropout1D +from colossalai.shardformer.layer import Dropout1D from colossalai.testing import assert_equal, assert_not_equal, rerun_if_address_is_in_use, spawn diff --git a/tests/test_shardformer/test_layer/test_embedding.py b/tests/test_shardformer/test_layer/test_embedding.py index 462349ecb93b..70500008cfff 100644 --- a/tests/test_shardformer/test_layer/test_embedding.py +++ b/tests/test_shardformer/test_layer/test_embedding.py @@ -4,7 +4,7 @@ from torch.testing import assert_close import colossalai -from colossalai.shardformer.layer.layers import Embedding1D +from colossalai.shardformer.layer import Embedding1D from colossalai.testing import rerun_if_address_is_in_use, spawn diff --git a/tests/test_shardformer/test_layer/test_linear_1d.py b/tests/test_shardformer/test_layer/test_linear_1d.py index 2a3ce99384cb..00ecc37ce2fa 100644 --- a/tests/test_shardformer/test_layer/test_linear_1d.py +++ b/tests/test_shardformer/test_layer/test_linear_1d.py @@ -4,7 +4,7 @@ from torch.testing import assert_close import colossalai -from colossalai.shardformer.layer.layers import Linear1D_Col, Linear1D_Row +from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row from colossalai.testing import rerun_if_address_is_in_use, spawn diff --git a/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py index 3df53e8a8458..bee44a2fb109 100644 --- a/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py +++ b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py @@ -4,7 +4,7 @@ from torch.testing import assert_close import colossalai -from colossalai.shardformer.layer.layers import VocabParallelEmbedding1D +from colossalai.shardformer.layer import VocabParallelEmbedding1D from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn diff --git a/tests/test_shardformer/test_module/test_dropout.py b/tests/test_shardformer/test_module/test_dropout.py deleted file mode 100644 index 4a13eb61c1fc..000000000000 --- a/tests/test_shardformer/test_module/test_dropout.py +++ /dev/null @@ -1,51 +0,0 @@ -import pytest -import torch -import torch.nn.functional as F - -import colossalai -from colossalai.logging import disable_existing_loggers -from colossalai.shardformer.layer.dropout import Dropout1D -from colossalai.testing import rerun_if_address_is_in_use, spawn - -CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) - - -def check_dropout(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, port=port, host='localhost', backend='nccl') - - # prepare data - input = torch.randn(5, 4).to('cuda') - dropout = Dropout1D(p=0.4).to('cuda') - output_list = [] - # compare the dropout pattern in each device - for i in range(2): - output = dropout(input) - output_list.append(output) - dist_output_list = [torch.zeros(*output.shape).to('cuda') for _ in range(world_size)] - torch.distributed.all_gather(dist_output_list, output) - for j in range(world_size): - for k in range(world_size): - if j != k: - mask = torch.eq(dist_output_list[j], 0.0) == torch.eq(dist_output_list[k], 0.0) - assert torch.all( - mask - ) == False, f"The dropout pattern in each device is not unique\n{dist_output_list[j]}\n{dist_output_list[k]}" - # compare the dropout pattern in loacl device - for i in range(len(output_list)): - for j in range(len(output_list)): - if i != j: - mask = torch.eq(output_list[i], 0.0) == torch.eq(output_list[j], 0.0) - assert torch.all( - mask - ) == False, f"The dropout pattern in one device is not unique\n{output_list[i]}\n{output_list[j]}" - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_dropout(): - spawn(check_dropout, 2) - - -if __name__ == '__main__': - test_dropout() diff --git a/tests/test_shardformer/test_module/test_slicer.py b/tests/test_shardformer/test_module/test_slicer.py deleted file mode 100644 index c72a0357573b..000000000000 --- a/tests/test_shardformer/test_module/test_slicer.py +++ /dev/null @@ -1,78 +0,0 @@ -import pytest -import torch -import torch.nn.functional as F - -import colossalai -from colossalai.logging import disable_existing_loggers -from colossalai.shardformer.policies.basepolicy import Col_Layer, Layer, Row_Layer -from colossalai.shardformer.shard.shard_config import ShardConfig -from colossalai.shardformer.shard.slicer import Slicer -from colossalai.testing import rerun_if_address_is_in_use, spawn - -CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) - - -def check_slicer(rank, world_size, port, in_feature, out_feature): - disable_existing_loggers() - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, port=port, host='localhost', backend='nccl') - # initialize slicer - shardconfig = ShardConfig(rank=rank, world_size=world_size) - slicer = Slicer(shardconfig) - # initialize test data - weight = torch.randn(in_feature, out_feature) - bias = torch.randn(out_feature) - policy_layer_cls_list = [Layer, Col_Layer, Row_Layer] - n_cast_list = [None, 2, 3, 4] - # weight and bias - for n_cast in n_cast_list: - sliced_weight, sliced_bias = slicer.slice_weight_bias(weight, bias, policy_layer_cls=Layer, n_cast=n_cast) - expected_sliced_weight = weight - expected_sliced_bias = bias - assert torch.equal( - sliced_weight, expected_sliced_weight - ), f"In Layer case, weight: sliced_weight is not equal to expected_sliced_weight\norg:{weight}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" - assert torch.equal( - sliced_bias, expected_sliced_bias - ), f"In Layer case, bias: sliced_bias is not equal to expected_sliced_bias\norg:{bias}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" - - sliced_weight, sliced_bias = slicer.slice_weight_bias(weight, bias, policy_layer_cls=Col_Layer, n_cast=n_cast) - if (n_cast is None): - expected_sliced_weight = weight.chunk(world_size, dim=0)[rank] - expected_sliced_bias = bias.chunk(world_size)[rank] - else: - chunks = weight.chunk(world_size * n_cast, dim=0) - expected_sliced_weight = torch.cat([chunks[i] for i in range(rank, n_cast * world_size, world_size)], dim=0) - chunks = bias.chunk(world_size * n_cast, dim=0) - expected_sliced_bias = torch.cat([chunks[i] for i in range(rank, n_cast * world_size, world_size)]) - assert torch.equal( - sliced_weight, expected_sliced_weight - ), f"In Col_Layer {n_cast} cast case, weight: sliced_weight is not equal to expected_sliced_weight\norg:{weight}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" - assert torch.equal( - sliced_bias, expected_sliced_bias - ), f"In Col_Layer {n_cast} cast case, bias: sliced_bias is not equal to expected_sliced_bias\norg:{bias}\nsliced:{sliced_bias}\nexpected:{expected_sliced_bias}" - - sliced_weight, sliced_bias = slicer.slice_weight_bias(weight, bias, policy_layer_cls=Row_Layer, n_cast=n_cast) - if (n_cast is None): - expected_sliced_weight = weight.chunk(world_size, dim=1)[rank] - expected_sliced_bias = bias - else: - chunks = weight.chunk(world_size * n_cast, dim=1) - expected_sliced_weight = torch.cat([chunks[i] for i in range(rank, n_cast * world_size, world_size)], dim=1) - expected_sliced_bias = bias - assert torch.equal( - sliced_weight, expected_sliced_weight - ), f"In Row_Layer {n_cast} cast case, weight: sliced_weight is not equal to expected_sliced_weight\norg:{weight}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" - assert torch.equal( - sliced_bias, expected_sliced_bias - ), f"In Row_Layer {n_cast} cast case, bias: sliced_bias is not equal to expected_sliced_bias\norg:{bias}\nsliced:{sliced_weight}\nexpected:{expected_sliced_weight}" - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_slicer(): - args = dict(in_feature=24, out_feature=48) - spawn(check_slicer, nprocs=2, in_feature=args['in_feature'], out_feature=args['out_feature']) - - -if __name__ == '__main__': - test_slicer() From 7740c55c55ff95c107991e79be9bef5f3c82de75 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Thu, 22 Jun 2023 10:33:06 +0800 Subject: [PATCH 378/413] support kit use for bert/gpt test (#4055) * support kit use for bert test * support kit test for gpt2 --- colossalai/shardformer/policies/autopolicy.py | 20 ++- colossalai/shardformer/policies/bert.py | 23 ++- colossalai/shardformer/policies/gpt2.py | 81 +++++++++- tests/kit/model_zoo/transformers/bert.py | 140 +++++++++++++----- tests/kit/model_zoo/transformers/gpt.py | 69 +++++++-- .../test_model/test_shard_bert.py | 116 +++------------ .../test_model/test_shard_gpt2.py | 116 ++++----------- 7 files changed, 319 insertions(+), 246 deletions(-) diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 5e7a285e3285..b1b8c6156f9f 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -25,17 +25,19 @@ class PolicyLocation: _POLICY_LIST = { # BERT "transformers.models.bert.modeling_bert.BertModel": - PolicyLocation(file_name="bert", class_name="BertPolicy"), + PolicyLocation(file_name="bert", class_name="BertModelPolicy"), "transformers.models.bert.modeling_bert.BertForPreTraining": PolicyLocation(file_name="bert", class_name="BertForPretrainingPolicy"), - "transformers.models.bert.modeling_bert.BertForMaskedLM": - PolicyLocation(file_name="bert", class_name="BertForMaskedLMPolicy"), "transformers.models.bert.modeling_bert.BertLMHeadModel": PolicyLocation(file_name="bert", class_name="BertLMHeadModelPolicy"), - "transformers.models.bert.modeling_bert.BertForNextSentencePrediction": - PolicyLocation(file_name="bert", class_name="BertForNextSentencePredictionPolicy"), + "transformers.models.bert.modeling_bert.BertForMaskedLM": + PolicyLocation(file_name="bert", class_name="BertForMaskedLMPolicy"), "transformers.models.bert.modeling_bert.BertForSequenceClassification": PolicyLocation(file_name="bert", class_name="BertForSequenceClassificationPolicy"), + "transformers.models.bert.modeling_bert.BertForTokenClassification": + PolicyLocation(file_name="bert", class_name="BertForTokenClassificationPolicy"), + "transformers.models.bert.modeling_bert.BertForNextSentencePrediction": + PolicyLocation(file_name="bert", class_name="BertForNextSentencePredictionPolicy"), "transformers.models.bert.modeling_bert.BertForMultipleChoice": PolicyLocation(file_name="bert", class_name="BertForMultipleChoicePolicy"), @@ -58,6 +60,14 @@ class PolicyLocation: # GPT2 "transformers.models.gpt2.modeling_gpt2.GPT2Model": PolicyLocation(file_name="gpt2", class_name="GPT2ModelPolicy"), + "transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel": + PolicyLocation(file_name="gpt2", class_name="GPT2LMHeadModelPolicy"), + "transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModel": + PolicyLocation(file_name="gpt2", class_name="GPT2DoubleHeadsModelPolicy"), + "transformers.models.gpt2.modeling_gpt2.GPT2ForTokenClassification": + PolicyLocation(file_name="gpt2", class_name="GPT2ForTokenClassificationPolicy"), + "transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification": + PolicyLocation(file_name="gpt2", class_name="GPT2ForSequenceClassificationPolicy"), } diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index d5e8e01cf154..8649c0dbeaa6 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -131,8 +131,8 @@ def postprocess(self): return self.model -# BertForMaskedLM -class BertForMaskedLMPolicy(BertPolicy): +# BertLMHeadModel +class BertLMHeadModelPolicy(BertPolicy): def __init__(self) -> None: super().__init__() @@ -162,8 +162,8 @@ def postprocess(self): return self.model -# BertLMHeadModel -class BertLMHeadModelPolicy(BertPolicy): +# BertForMaskedLM +class BertForMaskedLMPolicy(BertPolicy): def __init__(self) -> None: super().__init__() @@ -193,15 +193,22 @@ def postprocess(self): return self.model -# BertForNextSentencePrediction -class BertForNextSentencePredictionPolicy(BertPolicy): +# BertForSequenceClassification +class BertForSequenceClassificationPolicy(BertPolicy): def __init__(self) -> None: super().__init__() -# BertForSequenceClassification -class BertForSequenceClassificationPolicy(BertPolicy): +# BertForTokenClassification +class BertForTokenClassificationPolicy(BertPolicy): + + def __init__(self) -> None: + super().__init__() + + +# BertForNextSentencePrediction +class BertForNextSentencePredictionPolicy(BertPolicy): def __init__(self) -> None: super().__init__() diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index da9e6b7bd32d..54ea2f6e3279 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -1,7 +1,9 @@ -from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model +import torch.nn as nn +from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2DoubleHeadsModel, GPT2LMHeadModel, GPT2Model import colossalai.shardformer.layer as col_nn +from .._utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -82,7 +84,6 @@ def module_policy(self): } def new_model_class(self): - return self.model def postprocess(self): @@ -94,3 +95,79 @@ class GPT2ModelPolicy(GPT2Policy): def __init__(self) -> None: super().__init__() + + +# GPT2LMHeadModel +class GPT2LMHeadModelPolicy(GPT2Policy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + module_policy = super().module_policy() + addon_module = { + GPT2LMHeadModel: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="lm_head", + target_module=col_nn.Linear1D_Col, + kwargs={"gather_output": True}) + ]) + } + module_policy.update(addon_module) + return module_policy + + def postprocess(self): + binding_map = {"transformer.wte.weight": "lm_head.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + param = nn.Parameter(param) + setattr_(self.model, k, param) + setattr_(self.model, v, param) + return self.model + + +# GPT22DoubleHeadsModel +class GPT2DoubleHeadsModelPolicy(GPT2Policy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + module_policy = super().module_policy() + addon_module = { + GPT2DoubleHeadsModel: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="lm_head", + target_module=col_nn.Linear1D_Col, + kwargs={"gather_output": True}) + ]) + } + module_policy.update(addon_module) + return module_policy + + def postprocess(self): + binding_map = {"transformer.wte.weight": "lm_head.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + param = nn.Parameter(param) + setattr_(self.model, k, param) + setattr_(self.model, v, param) + return self.model + + +# GPT2ForTokenClassification +class GPT2ForTokenClassificationPolicy(GPT2Policy): + + def __init__(self) -> None: + super().__init__() + + +# GPT2ForSequenceClassification +class GPT2ForSequenceClassificationPolicy(GPT2Policy): + + def __init__(self) -> None: + super().__init__() diff --git a/tests/kit/model_zoo/transformers/bert.py b/tests/kit/model_zoo/transformers/bert.py index 99135704da70..d2d3de7b7bee 100644 --- a/tests/kit/model_zoo/transformers/bert.py +++ b/tests/kit/model_zoo/transformers/bert.py @@ -6,83 +6,147 @@ # =============================== # Register single-sentence BERT # =============================== -BATCH_SIZE = 2 -SEQ_LENGTH = 16 -def data_gen_fn(): - input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) - token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) - attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) +# define data gen function +def data_gen(): + # Generated from following code snippet + # + # from transformers import BertTokenizer + # input = 'Hello, my dog is cute' + # tokenized_input = tokenizer(input, return_tensors='pt') + # input_ids = tokenized_input['input_ids'] + # attention_mask = tokenized_input['attention_mask'] + # token_type_ids = tokenized_input['token_type_ids'] + input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]], dtype=torch.int64) + token_type_ids = torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) +def data_gen_for_lm(): + # LM data gen + # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels` + data = data_gen() + data['labels'] = data['input_ids'].clone() + return data + + +def data_gen_for_pretraining(): + # pretraining data gen + # `next_sentence_label` is the label for next sentence prediction, 0 or 1 + data = data_gen_for_lm() + data['next_sentence_label'] = torch.tensor([1], dtype=torch.int64) + return data + + +def data_gen_for_sequence_classification(): + # sequence classification data gen + # `labels` is the label for sequence classification, 0 or 1 + data = data_gen() + data['labels'] = torch.tensor([1], dtype=torch.int64) + return data + + +def data_gen_for_token_classification(): + # token classification data gen + # `labels` is the type not the token id for token classification, 0 or 1 + data = data_gen() + data['labels'] = torch.tensor([[1, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64) + return data + + +def data_gen_for_mcq(): + # multiple choice question data gen + # Generated from following code snippet + # + # tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased") + # prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." + # choice0 = "It is eaten with a fork and a knife." + # choice1 = "It is eaten while held in the hand." + # data = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True) + # data = {k: v.unsqueeze(0) for k, v in encoding.items()} + # data['labels'] = torch.tensor([0], dtype=torch.int64) + input_ids = torch.tensor([[[ + 101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591, + 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102 + ], + [ + 101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, + 4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096, + 2218, 1999, 1996, 2192, 1012, 102, 0 + ]]]) + token_type_ids = torch.tensor( + [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]]) + attention_mask = torch.tensor( + [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]]) + labels = torch.tensor([0], dtype=torch.int64) + + return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels) + + +# define output transform function output_transform_fn = lambda x: x -config = transformers.BertConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4, intermediate_size=256) +# define loss funciton +loss_fn_for_bert_model = lambda x: x.pooler_output.mean() +loss_fn = lambda x: x.loss + +config = transformers.BertConfig(hidden_size=128, + num_hidden_layers=2, + num_attention_heads=4, + intermediate_size=256, + hidden_dropout_prob=0, + attention_probs_dropout_prob=0) # register the BERT variants model_zoo.register(name='transformers_bert', model_fn=lambda: transformers.BertModel(config), - data_gen_fn=data_gen_fn, + data_gen_fn=data_gen, output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_bert_model, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_bert_for_pretraining', model_fn=lambda: transformers.BertForPreTraining(config), - data_gen_fn=data_gen_fn, + data_gen_fn=data_gen_for_pretraining, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_bert_lm_head_model', model_fn=lambda: transformers.BertLMHeadModel(config), - data_gen_fn=data_gen_fn, + data_gen_fn=data_gen_for_lm, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_bert_for_masked_lm', model_fn=lambda: transformers.BertForMaskedLM(config), - data_gen_fn=data_gen_fn, + data_gen_fn=data_gen_for_lm, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_bert_for_sequence_classification', model_fn=lambda: transformers.BertForSequenceClassification(config), - data_gen_fn=data_gen_fn, + data_gen_fn=data_gen_for_sequence_classification, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_bert_for_token_classification', model_fn=lambda: transformers.BertForTokenClassification(config), - data_gen_fn=data_gen_fn, + data_gen_fn=data_gen_for_token_classification, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) - - -# =============================== -# Register multi-sentence BERT -# =============================== -def data_gen_for_next_sentence(): - tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased") - prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." - next_sentence = "The sky is blue due to the shorter wavelength of blue light." - encoding = tokenizer(prompt, next_sentence, return_tensors="pt") - return encoding - - -def data_gen_for_mcq(): - tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased") - prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." - choice0 = "It is eaten with a fork and a knife." - choice1 = "It is eaten while held in the hand." - encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True) - encoding = {k: v.unsqueeze(0) for k, v in encoding.items()} - return encoding - - -# register the following models model_zoo.register(name='transformers_bert_for_next_sentence', model_fn=lambda: transformers.BertForNextSentencePrediction(config), - data_gen_fn=data_gen_for_next_sentence, + data_gen_fn=data_gen_for_sequence_classification, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_bert_for_mcq', model_fn=lambda: transformers.BertForMultipleChoice(config), data_gen_fn=data_gen_for_mcq, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index 5ed4fbe70dc9..c598fa8f48e0 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -11,47 +11,86 @@ def data_gen(): - input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) - token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) - attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) - return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) + # Generated from following code snippet + # + # from transformers import GPT2Tokenizer + # input = 'Hello, my dog is cute' + # tokenized_input = tokenizer(input, return_tensors='pt') + # input_ids = tokenized_input['input_ids'] + # attention_mask = tokenized_input['attention_mask'] + input_ids = torch.tensor([[15496, 11, 616, 3290, 318, 13779]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]], dtype=torch.int64) + return dict(input_ids=input_ids, attention_mask=attention_mask) -def seq_classification_data_gen(): - # batch sizes should be 1 if no padding token is defined. - input_ids = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64) - token_type_ids = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64) - attention_mask = torch.zeros((1, SEQ_LENGTH), dtype=torch.int64) - return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) +def data_gen_for_lm(): + # LM data gen + # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels` + data = data_gen() + data['labels'] = data['input_ids'].clone() + return data +def data_gen_for_token_classification(): + # token classification data gen + # `labels` is the type not the token id for token classification, 0 or 1 + data = data_gen() + data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 0]], dtype=torch.int64) + return data + + +def data_gen_for_sequence_classification(): + # sequence classification data gen + data = data_gen() + data['labels'] = torch.tensor([0], dtype=torch.int64) + return data + + +# define output transform function output_transform_fn = lambda x: x -config = transformers.GPT2Config(n_position=64, n_layer=2, n_head=4) +# define loss function +loss_fn_for_gpt2_model = lambda x: x.last_hidden_state.mean() +loss_fn = lambda x: x.loss + +config = transformers.GPT2Config(n_layer=2, + n_head=4, + vocab_size=50258, + attn_pdrop=0, + embd_pdrop=0, + resid_pdrop=0, + summary_first_dropout=0, + hidden_dropout=0, + problem_type="single_label_classification") # register the following models model_zoo.register(name='transformers_gpt', model_fn=lambda: transformers.GPT2Model(config), data_gen_fn=data_gen, output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_gpt2_model, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_gpt_lm', model_fn=lambda: transformers.GPT2LMHeadModel(config), - data_gen_fn=data_gen, + data_gen_fn=data_gen_for_lm, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_gpt_double_heads', model_fn=lambda: transformers.GPT2DoubleHeadsModel(config), - data_gen_fn=data_gen, + data_gen_fn=data_gen_for_lm, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_gpt_for_token_classification', model_fn=lambda: transformers.GPT2ForTokenClassification(config), - data_gen_fn=data_gen, + data_gen_fn=data_gen_for_token_classification, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_gpt_for_sequence_classification', model_fn=lambda: transformers.GPT2ForSequenceClassification(config), - data_gen_fn=seq_classification_data_gen, + data_gen_fn=data_gen_for_sequence_classification, output_transform_fn=output_transform_fn, + loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 043ed1a74a27..ad98e3d073d4 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -1,86 +1,30 @@ -import copy -import os - import pytest import torch -from transformers import ( - AutoTokenizer, - BertConfig, - BertForMaskedLM, - BertForNextSentencePrediction, - BertForPreTraining, - BertForSequenceClassification, - BertLMHeadModel, - BertModel, -) import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.shardformer import ShardConfig, ShardFormer -from colossalai.testing import rerun_if_address_is_in_use, spawn - -os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' -CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) -tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - - -def build_model(world_size, model_fn): - config = BertConfig() - config.hidden_dropout_prob = 0 - config.attention_probs_dropout_prob = 0 - - org_model = model_fn(config=config) - org_model_forshard = copy.deepcopy(org_model) - - org_model.to('cuda') - # TODO: no need to transfer to cuda - org_model_forshard.to('cuda') - shard_config = ShardConfig(tensor_parallel_size=world_size,) - shard_former = ShardFormer(shard_config=shard_config) - shard_former.init_distributed() - sharded_model = shard_former.shard_model(org_model_forshard).to('cuda') - - return org_model, sharded_model - +from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward -def check_forward(org_model, sharded_model): - input = 'Hello, my dog is cute' - tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') - #orgin model - org_model.eval() - org_out = org_model(**tokenized_input) +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output) - #shard model - sharded_model.eval() - shard_out = sharded_model(**tokenized_input) - - assert torch.allclose( - org_out[0], shard_out[0], - atol=1e-5), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}" - - -def check_backward(org_model, sharded_model): - # prepare input - input = 'Hello, my dog is cute' - tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') - labels = tokenized_input['input_ids'].clone() - labels[labels == tokenizer.pad_token_id] = -100 - tokenized_input['labels'] = labels - - #orgin model - org_model.train() - org_out = org_model(**tokenized_input) - org_loss = org_out.loss + # do backward org_loss.backward() - org_grad = org_model.bert.encoder.layer[0].attention.self.query.weight.grad - - #shard model - sharded_model.train() - shard_out = sharded_model(**tokenized_input) - shard_loss = shard_out.loss shard_loss.backward() - shard_grad = sharded_model.bert.encoder.layer[0].attention.self.query.weight.grad + + # check grad equality + if org_model.__class__.__name__ == 'BertModel': + org_grad = org_model.encoder.layer[0].attention.self.query.weight.grad + shard_grad = sharded_model.encoder.layer[0].attention.self.query.weight.grad + else: + org_grad = org_model.bert.encoder.layer[0].attention.self.query.weight.grad + shard_grad = sharded_model.bert.encoder.layer[0].attention.self.query.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) @@ -89,36 +33,24 @@ def check_backward(org_model, sharded_model): assert torch.allclose(org_loss, shard_loss, atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" def check_bert(rank, world_size, port): disable_existing_loggers() - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - forward_list = [ - BertForMaskedLM, - BertForPreTraining, - BertLMHeadModel, + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - # TODO: do not work yet - # BertModel, - # BertForSequenceClassification - # BertForNextSentencePrediction, - ] - backward_lsit = [BertForMaskedLM, BertLMHeadModel] - - for model_fn in forward_list: + sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(world_size, model_fn) - check_forward(org_model, sharded_model) - - if model_fn in backward_lsit: - check_backward(org_model, sharded_model) + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - torch.cuda.empty_cache() + torch.cuda.empty_cache() @pytest.mark.dist @rerun_if_address_is_in_use() +@clear_cache_before_run() def test_bert(): spawn(check_bert, 2) diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 2f679b83f99b..0c07f44401c7 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -1,117 +1,61 @@ -import copy -import os - import pytest import torch -from transformers import AutoTokenizer, GPT2Config, GPT2Model import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.shardformer import ShardConfig, ShardFormer -from colossalai.testing import rerun_if_address_is_in_use, spawn - -os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' -CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),) -tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - - -def build_model(world_size, model_fn): - config = GPT2Config() - config.attn_pdrop = 0 - config.embd_pdrop = 0 - config.resid_pdrop = 0 - config.summary_first_dropout - - org_model = model_fn(config=config) - org_model_forshard = copy.deepcopy(org_model) - - org_model.to('cuda') - # TODO: no need to transfer to cuda - org_model_forshard.to('cuda') - shard_config = ShardConfig(tensor_parallel_size=world_size,) - shard_former = ShardFormer(shard_config=shard_config) - shard_former.init_distributed() - sharded_model = shard_former.shard_model(org_model_forshard).to('cuda') - - return org_model, sharded_model - +from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward -def check_forward(org_model, sharded_model): - input = 'Hello, my dog is cute' - tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') - #orgin model - org_model.eval() - org_out = org_model(**tokenized_input) +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) - #shard model - sharded_model.eval() - shard_out = sharded_model(**tokenized_input) - - assert torch.allclose( - org_out[0], shard_out[0], - atol=1e-5), f"shard model output is not equal to orgin model output\n{org_out[0]}\n{shard_out[0]}" - - -def check_backward(org_model, sharded_model): - # prepare input - input = 'Hello, my dog is cute' - tokenized_input = tokenizer(input, return_tensors='pt').to('cuda') - labels = tokenized_input['input_ids'].clone() - labels[labels == tokenizer.pad_token_id] = -100 - # tokenized_input['labels'] = labels - - #orgin model - org_model.train() - org_out = org_model(**tokenized_input) - org_loss = org_out.loss + # do backward org_loss.backward() - org_grad = org_model.h[0].attn.c_attn.weight.grad - - #shard model - sharded_model.train() - shard_out = sharded_model(**tokenized_input) - shard_loss = shard_out.loss shard_loss.backward() - shard_grad = sharded_model.h[0].attn.c_attn.weight.grad + + # check grad equality + if org_model.__class__.__name__ == 'GPT2Model': + org_grad = org_model.h[0].attn.c_attn.weight.grad + shard_grad = sharded_model.h[0].attn.c_attn.weight.grad.transpose(0, 1).contiguous() + else: + org_grad = org_model.transformer.h[0].mlp.c_fc.weight.grad + shard_grad = sharded_model.transformer.h[0].mlp.c_fc.weight.grad.transpose(0, 1).contiguous() shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + all_shard_grad = torch.cat(shard_grad_list, dim=1) assert torch.allclose(org_loss, shard_loss, atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" -def check_bert(rank, world_size, port): +def check_gpt2(rank, world_size, port): disable_existing_loggers() - colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - forward_list = [ - GPT2Model, + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - # TODO: do not work yet - # BertModel, - # BertForSequenceClassification - # BertForNextSentencePrediction, - ] - backward_lsit = [] - - for model_fn in forward_list: + sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + print(name) + # if name == 'transformers_gpt': + # continue org_model, sharded_model = build_model(world_size, model_fn) - check_forward(org_model, sharded_model) - - if model_fn in backward_lsit: - check_backward(org_model, sharded_model) + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - torch.cuda.empty_cache() + torch.cuda.empty_cache() @pytest.mark.dist @rerun_if_address_is_in_use() +@clear_cache_before_run() def test_gpt2(): - spawn(check_bert, 2) + spawn(check_gpt2, 2) if __name__ == "__main__": From 8eb09a4c6946b40930cffc7f2d9bb150ee714b63 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Thu, 22 Jun 2023 11:42:11 +0800 Subject: [PATCH 379/413] [shardformer] support module saving and loading (#4062) * [shardformer] support module saving and loading * polish code --- colossalai/checkpoint_io/utils.py | 4 +- colossalai/lazy/lazy_init.py | 7 +- colossalai/shardformer/layer/embedding.py | 25 +- colossalai/shardformer/layer/linear.py | 30 +- colossalai/shardformer/layer/linear_conv.py | 2 - .../shardformer/layer/parallel_module.py | 142 ++++++++++ colossalai/tensor/d_tensor/__init__.py | 24 ++ colossalai/tensor/d_tensor/api.py | 263 ++++++++++++++++-- colossalai/tensor/d_tensor/layout.py | 3 +- .../tensor/d_tensor/layout_converter.py | 13 +- colossalai/tensor/d_tensor/utils.py | 2 +- test.py | 1 + tests/test_lazy/lazy_init_utils.py | 9 +- .../test_layer/test_embedding.py | 4 + .../test_layer/test_linear_1d.py | 12 +- .../test_vocab_parallel_embedding_1d.py | 6 +- .../test_dtensor/test_comm_spec.py | 3 - .../test_tensor/test_dtensor/test_dtensor.py | 43 ++- .../test_dtensor/test_layout_converter.py | 2 +- 19 files changed, 493 insertions(+), 102 deletions(-) create mode 100644 test.py diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 3dada00cd9b5..68981dff0d0a 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -10,7 +10,7 @@ import torch.nn as nn from torch.optim import Optimizer -from colossalai.tensor.d_tensor.d_tensor import DTensor +from colossalai.tensor.d_tensor import is_distributed_tensor SAFE_WEIGHTS_NAME = "model.safetensors" WEIGHTS_NAME = "pytorch_model.bin" @@ -99,7 +99,7 @@ def shard_model_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) for key, weight in state_dict.items(): ret_block = None ret_block_size = 0 - if type(weight) != DTensor: + if is_distributed_tensor(weight): weight_size = calculate_tensor_size(weight) # If this weight is going to tip up over the maximal size, we split. diff --git a/colossalai/lazy/lazy_init.py b/colossalai/lazy/lazy_init.py index 76f550dc4392..1e45eced5f34 100644 --- a/colossalai/lazy/lazy_init.py +++ b/colossalai/lazy/lazy_init.py @@ -8,8 +8,9 @@ from torch.utils._pytree import tree_map from colossalai._analyzer._subclasses import MetaTensor -from colossalai.tensor.d_tensor.d_tensor import DTensor -from colossalai.tensor.d_tensor.layout import Layout +from colossalai.device.device_mesh import DeviceMesh +from colossalai.tensor.d_tensor import distribute_tensor +from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec # reference: https://pytorch.org/cppdocs/notes/tensor_creation.html _NORMAL_FACTORY = [ @@ -183,7 +184,7 @@ def distribute(self, layout: Layout) -> torch.Tensor: """ target = self._materialize_data() self.clean() - local_tensor = DTensor(target, layout).local_tensor + local_tensor = distribute_tensor(target, device_mesh, sharding_spec) return _convert_cls(self, local_tensor) def clean(self) -> None: diff --git a/colossalai/shardformer/layer/embedding.py b/colossalai/shardformer/layer/embedding.py index 8b9fb03ec798..23601a04a27b 100644 --- a/colossalai/shardformer/layer/embedding.py +++ b/colossalai/shardformer/layer/embedding.py @@ -13,8 +13,7 @@ from colossalai.nn import init as init from colossalai.nn.layer.utils import divide -from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise -from colossalai.utils.cuda import get_current_device +from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise, sharded_tensor_to_param from ._operation import gather_forward_split_backward, reduce_input from .parallel_module import ParallelModule @@ -69,18 +68,17 @@ def __init__(self, self.num_embeddings = num_embeddings self.embedding_dim = embedding_dim self.process_group = process_group - self.num_partitions = dist.get_world_size(process_group) - self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions) self.padding_idx = padding_idx self.embed_args = args self.embed_kwargs = kwargs self.gather_output = gather_output - if device is None: - device = get_current_device() - - self.weight = Parameter(torch.empty((num_embeddings, self.embed_dim_per_partition), device=device, dtype=dtype)) + # Parameters. + factory_kwargs = {'device': device, 'dtype': dtype} + weight = torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs) + sharded_weight = shard_colwise(weight, process_group) + self.weight = sharded_tensor_to_param(sharded_weight) # offset the seed with randomizer index and rank seed = torch.random.initial_seed() @@ -194,7 +192,7 @@ def __init__(self, **kwargs): super().__init__() self.num_embeddings = num_embeddings - self.embed_dim = embedding_dim + self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.embed_args = args self.embed_kwargs = kwargs @@ -208,8 +206,11 @@ def __init__(self, self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition - self.weight = Parameter( - torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=device, dtype=dtype)) + # parameter + factory_kwargs = {'device': device, 'dtype': dtype} + weight = torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs) + sharded_weight = shard_rowwise(weight, process_group) + self.weight = sharded_tensor_to_param(sharded_weight) # offset the seed with randomizer index and rank seed = torch.random.initial_seed() @@ -252,7 +253,7 @@ def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, def reset_parameters(self, weight_initializer) -> None: with self.randomizer.fork_rng(enable_cpu=True): - fan_in, fan_out = self.num_embeddings, self.embed_dim + fan_in, fan_out = self.num_embeddings, self.embedding_dim weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) self._fill_padding_idx_with_zero() diff --git a/colossalai/shardformer/layer/linear.py b/colossalai/shardformer/layer/linear.py index b87981c6db42..912be26b99ba 100644 --- a/colossalai/shardformer/layer/linear.py +++ b/colossalai/shardformer/layer/linear.py @@ -14,7 +14,7 @@ from colossalai.nn import init as init from colossalai.nn.layer.utils import divide -from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise +from colossalai.tensor.d_tensor import shard_colwise, shard_rowwise, sharded_tensor_to_param from colossalai.utils.cuda import get_current_device from ._operation import ( @@ -76,22 +76,21 @@ def __init__(self, self.skip_bias_add = skip_bias_add self.device = device self.process_group = process_group - self.num_partitions = dist.get_world_size(self.process_group) if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') - self.out_features_per_partition = divide(out_features, self.num_partitions) - # Parameters. - # Initialize weight. - if device is None: - device = get_current_device() factory_kwargs = {'device': device, 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) + + weight = torch.empty(self.out_features, self.in_features, **factory_kwargs) + sharded_weight = shard_rowwise(weight, self.process_group) + self.weight = sharded_tensor_to_param(sharded_weight) if bias: - self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) + bias = torch.empty(self.out_features, **factory_kwargs) + sharded_bias = shard_colwise(bias, self.process_group) + self.bias = sharded_tensor_to_param(sharded_bias) else: self.bias = None @@ -128,7 +127,6 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis *args, **kwargs) - # TODO: copy the sharded weights with torch.no_grad(): # the weigh to the linear layer is a transpose # thus shard on row is equal to shard on column @@ -137,7 +135,6 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis if bias: sharded_bias = shard_colwise(module.bias.data, process_group) linear_1d.bias.copy_(sharded_bias) - return linear_1d def reset_parameters(self, weight_initializer, bias_initializer) -> None: @@ -212,21 +209,20 @@ def __init__(self, self.parallel_input = parallel_input self.skip_bias_add = skip_bias_add self.process_group = process_group - self.num_partitions = dist.get_world_size(self.process_group) if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') - # Divide the weight matrix along the last dimension. - self.input_size_per_partition = divide(in_features, self.num_partitions) - # Parameters. # Initialize weight. if device is None: device = get_current_device() factory_kwargs = {'device': device, 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) + + weight = torch.empty(self.out_features, self.in_features, **factory_kwargs) + sharded_weight = shard_colwise(weight, self.process_group) + self.weight = sharded_tensor_to_param(sharded_weight) if self.stream_chunk_num > 1: # TODO() work for inference only @@ -340,3 +336,5 @@ def forward(self, input_: Tensor) -> Tensor: return output else: return output, self.bias + return output, self.bias + return output, self.bias diff --git a/colossalai/shardformer/layer/linear_conv.py b/colossalai/shardformer/layer/linear_conv.py index b4599f48942d..2adfc182895e 100644 --- a/colossalai/shardformer/layer/linear_conv.py +++ b/colossalai/shardformer/layer/linear_conv.py @@ -31,7 +31,6 @@ class LinearConv1D_Col(ParallelModule): r"""Linear layer with column parallelism. - Specially created for HuggingFace's GPT2 model. The linear layer is defined as :math:`Y = XA + b`. A is parallelized along its second dimension as :math:`A = [A_1, ..., A_p]`. This layer is used to fit `Conv1D` layer in gpt2 of huggingface. @@ -189,7 +188,6 @@ def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: class LinearConv1D_Row(ParallelModule): r""" Linear layer with row parallelism - Specially created for HuggingFace's GPT2 model. Args: in_features (int): size of each input sample. diff --git a/colossalai/shardformer/layer/parallel_module.py b/colossalai/shardformer/layer/parallel_module.py index c68cd57786ab..5edcb9dde748 100644 --- a/colossalai/shardformer/layer/parallel_module.py +++ b/colossalai/shardformer/layer/parallel_module.py @@ -1,11 +1,23 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- +import itertools from abc import ABC, abstractmethod from typing import List, Union +import torch import torch.nn as nn from torch.distributed import ProcessGroup +from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX, Module + +from colossalai.tensor.d_tensor import ( + distribute_tensor, + get_device_mesh, + get_sharding_spec, + is_distributed_tensor, + sharded_tensor_to_param, + to_global, +) __all__ = ['ParallelModule'] @@ -25,3 +37,133 @@ def from_native_module(module: nn.Module, in the ith axis of the device mesh. Defaults to None, which means the global process group. """ pass + + def _save_to_state_dict(self, destination, prefix, keep_vars): + r"""Saves module state to `destination` dictionary, containing a state + of the module, but not its descendants. This is called on every + submodule in :meth:`~torch.nn.Module.state_dict`. + + In rare cases, subclasses can achieve class-specific behavior by + overriding this method with custom logic. + + Args: + destination (dict): a dict where state will be stored + prefix (str): the prefix for parameters and buffers used in this + module + """ + for name, param in self._parameters.items(): + if param is not None: + param_ = param if keep_vars else param.detach() + + if is_distributed_tensor(param_): + destination[prefix + name] = to_global(param_) + else: + destination[prefix + name] = param_ + + for name, buf in self._buffers.items(): + if buf is not None and name not in self._non_persistent_buffers_set: + destination[prefix + name] = buf if keep_vars else buf.detach() + extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX + if getattr(self.__class__, "get_extra_state", Module.get_extra_state) is not Module.get_extra_state: + destination[extra_state_key] = self.get_extra_state() + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, + error_msgs): + r"""Copies parameters and buffers from :attr:`state_dict` into only + this module, but not its descendants. This is called on every submodule + in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this + module in input :attr:`state_dict` is provided as :attr:`local_metadata`. + For state dicts without metadata, :attr:`local_metadata` is empty. + Subclasses can achieve class-specific backward compatible loading using + the version number at `local_metadata.get("version", None)`. + + .. note:: + :attr:`state_dict` is not the same object as the input + :attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. So + it can be modified. + + Args: + state_dict (dict): a dict containing parameters and + persistent buffers. + prefix (str): the prefix for parameters and buffers used in this + module + local_metadata (dict): a dict containing the metadata for this module. + See + strict (bool): whether to strictly enforce that the keys in + :attr:`state_dict` with :attr:`prefix` match the names of + parameters and buffers in this module + missing_keys (list of str): if ``strict=True``, add missing keys to + this list + unexpected_keys (list of str): if ``strict=True``, add unexpected + keys to this list + error_msgs (list of str): error messages should be added to this + list, and will be reported together in + :meth:`~torch.nn.Module.load_state_dict` + """ + for hook in self._load_state_dict_pre_hooks.values(): + hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) + + persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set} + local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items()) + local_state = {k: v for k, v in local_name_params if v is not None} + + for name, param in local_state.items(): + key = prefix + name + + if key in state_dict: + input_param = state_dict[key] + if not torch.overrides.is_tensor_like(input_param): + error_msgs.append('While copying the parameter named "{}", ' + 'expected torch.Tensor or Tensor-like object from checkpoint but ' + 'received {}'.format(key, type(input_param))) + continue + + if is_distributed_tensor(param): + # shard the input param + device_mesh = get_device_mesh(param) + sharding_spec = get_sharding_spec(param) + sharded_tensor = distribute_tensor(input_param, device_mesh, sharding_spec) + input_param = sharded_tensor_to_param(sharded_tensor) + + # This is used to avoid copying uninitialized parameters into + # non-lazy modules, since they dont have the hook to do the checks + # in such case, it will error when accessing the .shape attribute. + is_param_lazy = torch.nn.parameter.is_lazy(param) + # Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+ + if not is_param_lazy and len(param.shape) == 0 and len(input_param.shape) == 1: + input_param = input_param[0] + + if not is_param_lazy and input_param.shape != param.shape: + # local shape should match the one in checkpoint + error_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, ' + 'the shape in current model is {}.'.format(key, input_param.shape, param.shape)) + continue + + try: + with torch.no_grad(): + param.copy_(input_param) + except Exception as ex: + error_msgs.append('While copying the parameter named "{}", ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}, ' + 'an exception occurred : {}.'.format(key, param.size(), input_param.size(), + ex.args)) + elif strict: + missing_keys.append(key) + + extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX + if getattr(self.__class__, "set_extra_state", Module.set_extra_state) is not Module.set_extra_state: + if extra_state_key in state_dict: + self.set_extra_state(state_dict[extra_state_key]) + elif strict: + missing_keys.append(extra_state_key) + elif strict and (extra_state_key in state_dict): + unexpected_keys.append(extra_state_key) + + if strict: + for key in state_dict.keys(): + if key.startswith(prefix) and key != extra_state_key: + input_name = key[len(prefix):] + input_name = input_name.split('.', 1)[0] # get the name of param/buffer/child + if input_name not in self._modules and input_name not in local_state: + unexpected_keys.append(key) diff --git a/colossalai/tensor/d_tensor/__init__.py b/colossalai/tensor/d_tensor/__init__.py index e69de29bb2d1..52eae0e14877 100644 --- a/colossalai/tensor/d_tensor/__init__.py +++ b/colossalai/tensor/d_tensor/__init__.py @@ -0,0 +1,24 @@ +from .api import ( + compute_global_numel, + distribute_tensor, + get_device_mesh, + get_global_shape, + get_layout, + get_sharding_spec, + is_distributed_tensor, + is_sharded, + redistribute, + shard_colwise, + shard_rowwise, + sharded_tensor_to_param, + to_global, +) +from .layout import Layout +from .sharding_spec import ShardingSpec + +__all__ = [ + 'is_distributed_tensor', 'distribute_tensor', 'to_global', 'is_sharded', 'shard_rowwise', 'shard_colwise', + 'sharded_tensor_to_param', 'compute_global_numel', 'get_sharding_spec', 'get_global_shape', 'get_device_mesh', + 'redistribute', 'get_layout' + 'Layout', 'ShardingSpec' +] diff --git a/colossalai/tensor/d_tensor/api.py b/colossalai/tensor/d_tensor/api.py index b58edadfef20..a38e5e6b7184 100644 --- a/colossalai/tensor/d_tensor/api.py +++ b/colossalai/tensor/d_tensor/api.py @@ -1,3 +1,6 @@ +import copy +import operator +from functools import reduce from typing import Union import torch @@ -6,13 +9,165 @@ from colossalai.device.device_mesh import DeviceMesh -from .d_tensor import DTensor +from .layout import Layout +from .layout_converter import LayoutConverter from .sharding_spec import ShardingSpec +layout_converter = LayoutConverter() -def shard_rowwise(tensor: torch.Tensor, - group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None, - inplace: bool = False) -> DTensor: + +def is_distributed_tensor(tensor: torch.Tensor) -> bool: + """ + Check whether the given tensor is a distributed tensor. + + Args: + tensor (torch.Tensor): The tensor to be checked. + + Returns: + bool: Whether the given tensor is a distributed tensor. + """ + return hasattr(tensor, "dist_layout") + + +def is_sharded(dtensor: torch.Tensor) -> bool: + """ + Check if a tensor is sharded. + + Args: + tensor (torch.Tensor): The tensor to be checked. + + Returns: + bool: True if the tensor is sharded, False otherwise. + """ + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + return list(dtensor.shape) == list(dtensor.dist_layout.global_shape) + + +def _hijack_detach_and_clone(dtensor: torch.Tensor) -> torch.Tensor: + """ + Hijack the detach and clone methods of the tensor to make sure the dist_layout is copied. + + Args: + tensor (torch.Tensor): The tensor to be hijacked. + + Returns: + torch.Tensor: The hijacked tensor. + """ + dtensor._old_detach = dtensor.detach + dtensor._old_clone = dtensor.clone + + def new_detach(self): + t_ = self._old_detach() + t_.dist_layout = copy.deepcopy(self.dist_layout) + return t_ + + def new_clone(self, *args, **kwargs): + t_ = self._old_clone(*args, **kwargs) + t_.dist_layout = copy.deepcopy(self.dist_layout) + return t_ + + # bind the new methods to the tensor + dtensor.detach = new_detach.__get__(dtensor) + dtensor.clone = new_clone.__get__(dtensor) + return dtensor + + +def _construct_default_sharding_spec(tensor: torch.Tensor,) -> ShardingSpec: + ''' + Construct the default sharding specification for the tensor. + + Args: + tensor (`torch.Tensor`): the tensor to be sharded. + + Returns: + A `ShardingSpec` object without any sharding specified. + ''' + return ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={}) + + +def _apply_layout(tensor, layout): + ''' + Apply the layout to the local tensor during initializing process. + ''' + # layout converter requires a source and target laytout + # we construct the source layer for an unsharded tensor + # and use self.dist_layer as the targer layout for the sharded tensor + source_spec = _construct_default_sharding_spec(tensor) + source_layout = Layout(device_mesh=layout.device_mesh, sharding_spec=source_spec, global_shape=tensor.shape) + sharded_tensor = layout_converter.apply(tensor=tensor, source_layout=source_layout, target_layout=layout) + return sharded_tensor + + +def distribute_tensor(tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> torch.Tensor: + """ + Convert the given tensor to a distributed tensor. + + Args: + tensor (torch.Tensor): The tensor to be converted. + device_mesh (DeviceMesh): The device mesh for abstraction of the compute devices. + sharding_spec (ShardingSpec): The sharding specification which describes how the tensor will be sharded. + + Returns: + torch.Tensor: The distributed tensor. + """ + assert not is_distributed_tensor(tensor), 'The input tensor is already a distributed tensor.' + dist_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=tensor.shape) + + # shard tensor + sharded_tensor = _apply_layout(tensor, dist_layout) + + # hack some tensor methods + _hijack_detach_and_clone(sharded_tensor) + + return sharded_tensor + + +def redistribute(dtensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> None: + ''' + Convert the layout of the tensor from source_spec to target_spec. + This will update the `local_tensor` and `dist_layout` in place. + + Args: + dtensor (torch.Tensor): the distributed tensor to be converted. + device_mesh (DeviceMesh): the device mesh for abstraction of the compute devices. + target_layout (Layout): the target layout specification. + ''' + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + global_shape = get_global_shape(dtensor) + target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=global_shape) + resharded_tensor = layout_converter.apply(tensor=dtensor, + source_layout=dtensor.dist_layout, + target_layout=target_layout) + return resharded_tensor + + +def to_global(dtensor: torch.Tensor) -> torch.Tensor: + """ + Convert a distributed tensor to the global tensor with the given layout. + This function returns a native `torch.Tensor` object. + + Args: + dtensor (torch.Tensor): the distributed tensor to be converted. + + Returns: + torch.Tensor: the global tensor. + """ + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + layout_converter = LayoutConverter() + + global_sharding_spec = ShardingSpec(dtensor.dim(), {}) + device_mesh = get_device_mesh(dtensor) + global_shape = get_global_shape(dtensor) + global_layout = Layout(device_mesh=device_mesh, sharding_spec=global_sharding_spec, global_shape=global_shape) + + global_tensor = layout_converter.apply(dtensor, dtensor.dist_layout, global_layout) + return global_tensor + + +def shard_rowwise( + tensor: torch.Tensor, + group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None, +) -> torch.Tensor: """ Shard the first dim of the given tensor. @@ -24,7 +179,7 @@ def shard_rowwise(tensor: torch.Tensor, inplace (bool, optional): Whether to shard the tensor in-place. Defaults to False. Returns: - DTensor: The sharded tensor. + torch.Tensor: The sharded tensor. """ # if the group_or_device_mesh is None, we shard the tensor with respect to the global process group if group_or_device_mesh is None: @@ -35,17 +190,13 @@ def shard_rowwise(tensor: torch.Tensor, else: assert len(group_or_device_mesh.shape) == 1, 'Only 1D DeviceMesh is accepted for row-wise sharding.' device_mesh = group_or_device_mesh - sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={0: [0]}) - if not inplace: - tensor = tensor.detach().clone() + sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={0: [0]}) - return DTensor(tensor, device_mesh, sharding_spec) + return distribute_tensor(tensor, device_mesh, sharding_spec) -def shard_colwise(tensor: torch.Tensor, - group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None, - inplace: bool = False) -> DTensor: +def shard_colwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None) -> torch.Tensor: """ Shard the first dim of the given tensor. @@ -57,7 +208,7 @@ def shard_colwise(tensor: torch.Tensor, inplace (bool, optional): Whether to shard the tensor in-place. Defaults to False. Returns: - DTensor: The sharded tensor. + torch.Tensor: The sharded tensor. """ # if the group_or_device_mesh is None, we shard the tensor with respect to the global process group if group_or_device_mesh is None: @@ -70,7 +221,87 @@ def shard_colwise(tensor: torch.Tensor, device_mesh = group_or_device_mesh sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={-1: [0]}) - if not inplace: - tensor = tensor.detach().clone() + return distribute_tensor(tensor, device_mesh, sharding_spec) + + +def sharded_tensor_to_param(dtensor: torch.Tensor, requires_grad: bool = True): + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + param = torch.nn.Parameter(dtensor, requires_grad=requires_grad) + + # make it distributed as well + param.dist_layout = dtensor.dist_layout + _hijack_detach_and_clone(param) + + return param + - return DTensor(tensor, device_mesh, sharding_spec) +def compute_global_numel(dtensor: torch.Tensor) -> int: + """ + Compute the global number of elements in the distributed tensor. + + Args: + dtensor (torch.Tensor): The distributed tensor. + + Returns: + int: The global number of elements in the distributed tensor. + """ + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + numel = reduce(operator.mul, dtensor.dist_layout.global_shape) + return numel + + +def get_layout(dtensor: torch.Tensor) -> Layout: + """ + Get the layout of the distributed tensor. + + Args: + dtensor (torch.Tensor): The distributed tensor. + + Returns: + Layout: The layout of the distributed tensor. + + """ + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + return dtensor.dist_layout + + +def get_global_shape(dtensor: torch.Tensor) -> torch.Size: + """ + Get the global shape of the distributed tensor. + + Args: + dtensor (torch.Tensor): The distributed tensor. + + Returns: + torch.Size: The global shape of the distributed tensor. + """ + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + return dtensor.dist_layout.global_shape + + +def get_device_mesh(dtensor: torch.Tensor) -> DeviceMesh: + """ + Get the device mesh of the distributed tensor. + + Args: + dtensor (torch.Tensor): The distributed tensor. + + Returns: + DeviceMesh: The device mesh of the distributed tensor. + """ + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + return dtensor.dist_layout.device_mesh + + +def get_sharding_spec(dtensor: torch.Tensor) -> ShardingSpec: + """ + Get the sharding spec of the distributed tensor. + + Args: + dtensor (torch.Tensor): The distributed tensor. + + Returns: + ShardingSpec: The sharding spec of the distributed tensor. + """ + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + return dtensor.dist_layout.sharding_spec diff --git a/colossalai/tensor/d_tensor/layout.py b/colossalai/tensor/d_tensor/layout.py index f15956ea3d52..4185b85860e3 100644 --- a/colossalai/tensor/d_tensor/layout.py +++ b/colossalai/tensor/d_tensor/layout.py @@ -1,12 +1,11 @@ import operator -from dataclasses import dataclass from functools import reduce import torch from colossalai.device.device_mesh import DeviceMesh -from .misc import DuplicatedShardingDimensionError, LayoutException, ShardingNotDivisibleError +from .misc import DuplicatedShardingDimensionError, ShardingNotDivisibleError from .sharding_spec import ShardingSpec diff --git a/colossalai/tensor/d_tensor/layout_converter.py b/colossalai/tensor/d_tensor/layout_converter.py index abc70e19a126..14f9c4561622 100644 --- a/colossalai/tensor/d_tensor/layout_converter.py +++ b/colossalai/tensor/d_tensor/layout_converter.py @@ -28,18 +28,6 @@ class LayoutConverterOptions: pass -def to_global(distributed_tensor: torch.Tensor, layout: Layout) -> torch.Tensor: - layout_converter = LayoutConverter() - global_sharding_spec = ShardingSpec(distributed_tensor.dim(), {}) - global_layout = Layout(device_mesh=layout.device_mesh, - device_type=layout.device_type, - sharding_spec=global_sharding_spec, - entire_shape=layout.entire_shape) - with torch.no_grad(): - global_tensor = layout_converter.apply(distributed_tensor, layout, global_layout) - return global_tensor - - def set_layout_converting_options(options: LayoutConverterOptions): """ Configure the shape consistency manager via function call. @@ -553,4 +541,5 @@ def apply(self, tensor: torch.Tensor, source_layout: Layout, target_layout: Layo _, comm_action_sequence = self.layout_converting(source_layout, target_layout) for comm_spec in comm_action_sequence: tensor = comm_spec.covert_spec_to_action(tensor) + tensor.dist_layout = target_layout return tensor diff --git a/colossalai/tensor/d_tensor/utils.py b/colossalai/tensor/d_tensor/utils.py index 644bb6306b42..fc22b990d879 100644 --- a/colossalai/tensor/d_tensor/utils.py +++ b/colossalai/tensor/d_tensor/utils.py @@ -29,7 +29,7 @@ def get_comm_cost(layout: Layout, comm_spec: CommSpec, forward_only: bool = Fals # the comm size for all gather is the size of the gathered tensor gather_dim = comm_spec.gather_dim all_gather_axis = layout.sharding_spec.dim_partition_dict[gather_dim][-1] - all_gather_size = device_mesh.mesh_shape[all_gather_axis] + all_gather_size = device_mesh.shape[all_gather_axis] comm_size_for_all_gather = comm_size * all_gather_size forward_communication_cost = device_mesh.all_gather_cost(comm_size_for_all_gather, logical_process_axis) # give a tiny cost to shard diff --git a/test.py b/test.py new file mode 100644 index 000000000000..f283e21a1ebd --- /dev/null +++ b/test.py @@ -0,0 +1 @@ +from colossalai.tensor.d_tensor.api import to_distributed_tensor diff --git a/tests/test_lazy/lazy_init_utils.py b/tests/test_lazy/lazy_init_utils.py index 2dd8d1ca3216..3879363bcd1b 100644 --- a/tests/test_lazy/lazy_init_utils.py +++ b/tests/test_lazy/lazy_init_utils.py @@ -7,7 +7,8 @@ from packaging import version from colossalai.lazy.lazy_init import LazyInitContext, LazyTensor, _MyTensor -from colossalai.tensor.d_tensor.layout_converter import to_global +from colossalai.tensor.d_tensor import to_global +from colossalai.tensor.d_tensor.layout import Layout from tests.kit.model_zoo.registry import ModelAttribute SUPPORT_LAZY = version.parse(torch.__version__) >= version.parse('1.12.0') @@ -91,6 +92,8 @@ def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn. assert n1 == n2 t1 = t1.cuda() t2 = t2.cuda() - if n2 in layout_dict: - t2 = to_global(t2, layout_dict[n2]) + if n2 in sharding_spec_dict: + layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_dict[n2], global_shape=t1.shape) + t2.dist_layout = layout + t2 = to_global(t2) assert torch.equal(t1, t2), f'{n1} {t1} vs {t2}' diff --git a/tests/test_shardformer/test_layer/test_embedding.py b/tests/test_shardformer/test_layer/test_embedding.py index 70500008cfff..8a6aa42a42f2 100644 --- a/tests/test_shardformer/test_layer/test_embedding.py +++ b/tests/test_shardformer/test_layer/test_embedding.py @@ -14,6 +14,10 @@ def check_embedding_1d(): assert embedding_1d.weight.shape == torch.Size([32, 64]) + # ensure state dict is reversibly loadable + embedding.load_state_dict(embedding_1d.state_dict()) + embedding_1d.load_state_dict(embedding.state_dict()) + # check computation correctness x = torch.randint(low=0, high=32, size=(4, 32)).cuda() out = embedding(x) diff --git a/tests/test_shardformer/test_layer/test_linear_1d.py b/tests/test_shardformer/test_layer/test_linear_1d.py index 00ecc37ce2fa..a2b8bf22c0b2 100644 --- a/tests/test_shardformer/test_layer/test_linear_1d.py +++ b/tests/test_shardformer/test_layer/test_linear_1d.py @@ -5,6 +5,7 @@ import colossalai from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row +from colossalai.tensor.d_tensor import is_distributed_tensor from colossalai.testing import rerun_if_address_is_in_use, spawn @@ -12,9 +13,18 @@ def check_linear_1d_col(): linear = nn.Linear(32, 128).cuda() linear_col = Linear1D_Col.from_native_module(linear, process_group=None, gather_output=True) + # ensure that the parameters are distributed + assert is_distributed_tensor(linear_col.weight) + assert is_distributed_tensor(linear_col.bias) + + # ensure the shape is correct assert linear_col.weight.shape == torch.Size([64, 32]) assert linear_col.bias.shape == torch.Size([64]) + # ensure state dict is reversibly loadable + linear.load_state_dict(linear_col.state_dict()) + linear_col.load_state_dict(linear.state_dict()) + # check computation correctness x = torch.rand(4, 32).cuda() out = linear(x) @@ -55,7 +65,7 @@ def check_linear_1d_row(): def run_dist(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') check_linear_1d_col() - check_linear_1d_row() + # check_linear_1d_row() @rerun_if_address_is_in_use() diff --git a/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py index bee44a2fb109..8991d9b304f5 100644 --- a/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py +++ b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py @@ -14,7 +14,11 @@ def check_vocab_embedding_1d(): assert dist_embedding_1d.weight.shape == torch.Size([64, 32]) assert dist_embedding_1d.num_embeddings == 64 - assert dist_embedding_1d.embed_dim == 32 + assert dist_embedding_1d.embedding_dim == 32 + + # ensure state dict is reversibly loadable + embedding.load_state_dict(dist_embedding_1d.state_dict()) + dist_embedding_1d.load_state_dict(embedding.state_dict()) # check embedding correctness x = torch.randint(0, 128, (4, 32)).to('cuda') diff --git a/tests/test_tensor/test_dtensor/test_comm_spec.py b/tests/test_tensor/test_dtensor/test_comm_spec.py index d1f5b9299397..958eabb65fac 100644 --- a/tests/test_tensor/test_dtensor/test_comm_spec.py +++ b/tests/test_tensor/test_dtensor/test_comm_spec.py @@ -1,14 +1,11 @@ import pytest import torch -import torch.distributed as dist -from torch.distributed import ReduceOp from colossalai.core import global_context as gpc from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers from colossalai.tensor.d_tensor.comm_spec import CollectiveCommPattern, CommSpec -from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec from colossalai.testing import rerun_if_address_is_in_use, spawn diff --git a/tests/test_tensor/test_dtensor/test_dtensor.py b/tests/test_tensor/test_dtensor/test_dtensor.py index 3ca369acbf87..8350fb3e7fe6 100644 --- a/tests/test_tensor/test_dtensor/test_dtensor.py +++ b/tests/test_tensor/test_dtensor/test_dtensor.py @@ -3,9 +3,7 @@ from colossalai.device.device_mesh import DeviceMesh from colossalai.initialize import launch from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.d_tensor import DTensor, distribute_tensor -from colossalai.tensor.d_tensor.layout import Layout -from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec +from colossalai.tensor.d_tensor import ShardingSpec, distribute_tensor, get_global_shape, redistribute, to_global from colossalai.testing import rerun_if_address_is_in_use, spawn @@ -31,22 +29,18 @@ def check_dtensor(rank, world_size, port): device_mesh = DeviceMesh(torch.Tensor([0, 1, 2, 3]), (2, 2), init_process_group=True) target_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict={0: [0]}) - layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=target_sharding_spec, - entire_shape=original_tensor.shape) - d_tensor = DTensor(original_tensor, layout) + d_tensor = distribute_tensor(original_tensor, device_mesh, target_sharding_spec) - assert d_tensor.entire_shape == original_tensor.shape - assert d_tensor.data_type == original_tensor.dtype + assert get_global_shape(d_tensor) == original_tensor.shape + assert d_tensor.dtype == original_tensor.dtype if rank in (0, 1): - assert d_tensor.to_local().equal(original_tensor.narrow(0, 0, 2)) + assert d_tensor.equal(original_tensor.narrow(0, 0, 2)) elif rank in (2, 3): - assert d_tensor.to_local().equal(original_tensor.narrow(0, 2, 2)) + assert d_tensor.equal(original_tensor.narrow(0, 2, 2)) else: raise ValueError(f'rank {rank} is not in the device mesh') - assert d_tensor.to_global().equal(original_tensor) + assert to_global(d_tensor).equal(original_tensor) output = test_model(d_tensor) if rank in (0, 1): @@ -57,34 +51,29 @@ def check_dtensor(rank, world_size, port): raise ValueError(f'rank {rank} is not in the device mesh') new_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict={0: [0, 1]}) - new_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=new_sharding_spec, - entire_shape=original_tensor.shape) - - d_tensor.layout_convert(new_layout) + d_tensor = redistribute(d_tensor, device_mesh, new_sharding_spec) if rank == 0: - assert d_tensor.local_tensor.equal(original_tensor.narrow(0, 0, 1)) + assert d_tensor.equal(original_tensor.narrow(0, 0, 1)) elif rank == 1: - assert d_tensor.local_tensor.equal(original_tensor.narrow(0, 1, 1)) + assert d_tensor.equal(original_tensor.narrow(0, 1, 1)) elif rank == 2: - assert d_tensor.local_tensor.equal(original_tensor.narrow(0, 2, 1)) + assert d_tensor.equal(original_tensor.narrow(0, 2, 1)) elif rank == 3: - assert d_tensor.local_tensor.equal(original_tensor.narrow(0, 3, 1)) + assert d_tensor.equal(original_tensor.narrow(0, 3, 1)) else: raise ValueError(f'rank {rank} is not in the device mesh') dtensor_from_local = distribute_tensor(original_tensor, new_layout) if rank == 0: - assert dtensor_from_local.local_tensor.equal(original_tensor.narrow(0, 0, 1)) + assert dtensor_from_local.equal(original_tensor.narrow(0, 0, 1)) elif rank == 1: - assert dtensor_from_local.local_tensor.equal(original_tensor.narrow(0, 1, 1)) + assert dtensor_from_local.equal(original_tensor.narrow(0, 1, 1)) elif rank == 2: - assert dtensor_from_local.local_tensor.equal(original_tensor.narrow(0, 2, 1)) + assert dtensor_from_local.equal(original_tensor.narrow(0, 2, 1)) elif rank == 3: - assert dtensor_from_local.local_tensor.equal(original_tensor.narrow(0, 3, 1)) + assert dtensor_from_local.equal(original_tensor.narrow(0, 3, 1)) else: raise ValueError(f'rank {rank} is not in the device mesh') diff --git a/tests/test_tensor/test_dtensor/test_layout_converter.py b/tests/test_tensor/test_dtensor/test_layout_converter.py index 5c3da5f2b9ff..d9dff8af933d 100644 --- a/tests/test_tensor/test_dtensor/test_layout_converter.py +++ b/tests/test_tensor/test_dtensor/test_layout_converter.py @@ -9,7 +9,7 @@ from colossalai.tensor.d_tensor.comm_spec import CollectiveCommPattern from colossalai.tensor.d_tensor.layout import Layout from colossalai.tensor.d_tensor.layout_converter import LayoutConverter -from colossalai.tensor.d_tensor.sharding_spec import DimSpec, ShardingSpec +from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec from colossalai.testing import rerun_if_address_is_in_use, spawn entire_shape = torch.Size((64, 32, 16)) From 0803a61412c5f57b7f784fbba19aa92f33cf6885 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Thu, 22 Jun 2023 14:40:37 +0800 Subject: [PATCH 380/413] [shardformer] add linearconv1d test (#4067) * add linearconv1d test * add linearconv1d test --- colossalai/shardformer/layer/linear_conv.py | 36 +++--- colossalai/shardformer/policies/gpt2.py | 10 +- .../test_layer/test_linearconv_1d.py | 107 ++++++++++++++++++ .../test_model/test_shard_gpt2.py | 3 - 4 files changed, 122 insertions(+), 34 deletions(-) create mode 100644 tests/test_shardformer/test_layer/test_linearconv_1d.py diff --git a/colossalai/shardformer/layer/linear_conv.py b/colossalai/shardformer/layer/linear_conv.py index 2adfc182895e..2d1dacf2cd39 100644 --- a/colossalai/shardformer/layer/linear_conv.py +++ b/colossalai/shardformer/layer/linear_conv.py @@ -103,10 +103,15 @@ def __init__(self, self.reset_parameters(weight_initializer, bias_initializer) @staticmethod - def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], n_cast: int, + def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], n_fused: int, *args, **kwargs) -> ParallelModule: r""" Convert a huggingface layer `Conv1D` in gpt2 to a parallelized linear layer. + + Args: + module (`nn.Linear`): The module to be converted. + process_group (`Union[ProcessGroup, List[ProcessGroup]]`): The process group to be used for weight sharding and communication. + n_fused (int): The number of layers to be fused. In GPT2, Q,K,V are fused in one weight. """ # get the attributes in_features = module.weight.shape[0] @@ -135,20 +140,20 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis # first rearange the order of weight and bias world_size = dist.get_world_size(group=process_group) - order = torch.arange(world_size * n_cast) + order = torch.arange(world_size * n_fused) new_order = [] for i in range(world_size): new_order.append(order[i::world_size]) new_order = torch.cat(new_order) - weight_chunks = torch.chunk(module.weight.data, world_size * n_cast, dim=1) + weight_chunks = torch.chunk(module.weight.data, world_size * n_fused, dim=1) rearanged_weight_chunks = [weight_chunks[i] for i in new_order] rearanged_weight = torch.cat(rearanged_weight_chunks, dim=1) sharded_weight = shard_colwise(rearanged_weight, process_group) linear_1d.weight.data.copy_(sharded_weight.T.contiguous()) if bias: - bias_chunks = torch.chunk(module.bias.data, world_size * n_cast, dim=0) + bias_chunks = torch.chunk(module.bias.data, world_size * n_fused, dim=0) rearanged_bias_chunks = [bias_chunks[i] for i in new_order] rearanged_bias = torch.cat(rearanged_bias_chunks, dim=0) sharded_bias = shard_colwise(rearanged_bias, process_group) @@ -260,8 +265,8 @@ def __init__(self, self.reset_parameters(weight_initializer, bias_initializer) @staticmethod - def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], n_cast: int, - *args, **kwargs) -> ParallelModule: + def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, + **kwargs) -> ParallelModule: r""" Convert a native PyTorch linear layer to a parallelized linear layer. """ @@ -289,26 +294,11 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis with torch.no_grad(): # the weigh to the linear layer is a transpose # thus shard on col is equal to shard on row - - # first rearange the order of weight and bias - world_size = dist.get_world_size(group=process_group) - order = torch.arange(world_size * n_cast) - new_order = [] - for i in range(world_size): - new_order.append(order[i::world_size]) - new_order = torch.cat(new_order) - - weight_chunks = torch.chunk(module.weight.data, world_size * n_cast, dim=0) - rearanged_weight_chunks = [weight_chunks[i] for i in new_order] - rearanged_weight = torch.cat(rearanged_weight_chunks, dim=0) - sharded_weight = shard_rowwise(rearanged_weight, process_group) + sharded_weight = shard_rowwise(module.weight.data, process_group) linear_1d.weight.data.copy_(sharded_weight.T.contiguous()) if bias: - bias_chunks = torch.chunk(module.bias.data, world_size * n_cast, dim=0) - rearanged_bias_chunks = [bias_chunks[i] for i in new_order] - rearanged_bias = torch.cat(rearanged_bias_chunks, dim=0) - linear_1d.bias.copy_(rearanged_bias.contiguous()) + linear_1d.bias.copy_(module.bias.data) return linear_1d diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 54ea2f6e3279..9d5d7d36aea3 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -44,29 +44,23 @@ def module_policy(self): suffix="attn.c_attn", target_module=col_nn.LinearConv1D_Col, kwargs={ - "n_cast": 3, + "n_fused": 3, }, ), SubModuleReplacementDescription( suffix="attn.c_proj", target_module=col_nn.LinearConv1D_Row, - kwargs={ - "n_cast": 1, - }, ), SubModuleReplacementDescription( suffix="mlp.c_fc", target_module=col_nn.LinearConv1D_Col, kwargs={ - "n_cast": 1, + "n_fused": 1, }, ), SubModuleReplacementDescription( suffix="mlp.c_proj", target_module=col_nn.LinearConv1D_Row, - kwargs={ - "n_cast": 1, - }, ), SubModuleReplacementDescription( suffix="attn.attn_dropout", diff --git a/tests/test_shardformer/test_layer/test_linearconv_1d.py b/tests/test_shardformer/test_layer/test_linearconv_1d.py new file mode 100644 index 000000000000..e0c97178d901 --- /dev/null +++ b/tests/test_shardformer/test_layer/test_linearconv_1d.py @@ -0,0 +1,107 @@ +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.testing import assert_close + +import colossalai +from colossalai.shardformer.layer import LinearConv1D_Col, LinearConv1D_Row +from colossalai.testing import rerun_if_address_is_in_use, spawn + + +# This code is copied from https://github.com/huggingface/transformers +class Conv1D(nn.Module): + """ + 1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2). + + Basically works like a linear layer but the weights are transposed. + + Args: + nf (`int`): The number of output features. + nx (`int`): The number of input features. + """ + + def __init__(self, nf, nx): + super().__init__() + self.nf = nf + self.weight = nn.Parameter(torch.empty(nx, nf)) + self.bias = nn.Parameter(torch.zeros(nf)) + nn.init.normal_(self.weight, std=0.02) + + def forward(self, x): + size_out = x.size()[:-1] + (self.nf,) + x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight) + x = x.view(size_out) + return x + + +def rearrange(tensor: torch.Tensor, dim: int): + tensor = tensor.clone() + world_size = 2 + order = torch.arange(world_size * 3) + new_order = [] + for i in range(world_size): + new_order.append(order[i::world_size]) + new_order = torch.cat(new_order) + + tensor_chunks = torch.chunk(tensor, world_size * 3, dim=dim) + rearanged_tensor_chunks = [tensor_chunks[i] for i in new_order] + rearanged_tensor = torch.cat(rearanged_tensor_chunks, dim=dim) + return rearanged_tensor + + +def check_linear_conv_1d_col(): + linear = Conv1D(192, 48).cuda() + linear_conv_col = LinearConv1D_Col.from_native_module(linear, process_group=None, gather_output=True, n_fused=3) + + assert linear_conv_col.weight.shape == torch.Size([96, 48]) + assert linear_conv_col.bias.shape == torch.Size([96]) + + # check computation correctness + x = torch.rand(4, 48).cuda() + out = linear(x) + gather_out = linear_conv_col(x) + assert_close(rearrange(out, 1), gather_out) + + # check backward correctness + out.sum().backward() + gather_out.sum().backward() + + rank = dist.get_rank() + target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank] + assert_close(target_grad.transpose(0, 1).contiguous(), linear_conv_col.weight.grad) + + +def check_linear_1d_row(): + linear = Conv1D(192, 48).cuda() + linear_row = LinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False) + + assert linear_row.weight.shape == torch.Size([192, 24]) + assert linear_row.bias.shape == torch.Size([192]) + + # check computation correctness + x = torch.rand(4, 48).cuda() + out = linear(x) + gather_out = linear_row(x) + assert_close(out, gather_out) + + # check backward correctness + out.sum().backward() + gather_out.sum().backward() + + rank = dist.get_rank() + target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank] + assert_close(target_grad, linear_row.weight.grad) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + check_linear_conv_1d_col() + + +@rerun_if_address_is_in_use() +def test_linearconv(): + spawn(run_dist, nprocs=2) + + +if __name__ == '__main__': + test_linearconv() diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 0c07f44401c7..9aa02ec34d17 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -42,9 +42,6 @@ def check_gpt2(rank, world_size, port): sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - print(name) - # if name == 'transformers_gpt': - # continue org_model, sharded_model = build_model(world_size, model_fn) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) From 70c58cfd4f81a157693b34694ad443da89a87cc8 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 23 Jun 2023 16:07:09 +0800 Subject: [PATCH 381/413] [shardformer] supported fused qkv checkpoint (#4073) --- colossalai/shardformer/layer/_operation.py | 86 +++++++++- colossalai/shardformer/layer/embedding.py | 4 +- colossalai/shardformer/layer/linear.py | 16 +- colossalai/shardformer/layer/linear_conv.py | 162 ++++++++++++------ .../shardformer/layer/parallel_module.py | 8 +- colossalai/tensor/d_tensor/__init__.py | 8 +- colossalai/tensor/d_tensor/api.py | 127 ++++++++++++++ .../test_layer/test_linear_1d.py | 64 ++++++- .../test_layer/test_linearconv_1d.py | 20 ++- .../test_model/test_shard_gpt2.py | 13 +- 10 files changed, 420 insertions(+), 88 deletions(-) diff --git a/colossalai/shardformer/layer/_operation.py b/colossalai/shardformer/layer/_operation.py index 280d5526342b..7e97bee01b33 100644 --- a/colossalai/shardformer/layer/_operation.py +++ b/colossalai/shardformer/layer/_operation.py @@ -1,5 +1,6 @@ import torch import torch.distributed as dist +import torch.nn.functional as F try: import fused_mix_prec_layer_norm_cuda @@ -46,7 +47,7 @@ def backward(ctx, grad_output): return grad_input, grad_weight, grad_bias, None, None -class LinearWithAsyncCommunication(torch.autograd.Function): +class MatmulWithAsyncCommunication(torch.autograd.Function): """ Linear layer execution with asynchronous communication in backprop. """ @@ -58,11 +59,59 @@ def forward(ctx, input_, weight, bias, process_group, async_grad_allreduce): ctx.process_group = process_group ctx.async_grad_allreduce = async_grad_allreduce - output = torch.matmul(input_, weight.t()) + output = torch.matmul(input_, weight) + if bias is not None: output = output + bias return output + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + use_bias = ctx.use_bias + + total_input = input + grad_input = grad_output.matmul(weight.T) + grad_output = grad_output.contiguous() + # Convert the tensor shapes to 2D for execution compatibility + if len(grad_output.shape) > 2: + grad_output = grad_output.view(-1, grad_output.shape[-1]) + total_input = total_input.view(-1, total_input.shape[-1]) + + if ctx.async_grad_allreduce: + # Asynchronous all-reduce + handle = dist.all_reduce(grad_input, group=ctx.process_group, async_op=True) + # Delay the start of weight gradient computation shortly (3us) to have + # all-reduce scheduled first and have GPU resources allocated + _ = torch.empty(1, device=grad_output.device) + 1 + + grad_weight = total_input.t().matmul(grad_output) + grad_bias = grad_output.sum(dim=0) if use_bias else None + + if ctx.async_grad_allreduce: + handle.wait() + + return grad_input, grad_weight, grad_bias, None, None, None + + +class LinearWithAsyncCommunication(torch.autograd.Function): + """ + Linear layer execution with asynchronous communication in backprop. + """ + + @staticmethod + def forward(ctx, input_, weight, bias, process_group, async_grad_allreduce): + ctx.save_for_backward(input_, weight) + ctx.use_bias = bias is not None + ctx.process_group = process_group + ctx.async_grad_allreduce = async_grad_allreduce + + if bias is not None: + output = F.linear(input_, weight, bias) + else: + output = F.linear(input_, weight) + return output + @staticmethod def backward(ctx, grad_output): input, weight = ctx.saved_tensors @@ -114,7 +163,7 @@ def backward(ctx, grad_output): return _gather(grad_output, ctx.dim, ctx.process_group), None, None -class _ReduceInput(torch.autograd.Function): +class _ReduceForward(torch.autograd.Function): """ All-reduce the input from the model parallel region. @@ -132,6 +181,25 @@ def backward(ctx, grad_output): return grad_output, None +class _ReduceBackward(torch.autograd.Function): + """ + All-reduce the input from the model parallel region. + + Args: + input_: input matrix. + parallel_mode: parallel mode. + """ + + @staticmethod + def forward(ctx, input_, process_group): + ctx.process_group = process_group + return input_ + + @staticmethod + def backward(ctx, grad_output): + return _reduce(grad_output, ctx.process_group), None + + def _reduce(input_, process_group): # skip if only one rank involved if dist.get_world_size(process_group) == 1: @@ -198,6 +266,10 @@ def backward(ctx, grad_output): return _split(grad_output, ctx.dim, ctx.process_group), None, None +def matmul_with_async_comm(input_, weight, bias, process_group, async_grad_allreduce): + return MatmulWithAsyncCommunication.apply(input_, weight, bias, process_group, async_grad_allreduce) + + def linear_with_async_comm(input_, weight, bias, process_group, async_grad_allreduce): return LinearWithAsyncCommunication.apply(input_, weight, bias, process_group, async_grad_allreduce) @@ -210,5 +282,9 @@ def split_forward_gather_backward(input_, dim, process_group): return _SplitForwardGatherBackward.apply(input_, dim, process_group) -def reduce_input(input_, process_group): - return _ReduceInput.apply(input_, process_group) +def reduce_forward(input_, process_group): + return _ReduceForward.apply(input_, process_group) + + +def reduce_backward(input_, process_group): + return _ReduceBackward.apply(input_, process_group) diff --git a/colossalai/shardformer/layer/embedding.py b/colossalai/shardformer/layer/embedding.py index 23601a04a27b..db39a457b7fd 100644 --- a/colossalai/shardformer/layer/embedding.py +++ b/colossalai/shardformer/layer/embedding.py @@ -15,7 +15,7 @@ from colossalai.nn.layer.utils import divide from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise, sharded_tensor_to_param -from ._operation import gather_forward_split_backward, reduce_input +from ._operation import gather_forward_split_backward, reduce_forward from .parallel_module import ParallelModule from .utils import create_randomizer_with_offset @@ -276,5 +276,5 @@ def forward(self, input_: Tensor) -> Tensor: # Mask the output embedding. output_parallel[input_mask, :] = 0. # Reduce across all the model parallel GPUs. - output = reduce_input(output_parallel, self.process_group) + output = reduce_forward(output_parallel, self.process_group) return output diff --git a/colossalai/shardformer/layer/linear.py b/colossalai/shardformer/layer/linear.py index 912be26b99ba..d952d5eecbee 100644 --- a/colossalai/shardformer/layer/linear.py +++ b/colossalai/shardformer/layer/linear.py @@ -15,12 +15,11 @@ from colossalai.nn import init as init from colossalai.nn.layer.utils import divide from colossalai.tensor.d_tensor import shard_colwise, shard_rowwise, sharded_tensor_to_param -from colossalai.utils.cuda import get_current_device from ._operation import ( gather_forward_split_backward, linear_with_async_comm, - reduce_input, + reduce_forward, split_forward_gather_backward, ) from .parallel_module import ParallelModule @@ -148,9 +147,10 @@ def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: assert input_.shape[-1] == self.weight.shape[-1], \ 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( input_.shape, self.weight.shape, self.weight.shape[-1]) + # Set up backprop all-reduce. - # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) input_parallel = input_ + # Matrix multiply. bias = self.bias if not self.skip_bias_add else None output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True) @@ -209,17 +209,14 @@ def __init__(self, self.parallel_input = parallel_input self.skip_bias_add = skip_bias_add self.process_group = process_group + self.num_partitions = dist.get_world_size(self.process_group) if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') # Parameters. # Initialize weight. - if device is None: - device = get_current_device() - factory_kwargs = {'device': device, 'dtype': dtype} - weight = torch.empty(self.out_features, self.in_features, **factory_kwargs) sharded_weight = shard_colwise(weight, self.process_group) self.weight = sharded_tensor_to_param(sharded_weight) @@ -327,8 +324,7 @@ def forward(self, input_: Tensor) -> Tensor: output = torch.cat(output_parallel_list, dim=-1) else: output_parallel = F.linear(input_, self.weight) - # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) - output = reduce_input(output_parallel, self.process_group) + output = reduce_forward(output_parallel, self.process_group) if not self.skip_bias_add: if self.bias is not None: @@ -336,5 +332,3 @@ def forward(self, input_: Tensor) -> Tensor: return output else: return output, self.bias - return output, self.bias - return output, self.bias diff --git a/colossalai/shardformer/layer/linear_conv.py b/colossalai/shardformer/layer/linear_conv.py index 2d1dacf2cd39..e856abc14be6 100644 --- a/colossalai/shardformer/layer/linear_conv.py +++ b/colossalai/shardformer/layer/linear_conv.py @@ -14,13 +14,18 @@ from colossalai.nn import init as init from colossalai.nn.layer.utils import divide -from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise -from colossalai.utils.cuda import get_current_device +from colossalai.tensor.d_tensor.api import ( + customized_distributed_tensor_to_param, + distribute_tensor_with_customization, + shard_rowwise, + sharded_tensor_to_param, +) from ._operation import ( gather_forward_split_backward, - linear_with_async_comm, - reduce_input, + matmul_with_async_comm, + reduce_backward, + reduce_forward, split_forward_gather_backward, ) from .parallel_module import ParallelModule @@ -29,11 +34,69 @@ __all__ = ['LinearConv1D_Col', 'LinearConv1D_Row'] +def split_fused_qkv(qkv: torch.Tensor, n_fused: int, process_group: ProcessGroup): + """ + The fused qkv tensor looks like [Q1, Q2, K1, K2, V1, V2], this function will split them into [Q1, K1, V1] and [Q2, K2, V2]. + """ + # get the number of slice for the fused qkv + rank = dist.get_rank(group=process_group) + world_size = dist.get_world_size(group=process_group) + order = torch.arange(world_size * n_fused) + + # split the fused qkv + # from + # [Q, K, V] + # to + # [Q1, Q2, K1, K2, V1, V2] + weight_chunks = torch.chunk(qkv, world_size * n_fused, dim=-1) + + # rearrange the slice into the final order + # from + # [Q1, Q2, K1, K2, V1, V2] + # to + # [Q1, K1, V1], [Q2, K2, V2] + weight_chunks_of_current_rank = [weight_chunks[i] for i in order[rank::world_size]] + weight_of_current_rank = torch.cat(weight_chunks_of_current_rank, dim=-1) + return weight_of_current_rank + + +def gather_fused_qkv(qkv: torch.Tensor, n_fused: int, process_group: ProcessGroup): + """ + The splitted qkv tensor looks like [Q1, K1, V1] and [Q2, K2, V2], this function will gather them into [Q1, Q2, K1, K2, V1, V2]. + """ + world_size = dist.get_world_size(group=process_group) + + # gather the tensors + # from + # [Q1, K1, V1], [Q2, K2, V2] + # to + # [Q1, K1, V1, Q2, K2, V2] + origin_device = qkv.device + qkv = qkv.cuda() + gather_list = [torch.zeros_like(qkv) for _ in range(world_size)] + dist.all_gather(gather_list, qkv, group=process_group) + gather_weight = torch.cat(gather_list, dim=-1) + gather_weight = gather_weight.to(origin_device) + qkv = qkv.to(origin_device) + + # rearrange the tensor slices + # from + # [Q1, K1, V1, Q2, K2, V2] + # to + # [Q1, Q2, K1, K2, V1, V2] + weight_chunks = torch.chunk(gather_weight, world_size * n_fused, dim=-1) + reordered_chunk_list = [] + for i in range(n_fused): + reordered_chunk_list.extend(weight_chunks[i::n_fused]) + reordered_gather_weight = torch.cat(reordered_chunk_list, dim=-1) + return reordered_gather_weight + + class LinearConv1D_Col(ParallelModule): r"""Linear layer with column parallelism. The linear layer is defined as :math:`Y = XA + b`. A is parallelized along - its second dimension as :math:`A = [A_1, ..., A_p]`. This layer is used to fit `Conv1D` layer in gpt2 of huggingface. + its second dimension as :math:`A = [A_1, ..., A_p]`. This layer is used to fit `Conv1D` layer (Fused QKV) in gpt2 of huggingface. Args: in_features (int): size of each input sample. @@ -41,6 +104,7 @@ class LinearConv1D_Col(ParallelModule): bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. dtype (`torch.dtype`): The dtype of parameters, defaults to None. device (`torch.device`): The device of parameters, defaults to None. + n_fused (int): The number items fused, defaults to 3 (QKV). process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None. gather_output (bool, optional): If true, call all-gather on output and make Y available to all GPUs, otherwise, every GPU will have its output @@ -63,8 +127,10 @@ def __init__(self, dtype: torch.dtype = None, device: torch.device = None, process_group: ProcessGroup = None, + async_communication: bool = False, gather_output: bool = False, skip_bias_add: bool = False, + n_fused: int = 3, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): super().__init__() @@ -75,23 +141,34 @@ def __init__(self, self.gather_output = gather_output self.skip_bias_add = skip_bias_add self.device = device + self.n_fused = n_fused self.process_group = process_group - self.num_partitions = dist.get_world_size(self.process_group) + self.async_communication = async_communication if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') - self.out_features_per_partition = divide(out_features, self.num_partitions) - # Parameters. # Initialize weight. - if device is None: - device = get_current_device() factory_kwargs = {'device': device, 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs)) + weight = torch.empty(self.in_features, self.out_features, **factory_kwargs) + + def shard_fn(tensor): + return split_fused_qkv(tensor, self.n_fused, self.process_group) + + def gather_fn(tensor): + return gather_fused_qkv(tensor, 3, self.process_group) + + with torch.no_grad(): + sharded_weight = distribute_tensor_with_customization(weight, shard_fn, gather_fn) + self.weight = customized_distributed_tensor_to_param(sharded_weight) if bias: - self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs)) + bias = torch.empty(self.out_features, **factory_kwargs) + + with torch.no_grad(): + sharded_bias = distribute_tensor_with_customization(bias, shard_fn, gather_fn) + self.bias = customized_distributed_tensor_to_param(sharded_bias) else: self.bias = None @@ -103,7 +180,7 @@ def __init__(self, self.reset_parameters(weight_initializer, bias_initializer) @staticmethod - def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], n_fused: int, + def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], n_fused: int, *args, **kwargs) -> ParallelModule: r""" Convert a huggingface layer `Conv1D` in gpt2 to a parallelized linear layer. @@ -135,29 +212,12 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis # TODO: copy the sharded weights with torch.no_grad(): - # the weigh to the linear layer is a transpose - # thus shard on row is equal to shard on column - - # first rearange the order of weight and bias - world_size = dist.get_world_size(group=process_group) - order = torch.arange(world_size * n_fused) - new_order = [] - for i in range(world_size): - new_order.append(order[i::world_size]) - new_order = torch.cat(new_order) - - weight_chunks = torch.chunk(module.weight.data, world_size * n_fused, dim=1) - rearanged_weight_chunks = [weight_chunks[i] for i in new_order] - rearanged_weight = torch.cat(rearanged_weight_chunks, dim=1) - sharded_weight = shard_colwise(rearanged_weight, process_group) - linear_1d.weight.data.copy_(sharded_weight.T.contiguous()) + sharded_weight = split_fused_qkv(module.weight.data, n_fused=n_fused, process_group=process_group) + linear_1d.weight.data.copy_(sharded_weight.data) if bias: - bias_chunks = torch.chunk(module.bias.data, world_size * n_fused, dim=0) - rearanged_bias_chunks = [bias_chunks[i] for i in new_order] - rearanged_bias = torch.cat(rearanged_bias_chunks, dim=0) - sharded_bias = shard_colwise(rearanged_bias, process_group) - linear_1d.bias.copy_(sharded_bias.contiguous()) + sharded_bias = split_fused_qkv(module.bias.data, n_fused=n_fused, process_group=process_group) + linear_1d.bias.data.copy_(sharded_bias.data) return linear_1d @@ -169,15 +229,18 @@ def reset_parameters(self, weight_initializer, bias_initializer) -> None: bias_initializer(self.bias, fan_in=fan_in) def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: - assert input_.shape[-1] == self.weight.shape[-1], \ + assert input_.shape[-1] == self.weight.shape[0], \ 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( input_.shape, self.weight.shape, self.weight.shape[-1]) # Set up backprop all-reduce. - # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D) - input_parallel = input_ + input_parallel = reduce_backward(input_, self.process_group) + # input_parallel = input_ + # Matrix multiply. bias = self.bias if not self.skip_bias_add else None - output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True) + + output_parallel = matmul_with_async_comm(input_parallel, self.weight, bias, self.process_group, + self.async_communication) if self.gather_output: # All-gather across the partitions. @@ -192,7 +255,8 @@ def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: class LinearConv1D_Row(ParallelModule): - r""" Linear layer with row parallelism + r""" Linear layer with row parallelism. + This layer is used to fit `Conv1D` layer (Fused QKV) in gpt2 of huggingface. Args: in_features (int): size of each input sample. @@ -243,11 +307,10 @@ def __init__(self, # Parameters. # Initialize weight. - if device is None: - device = get_current_device() - factory_kwargs = {'device': device, 'dtype': dtype} - self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs)) + weight = torch.empty(self.in_features, self.out_features, **factory_kwargs) + sharded_weight = shard_rowwise(weight, self.process_group) + self.weight = sharded_tensor_to_param(sharded_weight) if self.stream_chunk_num > 1: # TODO() work for inference only @@ -295,7 +358,7 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis # the weigh to the linear layer is a transpose # thus shard on col is equal to shard on row sharded_weight = shard_rowwise(module.weight.data, process_group) - linear_1d.weight.data.copy_(sharded_weight.T.contiguous()) + linear_1d.weight.data.copy_(sharded_weight.data) if bias: linear_1d.bias.copy_(module.bias.data) @@ -325,12 +388,12 @@ def reset_parameters(self, weight_initializer, bias_initializer) -> None: def forward(self, input_: Tensor) -> Tensor: # Set up backprop all-reduce. if self.parallel_input: - assert input_.shape[-1] == self.weight.shape[-1], \ + assert input_.shape[-1] == self.weight.shape[0], \ 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( input_.shape, self.weight.shape, self.weight.shape[-1]) input_ = input_ else: - assert divide(input_.shape[-1], self.num_partitions) == self.weight.shape[-1], \ + assert divide(input_.shape[-1], self.num_partitions) == self.weight.shape[0], \ 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( input_.shape, self.weight.shape, self.weight.shape[-1] * self.num_partitions) input_ = split_forward_gather_backward(input_, dim=-1, process_group=self.process_group) @@ -342,7 +405,7 @@ def forward(self, input_: Tensor) -> Tensor: output_parallel_list = [None for i in range(self.stream_chunk_num)] handle_list = [] for i in range(self.stream_chunk_num): - output_parallel_list[i] = F.linear(input_, self.weight_list[i]) + output_parallel_list[i] = torch.matmul(input_, self.weight_list[i]) handle = torch.distributed.all_reduce(output_parallel_list[i], group=self.process_group, async_op=True) @@ -352,9 +415,8 @@ def forward(self, input_: Tensor) -> Tensor: handle.wait() output = torch.cat(output_parallel_list, dim=-1) else: - output_parallel = F.linear(input_, self.weight) - # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False) - output = reduce_input(output_parallel, self.process_group) + output_parallel = torch.matmul(input_, self.weight) + output = reduce_forward(output_parallel, self.process_group) if not self.skip_bias_add: if self.bias is not None: diff --git a/colossalai/shardformer/layer/parallel_module.py b/colossalai/shardformer/layer/parallel_module.py index 5edcb9dde748..bda147b121ab 100644 --- a/colossalai/shardformer/layer/parallel_module.py +++ b/colossalai/shardformer/layer/parallel_module.py @@ -12,11 +12,14 @@ from colossalai.tensor.d_tensor import ( distribute_tensor, + distribute_tensor_with_customization, get_device_mesh, get_sharding_spec, + is_customized_distributed_tensor, is_distributed_tensor, sharded_tensor_to_param, to_global, + to_global_for_customized_distributed_tensor, ) __all__ = ['ParallelModule'] @@ -54,9 +57,10 @@ def _save_to_state_dict(self, destination, prefix, keep_vars): for name, param in self._parameters.items(): if param is not None: param_ = param if keep_vars else param.detach() - if is_distributed_tensor(param_): destination[prefix + name] = to_global(param_) + elif is_customized_distributed_tensor(param_): + destination[prefix + name] = to_global_for_customized_distributed_tensor(param_) else: destination[prefix + name] = param_ @@ -124,6 +128,8 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, miss sharding_spec = get_sharding_spec(param) sharded_tensor = distribute_tensor(input_param, device_mesh, sharding_spec) input_param = sharded_tensor_to_param(sharded_tensor) + elif is_customized_distributed_tensor(param): + input_param = distribute_tensor_with_customization(input_param, param.shard_fn, param.gather_fn) # This is used to avoid copying uninitialized parameters into # non-lazy modules, since they dont have the hook to do the checks diff --git a/colossalai/tensor/d_tensor/__init__.py b/colossalai/tensor/d_tensor/__init__.py index 52eae0e14877..3ae38a12555b 100644 --- a/colossalai/tensor/d_tensor/__init__.py +++ b/colossalai/tensor/d_tensor/__init__.py @@ -1,10 +1,13 @@ from .api import ( compute_global_numel, + customized_distributed_tensor_to_param, distribute_tensor, + distribute_tensor_with_customization, get_device_mesh, get_global_shape, get_layout, get_sharding_spec, + is_customized_distributed_tensor, is_distributed_tensor, is_sharded, redistribute, @@ -12,6 +15,7 @@ shard_rowwise, sharded_tensor_to_param, to_global, + to_global_for_customized_distributed_tensor, ) from .layout import Layout from .sharding_spec import ShardingSpec @@ -19,6 +23,6 @@ __all__ = [ 'is_distributed_tensor', 'distribute_tensor', 'to_global', 'is_sharded', 'shard_rowwise', 'shard_colwise', 'sharded_tensor_to_param', 'compute_global_numel', 'get_sharding_spec', 'get_global_shape', 'get_device_mesh', - 'redistribute', 'get_layout' - 'Layout', 'ShardingSpec' + 'redistribute', 'get_layout', 'is_customized_distributed_tensor', 'distribute_tensor_with_customization', + 'to_global_for_customized_distributed_tensor', 'customized_distributed_tensor_to_param', 'Layout', 'ShardingSpec' ] diff --git a/colossalai/tensor/d_tensor/api.py b/colossalai/tensor/d_tensor/api.py index a38e5e6b7184..95a44e09e16a 100644 --- a/colossalai/tensor/d_tensor/api.py +++ b/colossalai/tensor/d_tensor/api.py @@ -305,3 +305,130 @@ def get_sharding_spec(dtensor: torch.Tensor) -> ShardingSpec: """ assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' return dtensor.dist_layout.sharding_spec + + +# ====================================================== +# Some sharding does not obey the SPMD style +# e.g. Fused QKV layer in GPT2 +# we support customize sharding with the following APIs +# ====================================================== +def is_customized_distributed_tensor(tensor: torch.Tensor): + """ + Check whether the given tensor is a customized distributed tensor. + + Args: + tensor (torch.Tensor): The tensor to be checked. + + Returns: + bool: Whether the given tensor is a customized distributed tensor. + """ + return hasattr(tensor, 'shard_fn') and hasattr(tensor, 'gather_fn') + + +def _hijack_detach_and_clone_for_customized_distributed_tensor(dtensor: torch.Tensor) -> torch.Tensor: + """ + Hijack the detach and clone methods of the tensor to make sure the dist_layout is copied. + + Args: + tensor (torch.Tensor): The tensor to be hijacked. + + Returns: + torch.Tensor: The hijacked tensor. + """ + dtensor._old_detach = dtensor.detach + dtensor._old_clone = dtensor.clone + + def new_detach(self): + t_ = self._old_detach() + t_.shard_fn = self.shard_fn + t_.gather_fn = self.gather_fn + return t_ + + def new_clone(self, *args, **kwargs): + t_ = self._old_clone(*args, **kwargs) + t_.shard_fn = self.shard_fn + t_.gather_fn = self.gather_fn + return t_ + + # bind the new methods to the tensor + dtensor.detach = new_detach.__get__(dtensor) + dtensor.clone = new_clone.__get__(dtensor) + return dtensor + + +def distribute_tensor_with_customization(tensor: torch.Tensor, shard_fn, gather_fn: callable): + """ + Distribute the given tensor with the given shard_fn and gather_fn. + + Example: + + ```python + # define shard and gather functions + def shard_fn(tensor): + rank = torch.distributed.get_rank() + world_size = torch.distributed.get_world_size() + return tensor.chunk(world_size, dim=0)[rank] + + def gather_fn(tensor): + rank = torch.distributed.get_rank() + world_size = torch.distributed.get_world_size() + shard_list = [torch.zeros_like(tensor) for _ in range(world_size)] + torch.distributed.all_gather(shard_list, tensor) + return torch.cat(shard_list, dim=0) + + # create a distributed tensor + tensor = torch.rand(4, 4) + dtensor = distribute_tensor_with_customization(tensor, shard_fn, gather_fn) + ``` + + Args: + tensor (torch.Tensor): The tensor to be distributed. + shard_fn (callable): The function to shard the tensor. + gather_fn (callable): The function to gather the tensor. + + Returns: + torch.Tensor: The distributed tensor. + """ + assert callable(shard_fn), 'The shard_fn must be callable.' + assert callable(gather_fn), 'The gather_fn must be callable.' + assert not is_distributed_tensor(tensor), 'The input tensor is already a distributed tensor.' + + sharded_tensor = shard_fn(tensor) + + # set the shard_fn and gather_fn as attributes of the distributed tensor + sharded_tensor.shard_fn = shard_fn + sharded_tensor.gather_fn = gather_fn + + # set the shard_fn and gather_fn as attributes of the distributed tensor + _hijack_detach_and_clone_for_customized_distributed_tensor(sharded_tensor) + + return sharded_tensor + + +def to_global_for_customized_distributed_tensor(dtensor: torch.Tensor) -> torch.Tensor: + """ + Gather the given tensor to the global tensor. + + Args: + dtensor (torch.Tensor): The distributed tensor. + + Returns: + torch.Tensor: The global tensor. + """ + assert is_customized_distributed_tensor(dtensor), 'The input tensor is not a customized distributed tensor.' + return dtensor.gather_fn(dtensor) + + +def customized_distributed_tensor_to_param(dtensor: torch.Tensor, requires_grad: bool = True): + """ + Convert the given customized distributed tensor to a parameter. + """ + assert is_customized_distributed_tensor(dtensor), 'The input tensor is not a customized distributed tensor.' + + param = torch.nn.Parameter(dtensor, requires_grad=requires_grad) + + # make it distributed as well + param.shard_fn = dtensor.shard_fn + param.gather_fn = dtensor.gather_fn + _hijack_detach_and_clone_for_customized_distributed_tensor(param) + return param diff --git a/tests/test_shardformer/test_layer/test_linear_1d.py b/tests/test_shardformer/test_layer/test_linear_1d.py index a2b8bf22c0b2..da3bdc1d78d3 100644 --- a/tests/test_shardformer/test_layer/test_linear_1d.py +++ b/tests/test_shardformer/test_layer/test_linear_1d.py @@ -27,8 +27,13 @@ def check_linear_1d_col(): # check computation correctness x = torch.rand(4, 32).cuda() - out = linear(x) - gather_out = linear_col(x) + x_for_unshard = x.expand_as(x.clone()) + x_for_unshard.requires_grad_(True) + x_for_shard = x.expand_as(x.clone()) + x_for_shard.requires_grad_(True) + + out = linear(x_for_unshard) + gather_out = linear_col(x_for_shard) assert_close(out, gather_out) # check backward correctness @@ -39,6 +44,11 @@ def check_linear_1d_col(): target_grad = torch.chunk(linear.weight.grad, 2, dim=0)[rank] assert_close(target_grad, linear_col.weight.grad) + # check the input gradients + assert x_for_shard.grad is not None + assert x_for_unshard.grad is not None + assert_close(x_for_unshard.grad, x_for_shard.grad) + def check_linear_1d_row(): linear = nn.Linear(32, 128).cuda() @@ -49,8 +59,14 @@ def check_linear_1d_row(): # check computation correctness x = torch.rand(4, 32).cuda() - out = linear(x) - gather_out = linear_row(x) + x_for_unshard = x.expand_as(x.clone()) + x_for_unshard.requires_grad_(True) + x_for_shard = x.expand_as(x.clone()) + x_for_shard.requires_grad_(True) + + # run forward + out = linear(x_for_unshard) + gather_out = linear_row(x_for_shard) assert_close(out, gather_out) # check backward correctness @@ -61,11 +77,49 @@ def check_linear_1d_row(): target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank] assert_close(target_grad, linear_row.weight.grad) + # check the input gradients + assert x_for_shard.grad is not None + assert x_for_unshard.grad is not None + assert_close(x_for_unshard.grad, x_for_shard.grad) + + +def check_linear_col_plus_row(): + linear_1 = nn.Linear(32, 128).cuda() + linear_2 = nn.Linear(128, 32).cuda() + linear_col = Linear1D_Col.from_native_module(linear_1, process_group=None, gather_output=False) + linear_row = Linear1D_Row.from_native_module(linear_2, process_group=None, parallel_input=True) + + # check computation correctness + x = torch.rand(4, 32).cuda() + x_for_unshard = x.expand_as(x.clone()) + x_for_unshard.requires_grad_(True) + x_for_shard = x.expand_as(x.clone()) + x_for_shard.requires_grad_(True) + + # run forward + unshard_out = linear_2(linear_1(x_for_unshard)) + shard_out = linear_row(linear_col(x_for_shard)) + assert_close(unshard_out, shard_out) + + # check backward correctness + unshard_out.sum().backward() + shard_out.sum().backward() + + rank = dist.get_rank() + target_1_grad = torch.chunk(linear_1.weight.grad, 2, dim=0)[rank] + assert_close(target_1_grad, linear_col.weight.grad) + + # check the input gradients + assert x_for_shard.grad is not None + assert x_for_unshard.grad is not None + assert_close(x_for_unshard.grad, x_for_shard.grad) + def run_dist(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') check_linear_1d_col() - # check_linear_1d_row() + check_linear_1d_row() + check_linear_col_plus_row() @rerun_if_address_is_in_use() diff --git a/tests/test_shardformer/test_layer/test_linearconv_1d.py b/tests/test_shardformer/test_layer/test_linearconv_1d.py index e0c97178d901..efdb88351519 100644 --- a/tests/test_shardformer/test_layer/test_linearconv_1d.py +++ b/tests/test_shardformer/test_layer/test_linearconv_1d.py @@ -5,6 +5,7 @@ import colossalai from colossalai.shardformer.layer import LinearConv1D_Col, LinearConv1D_Row +from colossalai.shardformer.layer.linear_conv import split_fused_qkv from colossalai.testing import rerun_if_address_is_in_use, spawn @@ -53,9 +54,15 @@ def check_linear_conv_1d_col(): linear = Conv1D(192, 48).cuda() linear_conv_col = LinearConv1D_Col.from_native_module(linear, process_group=None, gather_output=True, n_fused=3) - assert linear_conv_col.weight.shape == torch.Size([96, 48]) + assert linear.weight.shape == torch.Size([48, 192]) + assert linear.bias.shape == torch.Size([192]) + assert linear_conv_col.weight.shape == torch.Size([48, 96]) assert linear_conv_col.bias.shape == torch.Size([96]) + # ensure weights are reversibly loadable + linear_conv_col.load_state_dict(linear.state_dict()) + linear.load_state_dict(linear_conv_col.state_dict()) + # check computation correctness x = torch.rand(4, 48).cuda() out = linear(x) @@ -66,16 +73,16 @@ def check_linear_conv_1d_col(): out.sum().backward() gather_out.sum().backward() - rank = dist.get_rank() - target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank] - assert_close(target_grad.transpose(0, 1).contiguous(), linear_conv_col.weight.grad) + target_grad = split_fused_qkv(linear.weight.grad, 3, None) + assert_close(target_grad, linear_conv_col.weight.grad) def check_linear_1d_row(): linear = Conv1D(192, 48).cuda() linear_row = LinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False) - assert linear_row.weight.shape == torch.Size([192, 24]) + assert linear.weight.shape == torch.Size([48, 192]) + assert linear_row.weight.shape == torch.Size([24, 192]) assert linear_row.bias.shape == torch.Size([192]) # check computation correctness @@ -89,13 +96,14 @@ def check_linear_1d_row(): gather_out.sum().backward() rank = dist.get_rank() - target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank] + target_grad = torch.chunk(linear.weight.grad, 2, dim=0)[rank] assert_close(target_grad, linear_row.weight.grad) def run_dist(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') check_linear_conv_1d_col() + check_linear_1d_row() @rerun_if_address_is_in_use() diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 9aa02ec34d17..676267c2ca2a 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -20,20 +20,21 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check grad equality if org_model.__class__.__name__ == 'GPT2Model': - org_grad = org_model.h[0].attn.c_attn.weight.grad - shard_grad = sharded_model.h[0].attn.c_attn.weight.grad.transpose(0, 1).contiguous() + org_grad = org_model.h[0].mlp.c_fc.weight.grad + shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad else: org_grad = org_model.transformer.h[0].mlp.c_fc.weight.grad - shard_grad = sharded_model.transformer.h[0].mlp.c_fc.weight.grad.transpose(0, 1).contiguous() + shard_grad = sharded_model.transformer.h[0].mlp.c_fc.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) all_shard_grad = torch.cat(shard_grad_list, dim=1) assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + atol=1e-5), f"shard model loss is not equal to origin model loss\n{org_loss}\n{shard_loss}" + assert torch.allclose( + org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}" def check_gpt2(rank, world_size, port): From 92f6791095491e44c5712e14f00f2e19b52dc9f6 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Fri, 23 Jun 2023 18:00:22 +0800 Subject: [PATCH 382/413] [shardformer] Add layernorm (#4072) * add layernorm to bert * add layernorm test * add layernorm test with load state dict * add use_mixedfusedLN in shard config * refactor policy to support fused_layernorm --- colossalai/shardformer/layer/__init__.py | 3 +- colossalai/shardformer/layer/layernorm.py | 89 +++++++++++++ colossalai/shardformer/policies/bert.py | 122 ++++++++++++++++-- colossalai/shardformer/shard/shard_config.py | 4 +- .../test_layer/test_layernorm.py | 45 +++++++ .../test_layer/test_linearconv_1d.py | 4 +- tests/test_shardformer/test_model/_utils.py | 2 +- 7 files changed, 252 insertions(+), 17 deletions(-) create mode 100644 colossalai/shardformer/layer/layernorm.py create mode 100644 tests/test_shardformer/test_layer/test_layernorm.py diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py index 808ebbc12aeb..3ce0ef68aa4f 100644 --- a/colossalai/shardformer/layer/__init__.py +++ b/colossalai/shardformer/layer/__init__.py @@ -1,10 +1,11 @@ from .dropout import Dropout1D from .embedding import Embedding1D, VocabParallelEmbedding1D +from .layernorm import LayerNorm1D from .linear import Linear1D_Col, Linear1D_Row from .linear_conv import LinearConv1D_Col, LinearConv1D_Row from .loss import cross_entropy_1d __all__ = [ "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", "LinearConv1D_Col", "LinearConv1D_Row", - "Dropout1D", "cross_entropy_1d" + "Dropout1D", "cross_entropy_1d", 'LayerNorm1D' ] diff --git a/colossalai/shardformer/layer/layernorm.py b/colossalai/shardformer/layer/layernorm.py new file mode 100644 index 000000000000..a8e1d7a2c082 --- /dev/null +++ b/colossalai/shardformer/layer/layernorm.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +from typing import List, Union + +import torch +import torch.nn as nn +from torch.distributed import ProcessGroup + +from colossalai.kernel import LayerNorm +from colossalai.nn import init as init + +from .parallel_module import ParallelModule + +__all__ = ['LayerNorm1D'] + +Fast_LN = None +try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm + Fast_LN = FastLayerNorm +except ImportError: + pass + + +class LayerNorm1D(ParallelModule): + r""" + Layer Normalization for colossalai + + Args: + normalized_shape (int): input shape from an expected input of size. + :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] + \times \ldots \times \text{normalized_shape}[-1]]` + If a single integer is used, it is treated as a singleton list, and this module will + normalize over the last dimension which is expected to be of that specific size. + eps (float): a value added to the denominator for numerical stability, defaults to 1e-05. + bias (bool, optional): Whether to add a bias, defaults to ``True``. + dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + """ + + _fast_ln_supported_sizes = [ + 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, + 24576, 25600, 30720, 32768, 40960, 49152, 65536 + ] + + def __init__(self, + normalized_shape: int, + eps: int = 1e-05, + bias: bool = True, + dtype: torch.dtype = None, + device: torch.device = None): + super().__init__() + if Fast_LN is not None and normalized_shape in self._fast_ln_supported_sizes: + norm = Fast_LN(normalized_shape, eps=eps).to(dtype) + else: + norm = None + try: + from apex.normalization import FusedLayerNorm + norm = FusedLayerNorm(normalized_shape, eps=eps).to(dtype) + except ImportError: + norm = LayerNorm(normalized_shape, eps=eps, device=device, dtype=dtype) + self.norm = norm + + @staticmethod + def from_native_module(module: nn.LayerNorm, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, + **kwargs) -> ParallelModule: + r""" + Convert a native pytorch layer norm module to colossalai layer norm module + """ + normalized_shape = module.normalized_shape + eps = module.eps + bias = module.bias is not None + dtype = module.weight.dtype + device = module.weight.device + + # ensure only one process group is passed + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + # create layer norm + layer_norm = LayerNorm1D(normalized_shape, eps=eps, bias=bias, device=device, dtype=dtype).norm + + with torch.no_grad(): + # copy weight and bias + layer_norm.weight.copy_(module.weight) + if bias: + layer_norm.bias.copy_(module.bias) + return layer_norm diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 8649c0dbeaa6..1baf67ef9c02 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,8 +1,14 @@ import torch.nn as nn -from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead +from transformers.models.bert.modeling_bert import ( + BertEmbeddings, + BertForMultipleChoice, + BertForSequenceClassification, + BertForTokenClassification, + BertLayer, + BertLMPredictionHead, +) import colossalai.shardformer.layer as col_nn -from colossalai.shardformer.layer.dropout import Dropout1D from .._utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -24,7 +30,7 @@ def preprocess(self): return self.model def module_policy(self): - return { + base_policy = { BertLayer: ModulePolicyDescription( attribute_replacement={ @@ -53,10 +59,18 @@ def module_policy(self): suffix="attention.self.value", target_module=col_nn.Linear1D_Col, ), + SubModuleReplacementDescription( + suffix="attention.self.dropout", + target_module=col_nn.Dropout1D, + ), SubModuleReplacementDescription( suffix="attention.output.dense", target_module=col_nn.Linear1D_Row, ), + SubModuleReplacementDescription( + suffix="attention.output.dropout", + target_module=col_nn.Dropout1D, + ), SubModuleReplacementDescription( suffix="intermediate.dense", target_module=col_nn.Linear1D_Col, @@ -66,12 +80,8 @@ def module_policy(self): target_module=col_nn.Linear1D_Row, ), SubModuleReplacementDescription( - suffix="attention.self.dropout", - target_module=Dropout1D, - ), - SubModuleReplacementDescription( - suffix="attention.output.dropout", - target_module=Dropout1D, + suffix="output.dropout", + target_module=col_nn.Dropout1D, ) ]), BertEmbeddings: @@ -81,10 +91,32 @@ def module_policy(self): SubModuleReplacementDescription( suffix="word_embeddings", target_module=col_nn.VocabParallelEmbedding1D, + ), + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.Dropout1D, ) ]) } + if self.shard_config.fused_layernorm: + base_policy[BertLayer].sub_module_replacement.append( + SubModuleReplacementDescription( + suffix="attention.output.LayerNorm", + target_module=col_nn.LayerNorm1D, + )) + base_policy[BertLayer].sub_module_replacement.append( + SubModuleReplacementDescription( + suffix="output.LayerNorm", + target_module=col_nn.LayerNorm1D, + )) + base_policy[BertEmbeddings].sub_module_replacement.append( + SubModuleReplacementDescription( + suffix="LayerNorm", + target_module=col_nn.LayerNorm1D, + ),) + return base_policy + def new_model_class(self): # do nothing return self.model @@ -115,9 +147,15 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription(suffix="decoder", target_module=col_nn.Linear1D_Col, - kwargs={"gather_output": True}) + kwargs={"gather_output": True}), ]) } + if self.shard_config.fused_layernorm: + addon_module[BertLMPredictionHead].sub_module_replacement.append( + SubModuleReplacementDescription( + suffix="transform.LayerNorm", + target_module=col_nn.LayerNorm1D, + )) module_policy.update(addon_module) return module_policy @@ -146,9 +184,15 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription(suffix="decoder", target_module=col_nn.Linear1D_Col, - kwargs={"gather_output": True}) + kwargs={"gather_output": True}), ]) } + if self.shard_config.fused_layernorm: + addon_module[BertLMPredictionHead].sub_module_replacement.append( + SubModuleReplacementDescription( + suffix="transform.LayerNorm", + target_module=col_nn.LayerNorm1D, + )) module_policy.update(addon_module) return module_policy @@ -177,9 +221,15 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription(suffix="decoder", target_module=col_nn.Linear1D_Col, - kwargs={"gather_output": True}) + kwargs={"gather_output": True}), ]) } + if self.shard_config.fused_layernorm: + addon_module[BertLMPredictionHead].sub_module_replacement.append( + SubModuleReplacementDescription( + suffix="transform.LayerNorm", + target_module=col_nn.LayerNorm1D, + )) module_policy.update(addon_module) return module_policy @@ -199,6 +249,22 @@ class BertForSequenceClassificationPolicy(BertPolicy): def __init__(self) -> None: super().__init__() + def module_policy(self): + module_policy = super().module_policy() + addon_module = { + BertForSequenceClassification: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.Dropout1D, + ) + ]) + } + module_policy.update(addon_module) + return module_policy + # BertForTokenClassification class BertForTokenClassificationPolicy(BertPolicy): @@ -206,6 +272,22 @@ class BertForTokenClassificationPolicy(BertPolicy): def __init__(self) -> None: super().__init__() + def module_policy(self): + module_policy = super().module_policy() + addon_module = { + BertForTokenClassification: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.Dropout1D, + ) + ]) + } + module_policy.update(addon_module) + return module_policy + # BertForNextSentencePrediction class BertForNextSentencePredictionPolicy(BertPolicy): @@ -219,3 +301,19 @@ class BertForMultipleChoicePolicy(BertPolicy): def __init__(self) -> None: super().__init__() + + def module_policy(self): + module_policy = super().module_policy() + addon_module = { + BertForMultipleChoice: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.Dropout1D, + ) + ]) + } + module_policy.update(addon_module) + return module_policy diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 7379a8208745..8d3fc225e894 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -11,8 +11,9 @@ class ShardConfig: The config for sharding the huggingface model Args: - data_parallel_size (int): The size of data parallel tensor_parallel_size (int): The size of tensor parallel + use_mixedfusedLN (bool): Whether to use the `MixedFusedLayerNorm` + data_parallel_size (int): The size of data parallel pipeline_parallel_size (int): The size of pipeline parallel tensor_parallel_mode (List): The mode of tensor parallel, choose from `['1d','2d','2.5d','3d'] inference_only (bool): Whether to use the inference only mode, when setting to `True`, the model @@ -20,6 +21,7 @@ class ShardConfig: gather_output (bool): Whether to gather the output of the model of the last layer """ tensor_parallel_size: int + fused_layernorm: bool = False # TODO: add support for tensor parallel # pipeline_parallel_size: int diff --git a/tests/test_shardformer/test_layer/test_layernorm.py b/tests/test_shardformer/test_layer/test_layernorm.py new file mode 100644 index 000000000000..334ae05bed95 --- /dev/null +++ b/tests/test_shardformer/test_layer/test_layernorm.py @@ -0,0 +1,45 @@ +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.testing import assert_close + +import colossalai +from colossalai.shardformer.layer import LayerNorm1D +from colossalai.testing import rerun_if_address_is_in_use, spawn + + +def check_layernorm_1d(): + norm = nn.LayerNorm(128, 0.00001).cuda() + norm1d = LayerNorm1D.from_native_module(norm, process_group=None) + + assert norm1d.weight.shape == torch.Size([128]) + + # ensure state dict is reversibly loadable + norm.load_state_dict(norm1d.state_dict()) + norm1d.load_state_dict(norm.state_dict()) + + # check computation correctness + x = torch.rand(4, 128).cuda() + out = norm(x) + gather_out = norm1d(x) + assert_close(out, gather_out) + + # check backward correctness + out.sum().backward() + gather_out.sum().backward() + + assert_close(norm.weight.grad, norm1d.weight.grad) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + check_layernorm_1d() + + +@rerun_if_address_is_in_use() +def test_layernorm_1d(): + spawn(run_dist, nprocs=2) + + +if __name__ == '__main__': + test_layernorm_1d() diff --git a/tests/test_shardformer/test_layer/test_linearconv_1d.py b/tests/test_shardformer/test_layer/test_linearconv_1d.py index efdb88351519..774e6340eee6 100644 --- a/tests/test_shardformer/test_layer/test_linearconv_1d.py +++ b/tests/test_shardformer/test_layer/test_linearconv_1d.py @@ -77,7 +77,7 @@ def check_linear_conv_1d_col(): assert_close(target_grad, linear_conv_col.weight.grad) -def check_linear_1d_row(): +def check_linear_conv_1d_row(): linear = Conv1D(192, 48).cuda() linear_row = LinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False) @@ -103,7 +103,7 @@ def check_linear_1d_row(): def run_dist(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') check_linear_conv_1d_col() - check_linear_1d_row() + check_linear_conv_1d_row() @rerun_if_address_is_in_use() diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index 52ca7fce895b..a282e0bb919e 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -8,7 +8,7 @@ def build_model(world_size, model_fn): org_model = model_fn().cuda() # shard model - shard_config = ShardConfig(tensor_parallel_size=world_size) + shard_config = ShardConfig(tensor_parallel_size=world_size, fused_layernorm=True) model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) shard_former.init_distributed() From c4b1b65931a7614bfbed180c1e18c39b7cb61c2d Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 26 Jun 2023 15:50:07 +0800 Subject: [PATCH 383/413] [test] fixed tests failed due to dtensor change (#4082) * [test] fixed tests failed due to dtensor change * polish code --- .../tensor_shard/node_handler/node_handler.py | 2 +- .../strategy/matmul_strategy_generator.py | 6 +- .../auto_parallel/tensor_shard/utils/misc.py | 4 +- colossalai/checkpoint_io/utils.py | 4 +- colossalai/lazy/lazy_init.py | 11 ++- colossalai/tensor/comm_spec.py | 97 +++++++++---------- colossalai/tensor/d_tensor/comm_spec.py | 88 ++++++++--------- colossalai/tensor/d_tensor/layout.py | 13 +-- .../tensor/d_tensor/layout_converter.py | 71 +++++++------- colossalai/tensor/shape_consistency.py | 6 +- colossalai/tensor/sharding_spec.py | 6 +- test.py | 1 - .../test_autochunk_unet.py | 11 +-- .../test_gemini_checkpoint_io.py | 4 +- tests/test_device/test_device_mesh.py | 10 +- tests/test_device/test_init_logical_pg.py | 16 ++- .../test_hf_model/hf_tracer_utils.py | 14 ++- .../test_hf_model/test_hf_albert.py | 2 +- .../test_tracer/test_hf_model/test_hf_bert.py | 4 +- .../test_hf_model/test_hf_diffuser.py | 2 +- .../test_tracer/test_hf_model/test_hf_gpt.py | 4 +- .../test_tracer/test_hf_model/test_hf_opt.py | 2 +- .../test_tracer/test_hf_model/test_hf_t5.py | 9 +- .../test_timm_model/test_timm_model.py | 2 +- .../test_torchaudio_model.py | 2 +- .../test_torchrec_model/test_deepfm_model.py | 2 +- .../test_torchrec_model/test_dlrm_model.py | 2 +- .../test_torchvision_model.py | 2 +- tests/test_lazy/lazy_init_utils.py | 4 +- tests/test_lazy/test_distribute.py | 30 +++--- tests/test_lazy/test_models.py | 2 +- .../test_dtensor/test_comm_spec.py | 33 ++----- .../test_tensor/test_dtensor/test_dtensor.py | 2 +- .../test_dtensor/test_layout_converter.py | 43 +++----- tests/test_tensor/test_shape_consistency.py | 7 +- tests/test_tensor/test_sharded_linear.py | 2 +- tests/test_tensor/test_sharding_spec.py | 2 +- 37 files changed, 233 insertions(+), 289 deletions(-) delete mode 100644 test.py diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py b/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py index 4262d76173e4..b4b7b0e794d1 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py @@ -188,7 +188,7 @@ def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesV remove_strategy_list = [] for strategy in self.strategies_vector: shard_axis_list = [] - last_axis = len(self.device_mesh.mesh_shape) - 1 + last_axis = len(self.device_mesh.shape) - 1 for op_data, sharding_spec in strategy.sharding_specs.items(): if op_data.data is not None and isinstance(op_data.data, torch.Tensor): for dim, shard_axes in sharding_spec.dim_partition_dict.items(): diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py index 1ce5a08f2d6b..aa1581b99e0f 100644 --- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py +++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py @@ -984,7 +984,7 @@ def split_batch_dim_both_contract(self, mesh_dim_0, mesh_dim_1): def collate_strategies(self) -> List[ShardingStrategy]: strategy_list = [] device_mesh_is_1d = True - if len(self.device_mesh.mesh_shape) == 2 and 1 not in self.device_mesh.mesh_shape: + if len(self.device_mesh.shape) == 2 and 1 not in self.device_mesh.shape: device_mesh_is_1d = False if device_mesh_is_1d: @@ -992,10 +992,10 @@ def collate_strategies(self) -> List[ShardingStrategy]: # Sb = Sb x Sb # can be None as it is only for 1D device mesh # only for 1D device mesh - if len(self.device_mesh.mesh_shape) == 1: + if len(self.device_mesh.shape) == 1: mesh_dim = 0 else: - mesh_dim = self.device_mesh.mesh_shape.index(1) + mesh_dim = self.device_mesh.shape.index(1) strategy_list.append(self.split_one_batch_dim(mesh_dim)) else: # for 2D device mesh diff --git a/colossalai/auto_parallel/tensor_shard/utils/misc.py b/colossalai/auto_parallel/tensor_shard/utils/misc.py index 9e402dab7578..475e95fc4326 100644 --- a/colossalai/auto_parallel/tensor_shard/utils/misc.py +++ b/colossalai/auto_parallel/tensor_shard/utils/misc.py @@ -46,8 +46,8 @@ def check_sharding_spec_validity(sharding_spec: ShardingSpec, tensor: torch.Tens # make sure all dims are covered in sharding spec sharding_len = len(sharding_spec.sharding_sequence) tensor_num_dim = tensor.dim() - num_devices_in_col = sharding_spec.device_mesh.mesh_shape[0] - num_devices_in_row = sharding_spec.device_mesh.mesh_shape[1] + num_devices_in_col = sharding_spec.device_mesh.shape[0] + num_devices_in_row = sharding_spec.device_mesh.shape[1] assert sharding_len == tensor_num_dim, \ f'The ShardingSpec ({sharding_spec.sharding_sequence}) is created for {sharding_len}-dimension tensor, but the given tensor is {tensor_num_dim}-dimension ({tensor.shape}).' diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 68981dff0d0a..485577b9650c 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -99,7 +99,7 @@ def shard_model_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) for key, weight in state_dict.items(): ret_block = None ret_block_size = 0 - if is_distributed_tensor(weight): + if not is_distributed_tensor(weight): weight_size = calculate_tensor_size(weight) # If this weight is going to tip up over the maximal size, we split. @@ -146,7 +146,7 @@ def shard_optimizer_checkpoint(state_dict: dict, max_shard_size: int = 1024) -> continue # If the states are stored as DTensors, mark isDTensor as true. - if type(state_tensor) == DTensor: + if is_distributed_tensor(state_tensor): isDTensor = True state_size += calculate_tensor_size(state_tensor) diff --git a/colossalai/lazy/lazy_init.py b/colossalai/lazy/lazy_init.py index 1e45eced5f34..8b911407307c 100644 --- a/colossalai/lazy/lazy_init.py +++ b/colossalai/lazy/lazy_init.py @@ -1,5 +1,5 @@ from types import MethodType -from typing import Callable, Optional, Union +from typing import Callable, Dict, Optional, Union import torch import torch.distributed as dist @@ -173,7 +173,7 @@ def materialize(self) -> torch.Tensor: self.clean() return _convert_cls(self, target) - def distribute(self, layout: Layout) -> torch.Tensor: + def distribute(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> torch.Tensor: """Distribute the ``LazyTensor`` to ``torch.Tensor`` by modifying __class__ (inplace), according to the layout. Args: @@ -537,7 +537,10 @@ def apply_fn(name: str, p: LazyTensor): return _apply_to_lazy_module(module, apply_fn, verbose) @staticmethod - def distribute(module: nn.Module, layout_dict: dict, verbose: bool = False) -> nn.Module: + def distribute(module: nn.Module, + device_mesh: DeviceMesh, + sharding_spec_dict: Dict[str, ShardingSpec], + verbose: bool = False) -> nn.Module: """Distribute all ``nn.Parameter`` from ``LazyTensor``. This function will modify the module in-place. Args: @@ -547,7 +550,7 @@ def distribute(module: nn.Module, layout_dict: dict, verbose: bool = False) -> n """ def apply_fn(name: str, p: LazyTensor): - p.distribute(layout_dict[name]) + p.distribute(device_mesh, sharding_spec_dict[name]) return _apply_to_lazy_module(module, apply_fn, verbose) diff --git a/colossalai/tensor/comm_spec.py b/colossalai/tensor/comm_spec.py index af38d2a502c2..204f81343199 100644 --- a/colossalai/tensor/comm_spec.py +++ b/colossalai/tensor/comm_spec.py @@ -16,69 +16,66 @@ def _all_gather(tensor, comm_spec): ''' Implement all gather operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - tensor_list = [ - torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) - for _ in range(comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis]) - ] - # without this contiguous operation, the all gather may get some unexpected results. - tensor = tensor.contiguous() - dist.all_gather(tensor_list, tensor, group=process_group) - output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() - return output + process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() + process_group = process_groups[comm_spec.logical_process_axis] + + tensor_list = [ + torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) + for _ in range(comm_spec.device_mesh.shape[comm_spec.logical_process_axis]) + ] + # without this contiguous operation, the all gather may get some unexpected results. + tensor = tensor.contiguous() + dist.all_gather(tensor_list, tensor, group=process_group) + output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() + return output def _split(tensor, comm_spec): ''' Implement shard operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, _ in process_groups_list: - if dist.get_rank() in rank_list: - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // len(rank_list) - start = length * rank_list.index(dist.get_rank()) - output = torch.narrow(tensor, dim, start, length).contiguous() - return output + process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() + process_group = process_groups[comm_spec.logical_process_axis] + + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // dist.get_world_size(process_group) + start = length * dist.get_rank(process_group) + output = torch.narrow(tensor, dim, start, length).contiguous() + return output def _all_to_all(tensor, comm_spec): ''' Implement all to all operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - new_shape = list(tensor.shape) - new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // len(rank_list) - new_shape = torch.Size(new_shape) - output_tensor_list = [ - torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list)) - ] - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // len(rank_list) - input_tensor_list = [ - torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(len(rank_list)) - ] - group = process_group - dist.all_to_all(output_tensor_list, input_tensor_list, group) - output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() - return output + process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() + process_group = process_groups[comm_spec.logical_process_axis] + world_size = dist.get_world_size(process_group) + + new_shape = list(tensor.shape) + new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // world_size + new_shape = torch.Size(new_shape) + output_tensor_list = [torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)] + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // world_size + input_tensor_list = [torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(world_size)] + group = process_group + dist.all_to_all(output_tensor_list, input_tensor_list, group) + output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() + return output def _all_reduce(tensor, comm_spec, async_op=False): ''' Implement all reduce operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - if not tensor.is_contiguous(): - tensor = tensor.contiguous() - dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) - return tensor + process_groups = comm_spec.device_mesh.get_process_group_for_all_axes() + process_group = process_groups[comm_spec.logical_process_axis] + + if not tensor.is_contiguous(): + tensor = tensor.contiguous() + dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) + return tensor def _mix_gather(tensor, comm_spec): @@ -128,7 +125,7 @@ def _mix_gather(tensor, comm_spec): process_group = "[0, 1, 2, 3, 4, 5, 6, 7]" tensor_list = [(0,0),(1,0),(2,0),(3,0),(4,0),(5,0),(6,0),(7,0)] ''' - total_slices = comm_spec.device_mesh.mesh_shape[0] + total_slices = comm_spec.device_mesh.shape[0] tensor_list = [torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) for _ in range(total_slices)] leading_group_dim = comm_spec.logical_process_axes[0] assert len(comm_spec.device_mesh.process_groups_dict) == 1 @@ -149,7 +146,7 @@ def _mix_gather(tensor, comm_spec): if comm_spec.logical_process_axes[0] == comm_spec.logical_process_axes[1]: output = torch.cat(tuple(tensor_list), comm_spec.gather_dim[0]).contiguous() else: - mesh_shape = comm_spec.device_meshes.mesh_shape + mesh_shape = comm_spec.device_meshes.shape cat_slice = [mesh_shape[comm_spec.logical_process_axes[0]], mesh_shape[comm_spec.logical_process_axes[1]]] tmp_tensor_shape = list(tensor.shape) tmp_tensor_shape[comm_spec.gather_dim[0]] *= cat_slice[0] @@ -181,9 +178,9 @@ def _mix_split(tensor, comm_spec): # [4, 5, 6, 7]] # return {0: [0, 4, 1, 5, 2, 6, 3, 7], 1: [0, 1, 2, 3, 4, 5, 6, 7]} ''' - mesh_shape = comm_spec.device_meshes.mesh_shape + mesh_shape = comm_spec.device_meshes.shape dim = comm_spec.gather_dim - total_slices = comm_spec.device_mesh.mesh_shape[0] + total_slices = comm_spec.device_mesh.shape[0] # Get global rank rank = dist.get_rank() @@ -414,7 +411,7 @@ def __init__(self, self.forward_only = forward_only if isinstance(self.logical_process_axis, list): if not mix_gather: - self.device_mesh = self.sharding_spec.device_mesh.flatten_device_mesh + self.device_mesh = self.sharding_spec.device_mesh.flatten() self.logical_process_axis = 0 else: self.device_meshes = self.sharding_spec.device_mesh.flatten_device_meshes diff --git a/colossalai/tensor/d_tensor/comm_spec.py b/colossalai/tensor/d_tensor/comm_spec.py index 159125fa16db..79b2e3ef936a 100644 --- a/colossalai/tensor/d_tensor/comm_spec.py +++ b/colossalai/tensor/d_tensor/comm_spec.py @@ -24,12 +24,12 @@ class CommSpec: ''' Communication spec is used to record the communication action. It converts the communication spec to real action which will be used in runtime. It contains comm_pattern to determine the - communication method, process_groups_dict to determine the process groups, gather_dim and shard_dim + communication method, process_group_dict to determine the process groups, gather_dim and shard_dim to determine the buffer shape, and logical_process_axis Argument: - comm_pattern(CollectiveCommPattern): describe the communication method used in this spec. - process_groups_dict(Dict): A dict which contains the process groups used to apply this CommSpec. + comm_pattern(CollectiveCommPattern): decribe the communication method used in this spec. + process_group_dict(Dict): A dict which contains the process groups used to apply this CommSpec. gather_dim(int, Optional): The gather_dim of the tensor will be gathered. shard_dim(int, Optional): The shard_dim of the tensor will be sharded. logical_process_axis(Union(int, List[int]), Optional): The mesh_dim to implement the communication action. @@ -37,7 +37,7 @@ class CommSpec: def __init__(self, comm_pattern: CollectiveCommPattern, - process_groups_dict: Dict, + process_group_dict: Dict, gather_dim: int = None, shard_dim: int = None, logical_process_axis: int = None): @@ -45,7 +45,7 @@ def __init__(self, self.gather_dim = gather_dim self.shard_dim = shard_dim self.logical_process_axis = logical_process_axis - self.process_groups_dict = process_groups_dict + self.process_group_dict = process_group_dict def __repr__(self): res_list = ["CommSpec:("] @@ -92,68 +92,56 @@ def _all_gather(tensor: torch.Tensor, comm_spec: CommSpec): ''' Implement all gather operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - tensor_list = [ - torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list)) - ] - # without this contiguous operation, the all gather may get some unexpected results. - tensor = tensor.contiguous() - dist.all_gather(tensor_list, tensor, group=process_group) - output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() - return output + process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] + world_size = dist.get_world_size(process_group) + tensor_list = [torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)] + # without this contiguous operation, the all gather may get some unexpected results. + tensor = tensor.contiguous() + dist.all_gather(tensor_list, tensor, group=process_group) + output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous() + return output def _split(tensor: torch.Tensor, comm_spec: CommSpec): ''' Implement shard operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, _ in process_groups_list: - if dist.get_rank() in rank_list: - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // len(rank_list) - start = length * rank_list.index(dist.get_rank()) - output = torch.narrow(tensor, dim, start, length).contiguous() - return output + process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // dist.get_world_size(process_group) + start = length * dist.get_rank(process_group) + output = torch.narrow(tensor, dim, start, length).contiguous() + return output def _all_to_all(tensor: torch.Tensor, comm_spec: CommSpec): ''' Implement all to all operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - new_shape = list(tensor.shape) - new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // len(rank_list) - new_shape = torch.Size(new_shape) - output_tensor_list = [ - torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list)) - ] - dim = comm_spec.shard_dim - length = tensor.shape[comm_spec.shard_dim] // len(rank_list) - input_tensor_list = [ - torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(len(rank_list)) - ] - group = process_group - dist.all_to_all(output_tensor_list, input_tensor_list, group) - output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() - return output + process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] + world_size = dist.get_world_size(process_group) + new_shape = list(tensor.shape) + new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // world_size + new_shape = torch.Size(new_shape) + output_tensor_list = [torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(world_size)] + dim = comm_spec.shard_dim + length = tensor.shape[comm_spec.shard_dim] // world_size + input_tensor_list = [torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(world_size)] + group = process_group + dist.all_to_all(output_tensor_list, input_tensor_list, group) + output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous() + return output def _all_reduce(tensor: torch.Tensor, comm_spec: CommSpec, async_op: bool = False): ''' Implement all reduce operation on device mesh based on information provided by comm_spec. ''' - process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis] - for rank_list, process_group in process_groups_list: - if dist.get_rank() in rank_list: - if not tensor.is_contiguous(): - tensor = tensor.contiguous() - dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) - return tensor + process_group = comm_spec.process_group_dict[comm_spec.logical_process_axis] + if not tensor.is_contiguous(): + tensor = tensor.contiguous() + dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op) + return tensor class _ReduceGrad(torch.autograd.Function): @@ -269,7 +257,7 @@ def symbolic(graph, input_): def forward(ctx, input_, comm_spec): output = _all_to_all(input_, comm_spec) comm_spec_for_backward = CommSpec(comm_pattern=comm_spec.comm_pattern, - process_groups_dict=comm_spec.process_groups_dict, + process_group_dict=comm_spec.process_group_dict, gather_dim=comm_spec.shard_dim, shard_dim=comm_spec.gather_dim, logical_process_axis=comm_spec.logical_process_axis) diff --git a/colossalai/tensor/d_tensor/layout.py b/colossalai/tensor/d_tensor/layout.py index 4185b85860e3..a35b2f43e44b 100644 --- a/colossalai/tensor/d_tensor/layout.py +++ b/colossalai/tensor/d_tensor/layout.py @@ -14,24 +14,21 @@ class Layout: Attributes: device_mesh: the device mesh to store the tensor distributed. - device_type: the type of the device mesh, e.g. 'cpu' or 'cuda'. sharding_spec: the sharding specification to describe how the tensor is sharded. - entire_shape: the entire shape of the global tensor. + global_shape: the entire shape of the global tensor. """ - def __init__(self, device_mesh: DeviceMesh, device_type: torch.device, sharding_spec: ShardingSpec, - entire_shape: torch.Size): + def __init__(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec, global_shape: torch.Size): self.device_mesh = device_mesh - self.device_type = device_type self.sharding_spec = sharding_spec - self.entire_shape = entire_shape + self.global_shape = global_shape self._sanity_check() def __hash__(self) -> int: return hash(f'{self.sharding_spec}') def get_sharded_shape_per_device(self): - sharded_shape = list(self.entire_shape) + sharded_shape = list(self.global_shape) for dim, shard_list in self.sharding_spec.dim_partition_dict.items(): mesh_list = [self.device_mesh.shape[mesh_dim] for mesh_dim in shard_list] shard_partitions = reduce(operator.mul, mesh_list, 1) @@ -56,7 +53,7 @@ def _sanity_check(self): # make sure that the sharding for a dimension is divisible by the number of devices for dim, shard_list in sharding_spec.dim_partition_dict.items(): - tensor_dim_size = self.entire_shape[dim] + tensor_dim_size = self.global_shape[dim] num_devices = 1 for element in shard_list: diff --git a/colossalai/tensor/d_tensor/layout_converter.py b/colossalai/tensor/d_tensor/layout_converter.py index 14f9c4561622..528ed7901c4f 100644 --- a/colossalai/tensor/d_tensor/layout_converter.py +++ b/colossalai/tensor/d_tensor/layout_converter.py @@ -3,10 +3,8 @@ from dataclasses import dataclass from typing import Dict, List, Tuple -import numpy as np import torch -from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, TrainCycleItem from colossalai.context.singleton_meta import SingletonMeta from colossalai.tensor.d_tensor.comm_spec import * from colossalai.tensor.d_tensor.layout import Layout @@ -37,6 +35,9 @@ def set_layout_converting_options(options: LayoutConverterOptions): class LayoutConverter(metaclass=SingletonMeta): + """ + LayoutConverter is a singleton class which converts the layout of a distributed tensor. + """ def __init__(self): self._options = None @@ -79,15 +80,14 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) dim_partition_dict = {0: [0], 1: [1]} # [S0,S1,R] sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec, - entire_shape=entire_shape) + global_shape=global_shape) rst_dict = layout_converter.all_gather_transform_layouts(layout) for layout, comm_spec in rst_dict.items(): @@ -100,7 +100,12 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co valid_spec_dict = {} comm_pattern = CollectiveCommPattern.GATHER_FWD_SPLIT_BWD source_spec = source_layout.sharding_spec - process_groups_dict = source_layout.device_mesh.process_groups_dict + + # the key of the dict is the axis + # the value is the process group + current_rank = source_layout.device_mesh._global_rank_of_current_process + process_group_dict = source_layout.device_mesh._process_group_dict[current_rank] + for target_pair in source_spec.dim_partition_dict.items(): shard_list = all_gather_simulator(target_pair) index = target_pair[0] @@ -118,7 +123,7 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co logical_process_axis = target_pair[1][-1] comm_spec = CommSpec( comm_pattern, - process_groups_dict=process_groups_dict, + process_group_dict=process_group_dict, gather_dim=gather_dim, # shard_dim will be used during backward shard_dim=gather_dim, @@ -129,8 +134,7 @@ def all_gather_transform_layouts(self, source_layout: Layout) -> Dict[Layout, Co new_sharding_spec = ShardingSpec(source_spec.dims, dim_partition_dict=new_dim_partition_dict) new_layout = Layout(device_mesh=source_layout.device_mesh, sharding_spec=new_sharding_spec, - device_type=source_layout.device_type, - entire_shape=source_layout.entire_shape) + global_shape=source_layout.global_shape) valid_spec_dict[new_layout] = comm_spec except LayoutException: @@ -155,15 +159,14 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) dim_partition_dict = {0: [0], 1: [1]} # [S0,S1,R] sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec, - entire_shape=entire_shape) + global_shape=global_shape) rst_dict = layout_converter.all_to_all_transform_layout(layout) for layout, comm_spec in rst_dict.items(): @@ -176,7 +179,12 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com ''' valid_spec_dict = {} comm_pattern = CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD - process_groups_dict = source_layout.device_mesh.process_groups_dict + + # the key of the dict is the axis + # the value is the process group + current_rank = source_layout.device_mesh._global_rank_of_current_process + process_group_dict = source_layout.device_mesh._process_group_dict[current_rank] + source_spec = source_layout.sharding_spec tensor_dims = source_spec.dims for f_index in range(tensor_dims - 1): @@ -217,7 +225,7 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com shard_dim = f_index logical_process_axis = b_target_pair[1][-1] comm_spec = CommSpec(comm_pattern, - process_groups_dict, + process_group_dict=process_group_dict, gather_dim=gather_dim, shard_dim=shard_dim, logical_process_axis=logical_process_axis) @@ -240,8 +248,7 @@ def all_to_all_transform_layout(self, source_layout: Layout) -> Dict[Layout, Com new_sharding_spec = ShardingSpec(source_spec.dims, dim_partition_dict=new_dim_partition_dict) new_layout = Layout(device_mesh=source_layout.device_mesh, sharding_spec=new_sharding_spec, - device_type=source_layout.device_type, - entire_shape=source_layout.entire_shape) + global_shape=source_layout.global_shape) valid_spec_dict[new_layout] = comm_spec except LayoutException: pass @@ -266,16 +273,15 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) dim_partition_dict = {0: [0]} # [S0,R,R] sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec, - entire_shape=entire_shape) + global_shape=global_shape) rst_dict = layout_converter.shard_transform_layout(layout) for layout, comm_spec in rst_dict.items(): @@ -289,7 +295,11 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec valid_spec_dict = {} comm_pattern = CollectiveCommPattern.SPLIT_FWD_GATHER_BWD source_spec = source_layout.sharding_spec - process_groups_dict = source_layout.device_mesh.process_groups_dict + + # the key of the dict is the axis + # the value is the process group + current_rank = source_layout.device_mesh._global_rank_of_current_process + process_group_dict = source_layout.device_mesh._process_group_dict[current_rank] # legal sharding dims means the mesh_id is still available to use. legal_sharding_dims = [i for i in range(len(source_layout.device_mesh.shape))] @@ -317,7 +327,7 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec shard_dim = index logical_process_axis = shard_list[-1] comm_spec = CommSpec(comm_pattern, - process_groups_dict, + process_group_dict=process_group_dict, gather_dim=shard_dim, shard_dim=shard_dim, logical_process_axis=logical_process_axis) @@ -328,8 +338,7 @@ def shard_transform_layout(self, source_layout: Layout) -> Dict[Layout, CommSpec dim_partition_dict=new_dim_partition_dict) new_layout = Layout(device_mesh=source_layout.device_mesh, sharding_spec=new_sharding_spec, - device_type=source_layout.device_type, - entire_shape=source_layout.entire_shape) + global_shape=source_layout.global_shape) valid_spec_dict[new_layout] = comm_spec except LayoutException: pass @@ -387,7 +396,7 @@ def layout_converting(self, source_layout: Layout, # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) dim_partition_source = {1: [0, 1]} dim_partition_target = {0: [0, 1]} @@ -395,16 +404,14 @@ def layout_converting(self, source_layout: Layout, # [R,S01,R] sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) source_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec_source, - entire_shape=entire_shape) + global_shape=global_shape) # [S01,R,R] sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) target_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec_target, - entire_shape=entire_shape) + global_shape=global_shape) transform_path, comm_action_sequence = layout_converter.layout_converting(source_layout, target_layout) transform_path_str = '->'.join([str(layout.sharding_spec.sharding_sequence) for layout in transform_path]) @@ -493,21 +500,19 @@ def apply(self, tensor: torch.Tensor, source_layout: Layout, target_layout: Layo # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - entire_shape = (4, 4, 4) + global_shape = (4, 4, 4) # [S0,R,R] sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) source_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec_source, - entire_shape=entire_shape) + global_shape=global_shape) # [R,S0,R] sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) target_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), sharding_spec=sharding_spec_target, - entire_shape=entire_shape) + global_shape=global_shape) if rank in (0, 1): sharded_tensor_0 = torch.zeros(2, 1) diff --git a/colossalai/tensor/shape_consistency.py b/colossalai/tensor/shape_consistency.py index 5bec552d69d5..99d782c3f6e8 100644 --- a/colossalai/tensor/shape_consistency.py +++ b/colossalai/tensor/shape_consistency.py @@ -285,7 +285,7 @@ def get_all_shard_spec(self, source_spec: ShardingSpec, orig_cost_dict): comm_pattern = CollectiveCommPattern.SPLIT_FWD_GATHER_BWD # legal sharding dims means the mesh_id is still available to use. - legal_sharding_dims = [i for i in range(len(source_spec.device_mesh.mesh_shape))] + legal_sharding_dims = [i for i in range(len(source_spec.device_mesh.shape))] for dim, shard_list in source_spec.dim_partition_dict.items(): for element in shard_list: legal_sharding_dims.remove(element) @@ -435,7 +435,7 @@ def gather_analysis(comm_spec: CommSpec, discard_input: bool, alloc_numel: int, """ input_shape = compute_shape(comm_spec.sharding_spec) input_numel = np.prod(input_shape) - output_numel = input_numel * comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis] + output_numel = input_numel * comm_spec.device_mesh.shape[comm_spec.logical_process_axis] peak_numel = max(peak_numel, alloc_numel + output_numel * 2) alloc_numel += output_numel if discard_input: @@ -461,7 +461,7 @@ def split_analysis(comm_spec: CommSpec, discard_input: bool, alloc_numel: int, p # generate a new tensor input_shape = compute_shape(comm_spec.sharding_spec) input_numel = np.prod(input_shape) - output_numel = input_numel // comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis] + output_numel = input_numel // comm_spec.device_mesh.shape[comm_spec.logical_process_axis] alloc_numel += output_numel peak_numel = max(peak_numel, alloc_numel) if discard_input: diff --git a/colossalai/tensor/sharding_spec.py b/colossalai/tensor/sharding_spec.py index 406ad49097b5..e594fd297dc4 100644 --- a/colossalai/tensor/sharding_spec.py +++ b/colossalai/tensor/sharding_spec.py @@ -195,7 +195,7 @@ def __init__(self, def __repr__(self): res_list = ["DistSpec:"] res_list.append(f"\n\tshard_sequence: " + ",".join(str(dimspec) for dimspec in self.sharding_sequence)) - res_list.append(f"\n\tdevice_mesh_shape: {self.device_mesh.mesh_shape}") + res_list.append(f"\n\tdevice_mesh_shape: {self.device_mesh.shape}") return ' '.join(res_list) def _sanity_check(self): @@ -222,7 +222,7 @@ def _sanity_check(self): num_devices = 1 for element in shard_list: - num_devices *= self.device_mesh.mesh_shape[element] + num_devices *= self.device_mesh.shape[element] if tensor_dim_size % num_devices != 0: raise ShardingNotDivisibleError( @@ -288,7 +288,7 @@ def get_sharded_shape_per_device(self): sharded_shape = list(self.entire_shape) for dim, shard_list in self.dim_partition_dict.items(): - mesh_list = [self.device_mesh.mesh_shape[mesh_dim] for mesh_dim in shard_list] + mesh_list = [self.device_mesh.shape[mesh_dim] for mesh_dim in shard_list] shard_partitions = reduce(operator.mul, mesh_list, 1) assert sharded_shape[ dim] % shard_partitions == 0, f'Cannot shard dimension {dim} into {shard_partitions} partitions.' diff --git a/test.py b/test.py deleted file mode 100644 index f283e21a1ebd..000000000000 --- a/test.py +++ /dev/null @@ -1 +0,0 @@ -from colossalai.tensor.d_tensor.api import to_distributed_tensor diff --git a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py index fc9d8455ed5c..f0cf2a5fcbca 100644 --- a/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py +++ b/tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py @@ -58,13 +58,4 @@ def test_evoformer_block(model, shape, max_memory): if __name__ == "__main__": - run_test( - rank=0, - data=get_data(LATENTS_SHAPE), - max_memory=None, - model=UNet2DModel, - print_code=False, - print_mem=True, - print_est_mem=False, - print_progress=False, - ) + test_evoformer_block() diff --git a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py index 14d69cab2176..602cf468c944 100644 --- a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py @@ -22,7 +22,7 @@ @parameterize('use_safetensors', [False, True]) def exam_state_dict_with_origin(placement_policy, model_name, use_safetensors: bool): from transformers import BertForSequenceClassification - (model_fn, data_gen_fn, output_transform_fn, _) = next(iter(model_zoo.get_sub_registry(model_name).values())) + (model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values())) bert_model = model_fn() with shared_tempdir() as tempdir: @@ -53,7 +53,7 @@ def exam_state_dict_with_origin(placement_policy, model_name, use_safetensors: b @parameterize('shard', [True, False]) @parameterize('model_name', ['transformers_gpt']) def exam_state_dict(placement_policy, shard: bool, model_name: str): - (model_fn, data_gen_fn, output_transform_fn, _) = next(iter(model_zoo.get_sub_registry(model_name).values())) + (model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values())) criterion = lambda x: x.mean() plugin = GeminiPlugin(placement_policy=placement_policy) booster = Booster(plugin=plugin) diff --git a/tests/test_device/test_device_mesh.py b/tests/test_device/test_device_mesh.py index e9f0f9477e4a..590d6966bff6 100644 --- a/tests/test_device/test_device_mesh.py +++ b/tests/test_device/test_device_mesh.py @@ -8,18 +8,16 @@ def test_device_mesh(): - physical_mesh_id = torch.arange(0, 16).reshape(2, 8) + physical_mesh_id = torch.arange(0, 16) mesh_shape = (4, 4) # [[0, 1, 2, 3], # [4, 5, 6, 7], # [8, 9, 10,11], # [12,13,14,15]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) - assert device_mesh.convert_map[5] == [1, 1] - assert device_mesh.convert_map[11] == [2, 3] - assert device_mesh.global_rank_to_process_groups_with_logical_rank(0)[0] == [[0, 0], [1, 0], [2, 0], [3, 0]] - assert device_mesh.global_rank_to_process_groups_with_logical_rank(2)[1] == [[0, 0], [0, 1], [0, 2], [0, 3]] - assert device_mesh.global_rank_to_process_groups_with_global_rank(2)[1] == [0, 1, 2, 3] + assert device_mesh.global_rank_to_local_rank(5) == [1, 1] + assert device_mesh.global_rank_to_local_rank(11) == [2, 3] + assert device_mesh.get_ranks_in_process_group(axis=1, global_rank=2) == [0, 1, 2, 3] def check_1d_device_mesh(): diff --git a/tests/test_device/test_init_logical_pg.py b/tests/test_device/test_init_logical_pg.py index 2b7060c4846a..7c6339eff67e 100644 --- a/tests/test_device/test_init_logical_pg.py +++ b/tests/test_device/test_init_logical_pg.py @@ -20,16 +20,12 @@ def check_layer(rank, world_size, port): # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - logical_pg_dict = {0: [[0, 2], [1, 3]], 1: [[0, 1], [2, 3]]} - logical_process_groups = device_mesh.process_groups_dict - - for mesh_dim, pgs in logical_pg_dict.items(): - for index, pg in enumerate(pgs): - if rank in pg: - tensor = torch.ones(4).cuda() - group = logical_process_groups[mesh_dim][index][1] - dist.all_reduce(tensor, op=ReduceOp.SUM, group=group) - assert tensor.equal(tensor_to_check) + + for axis in range(len(mesh_shape)): + tensor = torch.ones(4).cuda() + pg = device_mesh.get_process_group(axis=axis) + dist.all_reduce(tensor, op=ReduceOp.SUM, group=pg) + assert tensor.equal(tensor_to_check) gpc.destroy() diff --git a/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py b/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py index 7a4bf131ae36..58c8132e1490 100644 --- a/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py +++ b/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py @@ -1,3 +1,5 @@ +from typing import List + import torch from numpy import isin from torch.fx import GraphModule @@ -7,19 +9,23 @@ from colossalai._analyzer.fx import symbolic_trace -def trace_model_and_compare_output(model, data_gen): +def trace_model_and_compare_output(model, data_gen, ignore_data: List[str] = None): # must turn on eval mode to ensure the output is consistent model.eval() + inputs = data_gen() + + if ignore_data is not None: + # drop the ignore_data key + inputs = {k: v for k, v in inputs.items() if k not in ignore_data} + try: - kwargs = data_gen() - meta_args = {k: v.to('meta') for k, v in kwargs.items()} + meta_args = {k: v.to('meta') for k, v in inputs.items()} gm = symbolic_trace(model, meta_args=meta_args) except Exception as e: raise RuntimeError(f"Failed to trace {model.__class__.__name__}, error: {e}") # run forward - inputs = data_gen() non_fx_out = model(**inputs) fx_out = gm(**inputs) diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py index f4d681221191..a1470400ad82 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py @@ -15,7 +15,7 @@ def test_albert(): sub_registry = model_zoo.get_sub_registry('transformers_albert') - for name, (model_fn, data_gen_fn, _, _) in sub_registry.items(): + for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items(): model = model_fn() trace_model_and_compare_output(model, data_gen_fn) diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py index a833bb30c056..632ad366ccc4 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py @@ -12,9 +12,9 @@ def test_bert(): sub_registry = model_zoo.get_sub_registry('transformers_bert') - for name, (model_fn, data_gen_fn, _, _) in sub_registry.items(): + for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items(): model = model_fn() - trace_model_and_compare_output(model, data_gen_fn) + trace_model_and_compare_output(model, data_gen_fn, ignore_data=['labels', 'next_sentence_label']) if __name__ == '__main__': diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py index ccbe2da58bf2..ac87a7fcb13b 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py @@ -47,7 +47,7 @@ def test_diffusers(): sub_model_zoo = model_zoo.get_sub_registry('diffusers') - for name, (model_fn, data_gen_fn, output_transform_fn, _, attribute) in sub_model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, _, attribute) in sub_model_zoo.items(): data = data_gen_fn() trace_and_compare(model_fn, data, output_transform_fn) torch.cuda.synchronize() diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py index 67107469d8bb..31bcb7028e25 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py @@ -12,7 +12,7 @@ def test_gpt(): sub_registry = model_zoo.get_sub_registry('transformers_gpt') - for name, (model_fn, data_gen_fn, _, _) in sub_registry.items(): + for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items(): model = model_fn() # TODO: support the following models @@ -21,7 +21,7 @@ def test_gpt(): if model.__class__.__name__ in ['GPT2DoubleHeadsModel']: continue - trace_model_and_compare_output(model, data_gen_fn) + trace_model_and_compare_output(model, data_gen_fn, ignore_data=['labels']) if __name__ == '__main__': diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py index 369545b03de1..f528db6a64ef 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py @@ -12,7 +12,7 @@ def test_opt(): sub_registry = model_zoo.get_sub_registry('transformers_opt') - for name, (model_fn, data_gen_fn, _, _) in sub_registry.items(): + for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items(): model = model_fn() trace_model_and_compare_output(model, data_gen_fn) diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py index 811cf3b21430..45e06bc2bbb0 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py @@ -12,9 +12,14 @@ def test_t5(): sub_registry = model_zoo.get_sub_registry('transformers_t5') - for name, (model_fn, data_gen_fn, _, _) in sub_registry.items(): + for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items(): + if name == "transformers_t5_for_conditional_generation": + # cannot trace for loss function yet + # so we use a data gen which does not produce labels + data_gen_fn = sub_registry.get('transformers_t5')[1] + model = model_fn() - trace_model_and_compare_output(model, data_gen_fn) + trace_model_and_compare_output(model, data_gen_fn, ignore_data=['labels']) if __name__ == '__main__': diff --git a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py index 117c70c84aa8..98433b8f7c3b 100644 --- a/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py +++ b/tests/test_fx/test_tracer/test_timm_model/test_timm_model.py @@ -56,7 +56,7 @@ def test_timm_models(): sub_model_zoo = model_zoo.get_sub_registry('timm') - for name, (model_fn, data_gen_fn, output_transform_fn, _, attribute) in sub_model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, _, attribute) in sub_model_zoo.items(): data = data_gen_fn() if attribute is not None and attribute.has_control_flow: meta_args = {k: v.to('meta') for k, v in data.items()} diff --git a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py index f73c5bb9a590..2b7def5bef85 100644 --- a/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py +++ b/tests/test_fx/test_tracer/test_torchaudio_model/test_torchaudio_model.py @@ -16,7 +16,7 @@ def test_torchaudio_models(): sub_model_zoo = model_zoo.get_sub_registry('torchaudio') - for name, (model_fn, data_gen_fn, output_transform_fn, _, attribute) in sub_model_zoo.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, _, attribute) in sub_model_zoo.items(): model = model_fn() trace_and_compare(model, data_gen_fn, diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py index df02568c0049..f969c8e6c3da 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py @@ -53,7 +53,7 @@ def test_torchrec_deepfm_models(): deepfm_models = model_zoo.get_sub_registry('deepfm') torch.backends.cudnn.deterministic = True - for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in deepfm_models.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, attribute) in deepfm_models.items(): data = data_gen_fn() if attribute is not None and attribute.has_control_flow: meta_args = {k: v.to('meta') for k, v in data.items()} diff --git a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py index 9776452be9c8..94fb24f33376 100644 --- a/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py +++ b/tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py @@ -53,7 +53,7 @@ def test_torchrec_dlrm_models(): torch.backends.cudnn.deterministic = True dlrm_models = model_zoo.get_sub_registry('dlrm') - for name, (model_fn, data_gen_fn, output_transform_fn, attribute) in dlrm_models.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, attribute) in dlrm_models.items(): data = data_gen_fn() # dlrm_interactionarch is not supported diff --git a/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py b/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py index bd259475ae5a..74cb753e2937 100644 --- a/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py +++ b/tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py @@ -10,7 +10,7 @@ def test_torchvision_models(): torch.backends.cudnn.deterministic = True tv_sub_registry = model_zoo.get_sub_registry('torchvision') - for name, (model_fn, data_gen_fn, output_transform_fn, model_attribute) in tv_sub_registry.items(): + for name, (model_fn, data_gen_fn, output_transform_fn, _, model_attribute) in tv_sub_registry.items(): data = data_gen_fn() if model_attribute is not None and model_attribute.has_stochastic_depth_prob: diff --git a/tests/test_lazy/lazy_init_utils.py b/tests/test_lazy/lazy_init_utils.py index 3879363bcd1b..73c3c5422d8a 100644 --- a/tests/test_lazy/lazy_init_utils.py +++ b/tests/test_lazy/lazy_init_utils.py @@ -6,6 +6,7 @@ import torch from packaging import version +from colossalai.device.device_mesh import DeviceMesh from colossalai.lazy.lazy_init import LazyInitContext, LazyTensor, _MyTensor from colossalai.tensor.d_tensor import to_global from colossalai.tensor.d_tensor.layout import Layout @@ -82,7 +83,8 @@ def check_lazy_init(entry: TestingEntry, seed: int = 42, verbose: bool = False, print(f'{model.__class__.__name__} pass') -def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn.Module, layout_dict: dict) -> None: +def assert_dist_model_equal(model: torch.nn.Module, distributed_model: torch.nn.Module, device_mesh: DeviceMesh, + sharding_spec_dict: dict) -> None: state = model.state_dict() distributed_state = distributed_model.state_dict() diff --git a/tests/test_lazy/test_distribute.py b/tests/test_lazy/test_distribute.py index f33c037e3de6..622d9deb601d 100644 --- a/tests/test_lazy/test_distribute.py +++ b/tests/test_lazy/test_distribute.py @@ -26,23 +26,19 @@ def find_shard_dim(shape: torch.Size) -> Optional[int]: return dim -def make_layout(device_mesh: DeviceMesh, original_tensor: torch.Tensor) -> Layout: +def make_sharding_spec(original_tensor: torch.Tensor) -> Layout: shard_dim = find_shard_dim(original_tensor.shape) dim_partition_dict = {shard_dim: [0]} if shard_dim is not None else {} target_sharding_spec = ShardingSpec(dim_size=original_tensor.dim(), dim_partition_dict=dim_partition_dict) - layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=target_sharding_spec, - entire_shape=original_tensor.shape) - return layout + return target_sharding_spec def _get_current_name(prefix: str, name: str) -> str: return f'{prefix}.{name}'.lstrip('.') -def generate_layout_dict(model: nn.Module, device_mesh: DeviceMesh) -> dict: - layout_dict = {} +def generate_sharding_spec_dict(model: nn.Module) -> dict: + sharding_spec_dict = {} @torch.no_grad() def generate_recursively(module: nn.Module, prefix: str = ''): @@ -53,17 +49,17 @@ def generate_recursively(module: nn.Module, prefix: str = ''): # initialize tensors directly attached to the current module for name, param in module.named_parameters(recurse=False): if isinstance(param, LazyTensor): - layout = make_layout(device_mesh, param) - layout_dict[_get_current_name(prefix, name)] = layout + sharding_spec = make_sharding_spec(param) + sharding_spec_dict[_get_current_name(prefix, name)] = sharding_spec for name, buf in module.named_buffers(recurse=False): if isinstance(buf, LazyTensor): - layout = make_layout(device_mesh, buf) - layout_dict[_get_current_name(prefix, name)] = layout + sharding_spec = make_sharding_spec(buf) + sharding_spec_dict[_get_current_name(prefix, name)] = sharding_spec generate_recursively(model) - return layout_dict + return sharding_spec_dict @parameterize('subset', ['torchvision', 'diffusers', 'timm', 'transformers', 'torchaudio', 'deepfm', 'dlrm']) @@ -75,7 +71,7 @@ def run_dist_lazy_init(subset, seed: int = 42): for name, entry in sub_model_zoo.items(): # TODO(ver217): lazy init does not support weight norm, skip these models - if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base'): + if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base') or name.startswith('transformers_llama'): continue print_rank_0(name) model_fn, data_gen_fn, output_transform_fn, _, model_attr = entry @@ -85,9 +81,9 @@ def run_dist_lazy_init(subset, seed: int = 42): ctx = LazyInitContext() with ctx: deferred_model = model_fn() - layout_dict = generate_layout_dict(deferred_model, device_mesh) - ctx.distribute(deferred_model, layout_dict, verbose=True) - assert_dist_model_equal(model, deferred_model, layout_dict) + sharding_spec_dict = generate_sharding_spec_dict(deferred_model) + ctx.distribute(deferred_model, device_mesh, sharding_spec_dict, verbose=True) + assert_dist_model_equal(model, deferred_model, device_mesh, sharding_spec_dict) def run_dist(rank, world_size, port) -> None: diff --git a/tests/test_lazy/test_models.py b/tests/test_lazy/test_models.py index f828b23a94c4..4b7aeed73a69 100644 --- a/tests/test_lazy/test_models.py +++ b/tests/test_lazy/test_models.py @@ -10,7 +10,7 @@ def test_torchvision_models_lazy_init(subset): sub_model_zoo = model_zoo.get_sub_registry(subset) for name, entry in sub_model_zoo.items(): # TODO(ver217): lazy init does not support weight norm, skip these models - if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base'): + if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base') or name.startswith('transformers_llama'): continue check_lazy_init(entry, verbose=True) diff --git a/tests/test_tensor/test_dtensor/test_comm_spec.py b/tests/test_tensor/test_dtensor/test_comm_spec.py index 958eabb65fac..95fcd2aaf8f3 100644 --- a/tests/test_tensor/test_dtensor/test_comm_spec.py +++ b/tests/test_tensor/test_dtensor/test_comm_spec.py @@ -122,23 +122,6 @@ def check_all_reduce_bwd(process_groups_dict, rank): assert tensor_to_comm.equal(tensor_to_check) -def check_all_reduce_in_flatten_device_mesh(process_groups_dict, rank): - # tensor to comm - tensor_to_comm = torch.ones(2, 2).cuda() * rank - - # reduce through logical process axis 0 at flatten device mesh - # tensor to check - # tensor([[6., 6.], - # [6., 6.]]) - tensor_to_check = torch.tensor([[6, 6], [6, 6]], dtype=tensor_to_comm.dtype).cuda() - - # CommSpec:(comm_pattern:all_reduce, logical_process_axis:[0, 1]) - comm_spec = CommSpec(CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD, process_groups_dict, logical_process_axis=0) - tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm) - - assert tensor_to_comm.equal(tensor_to_check) - - def check_comm(rank, world_size, port): disable_existing_loggers() launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') @@ -150,24 +133,22 @@ def check_comm(rank, world_size, port): # [[0, 1, # [2, 3]] device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True) - process_groups_dict = device_mesh.process_groups_dict + + process_group_dict = device_mesh._process_group_dict[rank] # test all gather - check_all_gather(process_groups_dict, rank) + check_all_gather(process_group_dict, rank) # test shard - check_shard(process_groups_dict, rank) + check_shard(process_group_dict, rank) # test all to all - check_all_to_all(process_groups_dict, rank) + check_all_to_all(process_group_dict, rank) # test all reduce - check_all_reduce_fwd(process_groups_dict, rank) - check_all_reduce_bwd(process_groups_dict, rank) + check_all_reduce_fwd(process_group_dict, rank) + check_all_reduce_bwd(process_group_dict, rank) - flatten_process_groups_dict = device_mesh.flatten_device_mesh.process_groups_dict - # test all reduce in 1D flatten device mesh - check_all_reduce_in_flatten_device_mesh(flatten_process_groups_dict, rank) gpc.destroy() diff --git a/tests/test_tensor/test_dtensor/test_dtensor.py b/tests/test_tensor/test_dtensor/test_dtensor.py index 8350fb3e7fe6..5a1aef79f332 100644 --- a/tests/test_tensor/test_dtensor/test_dtensor.py +++ b/tests/test_tensor/test_dtensor/test_dtensor.py @@ -64,7 +64,7 @@ def check_dtensor(rank, world_size, port): else: raise ValueError(f'rank {rank} is not in the device mesh') - dtensor_from_local = distribute_tensor(original_tensor, new_layout) + dtensor_from_local = distribute_tensor(original_tensor, device_mesh, new_sharding_spec) if rank == 0: assert dtensor_from_local.equal(original_tensor.narrow(0, 0, 1)) diff --git a/tests/test_tensor/test_dtensor/test_layout_converter.py b/tests/test_tensor/test_dtensor/test_layout_converter.py index d9dff8af933d..5388fd901e09 100644 --- a/tests/test_tensor/test_dtensor/test_layout_converter.py +++ b/tests/test_tensor/test_dtensor/test_layout_converter.py @@ -12,9 +12,9 @@ from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec from colossalai.testing import rerun_if_address_is_in_use, spawn -entire_shape = torch.Size((64, 32, 16)) +global_shape = torch.Size((64, 32, 16)) layout_converter = LayoutConverter() -physical_mesh_id = torch.arange(0, 4).reshape(2, 2) +physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) @@ -30,10 +30,7 @@ def check_one_step_transform(rank, world_size, port): # shard_sequence: S0,S1,R # device_mesh_shape: (2, 2) sharding_spec = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict) - layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec, - entire_shape=entire_shape) + layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=global_shape) rst_dict = layout_converter.all_gather_transform_layouts(layout) @@ -49,10 +46,7 @@ def check_one_step_transform(rank, world_size, port): # shard_sequence: S0,S1,R # device_mesh_shape: (4, 4) sharding_spec_all2all = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_dict_all2all) - layout_all2all = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_all2all, - entire_shape=entire_shape) + layout_all2all = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_all2all, global_shape=global_shape) rst_dict_all2all = layout_converter.all_to_all_transform_layout(layout_all2all) @@ -71,10 +65,7 @@ def check_one_step_transform(rank, world_size, port): # shard_sequence: S0,R,R # device_mesh_shape: (4, 4) sharding_spec_shard = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_shard) - shard_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_shard, - entire_shape=entire_shape) + shard_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_shard, global_shape=global_shape) rst_dict_shard = layout_converter.shard_transform_layout(shard_layout) @@ -100,19 +91,13 @@ def check_layout_converting(rank, world_size, port): # shard_sequence: R,S01,R # device_mesh_shape: (4, 4) sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) - source_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_source, - entire_shape=entire_shape) + source_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_source, global_shape=global_shape) # DistSpec: # shard_sequence: S01,R,R # device_mesh_shape: (4, 4) sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) - target_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_target, - entire_shape=entire_shape) + target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_target, global_shape=global_shape) transform_path, comm_action_sequence = layout_converter.layout_converting(source_layout, target_layout) @@ -137,7 +122,7 @@ def check_layout_converting(rank, world_size, port): assert comm_action_sequence[2].shard_dim == 0 assert comm_action_sequence[2].logical_process_axis == 1 - # checkout cached_spec_pairs_transform_path + # checkout chached_spec_pairs_transform_path assert layout_converter.cached_solution[('[R, S01, R]', '[S01, R, R]')][0] == transform_path assert layout_converter.cached_solution[('[R, S01, R]', '[S01, R, R]')][1] == comm_action_sequence @@ -159,21 +144,15 @@ def check_layout_converting_apply(rank, world_size, port): # shard_sequence: R,S01,R # device_mesh_shape: (4, 4) sharding_spec_source = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_source) - source_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_source, - entire_shape=entire_shape) + source_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_source, global_shape=global_shape) # DistSpec: # shard_sequence: S01,R,R # device_mesh_shape: (4, 4) sharding_spec_target = ShardingSpec(dim_size=3, dim_partition_dict=dim_partition_target) - target_layout = Layout(device_mesh=device_mesh, - device_type=torch.device('cuda'), - sharding_spec=sharding_spec_target, - entire_shape=entire_shape) + target_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec_target, global_shape=global_shape) - original_tensor = torch.rand(entire_shape).cuda() + original_tensor = torch.rand(global_shape).cuda() # tensor_to_apply: [R, S01, R] tensor_to_apply = original_tensor.narrow(1, rank * 8, 8) diff --git a/tests/test_tensor/test_shape_consistency.py b/tests/test_tensor/test_shape_consistency.py index 6fe9ee292cd0..859eef051256 100644 --- a/tests/test_tensor/test_shape_consistency.py +++ b/tests/test_tensor/test_shape_consistency.py @@ -1,9 +1,10 @@ -from colossalai.tensor.shape_consistency import ShapeConsistencyManager, CollectiveCommPattern import torch -from colossalai.tensor.sharding_spec import _DimSpec, ShardingSpec + from colossalai.device.device_mesh import DeviceMesh +from colossalai.tensor.shape_consistency import CollectiveCommPattern, ShapeConsistencyManager +from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec -physical_mesh_id = torch.arange(0, 16).reshape(2, 8) +physical_mesh_id = torch.arange(0, 16) mesh_shape = (4, 4) # [[0, 1, 2, 3], # [4, 5, 6, 7], diff --git a/tests/test_tensor/test_sharded_linear.py b/tests/test_tensor/test_sharded_linear.py index d66d4fec14d1..9bd9805e9b8f 100644 --- a/tests/test_tensor/test_sharded_linear.py +++ b/tests/test_tensor/test_sharded_linear.py @@ -26,7 +26,7 @@ def run_dist(rank, world_size, port): # the mesh is in the following topo # [[0, 1], # [2, 3]] - physical_mesh_id = torch.arange(0, 4).reshape(2, 2) + physical_mesh_id = torch.arange(0, 4) mesh_shape = (2, 2) device_mesh = DeviceMesh(physical_mesh_id, mesh_shape) row_id = rank // 2 diff --git a/tests/test_tensor/test_sharding_spec.py b/tests/test_tensor/test_sharding_spec.py index 909c84ef0f0e..5007c4141849 100644 --- a/tests/test_tensor/test_sharding_spec.py +++ b/tests/test_tensor/test_sharding_spec.py @@ -5,7 +5,7 @@ def test_sharding_spec(): - physical_mesh_id = torch.arange(0, 16).reshape(2, 8) + physical_mesh_id = torch.arange(0, 16) mesh_shape = (4, 4) # [[0, 1, 2, 3], # [4, 5, 6, 7], From d33a44e8c33e5fc3300f55bf68be18419ae47e6c Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 26 Jun 2023 18:05:00 +0800 Subject: [PATCH 384/413] [shardformer] refactored layernorm (#4086) --- colossalai/shardformer/layer/__init__.py | 4 +- colossalai/shardformer/layer/layernorm.py | 101 +++++++----------- colossalai/shardformer/policies/bert.py | 12 +-- .../test_layer/test_layernorm.py | 11 +- 4 files changed, 51 insertions(+), 77 deletions(-) diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py index 3ce0ef68aa4f..3ece2583132c 100644 --- a/colossalai/shardformer/layer/__init__.py +++ b/colossalai/shardformer/layer/__init__.py @@ -1,11 +1,11 @@ from .dropout import Dropout1D from .embedding import Embedding1D, VocabParallelEmbedding1D -from .layernorm import LayerNorm1D +from .layernorm import FusedLayerNorm from .linear import Linear1D_Col, Linear1D_Row from .linear_conv import LinearConv1D_Col, LinearConv1D_Row from .loss import cross_entropy_1d __all__ = [ "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", "LinearConv1D_Col", "LinearConv1D_Row", - "Dropout1D", "cross_entropy_1d", 'LayerNorm1D' + "Dropout1D", "cross_entropy_1d", 'FusedLayerNorm' ] diff --git a/colossalai/shardformer/layer/layernorm.py b/colossalai/shardformer/layer/layernorm.py index a8e1d7a2c082..83854239cf90 100644 --- a/colossalai/shardformer/layer/layernorm.py +++ b/colossalai/shardformer/layer/layernorm.py @@ -1,89 +1,64 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from typing import List, Union - import torch import torch.nn as nn -from torch.distributed import ProcessGroup - -from colossalai.kernel import LayerNorm -from colossalai.nn import init as init -from .parallel_module import ParallelModule +__all__ = ['FusedLayerNorm'] -__all__ = ['LayerNorm1D'] +FAST_LAYERNORM_SUPPORTED_SIZE = [ + 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, 24576, + 25600, 30720, 32768, 40960, 49152, 65536 +] -Fast_LN = None -try: - from apex.contrib.layer_norm.layer_norm import FastLayerNorm - Fast_LN = FastLayerNorm -except ImportError: - pass - -class LayerNorm1D(ParallelModule): +class FusedLayerNorm(): r""" - Layer Normalization for colossalai - - Args: - normalized_shape (int): input shape from an expected input of size. - :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] - \times \ldots \times \text{normalized_shape}[-1]]` - If a single integer is used, it is treated as a singleton list, and this module will - normalize over the last dimension which is expected to be of that specific size. - eps (float): a value added to the denominator for numerical stability, defaults to 1e-05. - bias (bool, optional): Whether to add a bias, defaults to ``True``. - dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None. + This is a wrapper around the apex fused layernorm implementation. It is meant to be used only with the from_native_module interface. """ - _fast_ln_supported_sizes = [ - 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, - 24576, 25600, 30720, 32768, 40960, 49152, 65536 - ] - - def __init__(self, - normalized_shape: int, - eps: int = 1e-05, - bias: bool = True, - dtype: torch.dtype = None, - device: torch.device = None): - super().__init__() - if Fast_LN is not None and normalized_shape in self._fast_ln_supported_sizes: - norm = Fast_LN(normalized_shape, eps=eps).to(dtype) - else: - norm = None - try: - from apex.normalization import FusedLayerNorm - norm = FusedLayerNorm(normalized_shape, eps=eps).to(dtype) - except ImportError: - norm = LayerNorm(normalized_shape, eps=eps, device=device, dtype=dtype) - self.norm = norm + def __init__(self) -> None: + raise NotImplementedError( + 'FusedLayerNorm is not implemented as a physical class. ' + 'It is meant to be used only with the from_native_module interface to wrap the fused layernorm implementation provided by apex.' + ) @staticmethod - def from_native_module(module: nn.LayerNorm, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, - **kwargs) -> ParallelModule: + def from_native_module(module: nn.LayerNorm, *args, **kwargs) -> nn.Module: r""" Convert a native pytorch layer norm module to colossalai layer norm module """ + # check if apex is installed + try: + import apex + except ImportError: + raise ImportError( + 'Please install apex from source (https://github.com/NVIDIA/apex) to use the fused layernorm kernel') + + # get the attributes of the module normalized_shape = module.normalized_shape eps = module.eps - bias = module.bias is not None + elementwise_affine = module.elementwise_affine dtype = module.weight.dtype device = module.weight.device - # ensure only one process group is passed - if isinstance(process_group, (list, tuple)): - assert len(process_group) == 1, \ - f'Expected only one process group, got {len(process_group)}.' - process_group = process_group[0] + # pick the suitable layernorm implementation + use_fast_ln = normalized_shape in FAST_LAYERNORM_SUPPORTED_SIZE + + if use_fast_ln: + try: + from apex.contrib.layer_norm.layer_norm import FastLayerNorm as ApexFusedLayerNorm + except ImportError: + # fall back to the normal fused layernorm is not built + from apex.normalization import FusedLayerNorm as ApexFusedLayerNorm + else: + from apex.normalization import FusedLayerNorm as ApexFusedLayerNorm - # create layer norm - layer_norm = LayerNorm1D(normalized_shape, eps=eps, bias=bias, device=device, dtype=dtype).norm + layernorm = ApexFusedLayerNorm(normalized_shape, eps=eps, + elementwise_affine=elementwise_affine).to(dtype).to(device) with torch.no_grad(): # copy weight and bias - layer_norm.weight.copy_(module.weight) - if bias: - layer_norm.bias.copy_(module.bias) - return layer_norm + layernorm.weight.copy_(module.weight) + layernorm.bias.copy_(module.bias) + return layernorm diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 1baf67ef9c02..7b0eaa5d8ab1 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -103,17 +103,17 @@ def module_policy(self): base_policy[BertLayer].sub_module_replacement.append( SubModuleReplacementDescription( suffix="attention.output.LayerNorm", - target_module=col_nn.LayerNorm1D, + target_module=col_nn.FusedLayerNorm, )) base_policy[BertLayer].sub_module_replacement.append( SubModuleReplacementDescription( suffix="output.LayerNorm", - target_module=col_nn.LayerNorm1D, + target_module=col_nn.FusedLayerNorm, )) base_policy[BertEmbeddings].sub_module_replacement.append( SubModuleReplacementDescription( suffix="LayerNorm", - target_module=col_nn.LayerNorm1D, + target_module=col_nn.FusedLayerNorm, ),) return base_policy @@ -154,7 +154,7 @@ def module_policy(self): addon_module[BertLMPredictionHead].sub_module_replacement.append( SubModuleReplacementDescription( suffix="transform.LayerNorm", - target_module=col_nn.LayerNorm1D, + target_module=col_nn.FusedLayerNorm, )) module_policy.update(addon_module) return module_policy @@ -191,7 +191,7 @@ def module_policy(self): addon_module[BertLMPredictionHead].sub_module_replacement.append( SubModuleReplacementDescription( suffix="transform.LayerNorm", - target_module=col_nn.LayerNorm1D, + target_module=col_nn.FusedLayerNorm, )) module_policy.update(addon_module) return module_policy @@ -228,7 +228,7 @@ def module_policy(self): addon_module[BertLMPredictionHead].sub_module_replacement.append( SubModuleReplacementDescription( suffix="transform.LayerNorm", - target_module=col_nn.LayerNorm1D, + target_module=col_nn.FusedLayerNorm, )) module_policy.update(addon_module) return module_policy diff --git a/tests/test_shardformer/test_layer/test_layernorm.py b/tests/test_shardformer/test_layer/test_layernorm.py index 334ae05bed95..a117845545be 100644 --- a/tests/test_shardformer/test_layer/test_layernorm.py +++ b/tests/test_shardformer/test_layer/test_layernorm.py @@ -1,16 +1,15 @@ import torch -import torch.distributed as dist import torch.nn as nn from torch.testing import assert_close import colossalai -from colossalai.shardformer.layer import LayerNorm1D +from colossalai.shardformer.layer import FusedLayerNorm from colossalai.testing import rerun_if_address_is_in_use, spawn -def check_layernorm_1d(): +def check_layernorm(): norm = nn.LayerNorm(128, 0.00001).cuda() - norm1d = LayerNorm1D.from_native_module(norm, process_group=None) + norm1d = FusedLayerNorm.from_native_module(norm, process_group=None) assert norm1d.weight.shape == torch.Size([128]) @@ -33,11 +32,11 @@ def check_layernorm_1d(): def run_dist(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - check_layernorm_1d() + check_layernorm() @rerun_if_address_is_in_use() -def test_layernorm_1d(): +def test_layernorm(): spawn(run_dist, nprocs=2) From ac8093713886193f4974445fc3b3baeb8ab92f8c Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Tue, 27 Jun 2023 17:39:29 +0800 Subject: [PATCH 385/413] [shardformer] shardformer support opt models (#4091) * [shardformer] shardformer support opt models * [shardformer] shardformer support opt models, fix * [shardformer] shardformer support opt models, fix * [shardformer] shardformer support opt models, fix --- colossalai/shardformer/policies/autopolicy.py | 10 ++ colossalai/shardformer/policies/opt.py | 133 ++++++++++++++++++ tests/kit/model_zoo/transformers/opt.py | 57 +++++++- .../test_tracer/test_hf_model/test_hf_opt.py | 3 +- tests/test_shardformer/test_model/_utils.py | 4 +- .../test_model/test_shard_opt.py | 67 +++++++++ 6 files changed, 264 insertions(+), 10 deletions(-) create mode 100644 colossalai/shardformer/policies/opt.py create mode 100644 tests/test_shardformer/test_model/test_shard_opt.py diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index b1b8c6156f9f..9cc583d58b11 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -68,6 +68,16 @@ class PolicyLocation: PolicyLocation(file_name="gpt2", class_name="GPT2ForTokenClassificationPolicy"), "transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification": PolicyLocation(file_name="gpt2", class_name="GPT2ForSequenceClassificationPolicy"), + + # OPT + "transformers.models.opt.modeling_opt.OPTModel": + PolicyLocation(file_name="opt", class_name="OPTModelPolicy"), + "transformers.models.opt.modeling_opt.OPTForCausalLM": + PolicyLocation(file_name="opt", class_name="OPTForCausalLMPolicy"), + "transformers.models.opt.modeling_opt.OPTForSequenceClassification": + PolicyLocation(file_name="opt", class_name="OPTForSequenceClassificationPolicy"), + "transformers.models.opt.modeling_opt.OPTForQuestionAnswering": + PolicyLocation(file_name="opt", class_name="OPTForQuestionAnsweringPolicy"), } diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py new file mode 100644 index 000000000000..f467726e5580 --- /dev/null +++ b/colossalai/shardformer/policies/opt.py @@ -0,0 +1,133 @@ +from transformers.models.opt.modeling_opt import ( + OPTAttention, + OPTDecoder, + OPTDecoderLayer, + OPTForCausalLM, + OPTForSequenceClassification, +) + +from colossalai.shardformer.layer import Embedding1D, FusedLayerNorm, Linear1D_Col, Linear1D_Row + +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + + +class OPTPolicy(Policy): + + def preprocess(self): + # reshape the embedding layer + r""" + Reshape the Embedding layer to make the embedding dimension divisible by world_size + """ + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + return self.model + + def module_policy(self): + base_policy = { + OPTDecoder: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=Embedding1D, + ) + ]), + OPTDecoderLayer: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="fc1", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="fc2", + target_module=Linear1D_Row, + ) + ]), + OPTAttention: + ModulePolicyDescription(attribute_replacement={ + "embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size + }, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="q_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="k_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="v_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="out_proj", + target_module=Linear1D_Row, + ), + ]), + } + if self.shard_config.fused_layernorm: + base_policy[OPTDecoder].sub_module_replacement.append( + SubModuleReplacementDescription(suffix="final_layer_norm", + target_module=FusedLayerNorm, + ignore_if_not_exist=True)) + base_policy[OPTDecoderLayer].sub_module_replacement.extend([ + SubModuleReplacementDescription(suffix="self_attn_layer_norm", + target_module=FusedLayerNorm, + ignore_if_not_exist=True), + SubModuleReplacementDescription(suffix="final_layer_norm", + target_module=FusedLayerNorm, + ignore_if_not_exist=True) + ]) + return base_policy + + def new_model_class(self): + return None + + def postprocess(self): + return self.model + + +class OPTModelPolicy(OPTPolicy): + + def __init__(self) -> None: + super().__init__() + + +class OPTForCausalLMPolicy(OPTPolicy): + + def module_policy(self): + policy = super().module_policy() + new_item = { + OPTForCausalLM: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="lm_head", + target_module=Linear1D_Col, + kwargs=dict(gather_output=True)) + ]) + } + + policy.update(new_item) + return policy + + +class OPTForSequenceClassificationPolicy(OPTPolicy): + + def __init__(self) -> None: + super().__init__() + + +class OPTForQuestionAnsweringPolicy(OPTPolicy): + + def __init__(self) -> None: + super().__init__() diff --git a/tests/kit/model_zoo/transformers/opt.py b/tests/kit/model_zoo/transformers/opt.py index d9c4a0b3c23c..4463ae12b901 100644 --- a/tests/kit/model_zoo/transformers/opt.py +++ b/tests/kit/model_zoo/transformers/opt.py @@ -11,14 +11,47 @@ def data_gen(): - input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) - attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64) + input_ids = torch.Tensor([[1, 15043, 29892, 590, 11203, 338, 274, 1082]]).long() + attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1]]).long() return dict(input_ids=input_ids, attention_mask=attention_mask) -output_transform_fn = lambda x: x +def data_gen_for_causal_lm(): + # LM data gen + # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels` + data = data_gen() + labels = data['input_ids'].clone() + data['labels'] = labels + return data + + +def data_gen_for_sequence_classification(): + # LM data gen + # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels` + data = data_gen() + labels = data['input_ids'].clone() + data['labels'] = torch.tensor([1]) + return data + + +def data_gen_for_question_answering(): + # LM data gen + # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels` + data = data_gen() + data['start_positions'] = torch.tensor([0]) + data['end_positions'] = torch.tensor([1]) + return data + -config = transformers.OPTConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4) +output_transform_fn = lambda x: x +loss_fn_for_opt_model = lambda x: x.last_hidden_state.mean() +loss_fn_for_lm = lambda x: x.loss +config = transformers.OPTConfig( + hidden_size=128, + num_hidden_layers=2, + num_attention_heads=4, + dropout=0, +) # register the following models # transformers.OPTModel, @@ -27,9 +60,23 @@ def data_gen(): model_fn=lambda: transformers.OPTModel(config), data_gen_fn=data_gen, output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_opt_model, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_opt_for_causal_lm', model_fn=lambda: transformers.OPTForCausalLM(config), - data_gen_fn=data_gen, + data_gen_fn=data_gen_for_causal_lm, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_lm, + model_attribute=ModelAttribute(has_control_flow=True)) +model_zoo.register(name='transformers_opt_for_question_answering', + model_fn=lambda: transformers.OPTForQuestionAnswering(config), + data_gen_fn=data_gen_for_question_answering, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_lm, + model_attribute=ModelAttribute(has_control_flow=True)) +model_zoo.register(name='transformers_opt_for_sequence_classification', + model_fn=lambda: transformers.OPTForSequenceClassification(config), + data_gen_fn=data_gen_for_sequence_classification, output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_lm, model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py index f528db6a64ef..c68b89e82fbe 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py @@ -11,10 +11,9 @@ @clear_cache_before_run() def test_opt(): sub_registry = model_zoo.get_sub_registry('transformers_opt') - for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items(): model = model_fn() - trace_model_and_compare_output(model, data_gen_fn) + trace_model_and_compare_output(model, data_gen_fn, ignore_data=['labels', 'start_positions', 'end_positions']) if __name__ == '__main__': diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index a282e0bb919e..ad7c408aeb38 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -25,7 +25,6 @@ def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, # switch to train mode original_model.train() sharded_model.train() - # run forward org_output = original_model(**data) org_output = output_transform_fn(org_output) @@ -34,5 +33,4 @@ def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, shard_output = sharded_model(**data) shard_output = output_transform_fn(shard_output) shard_loss = loss_fn(shard_output) - - return org_output, org_loss, shard_output, shard_loss + return org_output, org_loss, shard_output, shard_loss \ No newline at end of file diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py new file mode 100644 index 000000000000..4d4c55770144 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -0,0 +1,67 @@ +import copy +import os + +import pytest +import torch + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.testing import ( + assert_hf_output_close, + check_state_dict_equal, + clear_cache_before_run, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward + +os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4) + + # run backward + org_loss.backward() + shard_loss.backward() + + # check grad + if hasattr(org_model, 'model'): + opt_model = org_model.model + shard_opt_model = sharded_model.model + else: + opt_model = org_model + shard_opt_model = sharded_model + + org_grad = opt_model.decoder.layers[0].self_attn.q_proj.weight.grad + shard_grad = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight.grad + + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + + +def check_OPTModel(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + org_model, sharded_model = build_model(world_size, model_fn) + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + + torch.cuda.empty_cache() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_OPTModel(): + spawn(check_OPTModel, 4) From 8af29ee47a274b7bc416a41732bd59208bc3d92c Mon Sep 17 00:00:00 2001 From: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Date: Wed, 28 Jun 2023 13:28:18 +0800 Subject: [PATCH 386/413] [shardformer] support vision transformer (#4096) * first v of vit shardformer * keep vit * update * vit shard add vitattention vitlayer * update num head shard para * finish test for vit * add new_model_class & postprocess * add vit readme * delete old files & fix the conflict * fix sth --- colossalai/shardformer/README.md | 2 +- colossalai/shardformer/layer/_operation.py | 2 +- colossalai/shardformer/layer/layernorm.py | 2 +- colossalai/shardformer/policies/bert.py | 2 +- colossalai/shardformer/policies/t5.py | 2 +- colossalai/shardformer/policies/vit.py | 96 +++++++++++++++++++ tests/test_device/test_device_mesh.py | 2 +- .../test_layer/test_layernorm.py | 2 +- .../test_model/test_shard_t5.py | 2 +- .../test_model/test_shard_vit.py | 55 +++++++++++ 10 files changed, 159 insertions(+), 8 deletions(-) create mode 100644 colossalai/shardformer/policies/vit.py create mode 100644 tests/test_shardformer/test_model/test_shard_vit.py diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index fee4cce7a28a..da80a7276b68 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -91,7 +91,7 @@ We will follow this roadmap to develop Shardformer: - [ ] GPT Neo - [ ] GPT-J - [ ] CV - - [ ] ViT + - [x] ViT - [ ] BEiT - [ ] SwinTransformer - [ ] SwinTransformer V2 diff --git a/colossalai/shardformer/layer/_operation.py b/colossalai/shardformer/layer/_operation.py index 7e97bee01b33..c025daaeccc7 100644 --- a/colossalai/shardformer/layer/_operation.py +++ b/colossalai/shardformer/layer/_operation.py @@ -287,4 +287,4 @@ def reduce_forward(input_, process_group): def reduce_backward(input_, process_group): - return _ReduceBackward.apply(input_, process_group) + return _ReduceBackward.apply(input_, process_group) \ No newline at end of file diff --git a/colossalai/shardformer/layer/layernorm.py b/colossalai/shardformer/layer/layernorm.py index 83854239cf90..6103380fe8a5 100644 --- a/colossalai/shardformer/layer/layernorm.py +++ b/colossalai/shardformer/layer/layernorm.py @@ -61,4 +61,4 @@ def from_native_module(module: nn.LayerNorm, *args, **kwargs) -> nn.Module: # copy weight and bias layernorm.weight.copy_(module.weight) layernorm.bias.copy_(module.bias) - return layernorm + return layernorm \ No newline at end of file diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 7b0eaa5d8ab1..fb70cdff8824 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -316,4 +316,4 @@ def module_policy(self): ]) } module_policy.update(addon_module) - return module_policy + return module_policy \ No newline at end of file diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 30433f751088..9a1b63e46d2c 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -167,4 +167,4 @@ def module_policy(self): class T5EncoderPolicy(T5ModelPolicy): - pass + pass \ No newline at end of file diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py new file mode 100644 index 000000000000..4a2b72057d05 --- /dev/null +++ b/colossalai/shardformer/policies/vit.py @@ -0,0 +1,96 @@ +from typing import Dict, Union + +import torch.nn as nn + +from transformers.models.vit.modeling_vit import ViTModel, ViTLayer, ViTEmbeddings, ViTAttention + +from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row, Dropout1D + +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + +class ViTPolicy(Policy): + + def preprocess(self): + # Resize embedding + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + + return self.model + + def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: + return { + ViTEmbeddings: + ModulePolicyDescription( + attribute_replacement{}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=Dropout1D, + ) + ] + ), + ViTLayer: + ModulePolicyDescription( + attribute_replacement{ + "attention.attention.num_attention_heads": + self.model.config.num_attention_heads//self.shard_config.tensor_parallel_size, + "attention.attention.all_head_size": + self.model.config.hidden_size//self.shard_config.tensor_parallel_size, + }, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attention.attention.query", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.key", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.value", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.dropout", + target_module=Dropout1D, + ), + SubModuleReplacementDescription( + suffix="attention.output.dense", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="attention.output.dropout", + target_module=Dropout1D, + ), + SubModuleReplacementDescription( + suffix="intermediate.dense", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="output.dense", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="output.dropout", + target_module=Dropout1D, + ), + ] + ), + } + + def new_model_class(self): + return None + + def postprocess(self): + return self.model + + + + + diff --git a/tests/test_device/test_device_mesh.py b/tests/test_device/test_device_mesh.py index 590d6966bff6..1f8db99c9236 100644 --- a/tests/test_device/test_device_mesh.py +++ b/tests/test_device/test_device_mesh.py @@ -86,4 +86,4 @@ def test_device_mesh_from_process_group(): if __name__ == '__main__': test_device_mesh() - test_device_mesh_from_process_group() + test_device_mesh_from_process_group() \ No newline at end of file diff --git a/tests/test_shardformer/test_layer/test_layernorm.py b/tests/test_shardformer/test_layer/test_layernorm.py index a117845545be..080fae034956 100644 --- a/tests/test_shardformer/test_layer/test_layernorm.py +++ b/tests/test_shardformer/test_layer/test_layernorm.py @@ -41,4 +41,4 @@ def test_layernorm(): if __name__ == '__main__': - test_layernorm_1d() + test_layernorm_1d() \ No newline at end of file diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 2698d7675c8e..6074a902e9b0 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -56,4 +56,4 @@ def test_t5(): if __name__ == "__main__": - test_t5() + test_t5() \ No newline at end of file diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py new file mode 100644 index 000000000000..d5d71d9e29fe --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -0,0 +1,55 @@ +import pytest +import torch + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output) + + # do backward + org_loss.backward() + shard_loss.backward() + + # check grad + org_grad = org_model.encoder.layer[0].attention.attention.query.weight.grad + shard_grad = sharded_model.encoder.layer[0].attention.attention.query.weight.grad + + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + +def check_vit(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + org_model, sharded_model = build_model(world_size, model_fn) + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + + torch.cuda.empty_cache() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_vit(): + spawn(check_vit, 4) + + +if __name__ == "__main__": + test_vit() From b1c2901530daa4f9e5a8008170b915a201e59415 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Wed, 28 Jun 2023 15:04:35 +0800 Subject: [PATCH 387/413] [shardformer] supported bloom model (#4098) --- colossalai/shardformer/README.md | 8 +- colossalai/shardformer/layer/__init__.py | 9 +- colossalai/shardformer/layer/dropout.py | 45 +++- colossalai/shardformer/layer/linear.py | 8 +- .../{linear_conv.py => qkv_fused_linear.py} | 103 ++++++--- colossalai/shardformer/layer/utils.py | 80 ++++++- colossalai/shardformer/policies/autopolicy.py | 12 + colossalai/shardformer/policies/basepolicy.py | 1 + colossalai/shardformer/policies/bert.py | 14 +- colossalai/shardformer/policies/bloom.py | 214 ++++++++++++++++++ colossalai/shardformer/policies/gpt2.py | 14 +- colossalai/shardformer/policies/t5.py | 14 +- colossalai/shardformer/policies/vit.py | 129 +++++------ colossalai/shardformer/shard/sharder.py | 16 +- tests/kit/model_zoo/transformers/__init__.py | 1 + tests/kit/model_zoo/transformers/bloom.py | 107 +++++++++ tests/kit/model_zoo/transformers/gpt.py | 2 - .../test_layer/test_dropout.py | 25 +- ...conv_1d.py => test_qkv_fused_linear_1d.py} | 15 +- .../test_model/test_shard_bloom.py | 59 +++++ 20 files changed, 723 insertions(+), 153 deletions(-) rename colossalai/shardformer/layer/{linear_conv.py => qkv_fused_linear.py} (79%) create mode 100644 colossalai/shardformer/policies/bloom.py create mode 100644 tests/kit/model_zoo/transformers/bloom.py rename tests/test_shardformer/test_layer/{test_linearconv_1d.py => test_qkv_fused_linear_1d.py} (81%) create mode 100644 tests/test_shardformer/test_model/test_shard_bloom.py diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index da80a7276b68..8a8ed0f792fd 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -83,8 +83,10 @@ We will follow this roadmap to develop Shardformer: - [x] BERT - [x] T5 - [x] LlaMa - - [ ] GPT2 - - [ ] BLOOM + - [x] GPT2 + - [x] OPT + - [x] BLOOM + - [ ] GLM - [ ] RoBERTa - [ ] ALBERT - [ ] ERNIE @@ -96,7 +98,7 @@ We will follow this roadmap to develop Shardformer: - [ ] SwinTransformer - [ ] SwinTransformer V2 - [ ] Audio - - [ ] To be added + - [ ] Whisper - [ ] Multi-modal - [ ] To be added diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py index 3ece2583132c..2826a8429f00 100644 --- a/colossalai/shardformer/layer/__init__.py +++ b/colossalai/shardformer/layer/__init__.py @@ -1,11 +1,12 @@ -from .dropout import Dropout1D +from .dropout import DropoutForParallelInput, DropoutForReplicatedInput from .embedding import Embedding1D, VocabParallelEmbedding1D from .layernorm import FusedLayerNorm from .linear import Linear1D_Col, Linear1D_Row -from .linear_conv import LinearConv1D_Col, LinearConv1D_Row from .loss import cross_entropy_1d +from .qkv_fused_linear import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row __all__ = [ - "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", "LinearConv1D_Col", "LinearConv1D_Row", - "Dropout1D", "cross_entropy_1d", 'FusedLayerNorm' + "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", 'GPT2FusedLinearConv1D_Col', + 'GPT2FusedLinearConv1D_Row', 'DropoutForParallelInput', 'DropoutForReplicatedInput', "cross_entropy_1d", + 'FusedLayerNorm' ] diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py index 2c49b49faad6..2625fe97889a 100644 --- a/colossalai/shardformer/layer/dropout.py +++ b/colossalai/shardformer/layer/dropout.py @@ -7,10 +7,10 @@ from .parallel_module import ParallelModule from .utils import create_randomizer_with_offset -__all__ = ['Dropout1D'] +__all__ = ['DropoutForParallelInput', 'DropoutForReplicatedInput'] -class Dropout1D(ParallelModule, nn.Dropout): +class DropoutForParallelInput(ParallelModule, nn.Dropout): """ The Dropout Layer will apply dropout mask to the input tensor. The dropout mask is generated with randomness on different ranks of the given process group. This can avoid the same dropout mask is generated @@ -32,13 +32,50 @@ def __init__(self, p: float = 0.5, inplace: bool = False, process_group: Process @staticmethod def from_native_module(module: nn.Dropout, - process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "Dropout1D": + process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "DropoutForParallelInput": + """ + Create a DropoutForParallelInput layer from a native dropout layer. + """ + p = module.p + inplace = module.inplace + return DropoutForParallelInput(p=p, inplace=inplace, process_group=process_group) + + def forward(self, input): + with self.randomizer.fork_rng(): + input = super().forward(input) + return input + + +class DropoutForReplicatedInput(ParallelModule, nn.Dropout): + """ + The Dropout Layer will apply dropout mask to the input tensor. The dropout mask is generated with + randomness on different ranks of the given process group. This can avoid the same dropout mask is generated + and applied on the same position of different ranks, leading to poor convergence performance. + + Args: + p (float): probability of an element to be zeroed. Defaults to 0.5. + inplace (bool): If set to True, will do this operation in-place. Defaults to False. + process_group (ProcessGroup): the process group to be used for generating randomness. Defaults to None. + """ + + def __init__(self, p: float = 0.5, inplace: bool = False, process_group: ProcessGroup = None): + # init with nn.Dropout + super(nn.Dropout, self).__init__(p=p, inplace=inplace) + + # offset the seed with randomizer index only + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=process_group, offset_by_rank=False) + + @staticmethod + def from_native_module( + module: nn.Dropout, + process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "DropoutForReplicatedInput": """ Create a Dropout1D layer from a native dropout layer. """ p = module.p inplace = module.inplace - return Dropout1D(p=p, inplace=inplace, process_group=process_group) + return DropoutForReplicatedInput(p=p, inplace=inplace, process_group=process_group) def forward(self, input): with self.randomizer.fork_rng(): diff --git a/colossalai/shardformer/layer/linear.py b/colossalai/shardformer/layer/linear.py index d952d5eecbee..26ba5883c64f 100644 --- a/colossalai/shardformer/layer/linear.py +++ b/colossalai/shardformer/layer/linear.py @@ -277,6 +277,7 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis def chunk_weight(self): self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0) + @torch.no_grad() def reset_parameters(self, weight_initializer, bias_initializer) -> None: fan_in, fan_out = self.in_features, self.out_features weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) @@ -289,9 +290,10 @@ def reset_parameters(self, weight_initializer, bias_initializer) -> None: src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) origin_device = self.bias.device - self.bias = self.bias.cuda() - dist.broadcast(self.bias, src=src_rank, group=self.process_group) - self.bias = self.bias.to(origin_device) + bias = self.bias.cuda() + dist.broadcast(bias, src=src_rank, group=self.process_group) + bias = bias.to(origin_device) + self.bias.copy_(bias) def forward(self, input_: Tensor) -> Tensor: # Set up backprop all-reduce. diff --git a/colossalai/shardformer/layer/linear_conv.py b/colossalai/shardformer/layer/qkv_fused_linear.py similarity index 79% rename from colossalai/shardformer/layer/linear_conv.py rename to colossalai/shardformer/layer/qkv_fused_linear.py index e856abc14be6..9d51670c65dd 100644 --- a/colossalai/shardformer/layer/linear_conv.py +++ b/colossalai/shardformer/layer/qkv_fused_linear.py @@ -31,12 +31,25 @@ from .parallel_module import ParallelModule from .utils import create_randomizer_with_offset -__all__ = ['LinearConv1D_Col', 'LinearConv1D_Row'] +__all__ = ['FusedLinear1D_Col', 'FusedLinear1D_Row'] +# ==================================== +# For GPT Only +# ==================================== -def split_fused_qkv(qkv: torch.Tensor, n_fused: int, process_group: ProcessGroup): + +def split_fused_qkv_in_gpt2_style(qkv: torch.Tensor, + n_fused: int, + process_group: ProcessGroup, + is_transposed: bool = False): """ The fused qkv tensor looks like [Q1, Q2, K1, K2, V1, V2], this function will split them into [Q1, K1, V1] and [Q2, K2, V2]. + + Args: + qkv (torch.Tensor): The fused qkv tensor. + n_fused (int): The number items fused together, defaults to 3 (query, key and value). + process_group (ProcessGroup): The process group for distributed communication. + is_transposed (bool): generally the tensor is the shape of (out_features, in_features). Set this to True if the tensor is in the shape (in_features, out_features). """ # get the number of slice for the fused qkv rank = dist.get_rank(group=process_group) @@ -48,7 +61,10 @@ def split_fused_qkv(qkv: torch.Tensor, n_fused: int, process_group: ProcessGroup # [Q, K, V] # to # [Q1, Q2, K1, K2, V1, V2] - weight_chunks = torch.chunk(qkv, world_size * n_fused, dim=-1) + if is_transposed: + weight_chunks = torch.chunk(qkv, world_size * n_fused, dim=-1) + else: + weight_chunks = torch.chunk(qkv, world_size * n_fused, dim=0) # rearrange the slice into the final order # from @@ -56,13 +72,26 @@ def split_fused_qkv(qkv: torch.Tensor, n_fused: int, process_group: ProcessGroup # to # [Q1, K1, V1], [Q2, K2, V2] weight_chunks_of_current_rank = [weight_chunks[i] for i in order[rank::world_size]] - weight_of_current_rank = torch.cat(weight_chunks_of_current_rank, dim=-1) + + if is_transposed: + weight_of_current_rank = torch.cat(weight_chunks_of_current_rank, dim=-1) + else: + weight_of_current_rank = torch.cat(weight_chunks_of_current_rank, dim=0) return weight_of_current_rank -def gather_fused_qkv(qkv: torch.Tensor, n_fused: int, process_group: ProcessGroup): +def gather_fused_qkv_in_gpt2_style(qkv: torch.Tensor, + n_fused: int, + process_group: ProcessGroup, + is_transposed: bool = False): """ The splitted qkv tensor looks like [Q1, K1, V1] and [Q2, K2, V2], this function will gather them into [Q1, Q2, K1, K2, V1, V2]. + + Args: + qkv (torch.Tensor): The fused qkv tensor. + n_fused (int): The number items fused together, defaults to 3 (query, key and value). + process_group (ProcessGroup): The process group for distributed communication. + is_transposed (bool): generally the tensor is the shape of (out_features, in_features). Set this to True if the tensor is in the shape (in_features, out_features). """ world_size = dist.get_world_size(group=process_group) @@ -75,7 +104,11 @@ def gather_fused_qkv(qkv: torch.Tensor, n_fused: int, process_group: ProcessGrou qkv = qkv.cuda() gather_list = [torch.zeros_like(qkv) for _ in range(world_size)] dist.all_gather(gather_list, qkv, group=process_group) - gather_weight = torch.cat(gather_list, dim=-1) + + if is_transposed: + gather_weight = torch.cat(gather_list, dim=-1) + else: + gather_weight = torch.cat(gather_list, dim=0) gather_weight = gather_weight.to(origin_device) qkv = qkv.to(origin_device) @@ -84,15 +117,23 @@ def gather_fused_qkv(qkv: torch.Tensor, n_fused: int, process_group: ProcessGrou # [Q1, K1, V1, Q2, K2, V2] # to # [Q1, Q2, K1, K2, V1, V2] - weight_chunks = torch.chunk(gather_weight, world_size * n_fused, dim=-1) + if is_transposed: + weight_chunks = torch.chunk(gather_weight, world_size * n_fused, dim=-1) + else: + weight_chunks = torch.chunk(gather_weight, world_size * n_fused, dim=0) + reordered_chunk_list = [] for i in range(n_fused): reordered_chunk_list.extend(weight_chunks[i::n_fused]) - reordered_gather_weight = torch.cat(reordered_chunk_list, dim=-1) + + if is_transposed: + reordered_gather_weight = torch.cat(reordered_chunk_list, dim=-1) + else: + reordered_gather_weight = torch.cat(reordered_chunk_list, dim=0) return reordered_gather_weight -class LinearConv1D_Col(ParallelModule): +class GPT2FusedLinearConv1D_Col(ParallelModule): r"""Linear layer with column parallelism. The linear layer is defined as :math:`Y = XA + b`. A is parallelized along @@ -154,10 +195,10 @@ def __init__(self, weight = torch.empty(self.in_features, self.out_features, **factory_kwargs) def shard_fn(tensor): - return split_fused_qkv(tensor, self.n_fused, self.process_group) + return split_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, True) def gather_fn(tensor): - return gather_fused_qkv(tensor, 3, self.process_group) + return gather_fused_qkv_in_gpt2_style(tensor, 3, self.process_group, True) with torch.no_grad(): sharded_weight = distribute_tensor_with_customization(weight, shard_fn, gather_fn) @@ -202,21 +243,27 @@ def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, Lis f'Expected only one process group, got {len(process_group)}.' process_group = process_group[0] - linear_1d = LinearConv1D_Col(in_features=in_features, - out_features=out_features, - bias=bias, - device=device, - process_group=process_group, - *args, - **kwargs) + linear_1d = GPT2FusedLinearConv1D_Col(in_features=in_features, + out_features=out_features, + bias=bias, + device=device, + process_group=process_group, + *args, + **kwargs) # TODO: copy the sharded weights with torch.no_grad(): - sharded_weight = split_fused_qkv(module.weight.data, n_fused=n_fused, process_group=process_group) + sharded_weight = split_fused_qkv_in_gpt2_style(module.weight.data, + n_fused=n_fused, + process_group=process_group, + is_transposed=True) linear_1d.weight.data.copy_(sharded_weight.data) if bias: - sharded_bias = split_fused_qkv(module.bias.data, n_fused=n_fused, process_group=process_group) + sharded_bias = split_fused_qkv_in_gpt2_style(module.bias.data, + n_fused=n_fused, + process_group=process_group, + is_transposed=True) linear_1d.bias.data.copy_(sharded_bias.data) return linear_1d @@ -254,7 +301,7 @@ def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: return output -class LinearConv1D_Row(ParallelModule): +class GPT2FusedLinearConv1D_Row(ParallelModule): r""" Linear layer with row parallelism. This layer is used to fit `Conv1D` layer (Fused QKV) in gpt2 of huggingface. @@ -345,13 +392,13 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis f'Expected only one process group, got {len(process_group)}.' process_group = process_group[0] - linear_1d = LinearConv1D_Row(in_features=in_features, - out_features=out_features, - bias=bias, - device=device, - process_group=process_group, - *args, - **kwargs) + linear_1d = GPT2FusedLinearConv1D_Row(in_features=in_features, + out_features=out_features, + bias=bias, + device=device, + process_group=process_group, + *args, + **kwargs) # TODO: copy the sharded weights with torch.no_grad(): diff --git a/colossalai/shardformer/layer/utils.py b/colossalai/shardformer/layer/utils.py index c3d6ab57e3e9..f2ac6563c46f 100644 --- a/colossalai/shardformer/layer/utils.py +++ b/colossalai/shardformer/layer/utils.py @@ -3,6 +3,7 @@ import torch import torch.distributed as dist from torch.distributed import ProcessGroup +from torch.distributed.distributed_c10d import _get_global_rank class Randomizer: @@ -112,27 +113,90 @@ def index(): """ idx = Randomizer._INDEX - Randomizer._INDEX += 1 return idx + @staticmethod + def increment_index(): + """ + Increment the index of the randomizer by one. + """ + Randomizer._INDEX += 1 + + @staticmethod + def is_randomizer_index_synchronized(process_group: ProcessGroup = None): + """ + Return whether the randomizer index is synchronized across processes. + """ + index = Randomizer.index() + if dist.is_initialized(): + # convert the index to tensor + index_tensor = torch.tensor(index, dtype=torch.int32).cuda() + + # all gather the index + gathered_index = [torch.zeros_like(index_tensor) for _ in range(dist.get_world_size(process_group))] + dist.all_gather(gathered_index, index_tensor, process_group) + + # make sure all the gathered index are the same + for i in range(1, dist.get_world_size(process_group)): + if gathered_index[i] != gathered_index[0]: + return False + + return True -def create_randomizer_with_offset(seed: int, process_group: ProcessGroup = None): + @staticmethod + def synchronize_index(process_group: ProcessGroup = None): + """ + All gather the index and pick the largest value. + """ + index = Randomizer.index() + + if dist.is_initialized(): + # convert the index to tensor + index_tensor = torch.tensor(index, dtype=torch.int32).cuda() + + # all gather the index + gathered_index = [torch.zeros_like(index_tensor) for _ in range(dist.get_world_size(process_group))] + dist.all_gather(gathered_index, index_tensor, process_group) + + # pick the largest index + for i in range(1, dist.get_world_size(process_group)): + if gathered_index[i] > index_tensor: + index_tensor = gathered_index[i] + + # set the index + Randomizer._INDEX = index_tensor.item() + + +def create_randomizer_with_offset(seed: int, + process_group: ProcessGroup = None, + offset_by_rank: bool = True, + offset_by_index: bool = True): """ Create a randomizer with an offset. The offset is equal to the rank of the process and the index of the randomizer. Args: seed (int): The base random seed to set. - enable_cpu (bool): fork the CPU RNG state as well. process_group (ProcessGroup): the process group to get the rank from. + offset_by_rank (bool): whether to offset by the rank of the process, i.e., the rank of the process will be added to the seed. Default: True. + offset_by_index (bool): whether to offset by the index of the randomizer, i.e., the index of the randomizer will be added to the seed. Default: True. Returns: Randomizer: the randomizer with offset. """ - offset = Randomizer.index() + base_seed = seed - if dist.is_initialized(): + if offset_by_rank and dist.is_initialized(): rank = dist.get_rank(process_group) - offset += rank + base_seed += rank + + if offset_by_index: + # check if the randomizer index is synchronized + is_synchronized = Randomizer.is_randomizer_index_synchronized(process_group) + assert is_synchronized, ("We detect that the randomizer index is not synchronized across processes." + "This is not allowed when we want to create a randomizer with offset by index." + "Please call Randomizer.synchronize_index() first.") + + base_seed += Randomizer.index() + Randomizer.increment_index() - seed += offset - return Randomizer(seed=seed) + return Randomizer(seed=base_seed) diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 9cc583d58b11..17c063c8d2cf 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -78,6 +78,18 @@ class PolicyLocation: PolicyLocation(file_name="opt", class_name="OPTForSequenceClassificationPolicy"), "transformers.models.opt.modeling_opt.OPTForQuestionAnswering": PolicyLocation(file_name="opt", class_name="OPTForQuestionAnsweringPolicy"), + + # Bloom + "transformers.models.bloom.modeling_bloom.BloomModel": + PolicyLocation(file_name="bloom", class_name="BloomModelPolicy"), + "transformers.models.bloom.modeling_bloom.BloomForCausalLM": + PolicyLocation(file_name="bloom", class_name="BloomForCausalLMPolicy"), + "transformers.models.bloom.modeling_bloom.BloomForSequenceClassification": + PolicyLocation(file_name="bloom", class_name="BloomForSequenceClassificationPolicy"), + "transformers.models.bloom.modeling_bloom.BloomForTokenClassification": + PolicyLocation(file_name="bloom", class_name="BloomForTokenClassificationPolicy"), + "transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering": + PolicyLocation(file_name="bloom", class_name="BloomForQuestionAnsweringPolicy"), } diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index b5d9cdbd7289..7e9bcf209573 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -52,6 +52,7 @@ def example_replace_weight(module: torch.nn.Module, process_group): attribute_replacement: Dict[str, Any] param_replacement: List[Callable] sub_module_replacement: List[SubModuleReplacementDescription] + method_replacement: List[Callable] = None class Policy(ABC): diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index fb70cdff8824..49ef53259321 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -61,7 +61,7 @@ def module_policy(self): ), SubModuleReplacementDescription( suffix="attention.self.dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ), SubModuleReplacementDescription( suffix="attention.output.dense", @@ -69,7 +69,7 @@ def module_policy(self): ), SubModuleReplacementDescription( suffix="attention.output.dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ), SubModuleReplacementDescription( suffix="intermediate.dense", @@ -81,7 +81,7 @@ def module_policy(self): ), SubModuleReplacementDescription( suffix="output.dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ) ]), BertEmbeddings: @@ -94,7 +94,7 @@ def module_policy(self): ), SubModuleReplacementDescription( suffix="dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ) ]) } @@ -258,7 +258,7 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ) ]) } @@ -281,7 +281,7 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ) ]) } @@ -311,7 +311,7 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ) ]) } diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py new file mode 100644 index 000000000000..d196bdbd6e4d --- /dev/null +++ b/colossalai/shardformer/policies/bloom.py @@ -0,0 +1,214 @@ +import torch +import torch.distributed as dist + +import colossalai.shardformer.layer as col_nn + +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + + +def build_bloom_alibi_tensor(self, attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor: + """ + Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it + relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value + `softmax(l+a) = softmax(l)`. Based on + https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742 + TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly. + + Args: + Returns tensor shaped (batch_size * num_heads, 1, max_seq_len) + attention_mask (`torch.Tensor`): + Token-wise attention mask, this should be of shape (batch_size, max_seq_len). + num_heads (`int`, *required*): + number of heads + dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`): + dtype of the output tensor + """ + import math + + if dist.is_initialized(): + world_size = dist.get_world_size() + num_heads = num_heads * world_size + + batch_size, seq_length = attention_mask.shape + closest_power_of_2 = 2**math.floor(math.log2(num_heads)) + base = torch.tensor(2**(-(2**-(math.log2(closest_power_of_2) - 3))), + device=attention_mask.device, + dtype=torch.float32) + powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32) + slopes = torch.pow(base, powers) + + if closest_power_of_2 != num_heads: + extra_base = torch.tensor(2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))), + device=attention_mask.device, + dtype=torch.float32) + num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2) + extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32) + slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) + + # Note: alibi will added to the attention bias that will be applied to the query, key product of attention + # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length) + # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length) + # => the query_length dimension will then be broadcasted correctly + # This is more or less identical to T5's relative position bias: + # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527 + arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :] + alibi = slopes[..., None] * arange_tensor + if dist.is_initialized(): + num_heads_per_rank = int(num_heads / dist.get_world_size()) + offset = dist.get_rank() * num_heads_per_rank + alibi = alibi.view(batch_size, num_heads, 1, seq_length) + alibi = alibi[:, offset:num_heads_per_rank + offset, :, :] + return alibi.reshape(batch_size * num_heads_per_rank, 1, seq_length).to(dtype) + else: + return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype) + + +class BloomPolicy(Policy): + + def preprocess(self): + # reshape the embedding layer + r""" + Reshape the Embedding layer to make the embedding dimension divisible by world_size + """ + # TODO: + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + return self.model + + def module_policy(self): + from transformers.models.bloom.modeling_bloom import BloomBlock, BloomModel + + return { + BloomBlock: + ModulePolicyDescription( + attribute_replacement={ + # 1. shard hidden size + "self_attention.hidden_size": + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "self_attention.split_size": + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + # 2. shard number of heads + "self_attention.num_heads": + self.model.config.n_head // self.shard_config.tensor_parallel_size, + }, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attention.query_key_value", + target_module=col_nn.Linear1D_Col, + # kwargs={'n_fused': 3} + ), + SubModuleReplacementDescription( + suffix="self_attention.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="self_attention.attention_dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="mlp.dense_h_to_4h", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="mlp.dense_4h_to_h", + target_module=col_nn.Linear1D_Row, + ), + ]), + BloomModel: + ModulePolicyDescription(attribute_replacement={ + "num_heads": self.model.config.n_head // self.shard_config.tensor_parallel_size, + }, + param_replacement=[], + method_replacement={"build_alibi_tensor": build_bloom_alibi_tensor}, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="word_embeddings", + target_module=col_nn.VocabParallelEmbedding1D, + ) + ]) + } + + def new_model_class(self): + # do nothing + return self.model + + def postprocess(self): + return self.model + + +# BertModel +class BloomModelPolicy(BloomPolicy): + pass + + +class BloomForCausalLMPolicy(BloomPolicy): + + def module_policy(self): + from transformers.models.bloom.modeling_bloom import BloomForCausalLM + policy = super().module_policy() + # add a new item for casual lm + new_item = { + BloomForCausalLM: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="lm_head", + target_module=col_nn.Linear1D_Col, + kwargs=dict(gather_output=True)) + ]) + } + policy.update(new_item) + return policy + + +class BloomForSequenceClassificationPolicy(BloomPolicy): + + def module_policy(self): + from transformers.models.bloom.modeling_bloom import BloomForSequenceClassification + policy = super().module_policy() + # add a new item for casual lm + new_item = { + BloomForSequenceClassification: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="score", + target_module=col_nn.Linear1D_Col, + kwargs=dict(gather_output=True)) + ]) + } + policy.update(new_item) + return policy + + +class BloomForTokenClassificationPolicy(BloomPolicy): + + def module_policy(self): + from transformers.models.bloom.modeling_bloom import BloomForTokenClassification + policy = super().module_policy() + # add a new item for casual lm + new_item = { + BloomForTokenClassification: + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription(suffix="classifier", + target_module=col_nn.Linear1D_Col, + kwargs=dict(gather_output=True)), + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForReplicatedInput, + ), + ]) + } + policy.update(new_item) + return policy + + +class BloomForQuestionAnsweringPolicy(BloomPolicy): + # No head sharding as the output features is only 2 + pass diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 9d5d7d36aea3..ebfaf8a8e1c3 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -42,37 +42,37 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription( suffix="attn.c_attn", - target_module=col_nn.LinearConv1D_Col, + target_module=col_nn.GPT2FusedLinearConv1D_Col, kwargs={ "n_fused": 3, }, ), SubModuleReplacementDescription( suffix="attn.c_proj", - target_module=col_nn.LinearConv1D_Row, + target_module=col_nn.GPT2FusedLinearConv1D_Row, ), SubModuleReplacementDescription( suffix="mlp.c_fc", - target_module=col_nn.LinearConv1D_Col, + target_module=col_nn.GPT2FusedLinearConv1D_Col, kwargs={ "n_fused": 1, }, ), SubModuleReplacementDescription( suffix="mlp.c_proj", - target_module=col_nn.LinearConv1D_Row, + target_module=col_nn.GPT2FusedLinearConv1D_Row, ), SubModuleReplacementDescription( suffix="attn.attn_dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ), SubModuleReplacementDescription( suffix="attn.resid_dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ), SubModuleReplacementDescription( suffix="mlp.dropout", - target_module=col_nn.Dropout1D, + target_module=col_nn.DropoutForParallelInput, ), ]) } diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 9a1b63e46d2c..8d8abc9f7204 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -9,7 +9,7 @@ T5Stack, ) -from colossalai.shardformer.layer import Dropout1D, Embedding1D, Linear1D_Col, Linear1D_Row +from colossalai.shardformer.layer import DropoutForParallelInput, Embedding1D, Linear1D_Col, Linear1D_Row from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -38,7 +38,7 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", - target_module=Dropout1D, + target_module=DropoutForParallelInput, ) ]), T5LayerSelfAttention: @@ -47,7 +47,7 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", - target_module=Dropout1D, + target_module=DropoutForParallelInput, ), ]), T5LayerCrossAttention: @@ -56,7 +56,7 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", - target_module=Dropout1D, + target_module=DropoutForParallelInput, ) ]), T5Attention: @@ -97,7 +97,7 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", - target_module=Dropout1D, + target_module=DropoutForParallelInput, ), ]), T5DenseGatedActDense: @@ -117,7 +117,7 @@ def module_policy(self): kwargs=dict(gather_output=True)), SubModuleReplacementDescription( suffix="dropout", - target_module=Dropout1D, + target_module=DropoutForParallelInput, ) ]), T5DenseActDense: @@ -134,7 +134,7 @@ def module_policy(self): ), SubModuleReplacementDescription( suffix="dropout", - target_module=Dropout1D, + target_module=DropoutForParallelInput, ) ]) } diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py index 4a2b72057d05..550f8f997ae1 100644 --- a/colossalai/shardformer/policies/vit.py +++ b/colossalai/shardformer/policies/vit.py @@ -1,15 +1,15 @@ from typing import Dict, Union import torch.nn as nn +from transformers.models.vit.modeling_vit import ViTAttention, ViTEmbeddings, ViTLayer, ViTModel -from transformers.models.vit.modeling_vit import ViTModel, ViTLayer, ViTEmbeddings, ViTAttention - -from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row, Dropout1D +from colossalai.shardformer.layer import DropoutForReplicatedInput, Linear1D_Col, Linear1D_Row from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + class ViTPolicy(Policy): - + def preprocess(self): # Resize embedding vocab_size = self.model.config.vocab_size @@ -20,77 +20,68 @@ def preprocess(self): self.model.resize_token_embeddings(new_vocab_size) return self.model - + def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - return { + return { ViTEmbeddings: - ModulePolicyDescription( - attribute_replacement{}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=Dropout1D, - ) - ] - ), + ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForReplicatedInput, + ) + ]), ViTLayer: - ModulePolicyDescription( - attribute_replacement{ - "attention.attention.num_attention_heads": - self.model.config.num_attention_heads//self.shard_config.tensor_parallel_size, - "attention.attention.all_head_size": - self.model.config.hidden_size//self.shard_config.tensor_parallel_size, - }, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="attention.attention.query", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.attention.key", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.attention.value", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.attention.dropout", - target_module=Dropout1D, - ), - SubModuleReplacementDescription( - suffix="attention.output.dense", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="attention.output.dropout", - target_module=Dropout1D, - ), - SubModuleReplacementDescription( - suffix="intermediate.dense", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="output.dense", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="output.dropout", - target_module=Dropout1D, - ), - ] - ), + ModulePolicyDescription(attribute_replacement={ + "attention.attention.num_attention_heads": + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + "attention.attention.all_head_size": + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + }, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attention.attention.query", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.key", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.value", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.dropout", + target_module=DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="attention.output.dense", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="attention.output.dropout", + target_module=DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="intermediate.dense", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="output.dense", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="output.dropout", + target_module=DropoutForParallelInput, + ), + ]), } - + def new_model_class(self): return None def postprocess(self): return self.model - - - - - diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 22f5f1c12d26..c2444e1f765c 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -95,8 +95,9 @@ def _replace_module(self,) -> None: attr_replacement = module_description[1].attribute_replacement param_replacement = module_description[1].param_replacement sub_module_replacement = module_description[1].sub_module_replacement + method_replacement = module_description[1].method_replacement self._recursive_replace_layer(self.model, origin_layer_cls, attr_replacement, param_replacement, - sub_module_replacement) + method_replacement, sub_module_replacement) def _recursive_replace_layer( self, @@ -104,6 +105,7 @@ def _recursive_replace_layer( origin_cls: nn.Module, attr_replacement: Dict[str, Any], param_replacement: List[Callable], + method_replacement: Dict[str, Callable], sub_module_replacement: List[Callable], ) -> None: r""" @@ -119,9 +121,11 @@ def _recursive_replace_layer( if module.__class__ == origin_cls: self._replace_attr(module, attr_replacement) self._replace_param(module, param_replacement) + self._replace_method(module, method_replacement) self._replace_sub_module(module, sub_module_replacement) + for name, child in module.named_children(): - self._recursive_replace_layer(child, origin_cls, attr_replacement, param_replacement, + self._recursive_replace_layer(child, origin_cls, attr_replacement, param_replacement, method_replacement, sub_module_replacement) def _replace_attr( @@ -154,6 +158,14 @@ def _replace_param( # TODO: support parameter shard pass + def _replace_method(self, module: nn.Module, method_replacement: Dict[str, Callable]): + if method_replacement is None: + return + + for method_name, new_method in method_replacement.items(): + # bind the new method to the module + setattr(module, method_name, new_method.__get__(module, module.__class__)) + def _replace_sub_module( self, org_layer: nn.Module, diff --git a/tests/kit/model_zoo/transformers/__init__.py b/tests/kit/model_zoo/transformers/__init__.py index ffaf4c566df9..4aa01abe13ee 100644 --- a/tests/kit/model_zoo/transformers/__init__.py +++ b/tests/kit/model_zoo/transformers/__init__.py @@ -1,5 +1,6 @@ from .albert import * from .bert import * +from .bloom import * from .gpt import * from .llama import * from .opt import * diff --git a/tests/kit/model_zoo/transformers/bloom.py b/tests/kit/model_zoo/transformers/bloom.py new file mode 100644 index 000000000000..71146c0b9819 --- /dev/null +++ b/tests/kit/model_zoo/transformers/bloom.py @@ -0,0 +1,107 @@ +import torch +import transformers + +from ..registry import ModelAttribute, model_zoo + +# =============================== +# Register Bloom +# =============================== + + +def data_gen(): + # Generated from following code snippet + # + # from transformers import BloomTokenizer + # input = 'Hello, my dog is cute' + # tokenized_input = tokenizer(input, return_tensors='pt') + # input_ids = tokenized_input['input_ids'] + # attention_mask = tokenized_input['attention_mask'] + input_ids = torch.tensor([[59414, 15, 2670, 35433, 632, 207595]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]], dtype=torch.int64) + return dict(input_ids=input_ids, attention_mask=attention_mask) + + +def data_gen_for_lm(): + # LM data gen + # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels` + data = data_gen() + data['labels'] = data['input_ids'].clone() + return data + + +def data_gen_for_token_classification(): + # token classification data gen + # `labels` is the type not the token id for token classification, 0 or 1 + data = data_gen() + data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 0]], dtype=torch.int64) + return data + + +def data_gen_for_sequence_classification(): + # sequence classification data gen + data = data_gen() + data['labels'] = torch.tensor([0], dtype=torch.int64) + return data + + +def data_gen_for_question_answering(): + # obtained with the following code + # + # from transformers import AutoTokenizer + # tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m") + # question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" + # inputs = tokenizer(question, text, return_tensors="pt") + + input_ids = torch.tensor( + [[57647, 1620, 23967, 620, 107373, 34, 91514, 620, 107373, 1620, 267, 35378, 48946, 18161]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) + return dict(input_ids=input_ids, attention_mask=attention_mask) + + +# define output transform function +output_transform_fn = lambda x: x + +# define loss function +loss_fn_for_bloom_model = lambda x: x.last_hidden_state.mean() +loss_fn_for_causal_lm = lambda x: x.loss +loss_fn_for_classification = lambda x: x.logits.mean() +loss_fn_for_question_answering = lambda x: x.end_logits.mean() + +config = transformers.BloomConfig(n_layer=1, + n_head=4, + vocab_size=250880, + hidden_dropout=0, + attention_dropout=0, + hidden_size=64) + +# register the following models +model_zoo.register(name='transformers_bloom', + model_fn=lambda: transformers.BloomModel(config), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_bloom_model, + model_attribute=ModelAttribute(has_control_flow=True)) +model_zoo.register(name='transformers_bloom_for_causal_lm', + model_fn=lambda: transformers.BloomForCausalLM(config), + data_gen_fn=data_gen_for_lm, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_causal_lm, + model_attribute=ModelAttribute(has_control_flow=True)) +model_zoo.register(name='transformers_bloom_for_sequence_classification', + model_fn=lambda: transformers.BloomForSequenceClassification(config), + data_gen_fn=data_gen_for_sequence_classification, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_classification, + model_attribute=ModelAttribute(has_control_flow=True)) +model_zoo.register(name='transformers_bloom_for_token_classification', + model_fn=lambda: transformers.BloomForTokenClassification(config), + data_gen_fn=data_gen_for_token_classification, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_classification, + model_attribute=ModelAttribute(has_control_flow=True)) +model_zoo.register(name='transformers_bloom_for_question_answering', + model_fn=lambda: transformers.BloomForQuestionAnswering(config), + data_gen_fn=data_gen_for_question_answering, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_question_answering, + model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index c598fa8f48e0..b9e0310780af 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -6,8 +6,6 @@ # =============================== # Register single-sentence GPT # =============================== -BATCH_SIZE = 1 # it can only be 1 as GPT cannot handle batch sizes > 1 if no padding token is defined. -SEQ_LENGTH = 16 def data_gen(): diff --git a/tests/test_shardformer/test_layer/test_dropout.py b/tests/test_shardformer/test_layer/test_dropout.py index c62d25d94aa4..332e377110a4 100644 --- a/tests/test_shardformer/test_layer/test_dropout.py +++ b/tests/test_shardformer/test_layer/test_dropout.py @@ -3,13 +3,13 @@ import torch.nn as nn import colossalai -from colossalai.shardformer.layer import Dropout1D +from colossalai.shardformer.layer import DropoutForParallelInput, DropoutForReplicatedInput from colossalai.testing import assert_equal, assert_not_equal, rerun_if_address_is_in_use, spawn -def check_dropout(): +def check_dropout_parallel_input(): dropout = nn.Dropout().cuda() - dropout_1d = Dropout1D.from_native_module(dropout, process_group=None) + dropout_1d = DropoutForParallelInput.from_native_module(dropout, process_group=None) # check computation correctness x = torch.rand(4, 128).cuda() @@ -39,9 +39,26 @@ def check_dropout(): assert_not_equal(out_1d_all[i], out_1d_all[0]) +def check_dropout_replicated_input(): + dropout = nn.Dropout().cuda() + dropout_replica = DropoutForReplicatedInput.from_native_module(dropout, process_group=None) + + # check computation correctness + x = torch.rand(4, 128).cuda() + out_1d = dropout_replica(x) + + # ensure out_1d is different across ranks + world_size = dist.get_world_size() + out_1d_all = [torch.zeros_like(out_1d) for _ in range(world_size)] + dist.all_gather(out_1d_all, out_1d) + for i in range(1, world_size): + assert_equal(out_1d_all[i], out_1d_all[0]) + + def run_dist(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - check_dropout() + check_dropout_parallel_input() + check_dropout_replicated_input() @rerun_if_address_is_in_use() diff --git a/tests/test_shardformer/test_layer/test_linearconv_1d.py b/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py similarity index 81% rename from tests/test_shardformer/test_layer/test_linearconv_1d.py rename to tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py index 774e6340eee6..681c4f6dd9f1 100644 --- a/tests/test_shardformer/test_layer/test_linearconv_1d.py +++ b/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py @@ -4,8 +4,8 @@ from torch.testing import assert_close import colossalai -from colossalai.shardformer.layer import LinearConv1D_Col, LinearConv1D_Row -from colossalai.shardformer.layer.linear_conv import split_fused_qkv +from colossalai.shardformer.layer import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row +from colossalai.shardformer.layer.qkv_fused_linear import split_fused_qkv_in_gpt2_style from colossalai.testing import rerun_if_address_is_in_use, spawn @@ -52,7 +52,10 @@ def rearrange(tensor: torch.Tensor, dim: int): def check_linear_conv_1d_col(): linear = Conv1D(192, 48).cuda() - linear_conv_col = LinearConv1D_Col.from_native_module(linear, process_group=None, gather_output=True, n_fused=3) + linear_conv_col = GPT2FusedLinearConv1D_Col.from_native_module(linear, + process_group=None, + gather_output=True, + n_fused=3) assert linear.weight.shape == torch.Size([48, 192]) assert linear.bias.shape == torch.Size([192]) @@ -73,13 +76,13 @@ def check_linear_conv_1d_col(): out.sum().backward() gather_out.sum().backward() - target_grad = split_fused_qkv(linear.weight.grad, 3, None) + target_grad = split_fused_qkv_in_gpt2_style(linear.weight.grad, 3, None, True) assert_close(target_grad, linear_conv_col.weight.grad) def check_linear_conv_1d_row(): linear = Conv1D(192, 48).cuda() - linear_row = LinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False) + linear_row = GPT2FusedLinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False) assert linear.weight.shape == torch.Size([48, 192]) assert linear_row.weight.shape == torch.Size([24, 192]) @@ -102,6 +105,8 @@ def check_linear_conv_1d_row(): def run_dist(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + # test for linear conv check_linear_conv_1d_col() check_linear_conv_1d_row() diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py new file mode 100644 index 000000000000..7e2e3dfa8f81 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -0,0 +1,59 @@ +import pytest +import torch + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) + + # do backward + org_loss.backward() + shard_loss.backward() + + # check grad equality + if org_model.__class__.__name__ == 'BloomModel': + org_grad = org_model.h[0].self_attention.query_key_value.weight.grad + shard_grad = sharded_model.h[0].self_attention.query_key_value.weight.grad + else: + org_grad = org_model.transformer.h[0].self_attention.query_key_value.weight.grad + shard_grad = sharded_model.transformer.h[0].self_attention.query_key_value.weight.grad + + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + +def check_bloom(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + org_model, sharded_model = build_model(world_size, model_fn) + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + + torch.cuda.empty_cache() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_bloom(): + spawn(check_bloom, 2) + + +if __name__ == "__main__": + test_bloom() From f3b6aaa6b7e7d47299019a52924f41eb513ee870 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 30 Jun 2023 09:32:37 +0800 Subject: [PATCH 388/413] [shardformer] supported fused normalization (#4112) --- colossalai/shardformer/layer/__init__.py | 4 +- .../layer/{layernorm.py => normalization.py} | 44 ++++++++++++++++++- colossalai/shardformer/policies/basepolicy.py | 8 ++++ colossalai/shardformer/policies/bert.py | 21 ++++++--- colossalai/shardformer/policies/bloom.py | 31 ++++++++++++- colossalai/shardformer/policies/gpt2.py | 29 +++++++++++- colossalai/shardformer/policies/llama.py | 28 +++++++++++- colossalai/shardformer/policies/opt.py | 8 +++- colossalai/shardformer/policies/t5.py | 22 ++++++++-- colossalai/shardformer/policies/vit.py | 27 +++++++++++- colossalai/shardformer/shard/shard_config.py | 10 +---- tests/test_shardformer/test_model/_utils.py | 6 +-- 12 files changed, 207 insertions(+), 31 deletions(-) rename colossalai/shardformer/layer/{layernorm.py => normalization.py} (59%) diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py index 2826a8429f00..7fad4948dfd0 100644 --- a/colossalai/shardformer/layer/__init__.py +++ b/colossalai/shardformer/layer/__init__.py @@ -1,12 +1,12 @@ from .dropout import DropoutForParallelInput, DropoutForReplicatedInput from .embedding import Embedding1D, VocabParallelEmbedding1D -from .layernorm import FusedLayerNorm from .linear import Linear1D_Col, Linear1D_Row from .loss import cross_entropy_1d +from .normalization import FusedLayerNorm, FusedRMSNorm from .qkv_fused_linear import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row __all__ = [ "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", 'GPT2FusedLinearConv1D_Col', 'GPT2FusedLinearConv1D_Row', 'DropoutForParallelInput', 'DropoutForReplicatedInput', "cross_entropy_1d", - 'FusedLayerNorm' + 'FusedLayerNorm', 'FusedRMSNorm' ] diff --git a/colossalai/shardformer/layer/layernorm.py b/colossalai/shardformer/layer/normalization.py similarity index 59% rename from colossalai/shardformer/layer/layernorm.py rename to colossalai/shardformer/layer/normalization.py index 6103380fe8a5..b27307154a76 100644 --- a/colossalai/shardformer/layer/layernorm.py +++ b/colossalai/shardformer/layer/normalization.py @@ -4,7 +4,7 @@ import torch import torch.nn as nn -__all__ = ['FusedLayerNorm'] +__all__ = ['FusedLayerNorm', 'FusedRMSNorm'] FAST_LAYERNORM_SUPPORTED_SIZE = [ 1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480, 24576, @@ -61,4 +61,44 @@ def from_native_module(module: nn.LayerNorm, *args, **kwargs) -> nn.Module: # copy weight and bias layernorm.weight.copy_(module.weight) layernorm.bias.copy_(module.bias) - return layernorm \ No newline at end of file + return layernorm + + +class FusedRMSNorm(): + """ + This is a wrapper around the apex fused rms norm implementation. It is meant to be used only with the from_native_module interface. + """ + + def __init__(self) -> None: + raise NotImplementedError( + 'FusedRMSNorm is not implemented as a physical class. ' + 'It is meant to be used only with the from_native_module interface to wrap the fused rms norm implementation provided by apex.' + ) + + @staticmethod + def from_native_module(module: nn.Module, *args, **kwargs) -> nn.Module: + try: + from apex.normalization import FusedRMSNorm as ApexFusedRMSNorm + except ImportError: + raise ImportError( + 'Please install apex from source (https://github.com/NVIDIA/apex) to use the fused RMS normalization kernel' + ) + + # to check if it is huggingface LlamaRMSNorm + if module.__class__.__name__ == "LlamaRMSNorm": + normalized_shape = module.weight.shape[0] + eps = module.variance_epsilon + elementwise_affine = True + else: + # get the attributes of the module + normalized_shape = module.normalized_shape + eps = module.eps + elementwise_affine = module.elementwise_affine + + rmsnorm = ApexFusedRMSNorm(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine) + + with torch.no_grad(): + # copy weight and bias + rmsnorm.weight.copy_(module.weight) + + return rmsnorm diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 7e9bcf209573..8835e38cbbe4 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -98,6 +98,14 @@ def set_shard_config(self, shard_config: ShardConfig) -> None: shard_config (:class:`ShardConfig`): The shard config to be perform """ self.shard_config = shard_config + self.config_sanity_check() + + @abstractmethod + def config_sanity_check(self): + """ + Check if the shard config is valid for the model. Raise an exception if the config is invalid. + """ + pass @abstractmethod def preprocess(self) -> nn.Module: diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 49ef53259321..545669f1f463 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -16,6 +16,9 @@ class BertPolicy(Policy): + def config_sanity_check(self): + pass + def preprocess(self): # reshape the embedding layer r""" @@ -99,7 +102,8 @@ def module_policy(self): ]) } - if self.shard_config.fused_layernorm: + # optimization configuration + if self.shard_config.enable_fused_normalization: base_policy[BertLayer].sub_module_replacement.append( SubModuleReplacementDescription( suffix="attention.output.LayerNorm", @@ -150,12 +154,16 @@ def module_policy(self): kwargs={"gather_output": True}), ]) } - if self.shard_config.fused_layernorm: + + # optimization configuration + if self.shard_config.enable_fused_normalization: addon_module[BertLMPredictionHead].sub_module_replacement.append( SubModuleReplacementDescription( suffix="transform.LayerNorm", target_module=col_nn.FusedLayerNorm, )) + + # append extra policy module_policy.update(addon_module) return module_policy @@ -187,7 +195,7 @@ def module_policy(self): kwargs={"gather_output": True}), ]) } - if self.shard_config.fused_layernorm: + if self.shard_config.enable_fused_normalization: addon_module[BertLMPredictionHead].sub_module_replacement.append( SubModuleReplacementDescription( suffix="transform.LayerNorm", @@ -224,12 +232,15 @@ def module_policy(self): kwargs={"gather_output": True}), ]) } - if self.shard_config.fused_layernorm: + + # optimization configuration + if self.shard_config.enable_fused_normalization: addon_module[BertLMPredictionHead].sub_module_replacement.append( SubModuleReplacementDescription( suffix="transform.LayerNorm", target_module=col_nn.FusedLayerNorm, )) + module_policy.update(addon_module) return module_policy @@ -316,4 +327,4 @@ def module_policy(self): ]) } module_policy.update(addon_module) - return module_policy \ No newline at end of file + return module_policy diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index d196bdbd6e4d..4e34f24643c2 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -65,6 +65,9 @@ def build_bloom_alibi_tensor(self, attention_mask: torch.Tensor, num_heads: int, class BloomPolicy(Policy): + def config_sanity_check(self): + pass + def preprocess(self): # reshape the embedding layer r""" @@ -81,7 +84,7 @@ def preprocess(self): def module_policy(self): from transformers.models.bloom.modeling_bloom import BloomBlock, BloomModel - return { + base_policy = { BloomBlock: ModulePolicyDescription( attribute_replacement={ @@ -99,7 +102,6 @@ def module_policy(self): SubModuleReplacementDescription( suffix="self_attention.query_key_value", target_module=col_nn.Linear1D_Col, - # kwargs={'n_fused': 3} ), SubModuleReplacementDescription( suffix="self_attention.dense", @@ -132,6 +134,31 @@ def module_policy(self): ]) } + # optimization configuration + if self.shard_config.enable_fused_normalization: + base_policy[BloomModel].sub_module_replacement.extend([ + SubModuleReplacementDescription( + suffix="ln_f", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="word_embeddings_layernorm", + target_module=col_nn.FusedLayerNorm, + ) + ]) + base_policy[BloomBlock].sub_module_replacement.extend([ + SubModuleReplacementDescription( + suffix="input_layernorm", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="post_attention_layernorm", + target_module=col_nn.FusedLayerNorm, + ) + ]) + + return base_policy + def new_model_class(self): # do nothing return self.model diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index ebfaf8a8e1c3..3d6d94b8e90d 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -9,6 +9,9 @@ class GPT2Policy(Policy): + def config_sanity_check(self): + pass + def preprocess(self): # reshape the embedding layer r""" @@ -22,7 +25,7 @@ def preprocess(self): return self.model def module_policy(self): - return { + base_policy = { GPT2Model: ModulePolicyDescription(attribute_replacement={}, param_replacement=[], @@ -77,6 +80,30 @@ def module_policy(self): ]) } + # optimization configuration + if self.shard_config.enable_fused_normalization: + base_policy[GPT2Model].sub_module_replacement.append( + SubModuleReplacementDescription( + suffix="ln_f", + target_module=col_nn.FusedLayerNorm, + )) + + base_policy[GPT2Block].sub_module_replacement.extend([ + SubModuleReplacementDescription( + suffix="ln_1", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="ln_2", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription(suffix="ln_cross_attn", + target_module=col_nn.FusedLayerNorm, + ignore_if_not_exist=True) + ]) + + return base_policy + def new_model_class(self): return self.model diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index a13f5f087da4..b36180ce3188 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -4,13 +4,16 @@ from transformers import LlamaForCausalLM, LlamaForSequenceClassification from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel -from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D +from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription class LlamaPolicy(Policy): + def config_sanity_check(self): + pass + def preprocess(self): # Resize embedding vocab_size = self.model.config.vocab_size @@ -23,7 +26,7 @@ def preprocess(self): return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - return { + base_policy = { LlamaDecoderLayer: ModulePolicyDescription( attribute_replacement={ @@ -75,6 +78,27 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: ]) } + # optimization configuration + if self.shard_config.enable_fused_normalization: + base_policy[LlamaDecoderLayer].sub_module_replacement.extend([ + SubModuleReplacementDescription( + suffix="input_layernorm", + target_module=FusedRMSNorm, + ), + SubModuleReplacementDescription( + suffix="post_attention_layernorm", + target_module=FusedRMSNorm, + ) + ]) + + base_policy[LlamaModel].sub_module_replacement.append( + SubModuleReplacementDescription( + suffix="norm", + target_module=FusedRMSNorm, + )) + + return base_policy + def new_model_class(self): return None diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index f467726e5580..ce3873954e15 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -13,6 +13,9 @@ class OPTPolicy(Policy): + def config_sanity_check(self): + pass + def preprocess(self): # reshape the embedding layer r""" @@ -74,7 +77,9 @@ def module_policy(self): ), ]), } - if self.shard_config.fused_layernorm: + + # optimization configuration + if self.shard_config.enable_fused_normalization: base_policy[OPTDecoder].sub_module_replacement.append( SubModuleReplacementDescription(suffix="final_layer_norm", target_module=FusedLayerNorm, @@ -87,6 +92,7 @@ def module_policy(self): target_module=FusedLayerNorm, ignore_if_not_exist=True) ]) + return base_policy def new_model_class(self): diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 8d8abc9f7204..d35f688a0b61 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -9,7 +9,7 @@ T5Stack, ) -from colossalai.shardformer.layer import DropoutForParallelInput, Embedding1D, Linear1D_Col, Linear1D_Row +from colossalai.shardformer.layer import DropoutForParallelInput, Embedding1D, FusedRMSNorm, Linear1D_Col, Linear1D_Row from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -18,6 +18,9 @@ class T5ModelPolicy(Policy): + def config_sanity_check(self): + pass + def preprocess(self): # reshape the embedding layer r""" @@ -31,7 +34,7 @@ def preprocess(self): return self.model def module_policy(self): - return { + base_policy = { T5Stack: ModulePolicyDescription(attribute_replacement={}, param_replacement=[], @@ -139,6 +142,19 @@ def module_policy(self): ]) } + # optimization configuration + if self.shard_config.enable_fused_normalization: + base_policy[T5LayerFF].sub_module_replacement.append( + SubModuleReplacementDescription(suffix="layer_norm", target_module=FusedRMSNorm)) + base_policy[T5LayerSelfAttention].sub_module_replacement.append( + SubModuleReplacementDescription(suffix="layer_norm", target_module=FusedRMSNorm)) + base_policy[T5LayerCrossAttention].sub_module_replacement.append( + SubModuleReplacementDescription(suffix="layer_norm", target_module=FusedRMSNorm)) + base_policy[T5Stack].sub_module_replacement.append( + SubModuleReplacementDescription(suffix="final_layer_norm", target_module=FusedRMSNorm)) + + return base_policy + def new_model_class(self): return None @@ -167,4 +183,4 @@ def module_policy(self): class T5EncoderPolicy(T5ModelPolicy): - pass \ No newline at end of file + pass diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py index 550f8f997ae1..5d8a235db7a9 100644 --- a/colossalai/shardformer/policies/vit.py +++ b/colossalai/shardformer/policies/vit.py @@ -3,13 +3,16 @@ import torch.nn as nn from transformers.models.vit.modeling_vit import ViTAttention, ViTEmbeddings, ViTLayer, ViTModel -from colossalai.shardformer.layer import DropoutForReplicatedInput, Linear1D_Col, Linear1D_Row +from colossalai.shardformer.layer import DropoutForReplicatedInput, FusedLayerNorm, Linear1D_Col, Linear1D_Row from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription class ViTPolicy(Policy): + def config_sanity_check(self): + pass + def preprocess(self): # Resize embedding vocab_size = self.model.config.vocab_size @@ -22,7 +25,7 @@ def preprocess(self): return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - return { + base_policy = { ViTEmbeddings: ModulePolicyDescription(attribute_replacement={}, param_replacement=[], @@ -80,6 +83,26 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: ]), } + # optimization configuration + if self.shard_config.enable_fused_normalization: + base_policy[ViTAttention].sub_module_replacement.extend([ + SubModuleReplacementDescription( + suffix="layernorm_before", + target_module=FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="layernorm_after", + target_module=FusedLayerNorm, + ) + ]) + base_policy[ViTModel].sub_module_replacement.append( + SubModuleReplacementDescription( + suffix="layernorm", + target_module=FusedLayerNorm, + )) + + return base_policy + def new_model_class(self): return None diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 8d3fc225e894..428ebc9780ba 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -12,16 +12,10 @@ class ShardConfig: Args: tensor_parallel_size (int): The size of tensor parallel - use_mixedfusedLN (bool): Whether to use the `MixedFusedLayerNorm` - data_parallel_size (int): The size of data parallel - pipeline_parallel_size (int): The size of pipeline parallel - tensor_parallel_mode (List): The mode of tensor parallel, choose from `['1d','2d','2.5d','3d'] - inference_only (bool): Whether to use the inference only mode, when setting to `True`, the model - will not calculate the loss and just return the output. - gather_output (bool): Whether to gather the output of the model of the last layer + enable_fused_normalization (bool): Whether to use fused layernorm, default is False """ tensor_parallel_size: int - fused_layernorm: bool = False + enable_fused_normalization: bool = False # TODO: add support for tensor parallel # pipeline_parallel_size: int diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index ad7c408aeb38..e49b0246ced5 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -8,11 +8,11 @@ def build_model(world_size, model_fn): org_model = model_fn().cuda() # shard model - shard_config = ShardConfig(tensor_parallel_size=world_size, fused_layernorm=True) + shard_config = ShardConfig(tensor_parallel_size=world_size, enable_fused_normalization=True) model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) shard_former.init_distributed() - sharded_model = shard_former.shard_model(model_copy) + sharded_model = shard_former.shard_model(model_copy).cuda() return org_model, sharded_model @@ -33,4 +33,4 @@ def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, shard_output = sharded_model(**data) shard_output = output_transform_fn(shard_output) shard_loss = loss_fn(shard_output) - return org_output, org_loss, shard_output, shard_loss \ No newline at end of file + return org_output, org_loss, shard_output, shard_loss From 6a88bae4ec26b11261047e5462a5c2ed6bfe41f4 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 30 Jun 2023 09:58:08 +0800 Subject: [PATCH 389/413] [shardformer] integrate with data parallelism (#4103) --- colossalai/shardformer/shard/shard_config.py | 16 ++-- colossalai/shardformer/shard/sharder.py | 11 +-- colossalai/shardformer/shard/shardformer.py | 25 +----- tests/test_shardformer/test_model/_utils.py | 6 +- .../test_model/test_shard_bert.py | 2 +- .../test_model/test_shard_bloom.py | 2 +- .../test_model/test_shard_gpt2.py | 2 +- .../test_model/test_shard_llama.py | 2 +- .../test_model/test_shard_opt.py | 2 +- .../test_model/test_shard_t5.py | 2 +- tests/test_shardformer/test_with_torch_ddp.py | 77 +++++++++++++++++++ 11 files changed, 97 insertions(+), 50 deletions(-) create mode 100644 tests/test_shardformer/test_with_torch_ddp.py diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 428ebc9780ba..e83191210a15 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -1,5 +1,8 @@ from dataclasses import dataclass +import torch.distributed as dist +from torch.distributed import ProcessGroup + from colossalai.cluster.dist_coordinator import DistCoordinator __all__ = ['ShardConfig'] @@ -11,10 +14,10 @@ class ShardConfig: The config for sharding the huggingface model Args: - tensor_parallel_size (int): The size of tensor parallel + tensor_parallel_process_group (int): The process group for tensor parallelism, defaults to None, which is the global process group. enable_fused_normalization (bool): Whether to use fused layernorm, default is False """ - tensor_parallel_size: int + tensor_parallel_process_group: int = None enable_fused_normalization: bool = False # TODO: add support for tensor parallel @@ -25,10 +28,5 @@ class ShardConfig: # gather_output: bool = True def __post_init__(self): - coordinator = DistCoordinator() - - # ensure the parallel size can match the world size - world_size = coordinator.world_size - self.data_parallel_size = world_size // self.tensor_parallel_size - assert world_size == self.data_parallel_size * self.tensor_parallel_size, \ - f"The world size ({world_size}) should be divisible by the data parallel size {self.data_parallel_size} and tensor parallel size {self.tensor_parallel_size}" + # get the parallel size + self.tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group) diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index c2444e1f765c..e9b27ea45959 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -22,16 +22,10 @@ class ModelSharder(object): shard_config: The setting of distributed model """ - def __init__( - self, - model: nn.Module, - policy: Policy, - shard_config: ShardConfig = None, # TODO - pg_manager: ProcessGroupManager = None) -> None: + def __init__(self, model: nn.Module, policy: Policy, shard_config: ShardConfig = None) -> None: self.model = model self.policy = get_autopolicy(self.model) if policy is None else policy self.shard_config = shard_config - self.pg_manager = pg_manager def shard(self) -> None: r""" @@ -198,7 +192,8 @@ def _replace_sub_module( continue try: - replace_layer = target_module.from_native_module(native_sub_module, self.pg_manager.pg_store['tp1d'], + replace_layer = target_module.from_native_module(native_sub_module, + self.shard_config.tensor_parallel_process_group, **kwargs) except Exception as e: raise RuntimeError( diff --git a/colossalai/shardformer/shard/shardformer.py b/colossalai/shardformer/shard/shardformer.py index 1208a9d090fb..7c4220c3a9fb 100644 --- a/colossalai/shardformer/shard/shardformer.py +++ b/colossalai/shardformer/shard/shardformer.py @@ -1,7 +1,6 @@ import torch.nn as nn -from torch.utils.data import Dataset -from colossalai.cluster import DistCoordinator, ProcessGroupManager +from colossalai.cluster import DistCoordinator from ..policies.basepolicy import Policy from .shard_config import ShardConfig @@ -28,7 +27,6 @@ class ShardFormer: tensor_parallel_mode='1d', ) shard_former = ShardFormer(shard_config=shard_config) - shard_former.init_distributed() model = shard_former.shard_model(org_model) ``` """ @@ -41,19 +39,6 @@ def __init__(self, shard_config: ShardConfig): """ self.coordinator = DistCoordinator() self.shard_config = shard_config - self.pg_manager = None - - def init_distributed(self) -> ProcessGroupManager: - """ - Initialize the distributed process group according to the - """ - # create process group manager and 1d process group - # TODO: may need to support other parallel mode when the config has such as field - pg_manager = ProcessGroupManager() - pg_manager.create_process_group(name='tp1d', ranks=range(self.coordinator.world_size)) - self.pg_manager = pg_manager - - return pg_manager def shard_model(self, model: nn.Module, policy: Policy = None): r""" @@ -64,12 +49,6 @@ def shard_model(self, model: nn.Module, policy: Policy = None): shard_config (`ShardConfig`): the config for distribute information policy (`Policy`): the custom policy for sharding """ - sharder = ModelSharder(model=model, shard_config=self.shard_config, policy=policy, pg_manager=self.pg_manager) + sharder = ModelSharder(model=model, shard_config=self.shard_config, policy=policy) sharder.shard() return model - - def shard_dataset(self, dataset: Dataset): - """ - Shard dataset for DP - """ - pass diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index e49b0246ced5..a6355bf1c75e 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -3,17 +3,15 @@ from colossalai.shardformer import ShardConfig, ShardFormer -def build_model(world_size, model_fn): +def build_model(model_fn): # create new model org_model = model_fn().cuda() # shard model - shard_config = ShardConfig(tensor_parallel_size=world_size, enable_fused_normalization=True) + shard_config = ShardConfig(enable_fused_normalization=True) model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) - shard_former.init_distributed() sharded_model = shard_former.shard_model(model_copy).cuda() - return org_model, sharded_model diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index ad98e3d073d4..a089a1ab33cc 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -42,7 +42,7 @@ def check_bert(rank, world_size, port): sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(world_size, model_fn) + org_model, sharded_model = build_model(model_fn) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index 7e2e3dfa8f81..2e7ae7067467 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -42,7 +42,7 @@ def check_bloom(rank, world_size, port): sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(world_size, model_fn) + org_model, sharded_model = build_model(model_fn) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 676267c2ca2a..4d4dc3c1e5b4 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -43,7 +43,7 @@ def check_gpt2(rank, world_size, port): sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(world_size, model_fn) + org_model, sharded_model = build_model(model_fn) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index 8b672af500bd..763fb2a6bf20 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -50,7 +50,7 @@ def check_llama(rank, world_size, port): sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(world_size, model_fn) + org_model, sharded_model = build_model(model_fn) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index 4d4c55770144..d70b5d8e57d9 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -54,7 +54,7 @@ def check_OPTModel(rank, world_size, port): sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(world_size, model_fn) + org_model, sharded_model = build_model(model_fn) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 6074a902e9b0..6f558e237970 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -42,7 +42,7 @@ def check_t5(rank, world_size, port): sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(world_size, model_fn) + org_model, sharded_model = build_model(model_fn) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_with_torch_ddp.py b/tests/test_shardformer/test_with_torch_ddp.py new file mode 100644 index 000000000000..61b672650965 --- /dev/null +++ b/tests/test_shardformer/test_with_torch_ddp.py @@ -0,0 +1,77 @@ +import pytest +import torch +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP + +import colossalai +from colossalai.cluster import DistCoordinator +from colossalai.logging import disable_existing_loggers +from colossalai.shardformer import ShardConfig, ShardFormer +from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo + + +def check_shardformer_with_ddp(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') + + # create shardformer + # ranks: [0, 1, 2, 3] + # tp ranks = [0, 1], [2, 3] + # dp ranks = [0, 2], [1, 3] + dp_process_group_1 = dist.new_group([0, 2]) + dp_process_group_2 = dist.new_group([1, 3]) + tp_process_group_1 = dist.new_group([0, 1]) + tp_process_group_2 = dist.new_group([2, 3]) + + coordinator = DistCoordinator() + + if coordinator.rank in [0, 1]: + tp_process_group = tp_process_group_1 + else: + tp_process_group = tp_process_group_2 + + if coordinator.rank in [0, 2]: + dp_process_group = dp_process_group_1 + else: + dp_process_group = dp_process_group_2 + + shard_config = ShardConfig(tensor_parallel_process_group=tp_process_group, enable_fused_normalization=True) + shardformer = ShardFormer(shard_config=shard_config) + + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + # create and shard model + model = model_fn().cuda() + sharded_model = shardformer.shard_model(model) + + # add ddp + sharded_ddp_model = DDP(sharded_model, process_group=dp_process_group) + + # prepare input + data = data_gen_fn() + data = {k: v.cuda() for k, v in data.items()} + + # switch to train mode + sharded_ddp_model.train() + + # run forward + output = sharded_ddp_model(**data) + loss = loss_fn(output) + + # backward + loss.backward() + torch.cuda.empty_cache() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_gpt2(): + spawn(check_shardformer_with_ddp, 4) + + +if __name__ == "__main__": + test_gpt2() + test_gpt2() From 44a190e6ac71976304838febcffe82fb61941926 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 30 Jun 2023 10:56:29 +0800 Subject: [PATCH 390/413] [shardformer] import huggingface implicitly (#4101) --- colossalai/shardformer/policies/autopolicy.py | 2 ++ colossalai/shardformer/policies/basepolicy.py | 2 ++ colossalai/shardformer/policies/bert.py | 30 +++++++++++++------ colossalai/shardformer/policies/gpt2.py | 14 +++++++-- colossalai/shardformer/policies/llama.py | 12 ++++++-- colossalai/shardformer/policies/opt.py | 17 ++++++----- colossalai/shardformer/policies/t5.py | 27 +++++++++-------- colossalai/shardformer/policies/vit.py | 7 +++-- colossalai/shardformer/shard/shard_config.py | 18 ++++++++++- 9 files changed, 91 insertions(+), 38 deletions(-) diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 17c063c8d2cf..8051433e8d71 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -5,6 +5,8 @@ from .basepolicy import Policy +__all__ = ["PolicyLocation", "get_autopolicy", "import_policy"] + @dataclass class PolicyLocation: diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 8835e38cbbe4..2b972606948c 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -8,6 +8,8 @@ from ..shard.shard_config import ShardConfig +__all__ = ["ParallelModule", "SubModuleReplacementDescription", "ModulePolicyDescription", "Policy"] + class ParallelModule(): diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 545669f1f463..cec7f0eb2a6d 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,18 +1,16 @@ import torch.nn as nn -from transformers.models.bert.modeling_bert import ( - BertEmbeddings, - BertForMultipleChoice, - BertForSequenceClassification, - BertForTokenClassification, - BertLayer, - BertLMPredictionHead, -) import colossalai.shardformer.layer as col_nn from .._utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +__all__ = [ + 'BertPolicy', 'BertModelPolicy', 'BertForPretrainingPolicy', 'BertLMHeadModelPolicy', 'BertForMaskedLMPolicy', + 'BertForNextSentencePredictionPolicy', 'BertForSequenceClassificationPolicy', 'BertForTokenClassificationPolicy', + 'BertForMultipleChoicePolicy' +] + class BertPolicy(Policy): @@ -33,6 +31,8 @@ def preprocess(self): return self.model def module_policy(self): + from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer + base_policy = { BertLayer: ModulePolicyDescription( @@ -123,7 +123,7 @@ def module_policy(self): def new_model_class(self): # do nothing - return self.model + return None def postprocess(self): return self.model @@ -143,6 +143,8 @@ def __init__(self) -> None: super().__init__() def module_policy(self): + from transformers.models.bert.modeling_bert import BertLMPredictionHead + module_policy = super().module_policy() addon_module = { BertLMPredictionHead: @@ -184,6 +186,8 @@ def __init__(self) -> None: super().__init__() def module_policy(self): + from transformers.models.bert.modeling_bert import BertLMPredictionHead + module_policy = super().module_policy() addon_module = { BertLMPredictionHead: @@ -221,6 +225,8 @@ def __init__(self) -> None: super().__init__() def module_policy(self): + from transformers.models.bert.modeling_bert import BertLMPredictionHead + module_policy = super().module_policy() addon_module = { BertLMPredictionHead: @@ -261,6 +267,8 @@ def __init__(self) -> None: super().__init__() def module_policy(self): + from transformers.models.bert.modeling_bert import BertForSequenceClassification + module_policy = super().module_policy() addon_module = { BertForSequenceClassification: @@ -284,6 +292,8 @@ def __init__(self) -> None: super().__init__() def module_policy(self): + from transformers.models.bert.modeling_bert import BertForTokenClassification + module_policy = super().module_policy() addon_module = { BertForTokenClassification: @@ -314,6 +324,8 @@ def __init__(self) -> None: super().__init__() def module_policy(self): + from transformers.models.bert.modeling_bert import BertForMultipleChoice + module_policy = super().module_policy() addon_module = { BertForMultipleChoice: diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 3d6d94b8e90d..c6108f5c0e85 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -1,11 +1,15 @@ import torch.nn as nn -from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2DoubleHeadsModel, GPT2LMHeadModel, GPT2Model import colossalai.shardformer.layer as col_nn from .._utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +__all__ = [ + 'GPT2Policy', 'GPT2ModelPolicy', 'GPT2LMHeadModelPolicy', 'GPT2DoubleHeadsModelPolicy', + 'GPT2ForTokenClassificationPolicy', 'GPT2ForSequenceClassificationPolicy' +] + class GPT2Policy(Policy): @@ -25,7 +29,9 @@ def preprocess(self): return self.model def module_policy(self): - base_policy = { + from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model + + return { GPT2Model: ModulePolicyDescription(attribute_replacement={}, param_replacement=[], @@ -125,6 +131,8 @@ def __init__(self) -> None: super().__init__() def module_policy(self): + from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel + module_policy = super().module_policy() addon_module = { GPT2LMHeadModel: @@ -156,6 +164,8 @@ def __init__(self) -> None: super().__init__() def module_policy(self): + from transformers.models.gpt2.modeling_gpt2 import GPT2DoubleHeadsModel + module_policy = super().module_policy() addon_module = { GPT2DoubleHeadsModel: diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index b36180ce3188..2fd2bc22303b 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -1,13 +1,13 @@ from typing import Dict, Union import torch.nn as nn -from transformers import LlamaForCausalLM, LlamaForSequenceClassification -from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +__all__ = ['LlamaPolicy', 'LlamaForCausalLMPolicy', 'LlamaForSequenceClassificationPolicy'] + class LlamaPolicy(Policy): @@ -26,7 +26,9 @@ def preprocess(self): return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - base_policy = { + from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel + + return { LlamaDecoderLayer: ModulePolicyDescription( attribute_replacement={ @@ -109,6 +111,8 @@ def postprocess(self): class LlamaForCausalLMPolicy(LlamaPolicy): def module_policy(self): + from transformers import LlamaForCausalLM + policy = super().module_policy() # add a new item for casual lm new_item = { @@ -128,6 +132,8 @@ def module_policy(self): class LlamaForSequenceClassificationPolicy(LlamaPolicy): def module_policy(self): + from transformers import LlamaForSequenceClassification + policy = super().module_policy() # add a new item for sequence classification diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index ce3873954e15..ec1bae20886a 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -1,15 +1,12 @@ -from transformers.models.opt.modeling_opt import ( - OPTAttention, - OPTDecoder, - OPTDecoderLayer, - OPTForCausalLM, - OPTForSequenceClassification, -) - from colossalai.shardformer.layer import Embedding1D, FusedLayerNorm, Linear1D_Col, Linear1D_Row from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +__all__ = [ + 'OPTPolicy', 'OPTModelPolicy', 'OPTForCausalLMPolicy', 'OPTForSequenceClassificationPolicy', + 'OPTForQuestionAnsweringPolicy' +] + class OPTPolicy(Policy): @@ -29,6 +26,8 @@ def preprocess(self): return self.model def module_policy(self): + from transformers.models.opt.modeling_opt import OPTAttention, OPTDecoder, OPTDecoderLayer + base_policy = { OPTDecoder: ModulePolicyDescription(attribute_replacement={}, @@ -111,6 +110,8 @@ def __init__(self) -> None: class OPTForCausalLMPolicy(OPTPolicy): def module_policy(self): + from transformers.models.opt.modeling_opt import OPTForCausalLM + policy = super().module_policy() new_item = { OPTForCausalLM: diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index d35f688a0b61..845bfe727745 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -1,15 +1,4 @@ -from transformers import T5ForConditionalGeneration -from transformers.models.t5.modeling_t5 import ( - T5Attention, - T5DenseActDense, - T5DenseGatedActDense, - T5LayerCrossAttention, - T5LayerFF, - T5LayerSelfAttention, - T5Stack, -) - -from colossalai.shardformer.layer import DropoutForParallelInput, Embedding1D, FusedRMSNorm, Linear1D_Col, Linear1D_Row +from colossalai.shardformer.layer import DropoutForParallelInput, Embedding1D, Linear1D_Col, Linear1D_Row from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -34,7 +23,17 @@ def preprocess(self): return self.model def module_policy(self): - base_policy = { + from transformers.models.t5.modeling_t5 import ( + T5Attention, + T5DenseActDense, + T5DenseGatedActDense, + T5LayerCrossAttention, + T5LayerFF, + T5LayerSelfAttention, + T5Stack, + ) + + return { T5Stack: ModulePolicyDescription(attribute_replacement={}, param_replacement=[], @@ -165,6 +164,8 @@ def postprocess(self): class T5ForConditionalGenerationPolicy(T5ModelPolicy): def module_policy(self): + from transformers import T5ForConditionalGeneration + policy = super().module_policy() new_item = { diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py index 5d8a235db7a9..6a404c2faf0f 100644 --- a/colossalai/shardformer/policies/vit.py +++ b/colossalai/shardformer/policies/vit.py @@ -1,12 +1,13 @@ from typing import Dict, Union import torch.nn as nn -from transformers.models.vit.modeling_vit import ViTAttention, ViTEmbeddings, ViTLayer, ViTModel from colossalai.shardformer.layer import DropoutForReplicatedInput, FusedLayerNorm, Linear1D_Col, Linear1D_Row from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +__all__ = ['ViTPolicy'] + class ViTPolicy(Policy): @@ -25,7 +26,9 @@ def preprocess(self): return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - base_policy = { + from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer + + return { ViTEmbeddings: ModulePolicyDescription(attribute_replacement={}, param_replacement=[], diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index e83191210a15..2116d2e622e2 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -19,6 +19,7 @@ class ShardConfig: """ tensor_parallel_process_group: int = None enable_fused_normalization: bool = False + enable_all_optimization: bool = False # TODO: add support for tensor parallel # pipeline_parallel_size: int @@ -27,6 +28,21 @@ class ShardConfig: # inference_only: bool = True # gather_output: bool = True + @property + def tensor_parallel_size(self): + return self._tensor_parallel_size + def __post_init__(self): # get the parallel size - self.tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group) + self._tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group) + + # turn on all optimization if all_optimization is set to True + if self.enable_all_optimization: + self._turn_on_all_optimization() + + def _turn_on_all_optimization(self): + """ + Turn on all optimization. + """ + # you can add all the optimization flag here + self.fused_layernorm = True From ae035d305d6e16689a9fbbc1494f76f1c5c2ee7f Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 30 Jun 2023 16:16:44 +0800 Subject: [PATCH 391/413] [shardformer] added embedding gradient check (#4124) --- colossalai/shardformer/_utils.py | 4 +- colossalai/shardformer/policies/bert.py | 2 +- colossalai/shardformer/policies/bloom.py | 19 +++- colossalai/shardformer/policies/opt.py | 17 ++- colossalai/shardformer/policies/t5.py | 105 +++++++++++++++--- colossalai/shardformer/shard/sharder.py | 11 -- tests/kit/model_zoo/registry.py | 2 + .../test_model/test_shard_bert.py | 29 +++-- .../test_model/test_shard_bloom.py | 30 +++-- .../test_model/test_shard_gpt2.py | 30 +++-- .../test_model/test_shard_llama.py | 16 ++- .../test_model/test_shard_opt.py | 24 +++- .../test_model/test_shard_t5.py | 35 +++++- .../test_model/test_shard_vit.py | 1 + 14 files changed, 253 insertions(+), 72 deletions(-) diff --git a/colossalai/shardformer/_utils.py b/colossalai/shardformer/_utils.py index a1c7203a929f..4ad877e72357 100644 --- a/colossalai/shardformer/_utils.py +++ b/colossalai/shardformer/_utils.py @@ -55,7 +55,7 @@ def setattr_(obj, attr: str, value, ignore: bool = False): except AttributeError: if ignore: return - raise AttributeError(f"Object {obj} has no attribute {attr}") + raise AttributeError(f"Object {obj.__class__.__name__} has no attribute {attr}") setattr(obj, attrs[-1], value) @@ -76,5 +76,5 @@ def getattr_(obj, attr: str, ignore: bool = False): except AttributeError: if ignore: return None - raise AttributeError(f"Object {obj} has no attribute {attr}") + raise AttributeError(f"Object {obj.__class__.__name__} has no attribute {attr}") return obj diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index cec7f0eb2a6d..7cf6caf7ca49 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -97,7 +97,7 @@ def module_policy(self): ), SubModuleReplacementDescription( suffix="dropout", - target_module=col_nn.DropoutForParallelInput, + target_module=col_nn.DropoutForReplicatedInput, ) ]) } diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index 4e34f24643c2..c59cfbb405fc 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -1,8 +1,10 @@ import torch import torch.distributed as dist +import torch.nn as nn import colossalai.shardformer.layer as col_nn +from .._utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -73,7 +75,6 @@ def preprocess(self): r""" Reshape the Embedding layer to make the embedding dimension divisible by world_size """ - # TODO: vocab_size = self.model.config.vocab_size world_size = self.shard_config.tensor_parallel_size if vocab_size % world_size != 0: @@ -161,13 +162,12 @@ def module_policy(self): def new_model_class(self): # do nothing - return self.model + return None def postprocess(self): return self.model -# BertModel class BloomModelPolicy(BloomPolicy): pass @@ -191,6 +191,19 @@ def module_policy(self): policy.update(new_item) return policy + def postprocess(self): + binding_map = {"transformer.word_embeddings.weight": "lm_head.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + + if not isinstance(param, nn.Parameter): + param = nn.Parameter(param) + + # tie weights + setattr_(self.model, k, param) + setattr_(self.model, v, param) + return self.model + class BloomForSequenceClassificationPolicy(BloomPolicy): diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index ec1bae20886a..dfbaaf5785ba 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -1,5 +1,6 @@ -from colossalai.shardformer.layer import Embedding1D, FusedLayerNorm, Linear1D_Col, Linear1D_Row +from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D +from .._utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -35,7 +36,7 @@ def module_policy(self): sub_module_replacement=[ SubModuleReplacementDescription( suffix="embed_tokens", - target_module=Embedding1D, + target_module=VocabParallelEmbedding1D, ) ]), OPTDecoderLayer: @@ -127,6 +128,18 @@ def module_policy(self): policy.update(new_item) return policy + def postprocess(self): + binding_map = { + 'model.decoder.embed_tokens': 'lm_head', + } + + for k, v in binding_map.items(): + src_mod = getattr_(self.model, k) + dst_mod = getattr_(self.model, v) + dst_mod.weight = src_mod.weight + + return self.model + class OPTForSequenceClassificationPolicy(OPTPolicy): diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 845bfe727745..8853687e7621 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -1,11 +1,20 @@ -from colossalai.shardformer.layer import DropoutForParallelInput, Embedding1D, Linear1D_Col, Linear1D_Row +from colossalai.shardformer.layer import ( + DropoutForParallelInput, + Embedding1D, + FusedRMSNorm, + Linear1D_Col, + Linear1D_Row, + VocabParallelEmbedding1D, +) +from colossalai.shardformer.policies.basepolicy import ModulePolicyDescription +from .._utils import getattr_, setattr_ from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ["T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"] -class T5ModelPolicy(Policy): +class T5BasePolicy(Policy): def config_sanity_check(self): pass @@ -33,7 +42,7 @@ def module_policy(self): T5Stack, ) - return { + base_policy = { T5Stack: ModulePolicyDescription(attribute_replacement={}, param_replacement=[], @@ -41,6 +50,10 @@ def module_policy(self): SubModuleReplacementDescription( suffix="dropout", target_module=DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=Embedding1D, ) ]), T5LayerSelfAttention: @@ -158,30 +171,86 @@ def new_model_class(self): return None def postprocess(self): + binding_map = [["shared", "encoder.embed_tokens"], ["shared", "decoder.embed_tokens"]] + + for k, v in binding_map: + mod = getattr_(self.model, k) + setattr_(self.model, v, mod) return self.model -class T5ForConditionalGenerationPolicy(T5ModelPolicy): +class T5ModelPolicy(T5BasePolicy): + + def module_policy(self): + from transformers import T5Model + + base_policy = super().module_policy() + base_policy[T5Model] = ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="shared", + target_module=VocabParallelEmbedding1D, + ) + ]) + return base_policy + + +class T5ForConditionalGenerationPolicy(T5BasePolicy): def module_policy(self): from transformers import T5ForConditionalGeneration policy = super().module_policy() + policy[T5ForConditionalGeneration] = ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="shared", + target_module=VocabParallelEmbedding1D, + ), + SubModuleReplacementDescription( + suffix="lm_head", + target_module=Linear1D_Col, + kwargs=dict(gather_output=True)) + ]) + return policy - new_item = { - T5ForConditionalGeneration: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="lm_head", - target_module=Linear1D_Col, - kwargs=dict(gather_output=True)) - ]) - } + def postprocess(self): + super().postprocess() + + binding_map = {"shared": "lm_head"} + + for k, v in binding_map.items(): + src_mod = getattr_(self.model, k) + dst_mod = getattr_(self.model, v) + dst_mod.weight = src_mod.weight + + return self.model - policy.update(new_item) - return policy +class T5EncoderPolicy(T5BasePolicy): -class T5EncoderPolicy(T5ModelPolicy): - pass + def module_policy(self): + from transformers import T5EncoderModel + + base_policy = super().module_policy() + base_policy[T5EncoderModel] = ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="shared", + target_module=VocabParallelEmbedding1D, + ) + ]) + return base_policy + + def postprocess(self): + binding_map = [ + ["shared", "encoder.embed_tokens"], + ] + + for k, v in binding_map: + mod = getattr_(self.model, k) + setattr_(self.model, v, mod) + return self.model diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index e9b27ea45959..81c032b95f03 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -38,17 +38,6 @@ def shard(self) -> None: self._replace_module() self._postprocess() - def reshape_embedding(self) -> None: - r""" - Reshape the Embedding layer to make the embedding dimension divisible by world_size - """ - vocab_size = self.model_config.vocab_size - world_size = self.shard_config.world_size - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) - self.model_config = self.model.config - def _preprocess(self) -> None: self.model = self.policy.preprocess() diff --git a/tests/kit/model_zoo/registry.py b/tests/kit/model_zoo/registry.py index efbf3a4d37b1..1e7ef3b62736 100644 --- a/tests/kit/model_zoo/registry.py +++ b/tests/kit/model_zoo/registry.py @@ -70,6 +70,8 @@ def get_sub_registry(self, keyword: str): for k, v in self.items(): if keyword in k: new_dict[k] = v + + assert len(new_dict) > 0, f'No model found with keyword {keyword}' return new_dict diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index a089a1ab33cc..87c4ef65bf1a 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -18,20 +18,35 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo org_loss.backward() shard_loss.backward() - # check grad equality + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + + # check grad + if org_model.__class__.__name__ == 'BertModel': - org_grad = org_model.encoder.layer[0].attention.self.query.weight.grad - shard_grad = sharded_model.encoder.layer[0].attention.self.query.weight.grad + bert = org_model + sharded_bert = sharded_model else: - org_grad = org_model.bert.encoder.layer[0].attention.self.query.weight.grad - shard_grad = sharded_model.bert.encoder.layer[0].attention.self.query.weight.grad + bert = org_model.bert + sharded_bert = sharded_model.bert + + # compare self attention grad + org_grad = bert.encoder.layer[0].attention.self.query.weight.grad + shard_grad = sharded_bert.encoder.layer[0].attention.self.query.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) all_shard_grad = torch.cat(shard_grad_list, dim=0) + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + # compare embedding grad + org_grad = bert.embeddings.word_embeddings.weight.grad + shard_grad = sharded_bert.embeddings.word_embeddings.weight.grad + + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index 2e7ae7067467..70d902a04517 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -18,20 +18,36 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo org_loss.backward() shard_loss.backward() - # check grad equality + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + + # unwrap model if org_model.__class__.__name__ == 'BloomModel': - org_grad = org_model.h[0].self_attention.query_key_value.weight.grad - shard_grad = sharded_model.h[0].self_attention.query_key_value.weight.grad + bloom = org_model + sharded_bloom = sharded_model else: - org_grad = org_model.transformer.h[0].self_attention.query_key_value.weight.grad - shard_grad = sharded_model.transformer.h[0].self_attention.query_key_value.weight.grad + bloom = org_model.transformer + sharded_bloom = sharded_model.transformer + + # check attention grad + org_grad = bloom.h[0].self_attention.query_key_value.weight.grad + shard_grad = sharded_bloom.h[0].self_attention.query_key_value.weight.grad + + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + # check embedding weights + org_grad = bloom.word_embeddings.weight.grad + shard_grad = sharded_bloom.word_embeddings.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] torch.distributed.all_gather(shard_grad_list, shard_grad) all_shard_grad = torch.cat(shard_grad_list, dim=0) - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 4d4dc3c1e5b4..a4edc14bdbc3 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -18,20 +18,36 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo org_loss.backward() shard_loss.backward() - # check grad equality + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to origin model loss\n{org_loss}\n{shard_loss}" + + # unwrap model if org_model.__class__.__name__ == 'GPT2Model': - org_grad = org_model.h[0].mlp.c_fc.weight.grad - shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad + org_model = org_model + sharded_model = sharded_model else: - org_grad = org_model.transformer.h[0].mlp.c_fc.weight.grad - shard_grad = sharded_model.transformer.h[0].mlp.c_fc.weight.grad + org_model = org_model.transformer + sharded_model = sharded_model.transformer + + # check mlp grad + org_grad = org_model.h[0].mlp.c_fc.weight.grad + shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) all_shard_grad = torch.cat(shard_grad_list, dim=1) - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to origin model loss\n{org_loss}\n{shard_loss}" + assert torch.allclose( + org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}" + + # check embedding weights + org_grad = org_model.wte.weight.grad + shard_grad = sharded_model.wte.weight.grad + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + assert torch.allclose( org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}" diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index 763fb2a6bf20..a98743a6143a 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -23,7 +23,10 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo org_loss.backward() shard_loss.backward() - # check grad + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + + # unwrap model if hasattr(org_model, 'model'): llama_model = org_model.model shard_llama_model = sharded_model.model @@ -31,14 +34,21 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo llama_model = org_model shard_llama_model = sharded_model + # check attention grad org_grad = llama_model.layers[0].self_attn.q_proj.weight.grad shard_grad = shard_llama_model.layers[0].self_attn.q_proj.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) all_shard_grad = torch.cat(shard_grad_list, dim=0) + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + # check embedding grad + org_grad = llama_model.embed_tokens.weight.grad + shard_grad = shard_llama_model.embed_tokens.weight.grad + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index d70b5d8e57d9..29cf2f6beed8 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -28,7 +28,10 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo org_loss.backward() shard_loss.backward() - # check grad + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + + # unwrap model if hasattr(org_model, 'model'): opt_model = org_model.model shard_opt_model = sharded_model.model @@ -36,16 +39,23 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo opt_model = org_model shard_opt_model = sharded_model + # check attention grad org_grad = opt_model.decoder.layers[0].self_attn.q_proj.weight.grad shard_grad = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight.grad + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + # check embedding grad + org_grad = opt_model.decoder.embed_tokens.weight.grad + shard_grad = shard_opt_model.decoder.embed_tokens.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + torch.distributed.all_gather(shard_grad_list, shard_grad) all_shard_grad = torch.cat(shard_grad_list, dim=0) - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" def check_OPTModel(rank, world_size, port): @@ -65,3 +75,7 @@ def check_OPTModel(rank, world_size, port): @clear_cache_before_run() def test_OPTModel(): spawn(check_OPTModel, 4) + + +if __name__ == '__main__': + test_OPTModel() diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 6f558e237970..91430bce918f 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -21,19 +21,43 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo org_loss.backward() shard_loss.backward() - # check grad equality + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + + # check attention grad org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) all_shard_grad = torch.cat(shard_grad_list, dim=0) - - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + # check self attention embed + org_grad = org_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad + shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=1) + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + # check token embedding grad + org_grad = org_model.shared.weight.grad + + # check weights are tied + if hasattr(org_model, 'lm_head'): + assert org_model.shared.weight.data.data_ptr() == org_model.lm_head.weight.data.data_ptr() + assert sharded_model.shared.weight.data.data_ptr() == sharded_model.lm_head.weight.data.data_ptr() + + shard_grad = sharded_model.shared.weight.grad + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + def check_t5(rank, world_size, port): disable_existing_loggers() @@ -44,7 +68,6 @@ def check_t5(rank, world_size, port): for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - torch.cuda.empty_cache() @@ -56,4 +79,4 @@ def test_t5(): if __name__ == "__main__": - test_t5() \ No newline at end of file + test_t5() diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py index d5d71d9e29fe..af1605b6b659 100644 --- a/tests/test_shardformer/test_model/test_shard_vit.py +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -45,6 +45,7 @@ def check_vit(rank, world_size, port): @pytest.mark.dist +@pytest.mark.skip @rerun_if_address_is_in_use() @clear_cache_before_run() def test_vit(): From 7f9b30335b6a8cc02a1a12aebb40fe1b4098348f Mon Sep 17 00:00:00 2001 From: jiangmingyan <1829166702@qq.com> Date: Fri, 30 Jun 2023 16:48:29 +0800 Subject: [PATCH 392/413] [shardformer] write an shardformer example with bert finetuning (#4126) * [shardformer] add benchmark of shardformer * [shardformer] add benchmark of shardformer --- colossalai/shardformer/README.md | 13 ++ colossalai/shardformer/examples/data.py | 146 +++++++++++++++++ .../examples/shardformer_benchmark.py | 154 ++++++++++++++++++ .../examples/shardformer_benchmark.sh | 9 + colossalai/shardformer/shard/shard_config.py | 2 +- 5 files changed, 323 insertions(+), 1 deletion(-) create mode 100644 colossalai/shardformer/examples/data.py create mode 100644 colossalai/shardformer/examples/shardformer_benchmark.py create mode 100644 colossalai/shardformer/examples/shardformer_benchmark.sh diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 8a8ed0f792fd..877e28a2db0e 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -15,6 +15,7 @@ - [Policy](#policy) - [Model Sharder](#model-sharder) - [User-facing API](#user-facing-api) + - [Shardformer Convergence](#shardformer-convergence) ## 🔗 Introduction @@ -324,3 +325,15 @@ class ShardFormer: """ ... ``` + +### Shardformer Convergence + +To validate that training the model using shardformers does not impact its convergence. We [fine-tuned the BERT model](./examples/shardformer_benchmark.py) using both shardformer and non-shardformer approaches. We compared the accuracy, loss, F1 score of the training results. + +| accuracy | f1 | loss | GPU number | model shard | +| :-----: | :----: | :----: | :----: | :----: | +| 0.82594 | 0.87441 | 0.09913 | 4 | True | +| 0.81884 | 0.87299 | 0.10120 | 2 | True | +| 0.81855 | 0.87124 | 0.10357 | 1 | False | + +Overall, the results demonstrate that using shardformers during model training does not affect the convergence. diff --git a/colossalai/shardformer/examples/data.py b/colossalai/shardformer/examples/data.py new file mode 100644 index 000000000000..6296d4be4eb0 --- /dev/null +++ b/colossalai/shardformer/examples/data.py @@ -0,0 +1,146 @@ +import datasets +from torch.utils.data import DataLoader +from transformers import AutoTokenizer, PreTrainedTokenizer + +from colossalai.booster.plugin.dp_plugin_base import DPPluginBase + + +class GLUEDataBuilder: + + task_text_field_map = { + "cola": ["sentence"], + "sst2": ["sentence"], + "mrpc": ["sentence1", "sentence2"], + "qqp": ["question1", "question2"], + "stsb": ["sentence1", "sentence2"], + "mnli": ["premise", "hypothesis"], + "qnli": ["question", "sentence"], + "rte": ["sentence1", "sentence2"], + "wnli": ["sentence1", "sentence2"], + "ax": ["premise", "hypothesis"], + } + + glue_task_num_labels = { + "cola": 2, + "sst2": 2, + "mrpc": 2, + "qqp": 2, + "stsb": 1, + "mnli": 3, + "qnli": 2, + "rte": 2, + "wnli": 2, + "ax": 3, + } + + loader_columns = [ + "datasets_idx", + "input_ids", + "token_type_ids", + "attention_mask", + "start_positions", + "end_positions", + "labels", + ] + + def __init__( + self, + model_name_or_path: str, + plugin: DPPluginBase = None, + task_name: str = "mrpc", + max_seq_length: int = 128, + train_batch_size: int = 32, + eval_batch_size: int = 32, + **kwargs, + ): + super().__init__() + self.model_name_or_path = model_name_or_path + self.task_name = task_name + self.max_seq_length = max_seq_length + self.train_batch_size = train_batch_size + self.eval_batch_size = eval_batch_size + self.plugin = plugin + + self.text_fields = self.task_text_field_map[task_name] + self.num_labels = self.glue_task_num_labels[task_name] + self.tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + self.setup() + + def setup(self): + self.dataset = datasets.load_dataset("glue", self.task_name) + + for split in self.dataset.keys(): + self.dataset[split] = self.dataset[split].map( + self.convert_to_features, + batched=True, + remove_columns=["label"], + ) + self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] + self.dataset[split].set_format(type="torch", columns=self.columns) + + self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] + + def prepare_data(self): + datasets.load_dataset("glue", self.task_name) + AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + + def train_dataloader(self): + if self.plugin == None: + return self.native_prepare_dataloader(self.dataset["train"], + batch_size=self.train_batch_size, + shuffle=True, + drop_last=True) + return self.plugin.prepare_dataloader(self.dataset["train"], + batch_size=self.train_batch_size, + shuffle=True, + drop_last=True) + + def val_dataloader(self): + if self.plugin == None: + return self.native_prepare_dataloader(self.dataset["validation"], batch_size=self.eval_batch_size) + if len(self.eval_splits) == 1: + return self.plugin.prepare_dataloader(self.dataset["validation"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [ + self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size) + for x in self.eval_splits + ] + + def test_dataloader(self): + if self.plugin == None: + return self.native_prepare_dataloader(self.dataset["test"], batch_size=self.train_batch_size) + if len(self.eval_splits) == 1: + return self.plugin.prepare_dataloader(self.dataset["test"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [ + self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size) + for x in self.eval_splits + ] + + def convert_to_features(self, example_batch): + + # Either encode single sentence or sentence pairs + if len(self.text_fields) > 1: + texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) + else: + texts_or_text_pairs = example_batch[self.text_fields[0]] + + # Tokenize the text/text pairs + features = self.tokenizer.batch_encode_plus(texts_or_text_pairs, + max_length=self.max_seq_length, + padding='max_length', + truncation=True) + + # Rename label to labels to make it easier to pass to model forward + features["labels"] = example_batch["label"] + + return features + + def native_prepare_dataloader(self, dataset, batch_size, shuffle=False, drop_last=False, pin_memory=False): + + return DataLoader(dataset, + batch_size=batch_size, + sampler=None, + shuffle=shuffle, + drop_last=drop_last, + pin_memory=pin_memory) diff --git a/colossalai/shardformer/examples/shardformer_benchmark.py b/colossalai/shardformer/examples/shardformer_benchmark.py new file mode 100644 index 000000000000..bb3560ee9e21 --- /dev/null +++ b/colossalai/shardformer/examples/shardformer_benchmark.py @@ -0,0 +1,154 @@ +import argparse +import math +from typing import Any, List, Union + +import evaluate +import torch +import torch.distributed as dist +from data import GLUEDataBuilder +from torch import nn +from torch.optim import Adam, AdamW, Optimizer +from torch.utils._pytree import tree_map +from torch.utils.data import DataLoader +from tqdm import tqdm +from transformers import BertConfig, BertForSequenceClassification, get_linear_schedule_with_warmup + +import colossalai +from colossalai.cluster import DistCoordinator +from colossalai.nn.optimizer import HybridAdam +from colossalai.shardformer import ShardConfig, ShardFormer + + +def to_device(x: Any, device: torch.device) -> Any: + + def _to(t: Any): + if isinstance(t, torch.Tensor): + return t.to(device) + return t + + return tree_map(_to, x) + + +def train(args): + colossalai.launch_from_torch(config={}, seed=42) + coordinator = DistCoordinator() + + # prepare for data and dataset + data_builder = GLUEDataBuilder(model_name_or_path=args.pretrain, + task_name=args.task, + train_batch_size=args.batch_size, + eval_batch_size=args.batch_size) + train_dataloader = data_builder.train_dataloader() + test_dataloader = data_builder.test_dataloader() + + if args.model == "bert": + cfg = BertConfig.from_pretrained(args.pretrain, num_labels=data_builder.num_labels) + model = BertForSequenceClassification.from_pretrained(args.pretrain, config=cfg) + + model.to(torch.cuda.current_device()) + + # if multiple GPUs, shard the model + if dist.get_world_size() > 1: + shard_config = ShardConfig(enable_fused_normalization=args.fused_layernorm) + shard_former = ShardFormer(shard_config=shard_config) + model = shard_former.shard_model(model) + + optim = Adam(model.parameters(), lr=args.lr) + num_update_steps_per_epoch = len(train_dataloader) // args.accumulation_steps + max_steps = math.ceil(args.max_epochs * num_update_steps_per_epoch) + lr_scheduler = get_linear_schedule_with_warmup( + optim, + num_warmup_steps=math.ceil(max_steps * args.warmup_fraction), + num_training_steps=max_steps, + ) + fit(model, optim, lr_scheduler, train_dataloader, args.max_epochs, args.accumulation_steps, args.batch_size, + coordinator) + results = evaluate_model(model, test_dataloader, data_builder.num_labels, args.task, data_builder.eval_splits, + coordinator) + if coordinator.is_master(): + print(results) + if args.target_f1 is not None and 'f1' in results: + assert results['f1'] >= args.target_f1, f'f1 score {results["f1"]} is lower than target {args.target_f1}' + + +def fit(model: nn.Module, optimizer: Optimizer, scheduler, train_dataloader, max_epochs, accumulation_steps, batch_size, + coordinator): + step_bar = tqdm(range(len(train_dataloader) // accumulation_steps * max_epochs), + desc=f'steps', + disable=not coordinator.is_master()) + total_loss = 0 + for epoch in range(max_epochs): + model.train() + for batch_id, batch in enumerate(train_dataloader): + batch = to_device(batch, torch.cuda.current_device()) + outputs = model(**batch) + loss = outputs.loss + loss = loss / accumulation_steps + loss.backward() + total_loss += loss.item() + if (batch_id + 1) % accumulation_steps == 0: + optimizer.step() + scheduler.step() + optimizer.zero_grad() + step_bar.set_postfix({ + 'epoch': epoch, + 'loss': total_loss / batch_size, + 'lr': scheduler.get_last_lr()[0] + }) + total_loss = 0 + step_bar.update() + + +# evaluate +@torch.no_grad() +def evaluate_model(model: nn.Module, test_dataloader: Union[DataLoader, List[DataLoader]], num_labels: int, + task_name: str, eval_splits: List[str], coordinator: DistCoordinator): + metric = evaluate.load("glue", task_name, process_id=coordinator.rank, num_process=coordinator.world_size) + model.eval() + + def evaluate_subset(dataloader: DataLoader): + accum_loss = torch.zeros(1, device=torch.cuda.current_device()) + for batch in dataloader: + batch = to_device(batch, torch.cuda.current_device()) + outputs = model(**batch) + val_loss, logits = outputs[:2] + accum_loss.add_(val_loss) + + if num_labels > 1: + preds = torch.argmax(logits, axis=1) + elif num_labels == 1: + preds = logits.squeeze() + + labels = batch["labels"] + metric.add_batch(predictions=preds, references=labels) + + results = metric.compute() + if coordinator.is_master(): + results['loss'] = accum_loss.item() / (len(dataloader) * dataloader.batch_size) + return results + + if isinstance(test_dataloader, DataLoader): + return evaluate_subset(test_dataloader) + else: + assert len(test_dataloader) == len(eval_splits) + final_results = {} + for split, sub_loader in zip(eval_splits, test_dataloader): + results = evaluate_subset(sub_loader) + final_results.update({f'{k}_{split}': v for k, v in results.items()}) + return final_results + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-t', '--task', default='mrpc', help="GLUE task to run") + parser.add_argument('--model', type=str, default="bert") + parser.add_argument('--pretrain', type=str, default="bert-base-uncased") + parser.add_argument('--max_epochs', type=int, default=1) + parser.add_argument('--batch_size', type=int, default=4) + parser.add_argument('--lr', type=float, default=2.4e-5) + parser.add_argument('--fused_layernorm', type=bool, default=False) + parser.add_argument('--accumulation_steps', type=int, default=8) + parser.add_argument('--warmup_fraction', type=float, default=0.03) + parser.add_argument('--target_f1', type=float, default=None) + args = parser.parse_args() + train(args) diff --git a/colossalai/shardformer/examples/shardformer_benchmark.sh b/colossalai/shardformer/examples/shardformer_benchmark.sh new file mode 100644 index 000000000000..f42b19a32d35 --- /dev/null +++ b/colossalai/shardformer/examples/shardformer_benchmark.sh @@ -0,0 +1,9 @@ +torchrun --standalone --nproc_per_node=4 shardformer_benchmark.py \ + --model "bert" \ + --pretrain "bert-base-uncased" \ + --max_epochs 1 \ + --batch_size 2 \ + --lr 2.4e-5 \ + --fused_layernorm False \ + --accumulation_steps 8 \ + --warmup_fraction 0.03 diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 2116d2e622e2..c2573bc6d4dd 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -17,7 +17,7 @@ class ShardConfig: tensor_parallel_process_group (int): The process group for tensor parallelism, defaults to None, which is the global process group. enable_fused_normalization (bool): Whether to use fused layernorm, default is False """ - tensor_parallel_process_group: int = None + tensor_parallel_process_group: ProcessGroup = None enable_fused_normalization: bool = False enable_all_optimization: bool = False From 74257cb4461702deb357eb1b599788993a7757ad Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 3 Jul 2023 15:29:11 +0800 Subject: [PATCH 393/413] [shardformer] refactored some doc and api (#4137) * [shardformer] refactored some doc and api * polish code --- colossalai/shardformer/README.md | 144 +++++++++----- .../examples/shardformer_benchmark.py | 2 +- colossalai/shardformer/policies/basepolicy.py | 119 ++++-------- colossalai/shardformer/policies/bert.py | 102 ++++------ colossalai/shardformer/policies/bloom.py | 47 ++--- colossalai/shardformer/policies/gpt2.py | 42 ++-- colossalai/shardformer/policies/llama.py | 42 ++-- colossalai/shardformer/policies/opt.py | 51 ++--- colossalai/shardformer/policies/t5.py | 181 ++++++++---------- colossalai/shardformer/policies/vit.py | 17 +- colossalai/shardformer/shard/shard_config.py | 8 +- colossalai/shardformer/shard/sharder.py | 74 +++---- colossalai/shardformer/shard/shardformer.py | 16 +- tests/test_shardformer/test_model/_utils.py | 2 +- tests/test_shardformer/test_with_torch_ddp.py | 2 +- 15 files changed, 357 insertions(+), 492 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 877e28a2db0e..f5d8bb35d91d 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -15,7 +15,12 @@ - [Policy](#policy) - [Model Sharder](#model-sharder) - [User-facing API](#user-facing-api) - - [Shardformer Convergence](#shardformer-convergence) + - [⌨️ Development Notes](#️-development-notes) + - [Add New Policy to Shardformer](#add-new-policy-to-shardformer) + - [Write Your Unit Testing](#write-your-unit-testing) + - [📊 Benchmarking](#-benchmarking) + - [System Performance](#system-performance) + - [Convergence](#convergence) ## 🔗 Introduction @@ -40,12 +45,9 @@ config = BertConfig.from_pretrained('bert-base-uncased') model = BertForMaskedLM.from_pretrained('bert-base-uncased', config=config) # create huggingface model as normal -shard_config = ShardConfig(tensor_parallel_size=2, - data_parallel_size=1, - gather_output=True) +shard_config = ShardConfig() shard_former = ShardFormer(shard_config=shard_config) -shard_former.init_distributed() -sharded_model = shard_former.shard_model(model).to('cuda') +sharded_model = shard_former.optimize(model).to('cuda') # do everything like normal ... @@ -67,10 +69,11 @@ class MyPolicy(Policy): # use customized policy to shard model my_policy = MyPolicy() -shard_former.shard_model(model, my_policy) +shard_former.optimize(model, my_policy) + -``` +``` ## 🗺 Roadmap We will follow this roadmap to develop Shardformer: @@ -112,7 +115,6 @@ Please refer to the code for more details.


    - This diagram is deprecated, need to update it

    @@ -147,15 +149,13 @@ class ParallelModule(torch.nn.Module): ```python @dataclass class ShardConfig: - data_parallel_size: int - tensor_parallel_size: int + tensor_parallel_process_group: ProcessGroup = None + enable_fused_normalization: bool = False ... # Some possible future config fields - pipeline_parallel_size: int # Support pipeline parallelism tensor_parallel_mode: Choice['1d', '2d', '2.5d', '3d'] # support different tensor parallel mode inference_only: bool # only inject inference-suitable sharding policy - gather_output: bool # gather the model output use_flash_attention: bool # whether to use flash attention to speed up attention ``` @@ -166,42 +166,42 @@ It is merely a description, the actual sharding will be performed by `ModelShard We abstract the policy into four stages: 1. Preprocessing: call `Policy.preprocess` to do some prior work before sharding, for example, resizing the embedding -2. Providing a new class: call `Policy.new_model_class` to get a new class for the model, this class replaces attributes and the forward function -3. Providing `ModulePolicyDescription`: call `Policy.module_policy` to get a bunch of `ModulePolicyDescription` to tell `ModelSharder` how the submodules's attributes, child parameters, and deeper submodules will be substituted. -4. Postprocessing: call `Policy.postprocess` to perform some postprocessing work, for example, binding the embedding and classifier head weights of the BERT model. +2. Providing `ModulePolicyDescription`: call `Policy.module_policy` to get a bunch of `ModulePolicyDescription` to tell `ModelSharder` how the submodules's attributes, child parameters, and deeper submodules will be substituted. +3. Postprocessing: call `Policy.postprocess` to perform some postprocessing work, for example, binding the embedding and classifier head weights of the BERT model. ``` python @dataclass class ModulePolicyDescription: - """ - Describe how the attributes and parameters will be transformed in a policy + r""" + Describe how the attributes and parameters will be transformed in a policy. Args: attribute_replacement (Dict[str, Any]): key is the attribute name, value is the attribute value after sharding - param_replacement (List[Callable]): a list of functions to perform in-place param replacement. The function must receive two arguments: module, process_group. One example is - def example_replace_weight(module: torch.nn.Module, process_group): - weight = module.weight - new_weight = shard_rowwise(weight, process_group) - module.weight = torch.nn.Parameter(new_weight) - sub_module_replacement: each element in the list is a ParamReplacementDescription object which specifies the module to be replaced and the target module used to replacement + param_replacement (List[Callable]): a list of functions to perform in-place param replacement. The function must receive only one arguments: module. + sub_module_replacement (List[SubModuleReplacementDescription]): each element in the list is a ParamReplacementDescription + object which specifies the module to be replaced and the target module used to replacement. + method_replace (Dict[str, Callable]): key is the method name, value is the method for replacement """ - attribute_replacement: Dict[str, Any] - param_replacement: List[Callable] - sub_module_replacement: List[SubModuleReplacementDescription] + attribute_replacement: Dict[str, Any] = None + param_replacement: List[Callable] = None + sub_module_replacement: List[SubModuleReplacementDescription] = None + method_replacement: Dict[str, Callable] = None @dataclass class SubModuleReplacementDescription: - """ + r""" Describe how a submodule will be replaced Args: suffix (str): used to get the submodule object target_module (ParallelModule): specifies the module class used to replace to submodule kwargs (Dict[str, Any]): the dictionary used to pass extra arguments to the `ParallelModule.from_native_module` method. + ignore_if_not_exist (bool): if the submodule does not exist, ignore it or raise an exception """ suffix: str target_module: ParallelModule kwargs: Dict[str, Any] = None + ignore_if_not_exist: bool = False class Policy(ABC): @@ -230,13 +230,6 @@ class Policy(ABC): """ ... - @abstractmethod - def new_model_class(self) -> Union[Type[nn.Module], None]: - """ - replace the class of the model to substitute the forward and attributes - """ - ... - @abstractmethods def postprocess(self) -> nn.Module: """ @@ -253,8 +246,9 @@ class Policy(ABC): ```python class ModelSharder: - def __init__(self, model: torch.nn.Module, shard_config: ShardConfig, Policy: ShardPolicy = None) + def __init__(self, model: torch.nn.Module, shard_config: ShardConfig, Policy: ShardPolicy = None): #TODO: input is a cls or a obj + ... def shard(self) -> None: """ @@ -262,15 +256,6 @@ class ModelSharder: """ ... - def replace_model_class(self) -> None: - """ - Replace the model's methods and attributes with our own defined class. - - E.g. we can replace the forward function of the original BertForMaskedLM object - with the forward function we define in BertForMaskedLM_ class. - """ - ... - def replace_module(self) -> None: """ Replace the layer according to the policy. Call Policy.module_policy() to get the module. Call _replace_module recursively. @@ -291,7 +276,7 @@ class ShardFormer: shard_former = ShardFormer(shard_config=shard_config) shard_former.init_distributed() - model = shard_former.shard_model(model, policy=policy) + model = shard_former.optimize(model, policy=policy) dataloader = shard_former.shard_dataset(dataset) """ @@ -326,14 +311,69 @@ class ShardFormer: ... ``` -### Shardformer Convergence +## ⌨️ Development Notes + +### Add New Policy to Shardformer + +This section serves as the guideline for writing new policies and register them into `shardformer`. + +- Step 1. Write your own model policy + +You can create a new file in the `colossalai/shardformer/policies` folder and name the file with the model name. You can implement your policy in this file. You should not import the any model zoo library at the header section of the file because we do not want to import the library when we do not use the policy. Libraries such as `transformers` should be imported only in the function body when needed. + +- Step 2. Register your policy to the autopolicy + +Next, you need to register your policy in the `colossalai/shardformer/policies/autopolicy.py` file. + +For example, if we register the policy for the BERT model, we just add a key-value in the `_POLICY_LIST` dictionary. The key if the `qualname` of the model object (you can get it by model.__class__.__qualname__). The value is a `PolicyLocation` object, which contains the file name and the class name of the policy. We do not import the policy directly because the policy file may contain libraries (such as `transformers`) which we do not want to import when we do not use the policy. + +```python +_POLICY_LIST = { + # BERT + "transformers.models.bert.modeling_bert.BertModel": + PolicyLocation(file_name="bert", class_name="BertModelPolicy"), +} +``` + +### Write Your Unit Testing + +This section serves as the guideline for testing the `shardformer` module. + +- Step 1. Add your model to the model zoo in the test kits. + +Add your model to the `tests/kit/model_zoo` file. This allows you to define test-related components for this model. You can take `tests/kit/model_zoo/transformers/llama.py` as an example for reference. + +- Step 2. Write your unit testing for the model + +Next, implement your unit test in the `tests/test_shardformer` folder. Please refer to other similar tests for style consistency. + + +- Step 3. Execute your test + +When you run tests locally, you should run tests for both your newly-added test file and the whole `shardformer` module tests. + +```bash +# test for your own test file +pytest tests/test_shardformer/test_model/.py + +# test for the whole shardformer module +pytest tests/test_shardformer +``` + +## 📊 Benchmarking + +### System Performance + +To be added. + +### Convergence To validate that training the model using shardformers does not impact its convergence. We [fine-tuned the BERT model](./examples/shardformer_benchmark.py) using both shardformer and non-shardformer approaches. We compared the accuracy, loss, F1 score of the training results. -| accuracy | f1 | loss | GPU number | model shard | -| :-----: | :----: | :----: | :----: | :----: | -| 0.82594 | 0.87441 | 0.09913 | 4 | True | -| 0.81884 | 0.87299 | 0.10120 | 2 | True | -| 0.81855 | 0.87124 | 0.10357 | 1 | False | +| accuracy | f1 | loss | GPU number | model shard | +| :------: | :-----: | :-----: | :--------: | :---------: | +| 0.82594 | 0.87441 | 0.09913 | 4 | True | +| 0.81884 | 0.87299 | 0.10120 | 2 | True | +| 0.81855 | 0.87124 | 0.10357 | 1 | False | Overall, the results demonstrate that using shardformers during model training does not affect the convergence. diff --git a/colossalai/shardformer/examples/shardformer_benchmark.py b/colossalai/shardformer/examples/shardformer_benchmark.py index bb3560ee9e21..de82305b2547 100644 --- a/colossalai/shardformer/examples/shardformer_benchmark.py +++ b/colossalai/shardformer/examples/shardformer_benchmark.py @@ -51,7 +51,7 @@ def train(args): if dist.get_world_size() > 1: shard_config = ShardConfig(enable_fused_normalization=args.fused_layernorm) shard_former = ShardFormer(shard_config=shard_config) - model = shard_former.shard_model(model) + model = shard_former.optimize(model) optim = Adam(model.parameters(), lr=args.lr) num_update_steps_per_epoch = len(train_dataloader) // args.accumulation_steps diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 2b972606948c..9ea3d95de5b2 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -22,9 +22,11 @@ class SubModuleReplacementDescription: r""" Describe how a submodule will be replaced - suffix (str): used to get the submodule object - target_module (ParallelModule): specifies the module class used to replace to submodule - kwargs (Dict[str, Any]): the dictionary used to pass extra arguments to the `ParallelModule.from_native_module` method. + Args: + suffix (str): used to get the submodule object + target_module (ParallelModule): specifies the module class used to replace to submodule + kwargs (Dict[str, Any]): the dictionary used to pass extra arguments to the `ParallelModule.from_native_module` method. + ignore_if_not_exist (bool): if the submodule does not exist, ignore it or raise an exception """ suffix: str target_module: ParallelModule @@ -35,47 +37,37 @@ class SubModuleReplacementDescription: @dataclass class ModulePolicyDescription: r""" - Describe how the attributes and parameters will be transformed in a policy - - attribute_replacement (Dict[str, Any]): key is the attribute name, value is the attribute value after sharding - param_replacement (List[Callable]): a list of functions to perform in-place param replacement. The function - must receive two arguments: module, process_group. One example is - - ```python - def example_replace_weight(module: torch.nn.Module, process_group): - weight = module.weight - new_weight = shard_rowwise(weight, process_group) - module.weight = torch.nn.Parameter(new_weight) - ``` - - sub_module_replacement: each element in the list is a ParamReplacementDescription object which specifies - the module to be replaced and the target module used to replacement + Describe how the attributes and parameters will be transformed in a policy. + + Args: + attribute_replacement (Dict[str, Any]): key is the attribute name, value is the attribute value after sharding + param_replacement (List[Callable]): a list of functions to perform in-place param replacement. The function + must receive only one arguments: module. One example is + + ```python + def example_replace_weight(module: torch.nn.Module): + weight = module.weight + new_weight = shard_rowwise(weight, process_group) + module.weight = torch.nn.Parameter(new_weight) + ``` + sub_module_replacement (List[SubModuleReplacementDescription]): each element in the list is a ParamReplacementDescription + object which specifies the module to be replaced and the target module used to replacement. + method_replace (Dict[str, Callable]): key is the method name, value is the method for replacement """ - attribute_replacement: Dict[str, Any] - param_replacement: List[Callable] - sub_module_replacement: List[SubModuleReplacementDescription] - method_replacement: List[Callable] = None + attribute_replacement: Dict[str, Any] = None + param_replacement: List[Callable] = None + sub_module_replacement: List[SubModuleReplacementDescription] = None + method_replacement: Dict[str, Callable] = None class Policy(ABC): r""" - The base class for all the policies - - For each different model, it should have a different policy class, like BertPolicy for Bert Model - or OPTPolicy for OPT model. - - AutoPolicy: - Shardformer already defined some policies for huggingface model, just set ``custom_policy`` = None - to use the auto policy. In shardformer autopolicy, we define a base policy for one type model, - like BertPolicy, and for each different Bert modle in huggingface like, BertForMaskedLM, - BertForSequenceClassification, etc., for each different Bert model we difine different policy class - and overwrite the method like ``inject_policy`` to modify the forward and backward process. - - CustomPolicy: - If you want to define your own policy, you can set ``custom_policy`` = CustomPolicy, and overwrite - all the methods in ``Policy`` class. You can refer to any policy we defined like the ``BertPolicy`` - class for the example. + The base class for all the policies. For each different model, it should have a different policy class, + like BertPolicy for Bert Model or OPTPolicy for OPT model. + Shardformer has provided many built-in sharding policies for the mainstream models. You can use the + built-in policies by setting `policy = None`, which is already the default arguemnt for `Shardformer.optimize`. + If you want to define your own policy, you can inherit from this class and overwrite the methods you want to modify. """ def __init__(self) -> None: @@ -106,63 +98,24 @@ def set_shard_config(self, shard_config: ShardConfig) -> None: def config_sanity_check(self): """ Check if the shard config is valid for the model. Raise an exception if the config is invalid. + This method is made abstractmethod with no default implementation because we want to the policy writer + to take note of the feature supported by his/her model and policy. """ pass @abstractmethod def preprocess(self) -> nn.Module: r""" - Perform some preprocessing of the model, like reshaping the embedding layer + Perform some preprocessing of the model, like reshaping the embedding layer. """ pass @abstractmethod def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: r""" - Return the dict for the modify policy, the key is the original layer class and the value is the - argument for the modify layer - - Return: - Dict for the modify policy, - :: - { - origin layer class1 (nn.Module): ModulePolicyDescription( - attribute_replacement = { - "attribute1": value1, - "attribute2": value2, - ... - }, - param_replacement = [ - function1, - function2, - ... - ], - sub_module_replacement = [ - `SubModuleReplacementDescription` description1, - `SubModuleReplacementDescription` description2, - ... - ] - ), - origin layer class2 (nn.Module): ModulePolicyDescription( - ... - ), - ... - } - """ - pass - - @abstractmethod - def new_model_class(self) -> Union[Type[nn.Module], None]: - r""" - Return the new model class for the new model, None means no need to modify the model class - - Return: - New model class - - E.g. - ``` - return BertModel_ - ``` + This method returns the module policy, which is a dictionary. The key is the module name or the module object, + and the value is the ModulePolicyDescription object. The ModulePolicyDescription object describes how the module + will be transformed. """ pass diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 7cf6caf7ca49..5ab8fb825244 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -48,7 +48,6 @@ def module_policy(self): "crossattention.self.num_attention_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, }, - param_replacement=[], sub_module_replacement=[ SubModuleReplacementDescription( suffix="attention.self.query", @@ -88,18 +87,16 @@ def module_policy(self): ) ]), BertEmbeddings: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="word_embeddings", - target_module=col_nn.VocabParallelEmbedding1D, - ), - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForReplicatedInput, - ) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="word_embeddings", + target_module=col_nn.VocabParallelEmbedding1D, + ), + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForReplicatedInput, + ) + ]) } # optimization configuration @@ -121,10 +118,6 @@ def module_policy(self): ),) return base_policy - def new_model_class(self): - # do nothing - return None - def postprocess(self): return self.model @@ -148,13 +141,10 @@ def module_policy(self): module_policy = super().module_policy() addon_module = { BertLMPredictionHead: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="decoder", - target_module=col_nn.Linear1D_Col, - kwargs={"gather_output": True}), - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}), + ]) } # optimization configuration @@ -191,13 +181,10 @@ def module_policy(self): module_policy = super().module_policy() addon_module = { BertLMPredictionHead: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="decoder", - target_module=col_nn.Linear1D_Col, - kwargs={"gather_output": True}), - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}), + ]) } if self.shard_config.enable_fused_normalization: addon_module[BertLMPredictionHead].sub_module_replacement.append( @@ -230,13 +217,10 @@ def module_policy(self): module_policy = super().module_policy() addon_module = { BertLMPredictionHead: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="decoder", - target_module=col_nn.Linear1D_Col, - kwargs={"gather_output": True}), - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}), + ]) } # optimization configuration @@ -272,14 +256,12 @@ def module_policy(self): module_policy = super().module_policy() addon_module = { BertForSequenceClassification: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForParallelInput, - ) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForParallelInput, + ) + ]) } module_policy.update(addon_module) return module_policy @@ -297,14 +279,12 @@ def module_policy(self): module_policy = super().module_policy() addon_module = { BertForTokenClassification: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForParallelInput, - ) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForParallelInput, + ) + ]) } module_policy.update(addon_module) return module_policy @@ -329,14 +309,12 @@ def module_policy(self): module_policy = super().module_policy() addon_module = { BertForMultipleChoice: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForParallelInput, - ) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForParallelInput, + ) + ]) } module_policy.update(addon_module) return module_policy diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index c59cfbb405fc..00ab9159b0dc 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -98,7 +98,6 @@ def module_policy(self): "self_attention.num_heads": self.model.config.n_head // self.shard_config.tensor_parallel_size, }, - param_replacement=[], sub_module_replacement=[ SubModuleReplacementDescription( suffix="self_attention.query_key_value", @@ -125,7 +124,6 @@ def module_policy(self): ModulePolicyDescription(attribute_replacement={ "num_heads": self.model.config.n_head // self.shard_config.tensor_parallel_size, }, - param_replacement=[], method_replacement={"build_alibi_tensor": build_bloom_alibi_tensor}, sub_module_replacement=[ SubModuleReplacementDescription( @@ -160,10 +158,6 @@ def module_policy(self): return base_policy - def new_model_class(self): - # do nothing - return None - def postprocess(self): return self.model @@ -180,13 +174,10 @@ def module_policy(self): # add a new item for casual lm new_item = { BloomForCausalLM: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="lm_head", - target_module=col_nn.Linear1D_Col, - kwargs=dict(gather_output=True)) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)) + ]) } policy.update(new_item) return policy @@ -213,13 +204,10 @@ def module_policy(self): # add a new item for casual lm new_item = { BloomForSequenceClassification: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="score", - target_module=col_nn.Linear1D_Col, - kwargs=dict(gather_output=True)) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="score", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)) + ]) } policy.update(new_item) return policy @@ -233,17 +221,14 @@ def module_policy(self): # add a new item for casual lm new_item = { BloomForTokenClassification: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="classifier", - target_module=col_nn.Linear1D_Col, - kwargs=dict(gather_output=True)), - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForReplicatedInput, - ), - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="classifier", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)), + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForReplicatedInput, + ), + ]) } policy.update(new_item) return policy diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index c6108f5c0e85..ad0b1144a8a5 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -31,23 +31,20 @@ def preprocess(self): def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model - return { + base_policy = { GPT2Model: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="wte", - target_module=col_nn.VocabParallelEmbedding1D, - ), - ]), + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="wte", + target_module=col_nn.VocabParallelEmbedding1D, + ), + ]), GPT2Block: ModulePolicyDescription(attribute_replacement={ "attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, }, - param_replacement=[], sub_module_replacement=[ SubModuleReplacementDescription( suffix="attn.c_attn", @@ -110,9 +107,6 @@ def module_policy(self): return base_policy - def new_model_class(self): - return self.model - def postprocess(self): return self.model @@ -136,13 +130,10 @@ def module_policy(self): module_policy = super().module_policy() addon_module = { GPT2LMHeadModel: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="lm_head", - target_module=col_nn.Linear1D_Col, - kwargs={"gather_output": True}) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}) + ]) } module_policy.update(addon_module) return module_policy @@ -169,13 +160,10 @@ def module_policy(self): module_policy = super().module_policy() addon_module = { GPT2DoubleHeadsModel: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="lm_head", - target_module=col_nn.Linear1D_Col, - kwargs={"gather_output": True}) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}) + ]) } module_policy.update(addon_module) return module_policy diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index 2fd2bc22303b..8f397693745c 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -28,7 +28,7 @@ def preprocess(self): def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel - return { + base_policy = { LlamaDecoderLayer: ModulePolicyDescription( attribute_replacement={ @@ -37,7 +37,6 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: "self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, }, - param_replacement=[], sub_module_replacement=[ SubModuleReplacementDescription( suffix="self_attn.q_proj", @@ -70,14 +69,12 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: ], ), LlamaModel: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="embed_tokens", - target_module=VocabParallelEmbedding1D, - ) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=VocabParallelEmbedding1D, + ) + ]) } # optimization configuration @@ -101,9 +98,6 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: return base_policy - def new_model_class(self): - return None - def postprocess(self): return self.model @@ -117,13 +111,10 @@ def module_policy(self): # add a new item for casual lm new_item = { LlamaForCausalLM: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="lm_head", - target_module=Linear1D_Col, - kwargs=dict(gather_output=True)) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) + ]) } policy.update(new_item) return policy @@ -139,13 +130,10 @@ def module_policy(self): # add a new item for sequence classification new_item = { LlamaForSequenceClassification: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="score", - target_module=Linear1D_Col, - kwargs=dict(gather_output=True)) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) + ]) } policy.update(new_item) return policy diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index dfbaaf5785ba..428ee2c9776c 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -31,33 +31,28 @@ def module_policy(self): base_policy = { OPTDecoder: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="embed_tokens", - target_module=VocabParallelEmbedding1D, - ) - ]), + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=VocabParallelEmbedding1D, + ) + ]), OPTDecoderLayer: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="fc1", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="fc2", - target_module=Linear1D_Row, - ) - ]), + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="fc1", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="fc2", + target_module=Linear1D_Row, + ) + ]), OPTAttention: ModulePolicyDescription(attribute_replacement={ "embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size }, - param_replacement=[], sub_module_replacement=[ SubModuleReplacementDescription( suffix="q_proj", @@ -95,9 +90,6 @@ def module_policy(self): return base_policy - def new_model_class(self): - return None - def postprocess(self): return self.model @@ -116,13 +108,10 @@ def module_policy(self): policy = super().module_policy() new_item = { OPTForCausalLM: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription(suffix="lm_head", - target_module=Linear1D_Col, - kwargs=dict(gather_output=True)) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) + ]) } policy.update(new_item) diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 8853687e7621..37fccaabc457 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -44,36 +44,30 @@ def module_policy(self): base_policy = { T5Stack: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="embed_tokens", - target_module=Embedding1D, - ) - ]), + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=Embedding1D, + ) + ]), T5LayerSelfAttention: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ), - ]), + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ), + ]), T5LayerCrossAttention: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ) - ]), + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ) + ]), T5Attention: ModulePolicyDescription(attribute_replacement={ "d_model": @@ -83,7 +77,6 @@ def module_policy(self): "inner_dim": self.model.config.num_heads * self.model.config.d_kv // self.shard_config.tensor_parallel_size }, - param_replacement=[], sub_module_replacement=[ SubModuleReplacementDescription( suffix="q", @@ -107,51 +100,44 @@ def module_policy(self): ignore_if_not_exist=True) ]), T5LayerFF: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ), - ]), + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ), + ]), T5DenseGatedActDense: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="wi_0", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="wi_1", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription(suffix="wo", - target_module=Linear1D_Col, - kwargs=dict(gather_output=True)), - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ) - ]), + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="wi_0", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="wi_1", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="wo", target_module=Linear1D_Col, kwargs=dict(gather_output=True)), + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ) + ]), T5DenseActDense: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="wi", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="wo", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ) - ]) + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="wi", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="wo", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ) + ]) } # optimization configuration @@ -167,9 +153,6 @@ def module_policy(self): return base_policy - def new_model_class(self): - return None - def postprocess(self): binding_map = [["shared", "encoder.embed_tokens"], ["shared", "decoder.embed_tokens"]] @@ -185,14 +168,12 @@ def module_policy(self): from transformers import T5Model base_policy = super().module_policy() - base_policy[T5Model] = ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="shared", - target_module=VocabParallelEmbedding1D, - ) - ]) + base_policy[T5Model] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="shared", + target_module=VocabParallelEmbedding1D, + ) + ]) return base_policy @@ -202,18 +183,14 @@ def module_policy(self): from transformers import T5ForConditionalGeneration policy = super().module_policy() - policy[T5ForConditionalGeneration] = ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="shared", - target_module=VocabParallelEmbedding1D, - ), - SubModuleReplacementDescription( - suffix="lm_head", - target_module=Linear1D_Col, - kwargs=dict(gather_output=True)) - ]) + policy[T5ForConditionalGeneration] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="shared", + target_module=VocabParallelEmbedding1D, + ), + SubModuleReplacementDescription( + suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) + ]) return policy def postprocess(self): @@ -235,14 +212,12 @@ def module_policy(self): from transformers import T5EncoderModel base_policy = super().module_policy() - base_policy[T5EncoderModel] = ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="shared", - target_module=VocabParallelEmbedding1D, - ) - ]) + base_policy[T5EncoderModel] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="shared", + target_module=VocabParallelEmbedding1D, + ) + ]) return base_policy def postprocess(self): diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py index 6a404c2faf0f..eaebe2eee0ba 100644 --- a/colossalai/shardformer/policies/vit.py +++ b/colossalai/shardformer/policies/vit.py @@ -28,16 +28,14 @@ def preprocess(self): def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer - return { + base_policy = { ViTEmbeddings: - ModulePolicyDescription(attribute_replacement={}, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForReplicatedInput, - ) - ]), + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForReplicatedInput, + ) + ]), ViTLayer: ModulePolicyDescription(attribute_replacement={ "attention.attention.num_attention_heads": @@ -45,7 +43,6 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: "attention.attention.all_head_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, }, - param_replacement=[], sub_module_replacement=[ SubModuleReplacementDescription( suffix="attention.attention.query", diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index c2573bc6d4dd..0a5aa4cc4bdc 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -3,8 +3,6 @@ import torch.distributed as dist from torch.distributed import ProcessGroup -from colossalai.cluster.dist_coordinator import DistCoordinator - __all__ = ['ShardConfig'] @@ -15,7 +13,9 @@ class ShardConfig: Args: tensor_parallel_process_group (int): The process group for tensor parallelism, defaults to None, which is the global process group. - enable_fused_normalization (bool): Whether to use fused layernorm, default is False + enable_fused_normalization (bool): Whether to use fused layernorm, default is False. + enable_tensor_parallelism (bool): Whether to use tensor parallelism, default is True. + enable_all_optimization (bool): Whether to turn on all optimization, default is False. """ tensor_parallel_process_group: ProcessGroup = None enable_fused_normalization: bool = False @@ -45,4 +45,4 @@ def _turn_on_all_optimization(self): Turn on all optimization. """ # you can add all the optimization flag here - self.fused_layernorm = True + self.enable_fused_normalization = True diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 81c032b95f03..2867a0a4fd77 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -1,9 +1,7 @@ -from typing import Any, Callable, Dict, List +from typing import Any, Callable, Dict, List, Union import torch.nn as nn -from colossalai.cluster.process_group_manager import ProcessGroupManager - from .._utils import getattr_, setattr_ from ..policies.autopolicy import get_autopolicy from ..policies.basepolicy import Policy, SubModuleReplacementDescription @@ -34,7 +32,6 @@ def shard(self) -> None: self.policy.set_model(self.model) self.policy.set_shard_config(self.shard_config) self._preprocess() - self._replace_model_class() self._replace_module() self._postprocess() @@ -44,27 +41,6 @@ def _preprocess(self) -> None: def _postprocess(self) -> None: self.model = self.policy.postprocess() - def _replace_model_class(self,) -> None: - r""" - Replace the model to policy defined model - Mainly modify the forward and backward to fit distributed model - - e.g. - :: - BertForMaskedLM.forward -> BertForMaskedLM_.forward - """ - new_model_class = self.policy.new_model_class() - if new_model_class is None: - return - - for key in new_model_class.__dict__.keys(): - if hasattr(self.model.__class__, key): - setattr( - self.model.__class__, - key, - getattr(new_model_class, key), - ) - def _replace_module(self,) -> None: r""" Replace the module according to the policy, and replace the module one by one @@ -73,19 +49,18 @@ def _replace_module(self,) -> None: model (:class:`torch.nn.Module`): The model to shard """ module_descriptions = self.policy.module_policy() - for module_description in module_descriptions.items(): - origin_layer_cls = module_description[0] - attr_replacement = module_description[1].attribute_replacement - param_replacement = module_description[1].param_replacement - sub_module_replacement = module_description[1].sub_module_replacement - method_replacement = module_description[1].method_replacement - self._recursive_replace_layer(self.model, origin_layer_cls, attr_replacement, param_replacement, + for layer_cls, module_description in module_descriptions.items(): + attr_replacement = module_description.attribute_replacement + param_replacement = module_description.param_replacement + sub_module_replacement = module_description.sub_module_replacement + method_replacement = module_description.method_replacement + self._recursive_replace_layer(self.model, layer_cls, attr_replacement, param_replacement, method_replacement, sub_module_replacement) def _recursive_replace_layer( self, module: nn.Module, - origin_cls: nn.Module, + origin_cls: Union[str, nn.Module], attr_replacement: Dict[str, Any], param_replacement: List[Callable], method_replacement: Dict[str, Callable], @@ -95,17 +70,25 @@ def _recursive_replace_layer( Reverse the replace layer operation Args: - layer (:class:`torch.nn.Module`): The object of layer to shard - origin_cls (:class:`transformers.model`): The origin layer class + layer (torch.nn.Module): The object of layer to shard + origin_cls (Union[str, torch.nn.Module]): The origin layer class or a string of layer class name. attr_replacement (Dict): The attribute dict to modify param_replacement (List[Callable]): The function list to get parameter shard information in polic sub_module_replacement (List[Callable]): The function list to get sub module shard information in policy """ - if module.__class__ == origin_cls: - self._replace_attr(module, attr_replacement) - self._replace_param(module, param_replacement) - self._replace_method(module, method_replacement) - self._replace_sub_module(module, sub_module_replacement) + if (isinstance(origin_cls, str) and origin_cls == module.__class__.__name__) or \ + (module.__class__ == origin_cls): + if attr_replacement is not None: + self._replace_attr(module, attr_replacement) + + if param_replacement is not None: + self._replace_param(module, param_replacement) + + if method_replacement is not None: + self._replace_method(module, method_replacement) + + if sub_module_replacement is not None: + self._replace_sub_module(module, sub_module_replacement) for name, child in module.named_children(): self._recursive_replace_layer(child, origin_cls, attr_replacement, param_replacement, method_replacement, @@ -138,13 +121,10 @@ def _replace_param( layer (:class:`torch.nn.Module`): The object of layer to shard param_replacement (List[Callable]): The function list to get parameter shard information in policy """ - # TODO: support parameter shard - pass + for param_func in param_replacement: + param_func(module) def _replace_method(self, module: nn.Module, method_replacement: Dict[str, Callable]): - if method_replacement is None: - return - for method_name, new_method in method_replacement.items(): # bind the new method to the module setattr(module, method_name, new_method.__get__(module, module.__class__)) @@ -158,8 +138,8 @@ def _replace_sub_module( Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict Args: - org_layer (:class:`torch.nn.Module`): The origin layer object to shard - param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class + org_layer (torch.nn.Module): The origin layer object to shard + sub_module_replacement (List[SubModuleReplacementDescription]): The sub module replacement description list """ for description in sub_module_replacement: diff --git a/colossalai/shardformer/shard/shardformer.py b/colossalai/shardformer/shard/shardformer.py index 7c4220c3a9fb..3fce12463414 100644 --- a/colossalai/shardformer/shard/shardformer.py +++ b/colossalai/shardformer/shard/shardformer.py @@ -22,27 +22,19 @@ class ShardFormer: colossalai.launch_from_torch(config={}) org_model = BertForMaskedLM.from_pretrained('bert-base-uncased') - shard_config = ShardConfig( - tensor_parallel_size=2, - tensor_parallel_mode='1d', - ) + shard_config = ShardConfig() shard_former = ShardFormer(shard_config=shard_config) - model = shard_former.shard_model(org_model) + model = shard_former.optimize(org_model) ``` """ def __init__(self, shard_config: ShardConfig): - """ - Do two things: - 1. Create a colossalai.cluster.process_group_manager to manage process groups for dp, tp and pp - 2. serve as a store for - """ self.coordinator = DistCoordinator() self.shard_config = shard_config - def shard_model(self, model: nn.Module, policy: Policy = None): + def optimize(self, model: nn.Module, policy: Policy = None): r""" - The function is used to shard the PyTorch model. + This method will optimize the model based on the given policy. Args: model (`torch.nn.Model`): the origin huggingface model diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index a6355bf1c75e..aa1424af3289 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -11,7 +11,7 @@ def build_model(model_fn): shard_config = ShardConfig(enable_fused_normalization=True) model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) - sharded_model = shard_former.shard_model(model_copy).cuda() + sharded_model = shard_former.optimize(model_copy).cuda() return org_model, sharded_model diff --git a/tests/test_shardformer/test_with_torch_ddp.py b/tests/test_shardformer/test_with_torch_ddp.py index 61b672650965..9f8a5db6c94f 100644 --- a/tests/test_shardformer/test_with_torch_ddp.py +++ b/tests/test_shardformer/test_with_torch_ddp.py @@ -44,7 +44,7 @@ def check_shardformer_with_ddp(rank, world_size, port): for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): # create and shard model model = model_fn().cuda() - sharded_model = shardformer.shard_model(model) + sharded_model = shardformer.optimize(model) # add ddp sharded_ddp_model = DDP(sharded_model, process_group=dp_process_group) From 1fb0d95df003135dd68c93bdcfc728ea419d266f Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 4 Jul 2023 09:57:03 +0800 Subject: [PATCH 394/413] [shardformer] made tensor parallelism configurable (#4144) * [shardformer] made tensor parallelism configurable * polish code --- colossalai/shardformer/policies/basepolicy.py | 25 ++ colossalai/shardformer/policies/bert.py | 299 ++++++++---------- colossalai/shardformer/policies/bloom.py | 166 +++++----- colossalai/shardformer/policies/gpt2.py | 163 +++++----- colossalai/shardformer/policies/llama.py | 150 ++++----- colossalai/shardformer/policies/opt.py | 114 ++++--- colossalai/shardformer/policies/t5.py | 266 ++++++++-------- colossalai/shardformer/shard/shard_config.py | 10 +- tests/test_shardformer/test_model/_utils.py | 5 +- .../test_model/test_shard_bert.py | 45 ++- .../test_model/test_shard_bloom.py | 45 ++- .../test_model/test_shard_gpt2.py | 46 ++- .../test_model/test_shard_llama.py | 48 ++- .../test_model/test_shard_opt.py | 41 ++- .../test_model/test_shard_t5.py | 59 +++- 15 files changed, 814 insertions(+), 668 deletions(-) diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 9ea3d95de5b2..85e6d509c81b 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -126,3 +126,28 @@ def postprocess(self) -> nn.Module: the classifier layer """ pass + + def append_or_create_submodule_replacement( + self, description: Union[SubModuleReplacementDescription, + List[SubModuleReplacementDescription]], policy: Dict[Union[str, nn.Module], + ModulePolicyDescription], + target_key: Union[str, nn.Module]) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: + r""" + Append or create a new submodule replacement description to the policy for the given key. + + Args: + submodule_replace_desc (Union[SubModuleReplacementDescription, List[SubModuleReplacementDescription]]): the submodule replacement description to be appended + policy (Dict[Union[str, nn.Module], ModulePolicyDescription]): the policy to be updated + target_key (Union[str, nn.Module]): the key of the policy to be updated + """ + # convert to list + if isinstance(description, SubModuleReplacementDescription): + description = [description] + + # append or create a new description + if target_key in policy: + policy[target_key].sub_module_replacement.extend(description) + else: + policy[target_key] = ModulePolicyDescription(sub_module_replacement=description) + + return policy diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 5ab8fb825244..9c2736cc64d3 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -33,89 +33,114 @@ def preprocess(self): def module_policy(self): from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer - base_policy = { - BertLayer: - ModulePolicyDescription( - attribute_replacement={ - # 1. shard hidden size - "attention.self.all_head_size": - self.model.config.hidden_size // self.shard_config.tensor_parallel_size, - "crossattention.self.all_head_size": - self.model.config.hidden_size // self.shard_config.tensor_parallel_size, - # 2. shard number of heads - "attention.self.num_attention_heads": - self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, - "crossattention.self.num_attention_heads": - self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, - }, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="attention.self.query", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.self.key", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.self.value", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.self.dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="attention.output.dense", - target_module=col_nn.Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="attention.output.dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="intermediate.dense", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="output.dense", - target_module=col_nn.Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="output.dropout", - target_module=col_nn.DropoutForParallelInput, - ) - ]), - BertEmbeddings: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="word_embeddings", - target_module=col_nn.VocabParallelEmbedding1D, - ), - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForReplicatedInput, - ) - ]) - } + policy = {} + + if self.shard_config.enable_tensor_parallelism: + policy[BertLayer] = ModulePolicyDescription(attribute_replacement={ + "attention.self.all_head_size": + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "crossattention.self.all_head_size": + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "attention.self.num_attention_heads": + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + "crossattention.self.num_attention_heads": + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attention.self.query", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.self.key", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.self.value", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.self.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="attention.output.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="attention.output.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="intermediate.dense", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="output.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="output.dropout", + target_module=col_nn.DropoutForParallelInput, + ) + ]) + + policy[BertEmbeddings] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="word_embeddings", + target_module=col_nn.VocabParallelEmbedding1D, + ), + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForReplicatedInput, + ) + ]) # optimization configuration if self.shard_config.enable_fused_normalization: - base_policy[BertLayer].sub_module_replacement.append( + # Handle bert layer + self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription( suffix="attention.output.LayerNorm", target_module=col_nn.FusedLayerNorm, - )) - base_policy[BertLayer].sub_module_replacement.append( + ), SubModuleReplacementDescription( suffix="output.LayerNorm", target_module=col_nn.FusedLayerNorm, - )) - base_policy[BertEmbeddings].sub_module_replacement.append( - SubModuleReplacementDescription( + ) + ], + policy=policy, + target_key=BertLayer) + + # handle embedding layer + self.append_or_create_submodule_replacement( + description=[SubModuleReplacementDescription( suffix="LayerNorm", target_module=col_nn.FusedLayerNorm, - ),) + )], + policy=policy, + target_key=BertEmbeddings) + return policy + + def add_lm_head_policy(self, base_policy): + from transformers.models.bert.modeling_bert import BertLMPredictionHead + + # optimize for tensor parallelism + if self.shard_config.enable_tensor_parallelism: + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}), + policy=base_policy, + target_key=BertLMPredictionHead) + + # optimize with fused normalization + if self.shard_config.enable_fused_normalization: + # Handle bert lm prediction head + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="transform.LayerNorm", + target_module=col_nn.FusedLayerNorm, + ), + policy=base_policy, + target_key=BertLMPredictionHead) return base_policy def postprocess(self): @@ -136,35 +161,14 @@ def __init__(self) -> None: super().__init__() def module_policy(self): - from transformers.models.bert.modeling_bert import BertLMPredictionHead - module_policy = super().module_policy() - addon_module = { - BertLMPredictionHead: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}), - ]) - } - - # optimization configuration - if self.shard_config.enable_fused_normalization: - addon_module[BertLMPredictionHead].sub_module_replacement.append( - SubModuleReplacementDescription( - suffix="transform.LayerNorm", - target_module=col_nn.FusedLayerNorm, - )) - - # append extra policy - module_policy.update(addon_module) + module_policy = self.add_lm_head_policy(module_policy) return module_policy def postprocess(self): binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) - param = nn.Parameter(param) - setattr_(self.model, k, param) setattr_(self.model, v, param) return self.model @@ -176,31 +180,14 @@ def __init__(self) -> None: super().__init__() def module_policy(self): - from transformers.models.bert.modeling_bert import BertLMPredictionHead - module_policy = super().module_policy() - addon_module = { - BertLMPredictionHead: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}), - ]) - } - if self.shard_config.enable_fused_normalization: - addon_module[BertLMPredictionHead].sub_module_replacement.append( - SubModuleReplacementDescription( - suffix="transform.LayerNorm", - target_module=col_nn.FusedLayerNorm, - )) - module_policy.update(addon_module) + module_policy = self.add_lm_head_policy(module_policy) return module_policy def postprocess(self): binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) - param = nn.Parameter(param) - setattr_(self.model, k, param) setattr_(self.model, v, param) return self.model @@ -212,34 +199,14 @@ def __init__(self) -> None: super().__init__() def module_policy(self): - from transformers.models.bert.modeling_bert import BertLMPredictionHead - module_policy = super().module_policy() - addon_module = { - BertLMPredictionHead: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}), - ]) - } - - # optimization configuration - if self.shard_config.enable_fused_normalization: - addon_module[BertLMPredictionHead].sub_module_replacement.append( - SubModuleReplacementDescription( - suffix="transform.LayerNorm", - target_module=col_nn.FusedLayerNorm, - )) - - module_policy.update(addon_module) + module_policy = self.add_lm_head_policy(module_policy) return module_policy def postprocess(self): binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) - param = nn.Parameter(param) - setattr_(self.model, k, param) setattr_(self.model, v, param) return self.model @@ -254,16 +221,18 @@ def module_policy(self): from transformers.models.bert.modeling_bert import BertForSequenceClassification module_policy = super().module_policy() - addon_module = { - BertForSequenceClassification: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForParallelInput, - ) - ]) - } - module_policy.update(addon_module) + + if self.shard_config.enable_tensor_parallelism: + addon_module = { + BertForSequenceClassification: + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForParallelInput, + ) + ]) + } + module_policy.update(addon_module) return module_policy @@ -277,16 +246,18 @@ def module_policy(self): from transformers.models.bert.modeling_bert import BertForTokenClassification module_policy = super().module_policy() - addon_module = { - BertForTokenClassification: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForParallelInput, - ) - ]) - } - module_policy.update(addon_module) + + if self.shard_config.enable_tensor_parallelism: + addon_module = { + BertForTokenClassification: + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForParallelInput, + ) + ]) + } + module_policy.update(addon_module) return module_policy @@ -307,14 +278,16 @@ def module_policy(self): from transformers.models.bert.modeling_bert import BertForMultipleChoice module_policy = super().module_policy() - addon_module = { - BertForMultipleChoice: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForParallelInput, - ) - ]) - } - module_policy.update(addon_module) + + if self.shard_config.enable_tensor_parallelism: + addon_module = { + BertForMultipleChoice: + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForParallelInput, + ) + ]) + } + module_policy.update(addon_module) return module_policy diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index 00ab9159b0dc..030774a919d7 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -85,57 +85,53 @@ def preprocess(self): def module_policy(self): from transformers.models.bloom.modeling_bloom import BloomBlock, BloomModel - base_policy = { - BloomBlock: - ModulePolicyDescription( - attribute_replacement={ - # 1. shard hidden size - "self_attention.hidden_size": - self.model.config.hidden_size // self.shard_config.tensor_parallel_size, - "self_attention.split_size": - self.model.config.hidden_size // self.shard_config.tensor_parallel_size, - # 2. shard number of heads - "self_attention.num_heads": - self.model.config.n_head // self.shard_config.tensor_parallel_size, - }, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="self_attention.query_key_value", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="self_attention.dense", - target_module=col_nn.Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="self_attention.attention_dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="mlp.dense_h_to_4h", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="mlp.dense_4h_to_h", - target_module=col_nn.Linear1D_Row, - ), - ]), - BloomModel: - ModulePolicyDescription(attribute_replacement={ + policy = {} + + if self.shard_config.enable_tensor_parallelism: + policy[BloomBlock] = ModulePolicyDescription(attribute_replacement={ + "self_attention.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "self_attention.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "self_attention.num_heads": self.model.config.n_head // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attention.query_key_value", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attention.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="self_attention.attention_dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="mlp.dense_h_to_4h", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="mlp.dense_4h_to_h", + target_module=col_nn.Linear1D_Row, + ), + ]) + + policy[BloomModel] = ModulePolicyDescription( + attribute_replacement={ "num_heads": self.model.config.n_head // self.shard_config.tensor_parallel_size, }, - method_replacement={"build_alibi_tensor": build_bloom_alibi_tensor}, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="word_embeddings", - target_module=col_nn.VocabParallelEmbedding1D, - ) - ]) - } + method_replacement={"build_alibi_tensor": build_bloom_alibi_tensor}, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="word_embeddings", + target_module=col_nn.VocabParallelEmbedding1D, + ) + ]) # optimization configuration if self.shard_config.enable_fused_normalization: - base_policy[BloomModel].sub_module_replacement.extend([ + # handle bloom model + self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription( suffix="ln_f", target_module=col_nn.FusedLayerNorm, @@ -144,8 +140,12 @@ def module_policy(self): suffix="word_embeddings_layernorm", target_module=col_nn.FusedLayerNorm, ) - ]) - base_policy[BloomBlock].sub_module_replacement.extend([ + ], + policy=policy, + target_key=BloomModel) + + # handle bloom block + self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription( suffix="input_layernorm", target_module=col_nn.FusedLayerNorm, @@ -154,9 +154,11 @@ def module_policy(self): suffix="post_attention_layernorm", target_module=col_nn.FusedLayerNorm, ) - ]) + ], + policy=policy, + target_key=BloomBlock) - return base_policy + return policy def postprocess(self): return self.model @@ -171,19 +173,19 @@ class BloomForCausalLMPolicy(BloomPolicy): def module_policy(self): from transformers.models.bloom.modeling_bloom import BloomForCausalLM policy = super().module_policy() - # add a new item for casual lm - new_item = { - BloomForCausalLM: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)) - ]) - } - policy.update(new_item) + + # handle tensor parallelism + if self.shard_config.enable_tensor_parallelism: + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)), + policy=policy, + target_key=BloomForCausalLM) + return policy def postprocess(self): binding_map = {"transformer.word_embeddings.weight": "lm_head.weight"} + for k, v in binding_map.items(): param = getattr_(self.model, k) @@ -191,7 +193,6 @@ def postprocess(self): param = nn.Parameter(param) # tie weights - setattr_(self.model, k, param) setattr_(self.model, v, param) return self.model @@ -201,15 +202,14 @@ class BloomForSequenceClassificationPolicy(BloomPolicy): def module_policy(self): from transformers.models.bloom.modeling_bloom import BloomForSequenceClassification policy = super().module_policy() - # add a new item for casual lm - new_item = { - BloomForSequenceClassification: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="score", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)) - ]) - } - policy.update(new_item) + + # handle tensor parallelism + if self.shard_config.enable_tensor_parallelism: + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="score", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)), + policy=policy, + target_key=BloomForSequenceClassification) + return policy @@ -218,19 +218,21 @@ class BloomForTokenClassificationPolicy(BloomPolicy): def module_policy(self): from transformers.models.bloom.modeling_bloom import BloomForTokenClassification policy = super().module_policy() - # add a new item for casual lm - new_item = { - BloomForTokenClassification: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="classifier", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)), - SubModuleReplacementDescription( - suffix="dropout", - target_module=col_nn.DropoutForReplicatedInput, - ), - ]) - } - policy.update(new_item) + + # handle tensor parallelism + if self.shard_config.enable_tensor_parallelism: + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription(suffix="classifier", + target_module=col_nn.Linear1D_Col, + kwargs=dict(gather_output=True)), + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForReplicatedInput, + ), + ], + policy=policy, + target_key=BloomForTokenClassification) + return policy diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index ad0b1144a8a5..549cdbf87a80 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -31,67 +31,67 @@ def preprocess(self): def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model - base_policy = { - GPT2Model: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="wte", - target_module=col_nn.VocabParallelEmbedding1D, - ), - ]), - GPT2Block: - ModulePolicyDescription(attribute_replacement={ - "attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, - "attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, - "attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, - }, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="attn.c_attn", - target_module=col_nn.GPT2FusedLinearConv1D_Col, - kwargs={ - "n_fused": 3, - }, - ), - SubModuleReplacementDescription( - suffix="attn.c_proj", - target_module=col_nn.GPT2FusedLinearConv1D_Row, - ), - SubModuleReplacementDescription( - suffix="mlp.c_fc", - target_module=col_nn.GPT2FusedLinearConv1D_Col, - kwargs={ - "n_fused": 1, - }, - ), - SubModuleReplacementDescription( - suffix="mlp.c_proj", - target_module=col_nn.GPT2FusedLinearConv1D_Row, - ), - SubModuleReplacementDescription( - suffix="attn.attn_dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="attn.resid_dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="mlp.dropout", - target_module=col_nn.DropoutForParallelInput, - ), - ]) - } + policy = {} - # optimization configuration - if self.shard_config.enable_fused_normalization: - base_policy[GPT2Model].sub_module_replacement.append( + if self.shard_config.enable_tensor_parallelism: + policy[GPT2Model] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( - suffix="ln_f", - target_module=col_nn.FusedLayerNorm, - )) + suffix="wte", + target_module=col_nn.VocabParallelEmbedding1D, + ), + ]) + policy[GPT2Block] = ModulePolicyDescription(attribute_replacement={ + "attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attn.c_attn", + target_module=col_nn.GPT2FusedLinearConv1D_Col, + kwargs={ + "n_fused": 3, + }, + ), + SubModuleReplacementDescription( + suffix="attn.c_proj", + target_module=col_nn.GPT2FusedLinearConv1D_Row, + ), + SubModuleReplacementDescription( + suffix="mlp.c_fc", + target_module=col_nn.GPT2FusedLinearConv1D_Col, + kwargs={ + "n_fused": 1, + }, + ), + SubModuleReplacementDescription( + suffix="mlp.c_proj", + target_module=col_nn.GPT2FusedLinearConv1D_Row, + ), + SubModuleReplacementDescription( + suffix="attn.attn_dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="attn.resid_dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="mlp.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + ]) - base_policy[GPT2Block].sub_module_replacement.extend([ + # optimization configuration + if self.shard_config.enable_fused_normalization: + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="ln_f", + target_module=col_nn.FusedLayerNorm, + ), + policy=policy, + target_key=GPT2Model) + + self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription( suffix="ln_1", target_module=col_nn.FusedLayerNorm, @@ -103,9 +103,10 @@ def module_policy(self): SubModuleReplacementDescription(suffix="ln_cross_attn", target_module=col_nn.FusedLayerNorm, ignore_if_not_exist=True) - ]) - - return base_policy + ], + policy=policy, + target_key=GPT2Block) + return policy def postprocess(self): return self.model @@ -128,22 +129,22 @@ def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel module_policy = super().module_policy() - addon_module = { - GPT2LMHeadModel: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}) - ]) - } - module_policy.update(addon_module) + + if self.shard_config.enable_tensor_parallelism: + addon_module = { + GPT2LMHeadModel: + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}) + ]) + } + module_policy.update(addon_module) return module_policy def postprocess(self): binding_map = {"transformer.wte.weight": "lm_head.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) - param = nn.Parameter(param) - setattr_(self.model, k, param) setattr_(self.model, v, param) return self.model @@ -158,22 +159,22 @@ def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2DoubleHeadsModel module_policy = super().module_policy() - addon_module = { - GPT2DoubleHeadsModel: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}) - ]) - } - module_policy.update(addon_module) + + if self.shard_config.enable_tensor_parallelism: + addon_module = { + GPT2DoubleHeadsModel: + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}) + ]) + } + module_policy.update(addon_module) return module_policy def postprocess(self): binding_map = {"transformer.wte.weight": "lm_head.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) - param = nn.Parameter(param) - setattr_(self.model, k, param) setattr_(self.model, v, param) return self.model diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index 8f397693745c..157785bdcf13 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -28,58 +28,58 @@ def preprocess(self): def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel - base_policy = { - LlamaDecoderLayer: - ModulePolicyDescription( - attribute_replacement={ - "self_attn.hidden_size": - self.model.config.hidden_size // self.shard_config.tensor_parallel_size, - "self_attn.num_heads": - self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, - }, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="self_attn.q_proj", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="self_attn.k_proj", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="self_attn.v_proj", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="self_attn.o_proj", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="mlp.gate_proj", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="mlp.up_proj", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="mlp.down_proj", - target_module=Linear1D_Row, - ) - ], - ), - LlamaModel: - ModulePolicyDescription(sub_module_replacement=[ + policy = {} + + if self.shard_config.enable_tensor_parallelism: + policy[LlamaDecoderLayer] = ModulePolicyDescription( + attribute_replacement={ + "self_attn.hidden_size": + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "self_attn.num_heads": + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attn.q_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.k_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.v_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.o_proj", + target_module=Linear1D_Row, + ), SubModuleReplacementDescription( - suffix="embed_tokens", - target_module=VocabParallelEmbedding1D, + suffix="mlp.gate_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="mlp.up_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="mlp.down_proj", + target_module=Linear1D_Row, ) - ]) - } + ], + ) + + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=VocabParallelEmbedding1D, + ), + policy=policy, + target_key=LlamaModel) # optimization configuration if self.shard_config.enable_fused_normalization: - base_policy[LlamaDecoderLayer].sub_module_replacement.extend([ + self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription( suffix="input_layernorm", target_module=FusedRMSNorm, @@ -88,15 +88,18 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: suffix="post_attention_layernorm", target_module=FusedRMSNorm, ) - ]) + ], + policy=policy, + target_key=LlamaDecoderLayer) - base_policy[LlamaModel].sub_module_replacement.append( - SubModuleReplacementDescription( - suffix="norm", - target_module=FusedRMSNorm, - )) + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="norm", + target_module=FusedRMSNorm, + ), + policy=policy, + target_key=LlamaModel) - return base_policy + return policy def postprocess(self): return self.model @@ -108,15 +111,17 @@ def module_policy(self): from transformers import LlamaForCausalLM policy = super().module_policy() - # add a new item for casual lm - new_item = { - LlamaForCausalLM: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) - ]) - } - policy.update(new_item) + + if self.shard_config.enable_tensor_parallelism: + # add a new item for casual lm + new_item = { + LlamaForCausalLM: + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) + ]) + } + policy.update(new_item) return policy @@ -127,13 +132,14 @@ def module_policy(self): policy = super().module_policy() - # add a new item for sequence classification - new_item = { - LlamaForSequenceClassification: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) - ]) - } - policy.update(new_item) + if self.shard_config.enable_tensor_parallelism: + # add a new item for sequence classification + new_item = { + LlamaForSequenceClassification: + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) + ]) + } + policy.update(new_item) return policy diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index 428ee2c9776c..b87db53f45f1 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -29,66 +29,67 @@ def preprocess(self): def module_policy(self): from transformers.models.opt.modeling_opt import OPTAttention, OPTDecoder, OPTDecoderLayer - base_policy = { - OPTDecoder: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="embed_tokens", - target_module=VocabParallelEmbedding1D, - ) - ]), - OPTDecoderLayer: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="fc1", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="fc2", - target_module=Linear1D_Row, - ) - ]), - OPTAttention: - ModulePolicyDescription(attribute_replacement={ - "embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, - "num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size - }, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="q_proj", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="k_proj", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="v_proj", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="out_proj", - target_module=Linear1D_Row, - ), - ]), - } + policy = {} + + if self.shard_config.enable_tensor_parallelism: + policy[OPTDecoder] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=VocabParallelEmbedding1D, + ) + ]) + policy[OPTDecoderLayer] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="fc1", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="fc2", + target_module=Linear1D_Row, + ) + ]) + + policy[OPTAttention] = ModulePolicyDescription(attribute_replacement={ + "embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + "num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="q_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="k_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="v_proj", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="out_proj", + target_module=Linear1D_Row, + ), + ]) # optimization configuration if self.shard_config.enable_fused_normalization: - base_policy[OPTDecoder].sub_module_replacement.append( - SubModuleReplacementDescription(suffix="final_layer_norm", - target_module=FusedLayerNorm, - ignore_if_not_exist=True)) - base_policy[OPTDecoderLayer].sub_module_replacement.extend([ + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="final_layer_norm", target_module=FusedLayerNorm, ignore_if_not_exist=True), + policy=policy, + target_key=OPTDecoder) + self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription(suffix="self_attn_layer_norm", target_module=FusedLayerNorm, ignore_if_not_exist=True), SubModuleReplacementDescription(suffix="final_layer_norm", target_module=FusedLayerNorm, ignore_if_not_exist=True) - ]) + ], + policy=policy, + target_key=OPTDecoderLayer) - return base_policy + return policy def postprocess(self): return self.model @@ -106,15 +107,12 @@ def module_policy(self): from transformers.models.opt.modeling_opt import OPTForCausalLM policy = super().module_policy() - new_item = { - OPTForCausalLM: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) - ]) - } - policy.update(new_item) + if self.shard_config.enable_tensor_parallelism: + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)), + policy=policy, + target_key=OPTForCausalLM) return policy def postprocess(self): diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 37fccaabc457..cde59ab77042 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -42,116 +42,126 @@ def module_policy(self): T5Stack, ) - base_policy = { - T5Stack: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="embed_tokens", - target_module=Embedding1D, - ) - ]), - T5LayerSelfAttention: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ), - ]), - T5LayerCrossAttention: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ) - ]), - T5Attention: - ModulePolicyDescription(attribute_replacement={ - "d_model": - self.model.config.d_model // self.shard_config.tensor_parallel_size, - "n_heads": - self.model.config.num_heads // self.shard_config.tensor_parallel_size, - "inner_dim": - self.model.config.num_heads * self.model.config.d_kv // self.shard_config.tensor_parallel_size - }, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="q", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="k", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="v", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="o", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription(suffix="relative_attention_bias", - target_module=Embedding1D, - kwargs=dict(gather_output=False), - ignore_if_not_exist=True) - ]), - T5LayerFF: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ), - ]), - T5DenseGatedActDense: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="wi_0", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="wi_1", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="wo", target_module=Linear1D_Col, kwargs=dict(gather_output=True)), - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ) - ]), - T5DenseActDense: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="wi", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="wo", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForParallelInput, - ) - ]) - } + policy = {} + + if self.shard_config.enable_tensor_parallelism: + policy[T5Stack] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=Embedding1D, + ) + ]) + policy[T5LayerSelfAttention] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ), + ]) + policy[T5LayerCrossAttention] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ) + ]) + policy[T5Attention] = ModulePolicyDescription(attribute_replacement={ + "d_model": + self.model.config.d_model // self.shard_config.tensor_parallel_size, + "n_heads": + self.model.config.num_heads // self.shard_config.tensor_parallel_size, + "inner_dim": + self.model.config.num_heads * self.model.config.d_kv // self.shard_config.tensor_parallel_size + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="q", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="k", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="v", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="o", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="relative_attention_bias", + target_module=Embedding1D, + kwargs=dict(gather_output=False), + ignore_if_not_exist=True) + ]) + policy[T5LayerFF] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ), + ]) + policy[T5DenseGatedActDense] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="wi_0", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="wi_1", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="wo", target_module=Linear1D_Col, kwargs=dict(gather_output=True)), + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ) + ]) + policy[T5DenseActDense] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="wi", + target_module=Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="wo", + target_module=Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="dropout", + target_module=DropoutForParallelInput, + ) + ]) # optimization configuration if self.shard_config.enable_fused_normalization: - base_policy[T5LayerFF].sub_module_replacement.append( - SubModuleReplacementDescription(suffix="layer_norm", target_module=FusedRMSNorm)) - base_policy[T5LayerSelfAttention].sub_module_replacement.append( - SubModuleReplacementDescription(suffix="layer_norm", target_module=FusedRMSNorm)) - base_policy[T5LayerCrossAttention].sub_module_replacement.append( - SubModuleReplacementDescription(suffix="layer_norm", target_module=FusedRMSNorm)) - base_policy[T5Stack].sub_module_replacement.append( - SubModuleReplacementDescription(suffix="final_layer_norm", target_module=FusedRMSNorm)) - - return base_policy + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="layer_norm", + target_module=FusedRMSNorm, + ), + policy=policy, + target_key=T5LayerFF) + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="layer_norm", + target_module=FusedRMSNorm, + ), + policy=policy, + target_key=T5LayerFF) + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="layer_norm", target_module=FusedRMSNorm), + policy=policy, + target_key=T5LayerSelfAttention) + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="layer_norm", target_module=FusedRMSNorm), + policy=policy, + target_key=T5LayerCrossAttention) + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="final_layer_norm", target_module=FusedRMSNorm), + policy=policy, + target_key=T5Stack) + return policy def postprocess(self): binding_map = [["shared", "encoder.embed_tokens"], ["shared", "decoder.embed_tokens"]] @@ -166,14 +176,15 @@ class T5ModelPolicy(T5BasePolicy): def module_policy(self): from transformers import T5Model - base_policy = super().module_policy() - base_policy[T5Model] = ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( + + if self.shard_config.enable_tensor_parallelism: + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="shared", target_module=VocabParallelEmbedding1D, - ) - ]) + ), + policy=base_policy, + target_key=T5Model) return base_policy @@ -183,14 +194,19 @@ def module_policy(self): from transformers import T5ForConditionalGeneration policy = super().module_policy() - policy[T5ForConditionalGeneration] = ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="shared", - target_module=VocabParallelEmbedding1D, - ), - SubModuleReplacementDescription( - suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) - ]) + + if self.shard_config.enable_tensor_parallelism: + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="shared", + target_module=VocabParallelEmbedding1D, + ), + SubModuleReplacementDescription(suffix="lm_head", + target_module=Linear1D_Col, + kwargs=dict(gather_output=True)) + ], + policy=policy, + target_key=T5ForConditionalGeneration) return policy def postprocess(self): @@ -212,12 +228,14 @@ def module_policy(self): from transformers import T5EncoderModel base_policy = super().module_policy() - base_policy[T5EncoderModel] = ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( + + if self.shard_config.enable_tensor_parallelism: + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="shared", target_module=VocabParallelEmbedding1D, - ) - ]) + ), + policy=base_policy, + target_key=T5EncoderModel) return base_policy def postprocess(self): diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 0a5aa4cc4bdc..83c08d275df3 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -13,11 +13,12 @@ class ShardConfig: Args: tensor_parallel_process_group (int): The process group for tensor parallelism, defaults to None, which is the global process group. + enable_tensor_parallelism (bool): Whether to turn on tensor parallelism, default is True. enable_fused_normalization (bool): Whether to use fused layernorm, default is False. - enable_tensor_parallelism (bool): Whether to use tensor parallelism, default is True. enable_all_optimization (bool): Whether to turn on all optimization, default is False. """ tensor_parallel_process_group: ProcessGroup = None + enable_tensor_parallelism: bool = True enable_fused_normalization: bool = False enable_all_optimization: bool = False @@ -33,8 +34,11 @@ def tensor_parallel_size(self): return self._tensor_parallel_size def __post_init__(self): - # get the parallel size - self._tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group) + if not self.enable_tensor_parallelism: + self._tensor_parallel_size = 1 + else: + # get the parallel size + self._tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group) # turn on all optimization if all_optimization is set to True if self.enable_all_optimization: diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index aa1424af3289..d83d9ecd39e0 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -3,12 +3,13 @@ from colossalai.shardformer import ShardConfig, ShardFormer -def build_model(model_fn): +def build_model(model_fn, enable_fused_normalization=True, enable_tensor_parallelism=True): # create new model org_model = model_fn().cuda() # shard model - shard_config = ShardConfig(enable_fused_normalization=True) + shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, + enable_tensor_parallelism=enable_tensor_parallelism) model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) sharded_model = shard_former.optimize(model_copy).cuda() diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 87c4ef65bf1a..1afedb7079ea 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -3,7 +3,14 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) from tests.kit.model_zoo import model_zoo from tests.test_shardformer.test_model._utils import build_model, run_forward @@ -33,36 +40,50 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # compare self attention grad org_grad = bert.encoder.layer[0].attention.self.query.weight.grad shard_grad = sharded_bert.encoder.layer[0].attention.self.query.weight.grad + shard_weight = sharded_bert.encoder.layer[0].attention.self.query.weight - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" # compare embedding grad org_grad = bert.embeddings.word_embeddings.weight.grad shard_grad = sharded_bert.embeddings.word_embeddings.weight.grad + shard_weight = sharded_bert.embeddings.word_embeddings.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" -def check_bert(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_bert_test(enable_fused_normalization, enable_tensor_parallelism): sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() +def check_bert(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_bert_test() + + @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index 70d902a04517..a3389652269c 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -3,7 +3,14 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) from tests.kit.model_zoo import model_zoo from tests.test_shardformer.test_model._utils import build_model, run_forward @@ -32,10 +39,14 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check attention grad org_grad = bloom.h[0].self_attention.query_key_value.weight.grad shard_grad = sharded_bloom.h[0].self_attention.query_key_value.weight.grad + shard_weight = sharded_bloom.h[0].self_attention.query_key_value.weight - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" @@ -43,27 +54,35 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check embedding weights org_grad = bloom.word_embeddings.weight.grad shard_grad = sharded_bloom.word_embeddings.weight.grad + shard_weight = sharded_bloom.word_embeddings.weight - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" -def check_bloom(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism): sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - torch.cuda.empty_cache() +def check_bloom(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_bloom_test() + + @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index a4edc14bdbc3..ee7737687d99 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -3,7 +3,14 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) from tests.kit.model_zoo import model_zoo from tests.test_shardformer.test_model._utils import build_model, run_forward @@ -32,11 +39,14 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check mlp grad org_grad = org_model.h[0].mlp.c_fc.weight.grad shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad + shard_weight = sharded_model.h[0].mlp.c_fc.weight - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=1) - + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=1) + else: + all_shard_grad = shard_grad assert torch.allclose( org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}" @@ -44,27 +54,35 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check embedding weights org_grad = org_model.wte.weight.grad shard_grad = sharded_model.wte.weight.grad - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + shard_weight = sharded_model.wte.weight + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose( org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}" -def check_gpt2(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism): sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - torch.cuda.empty_cache() +def check_gpt2(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_gpt2_test() + + @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index a98743a6143a..74b5fdd18af8 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -5,7 +5,14 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) from tests.kit.model_zoo import model_zoo from tests.test_shardformer.test_model._utils import build_model, run_forward @@ -37,35 +44,48 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check attention grad org_grad = llama_model.layers[0].self_attn.q_proj.weight.grad shard_grad = shard_llama_model.layers[0].self_attn.q_proj.weight.grad - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + shard_weight = shard_llama_model.layers[0].self_attn.q_proj.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" # check embedding grad org_grad = llama_model.embed_tokens.weight.grad shard_grad = shard_llama_model.embed_tokens.weight.grad - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + shard_weight = shard_llama_model.embed_tokens.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" -def check_llama(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_gpt2_llama(enable_fused_normalization, enable_tensor_parallelism): sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') - for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - torch.cuda.empty_cache() +def check_llama(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_gpt2_llama() + + @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index 29cf2f6beed8..25bccb13b1a8 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -6,10 +6,11 @@ import colossalai from colossalai.logging import disable_existing_loggers +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, - check_state_dict_equal, clear_cache_before_run, + parameterize, rerun_if_address_is_in_use, spawn, ) @@ -42,34 +43,48 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check attention grad org_grad = opt_model.decoder.layers[0].self_attn.q_proj.weight.grad shard_grad = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight.grad - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + shard_weight = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" # check embedding grad org_grad = opt_model.decoder.embed_tokens.weight.grad shard_grad = shard_opt_model.decoder.embed_tokens.weight.grad - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + shard_weight = shard_opt_model.decoder.embed_tokens.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" -def check_OPTModel(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_t5_test(enable_fused_normalization, enable_tensor_parallelism): sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - torch.cuda.empty_cache() +def check_OPTModel(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_t5_test() + + @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 91430bce918f..0762dc09e5af 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -5,7 +5,14 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) from tests.kit.model_zoo import model_zoo from tests.test_shardformer.test_model._utils import build_model, run_forward @@ -27,19 +34,28 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check attention grad org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad - - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" # check self attention embed org_grad = org_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=1) + shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=1) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" @@ -52,25 +68,34 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo assert sharded_model.shared.weight.data.data_ptr() == sharded_model.lm_head.weight.data.data_ptr() shard_grad = sharded_model.shared.weight.grad - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + shard_weight = sharded_model.shared.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad assert torch.allclose(org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" -def check_t5(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_t5_test(enable_fused_normalization, enable_tensor_parallelism): sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') - for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() +def check_t5(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_t5_test() + + @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() From 89f45eda5a8f40462c2c5462d715867685609976 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 4 Jul 2023 10:28:31 +0800 Subject: [PATCH 395/413] [shardformer] added development protocol for standardization (#4149) --- colossalai/shardformer/README.md | 13 ++++ colossalai/shardformer/model/modeling_bert.py | 67 ------------------ .../{model => modeling}/__init__.py | 0 colossalai/shardformer/modeling/bloom.py | 69 +++++++++++++++++++ colossalai/shardformer/policies/bloom.py | 64 ++--------------- 5 files changed, 86 insertions(+), 127 deletions(-) delete mode 100644 colossalai/shardformer/model/modeling_bert.py rename colossalai/shardformer/{model => modeling}/__init__.py (100%) create mode 100644 colossalai/shardformer/modeling/bloom.py diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index f5d8bb35d91d..fca401562be6 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -321,6 +321,19 @@ This section serves as the guideline for writing new policies and register them You can create a new file in the `colossalai/shardformer/policies` folder and name the file with the model name. You can implement your policy in this file. You should not import the any model zoo library at the header section of the file because we do not want to import the library when we do not use the policy. Libraries such as `transformers` should be imported only in the function body when needed. +Please follow the following protocols when writing your policy: + +- You have to make a clear decision what you want to replace exactly in the original PyTorch module + - Use `ModulePolicyDescription.attribute_replacement` to replace the module attributes + - Use `ModulePolicyDescription.param_replacement` to replace the module parameters + - Use `ModulePolicyDescription.sub_module_replacement` to replace the submodules completely. The target module should implement the `from_native_module` for the . + - Use `ModulePolicyDescription.method_replacement` to replace the module methods. **These replacement methods should be put in the `shardformer/modeling/.py`**. +- You can implement the `ParallelModule` for primitive modules in the `shardformer/layer/.py` file. Primitive modules refer to modules which are not composed of other modules. For example, the `torch.nn.Linear` module is a primitive module while modules such as `BertEncoder` module in the `transformers` library is a composite module. Primitive modules do not nested inner `nn.Module` members. For composite modules, you should consider using `ModulePolicyDescription` to implement your replacement. +- `ParallelModule` is meant to be used in two ways: `ParallelModule.from_native_module` to convert native PyTorch module to the `ParallelModule` and `ParallelModule(...)` to instantiate the module directly just like a normal PyTorch module. `ParallelModule` should be only implemented for modules whose weights are sharded. If you want to make your module compatible with the `ModulePolicyDescription.sub_module_replacement` and there is no weight sharding in your module, you can just implement the `from_native_module` method without inheriting the `ParallelModule` like `colossalai/shardformer/layer/normalization.py`. +- **Do not import any file in the `colossalai/shardformer/policies` and `colossalai/shardformer/modeling` to avoid unwanted import error**. For example, a file in these folders accidentally imports `transformers` library at the top of the file, then the user will have to install `transformers` library even if they do not use this file. Any file in the `modeling` folder should be only imported by the policy file. A policy implementation should be only imported dynamically via the autopolicy or manually via the `ShardFormer` module. +- Try to keep your import statement on third-party libraries such as `transformers` within the function body instead of the header section of the file. This is because we do not want to import the library when we do not use the policy. + + - Step 2. Register your policy to the autopolicy Next, you need to register your policy in the `colossalai/shardformer/policies/autopolicy.py` file. diff --git a/colossalai/shardformer/model/modeling_bert.py b/colossalai/shardformer/model/modeling_bert.py deleted file mode 100644 index bd07ab80c00d..000000000000 --- a/colossalai/shardformer/model/modeling_bert.py +++ /dev/null @@ -1,67 +0,0 @@ -from typing import Any, Dict, List, Type - -import torch -import torch.nn as nn -from torch.nn import CrossEntropyLoss -from transformers import BertForMaskedLM -from transformers.models.bert.modeling_bert import MaskedLMOutput - -from ..layer.dist_crossentropy import applyDistCrossEntropy - - -class BertForMaskedLM_(BertForMaskedLM): - - def forward( - self, - input_ids=None, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - labels=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - **kwargs, - ): - # print("[Inject OK] Injected forward method") - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = self.bert( - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - prediction_scores = self.cls(sequence_output) - - masked_lm_loss = None - - if labels is not None: - masked_lm_loss = applyDistCrossEntropy(prediction_scores, labels) - # if labels is not None: - # loss_fct = CrossEntropyLoss() # -100 index = padding token - # masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output - - return MaskedLMOutput( - loss=masked_lm_loss, - logits=prediction_scores, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) diff --git a/colossalai/shardformer/model/__init__.py b/colossalai/shardformer/modeling/__init__.py similarity index 100% rename from colossalai/shardformer/model/__init__.py rename to colossalai/shardformer/modeling/__init__.py diff --git a/colossalai/shardformer/modeling/bloom.py b/colossalai/shardformer/modeling/bloom.py new file mode 100644 index 000000000000..a3d774ff2abb --- /dev/null +++ b/colossalai/shardformer/modeling/bloom.py @@ -0,0 +1,69 @@ +import torch +import torch.distributed as dist +from torch.distributed import ProcessGroup + + +def build_bloom_alibi_tensor_fn(process_group: ProcessGroup) -> torch.Tensor: + + def build_bloom_alibi_tensor(self, attention_mask: torch.Tensor, num_heads: int, + dtype: torch.dtype) -> torch.Tensor: + """ + Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it + relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value + `softmax(l+a) = softmax(l)`. Based on + https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742 + TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly. + + Args: + Returns tensor shaped (batch_size * num_heads, 1, max_seq_len) + attention_mask (`torch.Tensor`): + Token-wise attention mask, this should be of shape (batch_size, max_seq_len). + num_heads (`int`, *required*): + number of heads + dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`): + dtype of the output tensor + """ + import math + + if dist.is_initialized(): + world_size = dist.get_world_size(process_group) + num_heads = num_heads * world_size + + batch_size, seq_length = attention_mask.shape + closest_power_of_2 = 2**math.floor(math.log2(num_heads)) + base = torch.tensor(2**(-(2**-(math.log2(closest_power_of_2) - 3))), + device=attention_mask.device, + dtype=torch.float32) + powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32) + slopes = torch.pow(base, powers) + + if closest_power_of_2 != num_heads: + extra_base = torch.tensor(2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))), + device=attention_mask.device, + dtype=torch.float32) + num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2) + extra_powers = torch.arange(1, + 1 + 2 * num_remaining_heads, + 2, + device=attention_mask.device, + dtype=torch.int32) + slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) + + # Note: alibi will added to the attention bias that will be applied to the query, key product of attention + # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length) + # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length) + # => the query_length dimension will then be broadcasted correctly + # This is more or less identical to T5's relative position bias: + # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527 + arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :] + alibi = slopes[..., None] * arange_tensor + if dist.is_initialized(): + num_heads_per_rank = int(num_heads / dist.get_world_size(process_group)) + offset = dist.get_rank(process_group) * num_heads_per_rank + alibi = alibi.view(batch_size, num_heads, 1, seq_length) + alibi = alibi[:, offset:num_heads_per_rank + offset, :, :] + return alibi.reshape(batch_size * num_heads_per_rank, 1, seq_length).to(dtype) + else: + return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype) + + return build_bloom_alibi_tensor diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index 030774a919d7..a0b5340f72bc 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -1,70 +1,12 @@ -import torch -import torch.distributed as dist import torch.nn as nn import colossalai.shardformer.layer as col_nn from .._utils import getattr_, setattr_ +from ..modeling.bloom import build_bloom_alibi_tensor_fn from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription -def build_bloom_alibi_tensor(self, attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor: - """ - Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it - relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value - `softmax(l+a) = softmax(l)`. Based on - https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742 - TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly. - - Args: - Returns tensor shaped (batch_size * num_heads, 1, max_seq_len) - attention_mask (`torch.Tensor`): - Token-wise attention mask, this should be of shape (batch_size, max_seq_len). - num_heads (`int`, *required*): - number of heads - dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`): - dtype of the output tensor - """ - import math - - if dist.is_initialized(): - world_size = dist.get_world_size() - num_heads = num_heads * world_size - - batch_size, seq_length = attention_mask.shape - closest_power_of_2 = 2**math.floor(math.log2(num_heads)) - base = torch.tensor(2**(-(2**-(math.log2(closest_power_of_2) - 3))), - device=attention_mask.device, - dtype=torch.float32) - powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32) - slopes = torch.pow(base, powers) - - if closest_power_of_2 != num_heads: - extra_base = torch.tensor(2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))), - device=attention_mask.device, - dtype=torch.float32) - num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2) - extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32) - slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) - - # Note: alibi will added to the attention bias that will be applied to the query, key product of attention - # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length) - # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length) - # => the query_length dimension will then be broadcasted correctly - # This is more or less identical to T5's relative position bias: - # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527 - arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :] - alibi = slopes[..., None] * arange_tensor - if dist.is_initialized(): - num_heads_per_rank = int(num_heads / dist.get_world_size()) - offset = dist.get_rank() * num_heads_per_rank - alibi = alibi.view(batch_size, num_heads, 1, seq_length) - alibi = alibi[:, offset:num_heads_per_rank + offset, :, :] - return alibi.reshape(batch_size * num_heads_per_rank, 1, seq_length).to(dtype) - else: - return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype) - - class BloomPolicy(Policy): def config_sanity_check(self): @@ -120,7 +62,9 @@ def module_policy(self): attribute_replacement={ "num_heads": self.model.config.n_head // self.shard_config.tensor_parallel_size, }, - method_replacement={"build_alibi_tensor": build_bloom_alibi_tensor}, + method_replacement={ + "build_alibi_tensor": build_bloom_alibi_tensor_fn(self.shard_config.tensor_parallel_process_group) + }, sub_module_replacement=[ SubModuleReplacementDescription( suffix="word_embeddings", From f447ca18111c2e37a2f14e7aecc98876dc7e3216 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 4 Jul 2023 14:47:53 +0800 Subject: [PATCH 396/413] [chat] removed cache file (#4155) --- applications/Chat/coati/trainer/.sft.py.swp | Bin 20480 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 applications/Chat/coati/trainer/.sft.py.swp diff --git a/applications/Chat/coati/trainer/.sft.py.swp b/applications/Chat/coati/trainer/.sft.py.swp deleted file mode 100644 index 302cf2a775338fb4fcd6b9b12c1a8e80f3969a01..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20480 zcmeHOU5q4E6)psY74VP5@FaJ89H$z3t7mp0aohBo-E~(9yMp_JSthfc>h9aq#Z*@{ zRn;uJ!vHZRh#HOZ#2YFJFB0QVSObsxWI{~Lk{BNh;)6b@tf-4W;QF2WQ+2DldsbdF zA-9unZdKiL?>+as=box_s;0Yq@0~~4UT@jK?*oo=(;FxI-#GnI=k?bd=l&oa`tc}; zsBW8IF4yNr58UI0{+tGnwG2>WBO9Z2IGI81(!sh@-T5aOh{vUW~J5 zmiR%$lV&=|yz$v>bg)&H0n5OZ7`V~dvwKBs^@Z9_cGHJWUa5htw+vVYECZGS%YbFT zGGH073|I!UFsSOzQumI2Ga|A7I&?>KiO*ng4|0C@keHBLF9S~i4+E<} z3%D7$h(y4zfBv-t%5*a`ZrO?|Z0V174>laU{aAzEycM%t>?or)eT z7)T}dwb^d(4(3OF7NniTk2X5XZoM_Fg3>&mWaCMedUQH1q-rrkF||?L=bgLq$?QqGObFGlw zsur=BXQ9tuXN* zT09OpAri(tKNK?cdNBm{(In*^F^V*UJ|D0FMx$9b)UIt9LaZCdA#DAyAEq3p115z* z{>`AYG)r^FmE&kK>WKXTk05G(MX;eL>PT*BWlX2cfHuQQt2;ICh7{ju+ zyrA2V(pmqIh0}G%TZ!m0! zuzJ@o<|mwylTIh&ahzlv_n``0Jis|OV;YhZYFtuXv)eOs7m2C0u1LIu6La(7W#S>G1>6z+*f z#61zckXD6kb!|=ILP}U%i`_kAn<|c4OtV$A62@4Zk{!!4@_f0?{%%}z zh}c@KCQ3RMT6H$ylGan^_JdwVH;sWm3A2=CG22`Ulcm-Q-*x*|y~VO2XG#`z@Wt%R zFbFxg4YI%wgL80=vP*JA`5^hSF}J*I<*`=R)DVR{MxhhdpCr_^V44oplT1EBv6jrm zLYMp9t-6In>cS1e&XpfS@1n=N8~JRPLiriptf+2;5EDXj_FAjFn-5K6S(muQS)X-u*-Q38O%Hk?52`wot8x1|xR_{*cYCBZT^NQ_SOZVw! zanc)lQRE$s>8_)Ckd-CA_rv0hN8c0j>twB9nOT-_SZH6mW=}9hM%)X1JmDWdd_pyL zRcYMl|6v-xGf3hQ^CnpkrYsnd3oM>j@#GPA`uxG5$CaF#cEEGtX}{LU_pEAE*sdDj zpz=|wzUza2Ri)H*WL1Xe-4qKm;z{b^gg(~RlBFB>Eb(}QN5MH6*NFMy$*>sco`lP> z`X{6yPY$3ucN_mSSki9eU&b}T8^sG+A39cQi1ijjdSs(oqVSidoG?T!l3S^x_z(|v zxl(UJ0sR=R1f+4{IX_E+?u71NeoE0trh5wgVA3!DOO z27ZE={t@69@Ht>7@G5lu4e%WB7*GRt0yKvuD(%lQU>UFsSOzQumVs>I)s>jiJTo%Io9T8B?Yr36Urjv+!=8{+FB~71M~<8-s$A*#%|0$B8Agnj(`}mV2&{9PSOKJJ^azp3@v(PnE z!oFvzi?B{4y2|MzYJiQ2?KN%6rMDy7Y}o~s2*;LnH)}M%TFW%rt+IY{$nyRcq1em~ zF7pyw>;PriS@Eig9mCdTo0XkxIsvt1$PMT)=8rgVmBZ54{$zwJq?-9#ATos!(2Ju* zOlloD$&4rDj-=CYOnH5*UxO5+=^C@#=FNgBHiJI1I)4ub(zU9NDNsGG2}&U?%K2M~ z+SS4Yg2J5gB)M>$NHb)e5Lw#Q_QrZG26NCeC8`7U!w>MbEwzq59?&SKNOkO z%6pM&i^`;E$toL3w-AzfzQ{w)NN-I?iWtKD+hn9b%7 From c77b3b19bec6a2bbba936acd3dbfdfeb75b92b13 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 4 Jul 2023 16:07:47 +0800 Subject: [PATCH 397/413] [format] applied code formatting on changed files in pull request 4152 (#4157) Co-authored-by: github-actions --- colossalai/device/device_mesh.py | 2 +- colossalai/shardformer/layer/_operation.py | 2 +- colossalai/shardformer/policies/autopolicy.py | 2 +- tests/test_device/test_device_mesh.py | 2 +- tests/test_shardformer/test_layer/test_layernorm.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/colossalai/device/device_mesh.py b/colossalai/device/device_mesh.py index 3e96310e1890..267c4529eb95 100644 --- a/colossalai/device/device_mesh.py +++ b/colossalai/device/device_mesh.py @@ -503,4 +503,4 @@ def all_to_all_cost(self, num_bytes, mesh_dim): num_devices = self.logical_mesh_id.shape[mesh_dim] penalty_factor = num_devices / 2.0 return (self.mesh_alpha[mesh_dim] + self.mesh_beta[mesh_dim] * - (num_devices - 1) / num_devices / num_devices * num_bytes * penalty_factor + 0.001) \ No newline at end of file + (num_devices - 1) / num_devices / num_devices * num_bytes * penalty_factor + 0.001) diff --git a/colossalai/shardformer/layer/_operation.py b/colossalai/shardformer/layer/_operation.py index c025daaeccc7..7e97bee01b33 100644 --- a/colossalai/shardformer/layer/_operation.py +++ b/colossalai/shardformer/layer/_operation.py @@ -287,4 +287,4 @@ def reduce_forward(input_, process_group): def reduce_backward(input_, process_group): - return _ReduceBackward.apply(input_, process_group) \ No newline at end of file + return _ReduceBackward.apply(input_, process_group) diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/autopolicy.py index 8051433e8d71..085e3150c697 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/autopolicy.py @@ -80,7 +80,7 @@ class PolicyLocation: PolicyLocation(file_name="opt", class_name="OPTForSequenceClassificationPolicy"), "transformers.models.opt.modeling_opt.OPTForQuestionAnswering": PolicyLocation(file_name="opt", class_name="OPTForQuestionAnsweringPolicy"), - + # Bloom "transformers.models.bloom.modeling_bloom.BloomModel": PolicyLocation(file_name="bloom", class_name="BloomModelPolicy"), diff --git a/tests/test_device/test_device_mesh.py b/tests/test_device/test_device_mesh.py index 1f8db99c9236..590d6966bff6 100644 --- a/tests/test_device/test_device_mesh.py +++ b/tests/test_device/test_device_mesh.py @@ -86,4 +86,4 @@ def test_device_mesh_from_process_group(): if __name__ == '__main__': test_device_mesh() - test_device_mesh_from_process_group() \ No newline at end of file + test_device_mesh_from_process_group() diff --git a/tests/test_shardformer/test_layer/test_layernorm.py b/tests/test_shardformer/test_layer/test_layernorm.py index 080fae034956..a117845545be 100644 --- a/tests/test_shardformer/test_layer/test_layernorm.py +++ b/tests/test_shardformer/test_layer/test_layernorm.py @@ -41,4 +41,4 @@ def test_layernorm(): if __name__ == '__main__': - test_layernorm_1d() \ No newline at end of file + test_layernorm_1d() From 2ac24040ebe3d6c140db1442fed2d33ff5ceb2ec Mon Sep 17 00:00:00 2001 From: digger yu Date: Tue, 4 Jul 2023 17:53:39 +0800 Subject: [PATCH 398/413] fix some typo colossalai/shardformer (#4160) --- colossalai/shardformer/README.md | 2 +- colossalai/shardformer/layer/loss.py | 12 ++++++------ colossalai/shardformer/policies/basepolicy.py | 2 +- colossalai/shardformer/shard/sharder.py | 2 +- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index fca401562be6..6ae32e4fbd42 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -252,7 +252,7 @@ class ModelSharder: def shard(self) -> None: """ - Shard model with parallelelism with the help of pre-processing, replace_model_class, replace_module, and post-processing. + Shard model with parallelism with the help of pre-processing, replace_model_class, replace_module, and post-processing. """ ... diff --git a/colossalai/shardformer/layer/loss.py b/colossalai/shardformer/layer/loss.py index 38a5395a0f57..7e3f6926b6d4 100644 --- a/colossalai/shardformer/layer/loss.py +++ b/colossalai/shardformer/layer/loss.py @@ -48,13 +48,13 @@ def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor, ignore_index: # [down, up) => false, other device and -100 => true delta = (global_vocab_size + world_size - 1) // world_size - down_shreshold = rank * delta - up_shreshold = down_shreshold + delta - mask = (target < down_shreshold) | (target >= up_shreshold) - masked_target = target.clone() - down_shreshold + down_threshold = rank * delta + up_threshold = down_threshold + delta + mask = (target < down_threshold) | (target >= up_threshold) + masked_target = target.clone() - down_threshold masked_target[mask] = 0 - # reshape the logist and target + # reshape the logits and target # reshape the vocab_logits to [bath_size * seq_len, vocab_size] # reshape the labels to [bath_size * seq_len] logits_2d = vocab_logits.view(-1, partition_vocab_size) @@ -79,7 +79,7 @@ def forward(ctx, vocab_logits: torch.Tensor, target: torch.Tensor, ignore_index: loss = torch.where(target == ignore_index, 0.0, torch.log(sum_exp_logits) - pred_logits) loss = torch.sum(loss).div_(torch.sum(loss != 0.0)) - # caculate the softmax + # calculate the softmax exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1)) ctx.save_for_backward(exp_logits, mask, masked_target_1d) diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 85e6d509c81b..2d347542fa7a 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -66,7 +66,7 @@ class Policy(ABC): like BertPolicy for Bert Model or OPTPolicy for OPT model. Shardformer has provided many built-in sharding policies for the mainstream models. You can use the - built-in policies by setting `policy = None`, which is already the default arguemnt for `Shardformer.optimize`. + built-in policies by setting `policy = None`, which is already the default argument for `Shardformer.optimize`. If you want to define your own policy, you can inherit from this class and overwrite the methods you want to modify. """ diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 2867a0a4fd77..201e0a08cbfe 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -73,7 +73,7 @@ def _recursive_replace_layer( layer (torch.nn.Module): The object of layer to shard origin_cls (Union[str, torch.nn.Module]): The origin layer class or a string of layer class name. attr_replacement (Dict): The attribute dict to modify - param_replacement (List[Callable]): The function list to get parameter shard information in polic + param_replacement (List[Callable]): The function list to get parameter shard information in policy sub_module_replacement (List[Callable]): The function list to get sub module shard information in policy """ if (isinstance(origin_cls, str) and origin_cls == module.__class__.__name__) or \ From 1908caad381dde68598d4d5341b0140667924f50 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Tue, 4 Jul 2023 17:54:40 +0800 Subject: [PATCH 399/413] [cli] hotfix launch command for multi-nodes (#4165) --- colossalai/cli/launcher/run.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/colossalai/cli/launcher/run.py b/colossalai/cli/launcher/run.py index daa5107caf90..5e74c2c4f5b8 100644 --- a/colossalai/cli/launcher/run.py +++ b/colossalai/cli/launcher/run.py @@ -164,9 +164,7 @@ def _arg_dict_to_list(arg_dict): ] else: # extra launch args for torch distributed launcher with torch >= 1.9 - default_torchrun_rdzv_args = dict(rdzv_backend="c10d", - rdzv_endpoint=f"{master_addr}:{master_port}", - rdzv_id="colossalai-default-job") + default_torchrun_rdzv_args = dict(master_addr=master_addr, master_port=master_port) # update rdzv arguments for key in default_torchrun_rdzv_args.keys(): From cc3cbe9f6f291af172252f097952bfe247200195 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 4 Jul 2023 18:11:46 +0800 Subject: [PATCH 400/413] [workflow] show test duration (#4159) --- .github/workflows/build_on_pr.yml | 2 +- .github/workflows/build_on_schedule.yml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build_on_pr.yml b/.github/workflows/build_on_pr.yml index 5f4e4feaa230..380c8e9f882c 100644 --- a/.github/workflows/build_on_pr.yml +++ b/.github/workflows/build_on_pr.yml @@ -208,7 +208,7 @@ jobs: - name: Execute Unit Testing run: | - CURL_CA_BUNDLE="" PYTHONPATH=$PWD pytest --testmon --testmon-cov=. tests/ + CURL_CA_BUNDLE="" PYTHONPATH=$PWD pytest --testmon --testmon-cov=. --durations=10 tests/ env: DATA: /data/scratch/cifar-10 NCCL_SHM_DISABLE: 1 diff --git a/.github/workflows/build_on_schedule.yml b/.github/workflows/build_on_schedule.yml index 0589cd617b80..03b47e6cb5b6 100644 --- a/.github/workflows/build_on_schedule.yml +++ b/.github/workflows/build_on_schedule.yml @@ -3,7 +3,7 @@ name: Build on Schedule on: schedule: # run at 00:00 of every Sunday - - cron: '0 0 * * *' + - cron: "0 0 * * *" workflow_dispatch: jobs: @@ -60,7 +60,7 @@ jobs: - name: Unit Testing if: steps.check-avai.outputs.avai == 'true' run: | - PYTHONPATH=$PWD pytest tests + PYTHONPATH=$PWD pytest --durations=0 tests env: DATA: /data/scratch/cifar-10 LD_LIBRARY_PATH: /github/home/.tensornvme/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64 From 190a6ea9c2d1c318779c68786e342daced2f8ac8 Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Tue, 4 Jul 2023 18:21:11 +0800 Subject: [PATCH 401/413] [dtensor] fixed readme file name and removed deprecated file (#4162) --- .../tensor/d_tensor/{RAEDME.md => README.md} | 0 colossalai/tensor/d_tensor/d_tensor.py | 142 ------------------ 2 files changed, 142 deletions(-) rename colossalai/tensor/d_tensor/{RAEDME.md => README.md} (100%) delete mode 100644 colossalai/tensor/d_tensor/d_tensor.py diff --git a/colossalai/tensor/d_tensor/RAEDME.md b/colossalai/tensor/d_tensor/README.md similarity index 100% rename from colossalai/tensor/d_tensor/RAEDME.md rename to colossalai/tensor/d_tensor/README.md diff --git a/colossalai/tensor/d_tensor/d_tensor.py b/colossalai/tensor/d_tensor/d_tensor.py deleted file mode 100644 index c1fe9d50a048..000000000000 --- a/colossalai/tensor/d_tensor/d_tensor.py +++ /dev/null @@ -1,142 +0,0 @@ -from typing import Optional - -import torch -from torch.utils._pytree import tree_map - -from .layout import Layout -from .layout_converter import LayoutConverter, to_global -from .sharding_spec import ShardingSpec - -layout_converter = LayoutConverter() - - -class DTensor(torch.Tensor): - - def __init__(self, local_tensor: torch.Tensor, dist_layout: Layout): - self.local_tensor = local_tensor - self.data_type = local_tensor.dtype - self.entire_shape = local_tensor.shape - self.dist_layout = dist_layout - self._apply_layout() - - @staticmethod - def __new__(cls, local_tensor, layout): - return torch.Tensor._make_subclass(cls, local_tensor, local_tensor.requires_grad) - - def __repr__(self): - return f"DTensor({self.to_global()}, {self.dist_layout})" - - def __str__(self): - return self.__repr__() - - def layout_convert(self, target_layout): - ''' - Convert the layout of the tensor from source_spec to target_spec. - ''' - self.local_tensor = layout_converter.apply(self.local_tensor, self.dist_layout, target_layout) - self.dist_layout = target_layout - - def _apply_layout(self): - ''' - Apply the layout to the local tensor during initializing process. - ''' - source_spec = construct_default_sharding_spec(self.local_tensor) - source_layout = Layout(device_mesh=self.dist_layout.device_mesh, - device_type=self.dist_layout.device_type, - sharding_spec=source_spec, - entire_shape=self.entire_shape) - self.local_tensor = layout_converter.apply(self.local_tensor, source_layout, self.dist_layout) - - @classmethod - def __torch_function__(cls, func, types, args=(), kwargs=None): - if kwargs is None: - kwargs = {} - - def filter_arg(arg): - if isinstance(arg, DTensor): - return arg.local_tensor - else: - return arg - - args = tree_map(filter_arg, args) - kwargs = tree_map(filter_arg, kwargs) - # if we want to convert the result into DTensor, we need to infer the layout of result from the layout of input tensors - # and op type. - - return func(*args, **kwargs) - - @property - def device_mesh(self): - ''' - Return the device mesh of the tensor. - ''' - return self.dist_layout.device_mesh - - @property - def sharding_spec(self): - ''' - Return the sharding specification of the tensor. - ''' - return self.dist_layout.sharding_spec - - def to(self, *args, **kwargs): - ''' - Move the tensor to a new device or convert the tensor to a new dtype. - ''' - self.local_tensor = self.local_tensor.to(*args, **kwargs) - self.data_type = self.local_tensor.dtype - self.dist_layout.device_type = self.local_tensor.device - # TODO: update the device mesh process groups or we should just cache - # both the cpu process groups and the cuda process groups? - return self - - def to_local(self): - ''' - Return the local tensor in this rank. - ''' - return self.local_tensor - - def to_global(self): - ''' - Recover the global tensor from the distributed tensor. - - Note: This function will all_gather the local tensor to the global tensor and it - will not change the layout of the DTensor. This function is mainly used for debugging or - check the correctness of the distributed tensor. - ''' - return to_global(self.local_tensor, self.dist_layout) - - -def distribute_tensor(local_tensor: torch.Tensor, dist_layout: Layout) -> DTensor: - ''' - Distribute the local tensor to the distributed tensor according to the dist_layout specified. - - Args: - local_tensor: tensor to be distributed. - dist_layout: the layout specification of the distributed tensor. - - Returns: - A 'DTensor' object. - ''' - return DTensor(local_tensor, dist_layout) - - -def distribute_module(module: torch.nn.Module, partition_fn: Optional[callable] = None) -> torch.nn.Module: - ''' - This function converts all the parameters in the module to DTensor(DParam). - - Note: This function is subject to future change as the DParam has not been implemented yet. - ''' - for name, param in module.named_parameters(): - if param is not None and not isinstance(param, DTensor): - # TODO: we could convert the parameter to DParam here, - # the type of the parameter could be an optional argument. - setattr(module, name, torch.nn.Parameter(partition_fn(name, param.data))) - return module - - -def construct_default_sharding_spec(tensor: torch.Tensor,) -> ShardingSpec: - ''' - Construct the default sharding specification for the tensor. - ''' - return ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={}) From fee32a3b785327d63ff9cdaea4451b4cfe071d2a Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Fri, 7 Jul 2023 15:31:51 +0800 Subject: [PATCH 402/413] [docker] added ssh and rdma support for docker (#4192) --- docker/Dockerfile | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/docker/Dockerfile b/docker/Dockerfile index 2c7bafd9604c..97399c939376 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -5,6 +5,18 @@ LABEL org.opencontainers.image.source = "https://github.com/hpcaitech/ColossalAI LABEL org.opencontainers.image.licenses = "Apache License 2.0" LABEL org.opencontainers.image.base.name = "docker.io/library/hpcaitech/cuda-conda:11.3" +# enable passwordless ssh +RUN mkdir ~/.ssh && \ + printf "Host * \n ForwardAgent yes\nHost *\n StrictHostKeyChecking no" > ~/.ssh/config && \ + ssh-keygen -t rsa -N "" -f ~/.ssh/id_rsa && \ + cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys + +# enable RDMA support +RUN apt-get update && \ + apt-get install -y infiniband-diags perftest ibverbs-providers libibumad3 libibverbs1 libnl-3-200 libnl-route-3-200 librdmacm1 && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + # install torch RUN conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch From 58913441a1bd5df3848a4766e2f75a8ae0942121 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Fri, 7 Jul 2023 16:33:06 +0800 Subject: [PATCH 403/413] Next commit [checkpointio] Unsharded Optimizer Checkpoint for Gemini Plugin (#4141) * [checkpointio] unsharded optimizer checkpoint for Gemini plugin * [checkpointio] unsharded optimizer checkpoint for Gemini using all_gather --- colossalai/booster/plugin/gemini_plugin.py | 75 ++-- .../checkpoint_io/checkpoint_io_base.py | 2 + .../checkpoint_io/general_checkpoint_io.py | 14 +- colossalai/checkpoint_io/utils.py | 24 +- colossalai/interface/optimizer.py | 6 + colossalai/testing/comparison.py | 64 +++- colossalai/zero/gemini/gemini_optimizer.py | 340 +++++++++++++++++- .../test_gemini_checkpoint_io.py | 69 ++-- .../test_gemini_torch_compability.py | 171 +++++++++ 9 files changed, 683 insertions(+), 82 deletions(-) create mode 100644 tests/test_checkpoint_io/test_gemini_torch_compability.py diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 1173589fcd49..6191f271c318 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -33,44 +33,40 @@ def __init__(self) -> None: super().__init__() self.coordinator = DistCoordinator() - def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool = True): - """ - Load model from checkpoint with automatic unwrapping. - """ - # the model should be unwrapped in self.load_model via ModelWrapper.unwrap - return super().load_unsharded_model(model, checkpoint, strict=strict) - def save_unsharded_model(self, model: GeminiDDP, checkpoint: str, gather_dtensor: bool, use_safetensors: bool): """ - Save model to checkpoint but only on master process. + Save sharded model to checkpoint but only on master process. + The model should be unwrapped in self.load_model via ModelWrapper.unwrap. + As there is communication when getting state dict, this must be called on all processes. """ - # the model should be unwrapped in self.load_model via ModelWrapper.unwrap - # as there is communication when get state dict, this must be called on all processes state_dict = model.state_dict(only_rank_0=True) if self.coordinator.is_master(): save_state_dict(state_dict, checkpoint, use_safetensors) - def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): + def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool = True): """ - Save optimizer to checkpoint but only on master process. + Load model from checkpoint with automatic unwrapping. + The model should be unwrapped in self.load_model via ModelWrapper.unwrap. """ - # TODO(ver217): optimizer state dict is sharded - warnings.warn('GeminiPlugin does not support save full optimizer checkpoint now. Save it on every process.') - checkpoint = f'{checkpoint}.rank{self.coordinator.rank}' - super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor) - - def load_optimizer(self, optimizer: Optimizer, checkpoint: str): - warnings.warn( - 'GeminiPlugin can only load optimizer checkpoint saved by itself with the same number of processes.') - checkpoint = f'{checkpoint}.rank{self.coordinator.rank}' - super().load_optimizer(optimizer, checkpoint) - - def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + super().load_unsharded_model(model, checkpoint, strict=strict) + + def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool): """ - Save model to checkpoint but only on master process. + Save unsharded optimizer state dict to checkpoint. + After calling optimizer.state_dict(), the complete optimizer states will be collected on master rank. + As there is communication when getting state dict, this must be called on all processes. + The saving process will only be executed by master rank. """ + state_dict = optimizer.state_dict() if self.coordinator.is_master(): - super().save_lr_scheduler(lr_scheduler, checkpoint) + save_state_dict(state_dict, checkpoint, use_safetensors=False) + + def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str): + """ + Loading unsharded optimizer from checkpoint file. + For each process, only loading optimizer states of parameters it controls. + """ + super().load_unsharded_optimizer(optimizer, checkpoint) def save_sharded_model(self, model: GeminiDDP, @@ -82,6 +78,12 @@ def save_sharded_model(self, """ Save sharded model """ + if os.path.isfile(checkpoint_path): + logging.error(f"Provided path ({checkpoint_path}) should be a directory, not a file") + return + + Path(checkpoint_path).mkdir(parents=True, exist_ok=True) + state_dict_shard = model.state_dict_shard(max_shard_size=max_shard_size, only_rank_0=True, dtype=torch.float32) weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors) total_size = 0 @@ -117,6 +119,23 @@ def load_sharded_model(self, """ return super().load_sharded_model(model, checkpoint_index_file, strict, use_safetensors, load_sub_module=False) + def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, gather_dtensor: bool, prefix: str, + size_per_shard: int): + """ + Save sharded optimizer state dict to checkpoint folder. + As there is communication when getting state dict, this must be called on all processes. + """ + Path(checkpoint).mkdir(parents=True, exist_ok=True) + super().save_sharded_optimizer(optimizer, checkpoint, gather_dtensor, prefix, size_per_shard) + + def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint_index_file: Path, prefix: str): + """ + Loading sharded optimizer from checkpoint folder, with index file given. + For each process, only loading optimizer states of parameters it controls. + """ + # TODO(Baizhou): To be implemented. + pass + class GeminiModel(ModelWrapper): @@ -193,7 +212,7 @@ class GeminiPlugin(DPPluginBase): which will be used when using hybrid CPU optimizer. This argument is meaningless when `placement_policy` of `GeminiManager` is not "auto". Defaults to 0.0. - initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32. + initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**16. min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1. growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2. backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5. @@ -219,7 +238,7 @@ def __init__( min_chunk_size_m: float = 32, memstats: Optional[MemStats] = None, gpu_margin_mem_ratio: float = 0.0, - initial_scale: float = 2**32, + initial_scale: float = 2**16, min_scale: float = 1, growth_factor: float = 2, backoff_factor: float = 0.5, diff --git a/colossalai/checkpoint_io/checkpoint_io_base.py b/colossalai/checkpoint_io/checkpoint_io_base.py index 8ff9d87c288e..baff24e1cb25 100644 --- a/colossalai/checkpoint_io/checkpoint_io_base.py +++ b/colossalai/checkpoint_io/checkpoint_io_base.py @@ -152,6 +152,7 @@ def load_optimizer(self, optimizer: Optimizer, checkpoint: str, prefix: str = No names to compose the keys in state_dict. Defaults to None. size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024. """ + index_file_exists, index_file_path = has_index_file(checkpoint) if Path(checkpoint).is_dir() and not index_file_exists: @@ -186,6 +187,7 @@ def save_optimizer(self, prefix (str): prefix for the optimizer checkpoint when shard = True. Default: None. size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard is set to True. """ + if shard: self.save_sharded_optimizer(optimizer, checkpoint, gather_dtensor, prefix, size_per_shard) else: diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index 26cafcada2c5..e1d9066948dd 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -28,6 +28,7 @@ shard_model_checkpoint, shard_optimizer_checkpoint, sharded_optimizer_loading_epilogue, + unwrap_optimizer, ) __all__ = ['GeneralCheckpointIO'] @@ -59,7 +60,7 @@ def load_sharded_optimizer(self, optimizer: Optimizer, index_file_path: str, pre # If optimizer is wrapped, unwrap it. if isinstance(optimizer, OptimizerWrapper): - optimizer = optimizer.optim + optimizer = unwrap_optimizer(optimizer) # Read checkpoint index file. ckpt_index_file = CheckpointIndexFile.from_file(index_file_path) @@ -96,6 +97,11 @@ def save_sharded_optimizer( - A group file (pytorch_optim_group.bin) recording information of param_groups - Multiple files (pytorch_optim-000XX.bin) that store state tensors of optimizer in a sharding way """ + + # If optimizer is wrapped, unwrap it. + if isinstance(optimizer, OptimizerWrapper): + optimizer = unwrap_optimizer(optimizer) + if os.path.isfile(checkpoint): logging.error(f"Provided path ({checkpoint}) should be a directory, not a file") return @@ -121,9 +127,8 @@ def save_sharded_optimizer( shard, current_size = shard_pair shard_file = get_shard_filename(states_name, idx) total_size = total_size + current_size - for param_id in shard.keys(): - index_file.append_weight_map(str(param_id), shard_file) - + for key in shard.keys(): + index_file.append_weight_map(key, shard_file) checkpoint_file_path = os.path.join(checkpoint, shard_file) save_state_dict(shard, checkpoint_file_path, use_safetensors=False) @@ -177,7 +182,6 @@ def save_sharded_model(self, total_size = total_size + shard_pair[1] for key in shard.keys(): index_file.append_weight_map(key, shard_file) - checkpoint_file_path = os.path.join(checkpoint_path, shard_file) save_state_dict(shard, checkpoint_file_path, use_safetensors) diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 485577b9650c..19e28c3f7068 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -10,6 +10,8 @@ import torch.nn as nn from torch.optim import Optimizer +from colossalai.interface import OptimizerWrapper +from colossalai.nn.optimizer import ColossalaiOptimizer from colossalai.tensor.d_tensor import is_distributed_tensor SAFE_WEIGHTS_NAME = "model.safetensors" @@ -88,6 +90,19 @@ def is_safetensor_checkpoint(checkpoint_file_path: str) -> bool: # ====================================== # Helper functions for saving shard file # ====================================== +def unwrap_optimizer(optimizer: OptimizerWrapper): + ''' + Unwrap a wrapped optimizer. + This method should be used before saving/loading it to/from sharded checkpoints. + ''' + + # TODO(Baizhou): ColossalaiOptimizer will be replaced with OptimizerWrapper in the future + unwrapped_optim = optimizer.optim + if isinstance(unwrapped_optim, ColossalaiOptimizer): + unwrapped_optim = unwrapped_optim.optim + return unwrapped_optim + + def shard_model_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> Iterator[Tuple[OrderedDict, int]]: """ Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a @@ -103,7 +118,7 @@ def shard_model_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) weight_size = calculate_tensor_size(weight) # If this weight is going to tip up over the maximal size, we split. - if current_block_size + weight_size > max_shard_size: + if current_block_size + weight_size > max_shard_size and current_block_size > 0: ret_block = current_block ret_block_size = current_block_size current_block = {} @@ -140,9 +155,10 @@ def shard_optimizer_checkpoint(state_dict: dict, max_shard_size: int = 1024) -> isDTensor = False for state_tensor in state.values(): - # When state_tensor is None (e.g., a SGD optimizer with momentum set to 0), + # When state_tensor is not of Tensor class, + # e.g., a SGD optimizer with momentum set to 0 can have None as state # The calculation of tensor size should be skipped to avoid error. - if state_tensor is None: + if not isinstance(state_tensor, torch.Tensor): continue # If the states are stored as DTensors, mark isDTensor as true. @@ -152,7 +168,7 @@ def shard_optimizer_checkpoint(state_dict: dict, max_shard_size: int = 1024) -> if not isDTensor: - if current_block_size + state_size > max_shard_size: + if current_block_size + state_size > max_shard_size and current_block_size > 0: ret_block = current_block ret_block_size = current_block_size current_block = {} diff --git a/colossalai/interface/optimizer.py b/colossalai/interface/optimizer.py index dd9acab17584..0eaf2e1ef8ba 100644 --- a/colossalai/interface/optimizer.py +++ b/colossalai/interface/optimizer.py @@ -119,3 +119,9 @@ def unscale_grad(self): """ raise NotImplementedError( "The method unscale_grad is only available for optimizers with mixed precision training") + + def unwrap(self): + """ + Unwrap the optimizer for checkpoint saving/loading. + """ + return self.optim diff --git a/colossalai/testing/comparison.py b/colossalai/testing/comparison.py index 5cbfb936b144..8d9ec8ab5f35 100644 --- a/colossalai/testing/comparison.py +++ b/colossalai/testing/comparison.py @@ -5,6 +5,7 @@ from torch import Tensor from torch.distributed import ProcessGroup from torch.testing import assert_close +from torch.utils._pytree import tree_flatten def assert_equal(a: Tensor, b: Tensor): @@ -16,7 +17,12 @@ def assert_not_equal(a: Tensor, b: Tensor): def assert_close_loose(a: Tensor, b: Tensor, rtol: float = 1e-3, atol: float = 1e-3): - assert_close(a, b, rtol=rtol, atol=atol) + assert_close(a, + b, + rtol=rtol, + atol=atol, + msg=f"Tensor not close, shape: {a.shape} vs {b.shape}, \ + dtype: {a.dtype} vs {b.dtype}") def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None): @@ -33,25 +39,51 @@ def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None): def check_state_dict_equal(d1: OrderedDict, d2: OrderedDict, ignore_device: bool = True): - for k, v in d1.items(): - if isinstance(v, dict): - check_state_dict_equal(v, d2[k]) - elif isinstance(v, list): - for i in range(len(v)): - if isinstance(v[i], torch.Tensor): + assert len(list(d1.keys())) == len(list(d2.keys())), \ + f"Number of keys unequal: {len(list(d1.keys()))} vs {len(list(d2.keys()))}" + for k, v1 in d1.items(): + assert k in d2 + v2 = d2[k] + if isinstance(v1, dict): + assert isinstance(v2, dict) + check_state_dict_equal(v1, v2, ignore_device) + elif isinstance(v1, list): + assert isinstance(v2, list) + for v1_i, v2_i in zip(v1, v2): + if isinstance(v1_i, torch.Tensor): + assert isinstance(v2_i, torch.Tensor) if not ignore_device: - v[i] = v[i].to("cpu") - d2[k][i] = d2[k][i].to("cpu") - assert torch.equal(v[i], d2[k][i]) + v1_i = v1_i.to("cpu") + v2_i = v2_i.to("cpu") + assert_close_loose(v1_i, v2_i) + elif isinstance(v1_i, dict): + assert isinstance(v2_i, dict) + check_state_dict_equal(v1_i, v2_i, ignore_device) else: - assert v[i] == d2[k][i] - elif isinstance(v, torch.Tensor): + assert v1_i == v2_i, f"{v1_i} not equals to {v2_i}" + elif isinstance(v1, torch.Tensor): + assert isinstance(v2, torch.Tensor) if not ignore_device: - v = v.to("cpu") - d2[k] = d2[k].to("cpu") - assert torch.equal(v, d2[k]) + v1 = v1.to("cpu") + v2 = v2.to("cpu") + assert_close_loose(v1, v2) else: - assert v == d2[k] + assert v1 == v2, f"{v1} not equals to {v2}" + + +def check_state_dict_equal_pytree(d1: OrderedDict, d2: OrderedDict, ignore_device: bool = True): + flat_d1, _ = tree_flatten(d1) + flat_d2, _ = tree_flatten(d2) + assert len(flat_d1) == len(flat_d2) + for v1, v2 in zip(flat_d1, flat_d2): + if isinstance(v1, torch.Tensor): + assert isinstance(v2, torch.Tensor) + if not ignore_device: + v1 = v1.to("cpu") + v2 = v2.to("cpu") + assert_close_loose(v1, v2) + else: + assert v1 == v2, f"{v1} not equals to {v2}" def assert_hf_output_close(out1: Any, diff --git a/colossalai/zero/gemini/gemini_optimizer.py b/colossalai/zero/gemini/gemini_optimizer.py index 267deb1e8699..99aff6f1c527 100644 --- a/colossalai/zero/gemini/gemini_optimizer.py +++ b/colossalai/zero/gemini/gemini_optimizer.py @@ -1,4 +1,6 @@ # this code is inspired by the DeepSpeed library and implemented with our own design from scratch +import copy +import gc import math import warnings from typing import Any, Dict, Set, Tuple @@ -101,6 +103,11 @@ def __init__(self, self.clipping_flag = clipping_norm > 0.0 self.max_norm = clipping_norm self.verbose = verbose + self.param_groups_backup = list() + + # Mapping from integer id to real/fake param tensor, used for checkpointing. + self.id_to_real_params: Dict[int, Parameter] = dict() + self.id_to_fake_params: Dict[int, Parameter] = dict() if self.clipping_flag: assert norm_type == 2.0, "ZeroOptimizer only supports L2 norm now" @@ -301,25 +308,352 @@ def get_range_pair(local_chunk: Chunk, local_param: Parameter): end = min(local_chunk.shard_size, param_info.end - local_chunk.shard_begin) return begin, end + param_id = -1 for group in self.optim.param_groups: fake_params_list = list() - + group_backup = {k: v for k, v in group.items() if k != 'params'} + group_ids = [] for param in group['params']: + + # Record the mapping of id to current param. + param_id += 1 + self.id_to_real_params[param_id] = param + group_ids.append(param_id) + + # If current param is controlled by current process, add it to fake_param. if is_ddp_ignored(param): continue chunk16 = self.chunk_manager.get_chunk(param) range_pair = get_range_pair(chunk16, param) if range_pair[0] >= range_pair[1]: continue - grad_device = self.module.grads_device[param] fake_param = torch.nn.Parameter(torch.empty([0], device=grad_device)) self.param_to_chunk32[fake_param] = chunk16.paired_chunk self.param_to_range[fake_param] = range_pair - + self.id_to_fake_params[param_id] = fake_param fake_params_list.append(fake_param) + # Update self.optim.param_groups as well as backup group. group['params'] = fake_params_list + group_backup['params'] = group_ids + self.param_groups_backup.append(group_backup) + + def get_offsets(self, param_id: int) -> tuple: + ''' + Args: + param_id(int): The id of parameter. + + Returns: + chunk_offset(int): Offset of parameter inside the chunk. + shard_offset(int): Offset of its optimizer state shard + relative to the whole optimizer state. + shard_size(int): Length of parameter shard owned by current process. + ''' + + if param_id not in self.id_to_fake_params: + return -1, -1, -1 + fake_param = self.id_to_fake_params[param_id] + chunk = self.param_to_chunk32[fake_param].paired_chunk + param = self.id_to_real_params[param_id] + param_info = chunk.tensors_info[param] + + begin_in_chunk, end_in_chunk = self.param_to_range[fake_param] + chunk_offset = begin_in_chunk + shard_offset = begin_in_chunk + chunk.shard_begin - param_info.offset + shard_size = end_in_chunk - begin_in_chunk + assert chunk_offset >= 0 and shard_offset >= 0 + + return chunk_offset, shard_offset, shard_size + + def collect_states(self, param_id: int, only_rank_0: bool = True) -> dict: + """ + Args: + param_id (int): id of the parameter whose state is to be gathered at master rank. + only_rank_0(bool): if True, states will be collected only on master rank, otherwise collected on every rank. + + Returns: + collected_states(dict): the gathered optimzier state of parameter with given id + if this method is called by master rank, otherwise an empty dict. + + This method can work only when called by all processes simultaneously. + """ + + # Get param & chunk & process group. + param = self.id_to_real_params[param_id] + fake_param = self.id_to_fake_params.get(param_id, None) + chunk = self.chunk_manager.get_chunk(param) + process_group = chunk.torch_pg + rank = dist.get_rank(process_group) + master_rank = 0 + collected_states = {} + + # Fetch names of states through all_gather. + local_state_names = None + if fake_param is not None: + local_state_names = list(self.optim.state[fake_param].keys()) + gathered_state_names = [None for _ in range(dist.get_world_size(process_group))] + dist.barrier() + dist.all_gather_object(gathered_state_names, local_state_names) + state_names = None + for names in gathered_state_names: + if names is not None: + # Assume different devices share the same set of state names if they have. + state_names = copy.deepcopy(names) + break + + # Directly return if this parameter doesn't have optimizer states. + # e.g. parameter freezed/layer dropped + if state_names is None: + return collected_states + + # Boolean variable is_collector indicates that whether the current rank + # needs to gather the whole optimizer states. + # Only master rank is collector when only_rank_0 is True. + # Every rank is collector when only_rank_0 is False. + is_collector = (rank == master_rank) or (not only_rank_0) + + # If the chunk is kept gathered, + # the parameteres are treated the same as that of those in strict DDP during training. + # So states can be directly fetched from current device. + if chunk.keep_gathered: + assert param_id in self.id_to_fake_params + if is_collector: + states = self.optim.state[fake_param] + for state_name in state_names: + if state_name == 'step': + # To keep aligned with pytorch, state 'step' is stored as a pytorch tensor with type float32. + collected_states[state_name] = torch.tensor(states['step'], + dtype=torch.float32, + requires_grad=False).cpu() + else: + collected_states[state_name] = states[state_name].detach().clone().to(torch.float32).cpu() + return collected_states + + # Check whether the param with given id is managed by current process. + own_param = param_id in self.id_to_fake_params + + # Collector gets prepared for state collecting. + if is_collector: + for state_name in state_names: + if state_name == 'step': + # To keep aligned with pytorch, state 'step' is stored as a pytorch tensor with type float32. + collected_states[state_name] = torch.tensor(0.0, dtype=torch.float32, requires_grad=False).cpu() + else: + collected_states[state_name] = torch.zeros(param.numel(), dtype=torch.float32, + requires_grad=False).cpu() + + # Materials for gathering, including compacted state tensors, and the offset of shard inside each state. + compacted_states = self.pack_optimizer_states_to_tensor(param_id, state_names) if own_param else None + _, shard_offset, shard_size = self.get_offsets(param_id) + + # Collectors gather state shards through all_gathering. + gathered_state_shards = [None for _ in range(dist.get_world_size(process_group))] + + dist.barrier() + dist.all_gather_object(gathered_state_shards, [compacted_states, shard_offset, shard_size]) + + if is_collector: + for state_shard in gathered_state_shards: + compacted_states = state_shard[0] + shard_offset = state_shard[1] + shard_size = state_shard[2] + if compacted_states is None: + continue + self.load_from_compacted_states(compacted_states, collected_states, state_names, shard_offset, + shard_size) + + # Clean gathered states + for state_shard in gathered_state_shards: + del state_shard[0] + gc.collect() + + # Reshape tensors + if is_collector: + for state_name, state_tensor in collected_states.items(): + if state_tensor.numel() == param.numel(): + collected_states[state_name] = torch.reshape(state_tensor, param.shape) + + return collected_states + + def pack_optimizer_states_to_tensor(self, + param_id: int, + state_names: list, + device: torch.device = torch.device('cuda'), + dtype: torch.dtype = torch.float32) -> torch.Tensor: + ''' + With param id given, pack its optimizer states into a compact tensor and return. + ''' + if param_id not in self.id_to_fake_params: + return None + + fake_param = self.id_to_fake_params[param_id] + param_range = self.param_to_range[fake_param] + states = self.optim.state[fake_param] + shard_size = param_range[1] - param_range[0] + compacted_size = 0 + for name in state_names: + if name == 'step': + compacted_size += 1 + else: + compacted_size += shard_size + compacted_states = torch.zeros(compacted_size, dtype=dtype, device=device, requires_grad=False) + + next_state_offset = 0 + for state_name, state_tensor in states.items(): + # State 'step' needs special operation. + if state_name == 'step': + if isinstance(state_tensor, torch.Tensor): + compacted_states[next_state_offset] = state_tensor[0].item() + else: + assert isinstance(state_tensor, int) + compacted_states[next_state_offset] = state_tensor + next_state_offset += 1 + else: + assert state_tensor.numel() == shard_size + compacted_states[next_state_offset:next_state_offset + shard_size].copy_(state_tensor) + next_state_offset += shard_size + + return compacted_states + + def load_from_compacted_states(self, compacted_states: torch.Tensor, collected_states: dict, state_names: list, + shard_start: int, shard_size: int): + ''' + Given a tensor carrying compacted optimizer states, + update these states to collected_states. + ''' + shard_end = shard_start + shard_size + next_state_offset = 0 + + for state_name in state_names: + if state_name == 'step': + collected_states['step'].data = torch.tensor(compacted_states[next_state_offset].item(), + dtype=torch.float32, + requires_grad=False).cpu() + next_state_offset += 1 + else: + target_segment = collected_states[state_name][shard_start:shard_end] + target_segment.copy_(compacted_states[next_state_offset:next_state_offset + shard_size]) + next_state_offset += shard_size + + def state_dict(self, only_rank_0: bool = True) -> dict: + """ + Args: + only_rank_0 (bool): a boolean value indicating whether the state_dict is collected + only on rank 0, dafault to True. + + Returns: + The complete state of the optimizer as a :class:`dict`. + It contains two entries: + + * state - a dict holding current optimization state. Its content + differs between optimizer classes. + * param_groups - a list containing all parameter groups where each + parameter group is a dict. + + Warning: This method will gather and return the whole optimizer state_dict, + so it should be called only when memory resources are abundant. + """ + state_dict = {} + state_dict['param_groups'] = copy.deepcopy(self.param_groups_backup) + + torch_special_hyperparameters = { + 'amsgrad': False, + 'maximize': False, + 'foreach': None, + 'capturable': False, + 'differentiable': False, + 'fused': False + } + + for group in state_dict['param_groups']: + for k, v in torch_special_hyperparameters.items(): + if k not in group: + group[k] = v + + # Collect optimizer states. + state_dict['state'] = dict() + for param_id in self.id_to_real_params.keys(): + dist.barrier() + state_dict['state'][param_id] = self.collect_states(param_id=param_id, only_rank_0=only_rank_0) + return state_dict + + def load_param_groups(self, saved_param_groups: list): + """ + Load saved_param_groups into + self.param_groups and self.param_groups_backup + """ + self.param_groups_backup = copy.deepcopy(saved_param_groups) + + # discard the older param_groups + self.optim.param_groups = [] + + for group in saved_param_groups: + fake_params_list = list() + updated_group = {k: v for k, v in group.items() if k != 'params'} + for param_id in group['params']: + if param_id not in self.id_to_fake_params: + continue + fake_param = self.id_to_fake_params[param_id] + fake_params_list.append(fake_param) + updated_group['params'] = fake_params_list + self.optim.param_groups.append(updated_group) + + def load_single_param_states(self, param_id: int, saved_states: dict): + """ + Load saved optimizer states into parameter with given id. + """ + + def cast(param, state_range, value, key=None): + """ + Make a copy of the needed segment of value and cast it to device of param. + """ + assert isinstance(value, torch.Tensor) + ret_val = value + if (key == "step"): + assert value.numel() == 1 + ret_val = int(value.item()) + else: + state_start, state_end = state_range + ret_val = torch.zeros(state_end - state_start, + dtype=torch.float32, + device=param.device, + requires_grad=False) + ret_val.copy_(value.flatten()[state_start:state_end]) + return ret_val + + assert param_id in self.id_to_fake_params + fake_param = self.id_to_fake_params[param_id] + _, state_offset, param_size = self.get_offsets(param_id) + state_range = (state_offset, state_offset + param_size) + + # Copy states assigned to param (and cast tensors to appropriate types). + updated_states = dict() + for k, v in saved_states.items(): + updated_states[k] = cast(fake_param, state_range, v, k) + del v # clean loaded states + self.optim.state[fake_param].update(updated_states) + + def load_state_dict(self, state_dict: dict): + """Loads optimizer state from whole optimizer state_dict. + During loading, filter out the part of states not considered by current process. + + Args: + state_dict (dict): optimizer state. Should be an object returned + from a call to :meth:`state_dict`. + """ + assert 'param_groups' in state_dict + self.load_param_groups(state_dict['param_groups']) + + state = state_dict['state'] + + for param_id, param_states in state.items(): + if param_id in self.id_to_fake_params: + self.load_single_param_states(param_id, param_states) + + # Epilogue for pytorch optimizer. + self.optim._hook_for_profile() # To support multiprocessing pickle/unpickle. + self.optim.defaults.setdefault('differentiable', False) class GeminiAdamOptimizer(ZeroOptimizer): diff --git a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py index 602cf468c944..0235ff2e2c81 100644 --- a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py @@ -8,15 +8,18 @@ import colossalai from colossalai.booster import Booster from colossalai.booster.plugin import GeminiPlugin -from colossalai.booster.plugin.gemini_plugin import GeminiCheckpointIO from colossalai.nn.optimizer import HybridAdam -from colossalai.testing import check_state_dict_equal, parameterize, rerun_if_address_is_in_use, spawn -from colossalai.zero import ZeroDDP -from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration -from colossalai.zero.gemini.gemini_mgr import GeminiManager +from colossalai.testing import ( + check_state_dict_equal, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) from tests.kit.model_zoo import model_zoo +@clear_cache_before_run() @parameterize('placement_policy', ['cuda', 'cpu']) @parameterize('model_name', ['transformers_bert_for_sequence_classification']) @parameterize('use_safetensors', [False, True]) @@ -29,33 +32,33 @@ def exam_state_dict_with_origin(placement_policy, model_name, use_safetensors: b pretrained_path = os.path.join(tempdir, 'pretrained') bert_model.config.save_pretrained(save_directory=pretrained_path) - # TODO(ver217): use boost api - config_dict, *_ = search_chunk_configuration(bert_model, search_range_m=1, search_interval=100) - chunk_manager = ChunkManager(config_dict) - gemini_manager = GeminiManager(placement_policy, chunk_manager) - bert_model = ZeroDDP(bert_model, gemini_manager) - bert_model.train() - - ckpt_io = GeminiCheckpointIO() + plugin = GeminiPlugin(placement_policy=placement_policy) + booster = Booster(plugin=plugin) + bert_model, _, _, _, _ = booster.boost(bert_model) model_size = sum(p.numel() * p.element_size() for p in bert_model.parameters()) / 1024**2 - ckpt_io.save_model(bert_model, (pretrained_path), + + booster.save_model(bert_model, + pretrained_path, True, True, '', (model_size / 3), use_safetensors=use_safetensors) dist.barrier() + new_bert_model = BertForSequenceClassification.from_pretrained(pretrained_path) - check_state_dict_equal(bert_model.state_dict(only_rank_0=False, dtype=torch.float32), + check_state_dict_equal(bert_model.unwrap().state_dict(only_rank_0=False, dtype=torch.float32), new_bert_model.state_dict(), False) +@clear_cache_before_run() @parameterize('placement_policy', ['cuda', 'cpu']) -@parameterize('shard', [True, False]) +@parameterize('shard', [False]) @parameterize('model_name', ['transformers_gpt']) -def exam_state_dict(placement_policy, shard: bool, model_name: str): +@parameterize('size_per_shard', [32]) +def exam_state_dict(placement_policy, shard: bool, model_name: str, size_per_shard: int): (model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values())) criterion = lambda x: x.mean() - plugin = GeminiPlugin(placement_policy=placement_policy) + plugin = GeminiPlugin(placement_policy=placement_policy, precision="fp16", initial_scale=(2**14)) booster = Booster(plugin=plugin) model = model_fn() @@ -78,18 +81,32 @@ def exam_state_dict(placement_policy, shard: bool, model_name: str): with shared_tempdir() as tempdir: model_ckpt_path = f"{tempdir}/model" optimizer_ckpt_path = f"{tempdir}/optimizer" - booster.save_model(model, model_ckpt_path) - if not shard: - # TODO(ver217): optimizer checkpointing is not supported for sharded checkpoint - booster.save_optimizer(optimizer, optimizer_ckpt_path) + booster.save_model(model, model_ckpt_path, shard=shard, size_per_shard=size_per_shard) + + booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard, size_per_shard=size_per_shard) dist.barrier() booster.load_model(new_model, model_ckpt_path) check_state_dict_equal(model.unwrap().state_dict(only_rank_0=False), new_model.unwrap().state_dict(only_rank_0=False), False) - if not shard: - booster.load_optimizer(new_optimizer, optimizer_ckpt_path) - check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False) + + booster.load_optimizer(new_optimizer, optimizer_ckpt_path) + check_state_dict_equal(optimizer.unwrap().state_dict(only_rank_0=False), + new_optimizer.unwrap().state_dict(only_rank_0=False), False) + + # Check the new model/optimizer can successfully run. + data = data_gen_fn() + data = { + k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items() + } + output = new_model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + booster.backward(loss, new_optimizer) + new_optimizer.step() + booster.save_model(new_model, model_ckpt_path, shard=shard) + booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard) def run_dist(rank, world_size, port): @@ -100,7 +117,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist -@pytest.mark.parametrize('world_size', [2]) +@pytest.mark.parametrize('world_size', [1, 2]) @rerun_if_address_is_in_use() def test_gemini_ckpIO(world_size): spawn(run_dist, world_size) diff --git a/tests/test_checkpoint_io/test_gemini_torch_compability.py b/tests/test_checkpoint_io/test_gemini_torch_compability.py new file mode 100644 index 000000000000..b34e3e3a1310 --- /dev/null +++ b/tests/test_checkpoint_io/test_gemini_torch_compability.py @@ -0,0 +1,171 @@ +import pytest +import torch +import torch.distributed as dist +from torch.optim import Adam +from utils import shared_tempdir + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import GeminiPlugin, TorchDDPPlugin +from colossalai.nn.optimizer import HybridAdam +from colossalai.testing import ( + check_state_dict_equal, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo + + +@clear_cache_before_run() +@parameterize('shard', [False]) +@parameterize('model_name', ['transformers_gpt']) +def exam_torch_load_from_gemini(shard: bool, model_name: str): + + (model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values())) + criterion = lambda x: x.mean() + plugin = GeminiPlugin(precision="fp16", initial_scale=(2**14)) + booster = Booster(plugin=plugin) + + model = model_fn() + optimizer = HybridAdam(model.parameters(), lr=0.001) + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + data = data_gen_fn() + data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()} + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + + booster.backward(loss, optimizer) + optimizer.step() + + with shared_tempdir() as tempdir: + model_ckpt_path = f"{tempdir}/model" + optimizer_ckpt_path = f"{tempdir}/optimizer" + + booster.save_model(model, model_ckpt_path, shard=shard) + booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard) + dist.barrier() + + new_model = model_fn() + new_optimizer = Adam(new_model.parameters(), lr=0.001) + new_plugin = TorchDDPPlugin() + new_booster = Booster(plugin=new_plugin) + new_model, new_optimizer, criterion, _, _ = new_booster.boost(new_model, new_optimizer, criterion) + + # Loading HybridAdam states to torch.Adam + new_booster.load_model(new_model, model_ckpt_path, strict=True) + + # Add prefix to get aligned with pytorch parameter names. + check_state_dict_equal( + model.unwrap().state_dict(only_rank_0=False, prefix='module.module.', dtype=torch.float32), + new_model.state_dict(), False) + + new_booster.load_optimizer(new_optimizer, optimizer_ckpt_path) + check_state_dict_equal(optimizer.unwrap().state_dict(only_rank_0=False), new_optimizer.state_dict(), False) + + # Check the new model/optimizer can successfully run. + data = data_gen_fn() + data = { + k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items() + } + output = new_model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + new_booster.backward(loss, new_optimizer) + new_optimizer.step() + new_booster.save_model(new_model, model_ckpt_path, shard=shard) + new_booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard) + + +@clear_cache_before_run() +@parameterize('shard', [False]) +@parameterize('model_name', ['transformers_gpt']) +def exam_gemini_load_from_torch(shard: bool, model_name: str): + + (model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values())) + criterion = lambda x: x.mean() + plugin = TorchDDPPlugin() + booster = Booster(plugin=plugin) + + model = model_fn() + optimizer = Adam(model.parameters(), lr=0.001) + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + data = data_gen_fn() + data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()} + output = model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + + booster.backward(loss, optimizer) + optimizer.step() + + with shared_tempdir() as tempdir: + model_ckpt_path = f"{tempdir}/model" + optimizer_ckpt_path = f"{tempdir}/optimizer" + + booster.save_model(model, model_ckpt_path, shard=shard) + booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard) + dist.barrier() + + new_model = model_fn() + new_optimizer = HybridAdam(new_model.parameters(), lr=0.001) + new_plugin = GeminiPlugin() + new_booster = Booster(plugin=new_plugin) + new_model, new_optimizer, criterion, _, _ = new_booster.boost(new_model, new_optimizer, criterion) + + # Loading torch.Adam states to HybridAdam + new_booster.load_model(new_model, model_ckpt_path, strict=True) + + # Add prefix to get aligned with pytorch parameter names. + check_state_dict_equal( + new_model.unwrap().state_dict(only_rank_0=False, prefix='module.module.', dtype=torch.float32), + model.state_dict(), False) + + new_booster.load_optimizer(new_optimizer, optimizer_ckpt_path) + old_state_dict = optimizer.state_dict() + new_state_dict = new_optimizer.unwrap().state_dict(only_rank_0=False) + + # Comparison of param_groups needs special care here, + # since not all hyperparameters in Adam are used by HybridAdam + hyperparameters_to_examine = ['params', 'lr', 'betas', 'eps', 'weight_decay'] + for old_group, new_group in zip(old_state_dict['param_groups'], new_state_dict['param_groups']): + for k in hyperparameters_to_examine: + assert k in old_group and k in new_group, \ + f"Old group's keys: {list(old_group.keys())}, New group's keys: {list(new_group.keys())}" + assert old_group[k] == new_group[k] + check_state_dict_equal(old_state_dict['state'], new_state_dict['state'], False) + + # Check the new model/optimizer can successfully run. + data = data_gen_fn() + data = { + k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items() + } + output = new_model(**data) + output = output_transform_fn(output) + output_key = list(output.keys())[0] + loss = criterion(output[output_key]) + new_booster.backward(loss, new_optimizer) + new_optimizer.step() + new_booster.save_model(new_model, model_ckpt_path, shard=shard) + new_booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard) + + +def run_dist(rank, world_size, port): + config = {} + colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + exam_torch_load_from_gemini() + exam_gemini_load_from_torch() + + +@pytest.mark.dist +@pytest.mark.parametrize('world_size', [1, 2]) +@rerun_if_address_is_in_use() +def test_gemini_ckpIO(world_size): + spawn(run_dist, world_size) From c1cf752021f3f9e6f578eca5827e3e87450d575b Mon Sep 17 00:00:00 2001 From: Frank Lee Date: Mon, 10 Jul 2023 11:48:27 +0800 Subject: [PATCH 404/413] [docker] fixed ninja build command (#4203) * [docker] fixed ninja build command * polish code --- docker/Dockerfile | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 97399c939376..a1e136ee58a5 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -21,7 +21,10 @@ RUN apt-get update && \ RUN conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch # install ninja -RUN apt-get install -y --no-install-recommends ninja-build +RUN apt-get update && \ + apt-get install -y --no-install-recommends ninja-build && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* # install apex RUN git clone https://github.com/NVIDIA/apex && \ @@ -31,7 +34,7 @@ RUN git clone https://github.com/NVIDIA/apex && \ pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" ./ # install colossalai -ARG VERSION=1 +ARG VERSION=main RUN git clone -b ${VERSION} https://github.com/hpcaitech/ColossalAI.git \ && cd ./ColossalAI \ && CUDA_EXT=1 pip install -v --no-cache-dir . From 4e9b09c222c9b3f78c4ad48eb55f09e3aaba10e1 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 12 Jul 2023 17:35:58 +0800 Subject: [PATCH 405/413] Automated submodule synchronization (#4217) Co-authored-by: github-actions --- examples/tutorial/fastfold/FastFold | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorial/fastfold/FastFold b/examples/tutorial/fastfold/FastFold index 05681304651b..eba496808a91 160000 --- a/examples/tutorial/fastfold/FastFold +++ b/examples/tutorial/fastfold/FastFold @@ -1 +1 @@ -Subproject commit 05681304651b1b29d7d887db169045ea3dd28fce +Subproject commit eba496808a91bbcd9661cf832349a418b197015f From 9a4842c571cd63e6a660182a234bc6ff60991ba0 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Mon, 17 Jul 2023 17:30:57 +0800 Subject: [PATCH 406/413] revise shardformer readme (#4246) --- colossalai/shardformer/README.md | 25 ++++++++++--------------- 1 file changed, 10 insertions(+), 15 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 6ae32e4fbd42..bf4215c52980 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -22,7 +22,6 @@ - [System Performance](#system-performance) - [Convergence](#convergence) - ## 🔗 Introduction **Shardformer** is a module that automatically parallelizes the mainstream models in libraries such as HuggingFace and TIMM. This module aims to make parallelization hassle-free for users who are not from the system background. @@ -33,7 +32,7 @@ The sample API usage is given below: -``` python +```python from colossalai.shardformer import ShardConfig, Shard from transformers import BertForMaskedLM @@ -74,6 +73,7 @@ shard_former.optimize(model, my_policy) ``` + ## 🗺 Roadmap We will follow this roadmap to develop Shardformer: @@ -117,15 +117,13 @@ Please refer to the code for more details.

    - - ### Distributed Modules `ShardFormer` replaces the original PyTorch module with a distributed module. The distributed module keeps the same attributes as the original module but replaces the original parameters with distributed parameters and defines a new `forward` function to execute distributed computation. Each distributed module implements its `from_native_module` static method to convert the PyTorch module to its corresponding distributed module. -```python +````python class ParallelModule(torch.nn.Module): @abstractmethod @@ -140,7 +138,7 @@ class ParallelModule(torch.nn.Module): my_linear = Linear1D_Col.from_native_module(my_linear, process_group) ``` """ -``` +```` ### Shard Config @@ -169,7 +167,7 @@ We abstract the policy into four stages: 2. Providing `ModulePolicyDescription`: call `Policy.module_policy` to get a bunch of `ModulePolicyDescription` to tell `ModelSharder` how the submodules's attributes, child parameters, and deeper submodules will be substituted. 3. Postprocessing: call `Policy.postprocess` to perform some postprocessing work, for example, binding the embedding and classifier head weights of the BERT model. -``` python +```python @dataclass class ModulePolicyDescription: r""" @@ -238,7 +236,6 @@ class Policy(ABC): ... ``` - ### Model Sharder `ModelSharder` is the class in charge of sharding the model based on the given policy. @@ -324,21 +321,20 @@ You can create a new file in the `colossalai/shardformer/policies` folder and na Please follow the following protocols when writing your policy: - You have to make a clear decision what you want to replace exactly in the original PyTorch module - - Use `ModulePolicyDescription.attribute_replacement` to replace the module attributes - - Use `ModulePolicyDescription.param_replacement` to replace the module parameters - - Use `ModulePolicyDescription.sub_module_replacement` to replace the submodules completely. The target module should implement the `from_native_module` for the . - - Use `ModulePolicyDescription.method_replacement` to replace the module methods. **These replacement methods should be put in the `shardformer/modeling/.py`**. + - Use `ModulePolicyDescription.attribute_replacement` to replace the module attributes + - Use `ModulePolicyDescription.param_replacement` to replace the module parameters + - Use `ModulePolicyDescription.sub_module_replacement` to replace the submodules completely. The target module should implement the `from_native_module` for the replacement. + - Use `ModulePolicyDescription.method_replacement` to replace the module methods. **These replacement methods should be put in the `shardformer/modeling/.py`**. - You can implement the `ParallelModule` for primitive modules in the `shardformer/layer/.py` file. Primitive modules refer to modules which are not composed of other modules. For example, the `torch.nn.Linear` module is a primitive module while modules such as `BertEncoder` module in the `transformers` library is a composite module. Primitive modules do not nested inner `nn.Module` members. For composite modules, you should consider using `ModulePolicyDescription` to implement your replacement. - `ParallelModule` is meant to be used in two ways: `ParallelModule.from_native_module` to convert native PyTorch module to the `ParallelModule` and `ParallelModule(...)` to instantiate the module directly just like a normal PyTorch module. `ParallelModule` should be only implemented for modules whose weights are sharded. If you want to make your module compatible with the `ModulePolicyDescription.sub_module_replacement` and there is no weight sharding in your module, you can just implement the `from_native_module` method without inheriting the `ParallelModule` like `colossalai/shardformer/layer/normalization.py`. - **Do not import any file in the `colossalai/shardformer/policies` and `colossalai/shardformer/modeling` to avoid unwanted import error**. For example, a file in these folders accidentally imports `transformers` library at the top of the file, then the user will have to install `transformers` library even if they do not use this file. Any file in the `modeling` folder should be only imported by the policy file. A policy implementation should be only imported dynamically via the autopolicy or manually via the `ShardFormer` module. - Try to keep your import statement on third-party libraries such as `transformers` within the function body instead of the header section of the file. This is because we do not want to import the library when we do not use the policy. - - Step 2. Register your policy to the autopolicy Next, you need to register your policy in the `colossalai/shardformer/policies/autopolicy.py` file. -For example, if we register the policy for the BERT model, we just add a key-value in the `_POLICY_LIST` dictionary. The key if the `qualname` of the model object (you can get it by model.__class__.__qualname__). The value is a `PolicyLocation` object, which contains the file name and the class name of the policy. We do not import the policy directly because the policy file may contain libraries (such as `transformers`) which we do not want to import when we do not use the policy. +For example, if we register the policy for the BERT model, we just add a key-value in the `_POLICY_LIST` dictionary. The key if the `qualname` of the model object (you can get it by model.\_\_class\_\_.\_\_qualname\_\_). The value is a `PolicyLocation` object, which contains the file name and the class name of the policy. We do not import the policy directly because the policy file may contain libraries (such as `transformers`) which we do not want to import when we do not use the policy. ```python _POLICY_LIST = { @@ -360,7 +356,6 @@ Add your model to the `tests/kit/model_zoo` file. This allows you to define test Next, implement your unit test in the `tests/test_shardformer` folder. Please refer to other similar tests for style consistency. - - Step 3. Execute your test When you run tests locally, you should run tests for both your newly-added test file and the whole `shardformer` module tests. From 7ff11b5537123b50d8b1b3b0fbaca0fa31d9481b Mon Sep 17 00:00:00 2001 From: binmakeswell Date: Mon, 17 Jul 2023 21:07:44 +0800 Subject: [PATCH 407/413] [example] add llama pretraining (#4257) --- README.md | 11 +++++++++++ docs/README-zh-Hans.md | 10 ++++++++++ examples/language/llama/README.md | 11 +++++++++++ 3 files changed, 32 insertions(+) create mode 100644 examples/language/llama/README.md diff --git a/README.md b/README.md index 34c8a6b730a3..21670e1e59fb 100644 --- a/README.md +++ b/README.md @@ -25,6 +25,7 @@
    ## Latest News +* [2023/07] [65B Model Pretraining Accelerated by 38%, Best Practices for Building LLaMA-Like Base Models Open-Source](https://www.hpc-ai.tech/blog/large-model-pretraining) * [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) * [2023/03] [Intel and Colossal-AI Partner to Deliver Cost-Efficient Open-Source Solution for Protein Folding Structure Prediction](https://www.hpc-ai.tech/blog/intel-habana) * [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs) @@ -49,6 +50,7 @@
  • Parallel Training Demo
      +
    • LLaMA
    • GPT-3
    • GPT-2
    • BERT
      • @@ -216,6 +218,15 @@ Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/) ## Parallel Training Demo +### LLaMA +

      + +

      + +- 65-billion-parameter large model pretraining accelerated by 38% +[[code]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama) +[[blog]](https://www.hpc-ai.tech/blog/large-model-pretraining) + ### GPT-3

      diff --git a/docs/README-zh-Hans.md b/docs/README-zh-Hans.md index 1dde7a816676..e229c65d890c 100644 --- a/docs/README-zh-Hans.md +++ b/docs/README-zh-Hans.md @@ -24,6 +24,7 @@

    • ## 新闻 +* [2023/07] [65B Model Pretraining Accelerated by 38%, Best Practices for Building LLaMA-Like Base Models Open-Source](https://www.hpc-ai.tech/blog/large-model-pretraining) * [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) * [2023/03] [Intel and Colossal-AI Partner to Deliver Cost-Efficient Open-Source Solution for Protein Folding Structure Prediction](https://www.hpc-ai.tech/blog/intel-habana) * [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs) @@ -49,6 +50,7 @@
  • 并行训练样例展示
      +
    • LLaMA
    • GPT-3
    • GPT-2
    • BERT
      • @@ -209,6 +211,14 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的

      (返回顶端)

      ## 并行训练样例展示 +### LLaMA +

      + +

      + +- 650亿参数大模型预训练加速38% +[[代码]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama) +[[博客]](https://www.hpc-ai.tech/blog/large-model-pretraining) ### GPT-3

      diff --git a/examples/language/llama/README.md b/examples/language/llama/README.md new file mode 100644 index 000000000000..871804f2ca86 --- /dev/null +++ b/examples/language/llama/README.md @@ -0,0 +1,11 @@ +# Pretraining LLaMA: best practices for building LLaMA-like base models + +

      + +

      + +- 65-billion-parameter large model pretraining accelerated by 38% +[[code]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama) +[[blog]](https://www.hpc-ai.tech/blog/large-model-pretraining) + +> Since the main branch is being updated, in order to maintain the stability of the code, this example is temporarily kept as an [independent branch](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama). From 4b977541a86c90946badc77a6a77fee64fdc8cce Mon Sep 17 00:00:00 2001 From: Cuiqing Li Date: Tue, 18 Jul 2023 23:53:38 +0800 Subject: [PATCH 408/413] [Kernels] added triton-implemented of self attention for colossal-ai (#4241) * added softmax kernel * added qkv_kernel * added ops * adding tests * upload tets * fix tests * debugging * debugging tests * debugging * added * fixed errors * added softmax kernel * clean codes * added tests * update tests * update tests * added attention * add * fixed pytest checking * add cuda check * fix cuda version * fix typo --- colossalai/kernel/triton/ops.py | 209 ++++++++++++++++++ colossalai/kernel/triton/qkv_matmul_kernel.py | 109 +++++++++ colossalai/kernel/triton/softmax_kernel.py | 44 ++++ tests/test_kernels/test_self_attention.py | 136 ++++++++++++ tests/test_kernels/test_softmax.py | 27 +++ 5 files changed, 525 insertions(+) create mode 100644 colossalai/kernel/triton/ops.py create mode 100644 colossalai/kernel/triton/qkv_matmul_kernel.py create mode 100644 colossalai/kernel/triton/softmax_kernel.py create mode 100644 tests/test_kernels/test_self_attention.py create mode 100644 tests/test_kernels/test_softmax.py diff --git a/colossalai/kernel/triton/ops.py b/colossalai/kernel/triton/ops.py new file mode 100644 index 000000000000..5e8d4ba3ec99 --- /dev/null +++ b/colossalai/kernel/triton/ops.py @@ -0,0 +1,209 @@ +import torch +from torch import nn + +try: + import triton + import triton.language as tl + HAS_TRITON = True +except ImportError: + HAS_TRITON = False + print("please install triton from https://github.com/openai/triton") + +if HAS_TRITON: + from .qkv_matmul_kernel import qkv_gemm_4d_kernel + from .softmax_kernel import softmax_kernel + + def self_attention_forward_without_fusion(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, input_mask: torch.Tensor, scale: float): + r""" A function to do QKV Attention calculation by calling GEMM and softmax triton kernels + Args: + q (torch.Tensor): Q embedding in attention layer, shape should be (batch, seq_len, num_heads, head_size) + k (torch.Tensor): K embedding in attention layer, shape should be (batch, seq_len, num_heads, head_size) + v (torch.Tensor): V embedding in attention layer, shape should be (batch, seq_len, num_heads, head_size) + input_mask (torch.Tensor): mask for softmax layer, shape should be (batch, num_heads, seq_lem, seq_len) + scale: the float scale value which is used to multiply with Q*K^T before doing softmax + + Return: + output (Torch.Tensor): The output shape is (batch, seq_len, num_heads, head_size) + """ + assert len(q.shape) == 4, "the shape of q val must be 4" + batches, M, H, K = q.shape + assert q.shape == k.shape, "the shape of q and the shape of k must be equal" + assert q.shape == v.shape, "the shape of q and the shape of v must be equal" + assert q.shape[-1] == k.shape[-1], "the last dimension of q and k must be equal" + + N = k.shape[1] + + # head_size * num_of_head + d_model = q.shape[-1] * q.shape[-2] + + score_output = torch.empty( + (batches, H, M, N), device=q.device, dtype=q.dtype) + + grid = lambda meta: ( + batches, + H, + triton.cdiv(M, meta["BLOCK_SIZE_M"]) * + triton.cdiv(N, meta["BLOCK_SIZE_N"]), + ) + + qkv_gemm_4d_kernel[grid]( + q, k, score_output, + M, N, K, + q.stride(0), q.stride(2), q.stride(1), q.stride(3), + k.stride(0), k.stride(2), k.stride(3), k.stride(1), + score_output.stride(0), score_output.stride(1), score_output.stride(2), score_output.stride(3), + scale=scale, + # currently manually setting, later on we can use auto-tune config to match best setting + BLOCK_SIZE_M=64, + BLOCK_SIZE_N=32, + BLOCK_SIZE_K=32, + GROUP_SIZE_M=8, + ) + + softmax_output = torch.empty( + score_output.shape, device=score_output.device, dtype=score_output.dtype) + score_output_shape = score_output.shape + + score_output = score_output.view(-1, score_output.shape[-1]) + n_rows, n_cols = score_output.shape + + if n_rows <= 350000: + + block_size = max(triton.next_power_of_2(n_cols), 2) + num_warps = 4 + if block_size >= 4096: + num_warps = 16 + elif block_size >= 2048: + num_warps = 8 + else: + num_warps = 4 + + softmax_kernel[(n_rows, )]( + softmax_output, + score_output, + score_output.stride(0), + n_cols, + mask_ptr = input_mask, + num_warps=num_warps, + BLOCK_SIZE=block_size, + ) + + else: + #TODO: change softmax kernel functions to make it suitable for large size dimension + softmax_output = torch.nn.functional.softmax(score_output, dim=-1) + softmax_output = softmax_output.view(*score_output_shape) + + batches, H, M, K = softmax_output.shape + N = v.shape[-1] + + output = torch.empty( + (batches, M, H, N), device=softmax_output.device, dtype=softmax_output.dtype) + + grid = lambda meta: ( + batches, + H, + triton.cdiv(M, meta["BLOCK_SIZE_M"]) * + triton.cdiv(N, meta["BLOCK_SIZE_N"]), + ) + + qkv_gemm_4d_kernel[grid]( + softmax_output, v, output, + M, N, K, + softmax_output.stride(0), + softmax_output.stride(1), + softmax_output.stride(2), + softmax_output.stride(3), + v.stride(0), + v.stride(2), + v.stride(1), + v.stride(3), + output.stride(0), + output.stride(2), + output.stride(1), + output.stride(3), + BLOCK_SIZE_M=128, + BLOCK_SIZE_N=64, + BLOCK_SIZE_K=64, + GROUP_SIZE_M=8, + scale=-1, + ) + return output.view(batches, -1, d_model) + + + def self_attention_compute_using_triton(qkv, + input_mask, + layer_past, + alibi, + scale, + head_size, + triangular=False, + use_flash=False): + + assert qkv.is_contiguous() + assert alibi is None, "current triton self-attention does not support alibi" + batches = qkv.shape[0] + d_model = qkv.shape[-1] // 3 + num_of_heads = d_model // head_size + + q = qkv[:, :, :d_model] + k = qkv[:, :, d_model:d_model * 2] + v = qkv[:, :, d_model * 2:] + q = q.view(batches, -1, num_of_heads, head_size) + k = k.view(batches, -1, num_of_heads, head_size) + v = v.view(batches, -1, num_of_heads, head_size) + + data_output_triton = self_attention_forward_without_fusion( + q, k, v, input_mask, scale) + + return data_output_triton + + + def softmax(input: torch.Tensor, mask: torch.Tensor = None, dim=-1) -> torch.Tensor: + if mask is not None: + assert input[-1] == mask[-1], "the last dimentions should be the same for input and mask" + assert dim == -1 or dim == len(input.shape)-1, "currently softmax layer only support last dimention" + + hidden_dim = input.shape[-1] + output = torch.empty_like(input) + input = input.view(-1, hidden_dim) + if mask is not None: + mask = mask.view(-1, hidden_dim) + assert input.shape[0] == mask.shape[0], "the fist dimention of mask and input should be the same" + + num_rows, num_cols = input.shape + block_size = max(triton.next_power_of_2(num_cols), 2) + num_warps = 16 + if block_size >= 4096: + num_warps = 16 + elif block_size >= 2048: + num_warps = 8 + else: + num_warps = 4 + + if num_rows <= 350000: + grid = (num_rows,) + softmax_kernel[grid](output, input, input.stride(0), num_cols, mask, BLOCK_SIZE = block_size, num_warps=num_warps) + else: + grid = lambda meta: () + + grid = lambda meta: ( + triton.cdiv(num_rows, meta["BLOCK_M"]), + ) + + BLOCK_M = 32 + if block_size >= 4096: + BLOCK_M = 4 + elif block_size >= 2048: + BLOCK_M = 8 + + softmax_kernel_2[grid](output_ptr = output, + input_ptr = input, + row_stride = input.stride(0), + n_rows = num_rows, + n_cols = num_cols, + mask_ptr = mask, + # currently manually setting up size + BLOCK_M = 32, + BLOCK_SIZE = block_size) + + return output \ No newline at end of file diff --git a/colossalai/kernel/triton/qkv_matmul_kernel.py b/colossalai/kernel/triton/qkv_matmul_kernel.py new file mode 100644 index 000000000000..62fc6bba0360 --- /dev/null +++ b/colossalai/kernel/triton/qkv_matmul_kernel.py @@ -0,0 +1,109 @@ +import torch +try: + import triton + import triton.language as tl + HAS_TRITON = True +except ImportError: + HAS_TRITON = False + print("please install triton from https://github.com/openai/triton") + + +if HAS_TRITON: + ''' + this kernel function is modified from https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html + ''' + @triton.jit + def qkv_gemm_4d_kernel( + a_ptr, + b_ptr, + c_ptr, + M, + N, + K, + stride_ab, + stride_ah, + stride_am, + stride_ak, + stride_bb, + stride_bh, + stride_bk, + stride_bn, + stride_cb, + stride_ch, + stride_cm, + stride_cn, + scale, + # Meta-parameters + BLOCK_SIZE_M : tl.constexpr = 64, + BLOCK_SIZE_N : tl.constexpr = 32, + BLOCK_SIZE_K : tl.constexpr = 32, + GROUP_SIZE_M : tl.constexpr = 8, + ): + r""" A kernel function which is used to do batch-matmul for Q*K^T or score_matrix * V for attention layer, + where score_matrix is softmax(Q*V^T/sqrt(hidden_size)) + Args: + a_ptr(torch.Tensor): pointer to input tensor array (bs, M, h, K) or (bs, h, M, K) + b_ptr(torch.Tensor): pointer to input tensor array (bs, N, h, K) or (bs, h, N, K) + c_ptr(torch.Tensor): pointer to output tensor array (bs, M, h, N) or (bs, h, M, N) + stride_ab(tl.constexpr): stride for bs-dimention for tensor array A + stride_ah(tl.constexpr): stride for h-dimention for tensor array A + stride_am(tl.constexpr): stride for m-dimention for tensor array A + stride_ak(tl.constexpr): stride for k-dimention for tensor array A + stride_bb(tl.constexpr): stride for bs-dimention for tensor array B + stride_bh(tl.constexpr): stride for h-dimention for tensor array B + stride_bk(tl.constexpr): stride for k-dimention for tensor array B + stride_bn(tl.constexpr): stride for n-dimention for tensor array B + stride_cb(tl.constexpr): stride for bs-dimention for tensor array output + stride_ch(tl.constexpr): stride for h-dimention for tensor array output + stride_cm(tl.constexpr): stride for m-dimention for tensor array output + stride_cn(tl.constexpr): stride for n-dimention for tensor array output + BLOCK_SIZE_M : tiling size for M-dimension of tensor Array a + BLOCK_SIZE_N : tiling size for N-dimension of tensor Array b + BLOCK_SIZE_K : tiling size for K-dimension of a and b + GROUP_SIZE_M : group size for reducing cache miss, more details: + """ + + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + batch = tl.program_id(axis = 0) + head = tl.program_id(axis = 1) + pid = tl.program_id(axis = 2) + + # the following is from tutorial: https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + (pid % group_size_m) + pid_n = (pid % num_pid_in_group) // group_size_m + + + offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + offs_k = tl.arange(0, BLOCK_SIZE_K) + a_ptrs = (a_ptr + batch * stride_ab + head * stride_ah + + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)) + b_ptrs = (b_ptr + batch * stride_bb + head * stride_bh + + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)) + + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for k in range(0, K, BLOCK_SIZE_K): + a_mask = (offs_am[:, None] < M) & (offs_k[None, :] + k < K) + b_mask = (offs_k[:, None] + k < K) & (offs_bn[None, :] < N) + a = tl.load(a_ptrs, mask=a_mask, other=0.) + b = tl.load(b_ptrs, mask=b_mask, other=0.) + accumulator += tl.dot(a, b) + a_ptrs += BLOCK_SIZE_K * stride_ak + b_ptrs += BLOCK_SIZE_K * stride_bk + + accumulator = accumulator.to(c_ptr.dtype.element_ty) + if scale > 0: + accumulator = accumulator * scale.to(c_ptr.dtype.element_ty) + + + offs_accumu_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_accumu_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptrs = (c_ptr + batch * stride_cb + head * stride_ch + stride_cm * offs_accumu_m[:, None] + + stride_cn * offs_accumu_n[None, :]) + accumulator_mask = (offs_accumu_m[:, None] < M) & (offs_accumu_n[None, :] < N) + tl.store(c_ptrs, accumulator, mask=accumulator_mask) diff --git a/colossalai/kernel/triton/softmax_kernel.py b/colossalai/kernel/triton/softmax_kernel.py new file mode 100644 index 000000000000..c215890badff --- /dev/null +++ b/colossalai/kernel/triton/softmax_kernel.py @@ -0,0 +1,44 @@ +try: + import triton + import triton.language as tl + HAS_TRITON = True +except ImportError: + HAS_TRITON = False + print("please install triton from https://github.com/openai/triton") + +if HAS_TRITON: + ''' + softmax kernel is modified based on + https://github.com/openai/triton/blob/34817ecc954a6f4ca7b4dfb352fdde1f8bd49ca5/python/tutorials/02-fused-softmax.py + ''' + @triton.jit + def softmax_kernel(output_ptr, input_ptr, row_stride, n_cols, mask_ptr, BLOCK_SIZE: tl.constexpr): + r""" the kernel function for implementing softmax operator + Args: + output_ptr: the output after finishing softmax operation, (N, hidden_dim) + input_ptr: the tensor of input, shape should be (N, hidden_dim) + n_cols(tl.constexpr): the number of cols of input + BLOCK_SIZE(tl.constexpr): the block_size of your hidden_dim dimension, typically BLOCK_SIZE >= hidden_dim + """ + row_idx = tl.program_id(0) + row_start_ptr = input_ptr + row_idx * row_stride + col_offsets = tl.arange(0, BLOCK_SIZE) + input_ptrs = row_start_ptr + col_offsets + row = tl.load(input_ptrs, mask=col_offsets < n_cols, other=-float('inf')).to(tl.float32) + row_minus_max = row - tl.max(row, axis=0) + + if mask_ptr is not None: + # load mask into SRAM + mask_ptrs = (mask_ptr + (row_indx * row_stride)) + col_offsets + mask = tl.load(mask_ptrs, mask=col_offsets < n_cols, other=0).to(tl.float32) + + # update + row_minus_max = row_minus_max + mask + + numerator = tl.exp(row_minus_max) + denominator = tl.sum(numerator, axis=0) + softmax_output = numerator / denominator + output_row_start_ptr = output_ptr + row_idx * row_stride + output_ptrs = output_row_start_ptr + col_offsets + # Write back output to DRAM + tl.store(output_ptrs, softmax_output, mask=col_offsets < n_cols) \ No newline at end of file diff --git a/tests/test_kernels/test_self_attention.py b/tests/test_kernels/test_self_attention.py new file mode 100644 index 000000000000..b316404a58db --- /dev/null +++ b/tests/test_kernels/test_self_attention.py @@ -0,0 +1,136 @@ +import pytest +from packaging import version +import torch +from torch import nn +import torch.nn.functional as F + +from colossalai.kernel.triton.ops import self_attention_compute_using_triton +from colossalai.kernel.triton.qkv_matmul_kernel import qkv_gemm_4d_kernel + +try: + import triton + import triton.language as tl + HAS_TRITON = True +except ImportError: + HAS_TRITON = False + print("please install triton from https://github.com/openai/triton") + +TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.4') + +@pytest.mark.skipif(not TRITON_CUDA_SUPPORT, reason="triton requires cuda version to be higher than 11.4") +def test_qkv_matmul(): + qkv = torch.randn((4, 24, 64*3), device="cuda", dtype=torch.float16) + scale = 1.2 + head_size = 32 + batches = qkv.shape[0] + d_model = qkv.shape[-1] // 3 + num_of_heads = d_model // head_size + + q = qkv[:, :, :d_model] + k = qkv[:, :, d_model:d_model * 2] + + q = q.view(batches, -1, num_of_heads, head_size) + k = k.view(batches, -1, num_of_heads, head_size) + q_copy = q.clone() + k_copy = k.clone() + q = torch.transpose(q, 1, 2).contiguous() + k = torch.transpose(k, 1, 2).contiguous() + k = torch.transpose(k, 2, 3).contiguous() + + torch_ouput = torch.einsum('bnij,bnjk->bnik', q, k) + torch_ouput *= 1.2 + + q, k = q_copy, k_copy + batches, M, H, K = q.shape + N = k.shape[1] + score_output = torch.empty( + (batches, H, M, N), device=q.device, dtype=q.dtype) + + grid = lambda meta: ( + batches, + H, + triton.cdiv(M, meta["BLOCK_SIZE_M"]) * + triton.cdiv(N, meta["BLOCK_SIZE_N"]), + ) + + K = q.shape[3] + qkv_gemm_4d_kernel[grid]( + q, k, score_output, + M, N, K, + q.stride(0), q.stride(2), q.stride(1), q.stride(3), + k.stride(0), k.stride(2), k.stride(3), k.stride(1), + score_output.stride(0), score_output.stride(1), score_output.stride(2), score_output.stride(3), + scale=scale, + # currently manually setting, later on we can use auto-tune config to match best setting + BLOCK_SIZE_M=64, + BLOCK_SIZE_N=32, + BLOCK_SIZE_K=32, + GROUP_SIZE_M=8, + ) + + check = torch.allclose(torch_ouput.cpu(), score_output.cpu(), rtol=1e-3, atol=1e-5) + assert check is True, "the outputs of triton and torch are not matched" + + +def self_attention_compute_using_torch(qkv, + input_mask, + scale, + head_size + ): + + batches = qkv.shape[0] + d_model = qkv.shape[-1] // 3 + num_of_heads = d_model // head_size + + q = qkv[:, :, :d_model] + k = qkv[:, :, d_model:d_model * 2] + v = qkv[:, :, d_model * 2:] + q = q.view(batches, -1, num_of_heads, head_size) + k = k.view(batches, -1, num_of_heads, head_size) + v = v.view(batches, -1, num_of_heads, head_size) + + q = torch.transpose(q, 1, 2).contiguous() + k = torch.transpose(k, 1, 2).contiguous() + v = torch.transpose(v, 1, 2).contiguous() + + k = torch.transpose(k, -1, -2).contiguous() + + score_output = torch.einsum('bnij,bnjk->bnik', q, k) + score_output *= scale + + softmax_output = F.softmax(score_output, dim = -1) + res = torch.einsum('bnij,bnjk->bnik', softmax_output, v) + res = torch.transpose(res, 1, 2) + res = res.contiguous() + + + return res.view(batches, -1, d_model), score_output, softmax_output + +@pytest.mark.skipif(not TRITON_CUDA_SUPPORT, reason="triton requires cuda version to be higher than 11.4") +def test_self_atttention_test(): + + qkv = torch.randn((4, 24, 64*3), device="cuda", dtype=torch.float16) + data_output_torch, score_output_torch, softmax_output_torch = self_attention_compute_using_torch( + qkv.clone(), + input_mask = None, + scale = 1.2, + head_size = 32 + ) + + data_output_triton = self_attention_compute_using_triton( + qkv.clone(), + alibi=None, + head_size=32, + scale=1.2, + input_mask=None, + layer_past=None, + use_flash=False, + triangular=True) + + check = torch.allclose(data_output_triton.cpu(), data_output_torch.cpu(), rtol=1e-4, atol=1e-2) + assert check is True, "the triton output is not matched with torch output" + + +if __name__ == "__main__": + test_qkv_matmul() + test_self_atttention_test() \ No newline at end of file diff --git a/tests/test_kernels/test_softmax.py b/tests/test_kernels/test_softmax.py new file mode 100644 index 000000000000..843d811d019c --- /dev/null +++ b/tests/test_kernels/test_softmax.py @@ -0,0 +1,27 @@ +import pytest +from packaging import version +import torch +from torch import nn + +from colossalai.kernel.triton.ops import softmax + +TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.4') + +@pytest.mark.skipif(not TRITON_CUDA_SUPPORT, reason="triton requires cuda version to be higher than 11.4") +def test_softmax_op(): + data_samples = [ + torch.randn((3, 4, 5, 32), device = "cuda", dtype = torch.float32), + torch.randn((320, 320, 78), device = "cuda", dtype = torch.float32), + torch.randn((2345, 4, 5, 64), device = "cuda", dtype = torch.float16) + ] + + for data in data_samples: + module = nn.Softmax(dim = -1) + data_torch_out = module(data) + data_triton_out = softmax(data) + check = torch.allclose(data_torch_out.cpu(), data_triton_out.cpu(), rtol=1e-3, atol=1e-3) + assert check is True, "softmax outputs from triton and torch are not matched" + + +if __name__ == "__main__": + test_softmax_op() \ No newline at end of file From fc5cef2c79265e36b585ef22c5e1d7f18be52a4e Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 19 Jul 2023 16:43:01 +0800 Subject: [PATCH 409/413] [lazy] support init on cuda (#4269) * [lazy] support init on cuda * [test] update lazy init test * [test] fix transformer version --- colossalai/lazy/lazy_init.py | 28 ++++++++++++++++++++-------- requirements/requirements-test.txt | 2 +- tests/test_lazy/lazy_init_utils.py | 10 +++++++--- tests/test_lazy/test_models.py | 5 +++-- 4 files changed, 31 insertions(+), 14 deletions(-) diff --git a/colossalai/lazy/lazy_init.py b/colossalai/lazy/lazy_init.py index 8b911407307c..1f5345015bf2 100644 --- a/colossalai/lazy/lazy_init.py +++ b/colossalai/lazy/lazy_init.py @@ -1,3 +1,4 @@ +from contextlib import contextmanager from types import MethodType from typing import Callable, Dict, Optional, Union @@ -61,12 +62,15 @@ class _MyTensor(Tensor): """ _pre_op_fn: Callable[['LazyTensor'], None] = lambda *args: None + default_device: Optional[torch.device] = None + def __new__(cls, func, *args, concrete_data=None, **kwargs) -> '_MyTensor': cls._pre_op_fn() if concrete_data is not None: # uniform api as LazyTensor data = concrete_data else: + kwargs['device'] = cls.default_device data = func(*args, **kwargs) return Tensor._make_subclass(cls, data, require_grad=data.requires_grad) @@ -142,6 +146,8 @@ class LazyTensor(torch.Tensor): _meta_data: Optional[MetaTensor] = None # shape, dtype, device _pre_op_fn: Callable[['LazyTensor'], None] = lambda *args: None + default_device: Optional[torch.device] = None + @staticmethod def __new__(cls, func, *args, meta_data=None, concrete_data=None, **kwargs): if concrete_data is not None: @@ -159,6 +165,8 @@ def __new__(cls, func, *args, meta_data=None, concrete_data=None, **kwargs): return r def __init__(self, func, *args, meta_data=None, concrete_data=None, **kwargs): + if func.__name__ in _NORMAL_FACTORY: + kwargs = {**kwargs, 'device': LazyTensor.default_device} self._factory_method = (func, args, kwargs) # (func, args, kwargs) self._op_buffer = [] # (func, args, kwargs, replace) self._materialized_data: Optional[torch.Tensor] = concrete_data # materialized data @@ -206,16 +214,11 @@ def _materialize_data(self) -> torch.Tensor: if self._materialized_data is None: # apply factory method func, args, kwargs = self._factory_method - # apply cached sequence self._pre_op_fn() - try: - init_val = func(*tree_map(self._replace_with_materialized, args), - **tree_map(self._replace_with_materialized, kwargs)) - except TypeError as e: - print(f'init fn: {func.__name__}') - raise e + init_val = func(*tree_map(self._replace_with_materialized, args), + **tree_map(self._replace_with_materialized, kwargs)) self._materialized_data = self._rerun_ops(init_val) return self._materialized_data @@ -305,6 +308,7 @@ def wrap(y, i=None): else: # out of place op, create new lazy tensor fn = lambda *a, **kw: func(*a, **kw) if i is None else func(*a, **kw)[i] + fn.__name__ = func.__name__ lazy_y = LazyTensor(fn, *args, meta_data=y, **kwargs) return lazy_y elif type(y) is Tensor: @@ -435,14 +439,21 @@ class LazyInitContext: """ _replaced: bool = False - def __init__(self, tensor_cls: Union[_MyTensor, LazyTensor] = LazyTensor): + def __init__(self, + tensor_cls: Union[_MyTensor, LazyTensor] = LazyTensor, + default_device: Optional[Union[torch.device, str, int]] = None): + assert tensor_cls is LazyTensor or tensor_cls is _MyTensor self.overrides = {} self.tensor_cls = tensor_cls + self.old_default_device = LazyTensor.default_device + self.default_device = default_device def __enter__(self): if LazyInitContext._replaced: raise RuntimeError(f'LazyInitContext is not reentrant') LazyInitContext._replaced = True + self.old_default_device = self.tensor_cls.default_device + self.tensor_cls.default_device = self.default_device def wrap_factory_method(target): # factory functions (eg. torch.empty()) @@ -518,6 +529,7 @@ def wrapper(*args, **kwargs): setattr(torch, name, wrapper) def __exit__(self, exc_type, exc_val, exc_tb): + self.tensor_cls.default_device = self.old_default_device LazyInitContext._replaced = False for name, (wrapper, orig) in self.overrides.items(): setattr(torch, name, orig) diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index 50121a9283f2..9f6580c72d1b 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -4,7 +4,7 @@ pytest coverage==7.2.3 git+https://github.com/hpcaitech/pytest-testmon torchvision -transformers +transformers==4.30.2 timm titans torchaudio diff --git a/tests/test_lazy/lazy_init_utils.py b/tests/test_lazy/lazy_init_utils.py index 73c3c5422d8a..9d9e9a3a5c76 100644 --- a/tests/test_lazy/lazy_init_utils.py +++ b/tests/test_lazy/lazy_init_utils.py @@ -61,14 +61,18 @@ def assert_forward_equal(m1: torch.nn.Module, m2: torch.nn.Module, data_gen_fn: f'{m1.__class__.__name__} has inconsistent outputs, {out1} vs {out2}' -def check_lazy_init(entry: TestingEntry, seed: int = 42, verbose: bool = False, check_forward: bool = False) -> None: +def check_lazy_init(entry: TestingEntry, + seed: int = 42, + verbose: bool = False, + check_forward: bool = False, + default_device: str = 'cpu') -> None: model_fn, data_gen_fn, output_transform_fn, _, model_attr = entry _MyTensor._pre_op_fn = lambda *args: set_seed(seed) LazyTensor._pre_op_fn = lambda *args: set_seed(seed) - ctx = LazyInitContext(tensor_cls=_MyTensor) + ctx = LazyInitContext(tensor_cls=_MyTensor, default_device=default_device) with ctx: model = model_fn() - ctx = LazyInitContext() + ctx = LazyInitContext(default_device=default_device) with ctx: deferred_model = model_fn() copied_deferred_model = deepcopy(deferred_model) diff --git a/tests/test_lazy/test_models.py b/tests/test_lazy/test_models.py index 4b7aeed73a69..e37184125d21 100644 --- a/tests/test_lazy/test_models.py +++ b/tests/test_lazy/test_models.py @@ -6,13 +6,14 @@ @pytest.mark.skipif(not SUPPORT_LAZY, reason='requires torch >= 1.12.0') @pytest.mark.parametrize('subset', ['torchvision', 'diffusers', 'timm', 'transformers', 'torchaudio', 'deepfm', 'dlrm']) -def test_torchvision_models_lazy_init(subset): +@pytest.mark.parametrize('default_device', ['cpu', 'cuda']) +def test_torchvision_models_lazy_init(subset, default_device): sub_model_zoo = model_zoo.get_sub_registry(subset) for name, entry in sub_model_zoo.items(): # TODO(ver217): lazy init does not support weight norm, skip these models if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base') or name.startswith('transformers_llama'): continue - check_lazy_init(entry, verbose=True) + check_lazy_init(entry, verbose=True, default_device=default_device) if __name__ == '__main__': From c6f6005990b182d7ee34c1fb84762d31ce7d3616 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Fri, 21 Jul 2023 14:39:01 +0800 Subject: [PATCH 410/413] [checkpointio] Sharded Optimizer Checkpoint for Gemini Plugin (#4302) * sharded optimizer checkpoint for gemini plugin * modify test to reduce testing time * update doc * fix bug when keep_gatherd is true under GeminiPlugin --- colossalai/booster/plugin/gemini_plugin.py | 131 +++++++++++++--- .../checkpoint_io/general_checkpoint_io.py | 38 +++-- colossalai/checkpoint_io/utils.py | 38 +++++ colossalai/zero/gemini/gemini_optimizer.py | 140 ++++++++++++++---- docs/source/en/basics/booster_api.md | 5 +- docs/source/en/basics/booster_checkpoint.md | 2 - docs/source/en/basics/booster_plugins.md | 2 - docs/source/zh-Hans/basics/booster_api.md | 5 +- .../zh-Hans/basics/booster_checkpoint.md | 1 - docs/source/zh-Hans/basics/booster_plugins.md | 1 - .../test_gemini_checkpoint_io.py | 4 +- .../test_gemini_torch_compability.py | 6 +- 12 files changed, 289 insertions(+), 84 deletions(-) diff --git a/colossalai/booster/plugin/gemini_plugin.py b/colossalai/booster/plugin/gemini_plugin.py index 6191f271c318..7b6e17337d36 100644 --- a/colossalai/booster/plugin/gemini_plugin.py +++ b/colossalai/booster/plugin/gemini_plugin.py @@ -1,3 +1,4 @@ +import gc import logging import os import warnings @@ -12,11 +13,19 @@ from torch.utils.data import DataLoader from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO, GeneralCheckpointIO -from colossalai.checkpoint_io.utils import get_model_base_filenames, get_shard_filename, save_state_dict +from colossalai.checkpoint_io.utils import ( + get_model_base_filenames, + get_optimizer_base_filenames, + get_shard_filename, + load_shard_state_dict, + save_state_dict, + save_state_dict_shards, +) from colossalai.cluster import DistCoordinator from colossalai.interface import ModelWrapper, OptimizerWrapper from colossalai.utils import get_current_device from colossalai.zero import GeminiDDP, zero_model_wrapper, zero_optim_wrapper +from colossalai.zero.gemini import ZeroOptimizer from colossalai.zero.gemini.memory_tracer import MemStats from .dp_plugin_base import DPPluginBase @@ -37,7 +46,7 @@ def save_unsharded_model(self, model: GeminiDDP, checkpoint: str, gather_dtensor """ Save sharded model to checkpoint but only on master process. The model should be unwrapped in self.load_model via ModelWrapper.unwrap. - As there is communication when getting state dict, this must be called on all processes. + As there is communication when getting state dict, model.state_dict() must be called on all processes. """ state_dict = model.state_dict(only_rank_0=True) if self.coordinator.is_master(): @@ -54,7 +63,7 @@ def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather """ Save unsharded optimizer state dict to checkpoint. After calling optimizer.state_dict(), the complete optimizer states will be collected on master rank. - As there is communication when getting state dict, this must be called on all processes. + As there is communication when getting state dict, optimizer.state_dict() must be called on all processes. The saving process will only be executed by master rank. """ state_dict = optimizer.state_dict() @@ -76,7 +85,8 @@ def save_sharded_model(self, max_shard_size: int = 1024, use_safetensors: bool = False): """ - Save sharded model + Save sharded model. + As there is communication when getting state dict, model.state_dict() must be called on all processes. """ if os.path.isfile(checkpoint_path): logging.error(f"Provided path ({checkpoint_path}) should be a directory, not a file") @@ -86,28 +96,24 @@ def save_sharded_model(self, state_dict_shard = model.state_dict_shard(max_shard_size=max_shard_size, only_rank_0=True, dtype=torch.float32) weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors) - total_size = 0 index_file = CheckpointIndexFile(checkpoint_path) - for idx, shard_pair in enumerate(state_dict_shard): - if not self.coordinator.is_master(): - continue - shard = shard_pair[0] - shard_file = get_shard_filename(weights_name, idx) - total_size = total_size + shard_pair[1] - for key in shard.keys(): - index_file.append_weight_map(key, shard_file) - - checkpoint_file_path = os.path.join(checkpoint_path, shard_file) - save_state_dict(shard, checkpoint_file_path, use_safetensors) - index_file.append_meta_data("total_size", total_size) + # Save shards of optimizer states. + is_master = self.coordinator.is_master() + total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard, + checkpoint=checkpoint_path, + index_file=index_file, + base_filename=weights_name, + is_master=is_master, + use_safetensors=use_safetensors) # only save the index file on the master rank if self.coordinator.is_master(): + index_file.append_meta_data("total_size", total_size) index_file.write_index_file(save_index_file) - logging.info(f"The model is split into checkpoint shards. " - f"You can find where each parameters has been saved in the " - f"index located at {save_index_file}.") + logging.info(f"The model is split into checkpoint shards. " + f"You can find where each parameters has been saved in the " + f"index located at {save_index_file}.") def load_sharded_model(self, model: GeminiDDP, @@ -115,7 +121,7 @@ def load_sharded_model(self, strict: bool = False, use_safetensors: bool = False): """ - load shard model, load model from multiple files + Load shard model, load model from multiple files. """ return super().load_sharded_model(model, checkpoint_index_file, strict, use_safetensors, load_sub_module=False) @@ -125,16 +131,93 @@ def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, gather_ Save sharded optimizer state dict to checkpoint folder. As there is communication when getting state dict, this must be called on all processes. """ + + # If optimizer is wrapped, unwrap it. + if isinstance(optimizer, OptimizerWrapper): + optimizer = optimizer.unwrap() + + assert isinstance(optimizer, ZeroOptimizer) + + if os.path.isfile(checkpoint): + logging.error(f"Provided path ({checkpoint}) should be a directory, not a file") + return + Path(checkpoint).mkdir(parents=True, exist_ok=True) - super().save_sharded_optimizer(optimizer, checkpoint, gather_dtensor, prefix, size_per_shard) + + # Preparing file paths and index file. + states_name, save_index_file, param_group_file = get_optimizer_base_filenames(prefix) + index_file = CheckpointIndexFile(checkpoint) + + # Store the information of param groups to param_group_file. + index_file.append_meta_data("param_groups", param_group_file) + group_file_path = os.path.join(checkpoint, param_group_file) + param_groups = optimizer.get_param_groups_for_saving() + torch.save(param_groups, group_file_path) + + # States are broken into shards within max_shard_size. + state_dict_shard = optimizer.state_shard(prefix=prefix, max_shard_size=size_per_shard, only_rank_0=True) + + # Save shards of optimizer states. + is_master = self.coordinator.is_master() + total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard, + checkpoint=checkpoint, + index_file=index_file, + base_filename=states_name, + is_master=is_master, + use_safetensors=False) + + # Wrap up index file. Only save it on master rank. + if self.coordinator.is_master(): + index_file.append_meta_data("total_size", total_size) + index_file.write_index_file(save_index_file) + logging.info(f"The optimizer is going to be split to checkpoint shards. " + f"You can find where each parameters has been saved in the " + f"index located at {save_index_file}.") def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint_index_file: Path, prefix: str): """ Loading sharded optimizer from checkpoint folder, with index file given. For each process, only loading optimizer states of parameters it controls. """ - # TODO(Baizhou): To be implemented. - pass + + if not os.path.isfile(checkpoint_index_file): + logging.error(f"Provided path ({checkpoint_index_file}) should be a file") + + # If optimizer is wrapped, unwrap it. + if isinstance(optimizer, OptimizerWrapper): + optimizer = optimizer.unwrap() + + assert isinstance(optimizer, ZeroOptimizer) + + # Read checkpoint index file. + ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file) + + # Load param_groups. + param_group_path = ckpt_index_file.get_param_group_filename() + if param_group_path is None: + raise RuntimeError(f'Invalid index file path {checkpoint_index_file} for an optimizer. \ + Lacking param group file under current directory.') + saved_param_groups = torch.load(param_group_path) + optimizer.load_param_groups(saved_param_groups) + + checkpoint_files, _ = ckpt_index_file.get_checkpoint_filenames() + + # Load optimizer states from shard files under checkpoint path. + # For each file, only load the states managed by current process. + for shard_file in checkpoint_files: + state_dict_shard = load_shard_state_dict(Path(shard_file), use_safetensors=False) + optimizer.load_param_states(state_dict_shard) + del state_dict_shard + gc.collect() + + optimizer.optimizer_loading_epilogue() + + def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str): + """ + Save model to checkpoint but only on master process. + """ + if self.coordinator.is_master(): + super().save_lr_scheduler(lr_scheduler, checkpoint) class GeminiModel(ModelWrapper): diff --git a/colossalai/checkpoint_io/general_checkpoint_io.py b/colossalai/checkpoint_io/general_checkpoint_io.py index e1d9066948dd..83e4bdcc863b 100644 --- a/colossalai/checkpoint_io/general_checkpoint_io.py +++ b/colossalai/checkpoint_io/general_checkpoint_io.py @@ -5,6 +5,7 @@ from pathlib import Path from typing import Iterator, Optional, OrderedDict, Tuple +import torch.distributed as dist import torch.nn as nn from torch.optim import Optimizer @@ -16,7 +17,6 @@ get_model_base_filenames, get_optimizer_base_filenames, get_shard_filename, - has_index_file, is_safetensors_available, load_param_groups_into_optimizer, load_shard_state_dict, @@ -25,6 +25,7 @@ load_states_into_optimizer, save_param_groups, save_state_dict, + save_state_dict_shards, shard_model_checkpoint, shard_optimizer_checkpoint, sharded_optimizer_loading_epilogue, @@ -122,15 +123,13 @@ def save_sharded_optimizer( save_param_groups(state_dict, group_file_path) # Save shards of optimizer states. - total_size = 0 - for idx, shard_pair in enumerate(sharded_state): - shard, current_size = shard_pair - shard_file = get_shard_filename(states_name, idx) - total_size = total_size + current_size - for key in shard.keys(): - index_file.append_weight_map(key, shard_file) - checkpoint_file_path = os.path.join(checkpoint, shard_file) - save_state_dict(shard, checkpoint_file_path, use_safetensors=False) + # In general cases, is_master is set to True to get the right behavior. + total_size = save_state_dict_shards(sharded_state_dict=sharded_state, + checkpoint=checkpoint, + index_file=index_file, + base_filename=states_name, + is_master=True, + use_safetensors=False) # Wrap up index file. index_file.append_meta_data("total_size", total_size) @@ -172,18 +171,17 @@ def save_sharded_model(self, # shard checkpoint state_dict = model.state_dict() state_dict_shard = shard_model_checkpoint(state_dict, max_shard_size=max_shard_size) - weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors) - total_size = 0 index_file = CheckpointIndexFile(checkpoint_path) - for idx, shard_pair in enumerate(state_dict_shard): - shard = shard_pair[0] - shard_file = get_shard_filename(weights_name, idx) - total_size = total_size + shard_pair[1] - for key in shard.keys(): - index_file.append_weight_map(key, shard_file) - checkpoint_file_path = os.path.join(checkpoint_path, shard_file) - save_state_dict(shard, checkpoint_file_path, use_safetensors) + + # Save shards of optimizer states. + # In general cases, is_master is set to True to get the right behavior. + total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard, + checkpoint=checkpoint_path, + index_file=index_file, + base_filename=weights_name, + is_master=True, + use_safetensors=use_safetensors) index_file.append_meta_data("total_size", total_size) index_file.write_index_file(save_index_file) diff --git a/colossalai/checkpoint_io/utils.py b/colossalai/checkpoint_io/utils.py index 19e28c3f7068..8837776aee4d 100644 --- a/colossalai/checkpoint_io/utils.py +++ b/colossalai/checkpoint_io/utils.py @@ -1,4 +1,5 @@ # coding=utf-8 +import os import re from collections import abc as container_abcs from collections import defaultdict @@ -103,6 +104,43 @@ def unwrap_optimizer(optimizer: OptimizerWrapper): return unwrapped_optim +def save_state_dict_shards(sharded_state_dict: Iterator[Tuple[OrderedDict, int]], + checkpoint: str, + index_file: "CheckpointIndexFile", + base_filename: str, + is_master: bool, + use_safetensors: bool = False) -> int: + ''' + Save sharded state dict only on master rank, this method can be used by both model and optimizer states. + Args: + sharded_state_dict (Iterator[Tuple[OrderedDict, int]]): a generator of shards, each shard contains state dict and shard size. + checkpoint (str): The path of checkpoint directory as string. + index_file (CheckpointIndexFile): The index file object to be updated. + base_filename (str): Decides the prefix of filenames of shards. + is_master (bool): Whether current rank is master. + use_safetensors (bool): Whether to use safetensors to save checkpoint. + + Returns: + int: the total size of shards + ''' + + total_size = 0 + for idx, shard_pair in enumerate(sharded_state_dict): + if not is_master: + continue + shard, current_size = shard_pair + shard_file = get_shard_filename(base_filename, idx) + total_size = total_size + current_size + for key in shard.keys(): + index_file.append_weight_map(key, shard_file) + checkpoint_file_path = os.path.join(checkpoint, shard_file) + + # Only save on master rank. + save_state_dict(shard, checkpoint_file_path, use_safetensors=use_safetensors) + + return total_size + + def shard_model_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> Iterator[Tuple[OrderedDict, int]]: """ Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a diff --git a/colossalai/zero/gemini/gemini_optimizer.py b/colossalai/zero/gemini/gemini_optimizer.py index 99aff6f1c527..7d0db6b1fa23 100644 --- a/colossalai/zero/gemini/gemini_optimizer.py +++ b/colossalai/zero/gemini/gemini_optimizer.py @@ -3,7 +3,7 @@ import gc import math import warnings -from typing import Any, Dict, Set, Tuple +from typing import Any, Dict, Iterator, OrderedDict, Set, Tuple import torch import torch.distributed as dist @@ -11,8 +11,10 @@ from torch.optim import Optimizer from colossalai.amp.naive_amp.mixed_precision_mixin import BF16MixedPrecisionMixin, FP16MixedPrecisionMixin +from colossalai.checkpoint_io.utils import calculate_tensor_size from colossalai.logging import get_dist_logger from colossalai.nn.optimizer import ColossalaiOptimizer, CPUAdam, FusedAdam, HybridAdam +from colossalai.tensor.d_tensor import is_distributed_tensor from colossalai.utils import disposable, get_current_device, is_ddp_ignored from .chunk import Chunk, ChunkManager @@ -360,10 +362,12 @@ def get_offsets(self, param_id: int) -> tuple: begin_in_chunk, end_in_chunk = self.param_to_range[fake_param] chunk_offset = begin_in_chunk - shard_offset = begin_in_chunk + chunk.shard_begin - param_info.offset + if chunk.keep_gathered: + shard_offset = 0 + else: + shard_offset = begin_in_chunk + chunk.shard_begin - param_info.offset shard_size = end_in_chunk - begin_in_chunk assert chunk_offset >= 0 and shard_offset >= 0 - return chunk_offset, shard_offset, shard_size def collect_states(self, param_id: int, only_rank_0: bool = True) -> dict: @@ -427,7 +431,8 @@ def collect_states(self, param_id: int, only_rank_0: bool = True) -> dict: dtype=torch.float32, requires_grad=False).cpu() else: - collected_states[state_name] = states[state_name].detach().clone().to(torch.float32).cpu() + state_tensor = states[state_name].detach().clone().to(torch.float32).cpu() + collected_states[state_name] = torch.reshape(state_tensor, param.shape) return collected_states # Check whether the param with given id is managed by current process. @@ -536,6 +541,31 @@ def load_from_compacted_states(self, compacted_states: torch.Tensor, collected_s target_segment.copy_(compacted_states[next_state_offset:next_state_offset + shard_size]) next_state_offset += shard_size + def get_param_groups_for_saving(self) -> list: + ''' + Return the param_groups in Pytorch format when saving to checkpoint. + ''' + + param_groups = copy.deepcopy(self.param_groups_backup) + + # To be compatible with pytorch checkpointing, + # store extra hyperparameters used by pytorch Adam optimizer. + torch_special_hyperparameters = { + 'amsgrad': False, + 'maximize': False, + 'foreach': None, + 'capturable': False, + 'differentiable': False, + 'fused': False + } + + for group in param_groups: + for k, v in torch_special_hyperparameters.items(): + if k not in group: + group[k] = v + + return param_groups + def state_dict(self, only_rank_0: bool = True) -> dict: """ Args: @@ -555,21 +585,7 @@ def state_dict(self, only_rank_0: bool = True) -> dict: so it should be called only when memory resources are abundant. """ state_dict = {} - state_dict['param_groups'] = copy.deepcopy(self.param_groups_backup) - - torch_special_hyperparameters = { - 'amsgrad': False, - 'maximize': False, - 'foreach': None, - 'capturable': False, - 'differentiable': False, - 'fused': False - } - - for group in state_dict['param_groups']: - for k, v in torch_special_hyperparameters.items(): - if k not in group: - group[k] = v + state_dict['param_groups'] = self.get_param_groups_for_saving() # Collect optimizer states. state_dict['state'] = dict() @@ -634,8 +650,24 @@ def cast(param, state_range, value, key=None): del v # clean loaded states self.optim.state[fake_param].update(updated_states) + def load_param_states(self, param_states: dict): + """Loads param states from a state_dict. The param_states can be complete or sharded. + During loading, filter out the part of states not considered by current process. + + Args: + param_states (dict): A mapping from param_id to its states. + """ + for param_id, states in param_states.items(): + if param_id in self.id_to_fake_params: + self.load_single_param_states(param_id, states) + + def optimizer_loading_epilogue(self): + # Epilogue when loading state_dict to pytorch optimizer. + self.optim._hook_for_profile() # To support multiprocessing pickle/unpickle. + self.optim.defaults.setdefault('differentiable', False) + def load_state_dict(self, state_dict: dict): - """Loads optimizer state from whole optimizer state_dict. + """Loads optimizer state from complete optimizer state_dict. During loading, filter out the part of states not considered by current process. Args: @@ -643,17 +675,71 @@ def load_state_dict(self, state_dict: dict): from a call to :meth:`state_dict`. """ assert 'param_groups' in state_dict + assert 'state' in state_dict self.load_param_groups(state_dict['param_groups']) + self.load_param_states(state_dict['state']) + self.optimizer_loading_epilogue() - state = state_dict['state'] + def state_shard(self, + prefix: str = '', + max_shard_size: int = 1024, + only_rank_0: bool = True) -> Iterator[Tuple[OrderedDict, int]]: + """Returns dictionaries containing shards of optimizer states one by one. + The max size of each dictionary shard is specified by ``max_shard_size``. - for param_id, param_states in state.items(): - if param_id in self.id_to_fake_params: - self.load_single_param_states(param_id, param_states) + Args: + prefix (str, optional): the prefix for states. Default to ''. + max_shard_size (int, optional): max size of state dict shard (in MB). Defaults to 1024. + only_rank_0 (bool, optional): a boolean value indicating whether the state_dict is collected + only on rank 0, dafault to True. - # Epilogue for pytorch optimizer. - self.optim._hook_for_profile() # To support multiprocessing pickle/unpickle. - self.optim.defaults.setdefault('differentiable', False) + Yields: + Iterator[OrderedDict]: A generator of state dict shard of optimizer states. + """ + + current_block = {} + current_block_size = 0 + + for param_id in self.id_to_real_params.keys(): + + dist.barrier() + state = self.collect_states(param_id=param_id, only_rank_0=only_rank_0) + + ret_block = None + ret_block_size = 0 + + # A state might contain more than one tensors. + # e.g. each Adam state includes: 'step', 'exp_avg', 'exp_avg_sq' + state_size = 0 + isDTensor = False + for state_tensor in state.values(): + + # When state_tensor is not of Tensor class, + # e.g., a SGD optimizer with momentum set to 0 can have None as state + # The calculation of tensor size should be skipped to avoid error. + if not isinstance(state_tensor, torch.Tensor): + continue + + # If the states are stored as DTensors, mark isDTensor as true. + if is_distributed_tensor(state_tensor): + isDTensor = True + state_size += calculate_tensor_size(state_tensor) + + if not isDTensor: + + if current_block_size + state_size > max_shard_size and current_block_size > 0: + ret_block = current_block + ret_block_size = current_block_size + current_block = {} + current_block_size = 0 + + current_block[param_id] = state + current_block_size += state_size + + if ret_block != None: + yield ret_block, ret_block_size + + yield current_block, current_block_size class GeminiAdamOptimizer(ZeroOptimizer): diff --git a/docs/source/en/basics/booster_api.md b/docs/source/en/basics/booster_api.md index 22d5ee818019..1e75c343c14f 100644 --- a/docs/source/en/basics/booster_api.md +++ b/docs/source/en/basics/booster_api.md @@ -21,10 +21,13 @@ Plugin is an important component that manages parallel configuration (eg: The ge **_GeminiPlugin:_** This plugin wraps the Gemini acceleration solution, that ZeRO with chunk-based memory management. -**_TorchDDPPlugin:_** This plugin wraps the DDP acceleration solution, it implements data parallelism at the module level which can run across multiple machines. +**_TorchDDPPlugin:_** This plugin wraps the DDP acceleration solution of Pytorch. It implements data parallelism at the module level which can run across multiple machines. **_LowLevelZeroPlugin:_** This plugin wraps the 1/2 stage of Zero Redundancy Optimizer. Stage 1 : Shards optimizer states across data parallel workers/GPUs. Stage 2 : Shards optimizer states + gradients across data parallel workers/GPUs. + +**_TorchFSDPPlugin:_** This plugin wraps the FSDP acceleration solution of Pytorch and can be used to train models with zero-dp. + ### API of booster {{ autodoc:colossalai.booster.Booster }} diff --git a/docs/source/en/basics/booster_checkpoint.md b/docs/source/en/basics/booster_checkpoint.md index adc0af60b7de..b2840fe87441 100644 --- a/docs/source/en/basics/booster_checkpoint.md +++ b/docs/source/en/basics/booster_checkpoint.md @@ -21,8 +21,6 @@ Model must be boosted by `colossalai.booster.Booster` before loading. It will de ## Optimizer Checkpoint -> ⚠ Saving optimizer checkpoint in a sharded way is not supported yet. - {{ autodoc:colossalai.booster.Booster.save_optimizer }} Optimizer must be boosted by `colossalai.booster.Booster` before saving. diff --git a/docs/source/en/basics/booster_plugins.md b/docs/source/en/basics/booster_plugins.md index 5e2586b836ad..c5c45abce8f7 100644 --- a/docs/source/en/basics/booster_plugins.md +++ b/docs/source/en/basics/booster_plugins.md @@ -51,8 +51,6 @@ This plugin implements Zero-3 with chunk-based and heterogeneous memory manageme {{ autodoc:colossalai.booster.plugin.GeminiPlugin }} -> ⚠ This plugin can only load optimizer checkpoint saved by itself with the same number of processes now. This will be fixed in the future. - ### Torch DDP Plugin More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel). diff --git a/docs/source/zh-Hans/basics/booster_api.md b/docs/source/zh-Hans/basics/booster_api.md index 1df821ce7d6e..b2235b73bca1 100644 --- a/docs/source/zh-Hans/basics/booster_api.md +++ b/docs/source/zh-Hans/basics/booster_api.md @@ -24,10 +24,13 @@ Booster 插件是管理并行配置的重要组件（eg：gemini 插件封装了 **_GeminiPlugin:_** GeminiPlugin 插件封装了 gemini 加速解决方案，即基于块内存管理的 ZeRO 优化方案。 -**_TorchDDPPlugin:_** TorchDDPPlugin 插件封装了 DDP 加速方案，实现了模型级别的数据并行，可以跨多机运行。 +**_TorchDDPPlugin:_** TorchDDPPlugin 插件封装了Pytorch的DDP加速方案，实现了模型级别的数据并行，可以跨多机运行。 **_LowLevelZeroPlugin:_** LowLevelZeroPlugin 插件封装了零冗余优化器的 1/2 阶段。阶段 1：切分优化器参数，分发到各并发进程或并发 GPU 上。阶段 2：切分优化器参数及梯度，分发到各并发进程或并发 GPU 上。 +**_TorchFSDPPlugin:_** TorchFSDPPlugin封装了 Pytorch的FSDP加速方案，可以用于零冗余优化器数据并行（ZeroDP）的训练。 + + ### Booster 接口 diff --git a/docs/source/zh-Hans/basics/booster_checkpoint.md b/docs/source/zh-Hans/basics/booster_checkpoint.md index d75f18c908ba..4ed049dcf44f 100644 --- a/docs/source/zh-Hans/basics/booster_checkpoint.md +++ b/docs/source/zh-Hans/basics/booster_checkpoint.md @@ -21,7 +21,6 @@ ## 优化器 Checkpoint -> ⚠ 尚不支持以分片方式保存优化器 Checkpoint。 {{ autodoc:colossalai.booster.Booster.save_optimizer }} diff --git a/docs/source/zh-Hans/basics/booster_plugins.md b/docs/source/zh-Hans/basics/booster_plugins.md index 5bd88b679000..0f355c43901c 100644 --- a/docs/source/zh-Hans/basics/booster_plugins.md +++ b/docs/source/zh-Hans/basics/booster_plugins.md @@ -51,7 +51,6 @@ Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累 {{ autodoc:colossalai.booster.plugin.GeminiPlugin }} -> ⚠ 该插件现在只能加载自己保存的且具有相同进程数的优化器 Checkpoint。这将在未来得到解决。 ### Torch DDP 插件 diff --git a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py index 0235ff2e2c81..7b664419b405 100644 --- a/tests/test_checkpoint_io/test_gemini_checkpoint_io.py +++ b/tests/test_checkpoint_io/test_gemini_checkpoint_io.py @@ -52,7 +52,7 @@ def exam_state_dict_with_origin(placement_policy, model_name, use_safetensors: b @clear_cache_before_run() @parameterize('placement_policy', ['cuda', 'cpu']) -@parameterize('shard', [False]) +@parameterize('shard', [False, True]) @parameterize('model_name', ['transformers_gpt']) @parameterize('size_per_shard', [32]) def exam_state_dict(placement_policy, shard: bool, model_name: str, size_per_shard: int): @@ -117,7 +117,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist -@pytest.mark.parametrize('world_size', [1, 2]) +@pytest.mark.parametrize('world_size', [2]) @rerun_if_address_is_in_use() def test_gemini_ckpIO(world_size): spawn(run_dist, world_size) diff --git a/tests/test_checkpoint_io/test_gemini_torch_compability.py b/tests/test_checkpoint_io/test_gemini_torch_compability.py index b34e3e3a1310..464fccb39103 100644 --- a/tests/test_checkpoint_io/test_gemini_torch_compability.py +++ b/tests/test_checkpoint_io/test_gemini_torch_compability.py @@ -19,7 +19,7 @@ @clear_cache_before_run() -@parameterize('shard', [False]) +@parameterize('shard', [False, True]) @parameterize('model_name', ['transformers_gpt']) def exam_torch_load_from_gemini(shard: bool, model_name: str): @@ -83,7 +83,7 @@ def exam_torch_load_from_gemini(shard: bool, model_name: str): @clear_cache_before_run() -@parameterize('shard', [False]) +@parameterize('shard', [False, True]) @parameterize('model_name', ['transformers_gpt']) def exam_gemini_load_from_torch(shard: bool, model_name: str): @@ -165,7 +165,7 @@ def run_dist(rank, world_size, port): @pytest.mark.dist -@pytest.mark.parametrize('world_size', [1, 2]) +@pytest.mark.parametrize('world_size', [2]) @rerun_if_address_is_in_use() def test_gemini_ckpIO(world_size): spawn(run_dist, world_size) From 917ac28961eb14ebd4a31975182f7db9c7699352 Mon Sep 17 00:00:00 2001 From: ver217 Date: Fri, 21 Jul 2023 18:07:35 +0800 Subject: [PATCH 411/413] [chat] train sft support tensorboard --- applications/Chat/coati/trainer/sft.py | 25 ++++++++++++++++--------- applications/Chat/examples/train_sft.py | 6 +++--- 2 files changed, 19 insertions(+), 12 deletions(-) diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index 0812ba165286..5214e9c98750 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -8,6 +8,7 @@ from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter from colossalai.logging import DistributedLogger @@ -49,12 +50,11 @@ def __init__( def _train(self, epoch: int): self.model.train() + start_step = epoch * len(self.train_dataloader) // self.accumulation_steps for batch_id, batch in enumerate(self.train_dataloader): batch = to_device(batch, torch.cuda.current_device()) - outputs = self.model(batch["input_ids"], - attention_mask=batch["attention_mask"], - labels=batch["labels"]) + outputs = self.model(batch["input_ids"], attention_mask=batch["attention_mask"], labels=batch["labels"]) loss = outputs.loss loss = loss / self.accumulation_steps @@ -68,9 +68,12 @@ def _train(self, epoch: int): self.strategy.optimizer_step(self.optimizer) self.optimizer.zero_grad() self.scheduler.step() + if self.writer: + self.writer.add_scalar('loss', self.total_loss, start_step + batch_id) + self.writer.add_scalar('lr', self.scheduler.get_last_lr()[0], start_step + batch_id) if is_rank_0() and self.use_wandb: wandb.log({ - "loss": self.total_loss / self.accumulation_steps, + "loss": self.total_loss, "lr": self.scheduler.get_last_lr()[0], "epoch": epoch, "batch_id": batch_id @@ -96,11 +99,14 @@ def _eval(self, epoch: int): loss_mean = loss_sum / num_seen if dist.get_rank() == 0: self.logger.info(f'Eval Epoch {epoch}/{self.max_epochs} loss {loss_mean}') + if self.writer: + self.writer.add_scalar('eval_loss', loss_mean, epoch) def _before_fit(self, train_dataloader: DataLoader, eval_dataloader: Optional[DataLoader] = None, logger: Optional[DistributedLogger] = None, + tensorboard_dir: Optional[str] = None, use_wandb: bool = False): """ Args: @@ -118,8 +124,9 @@ def _before_fit(self, self.total_loss = 0 self.no_epoch_bar = True - self.step_bar = tqdm.trange( - len(self.train_dataloader) // self.accumulation_steps * self.max_epochs, - desc=f'steps', - disable=not is_rank_0() - ) + self.step_bar = tqdm.trange(len(self.train_dataloader) // self.accumulation_steps * self.max_epochs, + desc=f'steps', + disable=not is_rank_0()) + self.writer = None + if tensorboard_dir and dist.get_rank() == 0: + self.writer = SummaryWriter(tensorboard_dir) diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index cb3eb649d76c..cb3737910adb 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -153,9 +153,7 @@ def train(args): optim, num_warmup_steps=math.ceil(max_steps * 0.03), num_training_steps=max_steps) - strategy_dict = strategy.prepare( - dict(model=model, optimizer=optim, lr_scheduler=lr_scheduler) - ) + strategy_dict = strategy.prepare(dict(model=model, optimizer=optim, lr_scheduler=lr_scheduler)) model = strategy_dict['model'] optim = strategy_dict['optimizer'] lr_scheduler = strategy_dict['lr_scheduler'] @@ -169,6 +167,7 @@ def train(args): trainer.fit(train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, logger=logger, + tensorboard_dir=args.tensorbard_dir, use_wandb=args.use_wandb) # save model checkpoint after fitting on only rank0 @@ -200,5 +199,6 @@ def train(args): parser.add_argument('--accumulation_steps', type=int, default=8) parser.add_argument('--use_wandb', default=False, action='store_true') parser.add_argument('--grad_checkpoint', default=False, action='store_true') + parser.add_argument('--tensorbard_dir', type=str, default=None) args = parser.parse_args() train(args) From fcb028071f06ca43fbab7b7252a968399b758604 Mon Sep 17 00:00:00 2001 From: ver217 Date: Fri, 21 Jul 2023 18:34:06 +0800 Subject: [PATCH 412/413] [chat] train sft support optimizer save load --- .../Chat/coati/trainer/strategies/base.py | 24 ++++--------------- applications/Chat/examples/train_sft.py | 15 ++++++------ 2 files changed, 13 insertions(+), 26 deletions(-) diff --git a/applications/Chat/coati/trainer/strategies/base.py b/applications/Chat/coati/trainer/strategies/base.py index 80bc3272872e..5352cd5fc4db 100644 --- a/applications/Chat/coati/trainer/strategies/base.py +++ b/applications/Chat/coati/trainer/strategies/base.py @@ -79,8 +79,7 @@ def prepare(self, *boost_args: _BoostArgSpec) -> Union[List[_BoostArgSpec], _Boo model, optimizer = arg except ValueError: raise RuntimeError(f'Expect (model, optimizer) pair, got a tuple with size "{len(arg)}"') - model, optimizer, *_ = self.booster.boost(model=model, - optimizer=optimizer) + model, optimizer, *_ = self.booster.boost(model=model, optimizer=optimizer) rets.append((model, optimizer)) elif isinstance(arg, Dict): model, optimizer, criterion, dataloader, lr_scheduler = self.booster.boost(**arg) @@ -90,10 +89,7 @@ def prepare(self, *boost_args: _BoostArgSpec) -> Union[List[_BoostArgSpec], _Boo dataloader=dataloader, lr_scheduler=lr_scheduler) # remove None values - boost_result = { - key: value - for key, value in boost_result.items() if value is not None - } + boost_result = {key: value for key, value in boost_result.items() if value is not None} rets.append(boost_result) else: raise RuntimeError(f'Type {type(arg)} is not supported') @@ -112,24 +108,14 @@ def unwrap_model(model: nn.Module) -> nn.Module: """ return model - def save_model(self, - model: nn.Module, - path: str, - only_rank0: bool = True, - **kwargs - ) -> None: + def save_model(self, model: nn.Module, path: str, only_rank0: bool = True, **kwargs) -> None: self.booster.save_model(model, path, shard=not only_rank0, **kwargs) def load_model(self, model: nn.Module, path: str, strict: bool = True) -> None: self.booster.load_model(model, path, strict) - def save_optimizer(self, - optimizer: Optimizer, - path: str, - only_rank0: bool = False, - **kwargs - ) -> None: - self.booster.save_optimizer(optimizer, path, shard=not only_rank0, **kwargs) + def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False, **kwargs) -> None: + self.booster.save_optimizer(optimizer, path, shard=False, **kwargs) def load_optimizer(self, optimizer: Optimizer, path: str) -> None: self.booster.load_optimizer(optimizer, path) diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index cb3737910adb..14ae1a5a33a1 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -157,6 +157,8 @@ def train(args): model = strategy_dict['model'] optim = strategy_dict['optimizer'] lr_scheduler = strategy_dict['lr_scheduler'] + if args.optim_load_path: + strategy.load_optimizer(optim, path=args.optim_load_path) trainer = SFTTrainer(model=model, strategy=strategy, optim=optim, @@ -167,16 +169,14 @@ def train(args): trainer.fit(train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, logger=logger, - tensorboard_dir=args.tensorbard_dir, + tensorboard_dir=args.tensorboard_dir, use_wandb=args.use_wandb) # save model checkpoint after fitting on only rank0 strategy.save_pretrained(model, path=args.save_path, only_rank0=True, tokenizer=tokenizer) # save optimizer checkpoint on all ranks - if args.need_optim_ckpt: - strategy.save_optimizer(trainer.optimizer, - 'rm_optim_checkpoint_%d.pt' % (torch.cuda.current_device()), - only_rank0=False) + if args.optim_save_path: + strategy.save_optimizer(trainer.optimizer, path=args.optim_save_path) if __name__ == '__main__': @@ -189,7 +189,8 @@ def train(args): parser.add_argument('--dataset', type=str, default=None) parser.add_argument('--max_datasets_size', type=int, default=None) parser.add_argument('--save_path', type=str, default='output') - parser.add_argument('--need_optim_ckpt', type=bool, default=False) + parser.add_argument('--optim_save_path', type=str, default=None) + parser.add_argument('--optim_load_path', type=str, default=None) parser.add_argument('--max_epochs', type=int, default=3) parser.add_argument('--batch_size', type=int, default=4) parser.add_argument('--max_len', type=int, default=512) @@ -199,6 +200,6 @@ def train(args): parser.add_argument('--accumulation_steps', type=int, default=8) parser.add_argument('--use_wandb', default=False, action='store_true') parser.add_argument('--grad_checkpoint', default=False, action='store_true') - parser.add_argument('--tensorbard_dir', type=str, default=None) + parser.add_argument('--tensorboard_dir', type=str, default=None) args = parser.parse_args() train(args) From 6be1cadd7d330f788f09b7e0dca00a1f2e54bfa3 Mon Sep 17 00:00:00 2001 From: CZYCW Date: Wed, 26 Jul 2023 09:37:47 +0000 Subject: [PATCH 413/413] add tensorboard close logic --- applications/Chat/coati/trainer/base.py | 8 +++++++- applications/Chat/coati/trainer/sft.py | 15 ++++++--------- applications/Chat/examples/train_sft.py | 2 +- 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/applications/Chat/coati/trainer/base.py b/applications/Chat/coati/trainer/base.py index 13571cdcc23a..8a826056edf2 100644 --- a/applications/Chat/coati/trainer/base.py +++ b/applications/Chat/coati/trainer/base.py @@ -8,7 +8,8 @@ from coati.replay_buffer import NaiveReplayBuffer from torch.optim import Optimizer from torch.utils.data import DataLoader - +from torch.utils.tensorboard import SummaryWriter +import torch.distributed as dist from .callbacks import Callback from .strategies import Strategy from .utils import CycledDataLoader, is_rank_0 @@ -30,12 +31,14 @@ def __init__(self, max_epochs: int, model: nn.Module, optimizer: Optimizer, + tensorboard_dir: str = None, ) -> None: super().__init__() self.strategy = strategy self.max_epochs = max_epochs self.model = model self.optimizer = optimizer + self.writer = SummaryWriter(tensorboard_dir) if tensorboard_dir and dist.get_rank() == 0 else None @abstractmethod def _train(self, epoch): @@ -56,6 +59,9 @@ def fit(self, *args, **kwargs): ): self._train(epoch) self._eval(epoch) + if dist.get_rank() == 0 and self.writer: + print("Closing tensorboard writer...") + self.writer.close() class OnPolicyTrainer(ABC): diff --git a/applications/Chat/coati/trainer/sft.py b/applications/Chat/coati/trainer/sft.py index 5214e9c98750..5ed0a05ca7eb 100644 --- a/applications/Chat/coati/trainer/sft.py +++ b/applications/Chat/coati/trainer/sft.py @@ -8,7 +8,6 @@ from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler from torch.utils.data import DataLoader -from torch.utils.tensorboard import SummaryWriter from colossalai.logging import DistributedLogger @@ -36,6 +35,7 @@ def __init__( strategy: Strategy, optim: Optimizer, lr_scheduler: _LRScheduler, + tensorboard_dir: str = None, max_epochs: int = 2, accumulation_steps: int = 8, ) -> None: @@ -43,8 +43,7 @@ def __init__( assert not isinstance(strategy, GeminiStrategy), \ "Accumulation steps are not supported in stage 3 of ColossalAI" - super().__init__(strategy, max_epochs, model, optim) - + super().__init__(strategy, max_epochs, model, optim, tensorboard_dir) self.accumulation_steps = accumulation_steps self.scheduler = lr_scheduler @@ -68,7 +67,7 @@ def _train(self, epoch: int): self.strategy.optimizer_step(self.optimizer) self.optimizer.zero_grad() self.scheduler.step() - if self.writer: + if dist.get_rank() == 0 and self.writer: self.writer.add_scalar('loss', self.total_loss, start_step + batch_id) self.writer.add_scalar('lr', self.scheduler.get_last_lr()[0], start_step + batch_id) if is_rank_0() and self.use_wandb: @@ -80,6 +79,7 @@ def _train(self, epoch: int): }) self.total_loss = 0 self.step_bar.update() + print(f"Epoch {epoch}/{self.max_epochs} batch {batch_id}/{len(self.train_dataloader)} loss {loss.item()}") def _eval(self, epoch: int): if self.eval_dataloader is not None: @@ -99,14 +99,14 @@ def _eval(self, epoch: int): loss_mean = loss_sum / num_seen if dist.get_rank() == 0: self.logger.info(f'Eval Epoch {epoch}/{self.max_epochs} loss {loss_mean}') - if self.writer: + if dist.get_rank() == 0 and self.writer: self.writer.add_scalar('eval_loss', loss_mean, epoch) + print(f'Eval Epoch {epoch}/{self.max_epochs} loss {loss_mean}') def _before_fit(self, train_dataloader: DataLoader, eval_dataloader: Optional[DataLoader] = None, logger: Optional[DistributedLogger] = None, - tensorboard_dir: Optional[str] = None, use_wandb: bool = False): """ Args: @@ -127,6 +127,3 @@ def _before_fit(self, self.step_bar = tqdm.trange(len(self.train_dataloader) // self.accumulation_steps * self.max_epochs, desc=f'steps', disable=not is_rank_0()) - self.writer = None - if tensorboard_dir and dist.get_rank() == 0: - self.writer = SummaryWriter(tensorboard_dir) diff --git a/applications/Chat/examples/train_sft.py b/applications/Chat/examples/train_sft.py index 14ae1a5a33a1..d643609b3a30 100644 --- a/applications/Chat/examples/train_sft.py +++ b/applications/Chat/examples/train_sft.py @@ -164,12 +164,12 @@ def train(args): optim=optim, lr_scheduler=lr_scheduler, max_epochs=args.max_epochs, + tensorboard_dir=args.tensorboard_dir, accumulation_steps=args.accumulation_steps) trainer.fit(train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, logger=logger, - tensorboard_dir=args.tensorboard_dir, use_wandb=args.use_wandb) # save model checkpoint after fitting on only rank0